Bridging the Gap Between Lagrangian and Eulerian Species Distribution Models for Abundance Estimation—A Simulation Experiment DOI Creative Commons
Charlotte Lambert, Anne‐Sophie Bonnet‐Lebrun, David Grémillet

et al.

Journal of Biogeography, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 5, 2025

ABSTRACT Aim In mobile species, individual movement decisions based on biotic and abiotic conditions determine how individuals interact with the environment, heterospecifics conspecifics. Accordingly, these underpin all ecological principles structure broader spatial patterns at population species level. Species distribution models (SDMs) are therefore paramount in ecology, implications for both fundamental applied studies. There many robust SDM techniques, from individual‐scale (Lagrangian) to population‐scale (Eulerian) models. Their outputs routinely support wildlife management, conservation, or risk assessments. Yet, it remains unclear whether SDMs built scales infer same processes, distributions they predict comparable. Here, we address this key question a simulation exercise. Location Virtual environment. Taxon species. Methods First, simulated movements of two highly one central‐place forager free ranger. Second, surveyed individual‐scale, replicating Lagrangian studies by tracking movements, population‐scale, Eulerian surveys censusing study area standardised protocols. The resulting data were analysed following well‐established statistical methods assess abundance distribution. We used Resource Selection Functions (RSFs) Density Surface Models (DSMs) data. Results Main Conclusions Both adequately estimated species' relationship environmental conditions. Although some fine‐scale differences occurred, perspectives yielded correlated (correlations 0.8–1.0 between pairs models), successfully predicted true 0.6–0.7 distribution). Our results demonstrate that statistically consistent directly comparable, which is great importance conservation science. This provides crucial guidance combination predictions model types inform planning within wide range management contexts.

Language: Английский

Bridging the Gap Between Lagrangian and Eulerian Species Distribution Models for Abundance Estimation—A Simulation Experiment DOI Creative Commons
Charlotte Lambert, Anne‐Sophie Bonnet‐Lebrun, David Grémillet

et al.

Journal of Biogeography, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 5, 2025

ABSTRACT Aim In mobile species, individual movement decisions based on biotic and abiotic conditions determine how individuals interact with the environment, heterospecifics conspecifics. Accordingly, these underpin all ecological principles structure broader spatial patterns at population species level. Species distribution models (SDMs) are therefore paramount in ecology, implications for both fundamental applied studies. There many robust SDM techniques, from individual‐scale (Lagrangian) to population‐scale (Eulerian) models. Their outputs routinely support wildlife management, conservation, or risk assessments. Yet, it remains unclear whether SDMs built scales infer same processes, distributions they predict comparable. Here, we address this key question a simulation exercise. Location Virtual environment. Taxon species. Methods First, simulated movements of two highly one central‐place forager free ranger. Second, surveyed individual‐scale, replicating Lagrangian studies by tracking movements, population‐scale, Eulerian surveys censusing study area standardised protocols. The resulting data were analysed following well‐established statistical methods assess abundance distribution. We used Resource Selection Functions (RSFs) Density Surface Models (DSMs) data. Results Main Conclusions Both adequately estimated species' relationship environmental conditions. Although some fine‐scale differences occurred, perspectives yielded correlated (correlations 0.8–1.0 between pairs models), successfully predicted true 0.6–0.7 distribution). Our results demonstrate that statistically consistent directly comparable, which is great importance conservation science. This provides crucial guidance combination predictions model types inform planning within wide range management contexts.

Language: Английский

Citations

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