Varying dataset resolution alters predictive accuracy of spatially explicit ensemble models for avian species distribution
| Autor: | Jeremy D. Ross, Claire M Curry, Eli S. Bridge, Andrea Contina |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
010504 meteorology & atmospheric sciences Scale (ratio) Computer science grassland birds Species distribution 010603 evolutionary biology 01 natural sciences Statistics spatiotemporal exploratory models Image resolution Ecology Evolution Behavior and Systematics 0105 earth and related environmental sciences Nature and Landscape Conservation Original Research landscape ecology Ecology Ensemble forecasting Pixel Oklahoma 15. Life on land data resolution Random forest machine learning Landscape ecology Predictive modelling random forest |
| Zdroj: | Ecology and Evolution |
| ISSN: | 2045-7758 |
| Popis: | 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 nonheterogeneity may depend on the spatial resolution of the examined dataset. Managers should be cautious if any regional differences are expected when developing policy from range‐wide results that show a single model or timeframe. We recommend use of standard species distribution models or other types of nonspatially explicit ensemble models for local species prediction models. Further study is necessary to understand at what point SEEMs become necessary with varying dataset resolutions. |
| Databáze: | OpenAIRE |
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