DOI: 10.5061/dryad.9cnp5hqsf
title: Data from: Corals that survive repeated thermal stress show signs of selection and acclimatization

description: Climate change is transforming coral reefs by increasing the frequency and
 intensity of marine heatwaves, often leading to coral bleaching and
 mortality. Coral communities have demonstrated modest increases in thermal
 tolerance following repeated exposure to moderate heat stress, but it is
 unclear whether these shifts represent acclimatization of individual
 colonies or mortality of thermally susceptible individuals. For corals
 that survive repeated bleaching events, it is important to understand how
 past bleaching responses impact future growth potential. Here, we track
 the bleaching responses of 1,832 corals in leeward Maui through multiple
 marine heatwaves and document patterns of coral growth and survivorship
 over a seven-year period. While we find limited evidence of
 acclimatization at population scales, we document reduced bleaching over
 time in specific individuals, primarily in the stress-tolerant taxa
 Porites lobata, indicative of acclimatization. For corals that survived
 both bleaching events, we find no relationship between bleaching response
 and coral growth in three of four taxa studied. This decoupling between
 bleaching and growth suggests that coral survivorship is a better
 indicator of future growth than is a coral’s bleaching history. Based on
 these results, we recommend restoration practitioners in Hawaiʻi obtain
 outplants from Porites and Montipora colonies with a proven track-record
 of growth and survivorship, rather than devote resources toward
 identifying and cultivating bleaching-resistant phenotypes. Survivorship
 followed a latitudinal thermal stress gradient, but because this gradient
 was small, it is likely that local environmental factors also drove
 differences in coral performance between sites. Efforts to reduce human
 impacts at low performing sites would likely improve coral survivorship in
 the future.
description: We used large-area imaging (photogrammetry) to capture a 3D
 snapshot of coral reef condition at our six sites in leeward Maui in 2014,
 2015, 2017, 2019, and 2021. Large-area imaging is the process of
 generating a composite visual reconstruction (i.e., 3D model,
 orthoprojection, DEM, etc.) via the overlap of many component images.
 Large-area imaging has been used with increased frequency to archive coral
 reef structure and condition, and provides a basis for <em>in
 silico</em> field work to answer a variety of ecological questions.
 Details of our large-area imaging workflow are available elsewhere and
 will be described in minimal detail here. At each fixed
 site, divers entered the water with two D7000 SLR Nikon cameras (focal
 lengths of 18mm and 55mm) in Ikelite underwater housings. Divers marked
 the boundaries of each 10 x 10 m long term monitoring plot with six
 calibration tiles, and placed four 0.5 m long scale bars within the plot.
 One diver swam approximately 1.5 m over the reef in a gridded pattern with
 the cameras, which were programmed to take a picture every second. This
 produced approximately 5,000 pictures per site. The camera diver imaged a
 core area of 10x10 m, and swam several meters beyond the calibration tiles
 to ensure that a buffer region (&gt; 1 m wide) around the core area
 was also imaged. Once imagery was collected, we used
 the software Agisoft Metashape (St. Petersburg, Russia) to build a dense
 point cloud, which we refer to here as a 3D model. After building the 3D
 models in Metashape, we loaded them into the custom software Viscore for
 postprocessing. These postprocessing steps included 1) scaling the 3D
 model using the 0.5 m scale bars, 2) entering depth measurements collected
 at each calibration tile so that the 3D model could be oriented with
 respect to the sea surface, 3) manually aligning 3D models of the same
 site collected in different years, and 4) exporting a high-resolution
 top-down view of the model known as an orthoprojection. Each
 orthoprojection was 12 x 12 m and had a resolution of 1 mm per pixel. We
 used these orthoprojections rather than the 3D models for all subsequent
 data collection steps.   Researchers used TagLab and
 ArcGIS Pro to trace patches of live coral tissue following the approach of
 Rodrguez et al. 2021. We found no effect of software on traced planar
 area, and a single annotator QCed all tracings in TagLab to control for
 any potential effect of annotator or software. We used the high-resolution
 imagery that underlies the 3D model (raw images) as a reference to assist
 with tracing and species ID. For this study, we chose to
 identify <em>Pocillopora</em> to the genus level because
 morphology is not a good indicator of species ID for
 <em>Pocillopora</em> in Hawaiʻi, whereas the three other taxa
 could be confidently identified to species. For <em>P.
