Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Related Publications
• Parameters
• Instruments
• Project Information
• Funding

Full methods are found in La Valle et al. 2020.

Site descriptions and SGD characterizations: The study sites are located along the southern shoreline of O'ahu, Hawai'i, in Maunalua Bay (21.2743°N, 157.7492°W). The two study sites, Black Point (21.2586°N, 157.7899°W) and Wailupe (21.2756°N, 157.7624°W), are on the western side of the bay and are macroalgae-dominated, with 50% and 15% fleshy macroalgal benthic cover at Wailupe and Black Point, respectively, from the shoreline to the reef crest. There is a close to conservative relationship between SGD-derived inorganic nutrients (nitrate, phosphate, and silicate) and salinity at Black Point and Wailupe (linear regressions statistics described in Table 1 of La Valle et al. 2019). SGD fluxes and nutrient delivery were described in detail in Holleman (2011), Nelson et al. (2015), Richardson et al. (2017a-b), Lubarsky et al. (2018) and summarized in La Valle et al. (2019).

Salinity time series (see instruments section) were used for EOF and community composition data analyses were related to the EOF loading scores.

Sampling and analytical procedures:
Benthic algal surveys were done at the grid intersection points (n = 97 for Wailupe and n = 115 for Black Point). The survey consisted of 25 cm by 25 cm quadrats, where species-specific percent algal cover, species-specific invertebrate percent cover and counts, and substrate type were measured. All taxa were identified to lowest taxonomic level.

Empirical orthogonal functions (EOFs) were applied to the spatially indexed salinity time series at both sites. The continuous time series were inputted in the EOFs. EOFs were used to reduce dimensionality of the spatially explicit time series and to reveal the spatial structure of the time series data. EOFs are the spatiotemporal manifestation of principal components analysis (PCA) (Wikle et al. 2018). The output for EOFs includes a spatial map of loadings and an associated normalized principal-component time series for the salinity dataset obtained using a singular value decomposition of a space-wide matrix. The singular value decomposition was done using the function svd in the base library in RStudio (version 1.0.44, R Core Team 2016).

Instruments:
Salinity time series were not included in this dataset because they are redundant for analyses (as EOF1 and EOF2 are included in the datasets) but are available from Florybeth La Valle (flavalle@ucsd.edu).

Twenty-three autonomous salinity sensors (Odyssey Temperature and Conductivity loggers, 3 to 60 mS cm-1) were deployed in a sparse grid across each site. The sensors were attached a few centimeters off of the benthos at Black Point for 30 days (29 May - 29 June 2015) and for 27 days at Wailupe (4 April - 21 May 2015) and took readings every 10 minutes. Two water samples were taken every week, synchronously with salinity sensor measurements, for the duration of the deployments at both sites and were analyzed using a Portasal Salinometer 8410A (accuracy 0.001) for quality control purposes and to check for sensor measurement drift.

Data Processing:
We ran distance-based linear models (DistLM) on the distance matrix of community data by site using EOF1 and EOF2 as fixed effects (predictors) and substrate type as a random effect was used to explore species composition to quantify variance in benthic community structure explained by EOFs. The DistLM enables us to quantify whether either or both EOF1s contribute significantly to patterns observed in the multivariate community structure while taking in account the variability in the substrate type. The function adonis in the R package vegan was used to create these models (Oksanen et al. 2017).

Species-specific relationships with variability in SGD:
In order to characterize how presence or absence of each benthic taxon was related to SGD, logistic regression models were run at each site using the glm function in the stats package (R Core Team) with EOF1 as the predictors. Only species that appeared in at least three benthic samples were analyzed for univariate relationships to SGD. All logistic regression p-values were controlled for false discovery rate (α = 0.05) using the function p.adjust with the Benjamini Hochberg method in the R package stats (Benjamini and Hochberg 1995).

BCO-DMO Processing:
- concatenated the two separate data files into one;
- replaced "NA" with "nd" (no data);
- renamed fields to conform with BCO-DMO naming conventions;
- replaced spaces with underscores in site column;
- changed date format to YYYY-MM-DD.

NSF Award Abstract:
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.

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.

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.