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Article

Submarine Groundwater Discharge Alters Benthic Community Composition and Functional Diversity on Coral Reefs

by
Danielle M. Barnas
1,2,*,
Maya Zeff
2,3 and
Nyssa J. Silbiger
4
1
Hawai‘i Institute of Marine Biology, University of Hawai‘i at Mānoa, Kāne‘ohe, HI 96744, USA
2
Department of Biology, California State University Northridge, Northridge, CA 91330, USA
3
Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA
4
Department of Oceanography, Uehiro Center for the Advancement of Oceanography, University of Hawai‘i at Mānoa, Honolulu, HI 96822, USA
*
Author to whom correspondence should be addressed.
Diversity 2025, 17(3), 161; https://doi.org/10.3390/d17030161
Submission received: 21 October 2024 / Revised: 10 February 2025 / Accepted: 20 February 2025 / Published: 25 February 2025
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Abstract

:
Coral reefs experience numerous natural and anthropogenic environmental gradients that alter biophysical conditions and affect biodiversity. While many studies have focused on drivers of reef biodiversity using traditional diversity metrics (e.g., species richness, diversity, evenness), less is known about how environmental variability may influence functional diversity. In this study, we tested the impact of submarine groundwater discharge (SGD) on taxonomic and functional diversity metrics in Mo‘orea, French Polynesia. SGD is the expulsion of terrestrial fresh or recirculated seawater into marine environments and is associated with reduced temperatures, pH, and salinity and elevated nutrient levels. Using a regression approach along the SGD gradient, we found that taxon and functional-entity richness displayed unimodal relationships to SGD parameters, primarily nitrate + nitrite and phosphate variability, with peak richness at moderate SGD for stony coral and the full benthic community. Macroalgae showed this unimodal pattern for functional-entity but not taxonomic richness. Functional community composition (presence and abundance of functional entities) increased along the gradient, while taxonomic composition showed a nonlinear relationship to SGD-related parameters. SGD is a common feature of many coastal ecosystems globally and therefore may be more important to structuring benthic functional diversity than previously thought. Further, studying community shifts through a functional-trait lens may provide important insights into the roles of community functions on ecosystem processes and stability, leading to improved management strategies.

