University of Southern California Dissertations and Theses

Genetic and environmental effects on symbiotic interactions across thermal gradients

(USC Thesis Other)

Genetic and environmental effects on symbiotic interactions across thermal gradients

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content

GENETIC AND ENVIRONMENTAL EFFECTS ON
SYMBIOTIC INTERACTIONS ACROSS THERMAL GRADIENTS

by

Maria Ruggeri

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOLOGICAL SCIENCES)

August 2023

Copyright 2023 Maria Ruggeri
ii
DEDICATION
I dedicate this dissertation to the family I was born with and the one that I made along the way.
Thank you all for your unwavering love and support, we did it!

iii
ACKNOWLEDGMENTS
This work would not have been possible without the support from faculty, students, and
staff at the University of Southern California. I would like to especially thank my advisor, Dr.
Carly Kenkel, who welcomed me into her lab with open arms and helped me grow as a scientist
and a person. I am forever grateful to have had such an amazing teacher and friend throughout
this process. Thank you for always believing in me, challenging me, and trusting me.
I would also like to thank the other members of my lab, who provided guidance and
support with experimentation, troubleshooting, and data analysis. I would particularly like to
thank my former lab mate, Wyatt Million, who provided both emotional and professional
support, and always pushed me to do my best. I would also like to specifically thank all the
undergraduate students that I have worked with over the years, especially Connie Machuca and
Lindsey Hamilton. This work would not have been possible without your hard work and
dedication. Lastly, I would like to thank the USC Wrigley Institute for Marine Science, which
provided the facilities and funding to explore projects outside of my advisors funded work and
allowed me to share my research with a broader public audience.
I would also like to thank those outside of USC who advised me. Special thanks to
Samuel Bedgood, who introduced me to both the Anthopleura and Aiptasia world and made
Chapters 2 and 4 possible. Thank you to Mote Marine Laboratory, which was home base for
many experiments and collections during my PhD. And of course, thank you to my fiancé,
Andrew Masco, who supported me every step of the way, from finding intertidal sites and
cleaning anemone tanks to always making me smile and reminding me of my worth. I wouldn’t
have gotten here without your love and support, thank you.

iv
TABLE OF CONTENTS
DEDICATION ................................................................................................................................ ii
ACKNOWLEDGMENTS ............................................................................................................. iii
LIST OF FIGURES ....................................................................................................................... vi
CHAPTER ONE: Introduction ....................................................................................................... 1
References ....................................................................................................................................... 8
CHAPTER TWO: Microhabitat acclimatization shifts physiological baselines and thermal
tolerance of the symbiotic anemone, Anthopleura elegantissima ................................................ 12
Abstract ......................................................................................................................................... 12
Introduction ................................................................................................................................... 13
Materials and methods .................................................................................................................. 16
Results ........................................................................................................................................... 23
Discussion ..................................................................................................................................... 30
References ..................................................................................................................................... 37
Appendix ....................................................................................................................................... 42
CHAPTER THREE: Divergent transcriptional response to thermal stress among life stages
could constrain coral adaptation to climate change ...................................................................... 54
Abstract ......................................................................................................................................... 54
Introduction ................................................................................................................................... 55
Materials and methods .................................................................................................................. 58
Results ........................................................................................................................................... 63
Discussion ..................................................................................................................................... 72
References ..................................................................................................................................... 81
Appendix ....................................................................................................................................... 87
CHAPTER FOUR: Population genomics and symbiont specificity in natural populations of
Exaiptasia diaphana ..................................................................................................................... 93
Abstract ......................................................................................................................................... 93
Introduction ................................................................................................................................... 94
Materials and methods .................................................................................................................. 97
v
Results ......................................................................................................................................... 103
Discussion ................................................................................................................................... 111
References ................................................................................................................................... 120
Appendix ..................................................................................................................................... 127
CHAPTER FIVE: Conclusion .................................................................................................... 137
References ................................................................................................................................... 142

vi
LIST OF FIGURES
Figure 2. 1: Diagram of sampling, experimental design, and thermal characteristics of sampling
locations. Temperature profiles contrasting the center (blue) versus the edge (orange) of a low
intertidal aggregation are shown in the top left (a) and temperature profiles contrasting the low (blue)
versus high (orange) intertidal are shown in the bottom left (b). ................................................................ 17
Figure 2. 2: Relationship between Maximum quantum yield (MQY) and symbiont to host cell
ratios across tidal zones and treatment. Density plots show the distribution of samples for a given
trait (top MQY, right log(sym/host)). Samples from the high intertidal and low intertidal are
represented in orange and blue, respectfully. Dark shading represents samples in control conditions
whereas light shading represents heat stressed samples. Asterisks (*) denote significance of fixed
factors from linear mixed effect models (p < 0.05). .................................................................................... 25
Figure 2. 3: Average trait values +/- SEM (MQY, symbiont to host cell ratios, and chlorophyll
a) under control and heat stress conditions of clonal aggregations sampled from different tidal
heights. Plots are grouped by genotype (top Genet 1, bottom Genet 10). Independent aggregations of
the same genet are colored by tidal height (meters). Cooler colors represent aggregations sampled
lower in the intertidal and warmer colors represent aggregations higher in the intertidal. ......................... 28

Figure 3. 1: Principal components analysis of variance stabilized count data for the host (A) and
symbiont (B) transcriptomes. Samples are colored by population and treatment. Symbols represent
different life history stages. ......................................................................................................................... 64
Figure 3. 2: Host transcriptional response to moderate-term thermal stress by life stage. (A)
Clustering of KOG enrichments by delta ranks and life stage. Bolded boxes represent KOG classes
significantly enriched in response to treatment (black p adj < 0.05 ; gray p adj < 0.1). (B) Pairwise
Pearson correlation coefficients of KOG delta ranks between life stages. Bolding denotes significant
correlation (p < 0.05). (C) Venn diagram of genes significantly differentially expressed in response to
treatment (p adj < 0.1) in each life stage. ....................................................................................................... 67
Figure 3. 3: Heatmap of log 2 fold change in expression of symbiont genes significantly
differentially regulated in response to treatment across host developmental stages. Rows
represent the 231 treatment responsive genes (Padj<0.1) detected in total for symbionts in adult and
recruit life stages, as indicated by the venn diagram. Adult corals were ranked by visual bleaching
score from 1 (lightest) – 6 (darkest) (Kenkel et al., 2015) which is denoted by the red/orange bar. .......... 68
vii
Figure 3. 4: Host transcriptional response to treatment among populations. Venn diagram of the
population-specific response to heat stress in adults and recruits (A) and larvae (B). Discriminant
analysis of principal components was performed on all treatment responsive genes to explore the
magnitude of expression change between populations. The discriminant function was defined by
contrasting samples in heat and control from one population and predicting the response of the other. .... 71

Figure 4. 1: Individual admixture proportions (K=2) of host populations sorted by site (N →S).
Admixture analyses were run for all unique genotypes (A) and subset to explore structure within
subpopulations (B and C). ......................................................................................................................... 105
Figure 4. 2: Host genetic differentiation based on geographic distance. a) Estimated effective
migration rates between sites. Colors indicate greater (blue) or less (orange) migration than would
be expected under IBD. Arrows indicate predominant directionality of sea surface currents following
(Lee and Smith, 2002; National Oceanic and Atmospheric Administration, 2011). b) Pairwise
geographic distance and genetic differentiation between sites. The dashed line indicates a positive
relationship between geographic distance and genetic differentiation (IBD), while the solid lines
indicate a negative relationship between geographic distance and genetic differentiation. Points labels
represent pairwise population comparisons between Otter Key (OK), Miami (MI), Tavernier Key
(TK), Long Key (LK), Big Pine Key (BP), and Stock Island (SI). ........................................................... 106
Figure 4. 3: Relative abundance of symbiont profiles across sites. Each bar represents an
individual sample. Samples are grouped by site (rows) and host genotype within site (bolded boxes).
Color families represent genus-level designations and gradation within color families denote unique
ITS2 profiles. ............................................................................................................................................. 108
Figure 4. 4: Co-divergence analysis based on host and symbiont relatedness. Filled rectangles
indicate whether or not (presence or absence) symbiont profiles were detected in host symbiont
communities across Symbiodiniaceae genera. Rows represent symbiont profiles clustered by genetic
relatedness and columns represent unique host genotypes clustered by IBS distance. Annotation bars
represent host clustering (gray scale) and site designations (color). ......................................................... 110

1

CHAPTER ONE: Introduction
As global sea surface temperatures rise it is unclear whether marine species will be able to
respond fast enough to persist over ecological and evolutionary timescales. To better predict
these responses, a mechanistic understanding of the processes that lead to thermal tolerance is
necessary. If greater thermal tolerance confers greater fitness, thermotolerant individuals will
survive and reproduce, facilitating adaptation. However, depending on the generation time, this
process may occur over long timescales and rapid environmental change may eliminate
populations before evolution can act. Conversely, acclimatization, or the ability for one genotype
to confer multiple phenotypes in response to environmental variation, occurs over short
timescales and may serve as a buffer to environmental change (Ghalambor et al., 2007).
Acclimatization and adaptation have previously been shown to influence thermal tolerance
divergence between populations of marine invertebrates (Stillman and Somero, 2000; Thomas et
al., 2018). However, the prevalence of these forces can be influenced by both spatial and
environmental factors. Further, larger organisms often form mutualistic symbioses with
microorganisms, whose tolerance limits can also be shaped by acclimatization and adaptation.
The interplay between the mechanisms setting environmental limits in both partners, as well as
their interaction, will affect collective fitness and ultimately determine evolutionary outcomes
under climate change (Rosenberg and Zilber-Rosenberg, 2018). In this dissertation, multi-partner
responses are explored across varying thermal gradients and spatial scales to better understand
the mechanisms, and potential constraints, underlying thermal adaptation in contemporary
symbiotic partnerships.

2
Adaptation vs acclimatization
Spatially heterogeneous thermal environments have been implicated in driving phenotypic
differentiation across species ranges. In ectotherms, thermal tolerance can limit species
distributions across both large, latitudinal gradients and small, local scales (Stillman and Somero,
2000; Willett, 2010; Sanford and Kelly, 2011; Silbiger et al., 2019). Variation in thermal
tolerance between local populations can be caused by processes that occur within generations,
such as acclimatization, and between generations, such as adaptation. By investigating the
contribution of these two processes in contemporary populations, we can better infer how
thermal tolerance may evolve under future warming scenarios.
Acclimatization is a type of phenotypic plasticity where an organism adjusts its
physiology to match a new environment. Both field and laboratory manipulations acclimating
organisms to higher temperatures have been shown to increase thermal tolerance (Tomanek and
Somero, 1999; Bay and Palumbi, 2015; Hawkins and Warner, 2017). However, plasticity is also
reversible, meaning thermal tolerance divergence between populations due to acclimatization
could again converge after acclimation/acclimatization to a common environment (Brahim et al.,
2018; Gleason et al., 2018). Although these changes can be adaptive if they increase the overall
fitness in a new environment, there may be tradeoffs associated with greater acclimation capacity
(Willett, 2010). Theoretically, plasticity is thought to be adaptive in environments with
predictable environmental variability, where organisms must frequently adjust their physiology
to survive (Bitter et al., 2021). Alternatively, in constant environments, plasticity is likely
maladaptive due to high underlying energetic and fitness costs and will be selected against
(Ghalambor et al., 2007). Physiological constraints, such as how close an organism lives to its
thermal maxima, might also impose limits to plasticity (Somero, 2010). Understanding the
3
potential to increase thermal tolerance through acclimation and its effect on fitness can help us
predict how long-lived organisms will respond to a changing environment.
Unlike acclimation, adaptation is caused by genetic effects which are selected for across
generations. Local adaptation is detected when a population has greater fitness in its native
environment compared to foreign populations or in comparison to that same population’s
performance in a novel environment (Kawecki and Ebert, 2004). Theory predicts that local
adaptation will occur when the selection differential outweighs the homogenizing effect of gene
flow (Wright, 1984). Environments with high spatial heterogeneity may promote adaptation by
allowing rare alleles to remain in local populations that would otherwise be removed by
purifying selection (Levene, 1953). Although adaptation in marine species with high dispersal
was originally thought to be limited to large spatial scales, empirical evidence of
microgeographic adaptation across fine scale environmental gradients within an organisms
dispersal neighborhood has been well documented (Kavanagh et al., 2010; Sanford and Kelly,
2011; Kenkel et al., 2015; Thomas et al., 2018) and challenges this view. For species with large
dispersal capacity, heat tolerant alleles may be selected for post-settlement in regions close to the
upper thermal limit of a given species. If heat tolerant alleles confer greater fitness than less
tolerant alleles in this environment, they will persist in the population, facilitating adaptation. By
exploring genetic and functional differentiation within a species across natural environmental
gradients, we can better understand the role of temperature as a selective force in shaping
populations across spatial scales.

4
Cnidarian-algal symbiosis
Symbiotic organisms are extremely vulnerable to environmental change as both partners must
respond in synchrony to increase holobiont tolerance. Many cnidarian species associate with
algal symbionts in the family Symbiodiniaceae, where algal cells are kept intracellularly in
specialized host cells. During a stable symbiosis, algal symbionts translocate photosynthetic
products to the host in return for shelter and inorganic nutrients (Muscatine, 1990). However,
stressful environmental conditions lead to the breakdown of this partnership, known as
bleaching. During this process, damage to the algal PSII reaction center generates reactive
oxygen species (ROS) which can cause DNA damage to both the host and symbiont (Weis,
2008). In response to this, the host may either expel or digest algal cells, resulting in an overall
reduction in symbiont density (Bieri et al., 2016). Alternatively, the symbiont may also decrease
the production of light capturing chlorophyll pigments to reduce cellular damage to itself and
consequently the host (Weis, 2008). Although under optimal environmental conditions this
relationship is beneficial to both partners, it exists in a delicate balance that is easily perturbed by
abiotic stressors.

Thermal bleaching
Bleaching due to thermal stress has been well characterized across symbiotic cnidarians. Global
bleaching events due to increasing temperatures have already caused widespread mortality in
reef-building corals (Hoegh-Guldberg, 1999; Hughes et al., 2018), which live very close to their
thermal maxima (Strong et al., 2011), emphasizing the need to understand mechanisms driving
variation in thermal tolerance. However, some symbiotic organisms continue to persist in
5
extreme thermal environments (Camp et al., 2018; Thomas et al., 2018) and can give insight into
the potential mechanisms behind bleaching resistance.
Mechanisms that have been previously implicated in thermal tolerance divergence
include a combination of fixed genetic effects, plastic effects, and symbiont effects. Plastic
effects, such as acclimation and acclimatization, have been shown to increase holobiont thermal
tolerance in both corals and symbiotic sea anemones (Bay and Palumbi, 2015; Hawkins and
Warner, 2017). Reciprocal transplant experiments across thermal environments indicate a role of
both plastic effects and fixed genetic effects, where corals from cooler reefs exhibit higher
bleaching resistance after transplantation to a warmer environment, but cannot reach the level of
thermal tolerance achieved by native corals (Thomas et al., 2018). Although many cnidarians
have large dispersal capacities, local thermal adaptation has been shown to occur across both
large, latitudinal gradients (Howells et al., 2013; Dixon et al., 2015; Silbiger et al., 2019) and
microgeographic scales (Kenkel et al., 2015; Thomas et al., 2018). It is therefore important to
characterize the thermal environments in which the selection differential exceeds the
homogenizing effects of gene flow in order to model population resistance to rising sea surface
temperatures.
Symbiont community composition can also have significant effects on holobiont thermal
tolerance and performance. Symbiont thermal tolerance varies in between genera (Robison and
Warner, 2006; Russnak et al., 2021), species (Díaz-Almeyda et al., 2017; Russnak et al., 2021),
and strains (Howells et al., 2011; Russnak et al., 2021), which is consistent with greater thermal
tolerance of the holobiont (Rowan, 2004; Berkelmans and van Oppen, 2006; Muller-Parker et al.,
2007). Changes in symbiont community composition can occur over an individual host’s
lifetime, by adjusting the relative abundance of symbiont types (shuffling, (Berkelmans and van
6
Oppen, 2006)) or acquiring novel community members from the environment (switching,
(Scharfenstein et al., 2022)), facilitating acclimation (Baker, 2003). Several studies have shown
increased abundance of more thermally tolerant symbiont genera after exposure to thermal stress
in corals, increasing bleaching resistance (Berkelmans and van Oppen, 2006; Cunning et al.,
2015; Bay et al., 2016). However, some host-symbiont associations are highly specific which
could limit the capacity for holobiont acclimation (Goulet, 2006). Although symbiont shuffling
promotes acclimation, symbiont specificity might allow for more efficient selection on the
holobiont, promoting coevolution (Koskella and Bergelson, 2020). Understanding the
contribution, and possible interplay, of fixed, plastic, and symbiont effects on thermal tolerance
divergence is paramount to predicting the response of symbiotic cnidarians to future oceans.

Dissertation overview
This dissertation explores the mechanisms driving thermal tolerance divergence between
populations of symbiotic cnidarians. Each chapter is centered across different spatial and
environmental scales to give insight into how genetic and environmental effects shape
phenotypic variation when demographic and evolutionary processes vary. Chapter 2 explores
thermal tolerance of the symbiotic anemone, Anthopleura elegantissima, across fine spatial
scales (meters) and environmental gradients. In this chapter, we determine whether thermal
tolerance is diverging within and between anemone genotypes across the intertidal, allowing us
to tease apart genetic effects on thermal tolerance from environmental history, and provide
insight into the scale of environmental divergence necessary to drive phenotypic variation.
Chapter 3 then uses a molecular approach to explore the genetic and environmental
effects driving known thermal tolerance variation in coral populations of Porites astreoides from
7
inshore and offshore reefs, with moderate spatial (<10-km) and environmental differentiation.
The effect of environmental history on the thermal stress response was distinguished from
heritable, population-level differences by comparing juvenile offspring with controlled
environmental history to their inshore and offshore parents. We additionally investigated the
robustness of the population-level response by comparing the molecular response to thermal
stress between different inshore and offshore families of an intermediate life stage, larvae, for
which I co-authored a corresponding study on larval physiological response to thermal stress
(Zhang et al., 2019).
Although symbiont effects due to acclimatization are explored in Chapters 2 and 3, both
A. elegantissima and P. astreoides form specific symbioses, so potential symbiont genetic effects
were obscured by limited population-level resolution. However, detectable differences in
symbiont genera and ITS2 types are well-known to have robust effects on thermal tolerance and
the capacity to adjust symbiont community composition in response to environmental variation
has important implications for evolutionary trajectories of symbiotic organisms (Berkelmans and
van Oppen, 2006). In Chapter 4, we investigate how genetic and environmental effects shape
host-symbiont associations across a latitudinal thermal gradient by comparing symbiont
community composition within and across populations of Exaiptasia diaphana. This chapter
gives insight into the evolution and potential constraints on symbiont specificity, which will
determine the capacity for symbiotic organisms to respond to environmental change over
ecological and evolutionary timescales.

8
References
Baker, A. C. (2003). Flexibility and Specificity in Coral-Algal Symbiosis: Diversity, Ecology,
and Biogeography of Symbiodinium. Annu. Rev. Ecol. Evol. Syst. 34, 661–689.
Bay, L. K., Doyle, J., Logan, M., and Berkelmans, R. (2016). Recovery from bleaching is
mediated by threshold densities of background thermo-tolerant symbiont types in a reef-
building coral. R Soc Open Sci 3, 160322.
Bay, R. A., and Palumbi, S. R. (2015). Rapid Acclimation Ability Mediated by Transcriptome
Changes in Reef-Building Corals. Genome Biol. Evol. 7, 1602–1612.
Berkelmans, R., and van Oppen, M. J. H. (2006). The role of zooxanthellae in the thermal
tolerance of corals: a “nugget of hope” for coral reefs in an era of climate change. Proc.
Biol. Sci. 273, 2305–2312.
Bieri, T., Onishi, M., Xiang, T., Grossman, A. R., and Pringle, J. R. (2016). Relative
Contributions of Various Cellular Mechanisms to Loss of Algae during Cnidarian
Bleaching. PLoS One 11, e0152693.
Bitter, M. C., Wong, J. M., Dam, H. G., Donelan, S. C., Kenkel, C. D., Komoroske, L. M., et al.
(2021). Fluctuating selection and global change: a synthesis and review on disentangling the
roles of climate amplitude, predictability and novelty. Proc. Biol. Sci. 288, 20210727.
Brahim, A., Mustapha, N., and Marshall, D. J. (2018). Non-reversible and Reversible Heat
Tolerance Plasticity in Tropical Intertidal Animals: Responding to Habitat Temperature
Heterogeneity. Front. Physiol. 9, 1909.
Camp, E. F., Schoepf, V., Mumby, P. J., Hardtke, L. A., Rodolfo-Metalpa, R., Smith, D. J., et al.
(2018). The Future of Coral Reefs Subject to Rapid Climate Change: Lessons from Natural
Extreme Environments. Frontiers in Marine Science 5. doi: 10.3389/fmars.2018.00004.
Cunning, R., Silverstein, R. N., and Baker, A. C. (2015). Investigating the causes and
consequences of symbiont shuffling in a multi-partner reef coral symbiosis under
environmental change. Proc. Biol. Sci. 282, 20141725.
Díaz-Almeyda, E. M., Prada, C., Ohdera, A. H., Moran, H., Civitello, D. J., Iglesias-Prieto, R., et
al. (2017). Intraspecific and interspecific variation in thermotolerance and photoacclimation
in Symbiodinium dinoflagellates. Proc. Biol. Sci. 284. doi: 10.1098/rspb.2017.1767.
Dixon, G. B., Davies, S. W., Aglyamova, G. V., Meyer, E., Bay, L. K., and Matz, M. V. (2015).
Genomic determinants of coral heat tolerance across latitudes. Science 348, 1460–1462.
Ghalambor, C. K., McKAY, J. K., Carroll, S. P., and Reznick, D. N. (2007). Adaptive versus
non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new
environments. Funct. Ecol. 21, 394–407.
Gleason, L. U., Strand, E. L., Hizon, B. J., and Dowd, W. W. (2018). Plasticity of thermal
tolerance and its relationship with growth rate in juvenile mussels (Mytilus californianus).
9
Proc. Biol. Sci. 285. doi: 10.1098/rspb.2017.2617.
Goulet, T. L. (2006). Most corals may not change their symbionts. Mar. Ecol. Prog. Ser. 321, 1–
7.
Hawkins, T. D., and Warner, M. E. (2017). Warm preconditioning protects against acute heat-
induced respiratory dysfunction and delays bleaching in a symbiotic sea anemone. J. Exp.
Biol. 220, 969–983.
Hoegh-Guldberg, O. (1999). Climate change, coral bleaching and the future of the world’s coral
reefs. Mar. Freshwater Res. 50, 839–866.
Howells, E. J., Beltran, V. H., Larsen, N. W., Bay, L. K., Willis, B. L., and van Oppen, M. J. H.
(2011). Coral thermal tolerance shaped by local adaptation of photosymbionts. Nat. Clim.
Chang. 2, 116–120.
Howells, E. J., Berkelmans, R., van Oppen, M. J. H., Willis, B. L., and Bay, L. K. (2013).
Historical thermal regimes define limits to coral acclimatization. Ecology 94, 1078–1088.
Hughes, T. P., Anderson, K. D., Connolly, S. R., Heron, S. F., Kerry, J. T., Lough, J. M., et al.
(2018). Spatial and temporal patterns of mass bleaching of corals in the Anthropocene.
Science 359, 80–83.
Kavanagh, K. D., Haugen, T. O., Gregersen, F., Jernvall, J., and Vøllestad, L. A. (2010).
Contemporary temperature-driven divergence in a Nordic freshwater fish under conditions
commonly thought to hinder adaptation. BMC Evol. Biol. 10, 350.
Kawecki, T. J., and Ebert, D. (2004). Conceptual issues in local adaptation. Ecol. Lett. 7, 1225–
1241.
Kenkel, C. D., Almanza, A. T., and Matz, M. V. (2015). Fine-scale environmental specialization
of reef-building corals might be limiting reef recovery in the Florida Keys. Ecology 96,
3197–3212.
Koskella, B., and Bergelson, J. (2020). The study of host-microbiome (co)evolution across levels
of selection. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190604.
Levene, H. (1953). Genetic Equilibrium When More Than One Ecological Niche is Available.
Am. Nat. 87, 331–333.
Muller-Parker, G., Pierce-Cravens, J., and Bingham, B. L. (2007). Broad thermal tolerance of the
symbiotic dinoflagellatesymbiodinium muscatinei(Dinophyta) in the sea
anemoneanthopleura elegantissima(Cnidaria) from northern latitudes. J. Phycol. 43, 25–31.
Muscatine, L. (1990). The role of symbiotic algae in carbon and energy flux in reef corals.
Ecosystems of the world. Coral Reefs. Available at: https://ci.nii.ac.jp/naid/10020710664/
[Accessed March 11, 2022].
10
Robison, J. D., and Warner, M. E. (2006). Differential impacts of photoacclimation and thermal
stress on the photobiology of four different phylotypes of Symbiodinium (pyrrhophyta)1. J.
Phycol. 42, 568–579.
Rosenberg, E., and Zilber-Rosenberg, I. (2018). The hologenome concept of evolution after 10
years. Microbiome 6, 78.
Rowan, R. (2004). Coral bleaching: thermal adaptation in reef coral symbionts. Nature 430, 742.
Russnak, V., Rodriguez-Lanetty, M., and Karsten, U. (2021). Photophysiological Tolerance and
Thermal Plasticity of Genetically Different Symbiodiniaceae Endosymbiont Species of
Cnidaria. Frontiers in Marine Science 8. doi: 10.3389/fmars.2021.657348.
Sanford, E., and Kelly, M. W. (2011). Local adaptation in marine invertebrates. Ann. Rev. Mar.
Sci. 3, 509–535.
Scharfenstein, H. J., Chan, W. Y., Buerger, P., Humphrey, C., and van Oppen, M. J. H. (2022).
Evidence for de novo acquisition of microalgal symbionts by bleached adult corals. ISME J.
doi: 10.1038/s41396-022-01203-0.
Silbiger, N. J., Goodbody-Gringley, G., Bruno, J. F., and Putnam, H. M. (2019). Comparative
thermal performance of the reef-building coral Orbicella franksi at its latitudinal range
limits. Mar. Biol. 166, 126.
Somero, G. N. (2010). The physiology of climate change: how potentials for acclimatization and
genetic adaptation will determine “winners” and “losers.” J. Exp. Biol. 213, 912–920.
Stillman, J. H., and Somero, G. N. (2000). A comparative analysis of the upper thermal tolerance
limits of eastern Pacific porcelain crabs, genus Petrolisthes: influences of latitude, vertical
zonation, acclimation, and phylogeny. Physiol. Biochem. Zool. 73, 200–208.
Strong, A. E., Liu, G., Skirving, W., and Eakin, C. M. (2011). NOAA’s Coral Reef Watch
program from satellite observations. Ann. GIS 17, 83–92.
Thomas, L., Rose, N. H., Bay, R. A., López, E. H., Morikawa, M. K., Ruiz-Jones, L., et al.
(2018). Mechanisms of Thermal Tolerance in Reef-Building Corals across a Fine-Grained
Environmental Mosaic: Lessons from Ofu, American Samoa. Frontiers in Marine Science 4.
doi: 10.3389/fmars.2017.00434.
Tomanek, L., and Somero, G. N. (1999). Evolutionary and acclimation-induced variation in the
heat-shock responses of congeneric marine snails (genus Tegula) from different thermal
habitats: implications for limits of thermotolerance and biogeography. J. Exp. Biol. 202,
2925–2936.
Weis, V. M. (2008). Cellular mechanisms of Cnidarian bleaching: stress causes the collapse of
symbiosis. J. Exp. Biol. 211, 3059–3066.
Willett, C. S. (2010). Potential fitness trade-offs for thermal tolerance in the intertidal copepod
11
Tigriopus californicus. Evolution 64, 2521–2534.
Wright, S. (1984). Evolution and the Genetics of Populations, Volume 2: Theory of Gene
Frequencies. University of Chicago Press.
Zhang, Y., Million, W. C., Ruggeri, M., and Kenkel, C. D. (2019). Family matters: Variation in
the physiology of brooded Porites astreoides larvae is driven by parent colony effects.
Comp. Biochem. Physiol. A Mol. Integr. Physiol. 238, 110562.

