Soft range limits shape sensitivity to forest cover more strongly than hard range limits

Land-use change is a major threat to biodiversity, yet there remains considerable unexplained variation in how it affects different populations of the same species. Here, we examine how sensitivity to forest cover changes depending on proximity to different limits of a species’ range. By comparing responses as species approach their coastal (‘hard’) and inland (‘soft’) range limits, we aim to provide insight into the relative influence of mass effects, as compared to abiotic and biotic environmental suitability in shaping population sensitivity. We combine data from several large databases to obtain a dataset of 2,543 bird species surveyed across 116 studies, spanning six continents. Using expert-verified range maps, we calculated the position of populations relative to their species’ nearest inland (‘soft’) and coastal (‘hard’) range limits and categorized the inland limits as equatorward- or poleward-facing. We investigated how distance to range limits and forest cover, derived from a..., , # Soft range limits shape sensitivity to forest cover more strongly than hard range limits

Dataset DOI: [10.5061/dryad.m905qfvd2](10.5061/dryad.m905qfvd2)

## Description of the data and file structure

\"RangeLimits_Scripts.zip\": Contains all R scripts used to process data, conduct analyses, and create figures.

\"Data_RangeLimits_Granvilleetal2025_GEB.xlsx\": Contains the explanatory variables used in the statistical models. The table shows site-level records for different species from the different databases and the associated study, with associated geographic and directional metrics. Each row represents a different sampling site.

**Raw data availability:**

The PREDICTS data can be downloaded from: [https://data.nhm.ac.uk/dataset/the-2016-release-of-the-predicts-database-v1-1](https://data.nhm.ac.uk/dataset/the-2016-release-of-the-predicts-database-v1-1) and [https://data.nhm.ac.uk/dataset/release-of-data-added-to-the-predicts-database-november-2022](https://data.nhm.ac.uk/dataset/re...,