Morphological plasticity in a reef-building coral is context-dependent and trades off with resistance to thermal stress

Morphological plasticity in a reef-building coral is context-dependent and trades off with resistance to thermal stress | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Morphological plasticity in a reef-building coral is context-dependent and trades off with resistance to thermal stress View ORCID Profile Jenna Dilworth , View ORCID Profile Maya Gomez , View ORCID Profile Ian Combs , Iliyan Hariyani , Joseph Kuehl , Sophia Lee , Tatianna Velicer , Natalie Villafranca , Hanna R. Koch , View ORCID Profile Erinn M. Muller , View ORCID Profile Carly D. Kenkel doi: https://doi.org/10.1101/2025.10.07.680072 Jenna Dilworth 1 Department of Biological Sciences, University of Southern California , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jenna Dilworth For correspondence: jennadilworth{at}gmail.com Maya Gomez 1 Department of Biological Sciences, University of Southern California , Los Angeles, CA, USA 2 Perry Institute for Marine Science , Waitsfield, VT, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maya Gomez Ian Combs 3 Coral Reef Restoration Research Program, Mote’s Elizabeth Moore International Center for Coral Reef Research & Restoration , Summerland Key, FL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ian Combs Iliyan Hariyani 1 Department of Biological Sciences, University of Southern California , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joseph Kuehl 3 Coral Reef Restoration Research Program, Mote’s Elizabeth Moore International Center for Coral Reef Research & Restoration , Summerland Key, FL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sophia Lee 1 Department of Biological Sciences, University of Southern California , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tatianna Velicer 1 Department of Biological Sciences, University of Southern California , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Natalie Villafranca 1 Department of Biological Sciences, University of Southern California , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hanna R. Koch 4 Coral Health and Disease Research Program, Mote Marine Laboratory , Sarasota, FL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Erinn M. Muller 4 Coral Health and Disease Research Program, Mote Marine Laboratory , Sarasota, FL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Erinn M. Muller Carly D. Kenkel 1 Department of Biological Sciences, University of Southern California , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carly D. Kenkel Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract In sessile, long-lived organisms, such as reef-building corals, adaptive phenotypic plasticity may play a key role in mitigating negative fitness impacts arising from rapidly shifting environmental conditions under climate change. However, plasticity is often context-dependent and can have difficult-to-detect costs and limits, resulting in effects that can be adaptive, neutral, or even maladaptive. Here we show that morphological plasticity in the foundational Caribbean reef-builder, Acropora cervicornis , is neutral under ambient temperature conditions, but trades off with thermal stress tolerance during a marine heatwave in the field. While morphological plasticity was previously identified as adaptive in this system, we expand our understanding of plasticity by demonstrating neutral to maladaptive effects depending on the environmental context. These contrasting results are strengthened by our finding that plasticity changes over time, underlining the importance of considering the temporal dynamics of plasticity in long-lived organisms such as scleractinian corals. Our results reveal the nuanced role of plasticity in this climate sentinel species by demonstrating context-dependent costs under real-world thermal stress, revealing a possible constraint on the evolution of plasticity in a changing climate. Main Text As climate change rapidly shifts environmental conditions, organisms must adapt to these changes or relocate to more favorable environments. In sessile, long-lived organisms, such as reef-building corals, phenotypic plasticity is hypothesized to play an important adaptive role in mitigating risk under accelerating climate change 1 , 2 . Organisms may respond to environmental change by adjusting their behaviour, physiology, or morphology, allowing for phenotypic changes within their lifetime without genetic adaptation. For example, a population of great tits in Britain shifted its mean egg-laying time via adaptive plasticity to track climate-driven environmental changes 3 . However, plastic responses may be adaptive, neutral, or maladaptive 4 , with ultimate effects on fitness being highly context-dependent 5 . For instance, a study exposing two species of Lobelia wildflowers to wet or dry conditions found varying levels of plasticity in several photosynthetic traits, with neutral, adaptive, or maladaptive effects depending on the specific environmental conditions 6 . In addition, plasticity is generally associated with costs and limits that can be difficult to detect 5 . Our ability to capture these costs under a changing climate may be influenced by how we measure plasticity and when we determine its fitness effects. As climate change continues to accelerate, it will be crucial to understand these dynamics and how the costs or limits associated with plasticity may constrain organisms’ ability to mitigate climate impacts. The Caribbean branching coral Acropora cervicornis is an excellent model to study phenotypic plasticity in real-world conditions. Asexual reproduction via clonal fragmentation 7 in this species allows for the production of replicate ramets of the same genotype. These ramets are common-gardened before transplantation to natural reefs as part of reef restoration practices 8 , providing the unique opportunity to study plastic responses of the same animal genotype in multiple natural environments. Previous research provided evidence for an adaptive role of plasticity in this system by demonstrating that different genotypes of A. cervicornis express varying levels of morphological plasticity, which correlated positively with growth and survival under ambient conditions 9 . While this prior study did not find any tradeoffs to morphological plasticity, it is likely that they exist in other contexts, particularly since the costs of plasticity in animals are often highest in stressful environments 10 . Potential costs under environmental