12
CHAPTER TWO: Microhabitat acclimatization shifts physiological baselines
and thermal tolerance of the symbiotic anemone, Anthopleura elegantissima
Maria Ruggeri, Wyatt Million, Lindsey Hamilton, Carly Kenkel

Abstract
Contemporary organisms in extreme environments can give insight into how species will
respond to environmental change. The intertidal forms an environmental gradient where stress
increases with tidal height. Here, we explore the contribution of fixed genotypic and plastic
environmental effects on thermal tolerance of the intertidal anemone Anthopleura elegantissima
and its algal symbionts using a laboratory-based tank experiment. High intertidal anemones had
lower baseline symbiont-to-host cell ratios under control conditions, but their symbionts had
higher baseline maximum quantum yield compared to low intertidal anemone symbionts, despite
identical symbiont communities. High intertidal anemones maintained greater maximum
quantum yield and symbiont-to-host cell ratios under heat stress compared to low intertidal
anemones, suggesting that high intertidal holobionts have greater thermal tolerance. However,
thermal tolerance of clonal anemones acclimatized to different zones was not explained by tidal
height alone, indicating emersion duration is not the sole environmental driver of physiological
variation. Fixed genotypic effects also influenced physiological baselines, but did not modulate
thermal tolerance, demonstrating thermal tolerance is largely driven by environmental history.
These results indicate that this symbiosis is highly plastic and may be able to rapidly acclimatize
to climate change, defying the convention that symbiotic organisms are more susceptible to
environmental stress.
13
Introduction
The ability to tolerate environmental extremes can be driven by fixed or plastic effects,
but both play a role in species persistence over ecological and evolutionary timescales
(Kumarathunge et al., 2019; Palumbi et al., 2014; Somero, 2010). Fixed genetic effects
determine standing trait variation and tolerance limits between individuals, populations, and
species, whereas plastic effects shift trait values within an individual. Fixed genetic effects can
drive population-level adaptation over generations, whereas plastic effects can influence survival
of an individual, potentially facilitating adaptation over longer time-scales (Kelly, 2019). At
small spatial scales, phenotypic differentiation is likely due to physiological plasticity, or
acclimatization, as high gene flow can limit adaptation (Lenormand, 2002; Richardson et al.,
2014; Sultan & Spencer, 2002). However, steep environmental gradients can increase the
strength of selection over high gene flow, which promotes adaptive divergence over
microgeographic scales, within a population's dispersal neighborhood (Felsenstein, 1976; Hendry
et al., 2001; Richardson et al., 2014). It is therefore important to understand the patterns of and
mechanisms facilitating stress tolerance in contemporary populations inhabiting extreme
environments to inform predictions of how species will respond to climate extremes expected
during global climate change.
Marine populations are generally well connected due to high dispersal, yet microgeographic
trait divergence has been documented, indicating the existence of strong selection gradients over
small spatial scales (Sanford & Kelly, 2011). The intertidal zone, in particular, forms a steep
environmental gradient that has been shown to outweigh the homogenizing effect of gene flow
(Sanford & Kelly, 2011; Somero, 2010). For example, congeneric species of crabs and snails
exhibit divergence in thermal tolerance traits within meters of intertidal height, reflecting the
vertical zonation of these species and implicating temperature as a potential driver of adaptation
14
and speciation (Stillman & Somero, 2000; Tomanek & Somero, 1999). However, few studies
have examined intraspecific variation in thermal tolerance traits across the intertidal. A common
garden experiment using F1 progeny of Crassostrea gigas found higher survival, metabolic rates,
and gene expression plasticity in intertidal-origin oysters compared to subtidal populations,
indicating adaptive divergence across microgeographic scales (Li et al., 2018). However, a
reciprocal transplant in Mytilus californianus found that juvenile mussels acclimatized to greater
thermal variability increased their thermal tolerance regardless of tidal height origin, suggesting
plasticity can also drive thermal tolerance divergence across the intertidal (Gleason et al., 2018).
Therefore, the relative roles of adaptation versus plasticity in driving thermal tolerance variation
across microgeographic scales remains unclear.
The importance of fixed and plastic effects at microgeographic scales can also be influenced
by symbiotic associations, especially when partners differ in their capacity for gene flow
(Thornhill et al., 2017) or when physiological thresholds are determined by holobiont rather than
individual partner limits (Rosenberg et al., 2007; Zilber-Rosenberg & Rosenberg, 2008).
Tropical corals are a particularly thermally sensitive symbiosis, where dysbiosis, or dissociation
of dinoflagellate symbionts from the host cnidarian commonly known as thermal bleaching,
occurs only 1-2°C above average maximum monthly temperatures (Hoegh-Guldberg, 1999). Yet
thermal tolerance limits can differ over microgeographic scales. Previous research in acroporid
corals from adjacent tidal pools 500 meters apart with contrasting daily thermal variability has
indicated a role for both fixed and plastic effects in driving divergence of thermal tolerance traits
in the holobiont (reviewed in Thomas et al., 2018). However, unlike the acroporids, poritid
corals from these same pools did not exhibit significant differences in thermal tolerance (Klepac
& Barshis, 2020), complicating the idea that more thermally variable environments always
15
increase thermal tolerance and highlighting the importance of understanding the mechanisms that
drive intraspecific variation in thermal tolerance across small spatial scales.
Populations of the symbiotic anemone, Anthopleura elegantissima, are distributed throughout
the intertidal zone along the Pacific coast of North America where it experiences extreme
environmental fluctuations in temperature, light, food and water availability (Bingham et al.,
2011; Dimond et al., 2011). Similar to reef-building corals, these anemones form a mutualistic
symbiosis with the dinoflagellate, Breviolum muscatinei (formerly Symbiodinium, ITS2 type B4,
Lajeunesse and Trench, 2000; LaJeunesse et al., 2018). Despite the general thermal sensitivity of
cnidarian-dinoflagellate symbioses, these anemones can experience temperature fluctuations of
up to 20°C during tidal exposure (Bingham et al., 2011). In addition to sexual reproduction, A.
elegantissima also reproduce asexually to form large clonal aggregations, allowing the
opportunity to leverage genetically identical individuals to tease apart plasticity from host
genotypic effects. Anemones in the center of aggregations experience reduced thermal
fluctuations compared to clonemates on the outer edges (Bingham et al., 2011), which could lead
to differences in thermal tolerance within clonal anemones of a single genotype. This provides a
unique system in which to explore the mechanisms leading to exceptional holobiont thermal
tolerance across small spatial scales.
In this study, we explored the contribution of fixed and plastic effects on holobiont
thermal tolerance of A. elegantissima acclimatized to different thermal environments. Replicate
anemone aggregations were sampled from high and low intertidal environments, including clonal
anemones taken from the center and edge of each aggregation, and exposed to control or elevated
temperature (+10°C) for 10 days (Figure 2.1). Thermal tolerance was assessed by measuring
maximum quantum yield of PSII, as an indicator of photodamage to the symbiont, and bleaching
16
phenotypes, including symbiont to host cell ratios and chlorophyll concentration. 2bRAD
genotyping was performed to verify host clonality within and among aggregations (Manzello et
al., 2019; Wang et al., 2012) and symbiont community composition was assessed using amplicon
sequencing of the ITS2 locus (Hume et al., 2019). Genotyping revealed that some independent
aggregations spanning multiple tidal heights were found to be clonal, which provided an
opportunity to explore thermal tolerance plasticity across the intertidal. We found that
physiological baselines were defined by both microhabitat and host genotype, but microhabitat
was the primary driver of thermal tolerance variation, suggesting acclimatization can greatly
increase holobiont tolerance.
Materials and methods
Environmental characterization
Onset HOBO Tidbit MX400 temperature loggers were deployed in-situ in the high
(+1.37m) and low (+0.47m) intertidal at Shark Harbor (latitude 33° 23' 0.8226", longitude -118°
28' 24.0594") from November 2019-November 2022 to characterize the range of environmental
conditions experienced within each microhabitat (Fig. 1a). Temperature was recorded every 5
minutes and Bluetooth Off Water Detect was used to record when loggers were exposed to air.
HOBO pendant loggers were also deployed in the center and edge of one low intertidal
aggregation to characterize temperature variation within an aggregation (Fig. 1b). Due to
difficulties in logger placement, no logger was able to be placed in the center of the high
aggregation. Therefore temperature data from the center and edge of a high aggregation at a
nearby site in Los Angeles County (Point Dume: latitude 34° 0' 7.5486", longitude -118° 48'
18.1044") were also collected.

17

Figure 2. 1: Diagram of sampling, experimental design, and thermal characteristics of
sampling locations. Temperature profiles contrasting the center (blue) versus the edge
(orange) of a low intertidal aggregation are shown in the top left (a) and temperature profiles
contrasting the low (blue) versus high (orange) intertidal are shown in the bottom left (b).

Heat stress experiment
Anemones were collected from Shark Harbor on Catalina Island, CA on November 13
th
,
2020 during low tide under a California Department of Fish and Wildlife specific-use scientific
collecting permit (S-183050013-18305-001). To capture an environmental gradient, 6 distinct
aggregations were sampled from the high intertidal and 6 distinct aggregations from the low
18
intertidal using a micro-spatula (Fig. 1b). Aggregations were marked with epoxy and absolute
tidal height was later measured with a DeWalt rotary laser level in November 2022 (Table A2.1).
Low intertidal anemones were sampled from an average tide height of 0.363m (range 0.233m-
0.783, Figure A2.1) and high intertidal anemones were sampled from an average tide height of
1.035m (range 0.834m-1.193m, Figure A2.1). Low aggregation 3 was partially buried during
tidal height sampling and actual sampling height was likely lower than +0.783m. From each
aggregation, 6 anemones were also collected from the inside (center) and 6 from the outer
margins of the aggregation (edge), totaling 144 samples (Figure 2.1a). Anemones were placed in
plastic bags full of seawater and transported to the Wrigley Marine Science Center, where they
were cleaned of all debris, blotted dry, and weighed prior to being settled onto aragonite plugs
set inside 29 mL plastic cups (Figure A2.2). Cups were covered with mesh for 24 hours to ensure
attachment and distributed into six aquaria, equipped with Marine LED lights (150
umol/photons/m
2
) and submersible pumps, so that each tank contained one replicate of each
aggregation-position combination (Figure 2.1). Aquaria were submerged in water baths in a
temperature controlled room at 18°C and anemones were acclimated to these control conditions
for 7 days prior to experimentation. Pulse-amplitude modulated fluorometry (PAM) was used
daily to monitor photo-acclimation. Stabilization of values, indicating photoacclimation, was
observed on day 5 (Figure A2.3).
On day 8, temperatures in treatment tanks (n=3) were increased at 1°C per hour using 100-Watt
heaters (Aquaneat) until they reached 28°C and were held at that temperature for 10 days (28.1±
0.239°C on average), while the control tanks (n=3) remained at 18°C (18.3 ± 0.223°C on
average, Figure A2.4). Anemones were fed frozen brine shrimp twice weekly and 25% water
changes were performed after feeding. Salinity was measured daily using a refractometer and
19
averaged 35 ppt. At the end of the experimental period, anemones were relaxed in a 1:1
seawater: 0.3M MgCl2 solution and tentacle biopsies were taken from each individual and
immediately frozen at -80°C.

Thermal physiology
Maximum quantum yield (MQY) of photosystem II was measured daily during the
experimental period following one hour of dark acclimation to assess photosynthetic
performance using a Walz diving-PAM with the following settings: measuring intensity = 6,
saturation intensity = 6, pulse width = 0.8, gain = 6, damp = 2. Contraction was induced by
gently prodding anemones and MQY measurements were taken of the body column.
Frozen tentacle biopsies were thawed and homogenized in 1 mL extraction buffer (100 mM Tris
HCl, 0.05 mM DTT, pH 7.8) using a rotor stator (VWR 200 homogenizer). Host and symbiont
cells were then separated via centrifugation for 3 min at 1500 x G. Resulting host supernatant
was removed and stored in 2 mL cell culture plates at -80°C and symbiont pellets were stored in
1.5 mL tubes at -80°C until protein and chlorophyll quantification, respectively. Protein
concentration was quantified from the host supernatant using a BCA protein assay kit and
normalized by the volume of elution buffer added during homogenization. To extract
chlorophyll, symbiont pellets were resuspended in 90% acetone with 5-6 1mm stainless steel
beads (McMaster-Carr) and homogenized in an Omni bead beater (30s at 4m/s for 3 cycles) to
lyse cell walls. Chlorophyll samples were incubated at -20°C overnight before measuring the
absorbance at 630, 647, and 664nm in triplicate using a Bio-tek, Synergy, H1M Microplate
reader. Chlorophyll a was calculated from absorbance values using the equation from Ritchie
(2008) and normalized by host soluble protein content.
20

Symbiont to host cell ratios
DNA was extracted from frozen tentacles following Wayne’s method (Wilson et al.,
2002), using an enzymatic lysis followed by alcohol precipitation. DNA was not obtained from
eight samples due to failed extractions, resulting in a final sample size of 136. Symbiont to host
cell ratios were quantified via qPCR by amplifying a host-specific ATPase gene and a symbiont-
specific cp23S-rDNA gene (Dziedzic, 2019, Dziedzic et al., in prep). Primer-specific reactions
were run in duplicate on an Agilent AriaMx with 2x Brilliant III Ultra-Fast SYBR MM, 1 mM
reference dye, 400 nM forward and reverse primer, and 20 ng of template DNA in 20 ul
reactions. Primer-specific Ct values of technical replicates were averaged after reference dye
correction and symbiont to host ratios were calculated as the fold change of symbiont to host Ct
multiplied by 2 to correct for copy number [(2^(Cthost – Ctsymbiont))*2] following (Cunning &
Baker, 2012).

Host genotyping
To verify clonality of the host, a subset of 48 DNA samples representing each
aggregation-position-treatment combination were genotyped using 2bRAD (Wang et al., 2012)
using a reduced representation approach targeting 1/16
th
of BcgI sites. 2bRAD libraries were
then sequenced on the NextSeq 500 by the USC Molecular Genomics core in June 2022. Two
sequencing runs were performed yielding a total of 26.6 M raw reads.
Bioinformatic analysis was performed on the USC CARC HPC system following the
pipeline described in https://github.com/z0on/2bRAD_denovo. First, reads were de-multiplexed
based on internal ligation adaptor barcodes prior to adaptor trimming and removal of PCR
21
duplicates using a custom perl script. Reads were then quality filtered to retain only 99%
accurate base calls over 100% of the read using the fastx-toolkit and reads containing library
adaptor sequences were removed. Following de-multiplexing, quality filtering and removal of
PCR duplicates, 133,597 reads per sample remained from run 1 and 115,373 reads from run 2,
totaling 248,170 reads per sample on average. High quality reads were concatenated across
samples and competitively mapped to a combined A. elegantissima genome (Elder, 2020) and
Breviolum minutum (formerly Symbiodinium minutum, ITS2 type B1) draft genome (Shoguchi et
al., 2013) using Bowtie2 (Langmead & Salzberg 2012). Cleaned reads are publicly available
under NCBI BioProject PRJNA933708.
ANGSD 0.933 (Korneliussen et al., 2014) was used to call genotype likelihoods for reads
exhibiting high quality matches to the anemone genome after filtering for high confidence SNPs
present in at least 75% of samples. Hierarchical clustering of the resulting identity by state (IBS)
matrix was used to identify clonal individuals based on known technical replicates as described
in (Manzello et al., 2019). The genotype designation for one sample (11-O-1) was unclear, as
this sample clustered with genet 13, while other replicates from this aggregation clustered with
genet 10 (Figure A2.5). We therefore decided to re-prepare this sample along with the remaining
replicates of aggregations 11 and 13, which were sequenced on the NextSeq 2000 by the USC
Molecular Genomics core in November 2022 resulting in 17.4 M raw reads. Samples were
processed and analyzed using the pipeline described above, which confirmed anemones from
aggregation 11 were unique from aggregation 13 (Figure A2.6).

22
Symbiont community profiling
Amplicon libraries of the ITS2 region were prepared using primers designed by Hume et al
(2019). PCR reactions were prepared with 50 ng of input DNA and amplified using the following
profile [98°C 0:30, (98°C 0:10 | 56°C 1:00 | 72°C 0:30) x 22, 72°C 5:00]. A second PCR was
then run to incorporate sample-specific barcodes [98°C 0:30, (98°C 0:10 | 59°C 0:30 | 72°C 0:30)
x 6, 72°C 2:00] and products were visualized by gel electrophoresis. 12-sample pools were
combined based on band intensity, which were quantified spectrophotometrically using the
Quant iT PicoGreen dsDNA assay kit. Equal amounts of each pool were added to the final
library. Paired-end 250 bp reads were sequenced on the MiSeq v2 by the USC Molecular
Genomics core with 30% PhiX spike-in yielding a read depth of ~29,000 reads per sample across
two runs (nano and v2 chemistry) in June and July 2022. Forward and reverse reads were
concatenated across runs and analyzed in SymPortal using default settings. ITS2 profiles were
generated in SymPortal by collapsing co-occuring defining intragenomic variants (DIVs) within
samples (Hume et al., 2019). Raw forward and reverse reads concatenated across runs can be
found under NCBI BioProject PRJNA933708.

Statistical Analyses
Statistical analyses were performed in R v4.2.1. Differences in environmental parameters
(temperature and exposure) were evaluated across intertidal zones and positions within
aggregations using Welch’s t-test assuming unequal variances across groups. All traits and their
residuals were screened for outliers and normality and transformations were applied where
appropriate. A log transformation was applied to both symbiont to host cell ratios and anemone
weight. For the full dataset, linear mixed models were used to evaluate the effects of treatment,
23
tidal height, position within aggregation, and their interaction on physiological traits (MQY,
symbiont to host cell ratios, chlorophyll), with random effects of tank and host genotype using
the lme4 package (Bates et al., 2015). Tidal height was evaluated both as a continuous metric as
well as binned by intertidal zone (i.e. high vs low). Binning low aggregation 3 (+0.783m) with
the high intertidal aggregations (Table A2.2) or using continuous tidal height measurements
(Table A2.3) did not change the overall model results, therefore we have elected to present
results binned by original tidal zone designations (Table A2.4). Where there was a significant
random effect of host genotype, an additional model was run with host genotype as an
independent fixed effect and as an interaction term with treatment to facilitate pairwise
comparisons across genets using the ‘emmeans’ package in R (Lenth, 2022). Linear regressions
between traits (MQY, symbiont to host cell ratios, and chlorophyll) were evaluated using linear
models to explore the relationship between commonly measured bleaching phenotypes. As
weight was only measured initially, treatment and tank were not included in models for anemone
weight.
2bRAD genotyping revealed multiple aggregations across tidal heights belonging to a
single genotype (Figure A2.5, Figure A2.1). These multi-aggregation genets, 1 and 10, were
subset to explore plastic trait variation within genotypes. Within each genet, linear mixed models
were run to evaluate the fixed effects of aggregation, treatment and their interaction on
physiological traits, with tank as a random effect. Statistical scripts and input files can be found
at https://github.com/mruggeri55/AeleHeatStress.
Results
Microhabitat gradients
24
The high intertidal (+1.37m) experienced warmer temperatures on average (+1.2°C) compared to
the low intertidal (+0.47m) in the summer through fall of 2022 (high 22.1°C; low 20.9°C,
p<0.001, Figure 2.1). The high intertidal also had 9.76°C greater daily thermal range on average
compared to the low intertidal during this same period (high 16.1°C, low 6.34°C, p<0.001,
Figure 2.1) and reached a maximum temperature of 42.6°C compared to 33°C in the low
intertidal. Average temperature did not differ between center and edge anemones in the low
intertidal at Shark Harbor (center 16.5°C, edge 16.5°C, p=0.64), but did significantly differ in the
high intertidal at Point Dume, where edge anemones experienced about 0.1°C greater
temperature on average (center 15.1°C, edge 15.2°C, p=0.002, Figure A2.7). Although not
significantly different, daily thermal range was on average 0.46°C greater on the edge of
aggregations versus the center in the low intertidal (center 5.76°C, edge 6.22°C, p=0.23, Figure
2.1). However, daily thermal range within aggregations did significantly differ at Point Dume,
with 0.94°C greater daily thermal range on the edge versus center of a high intertidal aggregation
(center 5.53°C, edge 6.47°C, p=0.001, Figure A2.7), and high intertidal edges reaching a
maximum temperature 3°C above the center of the aggregation (center 26°C, edge 29°C, Figure
A2.7).
25

Figure 2. 2: Relationship between Maximum quantum yield (MQY) and symbiont to host cell
ratios across tidal zones and treatment. Density plots show the distribution of samples for a
given trait (top MQY, right log(sym/host)). Samples from the high intertidal and low intertidal
are represented in orange and blue, respectfully. Dark shading represents samples in control
conditions whereas light shading represents heat stressed samples. Asterisks (*) denote
significance of fixed factors from linear mixed effect models (p < 0.05).

Shifting physiological baselines among intertidal zones and host genotypes
All genotyped samples hosted an identical symbiont profile (1379_B/1393_B-1607_B-
B4-1608_B-1418_B-1612_B) corresponding to the species Breviolum muscatinei (LaJeunesse &
Trench 2000 ; blastn 98.51% identity, E=5e
-129
, Figure A2.8), previously identified as the
26
dominant associate of A. elegantissima in southern California (Sanders & Palumbi, 2011; Secord
& Augustine, 2005).
Among aggregations, low intertidal anemones tended to have higher symbiont to host cell
ratios, but their symbionts had reduced maximum quantum yield in comparison to their higher
intertidal counterparts (Figure 2.2). This was supported by a significant fixed effect of intertidal
zone on MQY (p=0.015, Table A2.4). Although there was no fixed effect of intertidal zone on
symbiont to host cell ratios (p=0.348, Table A2.4), MQY (p<0.001) was a significant predictor
of symbiont to host cell ratios and varied by tidal zone (p=0.011) (Figure 2.2, Table A2.5),
indicating that symbiont performance differs among anemone aggregations inhabiting different
intertidal zones and may influence symbiont density. Chlorophyll a was weakly correlated with
both MQY (p<0.01, R
2
=0.06, Figure A2.9a) and symbiont to host cell ratios (p<0.001, R
2
=0.17,
Figure A2.9b), but there was no effect of intertidal zone. Position within an aggregation did not
affect MQY, symbiont to host cell ratios, or chlorophyll concentration (Figure A2.10, Table
A2.4). However, position did have a significant effect on weight (p<0.001), where clonemates in
the center of aggregations weighed significantly more than those on the edges of aggregations
(Figure A2.11).
Although independent aggregations were initially assumed to represent different host
genotypes (McFadden et al., 1997), IBS clustering of 428 SNPs revealed that some aggregations
in different zones belonged to the same clonal group (Figure A2.5). Aggregations 1, 2, 3, 7, and
8 were collapsed into a single genet (Genet 1), as well as aggregations 10 and 11 (Genet 10),
resulting in seven, rather than twelve, unique host genotypes. Because clonal aggregations of a
single genotype spanned different tidal heights and zones, we were able to explore the effect of
microhabitat acclimatization on physiological performance while controlling for host genetic
27
background. Aggregation had a significant effect on MQY (p<0.001) and symbiont to host cell
ratios (p<0.001) in Genet 1 (Figure 2.3, Table A2.6). However, trait values among aggregations
do not perfectly align with tidal height (Figure 2.3), suggesting additional environmental factors
are shifting baselines within clonal groups. Aggregations within Genet 10 reflected population-
level trends (Figure 2.2), where clonal aggregations at elevated tidal heights tended to have
higher MQY but lower symbiont to host cell ratios (Figure 2.3), although these trends were not
statistically significant (Table A2.7). Finally, genotype of the host also affected baseline trait
values. Host genet had a significant effect on symbiont to host cell ratios (p<0.01), chlorophyll
concentration (p<0.05), and weight (p<0.001), regardless of tidal height or intertidal zone. Genet
1 appears to be driving most of the genotypic effects in symbiont to host cell ratios and
chlorophyll, despite significant phenotypic variation among aggregations. Genet 1 had a
significantly greater symbiont to host cell ratios than Genet 13 (p<0.01, Figure A2.12) and
marginally higher ratios than Genet 12 (p=0.0536). Genet 1 also had significantly lower
chlorophyll concentration compared to Genet 10 (p<0.0203) and marginally less than Genet 4
(p=0.0713, Figure A2.13). Anemone weight differed significantly across 33% of pairwise
comparisons between genets (Figure A2.14). In contrast, MQY of the symbiont was not affected
by host genotype.
28

Figure 2. 3: Average trait values +/- SEM (MQY, symbiont to host cell ratios, and
chlorophyll a) under control and heat stress conditions of clonal aggregations sampled
from different tidal heights. Plots are grouped by genotype (top Genet 1, bottom Genet 10).
Independent aggregations of the same genet are colored by tidal height (meters). Cooler
colors represent aggregations sampled lower in the intertidal and warmer colors represent
aggregations higher in the intertidal.

Response to heat stress
MQY (9%, p<0.001), symbiont to host cell ratios (58%, p<0.001), and chlorophyll a
concentration (40%, p<0.001) all declined in anemones exposed to heat stress. There was a
significant treatment by intertidal zone interaction for MQY (p<0.05), indicating that symbionts
responded differently to heat stress based on intertidal origin. Symbionts from high intertidal
anemones had higher baseline MQY which remained elevated under thermal stress compared to
symbionts from low intertidal anemones (Figure 2.2, Figure A2.15). MQY of high intertidal
anemone symbionts declined by 7% on average in heat stress conditions versus a 12% decline in
low intertidal anemone symbionts. There was also a significant treatment by intertidal zone
interaction on symbiont to host cell ratios (p<0.05). Low intertidal anemones had higher
29
symbiont to host cell ratios under control conditions, but ratios declined more under heat stress
(71%) compared to high intertidal anemones (46%, Figure 2.2). Chlorophyll a concentration
declined by 40% under heat stress on average, with a reduction of 45% in high intertidal
anemones compared to 35% in low intertidal anemones, but this effect was not statistically
significant (Figure A2.15).
No fixed effect of host genotype was observed for the response to heat stress in any
physiological trait. However, among aggregations within a genet, microhabitat acclimatization
had a variable effect on the response to heat stress. Genet 10’s response reflects the additive
zone-level patterns (Figure 2.2) where aggregations positioned higher in the intertidal (0.834m)
maintained about 5% higher MQY values and 11% higher symbiont to host cell ratios on average
under heat stress compared to clones from lower intertidal origin (0.389m), although these
differences were not statistically significant (Figure 2.3). For Genet 1, aggregation and treatment
did have a significant interactive effect on MQY (p<0.05) but the response was not driven by
tidal height or zone. Under heat stress, MQY was significantly lower for symbionts acclimatized
to 0.253m compared to symbionts acclimatized to 1.038m and 1.108m (p<0.001, Figure 2.3),
consistent with results for Genet 10 and the full dataset. However, MQY was also significantly
different between clonemates acclimatized to similar tidal heights (0.233m vs 0.253m) under a
common heat stress (p<0.001). In this case, symbionts inhabiting clonemates from the lower
tidal height maintained higher MQY values under heat stress (Figure 2.3). Overall symbiont to
host cell ratios were not differentially affected by treatment across aggregations of Genet 1.
However, anemones from a tidal height of 1.108m had 31% higher symbiont to host cell ratios
after heat stress compared to clonemates sampled from 0.253m (p<0.05), demonstrating that
microhabitat acclimatization can affect thermal dysbiosis.
30

Discussion
Thermal variability is well known to increase thermal tolerance in intertidal organisms across
moderate (shorelines) to large spatial scales (latitudinal) (Brahim et al., 2018; Gaitán-Espitia et
al., 2014; Gleason et al., 2018; Willett, 2010), but here we show intraspecific divergence in
thermal tolerance traits of two cooperative species can occur within meters. Microhabitat
acclimatization affected both baseline differences in symbiotic traits and their sensitivity to
thermal stress. Higher intertidal anemones had lower symbiont to host cell ratios, but greater
photosynthetic efficiency under control conditions, which was better maintained under heat
stress, indicating greater thermal tolerance of high intertidal anemones compared to those lower
in the intertidal (Figure 2.2). Symbiont profiles were consistent among all individuals (Figure
A2.8), indicating community-level variation is not driving differences in performance, though
population-level variation could not be resolved. Host genotype contributed to physiological
variation, but differing baselines persisted for 17 days in common garden conditions, even in
genetically identical individuals (Figure 2.3), suggesting a long-term effect of environmental
acclimatization. Further, there was no effect of host genotype on the response to thermal stress in
any physiological trait, indicating that environmental history is the primary driver of phenotypic
variation in anemones and their symbionts under thermal stress.

Acclimatization to more extreme environments increases symbiont photosynthetic efficiency but
decreases symbiont to host cell ratios
There was a positive correlation between symbiont performance (MQY) and symbiont to host
cell ratios, which was modulated by intertidal zone (Figure 2.2). Lower intertidal anemones had
higher symbiont to host cell ratios, but lower MQY in contrast to the lower symbiont to host cell
31
ratios and greater MQY observed in higher intertidal anemones (Figure 2.2). This suggests that
more extreme environments shift symbiotic baselines toward less dense symbiont communities
with greater photosynthetic performance. Consistent with population-level results, MQY was
also elevated in clonal aggregations from greater tidal heights (Figure 2.3), despite no change in
host genetic background or symbiont community profile, suggesting photosynthetic performance
is the result of acclimatization. However, there is some indication that genotypic differences can
modulate physiological acclimatization. Genet 10’s symbiont to host cell ratios reflected
population-level patterns, but high intertidal aggregations from Genet 1 tended to have greater
symbiont to host cell ratios than low intertidal aggregations, opposite of population-level patterns
(Figure 2.3). Host genotype also had a significant effect on symbiont to host cell ratios, but not
MQY, suggesting baseline symbiont to host cell ratios may be driven by a combination of
genetic and environmental effects.
Symbioses are known to be environmentally responsive with acclimatization reported in
several systems, including cnidarian-algal (Putnam, 2021), Wolbachia-insect (Mouton et al.,
2007), and plant-mycorrhizal symbioses (Ma et al., 2021). In reef-building corals, seasonal
acclimatization induces shifts in many physiological traits in both partners, including symbiont
pigmentation and photosynthetic performance, host protein and respiration, and holobiont traits,
such as symbiont density and photosynthesis/respiration ratios (Scheufen et al., 2017). Seasonal
acclimatization to elevated temperature and irradiance during summer months is associated with
greater MQY of coral symbionts (Jurriaans & Hoogenboom, 2020; Scheufen et al., 2017) and
reduced symbiont densities (Fagoonee et al., 1999; Fitt et al., 2000; Scheufen et al., 2017), which
enhances photosynthetic productivity (Scheufen et al., 2017). This pattern is consistent with
acclimatization to higher tidal positions observed in the present study, suggesting that light
32
and/or temperature are also the primary environmental drivers. Although we did not observe
differences in chlorophyll concentration by tidal zone, chlorophyll was normalized by host
protein, which could also be affected by prior environmental history. In tropical corals, both total
protein and chlorophyll are lower in summer months compared to winter (Scheufen et al., 2017).
Although we could not measure total protein in this study, chlorophyll and protein may co-vary
in anemones acclimatized to different thermal conditions resulting in no detectable difference in
this trait across tidal zones.
Thermal stress did not affect the relationship between symbiont to host cell ratios and
MQY, but both traits exhibited greater proportional declines in low intertidal anemones (Figure
2.2), indicating a greater thermal tolerance of anemone holobionts acclimatized to more extreme
environments. Small but consistent declines in MQY were observed under thermal stress, with
low intertidal symbionts experiencing more photodamage than high intertidal symbionts.
Reactive oxygen species (ROS) produced by photodamage to symbionts is a key player in the
coral bleaching cascade (Lesser, 1996; Weis, 2008) and may have triggered greater bleaching in
low intertidal anemones observed here, despite higher baseline densities (Figure 2.2). Coral
symbionts can acclimate to prior thermal exposure by increasing photoprotective mechanisms,
which reduces symbiont photodamage and bleaching under subsequent thermal stress
(Middlebrook et al., 2008). As symbiont communities are homogenous across the intertidal,
greater thermal performance of high intertidal symbionts could be due to acclimatization to more
frequent thermal stress. However, higher symbiont densities in coral also increase bleaching
susceptibility (Cunning & Baker, 2012), due to greater cumulative oxidative stress produced by
larger symbiont populations. Therefore reduced baseline symbiont densities of high intertidal
33
anemones could also be an acclimatory mechanism of the host that increases bleaching
resistance.