1. Introduction

Biodiversity has traditionally been assessed through the lens of genetic or taxonomic diversity [1]; however, functional diversity plays an important role in understanding the connection between communities and ecological functioning [2,3,4,5,6]. Functional traits, which are organismal attributes that contribute to ecosystem processes, define broader functional groups within a community. Examples of community functional traits include trophic groups within food webs [7], varying leaf surface areas in plants [8], morphologies of reef-building corals [9], and nutrient-uptake strategies of macroalgae [10]. Functional traits can be characterized by trait richness—the number of unique traits present—and trait redundancy—the occurrence of identical traits across multiple species [11,12]. Classic species-diversity metrics such as abundance, richness, and evenness offer valuable perspectives into community characteristics and are frequently employed to evaluate community dynamics [13]. Still, studies show that while species and functional diversity are often correlated, functional metrics show stronger relationships with ecosystem processes than do traditional diversity metrics [6,14] due to the relationship between community functional traits and stabilizing ecosystem processes such as primary production, nutrient cycling, and carbonate dynamics [15,16,17]. Here, we highlight the importance of functional traits as indicators of community stability and strong drivers of shifts in ecosystem functioning.
Understanding the connection between the differing roles of functional and species diversity is a key step toward characterizing drivers of ecosystem functioning [18]. Species-rich communities have greater potential for high functional richness and redundancy, which support ecological stability and resilience in the face of disturbances [11]. This ‘buffering effect’ is explained through the insurance hypothesis [11], which suggests that greater richness decreases variability in ecosystem functioning over time. Communities with high species and functional richness are able to better withstand disturbances by compensating for possible species loss with overlapping functionality across species [11]. The buffering and enhancing effects predicted by the insurance hypothesis through increased functional diversity provide possible mechanisms for how communities may respond to environmental shifts or disturbances.
Coastal coral reef ecosystems are undergoing constant disturbances through natural (i.e., wave and storm events [19]), anthropogenic (i.e., overfishing [20,21] and pollution [22,23]), and climate change-related [24] pressures. These disturbances impact coral reef communities across multiple spatial and temporal scales, affecting both species and functional diversity. For example, storms occur over yearly or decadal timescales and can change the diversity and structure of coral reefs on the order of hundreds of km [25]. Environmental disturbances can also occur on much smaller spatiotemporal scales. River plumes pulse nutrients onto coastal reef ecosystems, creating salinity, temperature, nutrient, and turbidity gradients over the order of tens of meters to kms and occur on seasonal to annual or multiyear temporal scales [26,27]. Studies have shown that species and functional diversity change with proximity to river mouths across various ecosystems. This includes decreases in coral cover and diversity [28], shifts in seagrass diversity [29], and the creation of distinct zones of zooplankton functional groups [30] near river plumes. Nearshore communities often experience disturbances in the form of strong environmental gradients (i.e., gradients in temperature or nutrient levels) due to proximity to land-based processes [31] and reduced water motion relative to offshore flow, leading to higher spatial and temporal variability within the coastal ecosystem [32]. Chronic exposure to high environmental variability can alter species and functional diversity compared to more stable environments [10,33]. Considering the high vulnerability of coral and their substantial role in providing ecosystem services, understanding how the functional breadth of coral reef communities shifts under environmental pressures is essential.
Submarine groundwater discharge (SGD), the flow of water from land through the seabed into the coastal ocean [34], is a common source of environmental and biogeochemical variability, while also being a conduit for anthropogenic pollutants [35,36,37,38]. Under high SGD influence, ecosystems often experience decreased salinity and temperature, elevated nutrient levels, lower pH, and variable total alkalinity compared to ambient conditions [38,39]. The unique physicochemical environment created by SGD can alter both the species and the functional diversity of coral reef communities. For example, low salinity impacts coral and algae physiology and survival [40,41], and coral reef communities in close proximity to groundwater often experience a shift toward more freshwater-tolerant species [42,43]. Reduced-pH environments alter benthic reef community composition [33,44] and influence calcium carbonate processes through reduced calcification or increased dissolution due to low aragonite and calcite saturation [38,45]. High nutrient input can also be a driver of changes to community composition. Corals thrive in an oligotrophic environment, and nutrient enrichment can lead to shifts from coral-dominated to macroalgae-dominated communities [46]. Notably, studies have found both negative [44] and positive effects [47,48] of SGD-driven nutrient inputs on coral cover and growth. While each of these variables independently influences community biodiversity [33,40,46], the simultaneous interaction of multiple variables in coral reef communities highlights SGD as a compelling system through which to study the drivers of community shifts and changes in functional diversity.
In the present study, we evaluated benthic community shifts in taxonomic and functional-trait diversity along a natural SGD gradient on a coastal coral reef in Mo‘orea, French Polynesia. Specifically, we surveyed multiple communities along the gradient to understand how the biogeochemical environment might influence species and functional community composition using a regression approach. We hypothesized that the environmental variability along the SGD gradient would drive shifts in the presence and relative abundance of taxa and functional traits from low to high SGD and that the relationship between taxonomic and functional diversity would change along the gradient, with lower taxonomic diversity in higher SGD and greater functional redundancy at the extreme ends of the SGD gradient. Climate change and coastal development exert immense pressure on coastal ecosystems by increasing the rate of disturbance events and the extent of warming, ocean acidification, and coastal pollution [31,49]. These pressures can modify natural environmental gradients such as SGD in coastal systems [50], thereby altering community responses to SGD and affecting ecological functioning. Understanding the effects of multiparameter variability, like that resulting from SGD, on community taxonomic and functional diversity can provide insights into global community responses under projected climatic and environmental shifts.

2. Materials and Methods

2.1. Study Site and Characterization

Mo‘orea, French Polynesia, is a tropical volcanic island with coastal fringing coral reefs where SGD is distributed through fissures in the reef plate [51,52]. Local fishers’ knowledge of an SGD seep informed the location of our survey site (Figure 1), and the presence of SGD was confirmed through spatial and temporal radon [52,53] and biogeochemical surveys [54]. 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 (Figure 1). Our field site experiences consistent northwestward unidirectional flow averaging 0.15 m/s [54], distributing SGD in a predictable alongshore gradient.
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. [54] 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, DC, 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 [SiO32−], phosphate [PO43−], 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%; PO43−: DL = 0.011 and CV = 0.2%; SiO32−: 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 [55].

2.2. Community Surveys

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 [56,57]. 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, Dictyosphaeria sp., Verongida sp., and turf [Supplementary Table S1]). Therefore, we use the term ‘taxa’ instead of ‘species’ for accuracy throughout the manuscript. 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.
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 [58]. 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.

2.3. Classification of Functional Traits

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 [18] (Table 1). 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 [59,60]. 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 [61]. 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 [62,63,64]. 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 [9,65]. Calcification functional traits provide insights into rates of calcification as well as to the resilience of calcifiers under environmental stress [66]. 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 (Supplementary Table S1). 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.

