http://lod.bco-dmo.org/id/dataset/964240 eng; USA utf8 dataset Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. pointOfContact 2025-06-09 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) Impacts of submarine groundwater discharge on benthic community composition and functional diversity on coral reefs in Mo'orea, French Polynesia from Aug 2021 to Jun 2022 2025-07-07 publication 2025-07-07 revision Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA) 2025-10-15 publication https://doi.org/10.26008/1912/bco-dmo.964240.1 Nyssa Silbiger California State University Northridge principalInvestigator Danielle M Barnas California State University Northridge principalInvestigator Maya Zeff California State University Northridge principalInvestigator Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. publisher Cite this dataset as: Barnas, D. M., Zeff, M., Silbiger, N. (2025) Impacts of submarine groundwater discharge on benthic community composition and functional diversity on coral reefs in Mo'orea, French Polynesia from Aug 2021 to Jun 2022. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2025-07-07 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.964240.1 [access date] Coral reef benthic composition in SGD Dataset Description: Methods and Sampling: &lt;p&gt;Detailed methods are outlined in the results publication Barnas et al. (2025) and summarized here.&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Study site and characterization&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Mo‘orea, French Polynesia, is a tropical volcanic island with coastal fringing coral reefs where SGD is distributed through fissures in the reef plate (Knee et al. 2016; Hagedorn et al. 2020). Local fishers’ knowledge of an SGD seep informed the location of our survey site, and the presence of SGD was confirmed through spatial and temporal radon (Hagedorn et al. 2020, 2024) and biogeochemical surveys (Silbiger et al. 2023). We identified a focal seepage point on the western shore of Mo‘orea and haphazardly chose 19 survey locations downstream of the seep to study the effects of SGD on taxonomic and functional diversity. All survey locations had hard substrate with an average depth of 0.6 m and were within 150 m of the SGD seep, experiencing a gradient of SGD influence. Our field site experiences consistent northwestward unidirectional flow averaging 0.15 m/s (Silbiger et al. 2023), distributing SGD in a predictable alongshore gradient.&lt;/p&gt; &lt;p&gt;Biogeochemical measurements associated with SGD influence were assessed through discrete water sampling from high and low tides during the day and nighttime in August 2021 (n = 4 measurements per survey location). See Silbiger et al. (2023) for detailed methods and descriptions of the SGD gradient. In brief, water samples were collected concurrently at each time point in acid-washed, triple-rinsed 1 L HDPE bottles. Salinity, temperature, and pH were immediately measured using portable sensors (salinity accuracy ± 1.0% psu and precision = 0.1 psu, temperature accuracy ± 0.3 °C and precision = 0.1 °C, YSI Pro2030, Xylem Inc., Washington D.C., USA.; pH [total scale] accuracy ± 0.002 and precision = 0.001, tris-calibrated ROSSTM double junction electrode, Orion Star A325, Thermo Fisher Scientific Inc., Waltham, MA, USA). The water samples were also filtered through a 0.22 μm Sterivex filter before being frozen at −20 °C for subsequent nutrient analysis for concentrations of silicate [SiO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;2−&lt;/sup&gt;], phosphate [PO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;3−&lt;/sup&gt;], and nitrate + nitrite [N+N]). The samples were brought to the S-LAB at the University of Hawai‘i, where they were analyzed on a Seal Analytical AA3 HR Nutrient Analyzer (N+N: detection limit [DL] = 0.009 and coefficient of variation [CV] = 0.3%; PO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;3−&lt;/sup&gt;: DL = 0.011 and CV = 0.2%; SiO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;2−&lt;/sup&gt;: DL = 0.03 and CV = 0.5%). We calculated the coefficient of variation (CV = 100 × standard deviation/mean) for each biogeochemical parameter to characterize the SGD gradient for this study. CV was selected because sites most affected by SGD experienced both more extreme mean values and higher variability as SGD is pulsed onto the reef in association with the tidal cycle—SGD fluxes are highest during low tide (Burnett et al. 2006).&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Community surveys&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Benthic communities were surveyed via snorkeling at each survey location and at the SGD seepage point in June–July 2022. Our survey methods captured the species composition of coral, macroalgae, sponges, corallimorphs, anemones, and cyanobacteria. Composition was assessed within 2 × 2 m plots using a uniform point-count method with 200 evenly distributed points. Organisms at each point were identified to the species level when possible, or to the lowest possible taxonomic unit (Payri et al. 2000; Bosserelle 2014). Of the 51 taxa identified in this study, only six of those taxa could not be identified to the species level. In these cases, broader taxonomic classifications were necessary when identifying organisms in the community (i.e., ‘Crustose Corallines’ [CCA], Cyanobacteria unknown, Porifera unknown, &lt;em&gt;Dictyosphaeria &lt;/em&gt;sp., &lt;em&gt;Verongida &lt;/em&gt;sp., and turf). Therefore, we use the term ‘taxa’ instead of ‘species’ for accuracy in this dataset. Importantly, given our understanding of the life history of these broader groups, the use of these broad taxonomic classifications did not hinder our trait-based identifications. Taxa unidentifiable in the field were photographed and fragmented or collected whole for later identification. Substrate types were also identified at each point as sand, rubble, dead coral, or live coral to give context for taxon presence and abundance at each location.