Microhabitat acclimatization drives thermal tolerance variation across fine-spatial scales
Microhabitat acclimatization was the primary driver of thermal tolerance variation across the
intertidal. MQY and symbiont to host cell ratios were differentially affected by treatment based
on intertidal zone (Figure 2.2), but not by host genotype. Additionally, within genetically
identical hosts and symbionts, clones acclimatized to higher intertidal positions performed better
under heat stress (Figure 2.3). In intertidal limpets, one-day exposure to low tide conditions
increased thermal tolerance by up to 6°C (Pasparakis et al., 2016), emphasizing the importance
of environmental history on tolerance limits. Although tidal height acclimatization has been
implicated in physiological variation in several invertebrates (Gleason et al., 2018; Pasparakis et
al., 2016), this is the first study to evaluate plasticity in thermal tolerance across the intertidal
while controlling for the influence of genetic effects. The present results support the evolutionary
theory that variable environments should select for more plastic individuals (Hendry, 2016; Van
Tienderen, 1991), especially over small spatial scales when migration is high (Lenormand, 2002;
Richardson et al., 2014; Sultan & Spencer, 2002). High intertidal anemones experienced 9.76°C
greater daily thermal variation on average and reached maximum temperatures of 9.6°C above
low intertidal anemones, which increased holobiont thermal tolerance under a sustained thermal
stress (+10°C for 10 days), indicating some cnidarian-algal symbioses do have the capacity to
acclimatize to extreme environmental conditions expected under climate change.
Although there is a clear effect of microhabitat acclimatization on physiological baselines and
thermal tolerance, there may be other environmental factors driving variation in addition to
34
emersion duration. Within host genotypes, thermally responsive traits were significantly
modulated by aggregation (Figure 2.2, Table A2.6). However, physiological trait values did not
perfectly align with tidal height measurements within genets (Figure 2.2), suggesting additional
environmental factors may differ between aggregations. The intertidal zone has been previously
described as an environmental mosaic (Helmuth et al., 2006), where solar and geothermal
heating can drive microscale thermal variation within tidal zones (Denny & Harley, 2006;
Marshall et al., 2010). It is therefore possible that thermal stress does not increase linearly with
tidal height. As other factors such as light stress can also affect thermal tolerance in symbiotic
cnidarians independent of solar heating (Brown et al., 2002), additional fine-scale data are
needed to pinpoint which environmental factors are affecting physiological variation at an
aggregation-level. Nevertheless, an additive effect of intertidal zone on the physiological
response to treatment among all genets (Figure 2.2), and the tendency for high intertidal
acclimatization to elevate thermal tolerance within genets (Figure 2.3), suggests more extreme
environments increase thermal tolerance, but experimental manipulations are necessary to test
which environmental factors or combinations thereof, modify thermal tolerance in this system.
Additional population-level variation in the symbionts beyond the detection limits of the
sequencing method applied here could also play a role in physiological differences. ITS2
amplicon sequencing is the field standard for characterizing symbiont communities, but the
multicopy nature of this marker complicates inter- versus intra-genomic variant calling,
preventing population-level analyses (Davies et al., 2022). Using 2bRAD sequencing, Cornwell
and Hernandez (2021) found Anthopleura-associated B. muscatinei symbiont genotypes were
correlated to benthic community composition within sites, indicating environmental variation
may influence symbiont population structure. As strains of B. minutum can differ in baseline
35
maximum quantum yield, chlorophyll concentration, growth rate, and the sensitivity of these
traits to thermal stress (Bayliss et al., 2019), symbiont population-level variation could also be
driving physiological divergence across the intertidal, but this remains to be tested. Additional
genetic variation could also be present in the host due to somatic mutations, which cannot be
accurately distinguished from technical errors in the absence of high sequencing coverage
(Coorens et al., 2021). Because A. elegantissima can reproduce asexually, novel variants
generated from somatic mutations can be fixed or lost in newly formed clones and drive
phenotypic variation (Reusch et al., 2021). Although these additional levels of genetic variation
cannot be resolved in the present study, clonal hosts and their symbionts are nonetheless closely
related, supporting acclimatization as the prominent driver of physiological variation.

Limits to microgeographic acclimatization
Position within an aggregation did not affect symbiotic traits, but did affect anemone weight,
suggesting anemone size does not tradeoff with thermal tolerance. Anemones from the center of
aggregations weighed significantly more than those on the edge of aggregations (Figure A2.11),
which did not affect trait baselines or thermal sensitivity, despite edge anemones experiencing
greater daily thermal variation (Figure 2.1). Size differences within an aggregation is supported
by previous research (Francis, 1976) which additionally found anemones in the center of
aggregations are more likely to be reproductive than those on the edge. Consistent trait baselines
and thermal sensitivity, despite differences in size, suggests there may not be fitness tradeoffs to
thermal tolerance maintenance, but rather to greater cumulative stress. This also suggests that
differences in daily range of only 0.5-1°C on average are not enough to induce thermal
acclimatization, and more extreme environmental divergence such as between aggregations and
36
tidal zones are necessary to increase thermal tolerance. A meta-analysis on coral bleaching
reported that a 1°C increase in daily range could reduce bleaching by a factor of 33 (Safaie et al.,
2018). However, this was based on thermal characteristics of contemporary reefs, which reflects
both adaptation and acclimatization, and may not accurately project within-generational
responses to climate change. Thermal priming of coral holobionts to 3-4°C daily variation has
had variable success (Bay & Palumbi, 2015; Dilworth et al., 2021), emphasizing a need to
understand the relationship between the scale of environmental variability and tolerance limits.

37
References
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using
lme4. Journal of Statistical Software, 67, 1–48.

Bayliss, S. L. J., Scott, Z. R., Coffroth, M. A., & terHorst, C. P. (2019). Genetic variation in Breviolum
antillogorgium, a coral reef symbiont, in response to temperature and nutrients. Ecology and
Evolution, 9(5), 2803–2813.

Bay, R. A., & Palumbi, S. R. (2015). Rapid Acclimation Ability Mediated by Transcriptome Changes in
Reef-Building Corals. Genome Biology and Evolution, 7(6), 1602–1612.

Bingham, B. L., Freytes, I., Emery, M., Dimond, J., & Muller-Parker, G. (2011). Aerial exposure and
body temperature of the intertidal sea anemoneAnthopleura elegantissima. Invertebrate Biology: A
Quarterly Journal of the American Microscopical Society and the Division of Invertebrate
Zoology/ASZ, 130(4), 291–301.

Brahim, A., Mustapha, N., & Marshall, D. J. (2018). Non-reversible and Reversible Heat Tolerance
Plasticity in Tropical Intertidal Animals: Responding to Habitat Temperature Heterogeneity.
Frontiers in Physiology, 9, 1909.

Brown, B., Dunne, R., Goodson, M., & Douglas, A. (2002). Experience shapes the susceptibility of a reef
coral to bleaching. Coral Reefs , 21(2), 119–126.

Coorens, T. H. H., Oliver, T. R. W., Sanghvi, R., Sovio, U., Cook, E., Vento-Tormo, R., Haniffa, M.,
Young, M. D., Rahbari, R., Sebire, N., Campbell, P. J., Charnock-Jones, D. S., Smith, G. C. S., &
Behjati, S. (2021). Inherent mosaicism and extensive mutation of human placentas. Nature,
592(7852), 80–85.

Cornwell, B. H., & Hernández, L. (2021). Genetic structure in the endosymbiont Breviolum “muscatinei”
is correlated with geographical location, environment and host species. Proceedings of the Royal
Society B: Biological Sciences, 288(1946), 20202896.

Cunning, R., & Baker, A. C. (2012). Excess algal symbionts increase the susceptibility of reef corals to
bleaching. Nature Climate Change, 3(3), 259–262.

Davies, S., Gamache, M. H., Howe-Kerr, L. I., Kriefall, N. G., Baker, A. C., Banaszak, A. T., Bay, L. K.,
Bellantuono, A. J., Bhattacharya, D., Chan, C. X., & Others. (2022). Building consensus around the
assessment and interpretation of Symbiodiniaceae diversity.
https://repository.kaust.edu.sa/handle/10754/679256

Denny, M. W., & Harley, C. D. G. (2006). Hot limpets: predicting body temperature in a conductance-
mediated thermal system. The Journal of Experimental Biology, 209(Pt 13), 2409–2419.

Dilworth, J., Caruso, C., Kahkejian, V. A., Baker, A. C., & Drury, C. (2021). Host genotype and stable
differences in algal symbiont communities explain patterns of thermal stress response of Montipora
capitata following thermal pre-exposure and across multiple bleaching events. Coral Reefs , 40(1),
151–163.

Dimond, J. L., Bingham, B. L., Muller-Parker, G., Wuesthoff, K., & Francis, L. (2011). Seasonal stability
of a flexible algal-cnidarian symbiosis in a highly variable temperate environment. Limnology and
38
Oceanography, 56(6), 2233–2242.

Elder, H. L. (2020). Genomic Resource Development and Studies of Thermal Tolerance in Symbiotic
Cnidarians. https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/rr172450q

Fagoonee, I., I., Wilson, H. B., Hassell, M. P., & Turner, J. R. (1999). The dynamics of zooxanthellae
populations: A long-term study in the field. Science, 283(5403), 843–845.

Felsenstein, J. (1976). The theoretical population genetics of variable selection and migration. Annual
Review of Genetics, 10, 253–280.

Fitt, W. K., McFarland, F. K., Warner, M. E., & Chilcoat, G. C. (2000). Seasonal patterns of tissue
biomass and densities of symbiotic dinoflagellates in reef corals and relation to coral bleaching.
Limnology and Oceanography, 45(3), 677–685.

Francis, L. (1976). SOCIAL ORGANIZATION WITHIN CLONES OF THE SEA ANEMONE
ANTHOPLEURA ELEGANTISSIMA. The Biological Bulletin, 150(3), 361–376.

Gaitán-Espitia, J. D., Bacigalupe, L. D., Opitz, T., Lagos, N. A., Timmermann, T., & Lardies, M. A.
(2014). Geographic variation in thermal physiological performance of the intertidal crab Petrolisthes
violaceus along a latitudinal gradient. The Journal of Experimental Biology, 217(Pt 24), 4379–4386.

Gleason, L. U., Strand, E. L., Hizon, B. J., & Dowd, W. W. (2018). Plasticity of thermal tolerance and its
relationship with growth rate in juvenile mussels (Mytilus californianus). Proceedings. Biological
Sciences / The Royal Society, 285(1877). https://doi.org/10.1098/rspb.2017.2617

Helmuth, B., Broitman, B. R., Blanchette, C. A., Gilman, S., Halpin, P., Harley, C. D. G., O’Donnell, M.
J., Hofmann, G. E., Menge, B., & Strickland, D. (2006). Mosaic patterns of thermal stress in the
rocky intertidal zone: Implications for climate change. Ecological Monographs, 76(4), 461–479.

Hendry, A. P. (2016). Key Questions on the Role of Phenotypic Plasticity in Eco-Evolutionary Dynamics.
The Journal of Heredity, 107(1), 25–41.

Hendry, A. P., Day, T., & Taylor, E. B. (2001). Population mixing and the adaptive divergence of
quantitative traits in discrete populations: a theoretical framework for empirical tests. Evolution;
International Journal of Organic Evolution, 55(3), 459–466.

Hoegh-Guldberg, O. (1999). Climate change, coral bleaching and the future of the world’s coral reefs.
Marine and Freshwater Research, 50(8), 839–866.

Hume, B. C. C., Smith, E. G., Ziegler, M., Warrington, H. J. M., Burt, J. A., LaJeunesse, T. C.,
Wiedenmann, J., & Voolstra, C. R. (2019). SymPortal: A novel analytical framework and platform
for coral algal symbiont next-generation sequencing ITS2 profiling. Molecular Ecology Resources,
19(4), 1063–1080.

Jurriaans, S., & Hoogenboom, M. O. (2020). Seasonal acclimation of thermal performance in two species
of reef-building corals. Marine Ecology Progress Series, 635, 55–70.

Kelly, M. (2019). Adaptation to climate change through genetic accommodation and assimilation of
plastic phenotypes. Philosophical Transactions of the Royal Society of London. Series B, Biological
Sciences, 374(1768), 20180176.
39

Kumarathunge, D. P., Medlyn, B. E., Drake, J. E., Tjoelker, M. G., Aspinwall, M. J., Battaglia, M., Cano,
F. J., Carter, K. R., Cavaleri, M. A., Cernusak, L. A., Chambers, J. Q., Crous, K. Y., De Kauwe, M.
G., Dillaway, D. N., Dreyer, E., Ellsworth, D. S., Ghannoum, O., Han, Q., Hikosaka, K., … Way, D.
A. (2019). Acclimation and adaptation components of the temperature dependence of plant
photosynthesis at the global scale. The New Phytologist, 222(2), 768–784.

Lenormand, T. (2002). Gene flow and the limits to natural selection. Trends in Ecology & Evolution,
17(4), 183–189.

Lenth, R. (2022). emmeans: estimated marginal means, aka least-squares means. R package version 1.4.
7. 2020.

Lesser, M. P. (1996). Elevated temperatures and ultraviolet radiation cause oxidative stress and inhibit
photosynthesis in ymbiotic dinoflagellates. Limnology and Oceanography, 41(2), 271–283.

Li, A., Li, L., Wang, W., Song, K., & Zhang, G. (2018). Transcriptomics and Fitness Data Reveal
Adaptive Plasticity of Thermal Tolerance in Oysters Inhabiting Different Tidal Zones. Frontiers in
Physiology, 9, 825.

Manzello, D. P., Matz, M. V., Enochs, I. C., Valentino, L., Carlton, R. D., Kolodziej, G., Serrano, X.,
Towle, E. K., & Jankulak, M. (2019). Role of host genetics and heat-tolerant algal symbionts in
sustaining populations of the endangered coral Orbicella faveolata in the Florida Keys with ocean
warming. Global Change Biology, 25(3), 1016–1031.

Marshall, D. J., McQuaid, C. D., & Williams, G. A. (2010). Non-climatic thermal adaptation:
implications for species’ responses to climate warming. Biology Letters, 6(5), 669–673.

Ma, X., Geng, Q., Zhang, H., Bian, C., Chen, H. Y. H., Jiang, D., & Xu, X. (2021). Global negative
effects of nutrient enrichment on arbuscular mycorrhizal fungi, plant diversity and ecosystem
multifunctionality. The New Phytologist, 229(5), 2957–2969.

McFadden, C. S., Grosberg, R. K., Cameron, B. B., Karlton, D. P., & Secord, D. (1997). Genetic
relationships within and between clonal and solitary forms of the sea anemone Anthopleura
elegantissima revisited: evidence for the existence of two species. Marine Biology, 128(1), 127–139.

Middlebrook, R., Hoegh-Guldberg, O., & Leggat, W. (2008). The effect of thermal history on the
susceptibility of reef-building corals to thermal stress. The Journal of Experimental Biology, 211(Pt
7), 1050–1056.

Mouton, L., Henri, H., Charif, D., Boulétreau, M., & Vavre, F. (2007). Interaction between host genotype
and environmental conditions affects bacterial density in Wolbachia symbiosis. Biology Letters, 3(2),
210–213.

Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N., & Bay, R. A. (2014). Mechanisms of reef coral
resistance to future climate change. Science, 344(6186), 895–898.

Pasparakis, C., Davis, B. E., & Todgham, A. E. (2016). Role of sequential low-tide-period conditions on
the thermal physiology of summer and winter laboratory-acclimated fingered limpets, Lottia
digitalis. Marine Biology, 163(2), 23.

40
Putnam, H. M. (2021). Avenues of reef-building coral acclimatization in response to rapid environmental
change. The Journal of Experimental Biology, 224(Pt Suppl 1). https://doi.org/10.1242/jeb.239319

Reusch, T. B. H., Baums, I. B., & Werner, B. (2021). Evolution via somatic genetic variation in modular
species. Trends in Ecology & Evolution, 36(12), 1083–1092.

Richardson, J. L., Urban, M. C., Bolnick, D. I., & Skelly, D. K. (2014). Microgeographic adaptation and
the spatial scale of evolution. Trends in Ecology & Evolution, 29(3), 165–176.

Rosenberg, E., Koren, O., Reshef, L., Efrony, R., & Zilber-Rosenberg, I. (2007). The role of
microorganisms in coral health, disease and evolution. Nature Reviews. Microbiology, 5(5), 355–
362.

Safaie, A., Silbiger, N. J., McClanahan, T. R., Pawlak, G., Barshis, D. J., Hench, J. L., Rogers, J. S.,
Williams, G. J., & Davis, K. A. (2018). High frequency temperature variability reduces the risk of
coral bleaching. Nature Communications, 9(1), 1671.

Sanders, J. G., & Palumbi, S. R. (2011). Populations of Symbiodinium muscatinei show strong
biogeographic structuring in the intertidal anemone Anthopleura elegantissima. The Biological
Bulletin, 220(3), 199–208.

Sanford, E., & Kelly, M. W. (2011). Local adaptation in marine invertebrates. Annual Review of Marine
Science, 3, 509–535.

Scheufen, T., Krämer, W. E., Iglesias-Prieto, R., & Enríquez, S. (2017). Seasonal variation modulates
coral sensibility to heat-stress and explains annual changes in coral productivity. Scientific Reports,
7(1), 4937.

Secord, D., & Augustine, L. (2005). Biogeography and microhabitat variation in temperate algal-
invertebrate symbioses: zooxanthellae and zoochlorellae in two Pacific intertidal sea anemones,
Anthopleura elegantissima and A. xanthogrammica. Invertebrate Biology: A Quarterly Journal of
the American Microscopical Society and the Division of Invertebrate Zoology/ASZ, 119(2), 139–146.

Shoguchi, E., Shinzato, C., Kawashima, T., Gyoja, F., Mungpakdee, S., Koyanagi, R., Takeuchi, T.,
Hisata, K., Tanaka, M., Fujiwara, M., Hamada, M., Seidi, A., Fujie, M., Usami, T., Goto, H.,
Yamasaki, S., Arakaki, N., Suzuki, Y., Sugano, S., … Satoh, N. (2013). Draft assembly of the
Symbiodinium minutum nuclear genome reveals dinoflagellate gene structure. Current Biology: CB,
23(15), 1399–1408.

Somero, G. N. (2010). The physiology of climate change: how potentials for acclimatization and genetic
adaptation will determine “winners” and “losers.” The Journal of Experimental Biology, 213(6),
912–920.

Stillman, J. H., & Somero, G. N. (2000). A comparative analysis of the upper thermal tolerance limits of
eastern Pacific porcelain crabs, genus Petrolisthes: influences of latitude, vertical zonation,
acclimation, and phylogeny. Physiological and Biochemical Zoology: PBZ, 73(2), 200–208.

Sultan, S. E., & Spencer, H. G. (2002). Metapopulation structure favors plasticity over local adaptation.
The American Naturalist, 160(2), 271–283.

Thornhill, D. J., Howells, E. J., Wham, D. C., Steury, T. D., & Santos, S. R. (2017). Population genetics
41
of reef coral endosymbionts (Symbiodinium, Dinophyceae). Molecular Ecology, 26(10), 2640–2659.

Tomanek, L., & Somero, G. N. (1999). Evolutionary and acclimation-induced variation in the heat-shock
responses of congeneric marine snails (genus Tegula) from different thermal habitats: implications
for limits of thermotolerance and biogeography. The Journal of Experimental Biology, 202(Pt 21),
2925–2936.

Van Tienderen, P. H. (1991). EVOLUTION OF GENERALISTS AND SPECIALISTS IN SPATIALLY
HETEROGENEOUS ENVIRONMENTS. Evolution; International Journal of Organic Evolution,
45(6), 1317–1331.

Wang, S., Meyer, E., McKay, J. K., & Matz, M. V. (2012). 2b-RAD: a simple and flexible method for
genome-wide genotyping. Nature Methods, 9(8), 808–810.

Weis, V. M. (2008). Cellular mechanisms of Cnidarian bleaching: stress causes the collapse of symbiosis.
The Journal of Experimental Biology, 211(Pt 19), 3059–3066.

Willett, C. S. (2010). Potential fitness trade-offs for thermal tolerance in the intertidal copepod Tigriopus
californicus. Evolution; International Journal of Organic Evolution, 64(9), 2521–2534.

Wilson, K., Li, Y., Whan, V., Lehnert, S., Byrne, K., Moore, S., Pongsomboon, S., Tassanakajon, A.,
Rosenberg, G., Ballment, E., Fayazi, Z., Swan, J., Kenway, M., & Benzie, J. (2002). Genetic
mapping of the black tiger shrimp Penaeus monodon with amplified fragment length polymorphism.
Aquaculture , 204(3), 297–309.

Zilber-Rosenberg, I., & Rosenberg, E. (2008). Role of microorganisms in the evolution of animals and
plants: the hologenome theory of evolution. FEMS Microbiology Reviews, 32(5), 723–735.

42
Appendix
Figure A2.1. Tidal height of aggregations above or below mean lower low water (MLLW) line.
Points represent top, middle, and bottom of aggregations and are colored by intertidal zone
designation (low – blue, high – pink).

Figure A2.2. Anemones settled on aragonite plugs in plastic cups to track individuals.

−0 .5
0.0
0.5
1.0
1.5
2 4 1 5 11 3 10 13 8 12 7 9
aggregation
tidal height (m)
zone
high
low
43
Figure A2.3. Mean maximum quantum yield (MQY) +/- 1 standard error during the 7-day
acclimation period and 10-day heat stress. Dashed line denotes the end of the acclimation
period and beginning of experimental heat stress.

Figure A2.4. Temperature profiles of three replicate control tanks (pink) and experimental
tanks (blue) over the 7-day acclimation period followed by the 10-day experiment.

0.55
0.60
0.65
0.70
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Day
MQY
ZoneTrmt
high C
high H
low C
low H
17.5
20.0
22.5
25.0
27.5
Nov 16 Nov 23 Nov 30
Time
Temperature ( °C)
treatment
C
H
44
Figure A2.5. Cluster dendogram of genetic distance (1-IBS) between samples. A subset of four
samples per aggregation were sequenced including a representative from each aggregation,
position, and treatment combination. Technical replicates colored in red were used to set a
distance threshold of 0.22, represented by the dotted line, to distinguish unique genotypes.

Figure A2.6. Cluster dendogram of genetic distance (1-IBS) between all replicates of
aggregations 11 and 13, confirming that these aggregations are unique genotypes. Technical
replicates colored in red were used to set a distance threshold of 0.32, represented by the
dotted line.

9 −I −6
9 −O −3_TR2
9 −O −4
9 −I −1
9 −O −3_TR1
4 −O −2_TR1
4 −I −6
4 −I −2
4 −O −2_TR2
4 −O −4
5 −I −2
5 −O −6
5 −I −6
5 −O −3
12 −I −3
12 −O −5
12 −I −6
12 −O −3
11 −O −1
13 −O −4
13 −I −2
13 −I −4
13 −O −2
8 −O −3
8 −I −6_TR2
1 −O −3
8 −I −6_TR1
3 −I −3
8 −O −6
3 −O −6
1 −I −2
2 −I −5
2 −I −3
7 −I −4_TR2
1 −I −5_TR1
8 −I −2
2 −O −1_TR2
2 −O −6
1 −I −5_TR2
3 −O −2
7 −I −4_TR1
7 −O −4
2 −O −1_TR1
1 −O −6
7 −I −1
3 −I −4
7 −O −6
11 −O −6
10 −O −5
10 −I −6
11 −I −1
11 −I −5
10 −I −3
10 −O −3
0.10 0.15 0.20 0.25 0.30 0.35
hclust (*, "average")
as.dist(ma)
(1 −IBS)
11 −I −4
11 −I −3
11 −O −1
11 −O −4
11 −I −2
11 −O −6
11 −I −6
11 −O −2
11 −O −3
11 −I −1
11 −O −5
11 −I −6
13 −O −4
13 −I −2
13 −O −2
13 −O −3
13 −I −6
13 −I −5
13 −I −3
13 −I −1
13 −I −6
0.15 0.25 0.35 0.45
hclust (*, "average")
as.dist(ma)
(1 −IBS)
45

Figure A2.7. Temperature profile for loggers placed on the center (blue) and edge (pink) of a
high intertidal anemone aggregation at an independent site in Los Angeles County, Point
Dume.

46
Figure A2.8. Relative abundance of ITS2 profiles for each individual. All indivuduals hosted a
single profile (1379_B/1393_B-1607_B-B4-1608_B-1418_B-1612_B) corresponding to
Breviolum muscatinei (blastn 98.51% identity, E=5e
-129
).

0.00
0.25
0.50
0.75
1.00
1 −I −1
1 −I −2
1 −I −3
1 −I −4
1 −I −5
1 −I −6
1 −O −1
1 −O −2
1 −O −3
1 −O −5
1 −O −6
11 −I −1
11 −I −2
11 −I −3
11 −I −4
11 −I −5
11 −I −6
11 −O −2
11 −O −3
11 −O −4
11 −O −5
11 −O −6
2 −I −1
2 −I −2
2 −I −3
2 −I −6
2 −I −7
2 −O −1
2 −O −2
2 −O −3
2 −O −4
2 −O −5
2 −O −6
3 −I −1
3 −I −2
3 −I −3
3 −I −4
3 −I −6
3 −O −1
3 −O −2
3 −O −4
3 −O −5
3 −O −6
4 −I −1
4 −I −2
4 −I −3
4 −I −4
4 −I −6
4 −O −2
4 −O −3
4 −O −4 −1
4 −O −4 −2
4 −O −5
4 −O −6
5 −I −1
5 −I −2
5 −I −3
5 −I −4
5 −I −5
5 −I −6
5 −O −1
5 −O −2
5 −O −4
5 −O −6
Abundance
0.00
0.25
0.50
0.75
1.00
10 −I −1
10 −I −2
10 −I −4
10 −I −5
10 −I −6
10 −O −1
10 −O −2
10 −O −3
10 −O −4
10 −O −5
10 −O −6
12 −I −1
12 −I −2
12 −I −3
12 −I −4
12 −I −5
12 −I −6
12 −O −1
12 −O −2
12 −O −3 −1
12 −O −3 −2
12 −O −4
12 −O −5
12 −O −6
13 −I −1
13 −I −4 −1
13 −I −4 −2
13 −I −5
13 −I −6
13 −O −1
13 −O −2
13 −O −3
13 −O −5
13 −O −Star
7 −I −2
7 −I −4 −1 −1
7 −I −4 −1 −2
7 −I −5
7 −I −6
7 −O −1
7 −O −2
7 −O −3
7 −O −5
7 −O −6
8 −I −2
8 −I −3
8 −I −4
8 −I −5
8 −I −6
8 −O −1
8 −O −2
8 −O −3
8 −O −4
8 −O −5
8 −O −6
9 −I −1
9 −I −3
9 −I −4
9 −I −5
9 −I −6
9 −O −2
9 −O −3
9 −O −4
9 −O −6
Abundance
ITS2.type.profile
1 3 7 9 _ B/1 3 9 3 _ B−1 6 0 7 _ B−B4 −1 6 0 8 _ B−1 4 1 8 _ B−1 6 1 2 _ B
47
Figure A2.9. Trait correlations between chlorophyll concentration and MQY (a) and symbiont
to host cell ratios (b).
a) b)

Figure A2.10. The effect of position within an aggregation (edge – orange, center – blue) on
physiological traits.

Figure A2.11. Initial anemone weight by position within an aggregation (edge vs center).

0
2
4
6
0.50 0.55 0.60 0.65 0.70
MQY
ug Chla/mg protein
0
2
4
6
−4 −3 −2 −1
log(sym/host)
ug Chla/mg protein
−2
−1
0
center edge
position
log(mg wet weight)
48
Figure A2.12. Symbiont to host cell ratios by host genotype.

Figure A2.13. Chlorophyll concentration by host genotype.

Figure A2.14. Anemone weight by host genotype.

−4
−3
−2
−1
G1 G10 G12 G13 G4 G5 G9
genet
logSymHost
0
2
4
6
G1 G10 G12 G13 G4 G5 G9
genet
ug Chla/mg protein
−2
−1
0
G1 G10 G12 G13 G4 G5 G9
genet
log(mg wet weight)
49
Figure A2.15. The effect of intertidal zone (high – sand, low – blue) on physiological traits.

Table A2.1. Tidal height measurements (m) of bottom, middle, and top of aggregations
relative to mean lower low water line.

aggregation bottom middle top
1 0.631 0.821 1.011
2 0.386 0.801 1.216
3 1.031 1.351 1.671
4 0.141 0.816 1.491
7 1.376 1.676 1.976
8 1.331 1.606 1.881
9 1.516 1.761 2.006
13 1.521 1.521 1.521
5 1.19 1.31 1.43
10 1.62 1.87 2.12
11 1.325 1.425 1.525
12 2.12 2.12 2.12

0.5
0.6
0.7
0.8
C H
MQY
−5
−4
−3
−2
−1
0
C H
treatment
log(sym/host ratio)
0
2
4
6
C H
ug Chla/mg protein
Zone
high
low
50
Table A2.2. Linear mixed effect model results for all physiological traits (trait ~
treatment*Zone v2 + position + (1|genet) + (1|tank:treatment)) binning aggregation 3 as a
high intertidal aggregation. Significant fixed effects below an alpha of 0.05 are bolded.

Table A2.3. Linear mixed effect model results for all physiological traits (trait ~
treatment*tidal height + position + (1|genet) + (1|tank:treatment)) using tidal height as a
continuous variable. Significant fixed effects below an alpha of 0.05 are bolded.

51

Table A2.4. Linear mixed effect model results for all physiological traits (trait ~
treatment*zone + position + (1|genet) + (1|tank:treatment)). Significant fixed effects below
an alpha of 0.05 are bolded.

Table A2.5. Linear model results for the relationship between symbiont to host cell ratios,
maximum quantum yield (MQY), and intertidal zone.

52

Table A2.6. Linear mixed effect model results within Genet 1 for all physiological traits (trait
~ treatment*aggregation + (1|tank:treatment)).

53

Table A2.7. Linear mixed effect model results within Genet 10 for all physiological traits (trait
~ treatment*aggregation + (1|tank:treatment)).