stress are ecologically relevant in this system, as reef-building corals are extremely vulnerable to climate change. Increasing sea surface temperatures are leading to more frequent and severe bleaching events 11 , in which the relationship between coral and their endosymbiotic algae is disrupted, often leading to host mortality or negative fitness impacts 12 . We conducted a field-based transplant experiment to investigate the context-dependent costs of morphological plasticity in A. cervicornis under thermal stress. By applying a multivariate method 13 to quantify morphological plasticity at several timepoints, we show that genotype-specific plasticity is dynamic over time. Additionally, we demonstrate that while it is neutral under ambient conditions, plasticity trades off with thermal tolerance during a marine heat wave in the field, providing evidence for context-dependent costs. These results illustrate the nuanced role of plasticity in the persistence of this endangered reef-building coral under a changing climate. Results Genotype by environment interactions influence morphology Branching coral morphology is influenced by a balance between growth and breakage, traits which we tracked in ten genotypes of A. cervicornis for nine months after outplanting to two reef sites in the Lower Florida Keys ( Fig. 1a ). Coral ramets with minimal morphological complexity ( Fig. 1b ) were selected from in-water nursery trees before outplanting. To track subsequent changes in morphology ( Fig. 1c ), we used 3D models generated from underwater photographs ( Fig. 1d ) to measure three morphological traits: total linear extension (TLE; Fig. 1d,1e ), surface area (SA), and volume (Vol), and to calculate the surface area to volume ratio (SA:Vol). TLE measurements were used to derive information on linear growth and breakage, where negative changes in TLE were interpreted as breakage and positive changes as growth. Initial measurements of these traits indicated baseline differences in size among genotypes prior to outplanting (ANOVA; TLE p = 6.67 x10 -11 , SA p = 4.74 x10 -13 , Vol p = 2.65 x10 -7 , SA:Vol p = 5.74 x10 -6 ), so initial size was accounted for in subsequent analyses (see methods for details). Linear mixed-effects models showed that linear growth, measured as the change in TLE between timepoints, varied by genotype, genotype x site, and timepoint, the latter driven by an accelerating growth rate over time as corals increased in size ( Fig. 1e , Table S1). Similarly, timepoint was the dominant effect on absolute size in all measured traits, with all mean trait values increasing over time and varying significantly by timepoint ( Fig. 1e , Fig S1, Table S1). SA, Vol, and SA:Vol also varied significantly by genotype (Fig. S1, Table S1). To determine the effect of genotype, site, and their interaction on absolute size without the influence of timepoint, we used linear mixed-effects models to investigate their relationship with absolute size at the final timepoint in June. At this timepoint, absolute size in Vol varied significantly by genotype, site, and their interaction, while SA varied significantly by the interaction term alone (Fig. S1, Table S2). Absolute size in TLE and SA:Vol did not vary significantly by any factor ( Fig. 1e , Fig S1, Table S2). Download figure Open in new tab Figure 1: Growth and absolute size vary by outplant site and genotype. a: Location of source nursery (grey star) and outplant sites (blue shapes) in the Lower Florida Keys. b-c: representative images of experimental replicates in October 2022, before outplanting ( b ) and in June 2023, after 9 months of growth in the field ( c ). d: Example 3D model used for phenotyping: yellow lines on each branch represent measurement method for total linear linear extension (TLE). e: Genotype mean TLE in cm at each outplant site from initial (October 2022, n = 159) through the following three-month monitoring intervals (January 2023, n = 144; April 2023, n = 141; June 2023, n = 139). Point colors represent genotype, shapes represent site. Error bars show standard error. d c Given the diverse effects of genotype, site, and their interaction on individual traits, we used redundancy analysis (RDA) to determine their effect on overall morphology by analyzing a matrix of morphological traits including TLE, SA, Vol, SA:Vol, growth, breakage, and breakage severity while controlling for timepoint. We found that timepoint explained 11.15% of the variance in morphology, while genotype, site, and their interaction explained an additional 8.82% of morphological variance. There was a significant effect of genotype (p = 0.001) and the genotype x site interaction (p = 0.001) on overall morphology, with the first RDA axis explaining a significant amount of the variance (RDA1 59.08% of variance, p = 0.001, RDA2 20.66% of variance, p = 0.503, Fig. 2a ). Ellipses representing the 95% confidence interval at each timepoint show that morphological diversity increased over time, while the directions of trait vectors in RDA space indicate that overall morphology is expressed via a balance between growth and breakage ( Fig 2a ). Download figure Open in new tab Figure 2: Morphological plasticity is temporally dynamic. Point colors indicate genotype, while shapes represent outplant site. Ellipses show the 95% confidence interval at each timepoint, represented by transparency. RDA plots in panels a, b and d are identical, but vectors along with labeled inset, and timepoint ellipses are omitted in some for visual clarity. a: RDA of morphological traits of experimental coral by genotype, site, and their interaction, conditioned on timepoint (January 2023, n = 144; April 2023, n = 141; June 2023, n = 139). Vectors represent morphological traits in RDA space, as labeled in inset. b: example of one genotype’s site-specific centroid (enlarged points) movement over time, representing the approach used to calculate the Euclidean distance between sites as a measure of plasticity for each genotype (panel c). Smaller points represent positions of individual ramets. Centroids are labeled by timepoint (1 = January, 2 = April, 3 = June). Segments with arrows represent the movement of centroids in RDA space over time. c: Plasticity, calculated as the Euclidean distance between site-specific centroids for each genotype, over time. d: RDA of morphological traits, faceted by plasticity at the June timepoint. Left: high plasticity genotypes (top 50th percentile in panel c); right: low plasticity genotypes (bottom 50th percentile in panel c). Genotypic variation in morphological plasticity is temporally dynamic To further explore the dynamics of morphology over time, we calculated the morphological plasticity of each genotype as the Euclidean distance in RDA space between the two site centroids at each timepoint following Leung et al . 