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ABSTRACT AimHalting 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. LocationWorldwide, with a case study in the western United States. MethodsWe 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. ResultsOur 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 ConclusionsThis 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 transferable to hundreds of species worldwide with spatially explicit trend estimates, allowing inference across multiple species at scales that are tractable for management to combat species declines.
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This content will become publicly available on May 1, 2026
Lost in Space: When Spatial Scale Terms Blur Actual Study Size in Plant Community Ecology
ABSTRACT QuestionsThe detection and interpretation of ecological processes are strongly influenced by the spatial scale at which studies are conducted. Scale terms (e.g., ‘local’ or ‘regional’) are frequently used to denote study scale and imply that studies using the same scale term should be directly comparable. However, whether the area encompassed by a particular scale term is consistent across studies remains unclear. LocationGlobal. MethodsWe reviewed 385 papers in plant community ecology and analysed 962 spatial scale terms and their reported areas. We tested whether variation in the use of individual scale terms could be explained by habitat, type of study or geographic region, and virtually sampled a simulated plant community to demonstrate the consequences of this variation for calculating common biodiversity metrics. ResultsSingle scale terms covered areas that vary by an average of 4.7 orders of magnitude, with significant overlap between distinct scale terms. Though this variation could be partly explained by habitat type (e.g., scale terms cover larger areas in forests than grasslands), we still found large variability (3.8 orders of magnitude) in the use of single terms within habitats. We also found overall high consistency (but still high variability) in the use of scale terms across geographic regions and study types. Our community simulation showed that Shannon's and Simpson's indices are highly sensitive to this variation, especially at finer spatial scales, suggesting that variation in the use of individual scale terms has major consequences for synthesising biodiversity trends. ConclusionsWhile terminology can make it appear that studies are directly comparable, they may cover vastly different areas and capture different ecological processes. Spatial scales should be reported in a standardised fashion by clearly stating the actual study size in abstracts and methods, and inconsistencies in scale term use should be accounted for when synthesising previous research.
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- Award ID(s):
- 1831944
- PAR ID:
- 10659622
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Journal of Vegetation Science
- Volume:
- 36
- Issue:
- 3
- ISSN:
- 1100-9233
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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