54
CHAPTER THREE: Divergent transcriptional response to thermal stress among
life stages could constrain coral adaptation to climate change
Maria Ruggeri, Yingqi Zhang, Galina V. Aglyamova, Carly D. Kenkel
Abstract
The ability for adaptation to track environmental change depends on how efficiently selection
can act on heritable genetic variation. Complex life cycles may promote or constrain adaptation
depending on the integration or independence of fitness-related traits over development. Reef-
building corals exhibit life cycle complexity and are sensitive to increasing temperatures,
highlighting the need to understand heritable potential of the thermal stress response and its
developmental regulation. We used tag-based RNA-seq to profile holobiont gene expression of
inshore and offshore P. astreoides adults and recruit offspring in response to a 16-day heat stress,
and larvae in response to a 4-day heat stress. Developmental stage affected both host and
symbiont expression and modulated the stress response, suggesting host life-stage influences the
holobiont response to selection. Populations also exhibited origin-specific treatment responses,
but response magnitude differed among life-stages. Inshore parents and recruit offspring
exhibited a more robust stress response than offshore-origin corals, indicating expression
plasticity may be heritable. However, larval populations exhibited the opposite response,
possibly due to stage-specific effects or exposure duration. Overall, these results show that
putatively adaptive regulatory variation can be heritable, but thermally responsive genes are
stage-specific, which will complicate the evolutionary response of coral to climate change.

55
Introduction
Central to predicting organismal responses to climate change is understanding how
fitness landscapes will change over time. If traits are heritable and unconstrained, adaptation may
keep pace with environmental change (Bay et al., 2017a). However, organisms with complex life
cycles can occupy different forms, functions, and even habitats within their lifetime (Wilbur,
1980). As life stages share a common genome, variable selection over the course of development
(Aguirre et al., 2014; Coronado-Zamora et al., 2019) can affect total fitness and, ultimately,
adaptation (Albecker et al., 2021). Gene expression is a mechanistic link between genotype and
phenotype and can be measured over development to explore the degree of integration or
independence between life stages. Though differential expression is common between
developmental transitions (Schmid et al., 2005; Reyes-Bermudez et al., 2016; Herrig et al.,
2021), it is unclear whether traits relevant to all life stages, such as the response to stress, also
vary over development. As selective pressures intensify in thermally vulnerable species under
climate change (Bell and Collins, 2008), understanding how the thermal stress response is
modulated over development is key to predicting whether life cycle complexity will promote or
constrain adaptation.
Reef-building corals, the foundation of tropical reef ecosystems, have complex life cycles
and are extremely sensitive to rising temperature. Like many marine invertebrates, coral
development involves dramatic metamorphosis, where free-swimming larvae are restructured
into sessile, juvenile polyps (Richmond and Hunter, 1990). Juvenile coral begin to form a
calcium carbonate skeleton and invest in growth until they reach the reproductively mature, adult
stage. Corals also form a nutritive symbiosis with dinoflagellates in the family Symbiodiniaceae
(Muscatine, 1990), which are acquired directly from maternal colonies during oogenesis (vertical
transmission) or from the external environment (horizontal transmission) during the larval or
56
polyp stage. Although this partnership is mutually beneficial under normal conditions, the loss of
algal cells or their photosynthetic pigments, known as coral bleaching, occurs only 1-2°C above
local summer temperatures (Strong et al., 2011), and can lead to starvation and eventual death of
the host under prolonged stress. Increasingly frequent and severe thermal anomalies are resulting
in mass bleaching and mortality events on a worldwide scale (Hoegh-Guldberg, 1999; Hughes et
al., 2018). Despite the importance of early life stages in the demographic recovery from
bleaching (Hughes and Tanner, 2000; Doropoulos et al., 2015), only 2% of all coral heat stress
experiments have been performed on larvae and 1% on juveniles, and even fewer studies have
included multiple stages (McLachlan et al., 2020). Monitoring of reefs during natural bleaching
suggests that adults may differ from earlier life stages in their susceptibility to thermal stress
(Álvarez-Noriega et al., 2018), emphasizing the need for comparative, mechanistic studies
spanning life stages. Further, earlier work examining gene regulation during coral development
suggests that life stages express unique parts of the genome (Schwarz et al., 2008; Reyes-
Bermudez et al., 2016), though it is unclear whether the molecular response to thermal stress is
also influenced by life stage. Developmental stage has been found to modulate the transcriptional
response to stress in other systems, including water and salinity stress in plants (Garg et al.,
2016; Liu et al., 2021), viral infection in insects (Schneweis et al., 2017), and pH stress in sea
urchins (Devens et al., 2020). It is therefore necessary to explore how functional variation is
regulated across developmental stages to inform understanding of coral adaptive capacity.
Contemporary populations that are locally adapted represent promising study systems to
understand whether patterns of regulation driving thermal tolerance are heritable and expressed
across life stages, thereby increasing total fitness and facilitating adaptation. Coral populations
from backreef pools in American Samoa and nearshore reefs in the Florida Keys exhibit local
57
adaptation, with more thermally variable reefs having more tolerant adults (Kenkel et al., 2013a;
Palumbi et al., 2014), possibly driven by differences in gene expression plasticity (Barshis et al.,
2013a; Kenkel and Matz, 2016). Corals from highly variable backreef pools constitutively up or
down-regulate stress responsive genes in anticipation of thermal stress, known as front or
backloading, and thus have reduced expression plasticity compared to susceptible corals from
less variable pools (Barshis et al., 2013b). In contrast, locally adapted corals from more variable,
inshore reefs in the Florida Keys exhibit greater plasticity of the environmental stress response
and reduced bleaching compared to offshore-origin corals (Kenkel et al., 2013a; Kenkel and
Matz, 2016). Despite these different regulatory responses, evidence for population-level
variation in expression plasticity suggests that plasticity may be heritable and selected for across
generations, leading to local thermal adaptation. However, these hypotheses have only been
tested in adult corals, where the effect of long-term acclimatization cannot be distinguished from
genetic adaptation.
Here, we compare the molecular response to heat stress across three life stages of Porites
astreoides and their symbionts from inshore and offshore reefs in the Florida Keys. P. astreoides
is a brooding coral, meaning gamete fertilization occurs internally and competent planula larvae
are released which then settle and metamorphose into juvenile recruits (Richmond and Hunter,
1990). All life stages are symbiotic, with symbionts being vertically inherited from maternal
colonies (Thornhill et al., 2006). Although adult populations and their symbionts differ in the
magnitude of gene expression plasticity, which could underpin differences in thermal tolerance
(Kenkel and Matz, 2016), it is unknown whether larvae and juveniles also exhibit these patterns
of regulation. In this study, adult colonies from inshore and offshore populations and their recruit
offspring, as well as larvae from separate families, were profiled for gene expression after
58
exposure to controlled thermal stress experiments to ask 1) What are the effects of
developmental stage and reef origin on host and symbiont expression? 2) How does host
developmental stage affect the response to heat stress? and 3) Are population-level patterns of
regulation present in thermally naïve life stages?

Materials and methods
Thermal stress experiments
Adults and recruit offspring were subject to a 16-day common garden heat stress experiment as
described in Kenkel et al. (2015). Briefly, adult colonies were collected from one inshore and
one offshore reef site in April 2012 during peak larval release and held at Mote Marine
Laboratory’s Tropical Research Lab in Summerland Key, FL under permit #FKNMS-2012-028.
Larvae were settled onto terra cotta tiles to obtain families of juvenile recruits and parent
colonies were halved using a tile saw after spawning. Parent colonies and their recruits were then
reared in a common garden environment for 5 weeks. After the acclimation period, paired parent
colony halves and recruits were distributed into experimental tanks and exposed to either control
(28 +/- 0.4°C) or elevated temperatures (30.9 +/- 1.1°C) for 14 days. Adult and recruit samples
from 7 inshore and 5 offshore families were then flash frozen in liquid nitrogen and stored at -
80°C.
Larvae from separate families were sampled after exposure to a moderate heat stress for 4
days as described in Zhang et al. (2019). In April 2018 parent colonies were collected from the
same inshore and offshore reef sites used in the 2012 experiment and brought to Mote Marine
Laboratory’s IC2R3 in Summerland Key, FL under permit #FKNMS-2018-033. Larvae were
collected from each parent colony (3 inshore and 4 offshore) and separated by family in floating
netwells, with 10 larvae per family per netwell across 3 replicate netwells per temperature
59
treatment. Temperature was ramped to 32°C over 24 hours in the thermal stress treatment, while
the control treatment remained at room temperature (24°C). Larvae were sampled after 4 days of
exposure and frozen at -80°C until processing.

RNA isolation, library preparation, and sequencing
Extraction and library preparation for the adult/recruit samples follow protocols described in
Kenkel & Matz (2016). For both sample sets, one microgram of total RNA per sample was used
to generate tag-based RNA-seq, or TagSeq, libraries (Meyer et al., 2011; Lohman et al., 2016),
with modifications for sequencing on the Illumina platform as described at
https://github.com/ckenkel/tag-based_RNAseq.
Adult/recruit and larval TagSeq libraries were sequenced independently, on an Illumina
HiSeq 2500 in 2013 by the Genomic Sequencing and Analysis Facility at UT Austin and in 2018
by the USC Genome Core, respectively. Adult/recruit libraries were sequenced at an average
depth of 6.27 million reads per sample (median = 5.89 ; range = 0.23-17.4) and larval libraries
were sequenced at an average depth of 5.60 million reads per sample (median 5.16 ; range =
3.06-10.61). Raw sequence data are available at NCBI PRJNA666709.

Data processing
Data cleaning and processing was performed on USC’s Center for Advanced Research
Computing following protocols described in https://github.com/ckenkel/tag-based_RNAseq.
First, PCR duplicates and reads missing adaptor sequences were removed using custom perl
scripts. Adaptor sequences, poly-A tails and sample-specific barcodes were trimmed, and reads
were filtered to retain only those exhibiting Q20 over 70% of the read. To generate a host-
60
specific transcriptome, a P. astreoides holobiont assembly (Mansour et al., 2016)
(https://www.ncbi.nlm.nih.gov/Traces/wgs/GEHP01) consisting of transcriptomic reads from
multiple developmental stages (larvae, recruits, adults) was re-filtered using a hierarchical series
of blast searches against potential contaminants following methods described in (Kitchen et al.,
2015) (see methods S1 for additional details on assembly quality). SHRiMP (Rumble et al.,
2009) was used to competitively map reads to the concatenated host-specific and Symbiodinium
spp. (KB8, formerly Clade A) transcriptome (Bayer et al., 2012; Mansour et al., 2016). The
number of reads mapping to each isogroup by reference were summed using a custom perl script,
separated by isogroup into host and symbiont datasets, and analyzed separately.

Differential gene expression analysis
Outliers were screened using the R package arrayQualityMetrics (Kauffmann et al., 2009) and
genes with low abundance transcripts (count <2 in 90% of samples) were removed. One sample
outlier was removed from the host dataset (47RH) and four were removed from the symbiont
dataset (8AH, 16AH, 31AH, 47RH). 20,185 and 14,124 high-expression genes remained for
adult/recruit host and symbiont datasets for 47 and 44 samples respectively. For the host larval
expression, 23,806 genes remained. Only ~3,000 genes remained after filtering symbiont
expression in the larval dataset and further analyses were not pursued. Variance stabilizing
transformation (VST) was applied to count data for principal components analysis (PCA) to
visualize the dominant factors driving transcriptional variation.
The package DESeq2 (Love et al., 2014) in R (v3.6.1) was used to statistically evaluate
differential expression across life stages, reef origin, and treatment groups (~ stage + origin +
treatment) using default settings for the adult and recruit dataset. Due to potential batch effects,
61
the effect of reef origin and treatment on larval differential expression was analyzed
independently from the adult and recruit dataset (~ origin + treatment), and the identity of
significantly differentially expressed genes by origin and treatment were then compared across
datasets. Significance testing was determined using a Wald test after independent filtering using
an FDR threshold of 0.1. Multiple test correction was applied to raw p-values following
(Benjamini and Hochberg, 1995) and adjusted p-values less than 0.1 were deemed significant.
Specific contrasts were used to evaluate the effects of life stage or reef origin on the treatment
response. Contrasts were created by using a grouping variable of life stage and treatment (i.e.
recruit-control, recruit-heat, adult-control, adult-heat) while controlling for origin effects (~
origin + group). A separate model was created to contrast the treatment response according to
reef origin while controlling for life stage (~ stage + group). Scripts and input files for DESeq2
and downstream analyses can be found at https://github.com/mruggeri55/PastGE-lifestage.

Front and backloading analysis
Candidate front/backloaded genes were identified as those that were significantly differentially
expressed in control conditions (constitutively up/down-regulated) and did not respond
significantly to treatment in one group, but were significantly differentially expressed in another
group. This analysis was repeated across life stages and reef origin for both the host and
symbiont in the adult and recruit dataset only. Front/backloading in larvae could not be
determined in relation to adults and recruits due to the potential for batch effects across
experiments. Therefore, only front/backloading based on reef origin was explored in larvae.

62
Discriminant analysis
Discriminant analysis of principal components was performed using the R package adegenet
(Jombart, 2008). To explore shifts in expression profiles based on population, a discriminant
function was defined by contrasting inshore samples in control and heat conditions including all
treatment responsive genes. The number of PCs used to create the function were chosen to
capture at least 80% of transcriptional variance. The function was then applied to offshore
samples and plotted on the same axis as the inshore response. MCMCglmm (Hadfield, 2010) was
used to model the population by treatment interaction to determine whether one population had a
significantly greater response to treatment. This analysis was also repeated by contrasting all
treatment responsive genes in offshore control and heated conditions and applying the function
to inshore samples. In order to determine whether differences in plasticity underlie differences in
bleaching phenotypes, a linear model was used to test whether distance along the discriminant
function for each family was correlated to changes in previously published bleaching
phenotypes, including larval chlorophyll content (Zhang et al., 2019) and adult bleaching score
(Kenkel et al., 2015).

Functional enrichment
Gene ontology (GO) annotations were performed by blasting nucleotide sequences to the
SwissProt-UniProt database (Boutet et al., 2007) allowing a maximum of 5 alignments and
retaining hits with a minimum e-value of 0.0001. Rank-based GO enrichments were performed
on signed log p-values for each DESEq2 contrast using the package GO_MWU (Wright et al.,
2015) with a FDR threshold of 10%. Protein sequences were further categorized into EuKaryotic
Orthologous Groups (KOG) using the online interface of Eggnog-mapper v5.0 (see Methods S1
63
for details) (Cantalapiedra et al., 2021). The KOGMWU R package (Dixon et al., 2015a) was
used to calculate delta ranks of KOG classes across life stages based on their treatment response.
To test whether KOG delta ranks were correlated across life stages, pairwise Pearson correlations
were calculated and visualized using the R package ‘corrplot’.

Results
Life stage drives expression variation in both hosts and symbionts
Life stage was the primary driver of transcriptional variation. Adult and recruit samples
separated by stage along the first principal component of variance stabilized counts for both
hosts (Figure 3.1A) and their symbionts (Figure 3.1B) explaining 31% and 28% of
transcriptional variance, respectively. Stage also accounted for the highest proportion of
differential expression, with 78.5% (6,944) of all significant host genes (8,851) differentially
expressed between adults and recruits (Figure A3.1a). Adult corals more highly expressed genes
involved in proteolysis, protein folding, immune system processes, and ossification compared to
recruits. In contrast, recruits overexpressed signal transduction, ion transport, system processes,
and DNA integration. Differential expression in the symbiont was also driven by the host
developmental stage, with 77% (7,169) of all differentially expressed genes (9,355) regulated
based on life stage (Figure A3.1b). Symbionts in adult corals overexpressed cellular metabolic
processes, including photosynthesis, whereas symbionts in recruits overexpressed genes involved
in reproduction, cell cycle processes, and DNA metabolic processes (Table A4.2).

Signatures of reef origin are maintained, even in naive juveniles
Host and symbiont expression was also distinguished by reef origin regardless of life
stage or treatment conditions. Host samples separated along the second principal component by
64
origin, which explained ~12.5% of the variance across life stages (Figure A3.2). Reef origin
accounted for the second highest proportion of differentially expressed genes across adult and
recruit samples (1,302 genes, 15% of all DEGs ; Figure A3.1a). Larval samples differentially
expressed 225 genes due to origin, although treatment had a marginally larger effect (283 genes;
Figure A3.3). Offshore corals exhibited greater expression of genes involved in carbohydrate
transport, the TCA cycle, and nervous system development compared to inshore corals and
reduced expression of genes involved in cellular component organization, localization, and
vesicle-mediated transport 3). Reef origin was also the primary driver of symbiont expression
profiles along PC2 (Figure 3.1B; 17% of variance) and accounted for 1,700 (18%) of
differentially expressed genes. No GO terms were significantly enriched for origin-associated
expression in symbionts.

Figure 3. 1: Principal components analysis of variance stabilized count data for the host
(A) and symbiont (B) transcriptomes. Samples are colored by population and treatment.
Symbols represent different life history stages.

65
Moderate duration thermal stress induces moderate transcriptional regulation
Treatment accounted for the smallest number of differentially expressed genes in adults
and recruits for both host (605 genes, 7% of all DEGs ; Figure A3.1a) and symbiont (466 genes,
5% of all DEGs ; Figure A3.1b). No discernable pattern was observed due to treatment across the
first two principal components in either partner (Figure 3.1). A significant interaction was
detected for 9 genes in which the treatment specific response differed according to host life
stage. Five of these were upregulated in adults and downregulated in recruits of which 2 were
annotated: mitogen activated protein kinase 6 (MAPK6) and endothelin converting enzyme 2
(ECE2). Whereas three were downregulated in adults and upregulated in recruits in response to
thermal stress. One unannotated gene was upregulated in both life stages, but was more strongly
regulated in adults compared to recruits (LFC 6.3 vs 1.4). No differential expression was
detected for the host origin by treatment interaction, nor for any symbiont interaction terms.
Although the majority of differentially expressed genes were regulated by treatment in larvae
(283 genes, 56.5% of all DEGs), larval samples did not align along the first or second principal
component by treatment (Figure A3.2). No genes were significantly differentially expressed due
to the interaction of treatment based on larval origin.

Treatment response is modulated by life stage
Although few genes were detected for the treatment by life stage interaction, within
group analysis revealed that the most treatment responsive genes were largely stage-specific.
Recruits mounted the largest detectable expression response (311 DEGs) followed by larvae (283
DEGs) and then adults (194 DEGs). Only 9 genes consistently responded to treatment across all
life stages (Figure 3.2C), including upregulation of the universal stress protein Sll1388,
66
calumenin A, soma ferritin, and malate dehydrogenase. Conversely, expression of cyp1A1
varied, with adults and recruits upregulating and larvae downregulating cyp1A1 under heat
stress. The treatment response in symbionts was also modulated by host life stage (Figure 3.3a).
Recruit symbionts had a greater expression response to treatment compared to symbionts in
adults (155 DEGs versus 92 DEGs respectively) with only 16 DEGs overlapping between the
two life stages (Figure 3.3b). These 16 core genes included annotations for fatty acid
metabolism, RNA processing, metal ion binding, and oxidoreductase activity.
The unique differential expression response to treatment between adults and recruits does
not appear to be due to differences in variance (detection limits) but rather to differences in
transcriptional baselines. Forty-four genes were constitutively expressed in adults, but responsive
in recruits. Whereas 27 treatment responsive genes unique to the adult life stage were
overexpressed in control conditions by the recruit life stage. Adults and recruits also exhibited
backloading of 54 and 29 genes, respectively. Symbionts exhibited a similar pattern of differing
baselines leading to divergent thermal stress responses. Symbionts in adults frontloaded 15 genes
and backloaded 28 genes that were responsive in recruit symbionts, while recruit symbionts
frontloaded 22 genes and backloaded 17 genes that were responsive in adult symbionts.
Functional enrichment analysis also indicates that life stages utilize different biological
processes to respond to heat stress. Ontology enrichments in adults were driven by genes
involved in upregulation of metabolic processes and downregulation of cellular amide metabolic
processes (Figure A3.4a), while recruits downregulated genes involved in cell communication,
membrane lipid biosynthetic processes, and cellular protein metabolic processes (Figure A3.4b).
Both adults and recruits upregulated organic acid catabolic processes and downregulated
heterochromatin assembly by small RNA (Figure A3.4ab). Like adults, larvae also
67
downregulated cellular amide metabolic processes. However, unlike either adults or recruits,
larvae upregulated carbohydrate and DNA metabolic processes and downregulated cellular
developmental processes (Figure A3.4c). Among all life stages, energy production and
conversion was the only KOG term significantly enriched among upregulated genes (Figure
3.2A). Although only 23 treatment responsive genes and 2 ontology term enrichments were
shared among adults and larvae, a significant correlation in KOG class enrichment in response to
treatment was evident (r = 0.46, p<0.05, Fig. 2B). However, KOG class enrichment was not
significantly correlated in adults and their recruit offspring, in spite of higher relatedness and
exposure to identical treatment conditions (r = 0.12; Figure 3.2B).

Figure 3. 2: Host transcriptional response to moderate-term thermal stress by life
stage. (A) Clustering of KOG enrichments by delta ranks and life stage. Bolded boxes
represent KOG classes significantly enriched in response to treatment (black padj < 0.05 ;
gray padj < 0.1). (B) Pairwise Pearson correlation coefficients of KOG delta ranks between
life stages. Bolding denotes significant correlation (p < 0.05). (C) Venn diagram of genes
significantly differentially expressed in response to treatment (padj < 0.1) in each life stage.

68
Despite the lack of shared regulation at the level of individual genes, the biological
processes upregulated by symbionts in response to heat stress were similar in each life stage, but
downregulated processes varied. Both adult and recruit symbionts upregulated genes involved in
RNA metabolism and cell cycle processes (Figure A3.5). Recruit symbionts also upregulated
genes involved in the cellular response to stress and downregulated nitrogen transport,
utilization, and metabolism, while adult symbionts downregulated photosynthetic processes
(Figure A3.5).

Figure 3. 3: Heatmap of log2 fold change in expression of symbiont genes significantly
differentially regulated in response to treatment across host developmental stages. Rows
represent the 231 treatment responsive genes (Padj<0.1) detected in total for symbionts in
adult and recruit life stages, as indicated by the venn diagram. Adult corals were ranked by
visual bleaching score from 1 (lightest) – 6 (darkest) (Kenkel et al., 2015) which is denoted
by the red/orange bar.

Treatment response is also modulated by reef origin, but patterns vary among life-stages
69
The magnitude of the expression response to heat stress also differed by population.
Inshore adults and recruits had a much larger transcriptional response, differentially expressing
316 genes across treatments, whereas only 43 genes were detected as differentially expressed by
offshore hosts (Figure 3.4A). Only 25 treatment responsive genes were differentially expressed
in both populations. Universal stress protein 1388, Calumenin-A, and Malate Dehydrogenase
were responsive to treatment in both populations, as well as in all life stages, suggesting these
may be core genes involved in the cellular stress response. Symbionts also exhibited population-
level variation in the magnitude of their treatment response, with inshore symbionts differentially
expressing 142 genes compared to 57 genes in offshore symbionts, and only 14 genes showing
consistent responses to heat stress between the two populations (Figure A3.6). Four of the five
symbiont core genes annotated among populations were also identified as core genes among life
stages: At2g29290, ppsD, PP1, and NUDT3. Regulatory protein flaEY, which plays a role in
flagellum biogenesis, was also upregulated in response to heat stress in both symbiont
populations.
Unlike life-stage patterns, the low number of treatment responsive genes in offshore
adults and recruits does not seem to be due to front or backloading, but rather reduced expression
plasticity compared with inshore origin coral. Only 1 inshore responsive gene was frontloaded
(LFC > 20 in control) in offshore hosts, while 14 were backloaded (LFC -0.3 to -4.7 in control).
Offshore symbionts may be transcriptionally loading some inshore responsive genes, with 11
genes strongly frontloading (fold change > 2) and one weakly backloaded (fold change < 2).
However, for both host and symbiont, the total number of front and back-loaded genes in
offshore samples is not enough to make up for the greater degree of differential expression in
inshore samples.
70
Discriminant analysis of principal components for all treatment responsive genes
supports a significantly muted expression response in offshore adults and recruits (PMCMC < 0.01;
Figure 3.4A). The robust inshore host response included upregulated genes involved in catabolic
and cellular metabolism and downregulated genes involved in actin-filament based processes and
heterochromatin assembly. While DNA metabolic processes were upregulated in inshore corals,
miRNA and protein metabolic processes were downregulated under heat stress. Specifically,
protein autoubiquitination was enriched among downregulated genes. Conversely, offshore hosts
upregulated two stress response genes (ERP29 and slr1101) and one apoptosis gene (Mzb1).
Although offshore-origin adults bleach significantly more than inshore-origin adults (Kenkel et
al., 2015), the distance between adult samples along the discriminant function axis was not
correlated with changes in bleaching score (Figure A3.7a).
71

Figure 3. 4: Host transcriptional response to treatment among populations. Venn
diagram of the population-specific response to heat stress in adults and recruits (A) and
larvae (B). Discriminant analysis of principal components was performed on all treatment
responsive genes to explore the magnitude of expression change between populations. The
discriminant function was defined by contrasting samples in heat and control from one
population and predicting the response of the other.

Despite a consistent pattern of greater transcriptional response in inshore hosts and
symbionts for adult and recruit life stages, larval hosts exhibited the opposite response. Offshore
larvae differentially expressed 130 genes in response to treatment, whereas inshore larvae only
differentially expressed 25 genes, with 11 genes shared among populations (Figure 3.4b).
Offshore larvae also exhibited greater change in their transcriptional profiles for treatment
responsive genes (DAPC, PMCMC<0.01; Figure 3.4b). Similar to adults, the distance between
larval samples along the discriminant function was not correlated with changes in chlorophyll
72
across treatments (Figure A3.7b). No genes were detected as front or backloaded by inshore
larvae.
Discussion
Population origin and developmental stage have major impacts on gene expression and
modulate the response of coral hosts and their symbionts to heat stress. A largely stage-specific
thermal stress response implies inconsistent selection among life stages that could delay or
confound adaptation. Despite life stage differences, a fixed effect of population regardless of
thermal history indicates a genetic basis for expression variation. Further, the magnitude of the
treatment response varied based on population and was consistent across adults and their recruit
offspring, suggesting patterns of regulation are heritable. However, differences in response
magnitude may be influenced by the duration of heat stress or developmental stage necessitating
further exploration. Considerable variation in baseline gene expression and the response to stress
was also observed for symbionts, despite vertical transmission and similar ITS2 haplotypes
between populations (Kenkel et al., 2013a), implying that there is likely more physiological
and/or genetic diversity in symbiont communities than has been previously described.

Developmental stage drives differential expression in coral hosts and modulates the thermal
stress response
Life stage was the prominent driver of host transcriptional variation (Figure 3.1a) and
modulated the response to thermal stress (Figure 3.2c). Surprisingly, the thermal stress response
was largely stage-specific, with few genes consistently responding to heat stress across adults,
recruits, and larvae (Figure 3.2c). Conversely, the only other study examining transcriptomic
responses across life stages reported similar responses to salinity stress between adult and
73
juvenile corals (2019). However, thousands of genes were exclusive to individual life stages with
only ~20% DEGs consistently responding to salinity stress (Aguilar et al., 2019), reinforcing a
largely stage-specific stress response. Although sparse, there is also evidence for development
modulating the stress response in plants (Garg et al., 2016; Liu et al., 2021) and other
invertebrates (Schneweis et al., 2017; Devens et al., 2020), suggesting complex life cycles can
decouple the molecular response to stress. The lack of consistency in the stress response across
life stages has major implications for the evolutionary trajectory of coral. If inducible genes in
one life stage affect fitness, but are not expressed in other stages or are co-opted for other
functions, it may delay or constrain adaptation (Bay et al., 2017b).
Stage-specific responses to thermal stress are partly due to differing baselines. Life
history stage was the prominent driver of differential expression in the host (Figure A3.1),
suggesting that genome-wide expression varies more over an individual’s lifetime than between
locally adapted populations or thermal environments. Developmental modulation of baseline
expression could potentially reduce plasticity during environmental perturbations. About one-
third of treatment responsive genes in parents or recruits were front/back-loaded by the other
stage. Front/backloading of stress responsive genes has been proposed as an adaptive mechanism
that increases coral thermal tolerance (Barshis et al., 2013b). However, if front/back-loading had
a heritable basis, offspring would be expected to exhibit constitutive expression similar to adults.
Although constitutive expression could induce a priming effect against heat stress (Barshis et al.,
2013b), the robust signal of developmental stage, regardless of genetic or environmental factors,
suggests these genes are pleiotropic, performing different functions over developmental time.
Pleiotropic genes could have a positive effect on fitness in every developmental stage,
and thus increase total fitness, or they could experience divergent selection pressures over time,
74
limiting adaptive potential (Cheverud et al., 1983). Pleiotropy is generally believed to constrain
adaptation, however, a meta-analysis on quantitative genetic studies found genetic covariances
were just as likely to promote, constrain, or have no effect on the rate of adaptation, though
covariances among life stages were not considered (Agrawal and Stinchcombe, 2009). In model
organisms, there is evidence suggesting that pleiotropy can constrain evolution through a change
in the direction of selection across life stages (Postma and Ågren, 2016; Coronado-Zamora et al.,
2019). Although functional genomic evidence for pleiotropy is currently lacking for corals,
bleaching resistance in adults can come at the cost of other fitness-related traits (Shore-Maggio et
al., 2018; Cornwell et al., 2021), suggesting pleiotropy can constrain thermal adaptation within
life stages. Putatively pleiotropic genes identified in the present study include tumor necrosis
factor receptor superfamily 19 and a peroxidasin homolog, which have been previously
implicated as key players in both the immune (Traylor-Knowles et al., 2021) and environmental
stress response (Barshis et al., 2013b; Louis et al., 2017) in coral, yet their role in developmental
modulation is unknown. The present study provides the first evidence for developmental
pleiotropy in corals at a molecular level and emphasizes the need for functional and quantitative
genetics to be conducted across multiple life stages to determine the costs and limits of thermal
adaptation.
In contrast to one gene having multiple functions, different genes could also perform the
same function, but be uniquely expressed during discrete developmental stages, resulting in a
stage-specific thermal response. Transcriptome sequencing of five life history stages in two coral
species, Acropora palmata and Montastrea cavernosa, found that expressed sequence tags
(ESTs) were stage-specific (Schwarz et al., 2008), suggesting that life stages utilize completely
different gene sets to achieve the same functions. Similarly, in P. astreoides, the inclusion of
75
sequence data from three life-stages, larvae, newly settled recruits, and adults, increased the size
of the transcriptome by 30 Mbp compared to the previously published adult-only reference
(Mansour et al., 2016). As developmental stage was the main factor differentiating genome-wide
expression in this study (Figure 3.1a), unique genes may be expressed during each life stage
resulting in different treatment responsive genes. Therefore, in addition to differing baselines,
ontogenetic decoupling could also explain the lack of consistency in the thermal stress response
among developmental stages.
Life stages could also be decoupled physiologically, rather than at the molecular-level
(reviewed in (Rivera et al., 2021)), leading to potential differences in thermal susceptibility, and
in the identity of treatment responsive genes. However, despite different genes, the response to
heat stress in adults and larvae was functionally similar (Figure 3.2b), consistent with previous
research in acroporid species (Dixon et al., 2015b), suggesting similar physiological states and a
common response to thermal stress regardless of treatment duration. Conversely, adult and
recruit responses were not correlated despite identical treatment conditions and high genetic
relatedness, suggesting the recruit stage is fundamentally different from adults and larvae, and
may experience stress differently. Physiologically, adults may be investing more energy in
sexual reproduction, but differentially expressed genes between these two stages were not
enriched for reproductive processes, indicating that there are likely other physiological and
functional differences. Future work should focus on bleaching phenotypes that can be compared
among all stages to establish the resilience/susceptibility of different developmental stages to
controlled thermal stress.
Ontogenetic decoupling could delay adaptation because expression of specific genes will
only have fitness effects during discrete developmental periods (Albecker et al., 2021). However,
76
decoupling could also break genetic correlations between discrete life stages, reducing
antagonistic pleiotropy, and therefore optimizing stage-specific fitness (Moran, 1994). As
metamorphosis is often accompanied by changes in habitat, ontogenetic decoupling is predicted
to be strongest across metamorphic transitions (Moran, 1994). Interestingly, in addition to
differences in the thermal stress response between larvae and post-metamorphic life stages, we
also see clear differences in the identity of treatment responsive genes between recruits and
adults (Figure 3.2c), which lack any major morphological or ecological transitions. This suggests
that adults and recruits, in addition to larvae, vary in their fitness optima and can evolve
somewhat independently. In Drosophila, artificial selection on larval or adult tolerance to
extreme temperatures did not confer tolerance in the other life stage (Loeschcke and Krebs,
1996) and the genetic architecture underlying tolerance in each stage was found to be
independent (Loeschcke and Krebs, 1996; Freda et al., 2017). A largely decoupled thermal stress
response among all three life stages in this study suggests that selection for tolerant coral
genotypes in one life stage will likely not translate into greater tolerance in subsequent stages, as
the genes under selection will not be expressed. Intervention strategies, such as artificial
selection and assisted gene-flow, have been proposed to save vulnerable coral populations from
bleaching events (van Oppen et al., 2015), but our results suggest further research is needed to
determine the effect of genetic variation on total organismal fitness, including all life stages,
before these can be successfully implemented.