 lobata</em>, we focused on massive and submassive growth forms to
 avoid confusion with the related branching coral <em>Porites
 compressa</em>. After corals were traced, we used
 TagLab to “match” patches of live tissue that represented the same
 individual through time. This created a network of temporally linked coral
 patches. This enabled us to more accurately identify individual genets of
 corals such as <em>Porites</em> and
 <em>Montipora</em>, which readily exhibit fusion and fission
 over time due to partial morality. Hereafter we use “patch” to denote a
 single contiguous region of live coral tissue in one timepoint, and
 “genet” to denote a network of patches interconnected through time. While
 we cannot definitively say that linked corals represent a single genetic
 individual (since we did no genetic testing), the precise tracking
 capabilities afforded by overlapping orthoprojections and associated raw
 images enable us to identify individual genets with a reasonable degree of
 confidence. Additionally, we visually inspected orthoprojections to
 identify potential instances of pseudoreplication arising from colony
 fission prior to our timeseries, and found minimal evidence to support
 this concern. We performed all subsequent analyses at the genet level, and
 considered a genet to have survived the full timeseries if at least one
 patch of that genet existed in both 2014 (our first sampling year) and
 2021 (our final sampling year). For each genet, we calculated the total
 planar area (cm<sup>2</sup>) in each timestep by summing the
 planar area of individual associated patches. To
 achieve an appropriate sample size of <em>M.</em>
 <em>capitata</em>, <em>M. patula</em>, and
 <em>P. lobata</em>, we traced corals within 10 randomly placed
 non-overlapping 0.5 m<sup>2</sup> quadrats within the
 orthoprojection. We identified and traced all coral patches with a
 diameter &gt; 5cm whose centroid fell within the boundaries of a
 quadrat. To account for fission and fusion dynamics, we also traced
 patches outside of a quadrat or &lt; 5cm in diameter if they were
 temporally linked to a genet inside a quadrat. At sites where &lt; 40
 genets were traced in our first timestep, we placed additional quadrats
 (up to 25 total) and continued tracing taxa until we reached 40 genets or
 until 25 quadrats had been placed. For <em>Pocillopora</em>,
 which was less abundant than other taxa, we identified and traced all
 patches within each 12 x 12 m orthoprojection. To ensure a balanced design
 for statistical analyses, we randomly selected 100 <em>P.
 lobata</em> genets for n = 2 sites where &gt; 100 genets had
 been traced.  We assessed the extent and severity of
 bleaching for every patch of coral tissue &gt;
 1cm<sup>2</sup> in each year of our timeseries. First, we
 visually estimated bleaching extent as the percent of a patch’s area
 (0-100%) with some degree of paling unrelated to coral growth or disease.
 Focusing on this bleached tissue only, we then scored bleaching severity
 (from 0 to 3) based on the overall degree of paling observed (0 for
 practically no pigmentation loss; 1 for slight paling, 2 for significant
 loss of pigmentation, and 3 for almost or completely stark white). When
 estimating both bleaching extent and severity, we used Viscore’s Virtual
 Point Intercept interface to reference the original imagery. This allowed
 us to view multiple angles of each coral patch to assess bleaching,
 enabling us to account for changes in tissue color due to lighting or
 image quality. Once we assessed bleaching extent and
 severity for all patches in all years, we calculated genet-level bleaching
 extent and severity in each timepoint by summing bleaching extent and
 severity across patches, weighted by patch area. Then, we incorporated
 both bleaching extent and severity into a single bleaching metric for each
 genet in each timepoint. According to this metric, genets fell into one of
 five categories: no bleaching or very minor paling, minor bleaching,
 moderate bleaching, severe bleaching, or extreme bleaching. If a genet
 changed by more than one step between 2015 and 2019, the genet was
 considered to have an increased or decreased bleaching response over time.