2.4. Taxonomic and Functional Diversity

We took a multi-framework approach to identifying taxonomic and functional diversity, using a combination of raw data, multidimensional space, and dissimilarity-based methods [67]. 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 (Table 1) [59]. 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 richnessT) or total number of functional entities (FE richnessT) observed across the full community, as follows:
% Taxon richness = 100 × (Taxon richness ÷ Taxon richnessT)
% FE richness = 100 × (FE richness ÷ FE richnessT)
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 < taxon richness indicates functional redundancy, where more than one taxon shared the same functional entity and occupied a similar functional role in the community [11]. 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 [33,59]. 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 [68] in the cluster package in R [33,69]. Volumes of each survey site were calculated using the convhulln function in the geometry package in R [70].

2.5. Statistical Analyses

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 [71]. 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 [71]. 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 [72,73], 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) [74], we used a model-selection approach to determine the dominant SGD-related physicochemical variables and possible interactive effect of structure; selection involved comparing the AICC (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.
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 (Table 1). 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 ordisurf function in the vegan package [75] to create a smooth fit of each parameter in ordination space. All analyses were completed in R version 4.3.2 [76], and all visuals were produced with ggplot2 [77].

3. Results

3.1. Community Composition

We observed taxa in the following groups: 18 stony coral (cnidaria), 13 chlorophyta, eight rhodophyta, six phaeophyta, three porifera, one corallimorpharia (cnidaria), one cyanobacterium, and one anemone (cnidaria) for a total of 51 total species or taxonomic units. From within this community taxon pool, we identified 22 unique FEs representing distinct combinations of functional traits (Table 1).

3.2. SGD Gradient Yields Variable Nutrients and Biogeochemistry

Physicochemical parameters associated with SGD changed substantially along the reef (Figure 1c–h; Supplementary Figure S1). The coefficients of variation of nitrate + nitrite (CV N+N) and phosphate (CV PO43−) were positively correlated (p = 0.003, r2 = 0.41) and displayed significant linear relationships with salinity, a conservative SGD proxy variable (N+N p = 0.023 r2 = 0.27; PO43−: p = 0.019 r2 = 0.28) [74]. CV N+N along the SGD gradient ranged from 13 to 59.8%, peaking at 125.2% at the seepage point. CV PO43− ranged from 2.2 to 19.5% along the reef sites, while CV PO43− at the seepage point (66.3%) was 3.5 times greater than the highest value along the reef. CV SiO32- ranged from 24.3 to 127.9% and was 120.4% at the seepage point. The lowest CV salinity was 0.11%, while the highest along the reef was 1.2%, and the seepage point had the highest value, at 6%. CV temperature along the reef ranged from 1.9 to 2.3%, with the greatest value showing slightly greater maximum variability than that at the seepage point (2.2%). Lastly, CV pHT ranged from 0.14 to 0.54%, with a value of 1.2% at the seep.

3.3. Model Selection

According to a comparison of the effects of SGD on diversity metrics, mean structural complexity was a main driver of changes in %Taxon richness (Supplementary Figure S2b), while mean structural complexity and CV salinity were equally strong drivers of %FE richness. According to a comparison of the residual models within %Taxon richness and %FE richness, the primary drivers of all three diversity metrics were biogeochemical parameters (i.e., CV N+N and CV PO43−), with structural complexity at a ΔAICC of 2.5 to 3.3, suggesting an interactive effect of structural complexity with SGD on community diversity (Supplementary Figure S3). Therefore, we chose to use residual values calculated from diversity~complexity models for further analyses to test the effects of SGD on the various biodiversity metrics above and beyond the effect of reef structure. CV N+N and CV PO43- were equally dominant drivers of community biodiversity, with a ΔAICC < 2 compared to other parameters across all residual models (Figure 2; Supplementary Table S2). Residual models for stony coral community diversity revealed that CV N+N and temperature were the dominant drivers of taxon and FE richness, while fleshy macroalgae diversity was driven by CV N+N and pH. We selected CV N+N as a representative for both top-ranking nutrient parameters and as the primary SGD gradient variable to visualize SGD-driven community responses.

