Dataset title: Freshwater Subtropical Ridge and Slough Wetland Metabolism, South Florida, USA: 2014-2016 Dataset ID: doi:10.6073/pasta/4097433552819a3c6958b5dbd0b8ef86 Dataset Creator Name: John Kominoski Position: Associate Professor Organization: Florida International University Address: 11200 SW 8th Street Miami, FL 33199 United States Phone: 3053487117 Email: jkominos@fiu.edu URL: https://kominoskilab.com Metadata Provider Name: John Kominoski Position: Associate Professor Organization: Florida International University Address: 11200 SW 8th Street Miami, FL 33199 United States Phone: 3053487117 Email: jkominos@fiu.edu URL: https://kominoskilab.com Dataset Abstract How climate and habitat drive variation in aquatic metabolism in wetlands remains uncertain. To quantify differences in seasonal aquatic metabolism among wetlands, we estimated aquatic ecosystem metabolism (gross primary productivity, GPP; ecosystem respiration, ER; net aquatic productivity, NAP) in subtropical ridge and slough wetlands of the Florida Everglades from more than 2 years of continuously measured water column dissolved oxygen (DO), photosynthetically active radiation (PAR), water temperature, and water depth. GPP and ER were modeled from light, temperature, and water depth using non-linear minimization and maximum likelihood. Reaeration rates were estimated from wind speed. Dissolved oxygen was below saturation at all sites during both wet and dry seasons. Water depth interacted with vegetation to influence PAR, water temperature, and spatiotemporal patterns in aquatic metabolism. GPP and ER were highest at the slough with lowest submerged aquatic vegetation (low-SAV slough), intermediate in the sawgrass (Cladium jamaicense) ridge site, and lowest at the slough with lowest submerged aquatic vegetation (high-SAV slough). ER was strongly positively correlated with GPP at the sawgrass ridge and low-SAV slough sites. GPP increased with water temperature and PAR across all habitat types, whereas ER decreased (more respiration) with water temperature and PAR. NAP was negatively correlated with water temperature and positively correlated with PAR, suggesting that ER was more sensitive than GPP to water temperature. Aquatic metabolism was largely net heterotrophic in all wetlands, and high-SAV appeared to buffer seasonal variation in PAR and water temperatures that drive NAP in subtropical wetlands. Our results suggest that aquatic ecosystem metabolism in wetlands with seasonal hydrology is sensitive to changes in water depth and vegetation density that influence temperature and light. Expanding our understanding of how metabolic processes and carbon cycling in wetland ecosystems vary across gradients in hydrology, vegetation, and organic matter could enhance our understanding and protection of conditions that maximize carbon storage. Geographic Coverage Bounding Coordinates Geographic description: Our study site (25.780859°, -80.726742) was located in ridge and slough wetlands of the southern Water Conservation Area 3A (WCA-3A) about 1.6 km North of Tamiami Trail with Everglades National Park to the South West bounding coordinate: -80.726742 East bounding coordinate: -80.726742 North bounding coordinate: 25.780859 South bounding coordinate: 25.780859 Temporal Coverage Start Date: 2014 End Date: 2016 Data Table Entity Name: floc.metab.010121 Entity Description: Data file for Freshwater Subtropical Ridge and Slough Wetland Metabolism Object Name: floc.metab.010121.csv Data Format Number of Header Lines: 1 Attribute Orientation: column Field Delimiter: , Number of Records: Attributes Attribute Name: Date Attribute Label: Date Attribute Definition: day/month/year Storage Type: dateTime Measurement Scale: Missing Value Code: Attribute Name: Habitat Attribute Label: Habitat Attribute Definition: Location within wetland (ridge, slough.high.sav, slough.low.sav), emergent vegetation (ridge) or submerged aquatic vegetation (SAV) dominate a high or low densities Storage Type: string Measurement Scale: ridge= Cladium jamaicense (sawgrass) dominated habitat in freshwater subtropical wetlands slough.high.sav= Habitat in freshwater subtropical wetlands that is dominated by high density of submerged aquatic vegetation (SAV) slough.low.sav= Habitat in freshwater