Predictive Seagrass Habitat Model.

摘要

Restoration of ecosystem services provided by seagrass habitats in estuaries requires a firm understanding of the modes of action of multiple interacting stressors including nutrients, climate change, coastal land-use change, and habitat modification. Often, managers have used the reported historic depth limits of seagrass to project the future distribution of seagrass in response to nitrogen load reductions. In general, these predictions are based on empirical or modeled estimates of the influence of phytoplankton production in the water column on the light environment, and do not account for the interaction of multiple factors. We explored the application of generalized linear mixed models ( GLMMs) and generalized additive mixed models (GAMMs) to describe the simple and interactive effects of environmental factors on the distribution of a common seagrass, Zostera marina, in Narragansett Bay, Rhode Island. We used a random shoreline effect to account for “founder” (random colonization or extinction) effects. We provide several strategies to overcome three challenges in developing empirical species distribution models to describe and predict seagrass distribution in estuaries: the fine-scale patchiness of seagrass distributions with attendant problems of spatial autocorrelation; the large areas of interest for model development and application entailing significant memory demands for modeling; and the potential co-variance of multiple interacting factors affecting seagrass. We developed a spatial framework describing the coordinates of spatial autocorrelation in estuarine systems, with the main axis parallel to the shoreline and a secondary axis perpendicular to the shoreline. We demonstrated an approach to incorporate a term for residual autocorrelation in GLMMs first introduced by Crase (Crase, B., Liedloff A.C., and B.A. Wintle. 2012). To account for anisotropy in the system, we calculated zonal averages of residual errors within rectangular boxes oriented parallel to the shoreline along the longer main axis. We successfully dealt with covariance of influential factors by centering variables, by using multiple strategies to describe the interaction of the light environment and wave energy with depth, and by excluding correlated variables where necessary. We predicted seagrass distribution at the scale of 10-meter grid cells, as presence/absence or average presence/absence associated with shoreline locations spaced at 10-meter intervals, and minimum or maximum depth of distributions at those locations. Prediction of seagrass absolute or average presence/absence at shoreline locations was very robust, with area-under-the-curve (AUC) values associated with Receiver Operating Characteristic (ROC) curves of 0.95 – 0.98 following 10-fold cross-validation of models. Random shoreline effects varied over several orders of magnitude, probably tied to the distribution of tidal currents. Tidal currents are Restoration of ecosystem services provided by seagrass habitats in estuaries requires a firm understanding of the modes of action of multiple interacting stressors including nutrients, climate change, coastal land-use change, and habitat modification. Often, managers have used the reported historic depth limits of seagrass to project the future distribution of seagrass in response to nitrogen load reductions. In general, these predictions are based on empirical or modeled estimates of the influence of phytoplankton production in the water column on the light environment, and do not account for the interaction of multiple factors. We explored the application of generalized linear mixed models ( GLMMs) and generalized additive mixed models (GAMMs) to describe the simple and interactive effects of environmental factors on the distribution of a common seagrass, Zostera marina, in Narragansett Bay, Rhode Island. We used a random shoreline effect to account for “founder” (random colonization or extinction) effects. We provide several strategies to overcome three challenges in developing empirical species distribution models to describe and predict seagrass distribution in estuaries: the fine-scale patchiness of seagrass distributions with attendant problems of spatial autocorrelation; the large areas of interest for model development and application entailing significant memory demands for modeling; and the potential co-variance of multiple interacting factors affecting seagrass. We developed a spatial framework describing the coordinates of spatial autocorrelation in estuarine systems, with the main axis parallel to the shoreline and a secondary axis perpendicular to the shoreline. We demonstrated an approach to incorporate a term for residual autocorrelation in GLMMs first introduced by Crase (Crase, B., Liedloff A.C., and B.A. Wintle. 2012). To account for anisotropy in the system, we calculated zonal averages of residual errors within rectangular boxes oriented parallel to the shoreline along the longer main axis. We successfully dealt with covariance of influential factors by centering variables, by using multiple strategies to describe the interaction of the light environment and wave energy with depth, and by excluding correlated variables where necessary. We predicted seagrass distribution at the scale of 10-meter grid cells, as presence/absence or average presence/absence associated with shoreline locations spaced at 10-meter intervals, and minimum or maximum depth of distributions at those locations. Prediction of seagrass absolute or average presence/absence at shoreline locations was very robust, with area-under-the-curve (AUC) values associated with Receiver Operating Characteristic (ROC) curves of 0.95 – 0.98 following 10-fold cross-validation of models. Random shoreline effects varied over several orders of magnitude, probably tied to the distribution of tidal currents. Tidal currents are weak enough to allow persistence of existing seagrass beds, but strong enough to interfere with successful recolonization through resuspension of fine sediments.