&lt;/p&gt; &lt;p&gt;Rugosity (an in situ measurement of structural heterogeneity) was measured by laying a 2 m length chain (15 mm link size) over the benthos at three parallel locations within the survey area at each location. We then calculated the ratio of the transect length of the draped chain to the total linear chain length for each measurement (Risk 1972). Mean rugosity was calculated by the average of these three ratios and subtracted from one, such that higher values reflect greater structural heterogeneity. We use the term ‘structural complexity’ as a synonym for ‘rugosity’ throughout for ease of interpretation.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Classification of functional traits&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Each identified taxon was categorized into functional groups, which were selected for their contribution to broader community ecosystem functioning: phyla, morphology, calcification type, and trophic group (McGill et al. 2006). The combination of these functional groups comprises each taxon’s functional entity (FE), which provides context for each taxon’s ecological role within its community (Villéger et al. 2011; Chao et al. 2014). For example, the morphology of stony corals has been linked to photosynthetic and calcification efficiency, such that weedy branching corals exhibit greater rates of calcification than digitate or encrusting species (Alvarez-Filip et al. 2013). Conversely, branching and encrusting corals with minimal self-shading exhibit higher rates of photosynthesis and respiration than dense digitate species with self-shading and reduced interstitial flow (Carlot et al. 2022; Gattuso et al. 1999; Dennison and Barnes 1988). Relative growth rates among scleractinian corals are also dependent on morphology, such that tabular and branching species exhibit faster growth compared to those with massive morphologies (Zawada et al. 2019; Madin et al. 2020). Calcification functional traits provide insights into rates of calcification as well as to the resilience of calcifiers under environmental stress (Pentecost 1991). The phyla and functional traits specified within each functional group encompass the possible phyla and traits available from the full surveyed community taxon pool. Functional identification of each taxon was accomplished using the World Register of Marine Species (WoRMS), CoralTrait Database, AlgaeTraits, species-identification guides, and primary literature. Notably, we were unable to identify all organisms to the species level. However, the functional entities ascribed to these broader classifications were consistent with characteristics of these taxa, both in the literature and according to our observations.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Taxonomic and functional diversity&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;We took a multi-framework approach to identifying taxonomic and functional diversity, using a combination of raw data, multidimensional space, and dissimilarity-based methods (Mammola et al. 2021). We calculated three diversity metrics to measure community shifts along the SGD gradient: proportional taxon richness (raw data), functional entity richness (raw data), and volume of functional entity trait space (multidimensional space). We also used Gower’s distance metric and Bray−Curtis dissimilarity matrices to characterize functional dispersion and taxa dissimilarity, respectively, as described in the statistical analyses section below (dissimilarity-based method). Taxon richness was determined as the total number of unique species or taxonomic units within each survey plot. Similarly, each taxon was represented by one functional entity (FE), where each FE encompassed the unique combination of functional traits from all functional groups—phyla, morphology, calcification type, and trophic group (Villéger et al. 2011). FE richness was determined by the total number of unique FEs within each survey plot. Relative taxon richness and FE richness were calculated as the total number of unique taxa or FEs present within each survey location relative to the total number of taxa (Taxon richness&lt;sub&gt;T&lt;/sub&gt;) or total number of functional entities (FE richness&lt;sub&gt;T&lt;/sub&gt;) observed across the full community, as follows:&lt;/p&gt; &lt;p&gt;% Taxon richness = 100 × (Taxon richness ÷ Taxon richness&lt;sub&gt;T&lt;/sub&gt;)&lt;br /&gt; % FE richness = 100 × (FE richness ÷ FE richness&lt;sub&gt;T&lt;/sub&gt;)&lt;/p&gt; &lt;p&gt;&lt;br /&gt; The number of functional entities present at each site may have been equal to or less than the total number of taxa, and FE richness &amp;lt; taxon richness indicates functional redundancy, where more than one taxon shared the same functional entity and occupied a similar functional role in the community (Yachi and Loreau 1999). Functional entity volume, described as the volume of FE in multidimensional trait space, represents the dispersion of functional entities in multidimensional space through FE dissimilarity (Teixidó et al. 2018; Villéger et al. 2011). High FE volume indicates greater richness and dissimilarity across functional entities in a given surveyed community and therefore a wider range of functional roles, with less overlap in functionality. To calculate FE volume, a dissimilarity matrix of each survey location was calculated for FE using the daisy function with Gower’s distance metric (de Bello et al. 2013) in the &lt;em&gt;cluster &lt;/em&gt;package in R,&amp;nbsp;version 2.1.3., accessed 24 June 2023 (Teixidó et al. 2018; Maechler et al. 1999). Volumes of each survey site were calculated using the &lt;em&gt;convhulln &lt;/em&gt;function in the &lt;em&gt;geometry &lt;/em&gt;package in R, version 0.4.7, accessed on 24 June 2023 (Roussel et al. 2005).