Data from: Varying dataset resolution alters predictive accuracy of spatially explicit ensemble models for avian species distribution

Species distribution models can be made more accurate by use of new
“Spatiotemporal Exploratory Models” (STEMs), a type of spatially explicit
ensemble model (SEEM) developed at the continental scale that averages
regional models pixel by pixel. Although SEEMs can generate more accurate
predictions of species distributions, they are computationally expensive.
We compared the accuracies of each model for 11 grassland bird species,
and examined whether they improve accuracy at a statewide scale for fine
and coarse predictor resolutions. We used a combination of survey data and
citizen science data for 11 grassland bird species in Oklahoma to test a
spatially explicit ensemble model at a smaller scale for its effects on
accuracy of current models. We found that only four species performed best
with either a statewide model or SEEM; the most accurate model for the
remaining seven species varied with data resolution and performance
measure. Policy implications: Determination of non-heterogeneity may
depend on the spatial resolution of the examined dataset. Managers should
be cautious if any regional differences are expected when developing
policy from rangewide results that show a single model or timeframe. We
recommend use of standard species distribution models or other types of
non-spatially explicit ensemble models for local species prediction
models. Further study is necessary to understand at what point SEEMs
become necessary with varying dataset resolutions.