13 ( Fig. 2b , Fig. S2, Fig. S3). We found that plasticity is dynamic over time, with different genotypes having higher or lower relative morphological plasticity at different timepoints ( Fig. 2c ). Overall, morphological plasticity increased over time, with the highest plasticity values occurring at the final survey timepoint in June and differences in plasticity between genotypes becoming more pronounced ( Fig. 2c ). When comparing the top 50% highest plasticity genotypes versus the lowest 50% plasticity genotypes based on the final June timepoint in RDA space, high plasticity genotypes demonstrate greater variation in morphology. This difference is particularly evident along the growth-breakage axis, as indicated by the 95% confidence intervals ( Fig. 2a, 2d ), indicating that high plasticity genotypes entered more diverse morphospaces than the lower plasticity genotypes. Additionally, higher plasticity genotypes tended to have more within-site variation, or noise, in morphology than the lower plasticity genotypes, particularly at the June timepoint when plasticity was highest (Fig. S4). Plasticity tradeoffs manifest under thermal stress We investigated whether genotypic differences in morphological plasticity affected survival under ambient conditions using Cox proportional hazards models. We used two models to explore the role of genotype and time-dependent plasticity on survival during the first nine months of the outplant experiment, before significant temperature stress was accumulated ( Fig. 3a ). In the genotype model, only genotype 62 had a significantly higher risk of mortality, by 11.6-fold (p = 0.02, Fig. 3b ) compared to the best-surviving reference genotype, 36. The plasticity model showed no significant effect of time-dependent plasticity on mortality risk (p = 0.29, Fig 3b ). Download figure Open in new tab Figure 3: Mortality risk under ambient conditions by genotype and plasticity. a: Average daily temperatures in ºC (lines) and standard error (shaded area) recorded at the two outplant sites (red = Dave’s Ledge, blue = Looe Key) from outplanting in October 2022 to the final sampling timepoint in June 2023. b: Cox proportional hazards ratios (colored squares) for mortality risk by individual genotype (variable “genotype”) with the highest surviving genotype, 36, as the reference; or changing plasticity values over time (variable “plasticity”). Error bars represent 95% confidence intervals. Bolded p-values indicate p<0.05. At our final regular monitoring timepoint in June, outplants at both sites appeared healthy and did not show visual signs of bleaching stress, although genotype 31 had significantly lighter coloration than genotype 7 (ANOVA, p = 0.000567, Figure S5), suggesting some ramets may have begun paling. However, beginning mid-June 2023, temperatures began exceeding local bleaching thresholds 14 ( Fig 4a ). By late July, we recorded mortality, bleaching and paling as measured by color scoring ( Fig 4b ) and loss of algal symbionts measured via qPCR (Fig S6) at both outplant sites. Temperatures were higher at Dave’s Ledge (max = 33.1ºC) than at Looe Key (max = 32.3ºC) ( Fig 4a ), and outplants at Dave’s Ledge accumulated 11.0 degree heating weeks (DHWs) while those at Looe Key accumulated 10.3 DHWs by late July. As a result, there was more widespread mortality, severe bleaching, and greater loss of algal symbionts across genotypes at Dave’s Ledge, and more genotypic variation in bleaching response at Looe Key ( Fig 4b , Fig S6). Download figure Open in new tab Figure 4: Morphological plasticity trades off with thermal stress resistance. a: Average daily temperatures in ºC (solid lines) and standard error (shaded areas) measured at the outplant sites immediately prior to and during the bleaching event. Colors represent the outplant site. Black vertical lines represent the June and July monitoring/sampling timepoints. Solid grey horizontal line represents the maximum monthly mean temperature (MMM) for Looe Key Reef (29.63ºC) 14 , dashed grey horizontal line represents the local bleaching threshold (MMM + 1ºC = 30.63ºC). b: Proportion (y-axis) of surviving experimental corals at the June timepoint (n=139) at each outplant site falling into one of the Coral Health Chart score bleaching (1-6) or mortality (0) categories in July, by genotype along the x-axis. c: Relationships between genotype-specific morphological plasticity in June and the Bleaching Stress Index of each genotype at each outplant site in July. For Looe Key, linear regression is represented by the grey line, and the grey shaded area represents the 95% confidence interval. Point colors represent genotype, shapes indicate outplant site. We integrated genotype bleaching color scores and mortality at the two outplant sites to generate a bleaching stress index (BSI) for each genotype by site combination following Humanes et al . 15 ranging from 0 (all ramets dead) to 1 (all ramets alive and visually healthy). To investigate whether symbiont community composition may have played a role in the variation in bleaching response, we performed qPCR assays checking for the presence of the more thermally tolerant symbiont genus Durusdinium (∼ trenchii ) in a subset of samples, but found that across genotypes and sites, most samples were dominated by Symbiodinium (∼ fitti ) and only contained background amounts of other symbiont genera (Fig. S6). While some samples did have elevated proportions of Durusdinium , they were characterized by very low symbiont concentrations overall (Fig. S6). Additionally, we found no significant correlation (p = 0.056) between the proportion of thermally tolerant Durusdinium detected in an outplant and its bleaching color score at the July timepoint (Fig. S6), indicating that symbiont community composition did not play a dominant role in the variation in response to thermal stress. To investigate potential tradeoffs between genotype-specific morphological plasticity and the response to thermal stress, we explored the relationships between plasticity values measured at the June monitoring timepoint and genotype BSIs at the two outplant sites in July. At Dave’s Ledge, where temperature stress was more extreme and genotypic variation in bleaching response was lower ( Fig 4a, 4b ), there was no relationship between plasticity in