subtropical wetlands that is dominated by low density of submerged aquatic vegetation (SAV) Missing Value Code: Attribute Name: GPP Attribute Label: GPP Attribute Definition: Gross Primary Productivity Storage Type: float Measurement Scale: Units: grams oxygen per square meter per day Number Type: real Missing Value Code: Attribute Name: ER Attribute Label: ER Attribute Definition: Ecosystem Respiration Storage Type: float Measurement Scale: Units: grams oxygen per square meter per day Number Type: real Missing Value Code: Attribute Name: NAP Attribute Label: NAP Attribute Definition: Net Aquatic Productivity Storage Type: float Measurement Scale: Units: grams oxygen per square meter per day Number Type: real Missing Value Code: Attribute Name: LLE Attribute Label: LLE Attribute Definition: Log-Likelihood Estimate Storage Type: float Measurement Scale: Units: unitless Number Type: real Missing Value Code: Attribute Name: z.mean Attribute Label: z.mean Attribute Definition: Daily Mean Water Depth (z) Storage Type: float Measurement Scale: Units: meter Number Type: real Missing Value Code: Attribute Name: temp.mean Attribute Label: temp.mean Attribute Definition: Daily Mean Water Temperature Storage Type: float Measurement Scale: Units: celsius Number Type: real Missing Value Code: Attribute Name: par.sum Attribute Label: par.sum Attribute Definition: Daily Sum of Photosynthetically Active Radiation Storage Type: float Measurement Scale: Units: micromols per meter square per second Number Type: real Missing Value Code: Methods Method Step Description Site description Our study site (25.780859°, -80.726742) was located in ridge and slough wetlands of the southern Water Conservation Area 3A (WCA-3A) about 1.6 km North of Tamiami Trail with Everglades National Park to the South (FIG. 2). We selected three sampling locations, all within 20 m of each other, two within a single slough and one in the adjacent sawgrass (Cladium jamaicense) ridge (FIG. 2). The slough was ~50 m wide; the ridge ~30 m wide; and the ridge sampling point was located ~3 m into the ridge. The high-SAV slough site contained water lily (Nymphaea odorata), spikerush (Eleocharis cellulosa), and bladderwort (Utricularia foliosa and U. purpurea), and the low-SAV slough site was dominated by few water lily (Nymphaea odorata) (FIG. 2). A benthic layer of unconsolidated floc – a heterogeneous mixture of algal and detrital organic matter – generally is found covering the peat of the sloughs and the ridges to a thickness that varies from 0 to 25 cm thick (McVoy et al. 2011, Pisani et al. 2015). The thickness of the layer may be thinner on the sawgrass ridges than in the sloughs, but on ridges, the thickness can be obscured by visible fragments of dead sawgrass leaves (C. McVoy, personal observation). Dissolved oxygen and temperature We deployed dissolved oxygen (DO) loggers (D-Opto SDI-12 Optical DO Sensor, ENVCO Global, New Zealand) by attaching them to vertical tripods adjusted every 60 d to mid-depth of the water column level at the two slough locations (~15 to 45-cm depth) and one ridge location (~5 to 30-cm depth). We positioned DO sensors facing upward and above benthic substrates to ensure that measurements were made in the water. When the DO sensor in the ridge site was above the water during peak dry periods, we were able to detect and exclude these data recorded as supersaturated %DO. The ENVCO Global loggers recorded dissolved oxygen concentrations and temperature at 15 min intervals at each location from 14 February 2014 to 23 March 2016. Sites were visited every 60 d to download data, remove any fouling of the sensors, check calibration, and vertically reposition the DO loggers at mid-depth in the water column, if necessary. Prior to deploying DO loggers, each logger was calibrated in air-saturated water (water that had been aerated with an airstone for 20 min). Upon retrieval from the field, DO loggers were tested for potential drift assessed by recording DO and temperature in air-saturated water and adjusted as necessary. Meteorological and Stage Measurements At the research platform, photosynthetically active radiation (PAR) (μmol m2 s-1) was measured continuously at approximately 3 m above the water surface and averaged every 15 min (Apogee