&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Statistical analyses&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;We used multiple statistical approaches to test the effect of SGD on taxa and functional richness as well as community composition. We employed a regression approach to assess continuous changes in environment and communities along the SGD gradient. Indeed, recent reviews highlight the power of using regression-based experimental designs, which better characterize mechanisms compared to ANOVA designs (Idjadi and Edmunds 2006). We used individual general linear models (GLM) to determine the effect of SGD on the suite of functional and taxonomic diversity metrics while controlling for structural complexity, which could impact benthic taxonomic diversity by affecting settlement substrate (Idjadi and Edmunds 2006). To test the effect of structural complexity on %Taxon richness, %FE richness, and %FE volume in trait space, we used GLMs with mean structural complexity as the independent variable. We then calculated residuals of each diversity metric as a function of structural complexity. These residuals were used to test the impact of SGD on diversity above and beyond the effect of structural complexity. Due to the overall dominance of stony coral and fleshy macroalgae within the study site, as well as the ecological relevance of these functional groups to overall ecosystem health within a coral reef (Hatcher 1990; Hoegh-Guldberg et al. 2007), we additionally assessed the taxonomic and functional diversity of coral and fleshy macroalgae separately along the SGD gradient. All taxa used for the coral and macroalgae analyses were identified to the species level. Because there are several biogeochemical metrics commonly associated with SGD (i.e., variability in salinity, temperature, pH, and nutrients) (Taniguchi et al. 2019), we used a model-selection approach to determine the dominant SGD-related physicochemical variables and possible interactive effect of structure; selection involved comparing the AIC&lt;sub&gt;C&lt;/sub&gt; (Akaike information criterion, corrected for small sample size) of regression models. We tested both linear and polynomial regressions because communities exposed to various intensities of SGD may exhibit different relationships with diversity along the gradient in response to distinct biogeochemistry at each location.&lt;/p&gt; &lt;p&gt;We assessed functional-trait dispersion across surveyed species in multidimensional functional space using a principal coordinate analysis (PCoA) with the Gower metric. The functional space was created by calculating pairwise distances between taxa for four functional groups. To test the effect of SGD on community composition along the gradient, we used generalized additive models (GAM) to fit nonlinear relationships to the full suite of SGD parameters on community composition. Taxa and FE composition dissimilarities were visualized through an nMDS with a Bray−Curtis dissimilarity index, and we used the &lt;em&gt;ordisurf &lt;/em&gt;function in the vegan package, versions 2.6.4-2.6.8 (Oksanen et al. 2003) to create a smooth fit of each parameter in ordination space. All analyses were completed in R version 4.3.2 (R Core Team 2023), and all visuals were produced with &lt;em&gt;ggplot2&lt;/em&gt;, versions 3.4.4-3.5.1 (Wickham 2016).&lt;/p&gt; Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1924281 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1924281 completed Nyssa Silbiger California State University Northridge 818-677-2901 HI USA silbiger@hawaii.edu pointOfContact Danielle M Barnas California State University Northridge dmbarnas@hawaii.edu pointOfContact Maya Zeff California State University Northridge mayazeff@ucsc.edu pointOfContact asNeeded Dataset Version: 1 Unknown CowTagID Date Taxa pcover Taxon_Group Morph2 Calc ER FE PC1 PC2 PC3 PC4 Location AlphaTag LiveCoral DeadCoral Rubble Sand lat lon meanRugosity complexity Salinity Temperature pH Phosphate_umolL Silicate_umolL NN_umolL NbSp NbSpP NbFEs NbFEsP resSp resSpp resFE resFEp Community photographed snorkeling Transect tapes YSI Pro2030 Seal Analytical AA3 HR Nutrient Analyzer theme None, User defined site date taxon percent coverage sample description No BCO-DMO term site description latitude longitude salinity water temperature at measurement depth pH phosphate Silicate, Si(OH)4, silicic acid nitrate plus nitrite count featureType BCO-DMO Standard Parameters Camera Diving Mask and Snorkel Measuring Tape Multi Parameter Portable Meter Seal Analytical AutoAnalyser 3HR instrument BCO-DMO Standard Instruments otherRestrictions otherRestrictions Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use. RUI: Collaborative Research: Defining the biogeochemical context and ecological impacts of submarine groundwater discharge on coral reefs https://osprey.bco-dmo.org/project/820158 RUI: Collaborative Research: Defining the biogeochemical context and ecological impacts of submarine groundwater discharge on coral reefs <p><em>NSF Award Abstract:</em><br /> Submarine groundwater discharge (SGD) is the flow of water from land through the coastal seafloor into the nearby ocean. Approximately 13,000 cubic kilometers of groundwater is discharged into coastal environments every year, yet the effects of this fresh and often nutrient rich SGD are still poorly understood for coral reefs. This SGD input is driven by changes in precipitation, human land use, sea-level rise, tidal amplitude, and groundwater usage, many of which are rapidly changing with climate and human impacts. This project improves our understanding of SGD effects on coral reefs to better predict how both natural and human-induced changes will affect coastal ecosystem functioning in the future. Working in one of the most comprehensively studied coral reef