Fine-grain predictions are key to accurately represent continental-scale biodiversity patterns

Aim
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.
Location: United States and Canada
Time Period: 2004-2021
Major taxa studied: Birds (Aves)
Methods
We fit presence-absence SDMs characterizing the summer and winter distributions of 572 bird species native to the US and Canada across five spatial grains from 1 to 50 km, using obse..., , , # Fine-grain predictions are key to accurately represent continental-scale biodiversity patterns

[https://doi.org/10.5061/dryad.mw6m9065c](https://doi.org/10.5061/dryad.mw6m9065c)

## Description of the data and file structure

## ## DATA FILES

### ebird\_prebreeding\_migration\_CEA.csv

### ebird\_nonbreeding\_CEA.csv

### ebird\_postbreeding\_migration\_CEA.csv

### ebird\_breeding\_CEA.csv

Processed, filtered eBird data for March-May, December-February, September-November, or June-August (respectively, in order of files listed) with spatially linked environmental covariates (landcover, climate, and topographic variables) at all resolutions (1, 3, 5, 10, 50km, as suffix of each variable), as well as checklist-level temporal and effort covariates. eBird data is structured with rows for each checklist (sampling event) and columns for presence (1) or absence (0) for every species. Cells containing NA have no associated environmental information available at a given location.

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