July and the response to thermal stress (p = 0.38, Fig 4c ). However, at Looe Key, where temperatures were slightly lower and more genotypic variation in the bleaching response remained ( Fig 4a, 4b ), there was a significant negative relationship between morphological plasticity and BSI (p = 0.010, Fig 4c ) with more plastic genotypes showing more negative bleaching outcomes. Discussion Due to their unique life-history, phenotypic plasticity may play an outsized role in the persistence of sessile and long-lived organisms under the rapid environmental shifts occurring due to climate change 1 , 2 . However, plasticity is often associated with costs and limits, which can sometimes be hard to detect 5 . Here we leveraged clonal reproduction via fragmentation in A. cervicornis to reveal reduced thermal stress resistance as one consequence of elevated morphological plasticity in this foundational but critically endangered coral species. At a site where, in July of 2023, we could still detect genotypic variation in the response to thermal stress, we found increased resistance to bleaching and mortality is negatively correlated with morphological plasticity. While previous work in this system reported no tradeoffs between plasticity and survival or growth under ambient conditions 9 , we found that morphological plasticity is neutral under ambient conditions and maladaptive under thermal stress, illustrating that the consequences of plasticity are context-dependent. Consistent with previous studies in animals 10 , the costs of morphological plasticity in A. cervicornis are highest under environmentally stressful conditions, manifesting under thermal stress. Additionally, we observed a negative tradeoff where the highest plasticity genotypes were the most sensitive to thermal stress, aligning with previous work which found that costs of plasticity tend to be strongest when plastic responses are large 5 . For example, a study in the common frog ( Rana temporaria ) found costs of plasticity in development time under dry conditions only in the populations which showed the largest plastic responses 16 . These results indicate that coral cannot maximize both plasticity and thermal tolerance, suggesting the benefits of morphological plasticity must be constrained by some costs and/or limits 17 . Thus, our findings of real-world climate-driven constraints on plasticity demonstrate the nuanced role that phenotypic plasticity plays in the response of climate-sensitive species to shifting environmental conditions. We demonstrate that morphological plasticity in A. cervicornis is expressed as a balance between growth and breakage, so one possible cost of plasticity is the high energetic investment required for growth 18 . While tradeoffs between coral thermal tolerance and growth have been demonstrated in several species, they are usually associated with major differences in the community composition of endosymbiotic algae 19 . Under ambient conditions, coral hosting algal symbionts in the more thermally tolerant genus Durusdinium may have reduced growth rates compared to conspecifics hosting the more thermally sensitive Cladocopium 20 , as Durusdinium endosymbionts translocate less carbon to the host, resulting in less energy available for growth 21 . In species with less variation in their algal symbiont communities, such as the A. cervicornis in our experiment, evidence for tradeoffs between growth and thermal tolerance is more mixed. Positive associations between growth, measured as change in live surface area and volumetric growth, and thermal tolerance were observed in an Indo-Pacific congener Acropora digitifera 22 , while a prior study in A. cervicornis found a genotype-mediated negative association between linear growth rates before thermal stress and percent tissue loss after bleaching 23 . The lack of a consistent relationship between growth and thermal tolerance in species with more uniform endosymbiont communities indicates that tradeoffs may be more subtle or involve growth-related plasticity. Particularly, associations between individual growth metrics and fitness traits may not be sufficient to detect potential tradeoffs. Capturing and integrating multiple growth metrics to track morphological plasticity over time allowed us to reveal the context-dependent tradeoff of plasticity with thermal tolerance in this study, demonstrating the need to incorporate more nuanced considerations of growth when measuring tradeoffs with thermal stress responses. The observation that high plasticity genotypes express more varied morphologies via a balance of growth and breakage suggests that the mechanistic tradeoff with thermal tolerance could occur via production costs, or the cost to implement a plastic response. In A. cervicornis , morphological plasticity is modulated in part by calcification-mediated skeletal extension 24 and potentially the complementary process of skeletal infilling 25 , which influences structural integrity and propensity to fragment 7 . Although the precise mechanism remains unresolved, multiple studies have produced evidence in support of active ion transport being involved in the formation of new skeleton 26 . Moreover, bleaching, the visual manifestation of starvation via the loss of photosynthate from algal endosymbionts, is known to yield long-term calcification deficits 27 . Baseline differences among genotypes in skeletal extension or infill rates could result in increased investment in these energy-intensive processes or the need to recover from more frequent breakage, resulting in less energy stores available for responses to thermal stress. In addition to revealing context-dependent costs, we find that morphological plasticity in A. cervicornis is dynamic over time: different genotypes had higher relative plasticity at different timepoints throughout our study. Since the balance between growth and breakage is vital to the expression of morphological plasticity, we hypothesize that the relative plasticity of different genotypes changes over time as corals increase in size and complexity, yielding more branches along which growth and breakage can occur. Additionally, the first nine months of outplant morphology tracked during this experiment represent a substantial change in morphological complexity, as simple ramets with no branches transformed into more complex colonies. It is possible that this study characterizes a highly dynamic phase in plasticity at the early stages of coral growout, and that the relative plasticity of the genotypes studied here could stabilize over time