ecosystems in the Pacific (Mo'orea, French Polynesia, home of the Mo'orea Coral Reef Ecosystem LTER); this project tests the influence of SGD on individual, community, and ecosystem-scale coral reef processes. Using mensurative studies, caging experiments, and a synthetic model, the investigators: 1) characterize SGD gradients and relate it to high resolution coral reef cover data, 2) determine how individual to ecosystem processes are influenced by SGD, and 3) develop a synthetic model to show how changes in SGD fluxes will alter reef ecosystem functioning. As SGD is a common feature on nearshore coral reefs worldwide, the results of this study have global implications for understanding the performance of coral reefs, which are essential economic, cultural, and scientific resources. This project is structured to provide training across multiple career levels, linking 13 undergraduate students, 2 graduate students, 2 senior personnel, 1 postdoctoral researcher, 1 female beginning lead investigator, and 2 senior co-investigators, with a focus on encouraging participation from underrepresented groups (e.g., through the Alaska Native and Native Hawaiian, Asian American and Native American Pacific Islander, and Hispanic-Serving Institutions of California State University Northridge, the University of Hawaiʻi at Mānoa, and California State University Long Beach). The investigators work with local K-12 students and teachers in Mo'orea and collaborate with an artist-in-residence to communicate science to the broader public through interactive and immersive art experiences in Mo'orea, Miami, and Los Angeles.</p> <p>SGD is a natural and understudied feature of many nearshore coral reef ecosystems, which can contribute substantial changes to marine biogeochemistry, with impacts for coastal organisms such as reef-building corals, macroalgae, and bioeroders. SGD may play a key role in coral reef ecosystem functioning because it alters key physicochemical parameters (e.g., temperature, salinity, and nutrient and carbonate chemistry) that substantially affect both biotic and abiotic processes on coral reefs. This project (i) characterizes the spatial extent and biogeochemical signal of SGD in Mo'orea, French Polynesia, (ii) identifies how SGD influences microbial processes, benthic organism growth rates and physiology, species interactions between corals, macroalgae, and herbivores, and net ecosystem calcification and production rates, and (iii) quantitatively assesses how changes in SGD fluxes will alter reef biogeochemistry and ecosystem functioning through an integrative modelling effort. Specifically, the hydrogeological, biogeochemical, and ecological data collected in this study are synthesized in a Bayesian structural equation model. This project characterizes and quantifies how SGD directly and indirectly affects ecosystem functioning via changes in biogeochemistry and altered individual to ecosystem responses, thereby providing a better capacity to track and predict alterations in reef ecosystem function.</p> <p>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.</p> Moorea SGD largerWorkCitation project eng; USA biota oceans -149.90056 -149.89933 -17.5405 -17.53975 2021-08-01 2022-06-14 Mo&#039;orea, French Polynesia 0 BCO-DMO catalogue of parameters from Impacts of submarine groundwater discharge on benthic community composition and functional diversity on coral reefs in Mo'orea, French Polynesia from Aug 2021 to Jun 2022 Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. pointOfContact http://lod.bco-dmo.org/id/dataset-parameter/967019.rdf Name: CowTagID Units: unitless Description: <p>Individual survey location identifier (1-20 and a Seep location)</p> http://lod.bco-dmo.org/id/dataset-parameter/967020.rdf Name: Date Units: unitless Description: <p>Date (year-month-day) of in situ community survey</p> http://lod.bco-dmo.org/id/dataset-parameter/967021.rdf Name: Taxa Units: unitless Description: <p>Benthic taxa observed at each survey site</p> http://lod.bco-dmo.org/id/dataset-parameter/967022.rdf Name: pcover Units: percent Description: <p>Proportional cover of each taxa at each survey site</p> http://lod.bco-dmo.org/id/dataset-parameter/967023.rdf Name: Taxon_Group Units: unitless Description: <p>Phyla of each taxon observed at each survey site</p> http://lod.bco-dmo.org/id/dataset-parameter/967024.rdf Name: Morph2 Units: unitless Description: <p>Morphology of each taxon observed at each survey site described as Branched (Br), Cushion-like (Cushion), Digitate (Dig), Encrusting (Enc), Filamentous (Fil), Foliose (Fol), Massive (Mas), Mushroom (Mush), Polypoid (Poly), Spherical (Sph), and Stolonial (Stol). </p> http://lod.bco-dmo.org/id/dataset-parameter/967025.rdf Name: Calc Units: unitless Description: <p>Calcification strategy of each taxon observed at each survey site described as Non-calcified (NC), Articulated (AC), Non-articulated (Non-AC), or Hermatypic (Herm).</p> http://lod.bco-dmo.org/id/dataset-parameter/967026.rdf Name: ER Units: unitless Description: <p>Trophic group of each taxon observed at each survey site described as Autotrophy (Auto), Heterotrophy (Het), or Mixotrophy (Mix).