DOI: 10.5061/dryad.8pk0p2nzf
title: A framework for assessing the habitat correlates of spatially explicit population trends

description: 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
 modification while evaluating potential co-benefits among species.Results.
 Our analysis revealed complex, non-linear relationships between land cover
 variables and species’ population trends as well as substantial
 interspecific variation in those relationships. Areas with the most
 positive impacts from a simulated land cover modification overlapped for
 two species, but these changes had little effect on the third species.
 Main conclusions. This framework can help conservation practitioners
 identify important relationships between species trends and habitat while
 also highlighting areas where potential modifications to the landscape
 could bring the biggest benefits. The analysis is transferrable to
 hundreds of species worldwide with spatially explicit trend estimates,
 allowing inference across multiple species at scales which are tractable
 for management to combat species declines. 
description: # 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 Trend estimates spanning 2007-2021 for three species, and eBird Status estimates of relative abundance for 2021 for three species. #### Code (Zenodo) Three R scripts necessary to complete the case study analysis. Files include a script to run the GAM models, a script to plot the results, and a script to simulate the potential effects of land cover change on bird population trends. #### Results (Zenodo) This folder structure is necessary to receive and store results from the scripts. Outputs in the ROI_example folder are provided here, and model results can be generated using the provided scripts labeled 1 through 3.  ### Files and variables #### File: landcover\_2007.csv **Description:** Land cover values corresponding to the year 2007. srd_id = Unique ID for each pixel in the study area. The study area covers the full geographic range of all three case study species (Brewer's sparrow, sage thrasher, and sagebrush sparrow). latitude = Latitude of pixel. longitude = Longitude of pixel. elevation = Elevation of pixel. Remaining columns (from Barren.PLAND to Herbaceous.Wetlands.PLAND) give the percent cover of each land cover type within the pixel. See appendices accompanying the publication for a list of data sources. #### File: landcover\_2021.csv **Description:** Land cover values corresponding to the year 2021. srd_id = Unique ID for each pixel in the study area. The study area covers the full geographic range of all three case study species (Brewer's sparrow, sage thrasher, and sagebrush sparrow). latitude = Latitude of pixel. longitude = Longitude of pixel. elevation = Elevation of pixel. Remaining columns (from Barren.PLAND to Herbaceous.Wetlands.PLAND) give the percent cover of each land cover type within the pixel. See appendices accompanying the publication for a list of data sources. #### File: trend\_reps\_wide\_2021.csv **Description:** Spatially explicit trend estimates in units of PPY (percent per year population change) from 2007 to 2021. For each of the three case study species (Brewer's sparrow, sage thrasher, and sagebrush sparrow), the dataset includes 100 replicates of trend estimates in each pixel. species = species code. "brespa" Brewer's sparrow, "sagthr" sage Thrasher, "sagspa1" sagebrush sparrow. fold = ID of the trend replicate ranging from 1-100. Remaining columns give the srd_id values, which are unique IDs for each pixel in the study area. #### File: abundance\_wide\_2021.csv **Description:** Spatially explicit relative abundance estimates from eBird Status for three case study species (Brewer's sparrow, sage thrasher, and sagebrush sparrow). Columns give the srd_id values, which are unique IDs for each pixel in the study area. Row 1 = Brewer's sparrow Row 2 = sage thrasher Row 3 = sagebrush sparrow. ## Code/software This analysis was run using R version 4.4.0. Software versions for R packages are recorded in the references section of the manuscript. \ R Core Team. (2024). R: A language and environment for statistical computing. (Version 4.4.0) \[Computer software]. R Foundation for Statistical Computing. [https://www.R-project.org/](https://www.R-project.org/) ### 1\_GAM\_and\_predict This script uses GAM models to uncover the correlates of eBird population trends for 3 species. Step 1. Prepare predictors and data for GAM models Step 2. Loop over species: fit a GAM to each trend replicate, predict to 2021 land cover, and export datasets for plotting ### 2\_Mapping This script produces plots to visualize results from the manuscript Step 1. Create map of species abundance Step 2. Create map of species trend Step 3. Create map of % land cover, geographic effect, & effect on abundance for each predictor Step 4. Create 1D effect plot for each predictor ### 3\_Land\_cover\_modification This script conducts a land cover modification scenario using 2021 land cover values Step 1. Loop through all species and predict to modified predictor surface Step 2. Write functions for results Step 3. Compare between species and export datasets Step 4. Mapping