Population-level signatures persist in thermally naive life stages
Origin effects on constitutive expression persisted in all life stages despite rearing adults
in a common garden for five weeks and larvae and recruits having no exposure to their home
77
environment independent from the maternal colony. Although adult P. astreoides from inshore
and offshore reefs in the Florida Keys exhibit population-level divergence (Kenkel et al., 2013b;
Kenkel and Matz, 2016), the underlying mechanisms driving expression variation remain
unclear. Here, clear differentiation between inshore and offshore expression profiles in juvenile
life stages reared in a common environment suggests a genetic basis for expression divergence.
Population-level differences were also present in the thermal stress response, where inshore
adults and recruit offspring exhibited a larger shift in expression of treatment responsive genes
and greater differential expression across treatments than offshore-origin adults and recruits
(Figure 3.4a). Inshore adults of P. astreoides also exhibit greater genome-wide expression
plasticity in response to transplantation (Kenkel and Matz, 2016) and are generally more
thermally tolerant compared to offshore adults (Kenkel et al., 2013a, 2015). Here we show that
this expression pattern is consistent in juvenile offspring, with no prior thermal stress experience,
indicating that plasticity is potentially heritable.
Though environmental history of juvenile life stages were controlled, origin specific
differences in expression could also be due to transgenerational effects induced by the parental
environment, including maternal effects or developmental plasticity. Some studies have
documented an effect of parental environment on offspring physiology in coral (Putnam and
Gates, 2015; Bellworthy et al., 2019; Wong et al., 2021), suggesting that underlying expression
patterns likely play a role. It is important to note that because P. astreoides employ a brooding
reproductive strategy, larvae and juveniles were exposed to their home environment during
embryonic development. Temperature profiles are similar between inshore and offshore reefs in
the fall and spring when embryonic development occurs, yet many other environmental
parameters diverge (Kenkel et al., 2015), which could have induced a plastic response. Though
78
we cannot rule out maternal effects or developmental plasticity, brooded offspring are commonly
retained within reefs (Ayre and Hughes, 2000; Underwood et al., 2007), so persistent population-
level differences could facilitate survival of early life stages in their local environment.
Despite consistent population-specific differences in parents and offspring, expression
plasticity may not be the primary mechanism driving differences in thermal tolerance. Similar to
adults, inshore larvae are more resistant to bleaching than offshore larvae (Zhang et al., 2019).
However, unlike population-specific plasticity observed in adults and recruits, offshore larvae
demonstrated larger shifts in expression profiles for all treatment responsive genes and
differentially expressed more genes across treatments compared to inshore-origin larvae (Figure
3.4b). Reduced expression plasticity due to constitutive expression of thermally responsive
genes, or front/back-loading, has been documented in tolerant coral adults in American-Samoa
(Barshis et al., 2013b) and the Red Sea (Voolstra et al., 2021). However, we did not detect any
front/back-loaded genes in inshore larvae, suggesting this is not the main mechanism driving
population variation. Additionally, expression plasticity was not correlated with plasticity in
adult bleaching score (Figure A3.7a) or larval chlorophyll content (Figure A3.7b), implying that
population-level differences in plasticity do not predict the magnitude of bleaching.
Alternatively, the timing of the cellular stress response may vary across populations and
underlie differences in thermal tolerance rather than its plasticity (Rivera et al., 2021).
Population-specific plasticity in larvae exposed to only 4 days of heat stress was opposite that of
adults and recruits exposed to 16 days of heat stress. As offshore-origin larvae had a more robust
response to acute stress (Figure 3.4b), but inshore-origin adults and recruits had a more robust
response to moderate heat stress (Figure 3.4a), the onset of the stress response might occur
earlier in offshore corals, artificially leading to differences in population-specific plasticity
79
across life stages. It is also possible that after 16-days of moderate thermal stress, population-
specific responses observed in adults and recruits are due to differences in cellular fate, rather
than the cellular stress response. Treatment responsive genes in inshore adults and recruits were
mainly involved in metabolism, actin-filament based processes, and heterochromatin
organization, whereas the offshore-specific response included genes involved in the cellular
stress response and apoptosis. This pattern could indicate that offshore adults and recruits may
have reached their thermal limit and activated cellular death pathways, while inshore corals are
utilizing other pathways to acclimate to warmer conditions. Multiple sampling timepoints during
controlled thermal stress are needed to resolve whether differences in response timing or
plasticity underlie differences in cell-fate and affect the bleaching response.

Symbiont expression is modulated by host and holobiont condition
Symbiont expression reflected that exhibited by the host, with life stage and population
both differentiating expression and modulating the response to heat stress. Stage was the
prominent driver of transcriptional variation (Figure 3.1b), providing the first evidence that
symbiont expression is affected by the host’s developmental stage. During development, carbon
translocation trades off with symbiont density (Kopp et al., 2016), supported by overexpression
of genes involved in somatic reproduction and underexpression of photosynthetic genes in
juvenile life stages observed here, which could reduce oxidative damage and increase bleaching
resilience (Lesser, 1996). Underexpression of photosynthetic genes in adults responding to heat
stress was correlated to increased bleaching (Figure A3.8), indicating that juvenile holobionts
may be more thermally resilient than adults, but comparable bleaching phenotypes must be
measured across life stages to test this hypothesis.
80
Consistent with host expression, inshore symbionts have a more robust response to heat
stress, even in environmentally naive recruits, further supporting heritability of holobiont
tolerance. Symbiont community composition is presumed to be similar across life stages and
populations, as symbionts are directly inherited by recruits through the process of vertical
transmission and the same symbiont haplotypes (ITS2 type A4/A4a) were previously found in
adult P. astreoides at these reef sites (Kenkel et al., 2013a). However, vertical transmission could
also cause local retention of symbiont strains within each population leading to genetic
differentiation and expression divergence (Barshis et al., 2014; Parkinson et al., 2016). Though
symbiont expression could differ due to long-term acclimatization, symbionts in environmentally
naive recruits also exhibit population-level differentiation, indicating genetics of the symbiont
and/or host are more likely driving this variation. Restricted gene flow in host populations
(Kenkel et al., 2013a) may be driving differences in symbiont expression across reefs, but fine-
scale population genetics is needed to test whether genetic differentiation in the symbiont is a
possible mechanism for population-level expression divergence observed here.

81
References
Agrawal, A. F., and Stinchcombe, J. R. (2009). How much do genetic covariances alter the rate
of adaptation? Proc. Biol. Sci. 276, 1183–1191.
Aguilar, C., Raina, J.-B., Fôret, S., Hayward, D. C., Lapeyre, B., Bourne, D. G., et al. (2019).
Transcriptomic analysis reveals protein homeostasis breakdown in the coral Acropora
millepora during hypo-saline stress. BMC Genomics 20, 148.
Aguirre, J. D., Blows, M. W., and Marshall, D. J. (2014). The genetic covariance between life
cycle stages separated by metamorphosis. Proc. Biol. Sci. 281, 20141091.
Albecker, M. A., Wilkins, L. G. E., Krueger-Hadfield, S. A., Bashevkin, S. M., Hahn, M. W.,
Hare, M. P., et al. (2021). Does a complex life cycle affect adaptation to environmental
change? Genome-informed insights for characterizing selection across complex life cycle.
Proc. Biol. Sci. 288, 20212122.
Álvarez-Noriega, M., Baird, A. H., Bridge, T. C. L., Dornelas, M., Fontoura, L., Pizarro, O., et
al. (2018). Contrasting patterns of changes in abundance following a bleaching event
between juvenile and adult scleractinian corals. Coral Reefs 37, 527–532.
Ayre, D. J., and Hughes, T. P. (2000). Genotypic diversity and gene flow in brooding and
spawning corals along the Great Barrier Reef, Australia. Evolution 54, 1590–1605.
Barshis, D. J., Ladner, J. T., Oliver, T. A., and Palumbi, S. R. (2014). Lineage-specific
transcriptional profiles of Symbiodinium spp. unaltered by heat stress in a coral host. Mol.
Biol. Evol. 31, 1343–1352.
Barshis, D. J., Ladner, J. T., Oliver, T. A., Seneca, F. O., Traylor-Knowles, N., and Palumbi, S.
R. (2013a). Genomic basis for coral resilience to climate change. Proceedings of the
National Academy of Sciences 110, 1387–1392. doi: 10.1073/pnas.1210224110.
Barshis, D. J., Ladner, J. T., Oliver, T. A., Seneca, F. O., Traylor-Knowles, N., and Palumbi, S.
R. (2013b). Genomic basis for coral resilience to climate change. Proc. Natl. Acad. Sci. U.
S. A. 110, 1387–1392.
Bayer, T., Aranda, M., Sunagawa, S., Yum, L. K., Desalvo, M. K., Lindquist, E., et al. (2012).
Symbiodinium transcriptomes: genome insights into the dinoflagellate symbionts of reef-
building corals. PLoS One 7, e35269.
Bay, R. A., Rose, N. H., Logan, C. A., and Palumbi, S. R. (2017a). Genomic models predict
successful coral adaptation if future ocean warming rates are reduced. Sci Adv 3, e1701413.
Bay, R. A., Rose, N. H., Logan, C. A., and Palumbi, S. R. (2017b). Genomic models predict
successful coral adaptation if future ocean warming rates are reduced. Sci Adv 3, e1701413.
Bell, G., and Collins, S. (2008). Adaptation, extinction and global change. Evol. Appl. 1, 3–16.
82
Bellworthy, J., Spangenberg, J. E., and Fine, M. (2019). Feeding increases the number of
offspring but decreases parental investment of Red Sea coral. Ecol. Evol. 9, 12245–12258.
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and
powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300.
Boutet, E., Lieberherr, D., Tognolli, M., Schneider, M., and Bairoch, A. (2007).
UniProtKB/Swiss-Prot. Methods Mol. Biol. 406, 89–112.
Cantalapiedra, C. P., Hernández-Plaza, A., Letunic, I., Bork, P., and Huerta-Cepas, J. (2021).
eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain
Prediction at the Metagenomic Scale. Molecular Biology and Evolution. doi:
10.1093/molbev/msab293.
Cheverud, J. M., Rutledge, J. J., and Atchley, W. R. (1983). QUANTITATIVE GENETICS OF
DEVELOPMENT: GENETIC CORRELATIONS AMONG AGE-SPECIFIC TRAIT
VALUES AND THE EVOLUTION OF ONTOGENY. Evolution 37, 895–905.
Cornwell, B., Armstrong, K., Walker, N. S., Lippert, M., Nestor, V., Golbuu, Y., et al. (2021).
Widespread variation in heat tolerance and symbiont load are associated with growth
tradeoffs in the coral Acropora hyacinthus in Palau. Elife 10. doi: 10.7554/eLife.64790.
Coronado-Zamora, M., Salvador-Martínez, I., Castellano, D., Barbadilla, A., and Salazar-
Ciudad, I. (2019). Adaptation and Conservation throughout the Drosophila melanogaster
Life-Cycle. Genome Biology and Evolution 11, 1463–1482. doi: 10.1093/gbe/evz086.
Devens, H. R., Davidson, P. L., Deaker, D. J., Smith, K. E., Wray, G. A., and Byrne, M. (2020).
Ocean acidification induces distinct transcriptomic responses across life history stages of
the sea urchin Heliocidaris erythrogramma. Mol. Ecol. 29, 4618–4636.
Dixon, G. B., Davies, S. W., Aglyamova, G. A., Meyer, E., Bay, L. K., and Matz, M. V. (2015a).
CORAL REEFS. Genomic determinants of coral heat tolerance across latitudes. Science
348, 1460–1462.
Dixon, G. B., Davies, S. W., Aglyamova, G. A., Meyer, E., Bay, L. K., and Matz, M. V. (2015b).
CORAL REEFS. Genomic determinants of coral heat tolerance across latitudes. Science
348, 1460–1462.
Doropoulos, C., Ward, S., Roff, G., González-Rivero, M., and Mumby, P. J. (2015). Linking
demographic processes of juvenile corals to benthic recovery trajectories in two common
reef habitats. PLoS One 10, e0128535.
Freda, P. J., Alex, J. T., Morgan, T. J., and Ragland, G. J. (2017). Genetic Decoupling of
Thermal Hardiness across Metamorphosis in Drosophila melanogaster. Integr. Comp. Biol.
57, 999–1009.
Garg, R., Shankar, R., Thakkar, B., Kudapa, H., Krishnamurthy, L., Mantri, N., et al. (2016).
Transcriptome analyses reveal genotype- and developmental stage-specific molecular
83
responses to drought and salinity stresses in chickpea. Sci. Rep. 6, 19228.
Hadfield, J. D. (2010). MCMC Methods for Multi-Response Generalized Linear Mixed Models:
The MCMCglmm R Package. J. Stat. Softw. 33, 1–22.
Herrig, D. K., Vertacnik, K. L., Kohrs, A. R., and Linnen, C. R. (2021). Support for the adaptive
decoupling hypothesis from whole-transcriptome profiles of a hypermetamorphic and
sexually dimorphic insect, Neodiprion lecontei. Mol. Ecol. 30, 4551–4566.
Hoegh-Guldberg, O. (1999). Climate change, coral bleaching and the future of the world’s coral
reefs. Mar. Freshwater Res. 50, 839–866.
Hughes, T. P., Anderson, K. D., Connolly, S. R., Heron, S. F., Kerry, J. T., Lough, J. M., et al.
(2018). Spatial and temporal patterns of mass bleaching of corals in the Anthropocene.
Science 359, 80–83.
Hughes, T. P., and Tanner, J. E. (2000). Recruitment failure, life histories, and long-term decline
of Caribbean corals. Ecology 81, 2250–2263.
Jombart, T. (2008). adegenet: a R package for the multivariate analysis of genetic markers.
Bioinformatics 24, 1403–1405.
Kauffmann, A., Gentleman, R., and Huber, W. (2009). arrayQualityMetrics--a bioconductor
package for quality assessment of microarray data. Bioinformatics 25, 415–416.
Kenkel, C. D., Goodbody-Gringley, G., Caillaud, D., Davies, S. W., Bartels, E., and Matz, M. V.
(2013a). Evidence for a host role in thermotolerance divergence between populations of the
mustard hill coral (Porites astreoides) from different reef environments. Mol. Ecol. 22,
4335–4348.
Kenkel, C. D., and Matz, M. V. (2016). Gene expression plasticity as a mechanism of coral
adaptation to a variable environment. Nat Ecol Evol 1, 14.
Kenkel, C. D., Meyer, E., and Matz, M. V. (2013b). Gene expression under chronic heat stress in
populations of the mustard hill coral (Porites astreoides) from different thermal
environments. Mol. Ecol. 22, 4322–4334.
Kenkel, C. D., Setta, S. P., and Matz, M. V. (2015). Heritable differences in fitness-related traits
among populations of the mustard hill coral, Porites astreoides. Heredity 115, 509–516.
Kitchen, S. A., Crowder, C. M., Poole, A. Z., Weis, V. M., and Meyer, E. (2015). De Novo
Assembly and Characterization of Four Anthozoan (Phylum Cnidaria) Transcriptomes. G3
5, 2441–2452.
Kopp, C., Domart-Coulon, I., Barthelemy, D., and Meibom, A. (2016). Nutritional input from
dinoflagellate symbionts in reef-building corals is minimal during planula larval life stage.
Sci Adv 2, e1500681.
84
Lesser, M. P. (1996). Elevated temperatures and ultraviolet radiation cause oxidative stress and
inhibit photosynthesis in ymbiotic dinoflagellates. Limnol. Oceanogr. 41, 271–283.
Liu, S., Zenda, T., Dong, A., Yang, Y., Wang, N., and Duan, H. (2021). Global transcriptome
and weighted gene co-expression network analyses of growth-stage-specific drought stress
responses in maize. Front. Genet. 12, 645443.
Loeschcke, V., and Krebs, R. A. (1996). Selection for heat-shock resistance in larval and in adult
Drosophila buzzatii: Comparing direct and indirect responses. Evolution 50, 2354–2359.
Lohman, B. K., Weber, J. N., and Bolnick, D. I. (2016). Evaluation of TagSeq, a reliable low-
cost alternative for RNAseq. Mol. Ecol. Resour. 16, 1315–1321.
Louis, Y. D., Bhagooli, R., Kenkel, C. D., Baker, A. C., and Dyall, S. D. (2017). Gene
expression biomarkers of heat stress in scleractinian corals: Promises and limitations.
Comp. Biochem. Physiol. C. Toxicol. Pharmacol. 191, 63–77.
Love, M. I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and
dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550.
Mansour, T. A., Rosenthal, J. J. C., Brown, C. T., and Roberson, L. M. (2016). Transcriptome of
the Caribbean stony coral Porites astreoides from three developmental stages. Gigascience
5, 33.
McLachlan, R. H., Price, J. T., Solomon, S. L., and Grottoli, A. G. (2020). Thirty years of coral
heat-stress experiments: a review of methods. Coral Reefs 39, 885–902.
Meyer, E., Aglyamova, G. V., and Matz, M. V. (2011). Profiling gene expression responses of
coral larvae (Acropora millepora) to elevated temperature and settlement inducers using a
novel RNA-Seq procedure. Mol. Ecol. 20, 3599–3616.
Moran, N. A. (1994). ADAPTATION AND CONSTRAINT IN THE COMPLEX LIFE
CYCLES OF ANIMALS. Annu. Rev. Ecol. Syst. 25, 573–600.
Muscatine, L. (1990). The role of symbiotic algae in carbon and energy flux in reef corals.
Ecosystems of the world. Coral Reefs. Available at: https://ci.nii.ac.jp/naid/10020710664/
[Accessed March 11, 2022].
Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N., and Bay, R. A. (2014). Mechanisms of reef
coral resistance to future climate change. Science 344, 895–898.
Parkinson, J. E., Baumgarten, S., Michell, C. T., Baums, I. B., LaJeunesse, T. C., and Voolstra,
C. R. (2016). Gene Expression Variation Resolves Species and Individual Strains among
Coral-Associated Dinoflagellates within the Genus Symbiodinium. Genome Biol. Evol. 8,
665–680.
Postma, F. M., and Ågren, J. (2016). Early life stages contribute strongly to local adaptation in
Arabidopsis thaliana. Proc. Natl. Acad. Sci. U. S. A. 113, 7590–7595.
85
Putnam, H. M., and Gates, R. D. (2015). Preconditioning in the reef-building coral Pocillopora
damicornis and the potential for trans-generational acclimatization in coral larvae under
future climate change conditions. J. Exp. Biol. 218, 2365–2372.
Reyes-Bermudez, A., Villar-Briones, A., Ramirez-Portilla, C., Hidaka, M., and Mikheyev, A. S.
(2016). Developmental Progression in the Coral Acropora digitifera Is Controlled by
Differential Expression of Distinct Regulatory Gene Networks. Genome Biol. Evol. 8, 851–
870.
Richmond, R. H., and Hunter, C. L. (1990). Reproduction and recruitment of corals:
comparisons among the Caribbean, the tropical Pacific, and the Red Sea. Marine ecology
progress series. Oldendorf 60, 185–203.
Rivera, H. E., Aichelman, H. E., Fifer, J. E., Kriefall, N. G., Wuitchik, D. M., Wuitchik, S. J. S.,
et al. (2021). A framework for understanding gene expression plasticity and its influence on
stress tolerance. Mol. Ecol. 30, 1381–1397.
Rumble, S. M., Lacroute, P., Dalca, A. V., Fiume, M., Sidow, A., and Brudno, M. (2009).
SHRiMP: accurate mapping of short color-space reads. PLoS Comput. Biol. 5, e1000386.
Schmid, M., Davison, T. S., Henz, S. R., Pape, U. J., Demar, M., Vingron, M., et al. (2005). A
gene expression map of Arabidopsis thaliana development. Nat. Genet. 37, 501–506.
Schneweis, D. J., Whitfield, A. E., and Rotenberg, D. (2017). Thrips developmental stage-
specific transcriptome response to tomato spotted wilt virus during the virus infection cycle
in Frankliniella occidentalis, the primary vector. Virology 500, 226–237.
Schwarz, J. A., Brokstein, P. B., Voolstra, C., Terry, A. Y., Manohar, C. F., Miller, D. J., et al.
(2008). Coral life history and symbiosis: functional genomic resources for two reef building
Caribbean corals, Acropora palmata and Montastraea faveolata. BMC Genomics 9, 97.
Shore-Maggio, A., Callahan, S. M., and Aeby, G. S. (2018). Trade-offs in disease and bleaching
susceptibility among two color morphs of the Hawaiian reef coral, Montipora capitata.
Coral Reefs 37, 507–517.
Strong, A. E., Liu, G., Skirving, W., and Eakin, C. M. (2011). NOAA’s Coral Reef Watch
program from satellite observations. Ann. GIS 17, 83–92.
Thornhill, D. J., Fitt, W. K., and Schmidt, G. W. (2006). Highly stable symbioses among western
Atlantic brooding corals. Coral Reefs 25, 515–519.
Traylor-Knowles, N., Connelly, M. T., Young, B. D., Eaton, K., Muller, E. M., Paul, V. J., et al.
(2021). Gene expression response to stony coral tissue loss disease transmission in M.
cavernosa and O. faveolata from Florida. Front. Mar. Sci. 8. doi:
10.3389/fmars.2021.681563.
Underwood, J. N., Smith, L. D., Van Oppen, M. J. H., and Gilmour, J. P. (2007). Multiple scales
of genetic connectivity in a brooding coral on isolated reefs following catastrophic
86
bleaching. Mol. Ecol. 16, 771–784.
van Oppen, M. J. H., Oliver, J. K., Putnam, H. M., and Gates, R. D. (2015). Building coral reef
resilience through assisted evolution. Proc. Natl. Acad. Sci. U. S. A. 112, 2307–2313.
Voolstra, C. R., Valenzuela, J. J., Turkarslan, S., Cárdenas, A., Hume, B. C. C., Perna, G., et al.
(2021). Contrasting heat stress response patterns of coral holobionts across the Red Sea
suggest distinct mechanisms of thermal tolerance. Mol. Ecol. 30, 4466–4480.
Wilbur, H. M. (1980). Complex Life Cycles. Annu. Rev. Ecol. Syst. 11, 67–93.
Wong, K. H., Goodbody-Gringley, G., de Putron, S. J., Becker, D. M., Chequer, A., and Putnam,
H. M. (2021). Brooded coral offspring physiology depends on the combined effects of
parental press and pulse thermal history. Glob. Chang. Biol. 27, 3179–3195.
Wright, R. M., Aglyamova, G. V., Meyer, E., and Matz, M. V. (2015). Gene expression
associated with white syndromes in a reef building coral, Acropora hyacinthus. BMC
Genomics 16, 371.
Zhang, Y., Million, W. C., Ruggeri, M., and Kenkel, C. D. (2019). Family matters: Variation in
the physiology of brooded Porites astreoides larvae is driven by parent colony effects.
Comp. Biochem. Physiol. A Mol. Integr. Physiol. 238, 110562.

87
Appendix

Supplemental methods
Reference assembly
A Porites astreoides holobiont assembly (https://www.ncbi.nlm.nih.gov/Traces/wgs/GEHP01)
consisting of transcriptomic reads from multiple developmental stages (larvae, recruits, adults)
was downloaded from NCBI’s Sequence Read Archive (PRJNA283441) on 25 Feb 2019
(Mansour et al. 2016). This assembly was filtered using a hierarchical series of blast searches
against potential contaminants, the Acropora digitifera (Shinzato et al. 2011) and Symbiodinium
kawagutii (Lin et al. 2015) proteomes and NCBI’s nr database (downloaded 14 Mar 2019) (Pruitt
et al. 2005) following methods described in (Kitchen et al. 2015) to produce a cnidarian host-
specific assembly. An assessment of transcriptome completeness was conducted following
methods described in (Kenkel and Bay 2017). The final assembly represented 43,685 isogroups
(∼genes) with an average length of 1842 bp and an N50 of 2650. Mean GC content of host-
specific assemblies was 41.3%, and protein coverage exceeded 0.75 for 44% of contigs. Of the
978 core BUSCO gene sets for metazoans, 86.20% were found to be complete, while an
additional 0.71% were partially assembled, resulting in an 18% increase in representation
compared to an earlier assembly(Kenkel et al. 2013).

Front/backloading analysis
To test whether treatment inducible genes in one population or life stage were not detected in the
other due to front/backloading or differences in variance, we generated a correlation plot of the
log fold change (LFC) across treatment groups for significant differentially expressed genes
following [31]. Genes deviating from the one to one line indicate potential candidates for
front/backloading whereas those along the one to one line responded similarly in both groups
and, therefore, were likely not detected due to differences in variance among groups.

KOG annotation
Contigs belonging to more than one KOG class were excluded from further analysis. In cases
where a single isogroup was made up of multiple contigs with variable KOG annotations, the
class that made up the majority of contigs was retained for that isogroup. If no class was in the
majority, the annotation with the best blast score was chosen.

88
Supplemental Figures

Figure A3.1. Venn diagram of significant differentially expressed genes by life stage, reef
origin, and treatment for (A) host expression and (B) symbiont expression in adult and recruit
corals.

A. B.

Figure A3.2. Principal components analysis of adult, recruit, and larval host expression.

Figure A3.3. Venn diagram of significant differentially expressed genes by reef origin, and
treatment for host expression in larvae.

89
Figure A3.4. Gene ontology enrichments for treatment responsive genes in each coral life
stage: (A) adult, (B) recruit, (C) larvae.

90
Figure A3.5. Gene ontology enrichments for treatment responsive genes in symbionts
inhabiting different life stages of its host: (A) adult and (B) recruit.

A. Adult symbiont response

B. Recruit symbiont response

Figure A3.6. Venn diagram of treatment responsive genes in the symbiont by population.

91
Figure A3.7. Correlation of plasticity in thermal stress response to plasticity in bleaching score
of adults (a) and larval chlorophyll content (b).

Figure A3.8. Expression of photosystem II reaction center protein L (psbL) is correlated with
bleaching score in adults.

92
Figure A3.9. Heatmap of photosystem II reaction center protein (psbL) expression of
symbionts in adults and recruits exposed to heat stress.