once ramets reach a certain size and their complexity is no longer as strongly in flux. In addition to temporal variation in our focal morphological traits, within-site morphological variation, or noise, between ramets of the highest plasticity genotypes was also elevated, particularly at the final timepoint when morphological complexity was greatest. This positive association between within-site noise and between-site plasticity is indicative of a trend that has previously only been demonstrated in model systems. In yeast, the relationship between noise and plasticity in gene expression is strongly coupled 28 , and in Drosophila , developmental noise and phenotypic plasticity in wing morphology are positively correlated 29 . While the underlying mechanisms leading to the genetic correlation between noise and plasticity remain poorly understood, our observations suggest they are also ecologically important. The dynamic fluctuation of morphological plasticity in these coral outplants over time indicates that phenotypic changes are likely influenced by many environmental variables, meaning that the adaptive value of morphological plasticity in one environment or at one particular timepoint may differ from other environmental or temporal contexts. This is supported by the contrasting results between this study, which finds a neutral effect of time-dependent plasticity under ambient temperature conditions, and a previous study of the same genotypes across a larger suite of outplant sites, which found that morphological plasticity was adaptive under ambient temperatures 9 . Similar patterns of environmentally dynamic plasticity were found in montane butterflies, where plasticity in wing absorptivity, which is important for adaptive thermal regulation, varied by elevation 30 . As such, while we can consider the adaptive value of plasticity in certain contexts, whether it is adaptive overall requires assessment in multiple environments 5 . Additionally, it is important to consider temporal dynamics in the context of life-history. In many other systems, temporal dynamics may only become important in a trans-generational context 31 , but in long-lived clonal organisms it is essential to consider the temporal dynamics that may occur within one individual’s lifetime. Caribbean acroporids, including A. cervicornis , are highly fragmentation-prone species, where asexual clonal ramets regularly establish populations in new habitats 7 , 32 . Similarly, ramets of the same genotypes are placed in many different reef environments for the purposes of reef restoration 8 . The capacity for morphological plasticity may therefore have been historically advantageous. Indeed, plasticity may be beneficial in establishing populations in new habitats: a meta-analysis of 75 invasive and noninvasive plant species pairs found that invading species had greater plasticity 33 . However, even if morphological plasticity evolved in A. cervicornis due to its neutral to adaptive fitness effects under historical environmental conditions, more plastic genotypes may now be at a disadvantage under contemporary conditions due to increasing temperatures and more frequent and severe marine heatwaves 34 . The implications of contemporary marine heatwaves are further underscored by the fact that though there was evidence of genotypic variation in the response to thermal stress at Looe Key early in the 2023 mass bleaching event, corals at Dave’s Ledge were exposed to much more severe thermal stress, resulting in widespread bleaching and mortality regardless of genotype. Additionally, the unprecedented magnitude and duration of the 2023 marine heatwave in the Florida Keys meant that the genotypic variation in bleaching response we observed in July was overwhelmed by extreme thermal stress of over 20 DHWs by the end of the summer 35 , and almost all of the outplants in this experiment were dead by November 2023 (Fig S7). While our results demonstrate that the tradeoff between thermal stress resistance and plasticity represents a key constraint on the evolution of morphological plasticity in A. cervicornis and may play an important role for the maintenance of genotypic variation, contemporary thermal stress events threaten to swamp the adaptive capacity of this key reef-building species. Methods Outplanting and Monitoring In October 2022, 20 ramets of each of 10 genotypes of A. cervicornis were sourced from Mote Marine Laboratory’s Looe Key in situ nursery (24.56257, -81.40009) ( Fig. 1a ), where they had been maintained under common garden conditions for 9+ years. Ramets were collected from nursery trees, limiting differences in size (mean total linear extension = 8.3cm, sd = 1.1cm) and ensuring a lack of secondary branches. Prior to outplanting, ramets were photographed with Olympus Tough cameras in batches of 10 on racks with scaling bars for higher throughput following an updated imaging protocol 36 first outlined in Million et al 37 . On October 14th and 15th of 2022, 10 ramets per genotype were randomly outplanted to one of 10 arrays at one of two reef sites in the Lower Florida Keys (n = 100 ramets per site for a total of 200 ramets): Dave’s Ledge (24.54672, -81.40159) or Looe Key (24.53154, -81.48356) ( Fig. 1a ) under FKNMS permit 2022-120. These sites were chosen as they had demonstrated intermediate levels of outplant survivorship over time in a previous outplant experiment 9 . Masonry nails were hammered into the substrate and corals were affixed to the nails with plastic zip ties. HOBO temperature loggers (Onset Computer Corporation, Bourne, MA) were deployed at both sites to log hourly temperatures throughout the duration of the experiment. Both sites were resurveyed in January 2023, April 2023, and June 2023 over 1-3 observation days per visit to re-photograph each outplant with individual scale bars following the updated protocol 36 adapted from Million et al 37 . Survival was recorded during these surveys and confirmed later with photographs. Outplants where the entire coral fragment and zip tie and/or masonry nail were missing (n = 41) were considered lost due to technical failure rather than biological mortality and were excluded from all analyses. 3D Model Construction and Phenotyping Photos were sorted into separate photosets for each rack of corals (October 2022) or individual outplant (all following timepoints). Three-dimensional models were generated from these photosets using Agisoft Metashape version 1.8.3 (Agisoft LLC, St. Petersburg, Russia). Custom Python scripts adapted from Million et al . 