3.4. Diversity Exhibits a Unimodal Shift Along SGD Gradient

Taxon richness at the 19 survey locations ranged from 8 to 29 benthic taxa comprising stony coral, anemone, corallimorphs, sponges, macroalgae, turf, and cyanobacteria. The site with the lowest taxonomic richness, which had 16% of the total community pool, also had the least amount of hard substrate within the survey area—82% of the substrate was sand. Conversely, the site with the highest taxonomic richness, at 57% of the total community, had only 12% sand within the 2 × 2 m quadrat (Supplementary Figure S4). Only one species (Padina boryana [Thivy 1966]) was observed at the seepage point, where there was a predominantly sandy substrate (97% sand cover); this species represented <2% of total community taxon richness. FE richness along the reef ranged from 6 to 16. Rugosity-normalized %Taxon richness, %FE richness, and %FE volume showed significant polynomial relationships with CV N+N (%Taxon richness p = 0.024, r2 = 0.29; %FE richness p = 0.022, r2 = 0.29; %FE volume p = 0.027, r2 = 0.27; Figure 2a,d), such that community diversity was highest at moderate levels of CV N+N. The lowest FE richness incorporated 27% of the total community FE at a site with higher variability in N+N concentrations, while the greatest FE richness incorporated 72% of the total community FE at sites with moderate and elevated variability. Relative FE volume ranged from 2% of total FE volume in sites with higher N+N variability to 65% in sites with moderate variability. According to an analysis of the relationship between FE and taxon richness along the SGD gradient, the FE richness:Taxon richness ratio was not significant. However, there was a strong positive relationship when the seep was included (with seep p < 0.001 r2 = 0.47) (Supplementary Figure S5). Stony corals and fleshy macroalgae dominate the coral reef community, ranging from 30.8 to 93.2% total cover across the SGD gradient. We observed unimodal relationships between residual richness and CV N+N for coral taxa (p = 0.029, r2 = 0.29), coral FE (p = 0.037, r2 = 0.28), and fleshy macroalgae FE (p = 0.015, r2 = 0.32), with peak richness residuals at moderate SGD (Figure 2b,c,e,f). There was no significant relationship between macroalgal taxa richness and the SGD gradient.

3.5. Taxonomic and Functional-Trait Patterns Shift Along the SGD Gradient

Pocillopora acuta (Lamarck 1816), Porites rus (Forskål 1775), Turbinaria ornata (Agardh 1848), filamentous algal ‘turf’, Dictyota bartayresiana (Lamouroux 1809) and ‘CCA’ were the six most abundant taxa. Percent cover of one of these taxa, T. ornata, displayed a significant unimodal pattern across the SGD nutrient gradient, exhibiting the greatest abundance in sites with moderate SGD (p = 0.008, r2 = 0.36; Supplementary Figure S6). P. rus displayed a nearly significant trend of decreasing abundance from low to moderate SGD, with a slight increase in sites with elevated SGD (p = 0.061, r2 = 0.40). Within each functional group, relative abundance of functional traits changed across the SGD gradient, with some traits exhibiting abundance shifts with respect to SGD-driven nutrient variability (Figure 3; Supplementary Figure S7). Specifically, branching taxa were in highest abundance at moderate SGD, with reduced abundance at sites with lower and elevated SGD influence (CV N+N p = 0.002, r2 = 0.44). Taxa with cushion-like morphologies (Valonia fastigiata [Agardh 1887], Valonia macrophysa [Kutzing 1843], and Dictyosphaeria sp. [Børgesen 1932]) were also present in higher abundance in sites with moderate SGD (CV N+N p = 0.027, r2 = 0.39). Conversely, filamentous taxa (‘turf’) abundance exhibited a trend of high abundance at sites with low and elevated CV N+N, with a reduction in abundance at moderate values (p = 0.065, r2 = 0.31). Non-calcifying and hermatypic taxa were significantly correlated to SGD, such that non-calcifiers showed a positive linear relationship with SGD-derived nutrients (CV N+N p = 0.012, r2 = 0.32) and hermatypic taxa (i.e., corals) exhibited a negative linear relationship to SGD (CV N+N p = 0.004, r2 = 0.54). There were no significant relationships across trophic group traits or phyla along the SGD gradient.

3.6. Dissimilarity of Functional Entities Along SGD

PCoA results indicated a positive linear relationship between community functional-entity variability and the SGD gradient, when the seep community was not included (CV N+N p = 0.034, r2 = 0.24) (Figure 4a). Distances between functional entities along PCoA1 were most strongly correlated to differences in trophic groups (F2,19 = 413.9, p < 0.001 r2 = 0.97), while distances between FE along PCoA2 were most strongly correlated to differences in calcification type (F3,18 = 12.75, p < 0.001, r2 = 0.68) (Figure 4b). In a comparison of presence and absence of traits along the SGD gradient, communities exposed to low CV N+N exhibited limited FE richness, with higher within-site trait similarity. Meanwhile, communities in sites with moderate-to-elevated CV N+N experienced increasing trait richness, with higher dispersion across sites. The seepage point, which experienced the greatest CV N+N, twice that of the reef site with the next-highest value, exhibited the lowest trait diversity and dispersion, with only one species and FE present.

3.7. SGD Alters Community Taxonomic Composition

SGD-driven changes in the environment resulted in shifts in taxonomic composition along the gradient. A nonlinear smoothed fit of CV N+N along the taxon-abundance dissimilarity matrix revealed that communities experiencing a moderately dynamic environment, as measured through moderate variability around the mean, also experienced greater consistency in community composition compared to sites in the most and least dynamic environments (p = 0.044, r2 = 0.33; Figure 5). The presence of rare species within a site, as seen by the peripheral species in Figure 5b, contributed to some of the site dissimilarity, as did the clumping of phaeophytes at sites with low to moderate CV N+N.