93
CHAPTER FOUR: Population genomics and symbiont specificity in
natural populations of Exaiptasia diaphana
Maria Ruggeri, Connie S. Machuca, Samuel A. Bedgood, Carly D. Kenkel

Abstract
The mechanisms that drive host-symbiont associations across space and time in contemporary
populations can give insight into the capacity for symbiotic organisms to respond to
environmental change. High specificity between partners can increase cooperation and facilitate
efficient holobiont selection, whereas low specificity can lead to reduced host benefit, but
facilitate adaptive associations across environments. The degree of specificity could also be
influenced by whether symbionts are acquired from parents (vertically transmitted) or the
environment (horizontally transmitted), yet how specificity evolves is not well understood. The
present study explores specificity in natural populations of a cnidarian-algal model, Exaiptasia
diaphana, across a latitudinal gradient to better understand the genetic and environmental effects
driving host-symbiont associations, and their relation to transmission mode. We found that
symbiotic associations were extremely flexible in E. diaphana, and unrelated to host genetics or
transmission mode. E. diaphana could associate with diverse symbiont communities across
genetically identical hosts, which are seeded with vertically transmitted symbionts, and across
highly connected host populations, when symbionts are acquired horizontally. Host population
connectivity was complex and unrelated to geographic distance, whereas symbiont community
composition tracked with latitude, potentially due to reduced dispersal and/or environmental
filtering. Contrasting biogeographic patterns between partners could constrain the evolution of
specificity, regardless of transmission mode, indicating that the evolution of each partner
independently will determine the future of this symbiosis.
94
Introduction
Mutualisms are particularly vulnerable to climate change as two or more species must adapt in
concert to persist. In many symbioses, generation time and dispersal are de-coupled between
hosts and their symbionts (Wisselink et al., 2020), posing an evolutionary challenge under a
changing environment. Theoretically, specific host-symbiont associations which persist across
multiple generations could increase cooperation between partners (Douglas, 1998). However, it
may also be advantageous to maintain flexible associations to facilitate acclimatization in the
short-term (Davies et al., 2020). As the degree of specificity is variable across symbioses (Baker,
2003; Mandel, 2010; Allen and Lendemer, 2022) and will influence the evolutionary trajectory
of ecological interactions in future environments (Koskella and Bergelson, 2020), it is important
to understand how genomic and environmental factors influence host-symbiont associations in
contemporary populations.
The diversity of microorganisms in the environment necessitates interaction mechanisms
that confer some level of specificity to establish beneficial partnerships and protect against
pathogens (Fraune and Bosch, 2007; Mandel et al., 2009). Host-symbiont associations can be
maintained across generations through vertical transmission, where symbionts are directly
inherited from parents. Environmental acquisition of symbionts, on the other hand, or horizontal
transmission, requires some degree of partner choice for specificity to be maintained. Partner
choice during horizontal transmission can be constrained by both genetic and environmental
effects including host-symbiont recognition mechanisms, the host environment, competition
between symbionts, and biogeography (Bright and Bulgheresi, 2010; Ohbayashi et al., 2020).
Horizontal transmitters tend to be more flexible in their associations than vertical transmitters,
yet there is evidence of highly specific environmentally acquired symbioses, such as legume-
rhizobium and squid-vibrio symbioses, driven in part by specific host and symbiont traits (Long,
95
2001; Mandel, 2010). Though vertical transmission may have evolved as a mechanism to
increase specificity between partners, thereby aligning fitness and dispersal (Douglas, 1998),
vertically transmitting species can also retain the ability to environmentally acquire symbionts,
leading to intermediate or mixed transmission modes. Therefore, transmission mode does not
necessarily determine specificity, but rather can act as a mechanism to increase specificity, yet
little is known about the patterns of specificity in symbioses with mixed transmission.
Specific interactions between species can lead to co-divergence (Mandel, 2010; O’Brien
et al., 2019), linking ecological and evolutionary processes. When specificity is high between
partners and heritable, selection may act on the holobiont, or collective fitness, leading to
reciprocal evolutionary changes in both partners, or co-evolution (Bennett and Moran, 2015;
Visick et al., 2021). Hence, high specificity observed in symbioses with heritable, vertical
transmission often leads to co-evolution to increase cooperation between beneficial partners.
Though co-evolution is a form of co-divergence, co-divergence does not necessarily imply co-
evolution or specificity. For example, hosts and symbionts could independently evolve in
response to the same environmental factors and still maintain the ability to associate with other
partners if encountered. Therefore, an understanding of both specificity and co-divergence are
necessary to disentangle genetic, environmental and holobiont effects on the evolution of host-
symbiont associations.
Cnidarian-algal symbioses are highly diverse and ecologically important marine
mutualisms. Three classes of cnidarians, including coral and anemones, host endosymbiotic
dinoflagellates in the family Symbiodiniaceae, including six symbiotic genera (LaJeunesse et al.,
2018), but specificity varies greatly across species (reviewed in (Baker, 2003). For example, the
tropical coral Orbicella faveolota can host up to four genera of Symbiodiniaceae within a single
96
host colony (Manzello et al., 2019), while other hosts with similar distribution, Acropora
cervicornis and A. palmata, typically associate with only one symbiont species and in some
cases, even specific strains (Baums et al., 2010; Parkinson et al., 2015; Reich et al., 2021).
Specificity in cnidarians also appears to be related to transmission mode (Fabina et al., 2012), yet
some cnidarians with horizontal symbiont transmission exhibit remarkable specificity, even
across broad geographic ranges (LaJeunesse, 2002; Reich et al., 2021) and mixed-mode
transmission is possible (Quigley et al., 2018; Scharfenstein et al., 2022). Both vertical (Forsman
et al., 2020; Turnham et al., 2021; Johnston et al., 2022) and horizontal transmission (Reich et
al., 2021) can lead to co-divergence and possibly co-evolution in cnidarians with high specificity,
but it is unclear whether co-divergence can also be exhibited by species with mixed-mode
transmission. The degree of specificity and its underlying mechanisms will determine how
efficiently selection can act on the holobiont and the potential for specificity to evolve (Koskella
and Bergelson, 2020), which is paramount to understanding the future stability of thermally
sensitive cnidarian-algal symbioses.
Here we explore the mechanisms driving host-symbiont associations and their level of
specificity in an anemone symbiosis with mixed transmission. Commonly known as Aiptasia,
Exaiptasia diaphana is an emerging model for the cnidarian-algal symbiosis (Weis et al., 2008;
Lehnert et al., 2012; Baumgarten et al., 2015; Rädecker et al., 2018). Aiptasia reproduces both
asexually through pedal laceration and sexually by broadcast spawning. During asexual
reproduction, symbionts are passed vertically to clonal offspring, which form through tissue
budding near the pedal disk (Clayton, 1985). Sexually produced gametes, on the other hand, are
aposymbiotic and must acquire symbionts horizontally (Hambleton et al., 2014; Wolfowicz et
al., 2016). In the laboratory, horizontal transmission can also be achieved within a generation, by
97
chemical disruption of the symbiosis, followed by introduction of novel symbiont types
(Matthews et al., 2016). Infection success and maintenance varies across host-symbiont
combinations in the lab (Hambleton et al., 2014; Gabay et al., 2019; Herrera et al., 2020, 2021),
implying differences in the degree of host and/or symbiont specificity, but the underlying
mechanisms remain unknown. Prior studies of natural Aiptasia imply that specificity may vary
across populations (Thornhill et al., 2013; Bellis et al., 2018), though limited sample size and
molecular techniques precluded population genomic or co-divergence analysis.
In this study, we use 2bRAD and ITS2 amplicon sequencing to profile genetic diversity
of naturally occurring Aiptasia and their symbionts across a latitudinal gradient in Southern
Florida. By capturing genetically unique hosts over a broad geographic range, as well as
asexually reproduced clones, we were able to explore genetic and environmental contributions to
symbiont community composition, the capacity for partner specificity, and their relation to
transmission mode. Lastly, we leverage this population genomic dataset to better understand
reproduction and gene flow in the host to inform laboratory-based attempts at sexual
reproduction in this system, which has the potential to greatly advance our understanding of
mechanisms underpinning symbiont specificity in cnidarian-algal symbioses (Bucher et al.,
2016).

Materials and methods
Sample collection
Aiptasia were sampled from six natural populations in Florida spanning a latitudinal gradient
from July to September 2019 (see Table A4.1 for GPS coordinates) under Florida Fish and
Wildlife Conservation Commission Marine Special Activity License #SAL-19-2145-SR. Five
out of the six sites (Otter Key, OK; Miami’s Virginia Key, MI; Long Key, LK; Tavernier Key,
98
TK; and Big Pine, BP) represented natural mangrove forests, where Aiptasia were found
colonizing outer roots within one meter of depth. The sixth site (Stock Island, SI) was a man-
made marina in the lower Florida Keys where Aiptasia were attached to floating docks. Each site
was sampled as 3 subsites with at least 30m between each subsite. A target of five Aiptasia from
four mangrove roots were sampled from each subsite, ensuring at least 3m between each root,
yielding a targeted total of 60 Aiptasia per population (see Table 1 for final sample sizes by
population). For the dock site (SI), five Aiptasia were sampled within a small area (~1m
2
) and
areas were separated by at least 3m to mimic spatial sampling between roots at mangrove sites,
despite the presence of continuous substrate along the floating docks. Aiptasia were pried off
substrates using a microspatula/scoopula, transferred to 5 mL tubes and transported on ice to
Mote Marine Laboratory’s Elizabeth Moore International Center for Coral Reef Research &
Restoration on Summerland Key where they were preserved in 100% ethanol and stored at -
20C. An individual representing the lab strain CC7, and an unknown strain (TX) obtained from
aquaria in Galveston, TX, were also sampled to serve as species controls.

Host genotyping and identification of clones
Holobiont genomic DNA was extracted using a Qiagen DNeasy blood & tissue kit. For
host genotyping, reduced representation 2bRAD libraries (Wang et al., 2012) were prepared
using a hybrid protocol described in Matz (2017) targeting 1/8
th
of BcgI sites
(https://github.com/z0on/2bRAD_denovo). Libraries were originally sequenced on the NextSeq
550 by the USC Genome Core in 2021. However, due to low diversity attributed to high
clonality in this library, read output was lower than expected (51M total, ~163k reads per sample
on average). Thus, these libraries were combined with sequencing libraries from two other
99
species to increase read diversity and two additional sequencing runs were completed in May
2022 on the NextSeq 2000 by the USC NCCC Molecular Genomics core. Combined these runs
yielded a total of 161.7M reads (~510k reads per sample on average).
Bioinformatic analysis was performed on the USC CARC HPC system following the
pipeline described in https://github.com/z0on/2bRAD_denovo. First, reads were de-multiplexed
based on internal ligation adaptor barcodes prior to adaptor trimming and removing PCR
duplicates using a custom perl script. Reads were then quality filtered to retain only 99%
accurate base calls over 100% of the read using the fastx-toolkit and reads containing adaptor
sequences were removed. Sequencing runs were processed separately to remove run-dependent
PCR duplicates. Clean reads were concatenated across runs resulting in 226.5k de-multiplexed,
high-quality reads per sample on average. High quality reads were then competitively mapped to
a combined E. diaphana host genome (Formerly Aiptasia pallida, Baumgarten et al., 2015) and
symbiont reference genomes from four species representing four genera (Symbiodinium
microadriaticum (ITS2 type A1) (Aranda et al., 2016), Breviolum minutum (ITS2 type B1)
(Shoguchi et al., 2013), Cladocopium goreaui (ITS2 type C1) (Liu et al., 2018), and
Durusdinium trenchii (Dougan et al., 2022) using Bowtie2 (Langmead and Salzberg, 2012).
Reads mapping to the host genome were subset from reads mapping to the symbiont genome for
subsequent analysis.
Remaining host read depth, coverage, and SNP quality was calculated in ANGSD 0.933
(Korneliussen et al., 2014) to determine quality filter thresholds. Twelve samples with <20% of
sites exhibiting at least 5x coverage were excluded from further analysis, resulting in 306
samples remaining. Genotype likelihoods were estimated in ANGSD after filtering to retain high
confidence SNPs (-removebads 1 -uniqueOnly 1 -minMapQ 20 -minQ 25 -snp_pval 1e-5) with
100
at least 2x coverage in at least 50% of samples and a minimum minor allele frequency of 0.05.
Additional filters were also used to remove sites with either extremely high depth of coverage
(10x number of samples) or strongly deviating from Hardy-Weinberg equilibrium (HWE) (p<1e
-
5
). A total of 516 sites were retained and used to generate an Identity by State (IBS) matrix across
samples using ANGSD. Hierarchical clustering of the resulting IBS matrix was then performed
in R v4.2.1 and used to identify clonal individuals using known technical replicates (N=4) to set
thresholds as described in (Manzello et al., 2019). Due to variance in IBS distances across
technical replicates, high confidence clones were first pruned based on an IBS threshold of 0.15
(Fig. SN). ANGSD was then rerun on the pruned dataset using the same filters as above, and a
new IBS matrix was generated for the remaining 116 individuals (including technical replicates)
using 923 sites. Hierarchical clustering was again performed based on IBS distance, and a final
cutoff of 0.17 was used to discern ramets from unique genets.

Reproductive mode
For calculating statistics indicative of reproductive strategy, ANGSD was rerun on the full
dataset (with clones) and pruned dataset (without clones) with the same filtering and run
parameters but this time retaining sites out of HWE, resulting in 2007 sites across 294 samples
for the full dataset and 1860 sites across 89 samples for the pruned dataset. The R packages
hierfstat (Goudet and Jombart, n.d.) and poppr (Kamvar et al., 2014) were used to calculate
observed and expected heterozygosity, the inbreeding coefficient (Fis), and index of association
(rd) on hard-called genotypes for each population. Genotypic richness (R) was also calculated by
dividing the number of unique multi-locus genotypes (MLGs) by the total number of individuals
sampled at each site. Pairwise genetic correlations between loci were also calculated from
101
genotype likelihoods using ngsLD (Fox et al., 2019) for each population, with the inclusion and
exclusion of clones.

Host population genomics without clones
The pruned dataset (without clones) was used for population structure and gene flow analyses.
Sites strongly deviating from HWE (p<1e
-5
) were removed, resulting in 1046 sites across 89
samples. A multi-dimensional scaling (MDS) analysis was performed on pairwise IBS distances
using the R package vegan (Oksanen, 2010) to visualize genetic distance between populations.
Admixture was evaluated from genotype likelihoods using NGSadmix (Skotte et al., 2013).
Admixture analysis was evaluated for 1-6 genetic clusters (k) and 10 independent runs were
generated for each k. Delta k was calculated following Evanno et al. (2005) to determine the
number of source populations (k). As the Stock Island (SI) population had a greater sample size
than all other populations and appeared to be genetically distinct (Figure 4.1), analyses were run
including and excluding individuals from the SI population, and SI individuals only.
Genetic differentiation was evaluated by calculating pairwise Fst values between
populations from hard-called genotypes using vcftools (Danecek et al., 2011). Isolation by
distance (IBD) was explored by plotting scaled Fst values against geographical distance (mean
over water distance measured in google earth) and evaluated using the function mantel.rtest from
the ‘ade4’ package (Thioulouse et al., 2018) in R with 999 permutations. Migration rates
between populations were modeled from a genetic dissimilarity matrix generated from hard-
called genotypes using EEMS (estimated effective migration surfaces) (Petkova et al., 2016).
EEMS was run separately for multiple deme sizes (200, 300, 400, 500) with 5M MCMC
102
iterations and a 2M burn-in period. Model convergence and migration rates were visualized
using the associated R package rEEMSplots (Petkova et al., 2016).

Symbiont community profiling
Symbiont DNA was simultaneously extracted with host DNA as described above. Amplicon
libraries were prepared by amplifying the ribosomal ITS2 region (x20-30 cycles) using the
SYM_VAR_5.8S2/SYM_VAR_REV primers (Hume et al., 2013, 2015, 2018), followed by a
second PCR (x6 cycles) to add sample-specific barcodes. Paired-end 250bp reads were
sequenced on the MiSeq v2 by the USC NCCC Molecular Genomics core with 30% PhiX spike-
in which yielded a depth of 64.1M reads across two runs (nano and v2 chemistry) in June and
July 2022. Forward and reverse reads were concatenated across runs (~98.3k paired reads per
sample on average) and analyzed in SymPortal using the default settings. To verify profiles for
samples with high cycle numbers (>25), 39 sample libraries were re-prepared from remaining
DNA with the inclusion of negative controls and re-sequenced on the MiSeq v2 by the USC
NCCC Molecular Genomics Core in December 2022. Re-sequenced samples and the negative
control were analyzed in SymPortal, and confirmed that the original symbiont profiles represent
true biological signals (Figure A4.1).
ITS2 profiles were generated in SymPortal by collapsing co-occuring defining
intragenomic variants (DIVs) present in at least four individuals (Hume et al., 2019). To explore
whether clonality increased the number of unique profiles, SymPortal was rerun including only
unique genets (Figure A4.2). Symbiont profile absolute abundance was imported into the R
package Phyloseq (McMurdie and Holmes, 2013) to generate relative abundance plots. Relative
abundance of symbiont profiles was explored within and between genets and sites. Community
103
composition and beta diversity was compared across sites through PERMANOVA and
PERMDISP tests in the R package vegan (Oksanen et al., 2013). To evaluate co-phylogeny
between hosts and symbionts, a binary matrix was generated to represent the presence or absence
of each symbiont profile detected within unique host genotypes. IBS distances were used to
cluster host relatedness and plotted against within-genus symbiont relatedness generated in
SymPortal using the package ‘pheatmap’ (Kolde, 2019).
Results
Host reproductive mode
Only 30% of individuals represented unique genotypes (89 unique genotypes / 294 total
individuals), indicating high levels of clonal reproduction in natural Aiptasia populations.
Genotypic diversity (R) was low for most populations (0.132-0.295 ; Table 1), with the
exception of Stock Island (SI), where unique genotypes outnumbered clonal individuals
(R=0.661 ; Table 1). Otter key (OK) and Long Key (LK) had the lowest genotypic diversity,
which was associated with a negative inbreeding coefficient (Fis) and relatively high index of
association (rd) when clones were included in the analysis (Table 1). However, following clone
removal, all populations had a positive inbreeding coefficient and low index of association
(Table 1). Despite high clonality, genomic indicators (positive Fis and low rd) suggest that Aiptasia
are not purely asexual, and employ a mixed reproductive mode.
Ramets of a single genet were found across roots, subsites, and sites, indicating migration of
clonal ramets . Clonemates were found across 1-2 roots on average, and ramets of a single genet
could be found on up to six roots within a site (Figure A4.3). Additionally, ramets of four genets
were present at multiple sites (Figure A4.4), with ramets of one genet (genet 37) occurring at
three of the six sites (Table A4.2). Clonal migration was detected in 50% of pairwise
104
comparisons across four of the five Florida Keys sites (MI, TK, LK, BP; Table A4.2). No clones
migrated to or from the OK or SI population.
Despite high clonality, genotypic diversity was also high across small spatial scales.
Mangrove roots were colonized by 1-2 genets on average, with up to 4 genets colonizing a single
root (Figure A4.3, Table A4.3). At the dock site (SI), six unique genotypes were identified from
one root area on average (Figure A4.3, Table A4.3), highlighting the greater genotypic diversity
of this site.
Table 4.1: Population genetic summary statistics with and without clonal individuals.
nMLG = number of unique multi-locus genotypes, nTotal = total number of individuals sampled,
R = genotypic diversity, He = expected heterozygosity, Ho = observed heterozygosity, rd = index
of association. Sites are ordered from north (top) to south (bottom).

Host population structure
Admixture analysis detected two genetic clusters (K=2) among all sites (Figure 4.1A; Figure
A4.5 ). The most prominent genetic break was between SI and all other populations (Figure
4.1A), which was consistent with MDS analysis of IBS distance (Figure A4.6). However, at least
two individuals from each site (15 total) shared greater than 30% ancestry with those from the SI
subpopulation, suggesting admixture between subpopulations. Consistently, two genetic clusters
remained after filtering out SI individuals (Figure 4.1B, Figure A4.7). In contrast, within-site
analysis of SI detected additional subpopulation structure (Figure 4.1C). Two genetic clusters
(K=2) were identified within SI (Figure 4.1C, Figure A4.8), with 24 individuals exhibiting >70%
site nMLG nTotal R He Ho Fis r
d He Ho Fis r
d
OK 5 38 0.132 0.215 0.227 -0.024 0.135 0.245 0.221 0.066 0.016
MI 13 44 0.295 0.239 0.239 0.027 0.031 0.255 0.254 0.019 0.006
TK 10 48 0.208 0.239 0.234 0.046 0.044 0.257 0.249 0.045 0.008
LK 10 54 0.185 0.238 0.247 -0.006 0.057 0.259 0.260 0.006 0.006
BP 12 51 0.235 0.248 0.255 0.001 0.057 0.256 0.261 0.002 0.011
SI 39 59 0.661 0.237 0.239 0.029 0.010 0.249 0.240 0.061 0.003
without clones with clones
105
assignment to a single ancestry and 15 individuals exhibiting admixture (at least 30% assignment
to each subpopulation, Figure 4.1C).

Figure 4.1: Individual admixture proportions (K=2) of host populations sorted by site
(N →S). Admixture analyses were run for all unique genotypes (A) and subset to explore
structure within subpopulations (B and C).

Despite subpopulation structure, gene flow was high between populations (Fst 0-0.05,
Figure A4.9). The fixation index (Fst) was lowest between the most geographically close sites
(LK and TK, 40.5-km, Fst = 0), but was highest between the second closest site pair (BP and SI,
55.12-km, Fst = 0.05). In support of this, effective migration analysis indicated high migration in
the middle keys, but a potential genetic break in the lower keys (Figure 4.2a) and no isolation by
distance (IBD) was detected when including all locations. However, significant IBD was
detected within the Florida Keys when excluding the SI population (r=0.83, p=0.04, Figure
4.2b). Spatial structuring of populations based on latitude was also evidenced by MDS analysis
of IBS distance when removing SI individuals (Figure A4.10). Interestingly, OK and MI have
some of the lowest fixation indexes (0.01), despite being the most geographically distant sites
106
(Figure A4.9). Pairwise comparisons of scaled fixation indexes between OK and all other
populations revealed a significant negative correlation between genetic and geographic distance
(r=0.9, p=0.04), opposite to what is expected under IBD (gray line, Figure 4.2b). Pairwise
comparisons between SI and other Florida Keys sites also exhibited a marginally significant
negative correlation between genetic and geographic distance (r=0.66,p=0.07), where the SI was
least genetically differentiated from the geographically furthest site (SI vs MI, Fst=0.01) and
most differentiated from the closest site (SI vs BP, Fst=0.05) (Figure 4.2b).

Figure 4.2: Host genetic differentiation based on geographic distance. a) Estimated
effective migration rates between sites. Colors indicate greater (blue) or less (orange)
migration than would be expected under IBD. Arrows indicate predominant directionality of sea
surface currents following (Lee and Smith, 2002; National Oceanic and Atmospheric
107
Administration, 2011). b) Pairwise geographic distance and genetic differentiation between sites.
The dashed line indicates a positive relationship between geographic distance and genetic
differentiation (IBD), while the solid lines indicate a negative relationship between geographic
distance and genetic differentiation. Points labels represent pairwise population comparisons
between Otter Key (OK), Miami (MI), Tavernier Key (TK), Long Key (LK), Big Pine Key (BP),
and Stock Island (SI).

Symbiont community composition and distribution
35 symbiont profiles comprising representatives of four Symbiodiniaceae genera (Symbiodinium,
Breviolum, Cladocopium, and Durusdinium) were identified as symbiotic partners of naturally
sourced Aiptasia. Symbiodinium was the most abundant and diverse genus, with 77% of samples
being dominated by one of 13 Symbiodinium profiles. Breviolum and Cladocopium were less
abundant, but were relatively diverse, with 11 and 9 unique profiles, respectively. Durusdinium
was the least abundant genus and was only detected in two samples at background levels (<10%
relative abundance). The majority of individuals (78%) hosted homogenous symbiont
communities whereas the minority (22%) hosted mixed symbiont communities composed of
more than one symbiont profile.
Symbiont community composition differed significantly across sites (PERMANOVA,
p=0.001; Table S5). Cladocopium and Breviolum symbionts more frequently dominated Aiptasia
in northern sites but were uncommon or absent in lower latitude populations (Figure 4.3).
Cladocopium symbionts were restricted to the two northernmost sites, and only dominant in
Otter Key individuals. Otter Key Aiptasia were also frequently dominated by Breviolum profiles,
and only two individuals were dominated by profiles from the most abundant genus,
Symbiodinium. In the upper keys (MI and TK), both Breviolum and Symbiodinium frequently
dominated Aiptasia. However, Breviolum was uncommon south of Tavernier Key (Figure 4.3),
and Symbiodinium A4 profiles dominated southern sites (LK, BP, and SI). Despite little variation
in symbionts at the ITS2 type-level, symbiont community composition significantly differed
108
between southern sites (pairwise adonis, p=0.001; Table S5). Beta diversity was significantly
lower in the southernmost population (SI) in comparison to all other populations (permutest,
p<0.001), but diversity was similar among remaining sites (Figure A4.11).

Figure 4.3: Relative abundance of symbiont profiles across sites. Each bar represents an
individual sample. Samples are grouped by site (rows) and host genotype within site (bolded
boxes). Color families represent genus-level designations and gradation within color families
denote unique ITS2 profiles.

Host-symbiont specificity
Symbiotic associations were highly flexible and unrelated to host genetics. Identical
symbiont profiles were distributed widely across genets, regardless of host relatedness,
indicating that more closely related hosts do not associate with more closely related symbionts
109
(Figure 4.4. Further, within genets, dominant symbiont profiles varied among clones (Figure
4.3). 52% of genets with multiple ramets exhibited symbiont flexibility (24/46 genets). Of these
flexible genets, 13 exhibited flexibility at the profile-level, 1 at the ITS2 type-level, and 10 at the
genus-level (Figure A4.12). Flexibility of symbiont associations within host clonal groups was
evident within and across sites, and even along individual mangrove roots. The capacity for
symbiont flexibility also showed no association with host genetic distance (Figure 4.4, indicating
that more closely related genets are not united in their ability to associate with different
symbionts.

110

Figure 4.4: Co-divergence analysis based on host and symbiont relatedness. Filled rectangles
indicate whether or not (presence or absence) symbiont profiles were detected in host symbiont
communities across Symbiodiniaceae genera. Rows represent symbiont profiles clustered by
genetic relatedness and columns represent unique host genotypes clustered by IBS distance.
Annotation bars represent host clustering (gray scale) and site designations (color).

111
Discussion
Host-symbiont associations are highly flexible in Aiptasia and unrelated to host genetics.
Both hosts and symbionts exhibited biogeographic structuring, but patterns differed between
partners and there was no evidence of co-divergence, indicating low specificity during horizontal
transmission. Host population structure differentiated the most southern site from the others, but
gene flow was high across populations and connectivity opposed geographic distance. On the
other hand, symbiont community distribution followed latitudinal gradients, indicating
biogeographic constraints, such as environmental filtering and/or dispersal limitation, determine
symbiont community composition. Together these results show that contrasting biogeographic
patterns should favor symbiont flexibility and suggest that the evolutionary response of
symbionts to environmental change may determine the future of this symbiosis.
Divergent symbiont communities were also identified in hosts with the same genetic
background (clones), despite vertical transmission, further supporting the lack of symbiont
specificity in this system. In some cases, variation in symbiont communities within clonal groups
may represent ‘shuffling’ (Berkelmans and van Oppen, 2006), or changes in the relative
abundance of existing symbiont community members inherited through vertical transmission
(Figure A4.12, e.g. Genet 45). However, in other genets, symbiont community members are
inconsistent across individuals (Figure A4.12, e.g. Genet 7), indicative of novel symbiont uptake
through horizontal transmission, or symbiont ‘switching’ (Buddemeier and Fautin, 1993;
Scharfenstein et al., 2022). Although the presence of undetectable background symbiont types
could artificially lead to the observation of symbiont switching rather than shuffling, clonal lines
of Aiptasia are able to be reliably infected with novel symbiont types in the laboratory
(Matthews et al., 2018), supporting the potential for mixed mode symbiont transmission. Here
112
we show that this flexibility is ecologically relevant and potentially adaptive, which could
constrain the evolution of specificity regardless of transmission mode.

Symbiont community composition is independent of host effects
Closely related hosts, and even genetically identical individuals, were capable of
associating with various strains, ITS2 types, and symbiont genera, indicating that host-symbiont
recognition is not specialized in Aiptasia (Figure 3, Figure 4.4. Further, symbiont communities
were divergent amongst genetically identical hosts, suggesting that the host environment also
does not play a significant role in driving symbiont community composition. Although the
capacity to associate with diverse symbionts was not detected in all genets, host clusters were not
genetically differentiated based on their degree of specificity (Figure 4.4, so flexibility appears to
be common amongst populations. Together, this extensive flexibility within and across
generations indicates that the host does not play a significant role in driving symbiont
community composition. Rather, ecological processes such as partner availability and symbiont
competition drive community composition in Aiptasia.
Due to symbiont flexibility in the host, symbiont community composition could be
representative of local environmental symbiont pools. Symbiont community composition
significantly differed between all sites, regardless of geographic distance. As symbionts are more
commonly found in sediments than the water column (Littman et al., 2008), dispersal between
sites may be limited, resulting in differential availability of symbionts across sites. Additionally,
local symbiont pools could differ due to environmental filtering. For example, Cladocopium are
consistently found in deeper and more turbid environments (Rowan et al., 1997; LaJeunesse et
al., 2009; Finney et al., 2010) and at higher latitudes (Sawall et al., 2014, 2015; Kennedy et al.,
113
2016; Rossbach et al., 2021; Buitrago-López et al., 2023), so may be restricted to low light
conditions. Symbiodinium also has greater thermal tolerance than Breviolum in culture (Russnak
et al., 2021), which could explain the restricted distribution of Breviolum to cooler, northern
sites. Unlike restricted dispersal which would indicate neutral processes, environmental filtering
could be adaptive and increase holobiont fitness, but further research is needed to test this
hypothesis.
Symbiont communities could also reflect variation in competitive dominance of
symbionts across environments, resulting in differences in the endosymbiotic communities
across sites without a change in partner availability. When offered multiple symbiont types in the
laboratory, Breviolum preferentially infected Aiptasia over Symbiodinium under ambient
conditions (25C), but infection dynamics were reversed at elevated temperature (32C) (Herrera
et al., 2021), which supports the latitudinal patterns observed here. Additionally, Symbiodinium
increased in abundance over previously Breviolum-dominated anemones following thermal
stress, consistent with symbiont shuffling/switching between northern (MI) and southern (BP)
sites (Genet 10, Figure A4.13). Therefore variation in competitive dominance across
environments could explain both biogeographic structuring of symbiont communities, as well as
differences in symbiont community composition among clonal individuals. Although clonal
migration was detected across distant sites with different environmental conditions, which could
potentially induce changes in symbiont community composition, symbiont shuffling/switching
also occurred within a site (Figure 4.3), and even along individual mangrove roots. Fine-scale
environmental measurements and laboratory manipulations are needed to test microscale
environmental variation as a potential driver of symbiont community composition.
114
Despite the capacity to associate with diverse symbiont types, the majority of individuals
hosted a homogenous symbiont community (Figure 4.3). Homogenous symbiont communities
could be explained by priority effects, where symbionts which infect early and become
established limit subsequent infection and/or establishment of alternative symbionts (Fukami,
2015). Variation in symbiont communities across clonal individuals demonstrates that priority
effects do not limit symbiont succession, as clonal offspring seeded with parental symbionts can
host novel symbiont communities. However, it is unclear whether these symbiont community
modifications occurred during stable associations or following disturbance, which could give
insight into the strength of competitive dynamics between symbiont types.
Symbiont community turnover following dysbiosis has been identified as an adaptive
mechanism, facilitating associations with symbionts adapted to novel environments (Baker et al.,
2004). However, competition between symbionts in the absence of disturbance could lead to
reduced host benefit (Frank, 1996). In Aiptasia, symbiont populations undergo population
bottlenecks during pedal laceration followed by rapid proliferation as hosts develop (Presnell et
al., 2022). It is therefore possible that novel, horizontally transmitted symbionts or low
abundance, vertically transmitted symbionts could competitively exclude the previously
dominant symbiont during re-establishment of symbiont populations in clonal offspring.
Alternatively, symbiont shuffling/switching could occur in established communities in the
absence of disturbance, suggesting the competitive disparity between symbiont profiles is large
enough to induce complete community turnover (Fukami and Nakajima, 2011). Symbiont
manipulations in Aiptasia are necessary to understand competitive dynamics between symbiont
types in the presence and absence of disturbance and its effects on host fitness.

