Contributors | Affiliation | Role |
---|---|---|
Silbiger, Nyssa | California State University Northridge (CSUN) | Principal Investigator |
Barnas, Danielle M | California State University Northridge (CSUN) | Co-Principal Investigator |
Zeff, Maya | California State University Northridge (CSUN) | Scientist |
York, Amber D. | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
See the "Related Datasets" section or the project page for other data collected as part of this study.
See the Project Page for more data collected as part of the project.
Detailed methods are outlined in Barnas et al. (2025) and summarized here.
(a) Study site and experimental design. We created two SGD exposure treatments based on environmental data from Silbiger et al. (2023) and two SGD assemblage treatments based on benthic community surveys from naturally high or low SGD influence to test the effect of SGD on organismal growth and community metabolism (i.e., photosynthesis, respiration, and calcification). Specifically, we subjected multiple reef taxa in curated assemblages to high or low SGD exposure for six weeks during the rainy season, when SGD fluxes are highest. Ten replicate assemblages of the two community types were placed in each exposure treatment (two assemblage treatments ✕ two exposure treatments) for a total of 40 assemblages. This is an in situ experiment, with organisms soaking at the exposure sites for nearly 2 months.
(b) SGD Exposure Treatments. Due to the predictable flow regime along the fringing reef, we selected two experimental locations: one 50 m upstream (low SGD exposure) and one 27 m downstream (high SGD exposure) of the known SGD seepage point. These sites were chosen to minimize differences in flow, sedimentation, and light regime, while also keeping distance offshore constant (18 m). To monitor SGD presence at each exposure site at high and low tides, we collected continuous measurements of salinity (accuracy ± 5%, precision = 2 uS/cm conductivity) and temperature (accuracy ± 0.1℃, precision = 0.01°C) (HOBO U24 Saltwater Conductivity Logger, Onset Computer Corporation, Massachusetts) at the seepage point and each exposure location at 5-minute intervals. We also collected four discrete water samples in acid-washed triple-rinsed 125 mL Nalgene bottles during low and high tides at day and nighttime within a 24-hour period from March 9-10, 2023. From each sample, we measured instantaneous salinity (accuracy ± 1.0% psu, precision = 0.1 psu) and temperature (accuracy ± 0.3°C, precision = 0.1°C) (YSI Pro2030, Xylem Inc., Washington D.C), pHT (tris-calibrated [total scale] ROSSTM double junction electrode, accuracy ± 0.002, precision = 0.001, Orion Star A325, Thermo Fisher Scientific Inc., Massachusetts), nutrients (Silicate [SiO 2-], phosphate [PO 3-], and nitrate+nitrite [N+N]) and total alkalinity (AT). pHT was corrected for in situ temperature using the seacarb package. Nutrient samples were filtered through a 0.22µm Sterivex filter before being frozen at -20℃ for subsequent nutrient analysis at the S-LAB at 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 3-: DL = 0.011 and CV = 0.2%; SiO 2-: DL = 0.03 and CV = 0.5%) at the UH SOEST Lab for Analytical Chemistry. Total alkalinity samples were fixed with 50% saturated mercuric chloride in deionized (DI) water following protocols by the Dickson lab (Dickson et al. 2007). We analyzed AT using a Mettler Toledo T5 autotitrator for open-cell potentiometric titrations, and accuracy and precision of titrations were tested against a certified reference material (CRM Reference Material for Oceanic CO2 Measurements, A. Dickson, Scripps Institution of Oceanography) at the beginning of each set of titrated samples (accuracy < 0.5% off from reference material, precision = 5 µmol kg-1).
(c) SGD Assemblage Treatments. Benthic community composition surveys were used to determine the species identities of benthic communities to deploy at high and low SGD exposure locations. We selected eight total survey sites, four exhibiting highest and four exhibiting lowest environmental SGD exposure based on prior data.
Benthic communities were surveyed via snorkeling at each site from June-July 2022 to estimate species composition of coral, macroalgae, sponges, corallimorphs, anemones, and cyanobacteria. Composition was assessed within 2 m ✕ 2 m plots using a uniform point- count method with 200 evenly distributed points at each site (4 m2 per site, 16 m2 total area per SGD exposure type). Organisms at each point were identified to species level when possible, or the lowest possible taxonomic unit. We determined the top eight most abundant benthic species from each environmental exposure area for our assemblage treatments). While species richness was consistent across assemblage types (n=8 species, abundance=1 per species).
