Building a predictive model of submerged aquatic vegetation for Atlantic brant using remote sensing and in-situ sampling

Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
University of Delaware
Abstract
Submerged aquatic vegetation (SAV), e.g. eelgrass (Zostera marina), and macroalgae (Ulva lactuca., Enteromorpha spp., Gracilaria tikvahiae) provides critical ecosystem functions. However, because of a changing coastal environment due to shifting climates and anthropogenic land alteration, it is becoming more important to protect this ecosystem. There is a need to create a predictive remotely sensed model to assess SAV abundance and thus potentially long-term degradation. Secondarily, because SAV has many benefits to organisms including the Atlantic brant (Branta bernicula hrota), which is a specialist on these food sources, building a predictive model will aid in assessing the potential energetic carrying capacity of wintering brant. To meet this need, I used Landsat 8 imagery to create a Normalized Difference Vegetation Index (NDVI) of potential SAV between the southern coast of Long Island, New York and intertidal zones of New Jersey, USA, December 2015-February 2016. To quantify the accuracy of this index, I assessed SAV presence at two hundred and fifty-six 900 m2 sample sites including 174 predicted SAV points and 82 null points. Further at each site, I collected SAV biomass within a 1 m2 quadrat, as well as two subsamples within the 900 m2 resolution. A series of microhabitat variables were collected at each sample site to better evaluate predicted presence including water depth, water temperature, NH3 – N, turbidity, bottom type, and salinity. The NDVI correctly identified presence of eelgrass with 46% accuracy and the Ulva/Enteromorpha and Rhodophyta with 61% accuracy. Using 22 a priori general linear models and AIC model averaging, Ulva/Enteromorpha was significantly predicted by water depth and NH3-N, Eelgrass was significantly predicted by NH3-N, and Rhodophyta was best predicted by water depth. Using the top averaged models, I produced predictive maps that show the potential presence/absence of SAV species throughout the study area and extrapolated presence to available biomass. Estimates of energetic carrying capacity for both study areas were similar to mid-winter survey population counts for 2015–2016, thus further validating the accuracy of the NDVI predictive modeling.
Description
Keywords
Atlantic brant, Remote sensing, Waterfowl
Citation