 We considered genets that changed one bleaching step or less between 2015
 and 2019 to have a stable bleaching response over time. Among these stable
 genets, those that exhibited moderate bleaching or higher were considered
 to have “high bleaching susceptibility”, and all other genets were
 considered to be “thermally tolerant”. These bleaching responses
 (thermally tolerant, decreased bleaching response, increased bleaching
 response, and high bleaching susceptibility) were used as fixed factors to
 compare genets over time. To test for signs of
 acclimatization among surviving genets in each population (corals of the
 same taxa within a given site), we employed a bootstrapping approach using
 only genets that were present in both bleaching events. We calculated the
 difference between each genet’s bleaching score in 2015 and 2019, which
 produced an integer response variable ranging from -4 to 4, where 0
 indicated no change in bleaching, -4 indicated extreme bleaching in 2015
 and no bleaching in 2019, and 4 indicated no bleaching in 2015 and extreme
 bleaching in 2019. Then, we generated a null distribution of the median
 change in bleaching scores for each coral population via 100,000
 resamples. P values were calculated as the proportion of all resamples
 where the population’s median change in bleaching score was ≥ 0, and we
 used α = 0.05 as our threshold of significance. We did not test for
 acclimatization in two populations of <em>Pocillopora</em>
 spp. which had a low sample size of surviving genets (&lt; 10
 genets). To test if coral populations at each site
 exhibited different bleaching responses, we created a contingency table of
 surviving genets based on their bleaching responses over time. We
 performed Fisher’s exact test to test the null hypothesis that there was
 no relationship between site and bleaching response. We performed this
 test between all permutations of sites for each taxa and calculated
 adjusted p values using the Bonferroni correction for multiple
 comparisons. We assigned post hoc letters at a significance level of α =
 0.05 to indicate significant differences in bleaching response between
 coral populations at different sites. We also pooled data by site and
 performed the same test to identify significant differences in bleaching
 response among coral taxa. Separately, we pooled observations across sites
 and ran a logistic regression of bleaching probability vs. genet size in
 2015 and 2019 to quantify the relationship between bleaching and genet
 size. For this analysis, we coded bleaching as a binary variable (0 for
 genets with no signs of bleaching or minor bleaching, 1 for genets with
 moderate, severe, or extreme bleaching) in order to perform the logistic
 regression. To test if acclimatized and thermally
 tolerant genets exhibited higher growth over the timeseries than other
 corals, we performed an ANCOVA for each coral taxa, focusing on surviving
 genets only. We used bleaching response (thermally tolerant, decreasing
 bleaching susceptibility, increasing bleaching susceptibility, and high
 bleaching susceptibility) as a fixed effect, only retaining those with a
 sample size ≥ 10 for analysis. Our continuous predictor variable was genet
 planar area at the start of the timeseries, and our response variable was
 genet planar area at the end of the timeseries. Genet planar area was
 natural log transformed for normality, and model assumptions were visually
 assessed using residual plots and quantitatively using Shapiro tests for
 normality and Cochran tests for homogeneity of variance. We performed a
 separate ANCOVA using site as a fixed effect to test for significant
 differences in coral growth between sites for each taxon. All models used
 a significance level of α = 0.05. Finally, to test for
 differences in coral survivorship between sites, we performed a logistic
 regression using all traced genets (not just survivors) for each taxon. We
 used site as a fixed effect, log transformed planar area of genets in 2014
 as our predictor variable, and coded genet survivorship as a binary
 response variable (0 = genet did not survive until 2021, 1 = genet
 survived from 2014 to 2021). There were significant interactions between
 multiple sites, so to facilitate interpretation we conducted pairwise
 comparisons between sites that didn’t interact. We calculated adjusted p
 values using the Bonferroni correction for multiple comparisons at a
 significance level of α = 0.05. To test for differences in coral
 survivorship between taxa, we also pooled genets by site and ran a
 logistic regression of survivorship vs. initial genet size. We completed
 statistical analyses using the ‘dplyr’, ‘emmeans’, ‘multcomp’, ‘coin’, and
 ‘rstatix’ packages in R v. 4.0.5.
