Search for: All records

Spartina alterniflorahas a distinct flood‐adapted morphology, and its physiological responses are likely to vary with differences in tidal submergence. To understand these responses, we examined the impacts of tidal inundation on the efficiency of Photosystem II (φPSII) photochemistry and leaf‐level photosynthesis at different canopy heights through a combination of in situ chlorophyll fluorescence (ChlF), incident photosynthetically active radiation, and tide levels. Our result showed small declines (7%–8.3%) in φPSII for air‐exposed leaves when the bottom canopies were tidally submerged. Submerged leaves produced large reductions (30.3%–41%) in φPSII. Our results suggest that when submerged, PSII reaction centers inS. alternifloraleaves are still active and able to transfer electrons, but only at ∼20% of the typical daily rate. We attribute this reduction in φPSII to the decrease in the fraction of “open” PSII reaction centers (10% of the total) and the stomatal conductance rate caused by the tidal submergence. To our knowledge, this flooding induced leaf‐level reduction of φPSII forS. alterniflorain field settings has not been reported before. Our findings suggest that canopy‐level φPSII is dependent on the proportion of submerged versus emerged leaves and highlight the complexities involved in estimating the photosynthetic efficiency of tidal marshes.

 

Tidal wetlands, widely considered the most extensive reservoir of soil organic carbon (SOC), can benefit from remote sensing studies enabling spatiotemporal estimation and mapping of SOC stock. We found that a majority of the remote-sensing-based SOC mapping efforts have been focused on upland ecosystems, not on tidal wetlands. We present a comprehensive review detailing the types of remote sensing models and methods used, standard input variables, results, and limitations for the handful of studies on tidal wetland SOC. Based on that synthesis, we pose several unexplored research questions and methods that are critical for moving tidal wetland SOC science forward. Among these, the applicability of machine learning and deep learning models for predicting surface SOC and the modeling requirements for SOC in subsurface soils (soils without a remote sensing signal, i.e., a soil depth greater than 5 cm) are the most important. We did not find any remote sensing study aimed at modeling subsurface SOC in tidal wetlands. Since tidal wetlands store a significant amount of SOC at greater depths, we hypothesized that surface SOC could be an important covariable along with other biophysical and climate variables for predicting subsurface SOC. Preliminary results using field data from tidal wetlands in the southeastern United States and machine learning model output from mangrove ecosystems in India revealed a strong nonlinear but significant relationship (r2 = 0.68 and 0.20, respectively, p < 2.2 × 10−16 for both) between surface and subsurface SOC at different depths. We investigated the applicability of the Soil Survey Geographic Database (SSURGO) for tidal wetlands by comparing the data with SOC data from the Smithsonian’s Coastal Blue Carbon Network collected during the same decade and found that the SSURGO data consistently over-reported SOC stock in tidal wetlands. We concluded that a novel machine learning framework that utilizes remote sensing data and derived products, the standard covariables reported in the limited literature, and more importantly, other new and potentially informative covariables specific to tidal wetlands such as tidal inundation frequency and height, vegetation species, and soil algal biomass could improve remote-sensing-based tidal wetland SOC studies.

 

To understand patterns in CO2 partial pressure (PCO2) over time in wetlands’ surface water and porewater, we examined the relationship between PCO2 and land–atmosphere flux of CO2 at the ecosystem scale at 22 Northern Hemisphere wetland sites synthesized through an open call. Sites spanned 6 major wetland types (tidal, alpine, fen, bog, marsh, and prairie pothole/karst), 7 Köppen climates, and 16 different years. Ecosystem respiration (Reco) and gross primary production (GPP), components of vertical CO2 flux, were compared to PCO2, a component of lateral CO2 flux, to determine if photosynthetic rates and soil respiration consistently influence wetland surface and porewater CO2 concentrations across wetlands. Similar to drivers of primary productivity at the ecosystem scale, PCO2 was strongly positively correlated with air temperature (Tair) at most sites. Monthly average PCO2 tended to peak towards the middle of the year and was more strongly related to Reco than GPP. Our results suggest Reco may be related to biologically driven PCO2 in wetlands, but the relationship is site-specific and could be an artifact of differently timed seasonal cycles or other factors. Higher levels of discharge do not consistently alter the relationship between Reco and temperature normalized PCO2. This work synthesizes relevant data and identifies key knowledge gaps in drivers of wetland respiration. 

Vegetation indices calculated from remotely sensed satellite imagery are commonly used within empirically derived models to estimate leaf area index in loblolly pine plantations in the southeastern United States. The data used to parameterize the models typically come with observation errors, resulting in biased parameters. The objective of this study was to quantify and reduce the effects of observation errors on a leaf area index (LAI) estimation model using imagery from Landsat 5 TM and 7 ETM+ and over 1500 multitemporal measurements from a Li-Cor 2000 Plant Canopy Analyzer. Study data comes from a 16 quarter 1 ha plot with 1667 trees per hectare (2 m × 3 m spacing) fertilization and irrigation research site with re-measurements taken between 1992 and 2004. Using error-in-variable methods, we evaluated multiple vegetation indices, calculated errors associated with their observations, and corrected for them in the modeling process. We found that the normalized difference moisture index provided the best correlation with below canopy LAI measurements (76.4%). A nonlinear model that accounts for the nutritional status of the stand was found to provide the best estimates of LAI, with a root mean square error of 0.418. The analysis in this research provides a more extensive evaluation of common vegetation indices used to estimate LAI in loblolly pine plantations and a modeling framework that extends beyond the typical linear model. The proposed model provides a simple to use form allowing forest practitioners to evaluate LAI development and its uncertainty in historic pine plantations in a spatial and temporal context.