</p> http://lod.bco-dmo.org/id/dataset-parameter/967027.rdf Name: FE Units: unitless Description: <p>Functional entity of each taxon observed at each survey site; a compilation of the unique functional traits assigned to the taxon from each functional group</p> http://lod.bco-dmo.org/id/dataset-parameter/967028.rdf Name: PC1 Units: unitless Description: <p>PCoA axis 1</p> http://lod.bco-dmo.org/id/dataset-parameter/967029.rdf Name: PC2 Units: unitless Description: <p>PCoA axis 2</p> http://lod.bco-dmo.org/id/dataset-parameter/967030.rdf Name: PC3 Units: unitless Description: <p>PCoA axis 3</p> http://lod.bco-dmo.org/id/dataset-parameter/967031.rdf Name: PC4 Units: unitless Description: <p>PCoA axis 4</p> http://lod.bco-dmo.org/id/dataset-parameter/967032.rdf Name: Location Units: unitless Description: <p>Indicates the coral reef site (Varari)</p> http://lod.bco-dmo.org/id/dataset-parameter/967033.rdf Name: AlphaTag Units: unitless Description: <p>Individual survey location identifier, alphabetized by linear distance from seepage point (Seep location, A, and B-T)</p> http://lod.bco-dmo.org/id/dataset-parameter/967034.rdf Name: LiveCoral Units: percent Description: <p>Percent cover of live coral substrate</p> http://lod.bco-dmo.org/id/dataset-parameter/967035.rdf Name: DeadCoral Units: percent Description: <p>Percent cover of dead coral substrate</p> http://lod.bco-dmo.org/id/dataset-parameter/967036.rdf Name: Rubble Units: percent Description: <p>Percent cover of rubble substrate</p> http://lod.bco-dmo.org/id/dataset-parameter/967037.rdf Name: Sand Units: percent Description: <p>Percent cover of sand substrate</p> http://lod.bco-dmo.org/id/dataset-parameter/967038.rdf Name: lat Units: decimal degrees Description: <p>Latitude of survey locations recorded on a Garmin GPS</p> http://lod.bco-dmo.org/id/dataset-parameter/967039.rdf Name: lon Units: decimal degrees Description: <p>Longitude of survey locations recorded on a Garmin GPS</p> http://lod.bco-dmo.org/id/dataset-parameter/967040.rdf Name: meanRugosity Units: unitless Description: <p>Average rugosity measured using a 2.03m link chain across 3 randomly chosen lines within the survey box of each survey location. Values range from 0-1, with 0 indicating higher substrate complexity and 1 indicating a flat surface</p> http://lod.bco-dmo.org/id/dataset-parameter/967041.rdf Name: complexity Units: unitless Description: <p>Average rugosity subtracted from 1 as an indication of structural complexity of the reef surface</p> http://lod.bco-dmo.org/id/dataset-parameter/967042.rdf Name: Salinity Units: psu Description: <p>Salinity measured during the high and low tide in the day and nighttime; coefficient of variation (%) used for analyses</p> http://lod.bco-dmo.org/id/dataset-parameter/967043.rdf Name: Temperature Units: degrees Celsius Description: <p>Temperature measured during the high and low tide in the day and nighttime at each survey location; coefficient of variation (%) used for analyses</p> http://lod.bco-dmo.org/id/dataset-parameter/967044.rdf Name: pH Units: unitless (pH scale) Description: <p>pH measured during the high and low tide in the day and nighttime at each survey location; coefficient of variation (%) used for analyses</p> http://lod.bco-dmo.org/id/dataset-parameter/967045.rdf Name: Phosphate_umolL Units: umol/L Description: <p>Raw values of phosphate measured during the high and low tide in the day and nighttime at each survey location; coefficient of variation (%) used for analyses</p> http://lod.bco-dmo.org/id/dataset-parameter/967046.rdf Name: Silicate_umolL Units: umol/L Description: <p>Raw values silicate measured during the high and low tide in the day and nighttime at each survey location; coefficient of variation (%) used for analyses</p> http://lod.bco-dmo.org/id/dataset-parameter/967047.rdf Name: NN_umolL Units: umol/L Description: <p>Raw values of nitrate + nitrite (N+N) measured during the high and low tide in the day and nighttime at each survey location; coefficient of variation (%) used for analyses</p> http://lod.bco-dmo.org/id/dataset-parameter/967048.rdf Name: NbSp Units: unitless Description: <p>Total tally of taxa per site</p> http://lod.bco-dmo.org/id/dataset-parameter/967049.rdf Name: NbSpP Units: percent Description: <p>Proportional taxonomic richness</p> http://lod.bco-dmo.org/id/dataset-parameter/967050.rdf Name: NbFEs Units: unitless Description: <p>Total tally of functional entities per site</p> http://lod.bco-dmo.org/id/dataset-parameter/967051.rdf Name: NbFEsP Units: percent Description: <p>Proportional functional entity richness</p> http://lod.bco-dmo.org/id/dataset-parameter/967052.rdf Name: resSp Units: unitless Description: <p>Complexity-normalized residuals of total taxonomic richness</p> http://lod.bco-dmo.org/id/dataset-parameter/967053.rdf Name: resSpp Units: percent Description: <p>Complexity-normalized residuals of proportional taxonomic richness</p> http://lod.bco-dmo.org/id/dataset-parameter/967054.rdf Name: resFE Units: unitless Description: <p>Complexity-normalized residuals of total functional entity richness</p> http://lod.bco-dmo.org/id/dataset-parameter/967055.rdf Name: resFEp Units: percent Description: <p>Complexity-normalized residuals of proportional functional entity richness</p> http://lod.bco-dmo.org/id/dataset-parameter/967056.rdf Name: Community Units: unitless Description: <p>Indicates whether the diversity metrics are representative of the &quot;Full Community&quot;, &quot;Stony Coral&quot;, or &quot;Macroalgae&quot;</p> GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. pointOfContact 1167796 https://darchive.mblwhoilibrary.org/server/api/core/bitstreams/598f641c-8ab1-43e4-a8ca-b9cfc10a84a0/content download https://doi.org/10.26008/1912/bco-dmo.964240.1 download onLine dataset &lt;p&gt;Detailed methods are outlined in the results publication Barnas et al. (2025) and summarized here.&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Study site and characterization&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Mo‘orea, French Polynesia, is a tropical volcanic island with coastal fringing coral reefs where SGD is distributed through fissures in the reef plate (Knee et al. 2016; Hagedorn et al. 2020). Local fishers’ knowledge of an SGD seep informed the location of our survey site, and the presence of SGD was confirmed through spatial and temporal radon (Hagedorn et al. 2020, 2024) and biogeochemical surveys (Silbiger et al. 2023). We identified a focal seepage point on the western shore of Mo‘orea and haphazardly chose 19 survey locations downstream of the seep to study the effects of SGD on taxonomic and functional diversity. All survey locations had hard substrate with an average depth of 0.6 m and were within 150 m of the SGD seep, experiencing a gradient of SGD influence. Our field site experiences consistent northwestward unidirectional flow averaging 0.15 m/s (Silbiger et al. 2023), distributing SGD in a predictable alongshore gradient.