4. Discussion

Submarine groundwater discharge (SGD) impacts community composition directly through variable environmental biogeochemistry and indirectly through changes in reef structure. SGD delivered excess nitrate + nitrite and phosphate, along with a suite of physicochemical changes, to the fringing coral reef, resulting in a polynomial diversity response across the benthic community, accounting for reef structure. Salinity is a common conservative proxy for SGD presence [50,74]; therefore, given their positive linear relationships with the coefficient of variation (CV) of salinity and their strong relationships to diversity metrics, CV N+N and CV PO43− were chosen to represent SGD influence through nutrient loading on the surveyed ecosystem. Previous studies on SGD variability in Hawai‘i have examined the relative abundance of benthic reef taxa, including turf, macroalgae, and zoanthids, finding unimodal relationships to environmental variability for multiple macroalgal species, with elevated abundances in sites with moderate-to-low SGD [42]. A study by Pisternick et al. [47] observing broad taxonomic-group cover on Mauritius Island also noted the positive influence of moderate SGD on coral and turf benthic cover, while observing a decrease in macroalgae. The latter study was conducted in a less extreme SGD regime with reduced nutrient fluxes compared to the study site in Hawai‘i, possibly accounting for the seemingly contradictory results. Our results align with those of prior studies; however, through an examination of the functional diversity of a coral-dominated reef, our research shows an overall unimodal effect of SGD on both taxon and functional richness when accounting for structural complexity, a positive effect on non-normalized functional-trait richness, and a nonlinear effect of SGD on taxonomic composition. The consideration of functional traits in our analysis revealed significant distinct responses to the SGD gradient, with certain trait groups exhibiting a positive or negative response to moderate or high SGD. Additionally, we saw different responses to SGD when considering functional traits vs. taxa for certain reef members (i.e., fleshy macroalgae). These findings highlight the importance of the variability of SGD regimes around the globe and underscore the need to understand the scale at which we study the impact of these environments.
Structural complexity was correlated with multiple SGD parameters, suggesting that SGD may impact the physical structure of the reef. The role SGD plays in modifying structure may be both direct, through the dissolution of coral skeleton and limestone in a low-pH environment [78], and indirect, by creating an environment that supports low-structure (i.e., macroalgal-dominated versus coral-dominated) communities [78]. Nutrient enrichment has been observed to shift communities from coral to macroalgal dominance on Mo‘orea [46] and, across a strong pH gradient, calcifying organisms are heavily reduced or completely absent from benthic communities in low-pH conditions [33,79]. In results similar to these past observations, the combined variable fluxes in high-SGD sites within the present study resulted in reduced species and functional richness, as well as shifts in community composition. Near the seepage point, high variability led to communities being dominated by fleshy macroalgae and lacking in structure-forming organisms.
The polynomial relationships between %Taxon richness, %FE richness, and %FE volume to the SGD variability gradient indicate a benefit to benthic community diversity from moderate environmental changes and nutrient pulses relative to more stable or highly variable nutrient regimes. Previous studies in Hawai‘i have observed a pattern of decreased richness and diversity of macroalgal communities on coral reefs under strong, and likely polluted, SGD influence, particularly with nutrient loading and freshwater contribution [44,80]. In low-salinity environments, macroalgal growth may be affected by the availability of ions required to maintain metabolic activity (e.g., K+ and Ca2+) [41,81,82]. However, in a nutrient-enriched environment, higher concentrations of nitrogen and phosphorus may enhance the metabolism of nutrient-limited organisms by increasing the efficiency of nutrient uptake, particularly in algae [10,83]. Within an environment of increasing variability, diversity may be augmented due to greater overlap of species niche breadths [84,85], leading to coexistence of more species that are tolerant to the environment [86]. However, when environmental variability exceeds certain species tolerances, species and possible functional losses are inevitable [84,85]. As in our study, a community experiencing strong dips in salinity and temperature, coupled with eutrophication, is likely to lose most coral species, along with salinity- or temperature-sensitive algal species.
