The role of climate data resolution on species distribution models

Species distribution models use climate data at varying spatial scales, but how may this impact their performance?

Data resources used via GBIF : 35,138 species occurrences
White leadtree (Leucaena leucocephala)

White leadtree (Leucaena leucocephala), one of the nine invasive species modelled in the study. Photo by Tony Iwane licensed under CC BY-NC 4.0.

Species distribution models (SDMs) can provide valuable information on the potential range of species and are particularly useful for predicting suitability for invasive species and thus potential spread in present day and future climate scenarios. The scale of climate data used is such models, however, may affect the outcome.

Using invasive trees as a case study, researchers addressed the role of spatial scale of climate data on species distribution models. By combining GBIF-mediated occurrences of nine invasive species with bioclimatic variables at three different resolutions, they built SDMs and compare the performance of the models and evaluate the impact of spatial scale.

In a general measure of model performance, the SDMs appear to perform well regardless of resolution. However, the average distribution probabilities increased significantly with resolution, and the most important climatic variable differed depending on resolution.

These findings suggest that the spatial scale of the climate data impacts the ability of SDMs to provide accurate predictions.

Citations

Wan J-Z, Wang C-J and Yu F-H (2016) Impacts of the spatial scale of climate data on the modeled distribution probabilities of invasive tree species throughout the world. Ecological Informatics. Elsevier BV 36: 42–49. Available at: https://doi.org/10.1016/j.ecoinf.2016.10.001.

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