&lt;/p&gt; &lt;p&gt;Biogeochemical measurements associated with SGD influence were assessed through discrete water sampling from high and low tides during the day and nighttime in August 2021 (n = 4 measurements per survey location). See Silbiger et al. (2023) for detailed methods and descriptions of the SGD gradient. In brief, water samples were collected concurrently at each time point in acid-washed, triple-rinsed 1 L HDPE bottles. Salinity, temperature, and pH were immediately measured using portable sensors (salinity accuracy ± 1.0% psu and precision = 0.1 psu, temperature accuracy ± 0.3 °C and precision = 0.1 °C, YSI Pro2030, Xylem Inc., Washington D.C., USA.; pH [total scale] accuracy ± 0.002 and precision = 0.001, tris-calibrated ROSSTM double junction electrode, Orion Star A325, Thermo Fisher Scientific Inc., Waltham, MA, USA). The water samples were also filtered through a 0.22 μm Sterivex filter before being frozen at −20 °C for subsequent nutrient analysis for concentrations of silicate [SiO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;2−&lt;/sup&gt;], phosphate [PO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;3−&lt;/sup&gt;], and nitrate + nitrite [N+N]). The samples were brought to the S-LAB at the University of Hawai‘i, where they were analyzed on a Seal Analytical AA3 HR Nutrient Analyzer (N+N: detection limit [DL] = 0.009 and coefficient of variation [CV] = 0.3%; PO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;3−&lt;/sup&gt;: DL = 0.011 and CV = 0.2%; SiO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;2−&lt;/sup&gt;: DL = 0.03 and CV = 0.5%). We calculated the coefficient of variation (CV = 100 × standard deviation/mean) for each biogeochemical parameter to characterize the SGD gradient for this study. CV was selected because sites most affected by SGD experienced both more extreme mean values and higher variability as SGD is pulsed onto the reef in association with the tidal cycle—SGD fluxes are highest during low tide (Burnett et al. 2006).&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Community surveys&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Benthic communities were surveyed via snorkeling at each survey location and at the SGD seepage point in June–July 2022. Our survey methods captured the species composition of coral, macroalgae, sponges, corallimorphs, anemones, and cyanobacteria. Composition was assessed within 2 × 2 m plots using a uniform point-count method with 200 evenly distributed points. Organisms at each point were identified to the species level when possible, or to the lowest possible taxonomic unit (Payri et al. 2000; Bosserelle 2014). Of the 51 taxa identified in this study, only six of those taxa could not be identified to the species level. In these cases, broader taxonomic classifications were necessary when identifying organisms in the community (i.e., ‘Crustose Corallines’ [CCA], Cyanobacteria unknown, Porifera unknown, &lt;em&gt;Dictyosphaeria &lt;/em&gt;sp., &lt;em&gt;Verongida &lt;/em&gt;sp., and turf). Therefore, we use the term ‘taxa’ instead of ‘species’ for accuracy in this dataset. Importantly, given our understanding of the life history of these broader groups, the use of these broad taxonomic classifications did not hinder our trait-based identifications. Taxa unidentifiable in the field were photographed and fragmented or collected whole for later identification. Substrate types were also identified at each point as sand, rubble, dead coral, or live coral to give context for taxon presence and abundance at each location.&lt;/p&gt; &lt;p&gt;Rugosity (an in situ measurement of structural heterogeneity) was measured by laying a 2 m length chain (15 mm link size) over the benthos at three parallel locations within the survey area at each location. We then calculated the ratio of the transect length of the draped chain to the total linear chain length for each measurement (Risk 1972). Mean rugosity was calculated by the average of these three ratios and subtracted from one, such that higher values reflect greater structural heterogeneity. We use the term ‘structural complexity’ as a synonym for ‘rugosity’ throughout for ease of interpretation.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Classification of functional traits&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Each identified taxon was categorized into functional groups, which were selected for their contribution to broader community ecosystem functioning: phyla, morphology, calcification type, and trophic group (McGill et al. 2006). The combination of these functional groups comprises each taxon’s functional entity (FE), which provides context for each taxon’s ecological role within its community (Villéger et al. 2011; Chao et al. 2014). For example, the morphology of stony corals has been linked to photosynthetic and calcification efficiency, such that weedy branching corals exhibit greater rates of calcification than digitate or encrusting species (Alvarez-Filip et al. 2013). Conversely, branching and encrusting corals with minimal self-shading exhibit higher rates of photosynthesis and respiration than dense digitate species with self-shading and reduced interstitial flow (Carlot et al. 2022; Gattuso et al. 1999; Dennison and Barnes 1988). Relative growth rates among scleractinian corals are also dependent on morphology, such that tabular and branching species exhibit faster growth compared to those with massive morphologies (Zawada et al. 2019; Madin et al. 2020). Calcification functional traits provide insights into rates of calcification as well as to the resilience of calcifiers under environmental stress (Pentecost 1991). The phyla and functional traits specified within each functional group encompass the possible phyla and traits available from the full surveyed community taxon pool. Functional identification of each taxon was accomplished using the World Register of Marine Species (WoRMS), CoralTrait Database, AlgaeTraits, species-identification guides, and primary literature. Notably, we were unable to identify all organisms to the species level. However, the functional entities ascribed to these broader classifications were consistent with characteristics of these taxa, both in the literature and according to our observations.