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Scale effects in species distribution models: implications for conservation planning under climate change
Changwan Seo1, James H Thorne, Lee Hannah
1University of Seoul, 90 Jeonnong-dong, Dongdaemun-gu, Seoul 130-743, Korea. dharmascw@uos.ac.kr
Biology Letters
|November 7, 2008
Summary
Species distribution models (SDMs) using coarse climate data overestimate suitable areas, potentially leading to flawed conservation planning. Using finer-resolution data is crucial for accurate species range predictions.
Area of Science:
- Ecology
- Conservation Biology
- Climate Change Science
Background:
- Species distribution models (SDMs) are vital for predicting species ranges under climate change.
- Global climate model (GCM) outputs, often coarse (≥50x50 km), can introduce bias into SDM predictions.
- Biologically coarse GCM grids may not accurately represent fine-scale habitat suitability.
Purpose of the Study:
- To assess how different grid sizes of climate data affect SDM performance and predicted species ranges.
- To determine if SDMs operating on coarse data reliably predict species distribution compared to finer data.
- To identify potential biases in conservation planning due to coarse-resolution climate data.
Main Methods:
- Compared four SDMs using seven different grid sizes for nine species with varying range sizes.
- Evaluated SDM performance statistics and the total area predicted as suitable.
- Analyzed spatial congruence of predicted ranges across different grid resolutions.
Main Results:
- Larger grid sizes disproportionately increased the predicted suitable area for species ranges.
- SDMs using grids >50x50 km predicted up to 2.89 times more area than those at 1 km².
- Spatial overlap of predicted ranges decreased significantly with increasing grid size, especially for mid-sized ranges.
Conclusions:
- Coarse climate data in SDMs can lead to unreliable and overly large predictions of species ranges.
- Caution is needed when using coarse GCM data for conservation planning and protected area selection.
- Finer-resolution data is essential for accurate SDM predictions and effective conservation strategies.
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