description: # Corals that survive repeated thermal stress show signs of selection and
 acclimatization
 [https://doi.org/10.5061/dryad.9cnp5hqsf](https://doi.org/10.5061/dryad.9cnp5hqsf) ## Coral tracing data and file structure One CSV file containing raw data is included (All_sites_bleaching_by_genet.csv). It contains data on nearly 2,000 corals that were traced and followed over time using large area imagery. Corals are from six sites in leeward Maui, surveyed in 2014, 2015, 2017, 2019, and 2021. Corals were exposed to thermal stress that caused widespread bleaching in 2015 and 2019. Fields in the CSV file are as follows: * ind: A unique identifier for each genet. Generated by concatenating the site name, species name, and genet number * TagLab.Date: The year in which the coral was surveyed (2014, 2015, 2017, 2019, or 2021) * genet: An integer ID number associated with an individual coral (or genet) for a given site. A new sequence of integers created for each site and taxa (for example, genet #1 may refer to the first genet of species A at site A, but also the first genet of species B at site A). For each genet's unique ID, use ind * species: The species of coral. Four options (Montipora capitata, Montipora patula, Pocillopora spp, and Porites lobata) * total_area: The planar area of the genet in a given year, in cm2. Genet planar area was calculated by summing the planar area of all associated patches. * total_surf_area: The surface area of the genet in a given year, in cm2 * total_perimeter: The total perimeter of a genet in a given year, in cm. * quadrat: Integer representing the quadrat that a genet was located within. Up to 25 quadrats were used per site. Each quadrat was 0.5cm2. * survivor: indicates whether a genet survive from the beginning of the timeseries (2014) to the end (2021). Options are YES or NO. * bleaching_extent: the proportion of genet area impacted by bleaching in a given year. Uses planar area. * bleaching_extent_SA: the proportion of genet area impacted by bleaching in a given year. Uses surface area. * bleaching_severity: The bleaching severity category of the genet in a given year. Options are S0 (practically no pigmentation loss), S1 (slight paling), S2 (significant loss of pigmentation), and S3 (almost or completely stark white). * site: The site where corals were surveyed. Six options (Kahekili, Keawakapu, Molokini, Olowalu, Ukumehame, or Wahikuli) ## Sea Surface Temperature Data Sea surface temperature data from Coral Reef Watch (CRW) can be downloaded from [https://pae-paha.pacioos.hawaii.edu/erddap/griddap/dhw_5km.html](https://pae-paha.pacioos.hawaii.edu/erddap/griddap/dhw_5km.html). Sea surface temperature data from the Regional Ocean Modeling System (ROMS) for the Main Hawaiian Islands can be found at [https://www.pacioos.hawaii.edu/metadata/roms_hiig.html.](https://www.pacioos.hawaii.edu/metadata/roms_hiig.html.) The latitude and longitude of each site are as follows, and can be used to extract sea surface temperature data from either CRW or ROMS products: | **Site** | **Latitude** | **Longitude** | | :------- | :----------- | :------------ | | Kahekili | 20.9368 | -156.6937 | | :------- | :------ | :-------- | | Keawakapu | 20.7038 | -156.4504 | | :-------- | :------ | :-------- | | Molokini | 20.6315 | -156.4966 | | :------- | :------ | :-------- | | Olowalu | 20.8047 | -156.6072 | | :------ | :------ | :-------- | | Ukumehame | 20.7910 | -156.5843 | | :-------- | :------ | :-------- | | Wahikuli | 20.9098 | -156.6917 | | :------- | :------ | :-------- | ## Code/Software Two R markdown files are included. The first (Maui bleaching final analysis) contains code to perform the tasks below. Use All_sites_bleaching_by_genet.csv as an input when running this code. 1. Data wrangling (assigns bleaching categories to colonies based on their bleaching history, and creates graphs of bleaching severity) 2. Defines functions for graphing, makes stacked barplots of proportions of colonies by bleaching response and survivorship 3. Analysis * Bootstrapping test for difference of medians * Fishers exact test for difference in bleaching response between sites * ANCOVA for difference in coral growth between bleaching responses * ANCOVA for difference in coral growth between sites * Logistic regression for differences in survivorship between sites The second R markdown file compares sea surface temperature from NOAA's Coral Reef Watch Program (CRW) and the Regional Ocean Modeling System (ROMS) for study sites in Maui. Use sea surface temperature data downloaded from CRW and ROMS as an input with this code.