A framework for assessing the habitat correlates of spatially explicit population trends

Aim. Halting widespread biodiversity loss will require detailed information on species’ trends and the habitat conditions correlated with population declines. However, constraints on conventional monitoring programs and commonplace approaches for trend estimation can make it difficult to obtain such information across species’ ranges. Here, we demonstrate how recent developments in machine learning and model interpretation, combined with data sources derived from participatory science, enable landscape-scale inferences on the habitat correlates of population trends across broad spatial extents.
Location. Worldwide, with a case study in the western United States.
Methods. We used interpretable machine learning to understand the relationships between land cover and spatially explicit bird population trends. Using a case study with three passerine birds in the western U.S. and spatially explicit trends derived from eBird data, we explore the potential impacts of simulated land cover modifi..., , , # A framework for assessing the habitat correlates of spatially explicit population trends

[https://doi.org/10.5061/dryad.8pk0p2nzf](https://doi.org/10.5061/dryad.8pk0p2nzf)

## Description of the data and file structure

This file contains information and explanation for the data and code that accompany the following project:

Stillman, A.N., C.L. Davis, K.D. Dunham, V. Ruiz-Gutierrez, A.D. Rodewald, A. Johnston, T. Auer, M. Strimas-Mackey, S. Ligocki, and D. Fink. 2025. A framework for assessing the habitat correlates of spatially explicit population trends. Diversity and Distributions.

This .README file accompanies the archived data for this project which are necessary to run the case study in the manuscript. Scripts for the case study analysis are available from Zenodo along with supplemental results files. 

#### Data (Dryad)

All data files necessary to run the analysis in this repository. Files include land cover descriptions for 2007, land cover descriptions for 2021, eBird T...,