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Taxonomic and functional diversity&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;We took a multi-framework approach to identifying taxonomic and functional diversity, using a combination of raw data, multidimensional space, and dissimilarity-based methods (Mammola et al. 2021). We calculated three diversity metrics to measure community shifts along the SGD gradient: proportional taxon richness (raw data), functional entity richness (raw data), and volume of functional entity trait space (multidimensional space). We also used Gower’s distance metric and Bray−Curtis dissimilarity matrices to characterize functional dispersion and taxa dissimilarity, respectively, as described in the statistical analyses section below (dissimilarity-based method). Taxon richness was determined as the total number of unique species or taxonomic units within each survey plot. Similarly, each taxon was represented by one functional entity (FE), where each FE encompassed the unique combination of functional traits from all functional groups—phyla, morphology, calcification type, and trophic group (Villéger et al. 2011). FE richness was determined by the total number of unique FEs within each survey plot. Relative taxon richness and FE richness were calculated as the total number of unique taxa or FEs present within each survey location relative to the total number of taxa (Taxon richness&lt;sub&gt;T&lt;/sub&gt;) or total number of functional entities (FE richness&lt;sub&gt;T&lt;/sub&gt;) observed across the full community, as follows:&lt;/p&gt; &lt;p&gt;% Taxon richness = 100 × (Taxon richness ÷ Taxon richness&lt;sub&gt;T&lt;/sub&gt;)&lt;br /&gt; % FE richness = 100 × (FE richness ÷ FE richness&lt;sub&gt;T&lt;/sub&gt;)&lt;/p&gt; &lt;p&gt;&lt;br /&gt; The number of functional entities present at each site may have been equal to or less than the total number of taxa, and FE richness &amp;lt; taxon richness indicates functional redundancy, where more than one taxon shared the same functional entity and occupied a similar functional role in the community (Yachi and Loreau 1999). Functional entity volume, described as the volume of FE in multidimensional trait space, represents the dispersion of functional entities in multidimensional space through FE dissimilarity (Teixidó et al. 2018; Villéger et al. 2011). High FE volume indicates greater richness and dissimilarity across functional entities in a given surveyed community and therefore a wider range of functional roles, with less overlap in functionality. To calculate FE volume, a dissimilarity matrix of each survey location was calculated for FE using the daisy function with Gower’s distance metric (de Bello et al. 2013) in the &lt;em&gt;cluster &lt;/em&gt;package in R,&amp;nbsp;version 2.1.3., accessed 24 June 2023 (Teixidó et al. 2018; Maechler et al. 1999). Volumes of each survey site were calculated using the &lt;em&gt;convhulln &lt;/em&gt;function in the &lt;em&gt;geometry &lt;/em&gt;package in R, version 0.4.7, accessed on 24 June 2023 (Roussel et al. 2005).&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Statistical analyses&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;We used multiple statistical approaches to test the effect of SGD on taxa and functional richness as well as community composition. We employed a regression approach to assess continuous changes in environment and communities along the SGD gradient. Indeed, recent reviews highlight the power of using regression-based experimental designs, which better characterize mechanisms compared to ANOVA designs (Idjadi and Edmunds 2006). We used individual general linear models (GLM) to determine the effect of SGD on the suite of functional and taxonomic diversity metrics while controlling for structural complexity, which could impact benthic taxonomic diversity by affecting settlement substrate (Idjadi and Edmunds 2006). To test the effect of structural complexity on %Taxon richness, %FE richness, and %FE volume in trait space, we used GLMs with mean structural complexity as the independent variable. We then calculated residuals of each diversity metric as a function of structural complexity. These residuals were used to test the impact of SGD on diversity above and beyond the effect of structural complexity. Due to the overall dominance of stony coral and fleshy macroalgae within the study site, as well as the ecological relevance of these functional groups to overall ecosystem health within a coral reef (Hatcher 1990; Hoegh-Guldberg et al. 2007), we additionally assessed the taxonomic and functional diversity of coral and fleshy macroalgae separately along the SGD gradient. All taxa used for the coral and macroalgae analyses were identified to the species level. Because there are several biogeochemical metrics commonly associated with SGD (i.e., variability in salinity, temperature, pH, and nutrients) (Taniguchi et al. 2019), we used a model-selection approach to determine the dominant SGD-related physicochemical variables and possible interactive effect of structure; selection involved comparing the AIC&lt;sub&gt;C&lt;/sub&gt; (Akaike information criterion, corrected for small sample size) of regression models. We tested both linear and polynomial regressions because communities exposed to various intensities of SGD may exhibit different relationships with diversity along the gradient in response to distinct biogeochemistry at each location.