Fine-Grain Predictions Are Key to Accurately Represent Continental-Scale Biodiversity Patterns

Abstract

As global change accelerates, accurate predictions of species distributions and biodiversity patterns are critical to limit biodiversity loss. Numerous studies have found that coarse-grain species distribution models (SDMs) perform poorly relative to fine-grain models because they mismatch environmental information with observations. However, it remains unclear how grain-size biases vary in intensity across space and time, possibly generating inaccurate predictions for specific regions, seasons or species. For example, coarse-grain biases may intensify in patchy, discontinuous landscapes. Such biases may accumulate to produce highly misleading estimates of continental and seasonal biodiversity patterns.