The benefit of SGD with intermediate inputs has been shown in other coral reef studies and is likely a result of nutrient addition. For example, the addition of moderate concentrations of nitrogen and phosphorus to a coral reef ecosystem can bolster the growth of macroalgae and some corals in field manipulations [48,87,88]. Moderate SGD has also previously been observed to enhance coral reef-community ecosystem functioning, increasing productivity and calcification [89]. It is possible that higher functional diversity as a result of SGD was one of the mechanisms that led to higher ecosystem metabolic rates. Taxonomic richness does not imply functional richness along the SGD gradient. Although we observed a unimodal response of taxonomic and functional-trait richness in the overall community and in coral species, the unimodal effect of nutrient variability on the functional traits but not the taxonomic richness of fleshy macroalgal may be a result of trait selection in the environment with more extreme variability. Coral presence becomes more scarce overall under higher SGD influence, and the unimodal response of coral-richness residuals may be attributed to the lower adaptability of coral taxa to a high-SGD environment [48], which leads to a similar response of taxa and traits. However, certain macroalgal taxa may have greater adaptability to a higher-SGD environment [43], leading to an increase in species richness, with greater similarity in functional traits [11]. We observed high variability in the number and identity of functional traits in sites with moderate SGD, indicating that a moderate level of influence may lead to greater heterogeneity in community diversity than is seen in extremely high- or low-SGD environments.
The ratio of functional richness to taxon richness (a measure of functional redundancy) was positively correlated with CV N+N only when the seepage point was included. Therefore, we cannot conclude that communities in high-SGD sites contain more or less redundancy than communities in moderate- or low-SGD sites, although all communities exhibited some redundancy (FE richness/Taxon richness < 1) (Supplementary Figure S5). The insurance hypothesis [11] suggests that redundancy of functional traits in any community helps maintain ecosystem stability under disturbances. When a community experiences species loss, redundant traits across multiple taxa help maintain ecosystem functioning [11]. While functional redundancy can stabilize a diverse community [14], it may also indicate extreme environmental conditions [33], where species with diverse traits were outcompeted or unable to survive, resulting in a low-diversity, high-redundancy community.
SGD provides a dynamic enriched environment for global coastal communities [39]. On many coral reefs, SGD is a source for nutrients in an otherwise oligotrophic system [37] and can modify surrounding communities through its impact on seawater biogeochemistry [38]. In this study, we observed the effect of chronic SGD influence on community composition and reef structure, providing context for changes in taxon and functional richness and composition along the SGD gradient. Understanding how communities respond to dynamic systems is crucial for predicting future responses to disturbances such as ocean warming, acidification, storms, and other global changes. This study shows that SGD can positively impact species and functional diversity on coral reef communities, even for reef-building coral, which are often more sensitive to environmental variability and nutrient loading [46,79], and studying communities through the lens of functional traits reveals patterns that might not be evident through species diversity alone.
Trait-based approaches for assessing ecosystem functioning have expanded the scope of possible investigations beyond just species-specific contributions [90]. For example, studies on traits of herbivorous reef fish have provided an understanding of functional roles on ecosystem processes and coral reef-community stability on local and global biogeographic scales [91]. In another study examining the role of functional diversity on coral reef resilience and recovery, Carturan et al. (2022) observed a positive effect of functional diversity on overall resilience and the prevalence of corals versus algae after disturbances. A functional-trait approach, rather than a taxonomic analysis emphasizing species identities, may lead to more ecological generality and predictability of community responses to environmental shifts [18]. Additionally, species-specific studies may be logistically complicated, particularly for a community comprising algal turf or other species that require genetic sampling. In such cases, a trait-based approach can provide critical and attainable information for community diversity and ecosystem functioning [16]. Complementary future studies further evaluating functional composition and the role of benthic community functions on ecosystem processes will provide important insights for conservation and management of benthic coral ecosystems.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d17030161/s1, Table S1: Taxa identified in benthic community surveys and their assigned functional traits within each of four functional groups; Table S2: Corrected Akaike information criterion (AICC) values compared to lowest AICC value (ΔAICC) for linear and polynomial regression models testing relationships between SGD predictor parameters, including structural complexity, and diversity metrics; Figure S1: Scatter plot of the coefficient of variation (CV) of physicochemical parameters used in model selection relative to CV phosphate (%); Figure S2: Scatter plots displaying polynomial regressions of the coefficient of variation of SGD parameters used for model testing against structural complexity; Figure S3: Corrected Akaike information criterion (AICC) values compared to the lowest AICC value (ΔAICC) for linear and polynomial regression models; Figure S4: Stacked bar plot of proportional substrate at each survey site; Figure S5: Relationship of functional entity richness to species richness along the SGD gradient; Figure S6: Scatter plots displaying taxa which significantly shifted in abundance along the CV nitrate + nitrite gradient; Figure S7: Scatter plots displaying linear and polynomial regressions of individual functional traits within groups relative to increasing CV nitrate + nitrite (%).