&lt;/p&gt; &lt;p&gt;We assessed functional-trait dispersion across surveyed species in multidimensional functional space using a principal coordinate analysis (PCoA) with the Gower metric. The functional space was created by calculating pairwise distances between taxa for four functional groups. To test the effect of SGD on community composition along the gradient, we used generalized additive models (GAM) to fit nonlinear relationships to the full suite of SGD parameters on community composition. Taxa and FE composition dissimilarities were visualized through an nMDS with a Bray−Curtis dissimilarity index, and we used the &lt;em&gt;ordisurf &lt;/em&gt;function in the vegan package, versions 2.6.4-2.6.8 (Oksanen et al. 2003) to create a smooth fit of each parameter in ordination space. All analyses were completed in R version 4.3.2 (R Core Team 2023), and all visuals were produced with &lt;em&gt;ggplot2&lt;/em&gt;, versions 3.4.4-3.5.1 (Wickham 2016).&lt;/p&gt; Specified by the Principal Investigator(s) &lt;p&gt;Data and code are available in the related dataset section (Barnas et al. 2025).&lt;/p&gt; Specified by the Principal Investigator(s) - Imported &quot;README_Variables.csv&quot; into the BCO-DMO system - Imported &quot;uniquetaxa_matched.xlsx&quot; into the BCO-DMO system - Replaced &quot;Taxa&quot; values with accepted WoRMS values: Gracilaria verrucosa -&gt; Gracilariopsis longissima, Heteractis magnifica -&gt; Radianthus magnifica, Phaeophyta -&gt; Phaeophyceae, Verongida -&gt; Verongiida - Exported primary file as &quot;964240_v1_coral_benthic_comp_sgd.csv&quot; and &quot;uniquetaxa_matched.xlsx&quot; as &quot;taxonomy_coral_benthic_comp_sgd.csv&quot; - Checked all scientific names referenced in methods section and the methods section of related dataset using World Register of Marine Species (WoRMS) Taxon Match. Specified by BCO-DMO Data Managers asNeeded 7.x-1.1 Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. pointOfContact photographed photographed PI Supplied Instrument Name: photographed PI Supplied Instrument Description:Taxa unidentifiable in the field were photographed and fragmented or collected whole for later identification. Instrument Name: Camera Instrument Short Name:camera Instrument Description: All types of photographic equipment including stills, video, film and digital systems. Community Standard Description: http://vocab.nerc.ac.uk/collection/L05/current/311/ snorkeling snorkeling PI Supplied Instrument Name: snorkeling PI Supplied Instrument Description:Benthic communities were surveyed via snorkeling at each survey location and at the SGD seepage point in June–July 2022.  Instrument Name: Diving Mask and Snorkel Instrument Short Name: Instrument Description: A diving mask (also half mask, dive mask or scuba mask) is an item of diving equipment that allows underwater divers, including, scuba divers, free-divers, and snorkelers to see clearly underwater. Snorkel: A breathing apparatus for swimmers and surface divers that allows swimming or continuous use of a face mask without lifting the head to breathe, consisting of a tube that curves out of the mouth and extends above the surface of the water. Transect tapes Transect tapes PI Supplied Instrument Name: Transect tapes PI Supplied Instrument Description:Transect tapes (30m) for benthic composition quadrat parameters and points Instrument Name: Measuring Tape Instrument Short Name: Instrument Description: A tape measure or measuring tape is a flexible ruler. It consists of a ribbon of cloth, plastic, fibre glass, or metal strip with linear-measurement markings. It is a common tool for measuring distance or length. YSI Pro2030 YSI Pro2030 PI Supplied Instrument Name: YSI Pro2030 PI Supplied Instrument Description:Salinity, temperature, and pH were immediately measured using portable sensors (salinity accuracy ± 1.0% psu and precision = 0.1 psu, temperature accuracy ± 0.3 °C and precision = 0.1 °C, YSI Pro2030, Xylem Inc., Washington D.C, USA.; pH [total scale] accuracy ± 0.002 and precision = 0.001, tris-calibrated ROSSTM double junction electrode, Orion Star A325, Thermo Fisher Scientific Inc., Waltham, MA, USA). Instrument Name: Multi Parameter Portable Meter Instrument Short Name: Instrument Description: An analytical instrument that can measure multiple parameters, such as pH, EC, TDS, DO and temperature with one device and is portable or hand-held. Seal Analytical AA3 HR Nutrient Analyzer Seal Analytical AA3 HR Nutrient Analyzer PI Supplied Instrument Name: Seal Analytical AA3 HR Nutrient Analyzer PI Supplied Instrument Description:The samples were brought to the S-LAB at the University of Hawai‘i, where they were analyzed on a Seal Analytical AA3 HR Nutrient Analyzer (N+N: detection limit [DL] = 0.009 and coefficient of variation [CV] = 0.3%; PO43−: DL = 0.011 and CV = 0.2%; SiO32−: DL = 0.03 and CV = 0.5%).  Instrument Name: Seal Analytical AutoAnalyser 3HR Instrument Short Name:Seal Analytical AutoAnalyser 3HR Instrument Description: A fully automated Segmented Flow Analysis (SFA) system, ideal for water and seawater analysis. It comprises a modular system which integrates an autosampler, peristaltic pump, chemistry manifold and detector. The sample and reagents are pumped continuously through the chemistry manifold, and air bubbles are introduced at regular intervals forming reaction segments which are mixed using glass coils. The AA3 uses segmented flow analysis principles to reduce inter-sample dispersion, and can analyse up to 100 samples per hour using stable LED light sources.