Model SQ-100, Logan, Utah, USA). Barometric pressure was recorded every 15 min (WXT-520, Vaisala, Helsinki, Finland). Stage in the slough was recorded at 15 min intervals using a level transducer installed in a PVC tube at a set depth (KPSI Model 500 SDI-12, 0-4 PSI range, 0.05% accuracy, TE Connectivity, Schaffhausen, Switzerland) and converted to water depths (cm) using the peat elevations of the bottom of the slough and surface of the ridge. Due to proximity (less than 20 m) and the absence of any hydrological barriers, it was assumed that stages at both slough locations and at the ridge location were identical. Wind speed (m s-1) at approximately 3 m above water surface was measured using a sonic anemometer (Vaisala WXT-520). Surface Water Physicochemistry In October 2014, during the peak of the wet season when water depths were highest, we recorded depth-specific PAR using triplicate measures at 15- to 20-cm depth intervals from the surface of the water to the surface of the soil in each habitat. We generated PAR attenuation curves for each site to quantify the change in light from the surface with increasing water depth. We used these discrete depth-specific PAR attenuation measurements to help interpret modeled estimates of metabolism from continuous water depth and surface PAR measurements. Diel Dissolved Oxygen and Aquatic Ecosystem Metabolism Oxygen flux was estimated by fitting the following model to the oxygen data (Van de Bogert et al. 2007, Hall et al. 2015) O_i= O_(i-t)+ (GPP × 〖PPFD〗_t)/(z × ∑ PPFD)+ (ER × ∆t)/z+K(O_s- O) × ∆t (1) where O2 at time i is equal to O2 at the previous time (i-1) plus time-step-specific rates of GPP and ER, z is water depth, and K is air-water gas exchange coefficient per unit time (based on the reaeration flux K(Os - O), and the difference between dissolved O2 and O2 at saturation for a given temperature and barometric pressure). Os is saturated oxygen concentration, estimated as a function of barometric pressure and water temperature (Garcia and Gordon 1992), and Os - O is the saturation deficit. PPFDt is photon flux density during the time interval t (µmol photons m-2 s-1). In this model, ER is a negative O2 flux because O2 is being consumed. The time-step () is the measurement interval of logged O2 data for each DO sonde. We assumed that GPP was a linear function of light (Van de Bogert et al. 2007) and that ER was constant throughout the day. During our measurements, we rarely observed DO saturation (see below Results). Net aquatic productivity (NAP) was measured as the change in DO after accounting for diffusive exchange with the atmosphere or reaeration (K) (Hagerthey et al. 2010). NAPt = GPPt -ERt (2) Kt =0.5e0.15*wt (3) In aquatic ecosystems, Kt is generally modeled as a function of wind speed (wt) (Cole and Caraco 1998, Hagerthey et al. 2010, Caffrey 2004, Solomon et al. 2013), although studies have shown that commonly used wind speed/gas exchange relationships may overestimate the gas transfer velocity in wetland ecosystems (Ho et al. 2018). To estimate GPP and ER, we fit the model of diel whole-stream metabolism to the measured oxygen data by finding estimates of GPP and ER that minimized the negative log-likelihood of the model to the data using function nlm() in R (Hall et al. 2015). We compared modeled estimates and fits based on the minimized log-likelihood estimate (LLE). We used the following exclusion criteria for modeled GPP and ER: we eliminated positive estimates of ER, repeated values for subsequent dates, and used LLE (values > 600) combined with the determination of realistic estimates of GPP and ER based on the distribution of all modeled values. Exclusion of data based on these criteria accounted for 3-20% of daily estimates of GPP and ER (ridge: n = 97 of 525; high-SAV slough: 122 of 597; low-SAV slough: 18 of 611), leaving n = 428 (ridge), n = 475 (high-SAV slough), and n = 593 (low-SAV slough) diel estimates (total N = 1496). Maintenance Maintenance of the dataset will be performed by the creator. Dataset Contact Name: John Kominoski Position: Associate Professor Organization: Florida International University Address: 11200 SW 8th Street Miami, FL 33199 United States Phone: 3053487117 Email: jkominos@fiu.edu URL: https://kominoskilab.com