37 with modifications for automatic scaling with Metashape targets were used to batch process using the High Performance Computing resources at the University of Southern California’s Center for Advanced Research Computing. High accuracy was used for point cloud alignment parameters, and high quality was selected for depth maps generation. Individual models were visually inspected and rebuilt if any artifacts were present in the focal area, if the scale bar error was >0.01, or if the model reprojection error was >5 pix. All models were imported into MeshLab 38 to obtain measurements of total linear extension (TLE), volume (Vol), and surface area (SA) as described in Million et al 37 . For measurements of corals photographed using racks (October 2022), the height of the small plastic holder used to attach corals to the rack ( Fig. 1b ) was added to TLE measurements. Additionally, height and interior diameter of the holder were used to calculate the volume and curved surface area of a cylinder, which were added to model Vol and SA to account for coral tissue obscured by this attachment method. Breakage type was recorded by comparing models to the previous timepoint and categorized by branch ordination: breakage on the primary branch (P), secondary branches (S, along with the number(s) of the secondary branch(es), counting from the base of the coral upwards), tertiary branches (T, along with the number(s) of the tertiary branch(es), counting from the base of the secondary branch outward), or catastrophic breakage (C, loss of most secondary/tertiary branches and a significant portion of the primary branch). Net growth and net breakage were calculated as the total gain (growth) or loss (breakage) in total linear extension between two timepoints. Breakage severity was categorized on a scale from 0 (no breakage) to 5 (catastrophic breakage) by integrating breakage type with net growth/breakage data as outlined in Table S3. Assessment of Thermal Tolerance On June 29th 2023, individual RAW images with Coral Health Chart (CoralWatch, St Lucia, Australia) color scales were taken of each outplant using Olympus Tough cameras to serve as baseline color references in anticipation of a potential bleaching event later on in the summer. After receiving regional reports of bleaching near our experimental outplant sites, we conducted a non-quarterly monitoring survey of all outplants on July 26th and 28th 2023 to assess their response to thermal stress. Mortality was recorded and individual RAW images with Coral Health Chart color scales were taken of each outplant. Additionally, ∼2 cm tissue samples were taken from branching tips of all living outplants using bone cutters. An assisting diver shuttled tissue samples back to the boat, where they were immediately snap frozen in liquid nitrogen. The amount of temperature stress accumulated at each outplant site was calculated using average daily temperatures calculated from in-water hourly temperatures recorded by HOBO loggers via the experimental Degree Heating Weeks method as described in Leggat et al . 39 To assess visual bleaching severity, color scores were assigned to each outplant using the baseline photos taken in June and during the marine heatwave in July using ImageJ 40 . The Coral Health Chart D1-D6 color reference in each image was used to generate a standard curve of mean grayscale values 41 . Grayscale values of three unshaded areas of each outplant were then measured and converted into a color score corresponding to the Coral Health Chart 42 . To assess symbiont to host (S:H) ratios in collected tissue samples, total DNA was extracted using a Qiagen DNeasy PowerBiofilm Kit (Qiagen, Aarhus, Denmark), and relative abundances of Symbiodinium and coral host DNA were quantified via qPCR using actin 43 and calmodulin 44 assays respectively. On a random subset of samples, additional multiplexed Cladocopium and Durusdinium actin assays 45 were run to determine whether there were significant levels of these symbiont genera present. All assays were run for 40 cycles on an Agilent AriaMX system with two technical replicates and reference dye corrections. C T values were corrected for fluorescence and gene copy number following Cunning and Baker 45 . The S:H ratios as measured via qPCR and color scores determined from the Coral Health Chart did not correlate (Pearson correlation, p = 0.3692167). This may be due to the fact that A. cervicornis can slough host tissue in addition to the loss of algal symbionts during severe bleaching stress, leading to the loss of both host and symbiont DNA 46 . Additionally, since color scores were determined using the average of three different measurement areas and tissue samples for S:H ratios were collected from only one area near branch tips, we opted to use color scores for further analyses. To integrate mortality and bleaching severity as measured by color score of surviving corals at each outplant site, we used a modified version of the Bleaching Stress Index (BSI) developed by Humanes et al . 15 , which reflects the weighted proportion of ramets of each genotype that died between June and July or fell into one of the six color score categories from the Coral Health Chart, yielding a BSI ranging from 0 (all ramets dead) to 1 (all ramets alive and visually healthy). Statistical Analyses Two-way ANOVAs were used to test for initial differences in TLE, SA, Vol, and SA:Vol by genotype and outplant site. Linear mixed-effects models 47 were used to investigate the effect of timepoint, genotype, site, and their interaction on growth (defined as positive change in TLE) and size metrics. As all traits demonstrated significant initial differences by genotype, initial size was included as a random effect. Linear mixed-effects models were also used to determine the effect of genotype, site, and the genotype x site interaction on absolute size at the final measurement timepoint in June. In these models, initial size was subtracted from absolute size in June to account for baseline differences in all traits. Redundancy analysis (RDA) was used to assess the effects of genotype and site on multivariate morphology over time. The effect of genotype, site, and their interaction, conditioned on timepoint, on a matrix of morphological traits (TLE, V, SA, SA:Vol, net breakage, net growth, and breakage severity) was modeled using the vegan package 48 , with initial size subtracted from all absolute size traits included in the matrix to account for baseline genotype differences. Following Leung et al . 