(d) Organism collection and deployment. A total of 320 organisms across all treatments (n = 80 individuals per treatment) were collected for this experiment. All species were hand collected by snorkel on the fringing reef at least 100m upcurrent of the known seepage point to avoid confounding effects of SGD on life history. To allow for direct comparison of individual response to SGD, replicate individuals of non-colonial organisms were collected in pairs to place one individual in each exposure treatment. Similarly, colonial species in each assemblage type were fragmented into two organisms, such that one organism was placed in high SGD exposure and one was placed in low SGD exposure. Both assemblage types therefore experienced each SGD exposure condition before being tested for changes in community metabolism (NEC and NEP). Replicates of species pairs or colonies were collected at least three meters away from each other to minimize genotypic duplication across assemblages in each exposure treatment.
All organisms were transported submerged to the Richard B. Gump South Pacific Research Station on Mo'orea and held in flow-through water tables. A supply of fresh seawater was continuously pumped from nearshore to provide an ambient coastal environment similar to the collection site, and the water was supplementally oxygenated using air bubblers (Tetra Whisper Air Pump, Virginia, USA). Water tables were cleaned daily to remove algae and avoid settlement. Organisms were fragmented and cleaned to remove excessive epiphytes and epifauna. We used forceps to remove epiphytes and epifauna from organisms and additionally removed epifauna from interstitial spaces of L. kotschyanum by submerging fragments in freshwater for up to 15 seconds, following protocols from Glanz (2021). Species with hard substrate (Scleractinia: P. acuta, P. rus, M. grisea; crustose coralline algae [CCA]: L, kotschyanum; Corallimorpharia: D. nummiforme; sponge: Porifera unk) were attached to wide flat-headed bolts with hot super glue (Gorilla Hot Glue, The Gorilla Glue Company, Ohio). Species not attached to the bolts (macroalgae: D. bartayresiana, T. ornata, H. opuntia, V. fastigiata; sponge: L. chondrodes) were wrapped loosely in clear nylon netting to allow sufficient space and light for growth (8 mm ✕ 8 mm mesh size). Organisms were deployed in either the high or low exposure location in situ for 5-6 weeks from February 8 - March 24, 2023. Species were held in situ in a metal cage (13 mm ✕ 13 mm mesh size) at each exposure site to reduce grazing and predation. Cages were fastened atop cinder block platforms raised above the benthos to 0.6 m depth at each site. Bolt-mounted species were fastened to the cage along the mesh base with washers and nuts, while netted organisms were attached to the base with zipties. Cages were consistently cleaned and checked for overall health condition of species throughout deployment.
(e) Individual growth rate. Species were measured before and after deployment for weight ( ± 0.1 g, Adam Equipment CQI- 2601 balance, NY, USA) to determine growth of species across exposure treatments. Bolt-mounted organisms were buoyant-weighed (Scleractinia and CCA) or wet-weighed (D. nummiforme and Porifera unk) by patting the organism with a dry cloth to remove excess water. All other species (macroalgae and L. chondrodes) were wet-weighed before being wrapped in mesh and after mesh removal by using a centrifugal spinner with a standard timed rotation. We estimated individual growth as the daily change in growth normalized to biomass (mg g-1 d-1), and we converted buoyant weight to dry weight using the skeletal density of each species: aragonite (2.93 g cm-3) for corals and calcite (2.71 g cm- 3) for CCA. All individual weights were normalized to biomass for consistency across species, but surface area-normalized growth rates were also calculated for the three coral species and the on CCA to verify that growth was consistent to previous literature.
All data and code are available on GitHub (https://github.com/dbarnas/SGD_drives_both_direct_and_indirect_effects_on_organismal_and_community_metabolism_on_coral_reefs) and Zenodo (https://zenodo.org/records/14285978)
* Table within submitted file "organism_growth.csv" was imported into the BCO-DMO data system for this dataset. Values "NA" imported as missing data values. Table will appear as Data File: *960128_v1_sgd-response-reef-organism-growth.csv (along with other download format options).
Missing Data Identifiers:
* In the BCO-DMO data system missing data identifiers are displayed according to the format of data you access. For example, in csv files it will be blank (null) values. In Matlab .mat files it will be NaN values. When viewing data online at BCO-DMO, the missing value will be shown as blank (null) values.
