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 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.

Funding provided by: Leon Levy FoundationROR ID: https://ror.org/033hnyq61Award Number:
Funding provided by: Wolf Creek FoundationCrossref Funder Registry ID: Award Number:
Funding provided by: U.S. National Science FoundationROR ID: https://ror.org/021nxhr62Award Number:
Funding provided by: Cornell Atkinson Center for SustainabilityROR ID: https://ror.org/03h0qhk21Award Number: