Haider, S.M., Romañach, S.S., Benscoter, A.M., D'Acunto, L.E., and Pearlstine, L.G., 2021, EverSparrow model scripts and outputs: U.S. Geological Survey data release, https://doi.org/10.5066/P9VNZH7I.
Summary
EverSparrow is a spatially explicit Bayesian model of Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis; CSSS) presence that quantifies the interdependent relationships between a range of environmental factors and CSSS presence. Using hydrologic conditions such as mean 4-year hydroperiod and maximum depth, fire occurrence history, and vegetation structure throughout the range of CSSS, EverSparrow provides weekly probabilities of CSSS presence on a 400 x 400 m grid. Here we provide the modeling scripts used to develop EverSparrow (including the frequentist model developed and included in the ensemble model), the validation scripts, a script to run the EverSparrow model and create predicted surfaces of CSSS probability of presence, [...]
Summary
EverSparrow is a spatially explicit Bayesian model of Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis; CSSS) presence that quantifies the interdependent relationships between a range of environmental factors and CSSS presence. Using hydrologic conditions such as mean 4-year hydroperiod and maximum depth, fire occurrence history, and vegetation structure throughout the range of CSSS, EverSparrow provides weekly probabilities of CSSS presence on a 400 x 400 m grid. Here we provide the modeling scripts used to develop EverSparrow (including the frequentist model developed and included in the ensemble model), the validation scripts, a script to run the EverSparrow model and create predicted surfaces of CSSS probability of presence, and a netcdf showing predicted probability of presence from 1995 through 2020. For full details of the model development process, see Larger Work citation.
EverSparrow was developed as a decision support tool for natural resource managers working to restore the Everglades landscape. It can be run with models of hydrologic restoration (such as the Regional Simulation Model) to compare alternative water management scenarios in restoration planning.