Author Contributions

D.M.B. wrote the paper and analyzed the data. D.M.B. and N.J.S. designed the experiments and provided funding. D.M.B. and M.Z. collected the data. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by funds from an NSF biological oceanography grant (#1924281) and the Uehiro Center for the Advancement of Oceanography to NJS as well as the NSF funded Mo‘orea LTER (#1637396), with additional financial support to the MCR LTER provided through a general gift from the Gordon and Betty Moore Foundation. Additional funding supporting this work included the NSF Graduate Research Fellowship Program, Thesis Support from CSU Northridge, Donald E. Bianchi Outstanding Graduate Student Award, Dr. Bob and Lori Luszczak Graduate Scholarship in Biology, and the Robert Schiffman Memorial Award honorable mention, all awarded to DMB.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data and code are available on GitHub (https://github.com/dbarnas/Natural_environmental_gradients_alter_community_composition_and_functional_diversity_on_coral_reefs) and at Zenodo (DOI: 10.5281/zenodo.14921067) [92].

Acknowledgments

We thank the administrative and support staff at the UC Berkeley Richard B. Gump Research Station on Mo‘orea, who maintained the lab and living spaces to provide a reliable working environment. We also thank the Mo‘orea Coral Reef Long-Term Ecological Research Network for access to essential resources, equipment, and facilities. Many thanks to P. Edmunds, M. Donahue, J. Kerlin, C. Nelson, H. Merges, and Nova and Momo Barnas for their direct and indirect assistance in formulating and completing this project. Additionally, we thank Flora, Bruno, Juliette, T. Cabral, their respective families, and the gracious and generous people of Mo‘orea who allowed our team to conduct field research near their homes. A great deal of this work would not have been possible without their support and their help in maintaining the safety of field equipment on the reef. This is HIMB contribution # 1987, SOEST contribution # 11906, CSUN contribution # 390, and UCAO contribution # 17. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Française (DTRT) (Protocole d’Accueil 2005–2023). This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Map of Mo‘orea, French Polynesia, (a) and a zoomed-in map of the survey area (b) with survey locations labeled by distance from the primary seepage point. The survey area is colored by the coefficient of variation (CV) for nitrate + nitrite (%) to show relative nutrient loading of the reef and seepage point (d–h). The sampling locations ranged from 13.0 to 59.8%, with the seepage point having a coefficient of variation of 125.2%. The color gradient is based on kriging interpolation from discrete samples at each survey location collected by Silbiger et al. [54] in August 2021. A violin plot displaying the range and distribution of biogeochemical parameter values across sample time points (c), ordered by increasing distance from the seepage point.
Figure 1. Map of Mo‘orea, French Polynesia, (a) and a zoomed-in map of the survey area (b) with survey locations labeled by distance from the primary seepage point. The survey area is colored by the coefficient of variation (CV) for nitrate + nitrite (%) to show relative nutrient loading of the reef and seepage point (d–h). The sampling locations ranged from 13.0 to 59.8%, with the seepage point having a coefficient of variation of 125.2%. The color gradient is based on kriging interpolation from discrete samples at each survey location collected by Silbiger et al. [54] in August 2021. A violin plot displaying the range and distribution of biogeochemical parameter values across sample time points (c), ordered by increasing distance from the seepage point.
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Figure 2. Polynomial regressions of complexity-normalized residual values for proportional taxon richness (a–c) and functional-entity richness (d–f) of the full sampled community (a,d), stony corals only (b,e), and fleshy macroalgae only (c,f) across CV nitrate + nitrite (%) along the SGD gradient. Negative residual values indicate lower proportional richness than expected from structural complexity alone, while positive values indicate higher proportions than expected. The relationship between CV nitrate + nitrite and diversity metrics was significant across nearly all community groups, with a notable nonsignificant relationship to fleshy macroalgae taxon-richness residuals.
Figure 2. Polynomial regressions of complexity-normalized residual values for proportional taxon richness (a–c) and functional-entity richness (d–f) of the full sampled community (a,d), stony corals only (b,e), and fleshy macroalgae only (c,f) across CV nitrate + nitrite (%) along the SGD gradient. Negative residual values indicate lower proportional richness than expected from structural complexity alone, while positive values indicate higher proportions than expected. The relationship between CV nitrate + nitrite and diversity metrics was significant across nearly all community groups, with a notable nonsignificant relationship to fleshy macroalgae taxon-richness residuals.
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Figure 3. Proportional abundance within survey sites of phyla (a) and functional traits within morphological (b), calcification (c), and trophic group (d) categories in each group along the SGD gradient. Site names correspond to site locations within Figure 1b and are ordered by increasing CV nitrate + nitrite, a proxy for the SGD gradient and a dominant driver of site biodiversity (Figure 2), as indicated by the minimum, maximum, and seepage-point CV nitrate + nitrite values along the x-axis.