13 , plasticity at each sampling timepoint was calculated as the Euclidean distance between the centroids of each genotype and site combination in RDA space ( Fig. 2b , Fig. S2, Fig. S3). To evaluate the effect of genotype and changing plasticity over time on survival under ambient temperature conditions (October 2022-June 2023), Cox proportional hazards models were fit to survival data using the packages coxme 49 and survival 50 . Correlations between color scores measured via ImageJ and S:H obtained via qPCR at each outplant site were tested to determine consistency between measures of bleaching stress. To investigate potential tradeoffs between plasticity and responses to thermal stress, linear regressions were used to investigate the relationship between genotype plasticity in June and BSI in July. All analyses were performed in R (version 4.2.1). Data Availability All data and analytical code are available at https://github.com/fraulein-jenna/ORCC_tradeoffs , and workflow and scripts for batch processing 3D models can be found at https://zenodo.org/records/17136227 . Acknowledgments We are grateful to Erich Bartels, Zachary Craig, Kyle Knoblock, Amanda Lewan, Samantha Simpson, and Cory Walter at Mote Marine Laboratory and Sibelle O’Donnell at USC for their assistance with fieldwork. Coral were outplanted and sampled under FKNMS permit 2022-120. Funding for this study was provided by NSF IOS 2222272 to CDK and NSF IOS 2222273 to HRK/EMM. Funder Information Declared U.S. National Science Foundation, https://ror.org/021nxhr62 , IOS 2222272 , IOS 2222273 Footnotes https://github.com/fraulein-jenna/ORCC_tradeoffs References 1. ↵ Chevin , L.-M. , Lande , R. & Mace , G. M. Adaptation, Plasticity, and Extinction in a Changing Environment: Towards a Predictive Theory . PLOS Biol . 8 , e1000357 ( 2010 ). OpenUrl CrossRef PubMed 2. ↵ Kelly , M. Adaptation to climate change through genetic accommodation and assimilation of plastic phenotypes . Philos. Trans. R. Soc. B Biol. Sci . 374 , 20180176 ( 2019 ). OpenUrl CrossRef PubMed 3. ↵ Charmantier , A. et al. Adaptive Phenotypic Plasticity in Response to Climate Change in a Wild Bird Population . Science 320 , 800 – 803 ( 2008 ). OpenUrl Abstract / FREE Full Text 4. ↵ Ghalambor , C. K. , McKay , J. K. , Carroll , S. P. & Reznick , D. N. Adaptive versus nonadaptive phenotypic plasticity and the potential for contemporary adaptation in new environments . Funct. Ecol . 21 , 394 – 407 ( 2007 ). OpenUrl 5. ↵ Hendry , A. P. Key Questions on the Role of Phenotypic Plasticity in Eco-Evolutionary Dynamics . J. Hered . 107 , 25 – 41 ( 2016 ). OpenUrl CrossRef PubMed 6. ↵ Caruso , C. M. , Maherali , H. & Sherrard , M. Plasticity of physiology in Lobelia: testing for adaptation and constraint . Evolution 60 , 980 – 990 ( 2006 ). OpenUrl PubMed Web of Science 7. ↵ Highsmith , R. Reproduction by Fragmentation in Corals . Mar. Ecol. Prog. Ser . 7 , 207 – 226 ( 1982 ). OpenUrl CrossRef Web of Science 8. ↵ Young , C. , Schopmeyer , S. & Lirman , D. A Review of Reef Restoration and Coral Propagation Using the Threatened Genus Acropora in the Caribbean and Western Atlantic . Bull. Mar. Sci . 88 , 1075 – 1098 ( 2012 ). OpenUrl CrossRef 9. ↵ Million , W. C. et al. Evidence for adaptive morphological plasticity in the Caribbean coral, Acropora cervicornis . Proc. Natl. Acad. Sci . 119 , e2203925119 ( 2022 ). OpenUrl CrossRef PubMed 10. ↵ Van Buskirk , J. & Steiner , U. K. The fitness costs of developmental canalization and plasticity . J. Evol. Biol . 22 , 852 – 860 ( 2009 ). OpenUrl CrossRef PubMed Web of Science 11. ↵ Hughes , T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene . Science 359 , 80 – 83 ( 2018 ). OpenUrl Abstract / FREE Full Text 12. ↵ Baird , A. & Marshall , P. Mortality, growth and reproduction in scleractinian corals following bleaching on the Great Barrier Reef . Mar. Ecol. Prog. Ser . 237 , 133 – 141 ( 2002 ). OpenUrl CrossRef Web of Science 13. ↵ Leung , C. , Rescan , M. , Grulois , D. & Chevin , L.-M. Reduced phenotypic plasticity evolves in less predictable environments . Ecol. Lett . 23 , 1664 – 1672 ( 2020 ). OpenUrl CrossRef PubMed 14. ↵ Daily 5km Satellite Regional Virtual Station Time Series Data for Looe Key Reef (v 3.1 ). NOAA Coral Reef Watch, College Park , Maryland ( 2025 ). 15. ↵ Humanes , A. et al. Selective breeding enhances coral heat tolerance to marine heatwaves . Nat. Commun . 15 , 8703 ( 2024 ). OpenUrl CrossRef PubMed 16. ↵ Lind , M. I. & Johansson , F. Costs and limits of phenotypic plasticity in island populations of the common frog Rana temporaria under divergent selection pressures . Evolution 63 , 1508 – 1518 ( 2009 ). OpenUrl CrossRef PubMed 17. ↵ DeWitt , T. J. , Sih , A. & Wilson , D. S. Costs and limits of phenotypic plasticity . Trends Ecol. Evol . 13 , 77 – 81 ( 1998 ). OpenUrl CrossRef PubMed Web of Science 18. ↵ Cohen , A. L. & McConnaughey , T. A. Geochemical Perspectives on Coral Mineralization . Rev. Mineral. Geochem . 54 , 151 – 187 ( 2003 ). OpenUrl FREE Full Text 19. ↵ Little , A. F. , Van Oppen , M. J. H. & Willis , B. L. Flexibility in Algal Endosymbioses Shapes Growth in Reef Corals . Science 304 , 1492 – 1494 ( 2004 ). OpenUrl Abstract / FREE Full Text 20. ↵ Cunning , R. , Gillette , P. , Capo , T. , Galvez , K. & Baker , A. C. Growth tradeoffs associated with thermotolerant symbionts in the coral Pocillopora damicornis are lost in warmer oceans . Coral Reefs 34 , 155 – 160 ( 2015 ). OpenUrl CrossRef 21. ↵ Cantin , N. E. , Van Oppen , M. J. H. , Willis , B. L. , Mieog , J. C. & Negri , A. P. Juvenile corals can acquire more carbon from high-performance algal symbionts . Coral Reefs 28 , 405 – 414 ( 2009 ). OpenUrl CrossRef Web of Science 22. ↵ Lachs , L. et al. No apparent trade-offs associated with heat tolerance in a reef-building coral . Commun. Biol . 6 , 1 – 10 ( 2023 ). OpenUrl PubMed 23. ↵ Ladd , M. , Shantz , A. , Bartels , E. & Burkepile , D. Thermal stress reveals a genotype-specific tradeoff between growth and tissue loss in restored Acropora cervicornis . Mar. Ecol. Prog. Ser . 572 , 129 – 139 ( 2017 ). OpenUrl 24. ↵ Pratchett , M. S. et al. Spatial, Temporal and Taxonomic Variation in Coral Growth— Implications for the Structure and Function of Coral Reef Ecosystems . in Oceanography and Marine Biology ( CRC Press , 2015 ). 25. ↵ Gladfelter , E. H. Skeletal development in Acropora cervicornis: I. Patterns of calcium carbonate accretion in the axial corallite . Coral Reefs 1 , 45 – 51 ( 1982 ). OpenUrl CrossRef 26. ↵ Allemand , D. , Tambutte , E. , Zoccola , D. & Tambutte , S. Coral Calcification, Cells to Reefs . in Coral Reefs: An Ecosystem in Transition 119 – 150 ( Springer , Dordrecht , 2011 ). 