115
Contrasting biogeographic patterns between partners favor host-symbiont flexibility
Differences in host and symbiont dispersal could explain contrasting biogeographic
patterns and constrain the evolution of specificity in this system. One mechanism driving
specificity in both vertical and horizontal transmitters is the alignment of host and symbiont
dispersal. Inheritance of symbionts during vertical transmission ensures that the beneficial
relationship can reliably persist over generations. Dispersal can also be aligned in horizontal
transmitters by seeding offspring environments with compatible symbionts. For example, Vibrio
fischeri is more abundant in areas with adult squid and which could facilitate successful
offspring infection (Lee and Ruby, 1994). However, the present study indicates that dispersal and
population differentiation are incongruent between partners, and flexibility may be necessary to
maintain symbiotic relationships within and across generations.
Unlike symbiont community composition which differed significantly across all sites,
host populations were highly connected. Host population structure indicated two populations
with frequent admixture (Figure 4.1), and low genetic differentiation overall (Fst 0-0.05). In
contrast, community composition was differentiated by phylogenetically distant symbionts over
broad spatial scales, as well as by Symbiodinium A4 profiles between neighboring sites (LK, BP,
and SI; Figure 4.3). This suggests that symbiont dispersal may be restricted between sites,
whereas hosts have broader dispersal. Broadcast spawning hosts, such as E. diaphana, have long
pelagic larval duration, which facilitates broad dispersal and commonly leads to low genetic
differentiation between geographically distant populations (Selkoe and Toonen, 2011). On the
other hand, symbionts may have reduced dispersal due to low abundance in the water column
(Littman et al., 2008), though population genetics of symbionts are lacking. Existing work on
population genetics of Symbiodiniaceae indicate both fine-scale differentiation and broad
116
dispersal (Chen et al., 2020; Davies et al., 2020). Though multilocus markers are necessary to
determine the extent of symbiont gene flow between populations in the present study, local and
latitudinal differentiation of symbiont communities suggests that symbiont dispersal is restricted.
Maintaining symbiont flexibility could therefore be essential to establishing symbiotic
associations across generations when offspring are unlikely to be retained within populations.
Host anemones did exhibit some degree of isolation by distance despite high gene flow,
but patterns of differentiation were complex and did not always follow latitudinal gradients.
Isolation by distance was identified among four out of the five Florida Keys sites (Figure 4.2b),
consistent with wind-driven inshore currents along the keys (Schomer and Drew, 1982) (Figure
4.2a). However, the opposite pattern was observed for pairwise comparisons with Otter Key and
Stock Island (Figure 4.2b), possibly due to northward transport via the Florida current (Figure
4.2a). Northward currents southwest of Florida could transport larvae from Otter Key to the Gulf
of Mexico Loop Current (Merrill and Salmon, 2011), which feeds into the Florida Current near
Stock Island (Lee et al., 1992), resulting in relatively low genetic differentiation between Miami
and Otter Key/Stock Island populations (Figure 4.2b). Therefore, genetic differentiation of the
host could be based on complex sea surface currents rather than geographic distance, a common
biogeographic pattern identified in marine animals (Gilg and Hilbish, 2003; Bradbury and
Bentzen, 2007; Baird et al., 2009; White et al., 2010). The complexity of host dispersal patterns
observed here, both with and against the latitudinal gradient, suggests that environmental
selection could be inconsistent across generations and limit local thermal adaptation. As
symbiont distribution appears to follow the environmental gradient, the ability for hosts to
associate with locally adapted symbionts across generations could increase fitness, favoring
flexibility during horizontal transmission.
117
Although larval dispersal would be expected to follow surface currents, clonal migration
was also detected between sites, which could contribute to the complex patterns of connectivity
seen here. For example, Tavernier Key and Long Key were not genetically differentiated (Fst=0)
and migration was higher than would be expected based on geographic distance (Figure 4.2a).
Although clones were not included in population genetic analysis, two migratory genets were
shared across these sites (Table A4.2), which could explain population mixing. E. diaphana has
been introduced globally, likely due to unintentional transport by aquarists, boats, or other
flotsam (Glon et al., 2020). As Aiptasia in this study originated from canals and dock habitats
with high anthropogenic influence and clonal migration was detected over 150 kilometers
(Miami to Big Pine Key), boat transport could contribute to lack of differentiation across large
geographic distances. Additionally, clonal migration between sites was associated with a change
in symbiont community in all but one genet (Figure A4.13), indicating that maintaining
flexibility as adults could facilitate survival of migratory clones across sites. Therefore,
flexibility could increase fitness across environments within a single generation, also
constraining the evolution of specificity during vertical transmission.
However, it is important to note that selection could be occurring between Stock Island
and other sites. Five of the six sites exhibited admixture, whereas almost all Stock Island hosts
were assigned to a single subpopulation (Figure 4.1A). Additionally, clonal migration was not
detected between Stock Island and any other population, despite many boats docking at this site,
which could indicate that the environmental gradient is strong enough to select against hosts
regardless of symbiotic association. Stock Island symbiont communities had significantly lower
beta diversity compared to all other sites, so selection could also be occurring in symbiont
populations along a similar gradient. Co-divergence across a common environmental gradient
118
could potentially increase holobiont fitness through independent evolution. However, the
symbiont profiles associated with Stock Island communities were also detected in genetically
and geographically distant hosts (Figure 4.4. This indicates that specificity is not evolving
between partners and environmental filtering, rather than local adaptation, likely defines
symbiont communities. Common garden experiments are necessary to test whether Stock Island
hosts and/or symbionts are locally adapted, and determine the environmental drivers of selection.

Development of the Aiptasia-Symbiodiniaceae model system
Lack of co-divergence in the present study contrasts previous results, which concluded
that specificity is diverging in natural Aiptasia populations (Thornhill et al., 2013; Bellis et al.,
2018). In contrast to previous studies which relied on PCR banding (Thornhill et al., 2013; Bellis
et al., 2018), the present study is the first to sequence symbiont communities of natural Aiptasia
populations. Therefore, lower resolution may have led to previously observed specificity.
However, in support of the present study, Thornhill et al (2013) also identified low specificity in
Aiptasia from Southern Florida, which can associate with Symbiodinium, Breviolum, and
Cladocopium. This contrasted a globally distributed population with high specificity to
Breviolum B1 (Thornhill et al., 2013). Breviolum B1 was observed in the present study, but hosts
were not genetically differentiated (Figure 4.4, indicating that we either failed to capture the
globally distributed population or the symbiosis is more flexible than previously described.
Broader population genetic studies are necessary to resolve specificity of global Aiptasia
populations and explore the potential drivers of symbiont specificity in species with mixed
transmission.
119
Despite its flexibility, Aiptasia remains a useful model to explore the molecular
underpinnings of specificity in cnidarian-algal symbioses through reverse genetics (Jones et al.,
2018; Cleves et al., 2020; Cleves, 2022). However, the inability to induce metamorphosis ex-situ,
and thus close the life cycle, has hindered these efforts. The present study is the first to explore
reproductive mode in natural populations of Aiptasia and shows that despite high clonality,
sexual reproduction occurs frequently in-situ. Genomic signatures, such as a positive inbreeding
coefficient (Fis) and low index of association between loci (r d) (Table 1), indicate that
reproduction is not predominantly asexuxal (Halkett et al., 2005; Arnaud-Haond et al., 2020).
Further, low genetic differentiation between populations (Fst) (Figure A4.9) and frequent
admixture (Figure 4.1) support that sexual reproduction is common. Currently, few Aiptasia lab
strains are used for ex-situ spawning, which have been sourced from geographically distant
populations and exhibit high genetic distances (Bellis et al., 2016). Although spawning in the lab
produces viable larvae (Grawunder et al., 2015), settlement and metamorphosis have not been
observed, restricting all manipulations to a single generation. Post-zygotic barriers could prevent
successful metamorphosis between diverging populations due to genetic incompatibilities (Hj,
1942; Dobzhansky, 1982).Therefore, the local populations identified in the present study
represent important source populations for future attempts to close the life-cycle ex-situ.

Implications for climate change evolution
Novel environments are frequently encountered by Aiptasia both within (clonal migration) and
across generations (larval dispersal), which could constrain the evolution of specificity regardless
of transmission mode. Maintaining symbiont flexibility within and across generations suggests
that Aiptasia has the capacity to respond to climate change over ecological and evolutionary
120
timescales. However, gene flow was high between host populations and dispersal did not follow
the environmental gradient, indicating limited potential for local adaptation. On the other hand,
symbiont distribution tracked latitudinal limits, possibly due to restricted dispersal and/or
environmental filtering. This indicates that eco-evolutionary dynamics of algal symbionts,
independent of the host, will drive the future of this symbiosis under climate change.
References
Allen, J. L., and Lendemer, J. C. (2022). A call to reconceptualize lichen symbioses. Trends Ecol. Evol.
37, 582–589.
Aranda, M., Li, Y., Liew, Y. J., Baumgarten, S., Simakov, O., Wilson, M. C., et al. (2016). Genomes of
coral dinoflagellate symbionts highlight evolutionary adaptations conducive to a symbiotic lifestyle.
Sci. Rep. 6, 39734.
Arnaud-Haond, S., Stoeckel, S., and Bailleul, D. (2020). New insights into the population genetics of
partially clonal organisms: When seagrass data meet theoretical expectations. Mol. Ecol. 29, 3248–
3260.
Baird, A. H., Guest, J. R., and Willis, B. L. (2009). Systematic and Biogeographical Patterns in the
Reproductive Biology of Scleractinian Corals. Annu. Rev. Ecol. Evol. Syst. 40, 551–571.
Baker, A. C. (2003). Flexibility and Specificity in Coral-Algal Symbiosis: Diversity, Ecology, and
Biogeography of Symbiodinium. Annu. Rev. Ecol. Evol. Syst. 34, 661–689.
Baker, A. C., Starger, C. J., McClanahan, T. R., and Glynn, P. W. (2004). Corals’ adaptive response to
climate change. Nature 430, 741–741.
Baumgarten, S., Simakov, O., Esherick, L. Y., Liew, Y. J., Lehnert, E. M., Michell, C. T., et al. (2015).
The genome of Aiptasia, a sea anemone model for coral symbiosis. Proc. Natl. Acad. Sci. U. S. A.
112, 11893–11898.
Baums, I. B., Johnson, M. E., Devlin-Durante, M. K., and Miller, M. W. (2010). Host population genetic
structure and zooxanthellae diversity of two reef-building coral species along the Florida Reef Tract
and wider Caribbean. Coral Reefs 29, 835–842.
Bellis, E. S., Edlund, R. B., Berrios, H. K., Lessios, H. A., and Denver, D. R. (2018). Molecular
signatures of host specificity linked to habitat specialization in Exaiptasia sea anemones. Ecol. Evol.
8, 5413–5426.
Bellis, E. S., Howe, D. K., and Denver, D. R. (2016). Genome-wide polymorphism and signatures of
selection in the symbiotic sea anemone Aiptasia. BMC Genomics 17, 160.
Bennett, G. M., and Moran, N. A. (2015). Heritable symbiosis: The advantages and perils of an
evolutionary rabbit hole. Proceedings of the National Academy of Sciences 112, 10169–10176.
121
Berkelmans, R., and van Oppen, M. J. H. (2006). The role of zooxanthellae in the thermal tolerance of
corals: a “nugget of hope” for coral reefs in an era of climate change. Proceedings of the Royal
Society B: Biological Sciences 273, 2305–2312.
Bradbury, I. R., and Bentzen, P. (2007). Non-linear genetic isolation by distance: implications for
dispersal estimation in anadromous and marine fish populations. Mar. Ecol. Prog. Ser. 340, 245–
257.
Bright, M., and Bulgheresi, S. (2010). A complex journey: transmission of microbial symbionts. Nat. Rev.
Microbiol. 8, 218–230.
Bucher, M., Wolfowicz, I., Voss, P. A., Hambleton, E. A., and Guse, A. (2016). Development and
Symbiosis Establishment in the Cnidarian Endosymbiosis Model Aiptasia sp. Sci. Rep. 6, 19867.
Buddemeier, R. W., and Fautin, D. G. (1993). Coral Bleaching as an Adaptive Mechanism: A testable
hypothesis. Bioscience 43, 320–326.
Buitrago-López, C., Cárdenas, A., Hume, B. C. C., Gosselin, T., Staubach, F., Aranda, M., et al. (2023).
Disparate population and holobiont structure of pocilloporid corals across the Red Sea gradient
demonstrate species-specific evolutionary trajectories. Mol. Ecol. doi: 10.1111/mec.16871.
Chen, B., Yu, K., Qin, Z., Liang, J., Wang, G., Huang, X., et al. (2020). Dispersal, genetic variation, and
symbiont interaction network of heat-tolerant endosymbiont Durusdinium trenchii: Insights into the
adaptive potential of coral to climate change. Sci. Total Environ. 723, 138026.
Clayton, W. S., Jr (1985). Pedal laceration by the anemone Aiptasia pallida. Marine ecology progress
series. Oldendorf 21, 75–80.
Cleves, P. A. (2022). “A Need for Reverse Genetics to Study Coral Biology and Inform Conservation
Efforts,” in Coral Reef Conservation and Restoration in the Omics Age, eds. M. J. H. van Oppen and
M. Aranda Lastra (Cham: Springer International Publishing), 167–178.
Cleves, P. A., Tinoco, A. I., Bradford, J., Perrin, D., Bay, L. K., and Pringle, J. R. (2020). Reduced
thermal tolerance in a coral carrying CRISPR-induced mutations in the gene for a heat-shock
transcription factor. Proc. Natl. Acad. Sci. U. S. A. 117, 28899–28905.
Danecek, P., Auton, A., Abecasis, G., Albers, C. A., Banks, E., DePristo, M. A., et al. (2011). The variant
call format and VCFtools. Bioinformatics 27, 2156–2158.
Davies, S. W., Moreland, K. N., Wham, D. C., Kanke, M. R., and Matz, M. V. (2020). Cladocopium
community divergence in two Acropora coral hosts across multiple spatial scales. Mol. Ecol. 29,
4559–4572.
Dobzhansky, T. (1982). Genetics and the Origin of Species. Columbia University Press.
Dougan, K. E., Bellantuono, A. J., Kahlke, T., Abbriano, R. M., Chen, Y., Shah, S., et al. (2022). Whole-
genome duplication in an algal symbiont serendipitously confers thermal tolerance to corals. bioRxiv,
2022.04.10.487810. doi: 10.1101/2022.04.10.487810.
Douglas, A. E. (1998). Host benefit and the evolution of specialization in symbiosis. Heredity 81, 599–
603.
122
Fabina, N. S., Putnam, H. M., Franklin, E. C., Stat, M., and Gates, R. D. (2012). Transmission mode
predicts specificity and interaction patterns in coral-Symbiodinium networks. PLoS One 7, e44970.
Finney, J. C., Pettay, D. T., Sampayo, E. M., Warner, M. E., Oxenford, H. A., and LaJeunesse, T. C.
(2010). The relative significance of host-habitat, depth, and geography on the ecology, endemism,
and speciation of coral endosymbionts in the genus Symbiodinium. Microb. Ecol. 60, 250–263.
Forsman, Z. H., Ritson-Williams, R., Tisthammer, K. H., Knapp, I. S. S., and Toonen, R. J. (2020). Host-
symbiont coevolution, cryptic structure, and bleaching susceptibility, in a coral species complex
(Scleractinia; Poritidae). Sci. Rep. 10, 16995.
Fox, E. A., Wright, A. E., Fumagalli, M., and Vieira, F. G. (2019). ngsLD: evaluating linkage
disequilibrium using genotype likelihoods. Bioinformatics 35, 3855–3856.
Frank, S. A. (1996). Host-symbiont conflict over the mixing of symbiotic lineages. Proc. Biol. Sci. 263,
339–344.
Fraune, S., and Bosch, T. C. G. (2007). Long-term maintenance of species-specific bacterial microbiota in
the basal metazoan Hydra. Proceedings of the National Academy of Sciences 104, 13146–13151.
Fukami, T. (2015). Historical Contingency in Community Assembly: Integrating Niches, Species Pools,
and Priority Effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23.
Fukami, T., and Nakajima, M. (2011). Community assembly: alternative stable states or alternative
transient states? Ecol. Lett. 14, 973–984.
Gabay, Y., Parkinson, J. E., Wilkinson, S. P., Weis, V. M., and Davy, S. K. (2019). Inter-partner
specificity limits the acquisition of thermotolerant symbionts in a model cnidarian-dinoflagellate
symbiosis. ISME J. 13, 2489–2499.
Gilg, M. R., and Hilbish, T. J. (2003). The geography of marine larval dispersal: Coupling genetics with
fine-scale physical oceanography. Ecology 84, 2989–2998.
Glon, H., Daly, M., Carlton, J. T., Flenniken, M. M., and Currimjee, Z. (2020). Mediators of invasions in
the sea: life history strategies and dispersal vectors facilitating global sea anemone introductions.
Biol. Invasions 22, 3195–3222.
Goudet, J., and Jombart, T. (n.d.). hierfstat: estimation and tests of hierarchical F-statistics. R package
version 0.04-22.
Grawunder, D., Hambleton, E. A., Bucher, M., Wolfowicz, I., Bechtoldt, N., and Guse, A. (2015).
Induction of Gametogenesis in the Cnidarian Endosymbiosis Model Aiptasia sp. Sci. Rep. 5, 15677.
Halkett, F., Simon, J.-C., and Balloux, F. (2005). Tackling the population genetics of clonal and partially
clonal organisms. Trends Ecol. Evol. 20, 194–201.
Hambleton, E. A., Guse, A., and Pringle, J. R. (2014). Similar specificities of symbiont uptake by adults
and larvae in an anemone model system for coral biology. J. Exp. Biol. 217, 1613–1619.
Herrera, M., Klein, S. G., Campana, S., Chen, J. E., Prasanna, A., Duarte, C. M., et al. (2021).
Temperature transcends partner specificity in the symbiosis establishment of a cnidarian. ISME J. 15,
141–153.
123
Herrera, M., Klein, S. G., Schmidt-Roach, S., Campana, S., Cziesielski, M. J., Chen, J. E., et al. (2020).
Unfamiliar partnerships limit cnidarian holobiont acclimation to warming. Glob. Chang. Biol. 26,
5539–5553.
Hj, M. (1942). Isolating mechanisms, evolution, and temperature. Biol Symp 6, 71–125.
Hume, B. C. C., D’Angelo, C., Smith, E. G., Stevens, J. R., Burt, J., and Wiedenmann, J. (2015).
Symbiodinium thermophilum sp. nov., a thermotolerant symbiotic alga prevalent in corals of the
world’s hottest sea, the Persian/Arabian Gulf. Sci. Rep. 5, 8562.
Hume, B. C. C., Ziegler, M., Poulain, J., Pochon, X., Romac, S., Boissin, E., et al. (2018). An improved
primer set and amplification protocol with increased specificity and sensitivity targeting the
Symbiodinium ITS2 region. PeerJ 6, e4816.
Hume, B., D’Angelo, C., Burt, J., Baker, A. C., Riegl, B., and Wiedenmann, J. (2013). Corals from the
Persian/Arabian Gulf as models for thermotolerant reef-builders: prevalence of clade C3
Symbiodinium, host fluorescence and ex situ temperature tolerance. Mar. Pollut. Bull. 72, 313–322.
Johnston, E. C., Cunning, R., and Burgess, S. C. (2022). Cophylogeny and specificity between cryptic
coral species (Pocillopora spp.) at Mo’orea and their symbionts (Symbiodiniaceae). Mol. Ecol. 31,
5368–5385.
Jones, V. A. S., Bucher, M., Hambleton, E. A., and Guse, A. (2018). Microinjection to deliver protein,
mRNA, and DNA into zygotes of the cnidarian endosymbiosis model Aiptasia sp. Sci. Rep. 8,
16437.
Kamvar, Z. N., Tabima, J. F., and Grünwald, N. J. (2014). Poppr: an R package for genetic analysis of
populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281.
Kennedy, E. V., Tonk, L., Foster, N. L., Chollett, I., Ortiz, J.-C., Dove, S., et al. (2016). Symbiodinium
biogeography tracks environmental patterns rather than host genetics in a key Caribbean reef-
builder, Orbicella annularis. Proc. Biol. Sci. 283. doi: 10.1098/rspb.2016.1938.
Kolde, R. (2019). pheatmap: Pretty Heatmaps. R package version 1.0. 12.
Korneliussen, T. S., Albrechtsen, A., and Nielsen, R. (2014). ANGSD: Analysis of Next Generation
Sequencing Data. BMC Bioinformatics 15, 356.
Koskella, B., and Bergelson, J. (2020). The study of host-microbiome (co)evolution across levels of
selection. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190604.
LaJeunesse, T. (2002). Diversity and community structure of symbiotic dinoflagellates from Caribbean
coral reefs. Mar. Biol. 141, 387–400.
LaJeunesse, T. C., Parkinson, J. E., Gabrielson, P. W., Jeong, H. J., Reimer, J. D., Voolstra, C. R., et al.
(2018). Systematic Revision of Symbiodiniaceae Highlights the Antiquity and Diversity of Coral
Endosymbionts. Curr. Biol. 28, 2570–2580.e6.
LaJeunesse, T. C., Smith, R. T., Finney, J., and Oxenford, H. (2009). Outbreak and persistence of
opportunistic symbiotic dinoflagellates during the 2005 Caribbean mass coral “bleaching” event.
Proc. Biol. Sci. 276, 4139–4148.
124
Langmead, B., and Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nat. Methods 9,
357–359.
Lee, K. H., and Ruby, E. G. (1994). Effect of the Squid Host on the Abundance and Distribution of
Symbiotic Vibrio fischeri in Nature. Appl. Environ. Microbiol. 60, 1565–1571.
Lee, T. N., Rooth, C., Williams, E., McGowan, M., Szmant, A. F., and Clarke, M. E. (1992). Influence of
Florida Current, gyres and wind-driven circulation on transport of larvae and recruitment in the
Florida Keys coral reefs. Cont. Shelf Res. 12, 971–1002.
Lee, T. N., and Smith, N. (2002). Volume transport variability through the Florida Keys tidal channels.
Cont. Shelf Res. 22, 1361–1377.
Lehnert, E. M., Burriesci, M. S., and Pringle, J. R. (2012). Developing the anemone Aiptasia as a
tractable model for cnidarian-dinoflagellate symbiosis: the transcriptome of aposymbiotic A. pallida.
BMC Genomics 13, 271.
Littman, R. A., van Oppen, M. J. H., and Willis, B. L. (2008). Methods for sampling free-living
Symbiodinium (zooxanthellae) and their distribution and abundance at Lizard Island (Great Barrier
Reef). J. Exp. Mar. Bio. Ecol. 364, 48–53.
Liu, H., Stephens, T. G., González-Pech, R. A., Beltran, V. H., Lapeyre, B., Bongaerts, P., et al. (2018).
Symbiodinium genomes reveal adaptive evolution of functions related to coral-dinoflagellate
symbiosis. Commun Biol 1, 95.
Long, S. R. (2001). Genes and signals in the rhizobium-legume symbiosis. Plant Physiol. 125, 69–72.
Mandel, M. J. (2010). Models and approaches to dissect host-symbiont specificity. Trends Microbiol. 18,
504–511.
Mandel, M. J., Wollenberg, M. S., Stabb, E. V., Visick, K. L., and Ruby, E. G. (2009). A single
regulatory gene is sufficient to alter bacterial host range. Nature 458, 215–218.
Manzello, D. P., Matz, M. V., Enochs, I. C., Valentino, L., Carlton, R. D., Kolodziej, G., et al. (2019).
Role of host genetics and heat-tolerant algal symbionts in sustaining populations of the endangered
coral Orbicella faveolata in the Florida Keys with ocean warming. Glob. Chang. Biol. 25, 1016–
1031.
Matthews, J. L., Oakley, C. A., Lutz, A., Hillyer, K. E., Roessner, U., Grossman, A. R., et al. (2018).
Partner switching and metabolic flux in a model cnidarian-dinoflagellate symbiosis. Proc. Biol. Sci.
285. doi: 10.1098/rspb.2018.2336.
Matthews, J. L., Sproles, A. E., Oakley, C. A., Grossman, A. R., Weis, V. M., and Davy, S. K. (2016).
Menthol-induced bleaching rapidly and effectively provides experimental aposymbiotic sea
anemones (Aiptasia sp.) for symbiosis investigations. J. Exp. Biol. 219, 306–310.
McMurdie, P. J., and Holmes, S. (2013). phyloseq: an R package for reproducible interactive analysis and
graphics of microbiome census data. PLoS One 8, e61217.
Merrill, M. W., and Salmon, M. (2011). Magnetic orientation by hatchling loggerhead sea turtles (Caretta
caretta) from the Gulf of Mexico. Mar. Biol. 158, 101–112.
125
National Oceanic and Atmospheric Administration (2011). Florida Keys National Marine Sanctuary
Condition Report 2011. CreateSpace Independent Publishing Platform.
O’Brien, P. A., Webster, N. S., Miller, D. J., and Bourne, D. G. (2019). Host-Microbe Coevolution:
Applying Evidence from Model Systems to Complex Marine Invertebrate Holobionts. MBio 10. doi:
10.1128/mBio.02241-18.
Ohbayashi, T., Mergaert, P., and Kikuchi, Y. (2020). “Chapter Two - Host-symbiont specificity in
insects: Underpinning mechanisms and evolution,” in Advances in Insect Physiology, eds. K. M.
Oliver and J. A. Russell (Academic Press), 27–62.
Oksanen, J. (2010). vegan : Community Ecology Package. http://CRAN.R-project.org/package=vegan.
Available at: https://ci.nii.ac.jp/naid/10027940965/ [Accessed February 16, 2023].
Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O’hara, R. B., et al. (2013). Package
“vegan.” Community ecology package, version 2, 1–295.
Parkinson, J. E., Banaszak, A. T., Altman, N. S., LaJeunesse, T. C., and Baums, I. B. (2015). Intraspecific
diversity among partners drives functional variation in coral symbioses. Sci. Rep. 5, 15667.
Petkova, D., Novembre, J., and Stephens, M. (2016). Visualizing spatial population structure with
estimated effective migration surfaces. Nat. Genet. 48, 94–100.
Presnell, J. S., Wirsching, E., and Weis, V. M. (2022). Tentacle patterning during Exaiptasia diaphana
pedal lacerate development differs between symbiotic and aposymbiotic animals. PeerJ 10, e12770.
Quigley, K. M., Warner, P. A., Bay, L. K., and Willis, B. L. (2018). Unexpected mixed-mode
transmission and moderate genetic regulation of Symbiodinium communities in a brooding coral.
Heredity 121, 524–536.
Rädecker, N., Raina, J.-B., Pernice, M., Perna, G., Guagliardo, P., Kilburn, M. R., et al. (2018). Using
Aiptasia as a Model to Study Metabolic Interactions in Cnidarian-Symbiodinium Symbioses. Front.
Physiol. 9, 214.
Reich, H. G., Kitchen, S. A., Stankiewicz, K. H., Devlin-Durante, M., Fogarty, N. D., and Baums, I. B.
(2021). Genomic variation of an endosymbiotic dinoflagellate (Symbiodinium “fitti”) among closely
related coral hosts. Mol. Ecol. 30, 3500–3514.
Rossbach, S., Hume, B. C. C., Cárdenas, A., Perna, G., Voolstra, C. R., and Duarte, C. M. (2021).
Flexibility in Red Sea Tridacna maxima-Symbiodiniaceae associations supports environmental niche
adaptation. Ecol. Evol. 11, 3393–3406.
Rowan, R., Knowlton, N., Baker, A., and Jara, J. (1997). Landscape ecology of algal symbionts creates
variation in episodes of coral bleaching. Nature 388, 265–269.
Russnak, V., Rodriguez-Lanetty, M., and Karsten, U. (2021). Photophysiological Tolerance and Thermal
Plasticity of Genetically Different Symbiodiniaceae Endosymbiont Species of Cnidaria. Frontiers in
Marine Science 8. doi: 10.3389/fmars.2021.657348.
Sawall, Y., Al-Sofyani, A., Banguera-Hinestroza, E., and Voolstra, C. R. (2014). Spatio-temporal
analyses of Symbiodinium physiology of the coral Pocillopora verrucosa along large-scale nutrient
and temperature gradients in the Red Sea. PLoS One 9, e103179.
126
Sawall, Y., Al-Sofyani, A., Hohn, S., Banguera-Hinestroza, E., Voolstra, C. R., and Wahl, M. (2015).
Extensive phenotypic plasticity of a Red Sea coral over a strong latitudinal temperature gradient
suggests limited acclimatization potential to warming. Sci. Rep. 5, 8940.
Scharfenstein, H. J., Chan, W. Y., Buerger, P., Humphrey, C., and van Oppen, M. J. H. (2022). Evidence
for de novo acquisition of microalgal symbionts by bleached adult corals. ISME J. 16, 1676–1679.
Schomer, N. S., and Drew, R. D. (1982). An ecological characterization of the lower Everglades, Florida
Bay and the Florida Keys. US Fish and Wildlife Service, Office of Biological Services. FWS/OBS-
82/58.
Selkoe, K. A., and Toonen, R. J. (2011). Marine connectivity: a new look at pelagic larval duration and
genetic metrics of dispersal. Mar. Ecol. Prog. Ser. 436, 291–305.
Shoguchi, E., Shinzato, C., Kawashima, T., Gyoja, F., Mungpakdee, S., Koyanagi, R., et al. (2013). Draft
assembly of the Symbiodinium minutum nuclear genome reveals dinoflagellate gene structure. Curr.
Biol. 23, 1399–1408.
Skotte, L., Korneliussen, T. S., and Albrechtsen, A. (2013). Estimating individual admixture proportions
from next generation sequencing data. Genetics 195, 693–702.
Thioulouse, J., Dray, S., Dufour, A.-B., Siberchicot, A., Jombart, T., and Pavoine, S. (2018). Multivariate
Analysis of Ecological Data with ade4. Springer New York.
Thornhill, D. J., Xiang, Y., Pettay, D. T., Zhong, M., and Santos, S. R. (2013). Population genetic data of
a model symbiotic cnidarian system reveal remarkable symbiotic specificity and vectored
introductions across ocean basins. Mol. Ecol. 22, 4499–4515.
Turnham, K. E., Wham, D. C., Sampayo, E., and LaJeunesse, T. C. (2021). Mutualistic microalgae co-
diversify with reef corals that acquire symbionts during egg development. ISME J. 15, 3271–3285.
Visick, K. L., Stabb, E. V., and Ruby, E. G. (2021). A lasting symbiosis: how Vibrio fischeri finds a squid
partner and persists within its natural host. Nat. Rev. Microbiol. 19, 654–665.
Weis, V. M., Davy, S. K., Hoegh-Guldberg, O., Rodriguez-Lanetty, M., and Pringle, J. R. (2008). Cell
biology in model systems as the key to understanding corals. Trends Ecol. Evol. 23, 369–376.
White, C., Selkoe, K. A., Watson, J., Siegel, D. A., Zacherl, D. C., and Toonen, R. J. (2010). Ocean
currents help explain population genetic structure. Proc. Biol. Sci. 277, 1685–1694.
Wisselink, M., Aanen, D. K., and van ’t Padje, A. (2020). The Longevity of Colonies of Fungus-Growing
Termites and the Stability of the Symbiosis. Insects 11. doi: 10.3390/insects11080527.
Wolfowicz, I., Baumgarten, S., Voss, P. A., Hambleton, E. A., Voolstra, C. R., Hatta, M., et al. (2016).
Aiptasia sp. larvae as a model to reveal mechanisms of symbiont selection in cnidarians. Sci. Rep. 6,
32366.