Figure 3. Proportional abundance within survey sites of phyla (a) and functional traits within morphological (b), calcification (c), and trophic group (d) categories in each group along the SGD gradient. Site names correspond to site locations within Figure 1b and are ordered by increasing CV nitrate + nitrite, a proxy for the SGD gradient and a dominant driver of site biodiversity (Figure 2), as indicated by the minimum, maximum, and seepage-point CV nitrate + nitrite values along the x-axis.
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Figure 4. Principal coordinate analysis (PCoA) of Gower distance for functional trait presence in multidimensional space (FE volume), reduced to two primary axes. Faceted figures, colored by CV nitrate + nitrite (%), represent volume of trait space occupied by functional entities at each survey location and are arranged by increasing FE volume residual values (FE volume normalized to structural complexity). Points indicate unique FE presence within each site, distance between points represents dissimilarity of entities, and point size is determined by relative abundance of each FE per survey location (a). Points from PCoA analysis in (a) are categorized by phyla and functional groups and labeled for the corresponding trait represented within its respective FE ((b); Table 1) (Morphology: Br = Branching, Enc = Encrusting, Fol = Foliose, Stol = Stolonial, Fil = Filamentous, Sph = Spherical, Poly = Polypoid, Dig = Digitate, Mas = Massive, Much = Mushroom; Calcification: AC = Articulated, Non-AC = Non-articulated, Herm = Hermatypic, NC = Non-calcifying; Trophic Group: Auto = Autotrophic, Het = Heterotrophic, Mix = Mixotrophic).
Figure 4. Principal coordinate analysis (PCoA) of Gower distance for functional trait presence in multidimensional space (FE volume), reduced to two primary axes. Faceted figures, colored by CV nitrate + nitrite (%), represent volume of trait space occupied by functional entities at each survey location and are arranged by increasing FE volume residual values (FE volume normalized to structural complexity). Points indicate unique FE presence within each site, distance between points represents dissimilarity of entities, and point size is determined by relative abundance of each FE per survey location (a). Points from PCoA analysis in (a) are categorized by phyla and functional groups and labeled for the corresponding trait represented within its respective FE ((b); Table 1) (Morphology: Br = Branching, Enc = Encrusting, Fol = Foliose, Stol = Stolonial, Fil = Filamentous, Sph = Spherical, Poly = Polypoid, Dig = Digitate, Mas = Massive, Much = Mushroom; Calcification: AC = Articulated, Non-AC = Non-articulated, Herm = Hermatypic, NC = Non-calcifying; Trophic Group: Auto = Autotrophic, Het = Heterotrophic, Mix = Mixotrophic).
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Figure 5. nMDS ordination plots showing sample locations (n = 19) as black points along the SGD gradient, represented as CV N+N (a), with taxa labeled and colored by their respective phyla (b). Generalized additive models were used to fit a smooth layer of CV N+N values across the ordination plot to display the nonlinear relationship between community abundances and the nutrient-variability gradient.
Figure 5. nMDS ordination plots showing sample locations (n = 19) as black points along the SGD gradient, represented as CV N+N (a), with taxa labeled and colored by their respective phyla (b). Generalized additive models were used to fit a smooth layer of CV N+N values across the ordination plot to display the nonlinear relationship between community abundances and the nutrient-variability gradient.
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Table 1. Taxonomic and functional groups representing functional traits identified within the surveyed community.
Table 1. Taxonomic and functional groups representing functional traits identified within the surveyed community.
MorphologyPhylaCalcificationTrophic Group
Branched (Br)ChlorophytaNon-calcified (NC)Autotrophy (Auto)
Cushion-like (Cushion)CnidariaArticulated (AC)Heterotrophy (Het)
Digitate (Dig)CyanobacteriaNon-articulated (Non-AC)Mixotrophy (Mix)
Encrusting (Enc)PhaeophytaHermatypic (Herm)
Filamentous (Fil)Porifera
Foliose (Fol)Rhodophyta
Massive (Mas)
Mushroom (Mush)
Polypoid (Poly)
Spherical (Sph)
Stolonial (Stol)
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MDPI and ACS Style

Barnas, D.M.; Zeff, M.; Silbiger, N.J. Submarine Groundwater Discharge Alters Benthic Community Composition and Functional Diversity on Coral Reefs. Diversity 2025, 17, 161. https://doi.org/10.3390/d17030161

AMA Style

Barnas DM, Zeff M, Silbiger NJ. Submarine Groundwater Discharge Alters Benthic Community Composition and Functional Diversity on Coral Reefs. Diversity. 2025; 17(3):161. https://doi.org/10.3390/d17030161

Chicago/Turabian Style

Barnas, Danielle M., Maya Zeff, and Nyssa J. Silbiger. 2025. "Submarine Groundwater Discharge Alters Benthic Community Composition and Functional Diversity on Coral Reefs" Diversity 17, no. 3: 161. https://doi.org/10.3390/d17030161

APA Style

Barnas, D. M., Zeff, M., & Silbiger, N. J. (2025). Submarine Groundwater Discharge Alters Benthic Community Composition and Functional Diversity on Coral Reefs. Diversity, 17(3), 161. https://doi.org/10.3390/d17030161

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