* Column names adjusted to conform to BCO-DMO naming conventions designed to support broad re-use by a variety of research tools and scripting languages. [Only numbers, letters, and underscores. Can not start with a number]
Parameter | Description | Units |
SpeciesID | Fragment unique ID | unitless |
DaysInSitu | Days in the field during experiment (Feb 2023 - April 2023) | days |
AT | Assemblage Treatment (High = assemblage from High SGD; LOW = Assemblage from low SGD site) | unitless |
ET | Environment Treatment (High = placement in high SGD environment; Low - placement in low SGD environment) | unitless |
growthrate | Growth rate of individual | mg per g per day (mg g-1 d-1) |
SA | surface are of individual | square centimeters (cm2) |
FullSp | Species name (see supplemental file 960128_species_list.csv for taxonomic identifiers) | unitless |
Location | Site Name | unitless |
Dataset-specific Instrument Name | HOBO U24 Saltwater Conductivity Logger |
Generic Instrument Name | Conductivity Meter |
Dataset-specific Description | HOBO U24 Saltwater Conductivity Logger, Onset Computer Corporation, Massachusetts: salinity (accuracy ± 5%, precision = 2 uS/cm conductivity) and temperature (accuracy ± 0.1℃, precision = 0.01°C) was used to measure salinity and temperature in the field.
|
Generic Instrument Description | Conductivity Meter - An electrical conductivity meter (EC meter) measures the electrical conductivity in a solution. Commonly used in hydroponics, aquaculture and freshwater systems to monitor the amount of nutrients, salts or impurities in the water. |
Dataset-specific Instrument Name | YSI Pro2030, Xylem Inc., Washington D.C |
Generic Instrument Name | Multi Parameter Portable Meter |
Dataset-specific Description | YSI Pro2030, Xylem Inc., Washington D.C: salinity (accuracy ± 1.0% psu, precision = 0.1 psu) and temperature (accuracy ± 0.3°C, precision = 0.1°C) was used for discrete salinity and temperature measurements in the lab. |
Generic 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. |
Dataset-specific Instrument Name | Orion Star A325 |
Generic Instrument Name | pH Sensor |
Dataset-specific Description | Orion Star A325, with ROSSTM double junction electrode Thermo Fisher Scientific Inc., Massachusetts: accuracy ± 0.002 mV, precision = 0.001 was used to measure Tris-calibrated pH |
Generic Instrument Description | An instrument that measures the hydrogen ion activity in solutions.
The overall concentration of hydrogen ions is inversely related to its pH. The pH scale ranges from 0 to 14 and indicates whether acidic (more H+) or basic (less H+). |
Dataset-specific Instrument Name | |
Generic Instrument Name | PME miniPAR logger |
Dataset-specific Description | miniPAR, accuracy ± 5% in air traceable to NIST, Precision Measurement Engineering, California was used to measure PAR in the field. |
Generic Instrument Description | A submersible instrument that logs PAR (Photosynthetically Active Radiation), temperature and tilt measurements. Data are recorded on an internal SD card. The sensor is a LI-192 Underwater Quantum Sensor, manufactured by LI-COR. The sensor uses a silicon photodiode and glass optical filters to create a uniform sensitivity to light wavelengths in the 400-700nm range. It measures PAR from all angles in one hemisphere. An anti-fouling wiper is available. The instrument can be configured to record at intervals between 1 and 60 minutes. It is submersible up to 100 metres. PAR measurement accuracy is dependent upon the stability of the sensor pointed towards the water surface. |
Dataset-specific Instrument Name | Adam Equipment CQI-2601 balance, NY, USA |
Generic Instrument Name | scale or balance |
Dataset-specific Description | Adam Equipment CQI-2601 balance, NY, USA (accuracy ± 0.1 g) was used to wet and buoyant weigh the organisms |
Generic Instrument Description | Devices that determine the mass or weight of a sample. |
Dataset-specific Instrument Name | Seal Analytical AA3 HR |
Generic Instrument Name | Seal Analytical AutoAnalyser 3HR |
Dataset-specific Description | Seal Analytical AA3 HR Nutrient Analyzer at the University of Hawaii S-Lab : (N+N: detection limit [DL] = 0.009 and coefficient of variation [CV] = 0.3%; PO 3-: DL = 0.011 and CV = 0.2%; SiO 2-: DL = 0.03 and CV = 0.5%) was used to process all the nutrient samples |
Generic 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. |
Dataset-specific Instrument Name | Mettler Toledo T5 autotitrator |
Generic Instrument Name | Titrator |
Dataset-specific Description | Mettler Toledo T5 autotitrator: (accuracy < 0.5% off from certified reference material, precision = 5 µmol kg-1) was used to measure total alkalinity. |
Generic Instrument Description | Titrators are instruments that incrementally add quantified aliquots of a reagent to a sample until the end-point of a chemical reaction is reached. |
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.
Funding Source | Award |
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NSF Division of Ocean Sciences (NSF OCE) |