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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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Similarity in spatial structure constrains ecosystem relationships: Building a macroscale understanding of lakes
Abstract AimWe aimed to measure the dominant spatial patterns in ecosystem properties (such as nutrients and measures of primary production) and the multi‐scaled geographical driver variables of these properties and to quantify how the spatial structure of pattern in all of these variables influences the strength of relationships among them. Location and time periodWe studied > 8,500 lakes in a 1.8 million km2area of Northeast U.S.A. Data comprised 10‐year medians (2002–2011) for measured ecosystem properties, long‐term climate averages and recent land use/land cover variables. Major taxa studiedWe focused on ecosystem properties at the base of aquatic food webs, including concentrations of nutrients and algal pigments that are proxies of primary productivity. MethodsWe quantified spatial structure in ecosystem properties and their geographical driver variables using distance‐based Moran eigenvector maps (dbMEMs). We then compared the similarity in spatial structure for all pairs of variables with the correlation between variables to illustrate how spatial structure constrains relationships among ecosystem properties. ResultsThe strength of spatial structure decreased in order for climate, land cover/use, lake ecosystem properties and lake and landscape morphometry. Having a comparable spatial structure is a necessary condition to observe a strong relationship between a pair of variables, but not a sufficient one; variables with very different spatial structure are never strongly correlated. Lake ecosystem properties tended to have an intermediary spatial structure compared with that of their main drivers, probably because climate and landscape variables with known ecological links induce spatial patterns. Main conclusionOur empirical results describe inherent spatial constraints that dictate the expected relationships between ecosystem properties and their geographical drivers at macroscales. Our results also suggest that understanding the spatial scales at which ecological processes operate is necessary to predict the effects of multi‐scaled environmental changes on ecosystem properties.
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- PAR ID:
- 10075369
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Global Ecology and Biogeography
- Volume:
- 27
- Issue:
- 10
- ISSN:
- 1466-822X
- Format(s):
- Medium: X Size: p. 1251-1263
- Size(s):
- p. 1251-1263
- Sponsoring Org:
- National Science Foundation
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