27. ↵ Rodrigues , L. J. & Grottoli , A. G. Calcification rate and the stable carbon, oxygen, and nitrogen isotopes in the skeleton, host tissue, and zooxanthellae of bleached and recovering Hawaiian corals . Geochim. Cosmochim. Acta 70 , 2781 – 2789 ( 2006 ). OpenUrl CrossRef GeoRef 28. ↵ Lehner , B. Conflict between noise and plasticity in yeast . PLoS Genet . 6 , e1001185 ( 2010 ). OpenUrl CrossRef PubMed 29. ↵ Saito , K. , Tsuboi , M. & Takahashi , Y. Developmental noise and phenotypic plasticity are correlated in Drosophila simulans . Evol. Lett . 8 , 397 – 405 ( 2024 ). OpenUrl PubMed 30. ↵ Kingsolver , J. G. & Buckley , L. B. Evolution of plasticity and adaptive responses to climate change along climate gradients . Proc. R. Soc. B Biol. Sci . 284 , 20170386 ( 2017 ). OpenUrl CrossRef PubMed 31. ↵ Fox , R. J. , Donelson , J. M. , Schunter , C. , Ravasi , T. & Gaitán-Espitia , J. D. Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change . Philos. Trans. R. Soc. B Biol. Sci . 374 , 20180174 ( 2019 ). OpenUrl CrossRef PubMed 32. ↵ Drury , C. , Greer , J. B. , Baums , I. , Gintert , B. & Lirman , D. Clonal diversity impacts coral cover in Acropora cervicornis thickets: Potential relationships between density, growth, and polymorphisms . Ecol. Evol . 9 , 4518 – 4531 ( 2019 ). OpenUrl PubMed 33. ↵ Davidson , A. M. , Jennions , M. & Nicotra , A. B. Do invasive species show higher phenotypic plasticity than native species and, if so, is it adaptive? A meta-analysis . Ecol. Lett . 14 , 419 – 431 ( 2011 ). OpenUrl CrossRef PubMed 34. ↵ Hughes , T. P. et al. Global warming and recurrent mass bleaching of corals . Nature 543 , 373 – 377 ( 2017 ). OpenUrl CrossRef PubMed 35. ↵ Neely , K. L. et al. Too hot to handle? The impact of the 2023 marine heatwave on Florida Keys coral . Front. Mar. Sci . 11 , ( 2024 ). 36. ↵ Gomez , M. I. & Kenkel , C. Image capture for 3D photogrammetry of stony corals V.2 . Preprint at doi: 10.17504/protocols.io.bp2l6xj1rlqe/v1 ( 2025 ). OpenUrl CrossRef 37. ↵ Million , W. C. , O’Donnell , S. , Bartels , E. & Kenkel , C. D. Colony-Level 3D Photogrammetry Reveals That Total Linear Extension and Initial Growth Do Not Scale With Complex Morphological Growth in the Branching Coral, Acropora cervicornis . Front. Mar. Sci . 8 , 384 ( 2021 ). OpenUrl 38. ↵ Cignoni , P. et al. MeshLab: an Open-Source Mesh Processing Tool. in vol. 1 129 – 136 ( Salerno , Italy , 2008 ). OpenUrl 39. ↵ Leggat , W. , Heron , S. F. , Fordyce , A. , Suggett , D. J. & Ainsworth , T. D. Experiment Degree Heating Week (eDHW) as a novel metric to reconcile and validate past and future global coral bleaching studies . J. Environ. Manage . 301 , 113919 ( 2022 ). OpenUrl CrossRef PubMed 40. ↵ Schneider , C. A. , Rasband , W. S. & Eliceiri , K. W. NIH Image to ImageJ: 25 years of image analysis . Nat. Methods 9 , 671 – 675 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 41. ↵ Siebeck , U. E. , Marshall , N. J. , Klüter , A. & Hoegh-Guldberg , O. Monitoring coral bleaching using a colour reference card . Coral Reefs 25 , 453 – 460 ( 2006 ). OpenUrl CrossRef Web of Science 42. ↵ Zhang , Y. , Barnes , S. J. & Kenkel , C. D. Cross-generational heritability analysis of physiological traits in Porites astreoides across an inshore-offshore gradient in the Lower Florida Keys . Coral Reefs 41 , 1681 – 1692 ( 2022 ). OpenUrl CrossRef 43. ↵ Winter , R. N. Environmental Controls on the Reassembly of Symbiodinium Communities in Reef Corals Following Perturbation: Implications for Reef Futures under Climate Change . ( University of Miami, Coral Gables , Florida , 2017 ). 44. ↵ Palacio-Castro , A. M. , Dennison , C. M. , Rosales , S. M. & Baker , A. C. Variation in susceptibility among three Caribbean coral species and their algal symbionts indicates the threatened staghorn coral, Acropora cervicornis, is particularly susceptible to elevated nutrients and heat stress . Coral Reefs 40 , 1601 – 1613 ( 2021 ). OpenUrl 45. ↵ Cunning , R. & Baker , A. C. Thermotolerant coral symbionts modulate heat stressresponsive genes in their hosts . Mol. Ecol . 29 , 2940 – 2950 ( 2020 ). OpenUrl CrossRef 46. ↵ DeMerlis , A. et al. Pre-exposure to a variable temperature treatment improves the response of Acropora cervicornis to acute thermal stress . Coral Reefs 41 , 435 – 445 ( 2022 ). OpenUrl CrossRef 47. ↵ Bates , D. , Mächler , M. , Bolker , B. & Walker , S. Fitting Linear Mixed-Effects Models Using lme4 . J. Stat. Softw . 67 , 1 – 48 ( 2015 ). OpenUrl CrossRef PubMed 48. ↵ Oksanen , J. et al. vegan: Community Ecology Package . ( 2025 ). 49. ↵ Therneau , T. M. coxme: Mixed Effects Cox Models . ( 2024 ). 50. ↵ Therneau , T. M. , Lumley , T. , Atkinson , E. & Crowson , C. survival: Survival Analysis . ( 2024 ). View the discussion thread. Back to top Previous Next Posted October 07, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Morphological plasticity in a reef-building coral is context-dependent and trades off with resistance to thermal stress Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Morphological plasticity in a reef-building coral is context-dependent and trades off with resistance to thermal stress Jenna Dilworth , Maya Gomez , Ian Combs , Iliyan Hariyani , Joseph Kuehl , Sophia Lee , Tatianna Velicer , Natalie Villafranca , Hanna R. Koch , Erinn M. Muller , Carly D. Kenkel bioRxiv 2025.10.07.680072; doi: https://doi.org/10.1101/2025.10.07.680072 Share This Article: Copy Citation Tools Morphological plasticity in a reef-building coral is context-dependent and trades off with resistance to thermal stress Jenna Dilworth , Maya Gomez , Ian Combs , Iliyan Hariyani , Joseph Kuehl , Sophia Lee , Tatianna Velicer , Natalie Villafranca , Hanna R. Koch , Erinn M. Muller , Carly D. Kenkel bioRxiv 2025.10.07.680072; doi: https://doi.org/10.1101/2025.10.07.680072 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Evolutionary Biology Subject Areas All Articles Animal Behavior and Cognition (7629) Biochemistry (17660) Bioengineering (13881) Bioinformatics (41913) Biophysics (21436) Cancer Biology (18578) Cell Biology (25482) Clinical Trials (138) Developmental Biology (13372) Ecology (19889) Epidemiology (2067) Evolutionary Biology (24302) Genetics (15599) Genomics (22483) Immunology (17728) Microbiology (40365) Molecular Biology (17163) Neuroscience (88540) Paleontology (666) Pathology (2830) Pharmacology and Toxicology (4821) Physiology (7637) Plant Biology (15130) Scientific Communication and Education (2045) Synthetic Biology (4290) Systems Biology (9818) Zoology (2269)