Predicting Fish Distributions in Remote Areas Using E-DNA, Satellites and Models

The lack of location data of threatened fish species can make the conservation of biodiversity difficult for land managers. This is especially true in remote places such as the North Slope of Alaska. Species Distribution Models (SDMs) are one way to predict fish distributions. To apply SDMs across landscapes we need environmental data characterizing the environmental spatial and temporal variation that could be related to species locations. As data cannot be effectively collected on the ground in the North Slope, remote sensing offers a way of characterizing the environment for these models. We characterized watershed environments using Earth Observations from a variety of platforms (i.e., measurements collected using aerial Synthetic Aperture Radar, MODIS, and LandSat satellites). Because river environments are controlled by up-stream conditions, we adapted a process of accumulating watershed environmental data for the contiguous US known as StreamCat (Hill et al. 2016) to the North Slope. The remote sensing data and the StreamCat process allowed us to measure spatial and temporal environmental variability for every stream segment across the entire North Slope. We saw several interesting patterns of inter-year & spatial trends. This includes noting that land surface temperature was warmer at lower latitudes and higher elevation than at higher latitudes. This approach helps us understand the arctic landscape and minimize the effects of oil and gas development on biodiversity across the North Slope