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.