127
Appendix
Supplementary figures

Figure A4.1. Absolute abundance of re-sequenced samples including three negative controls
(NC1-3). Colors indicate symbiont genera (Symbiodinium – pink, Breviolum – green,
Cladocopium – blue, and Durusdinium – purple).

Figure A4.2. Relative abundance of symbiont profiles analyzed with (top) and without (bottom)
clones.

0
25000
50000
75000
100000
115_S179
142_S11
18_S175
63_S72
84_S180
86_S146
MI_sub1_rt1_D_S202
MI_sub2_rt4_E_S67
MI_sub3_rt1_B_S15
MI_sub3_rt3_B_S21
MI_sub3_rt4_C_S149
NC1_S200
NC2_S178
NC3_S206
OK_sub1_r2_E_S70
OK_sub1_rt1_D_S23
OK_sub1_rt2_A_S205
OK_sub1_rt2_D_S181
OK_sub1_rt3_B_S17
OK_sub1_rt3_D_S68
OK_sub2_rt1_A_S16
OK_sub2_rt1_B_S3
OK_sub2_rt1_C_S22
OK_sub2_rt1_D_S176
OK_sub2_rt1_E_S20
OK_sub2_rt2_A_S18
OK_sub2_rt2_B_S12
OK_sub2_rt2_C_S69
OK_sub2_rt2_D_S150
OK_sub2_rt2_E_S199
OK_sub2_rt3_A_S6
OK_sub2_rt3_B_S71
OK_sub2_rt3_C_S13
OK_sub2_rt3_D_S148
OK_sub2_rt3_E_S195
OK_sub2_rt4_A_S203
OK_sub2_rt4_B_S177
OK_sub2_rt4_C_S14
OK_sub2_rt4_D_S19
OK_sub2_rt4_E_S204
name
Abundance
clade
A
B
C
D
0.00
0.25
0.50
0.75
1.00
102
107
111
113
64
74
74B
85
96
99
MI1pt1E
Mi1pt2B
Mi1pt2C
Mi1pt3C
Mi1pt4A
Mi2pt1B
Mi2pt2B
Mi2pt4E
Mi3pt1D
Mi3pt2B
Mi3pt2C
Mi3pt4C
Mi3pt4E
OK1pt3D
OK2pt1C
OK2pt2B
OK2pt4A
OK2pt4C
sample
relative abundance
Reference
A1
A1.A4.A1ac.A1bn
A4
A4.A1
A4.A4cs
A4.A4ct.A4cs
A4.A4ct.A4cs.A1il
A4.A4ct.A4cs.A4q
A4.A4cu.A4ab.A4t.A4cv
A4.A4m
A4.A4m.A4cs.A4ct
A4.A4q.A1il
A4.A4q.A4cw
B1
B1.B1a.B1g
B1.B1ar
B1.B1b
B1.B1g
B1.B1o.B1g.B1p
B19
B2
B2.B2al
B2.B2j
B2.B2m
C1.C1l
C15.C116aa
C15.C15ch
C15.C15dq.C15dr
C15.C15dq.C15dr.C15ch
C3
C3.C1
C3.C15
C3.C1bp.C3ag
D1.D2
D1.D4.D4c.D1c.D2
0.00
0.25
0.50
0.75
1.00
102
107
111
113
64
74
74B
85
96
99
MI1pt1E
Mi1pt2B
Mi1pt2C
Mi1pt3C
Mi1pt4A
Mi2pt1B
Mi2pt2B
Mi2pt4E
Mi3pt1D
Mi3pt2B
Mi3pt2C
Mi3pt4C
Mi3pt4E
OK1pt3D
OK2pt1C
OK2pt2B
OK2pt4A
OK2pt4C
sample
relative abundance
Reference
A1
A4
A4.1365.1366.A4q
A4.A4m.A4q
A4.A4q
B1
B2
C15.C15dq.C15dr
C3
128

Figure A4.3. IBS dendrogram within sites, colored by root.

0.05 0.15 0.25
MI
1-IBS
0.05 0.15 0.25
TK
1-IBS
0.05 0.15 0.25
LK
1-IBS
0.05 0.15 0.25
BP
1-IBS
0.05 0.15 0.25
OK
1-IBS
0.05 0.15 0.25
SI
1-IBS
129
Figure A4.4. IBS dendrogram colored by site from north to south: OK (purple), MI (blue), TK
(green), LK (yellow), BP (orange), SI (red). Note: can see some clones from different sites.

Figure A4.5. Choosing a K from NGSadmix results for full dataset.

0.05 0.10 0.15 0.20 0.25
1-IBS
−7 2 5 0 0
−7 0 0 0 0
−6 7 5 0 0
−6 5 0 0 0
−6 2 5 0 0
0.0 2.5 5.0 7.5 10.0
K
L(k)
−1 2 0 0 0
−8 0 0 0
−4 0 0 0
0
2.5 5.0 7.5 10.0
K
L '(k)
0
5000
10000
15000
2.5 5.0 7.5 10.0
K
L ''(k)
0
20000
40000
60000
2.5 5.0 7.5 10.0
K
delK
NGSadmix no clones
130
Figure A4.6. MDS analysis of IBS distances without clonal individuals.

Figure A4.7. Choosing a K from NGSadmix results without Sis.

-1.0 -0.5 0.0 0.5 1.0
-0.5 0.0 0.5 1.0
MDS1
MDS2
BP
CC7
LK
MI
OK
SA
SI
TK
TX
−4 7 5 0 0
−4 5 0 0 0
−4 2 5 0 0
−4 0 0 0 0
−3 7 5 0 0
0.0 2.5 5.0 7.5 10.0
K
L(k)
−1 2 0 0 0
−8 0 0 0
−4 0 0 0
0
2.5 5.0 7.5 10.0
K
L '(k)
0
5000
10000
2.5 5.0 7.5 10.0
K
L ''(k)
0
20000
40000
60000
2.5 5.0 7.5 10.0
K
delK
NGSadmix no SIs
131
Figure A4.8. Choosing a K for NGSadmix – SI only.

Figure A4.9. Pairwise Fst (fixation indices) between sites.

−3 4 0 0 0
−3 2 0 0 0
−3 0 0 0 0
−2 8 0 0 0
0.0 2.5 5.0 7.5 10.0
K
L(k)
−8 0 0 0
−6 0 0 0
−4 0 0 0
−2 0 0 0
0
2.5 5.0 7.5 10.0
K
L '(k)
0
2500
5000
7500
2.5 5.0 7.5 10.0
K
L ''(k)
0
20000
40000
60000
2.5 5.0 7.5 10.0
K
delK
NGSadmix SIs only
0.01
0.02
0.04
0.03
0.04
0.01
0.03
0.03
0.03
0
0.01
0.03
0.01
0.04 0.05
0 0.01 0.01 0.02 0.02 0.03 0.04 0.04 0.05 0.05 0.06
OK
MI
TK
LK
BP
MI
TK
LK
BP
SI
132
Figure A4.10. MDS analysis of IBS distance excluding SI individuals.

Figure A4.11. Mean beta diversity between sites.

-1.5 -1.0 -0.5 0.0 0.5
-0.5 0.0 0.5 1.0
MDS1
MDS2
BP
CC7
LK
MI
OK
SA
TK
TX
-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6
TK-SI
TK-OK
SI-OK
TK-MI
SI-MI
OK-MI
TK-LK
SI-LK
OK-LK
MI-LK
TK-BP
SI-BP
OK-BP
MI-BP
LK-BP
95% family-wise confidence level
Differences in mean levels of group
133
Figure A4.12. Relative abundance of symbiont profiles within genets showing both symbiont
shuffling and switching. Colors correspond to legend in Figure 4.1.

Figure A4.13. Relative abundance of symbiont profiles for genets with clonal migration across
sites. Colors correspond to legend in Figure 4.1.

0.00
0.25
0.50
0.75
1.00
Genet 7
0.00
0.25
0.50
0.75
1.00
Genet 10
0.00
0.25
0.50
0.75
1.00
Genet 39
0.00
0.25
0.50
0.75
1.00
Genet 43
0.00
0.25
0.50
0.75
1.00
Genet 45
0.00
0.25
0.50
0.75
1.00
Genet 56
0.00
0.25
0.50
0.75
1.00
Genet 57
0.00
0.25
0.50
0.75
1.00
Genet 58
0.00
0.25
0.50
0.75
1.00
Genet 102
0.00
0.25
0.50
0.75
1.00
Genet 104
0.00
0.25
0.50
0.75
1.00
Genet 109
0.00
0.25
0.50
0.75
1.00
BP
BP
BP
BP
BP
BP
BP
BP
BP
BP
BP
BP
TK
Genet 1
0.00
0.25
0.50
0.75
1.00
BP
BP
BP
BP
MI
Genet 7
0.00
0.25
0.50
0.75
1.00
BP
BP
BP
BP
MI
Genet 10
0.00
0.25
0.50
0.75
1.00
TK
TK
TK
TK
LK
TK
TK
TK
TK
TK
TK
TK
Genet 35
0.00
0.25
0.50
0.75
1.00
LK
LK
LK
LK
LK
LK
LK
LK
LK
LK
LK
TK
Genet 37
134

Supplemental tables

Table A4.1. GPS coordinates of sampling sites.

Table A4.2. Presence (1) or absence (0) of genets across sites where clonal migration was
detected.

OK MI TK LK BP SI
genet 1 0 0 1 0 1 0
genet 10 0 1 0 0 1 0
genet 35 0 0 1 1 0 0
genet 37 0 1 1 1 0 0
genet 7 0 1 0 0 1 0

Site Latitude Longitude
Stock Island (SI) 24.565532 -81.738498
Big Pine (BP) 24.705185 -81.35676
Long Key (LK) 24.824003 -80.810071
Tavernier Key (TK) 25.02205 -80.512955
Miami (MI) 25.747922 -80.144946
Otter Key (OK) 27.313934 -82.569941
135
Table A4.3. Number of roots colonized by each genet.

genotype roots_per_genet genotype roots_per_genet
1 6 63 1
37 5 64 1
35 4 65 1
39 4 66 1
54 4 67 1
7 3 68 1
13 3 69 1
26 3 70 1
30 3 71 1
33 3 72 1
104 3 73 1
10 2 75 1
21 2 76 1
40 2 77 1
47 2 78 1
51 2 79 1
57 2 80 1
88 2 81 1
102 2 82 1
107 2 83 1
5 1 84 1
6 1 85 1
9 1 86 1
12 1 87 1
17 1 89 1
19 1 90 1
20 1 91 1
22 1 92 1
23 1 93 1
24 1 94 1
27 1 95 1
28 1 96 1
29 1 103 1
31 1 105 1
32 1 106 1
36 1 108 1
43 1 109 1
44 1
45 1
46 1
48 1
49 1
52 1
56 1
58 1
59 1
60 1
61 1
62 1
136

Table A4.4. Pairwise adonis of symbiont community composition between sites.

Table A4.5. Differences in beta diversity across sites (permutest).

pairs F.Model R2 p.value p.adjusted
MI vs SI 23.2536522 0.17990712 0.001 0.001
MI vs BP 6.56074113 0.05880869 0.001 0.001
MI vs LK 6.33278584 0.05490881 0.001 0.001
MI vs TK 17.1659997 0.14527021 0.001 0.001
MI vs OK 12.2782027 0.12752837 0.001 0.001
SI vs BP 24.1595327 0.18143299 0.001 0.001
SI vs LK 52.954798 0.3190917 0.001 0.001
SI vs TK 64.5192685 0.38060139 0.001 0.001
SI vs OK 49.1050787 0.35815653 0.001 0.001
BP vs LK 9.75718833 0.08013645 0.001 0.001
BP vs TK 11.8593688 0.10236003 0.001 0.001
BP vs OK 9.99870627 0.10308082 0.001 0.001
LK vs TK 21.9720492 0.16905211 0.001 0.001
LK vs OK 15.1843925 0.14300023 0.001 0.001
TK vs OK 8.71968211 0.09506882 0.001 0.001
pair diff lwr upr p adj
LK-BP -0.095651 -0.2483699 0.05706792 0.46961835
MI-BP -0.03649 -0.1940901 0.12111014 0.985668
OK-BP -0.0331508 -0.2109058 0.14460425 0.99470894
SI-BP -0.5091054 -0.6637853 -0.3544255 9.33E-13
TK-BP -0.0843032 -0.2426955 0.07408919 0.64739358
MI-LK 0.05916099 -0.0958211 0.21414309 0.88321253
OK-LK 0.0625002 -0.1129378 0.23793823 0.9104187
SI-LK -0.4134544 -0.565466 -0.2614429 2.46E-12
TK-LK 0.01134782 -0.1444398 0.16713545 0.99994494
OK-MI 0.00333921 -0.176364 0.18304242 0.99999994
SI-MI -0.4726154 -0.6295302 -0.3157006 9.39E-13
TK-MI -0.0478132 -0.2083888 0.11276244 0.95685799
SI-OK -0.4759546 -0.6531023 -0.2988069 3.77E-12
TK-OK -0.0511524 -0.2315508 0.12924601 0.96496239
TK-SI 0.42480226 0.2670918 0.58251273 3.43E-12
137
CHAPTER FIVE: Conclusion
The predicted magnitude of climate change will exceed the current physiological limits of
many species. However, physiological limits are dynamic over time and space, so understanding
the mechanisms that lead to thermal resilience in contemporary populations can give insight into
future adaptive capacity. This dissertation investigates the interactions between symbiotic algae
and their cnidarian hosts across varying thermal gradients and spatial scales to better understand
thermal acclimatization and adaptation, and their potential constraints. Acclimatization had a
strong effect on symbiotic interactions across both fine (meters) and large-scale (latitudinal)
environmental gradients. Plasticity, or the capacity for an individual to shift its phenotype in
response to the environment, also appears to drive local adaptation between populations and is
potentially heritable. Together this suggests that symbiotic cnidarians are highly plastic and that
plasticity itself can evolve, providing a mechanism for survival and adaptation in future oceans.

Host acclimatization prevails over spatial and environmental scales
Local adaptation is predicted to occur when the strength of divergent selection outweighs
gene flow between populations (Richardson et al., 2014). Therefore, both spatial and
environmental scales can promote or constrain adaptation. In chapters 2 and 4, I explored the
potential for adaptation in symbiotic anemones with large dispersal capacities across contrasting
spatial and thermal gradients. Both Anthopleura elegantissima and Exaiptasia diaphana
reproduce by broadcast spawning, where pelagic larvae are dispersed through surface currents.
Therefore, adaptation could be expected to occur over relatively large spatial scales along
moderate environmental gradients, or small spatial scales along a steep environmental gradient.
The intertidal forms a steep environmental gradient, where physiological limits are
known to set species distributions (Stillman and Somero, 1996; Tomanek and Somero, 1999;
138
Dong et al., 2008), yet selection along this gradient was not strong enough to differentiate A.
elegantissima populations. Similarly, even across latitudinal gradients, little genetic
differentiation was detected across E. diaphana populations, suggesting high gene flow and large
dispersal capacity. However, symbiont communities of E. diaphana exhibited latitudinal
structuring, which could reflect symbiont physiological and/or dispersal limits. In contrast,
symbiont communities were invariable in A. elegantissima across the intertidal, possibly
indicative of high symbiont dispersal across small spatial scales (meters) or high environmental
tolerance of Breviolum muscatinei (Muller-Parker et al., 2007). Despite no detectable change in
host or symbiont communities, A. elegantissima holobionts achieved higher thermal tolerance
after acclimatization to greater thermal variability. Together this suggests that host genetic
adaptation is limited in cnidarians with high dispersal capacity, yet holobionts can acclimatize to
greater temperatures with or without dramatic changes in symbiont community composition.
Symbiont dispersal is considered more restricted than cnidarian hosts, which could
facilitate local adaptation and increase holobiont performance, however little is known about the
spatial and environmental scales necessary to promote local adaptation of Symbiodiniaceae.
Chapter 4 suggests symbiont genera are distributed based on physiological niches across broad
spatial and environmental scales, but also that the community composition of closely related
ITS2 type profiles vary significantly between neighboring sites. Currently, population structure
detection is limited in symbionts due to the challenges associated with mixed communities,
availability of symbiont genomes, and their genomic architecture (Davies et al., 2022). However,
recent work in homogenous communities, such as those hosted by A. elegantissima at lower
latitudes, has suggested that populations could be differentiated across the intertidal (Cornwell
and Hernández, 2021), despite the inability to detect population-level differences in Chapter 3.
139
Additionally, symbiont populations can be locally adapted across latitudinal gradients (Howells
et al., 2011), although higher resolution genetics are needed to understand the balance between
gene flow and selection. Population genetics, physiological assays, and common garden
experiments are needed across different levels of Symbiodiniaceae relatedness to better
understand the potential for symbiont acclimatization and adaptation.
In E. diaphana, symbiont switching/shuffling occurred between diverse symbiont types
over broad spatial scales, where environmental filtering and/or local adaptation of symbionts has
the potential to facilitate host acclimatization. Another Anthopleura species has also been
previously shown to employ symbiont switching/shuffling between phylogenetically diverse
microalgae (Bates, 2000), so symbiont switching/shuffling between symbiont populations could
also be a potential mechanism for thermal acclimatization in A. elegantissima. However,
community composition also changed over very fine spatial scales in E. diaphana, such as within
a single mangrove root, and it is unclear whether competitive dynamics between symbionts could
also modify symbiont community composition in the absence of disturbance. Symbiont
switching/shuffling in the absence of disturbance could be detrimental to the host, as competition
between symbionts could reduce host nutritional benefits (Frank, 1996). However, re-
establishment of symbiosis following environmental disturbance could be adaptive if novel
communities are locally adapted (Baker, 2004). Future work should focus on understanding
competitive dominance between symbionts in the presence and absence of disturbance, and its
effects on host fitness, to better predict eco-evolutionary dynamics under climate change.

Evolution of plasticity
Unlike A. elegantissima and E. diaphana, the coral Porites astreoides has internal
fertilization and releases competent planula larvae, which typically restricts dispersal to smaller
140
geographic distances (Ayre and Hughes, 2000; Buitrago-López et al., 2023), allowing
differential selection for locally adapted host alleles to persist within populations. Consistent
with moderate environmental variation and restricted gene flow, inshore and offshore
populations of P. astreoides, exhibit local adaptation to reefs with divergent environmental
variability (Kenkel et al., 2015). We found that gene expression plasticity was greater in inshore-
origin adults, which have higher thermal tolerance than offshore adults. Although adult plasticity
could be environmentally induced due to acclimatization, juvenile offspring reared in a common
garden also exhibited divergent gene expression plasticity, consistent with their adult
counterparts, indicating that gene expression plasticity is potentially heritable. This implies that
selection can act on plasticity itself, possibly increasing environmental responsiveness and
tolerance to future ocean conditions.
In addition to dispersal, the capacity for selection to act on plasticity may depend on the
scale of environmental variation experienced by an organism. Existing plasticity facilitated
thermal acclimatization of clonal A. elegantissima across the intertidal, regardless of the
magnitude of environmental variation. This indicates that plasticity may be adaptive, but is not
differentially selected for along the intertidal gradient, and instead drives broad colonization and
survival. Daily environmental variation, though less extreme than the high intertidal, still
fluctuates predictably in the low intertidal, so plasticity may be advantageous in both tidal zones
(Bitter et al., 2021). In contrast, inshore and offshore reef environments are distinguished by the
magnitude of lower frequency, annual seasonal variation (Kenkel et al., 2015), and divergent
plasticity in P. astreoides reflects the magnitude of seasonal variation across populations. This
suggests that the magnitude of environmental variation can drive the evolution of plasticity,
potentially when the time lag between environmental cues and selection are greater and dispersal
141
is restricted between populations. Rapid plastic responses may be selected for under daily
environmental fluctuations regardless of magnitude, such as across intertidal habitats, whereas
magnitude may be more important in predicting plastic responses under low frequency variation,
such as seasonal fluctuations. A better understanding of the timing between environmental cues
and phenotypic responses combined with experimental evolution and/or modeling across
different variability regimes are necessary to predict the evolution of plasticity and its potential
to increase climate change resilience.

Final remarks
Despite the general sensitivity of symbiotic cnidarians to environmental perturbations, this
dissertation shows that acclimatization, adaptation, and their interaction have the potential to
maintain symbiotic interactions over ecological and evolutionary timescales. In the face of high
gene flow, acclimatization to novel environments is possible with or without a change in
symbiont community composition, which is particularly important for the persistence of long-
lived organisms, like coral, under a changing climate. Adaptation of symbionts will therefore
play an important role in driving acclimatization within generations, and could also be essential
for holobiont success across generations. Finally, plasticity itself can evolve, increasing
environmental tolerance both within and across generations. Though a greater understanding of
these processes are necessary to fully predict eco-evolutionary outcomes, this dissertation
provides valuable insight on the mechanisms leading to acclimatization and adaptation of
symbiotic cnidarians and their potential constraints.

142
References
Ayre, D. J., and Hughes, T. P. (2000). Genotypic diversity and gene flow in brooding and
spawning corals along the Great Barrier Reef, Australia. Evolution 54, 1590–1605.
Baker, A. C. (2004). “Symbiont Diversity on Coral Reefs and Its Relationship to Bleaching
Resistance and Resilience,” in Coral Health and Disease, eds. E. Rosenberg and Y. Loya
(Berlin, Heidelberg: Springer Berlin Heidelberg), 177–194.
Bates, A. (2000). The intertidal distribution of two algal symbionts hosted by Anthopleura
xanthogrammica (Brandt 1835). J. Exp. Mar. Bio. Ecol. 249, 249–262.
Bitter, M. C., Wong, J. M., Dam, H. G., Donelan, S. C., Kenkel, C. D., Komoroske, L. M., et al.
(2021). Fluctuating selection and global change: a synthesis and review on disentangling the
roles of climate amplitude, predictability and novelty. Proc. Biol. Sci. 288, 20210727.
Buitrago-López, C., Cárdenas, A., Hume, B. C. C., Gosselin, T., Staubach, F., Aranda, M., et al.
(2023). Disparate population and holobiont structure of pocilloporid corals across the Red
Sea gradient demonstrate species-specific evolutionary trajectories. Mol. Ecol. doi:
10.1111/mec.16871.
Cornwell, B. H., and Hernández, L. (2021). Genetic structure in the endosymbiont Breviolum
“muscatinei” is correlated with geographical location, environment and host species.
Proceedings of the Royal Society B: Biological Sciences 288, 20202896.
Davies, S., Gamache, M. H., Howe-Kerr, L. I., Kriefall, N. G., Baker, A. C., Banaszak, A. T., et
al. (2022). Building consensus around the assessment and interpretation of Symbiodiniaceae
diversity. Preprints. doi: 10.20944/preprints202206.0284.v1.
Dong, Y., Miller, L. P., Sanders, J. G., and Somero, G. N. (2008). Heat-shock protein 70 (Hsp70)
expression in four limpets of the genus Lottia: interspecific variation in constitutive and
inducible synthesis correlates with in situ exposure to heat stress. Biol. Bull. 215, 173–181.
Frank, S. A. (1996). Host-symbiont conflict over the mixing of symbiotic lineages. Proc. Biol.
Sci. 263, 339–344.
Howells, E. J., Beltran, V. H., Larsen, N. W., Bay, L. K., Willis, B. L., and van Oppen, M. J. H.
(2011). Coral thermal tolerance shaped by local adaptation of photosymbionts. Nat. Clim.
Chang. 2, 116–120.
Kenkel, C. D., Almanza, A. T., and Matz, M. V. (2015). Fine-scale environmental specialization
of reef-building corals might be limiting reef recovery in the Florida Keys. Ecology 96,
3197–3212.
Muller-Parker, G., Pierce-Cravens, J., and Bingham, B. L. (2007). Broad thermal tolerance of the
symbiotic dinoflagellatesymbiodinium muscatinei(Dinophyta) in the sea
anemoneanthopleura elegantissima(Cnidaria) from northern latitudes. J. Phycol. 43, 25–31.
143
Richardson, J. L., Urban, M. C., Bolnick, D. I., and Skelly, D. K. (2014). Microgeographic
adaptation and the spatial scale of evolution. Trends Ecol. Evol. 29, 165–176.
Stillman, J., and Somero, G. (1996). Adaptation to temperature stress and aerial exposure in
congeneric species of intertidal porcelain crabs (genus Petrolisthes): correlation of
physiology, biochemistry and morphology with vertical distribution. J. Exp. Biol. 199,
1845–1855.
Tomanek, L., and Somero, G. N. (1999). Evolutionary and acclimation-induced variation in the
heat-shock responses of congeneric marine snails (genus Tegula) from different thermal
habitats: implications for limits of thermotolerance and biogeography. J. Exp. Biol. 202,
2925–2936.

Abstract (if available)

Abstract The predicted magnitude of climate change will exceed the current physiological limits of many species. The ability to acclimate, adapt, or migrate could determine species persistence in future oceans, but this response may be further complicated by ecological interactions, such as symbioses, where the fitness of one species is dependent on another. To better understand acclimatization and adaptation in symbiotic organisms, and their potential constraints, I investigated interactions between symbiotic algae and their cnidarian hosts across varying thermal gradients and spatial scales. Acclimatization had a strong effect on symbiotic interactions across both fine (meters) and large-scale (latitudinal) environmental gradients. Intertidal anemones acclimatized to greater thermal variability had elevated thermal tolerance compared to those meters away in more stable conditions, which was possible without any change in host or symbiont genetics. At latitudinal scales, clonal migration and high larval dispersal resulted in little genetic differentiation between host populations, but symbiont community composition tracked environmental gradients, suggesting maintaining flexible symbiotic relationships could facilitate acclimatization. Plasticity, or the capacity for an individual to shift its phenotype in response to the environment, also appears to drive local adaptation between coral populations with divergent thermal tolerance. Parents and their lab-reared offspring from thermally variable reefs both exhibited a more robust thermal stress response than those originating from a less variable reef site, indicating these patterns of regulation are potentially heritable. Together this suggests that symbiotic cnidarians are highly plastic and that plasticity itself can evolve, providing a mechanism for survival and adaptation in future oceans.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Transgenerational inheritance of thermal tolerance in two coral species in the Florida Keys

PDF

Disentangling the ecology of bacterial communities in cnidarian holobionts

PDF

Thermal acclimation and adaptation of key phytoplankton groups and interactions with other global change variables

PDF

Developing genetic tools to assist in the domestication of giant kelp

PDF

Evolutionary mechanisms responsible for genetic and phenotypic variation

PDF

Phenotypic plasticity and its ecological and evolutionary significance for reef building coral

PDF

Population genetics and recruitment of the kelp bass, Paralabrax clathratus

PDF

From gamete to genome: evolutionary consequences of sexual conflict in house mice

PDF

Exploring the genetic basis of quantitative traits

PDF

The evolution of pollution tolerance in the marine copepod Tigriopus

PDF

Genetic architectures of phenotypic capacitance

PDF

Biological interactions on the behavioral, genomic, and ecological scale: investigating patterns in Drosophila melanogaster of the southeast United States and Caribbean islands

PDF

Characterizing and developing E. coli Type I-E CRISPR adaptation as a DNA recording and genome engineering tool

PDF

Simulated and field environmental effects on the transcriptome and metabolome of mussel Mytilus californianus

PDF

Sex differences in aging and the effects of mitochondria

PDF

Genetic architecture underlying variation in different traits in the Pacific oyster Crassostrea gigas

PDF

Robustness and stochasticity in Drosophila development

PDF

Essays in environmental economics

PDF

Computational algorithms for studying human genetic variations -- structural variations and variable number tandem repeats

PDF

Application of evolutionary theory and methods to aquatic ecotoxicology

Asset Metadata

Creator Ruggeri, Maria (author)

Core Title Genetic and environmental effects on symbiotic interactions across thermal gradients

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Biology

Degree Conferral Date 2023-08

Publication Date 05/18/2023

Defense Date 05/18/2023

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag acclimatization,adaptation,climate change,cnidarians,OAI-PMH Harvest,symbiosis

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Edge, Doc (committee member), Edmands, Suzanne (committee member), Nuzhdin, Sergey (committee member)

Creator Email mruggeri@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113134163

Unique identifier UC113134163

Identifier etd-RuggeriMar-11872.pdf (filename)

Legacy Identifier etd-RuggeriMar-11872

Document Type Dissertation

Format theses (aat)

Rights Ruggeri, Maria

Internet Media Type application/pdf

Type texts

Source 20230522-usctheses-batch-1047 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu