Understanding habitat selection of range‐expanding populations of large carnivores: 20 years of grey wolves ( Canis lupus) recolonizing Germany
Abstract Aim The non‐stationarity in habitat selection of expanding populations poses a significant challenge for spatial forecasting. Focusing on the grey wolf ( Canis lupus ) natural recolonization of Germany, we compared the performance of different distribution modelling approaches for predictin...
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crwiley:10.1111/ddi.13789 2024-10-13T14:06:32+00:00 Understanding habitat selection of range‐expanding populations of large carnivores: 20 years of grey wolves ( Canis lupus) recolonizing Germany Planillo, Aimara Wenzler‐Meya, Moritz Reinhardt, Ilka Kluth, Gesa Michler, Frank‐Uwe Stier, Norman Louvrier, Julie Steyer, Katharina Gillich, Benjamin Rieger, Siegfried Knauer, Felix Kuemmerle, Tobias Kramer‐Schadt, Stephanie Bundesamt für Naturschutz Deutsche Forschungsgemeinschaft Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz 2023 http://dx.doi.org/10.1111/ddi.13789 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ddi.13789 en eng Wiley http://creativecommons.org/licenses/by/4.0/ Diversity and Distributions volume 30, issue 1, page 71-86 ISSN 1366-9516 1472-4642 journal-article 2023 crwiley https://doi.org/10.1111/ddi.13789 2024-09-23T04:37:04Z Abstract Aim The non‐stationarity in habitat selection of expanding populations poses a significant challenge for spatial forecasting. Focusing on the grey wolf ( Canis lupus ) natural recolonization of Germany, we compared the performance of different distribution modelling approaches for predicting habitat suitability in unoccupied areas. Furthermore, we analysed whether grey wolf showed non‐stationarity in habitat selection in newly colonized areas, which will impact the predictions for potential habitat. Location Germany. Methods Using telemetry data as presence points, we compared the predictive performance of five modelling approaches based on combinations of distribution modelling algorithms—GLMM, MaxEnt and ensemble modelling—and two background point selection strategies. We used a homogeneous Poisson point process to draw background points from either the minimum convex polygons derived from telemetry or the whole area known to be occupied by wolves. Models were fit to the data of the first years and validated against independent data representing the expansion of the species. The best‐performing approach was then used to further investigate non‐stationarity in the species' response in spatiotemporal restricted datasets that represented different colonization steps. Results While all approaches performed similarly when evaluated against a subset of the data used to fit the models, the ensemble model based on integrated data performed best when predicting range expansion. Models for subsequent colonization steps differed substantially from the global model, highlighting the non‐stationarity of wolf habitat selection towards human disturbance during the colonization process. Main Conclusions While telemetry‐only data overfitted the models, using all available datasets increased the reliability of the range expansion forecasts. The non‐stationarity in habitat selection pointed to wolves settling in the best areas first, and filling in nearby lower‐quality habitat as the population increases. Our results ... Article in Journal/Newspaper Canis lupus Wiley Online Library PLOS ONE 17 10 e0265293 |
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Wiley Online Library |
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crwiley |
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English |
description |
Abstract Aim The non‐stationarity in habitat selection of expanding populations poses a significant challenge for spatial forecasting. Focusing on the grey wolf ( Canis lupus ) natural recolonization of Germany, we compared the performance of different distribution modelling approaches for predicting habitat suitability in unoccupied areas. Furthermore, we analysed whether grey wolf showed non‐stationarity in habitat selection in newly colonized areas, which will impact the predictions for potential habitat. Location Germany. Methods Using telemetry data as presence points, we compared the predictive performance of five modelling approaches based on combinations of distribution modelling algorithms—GLMM, MaxEnt and ensemble modelling—and two background point selection strategies. We used a homogeneous Poisson point process to draw background points from either the minimum convex polygons derived from telemetry or the whole area known to be occupied by wolves. Models were fit to the data of the first years and validated against independent data representing the expansion of the species. The best‐performing approach was then used to further investigate non‐stationarity in the species' response in spatiotemporal restricted datasets that represented different colonization steps. Results While all approaches performed similarly when evaluated against a subset of the data used to fit the models, the ensemble model based on integrated data performed best when predicting range expansion. Models for subsequent colonization steps differed substantially from the global model, highlighting the non‐stationarity of wolf habitat selection towards human disturbance during the colonization process. Main Conclusions While telemetry‐only data overfitted the models, using all available datasets increased the reliability of the range expansion forecasts. The non‐stationarity in habitat selection pointed to wolves settling in the best areas first, and filling in nearby lower‐quality habitat as the population increases. Our results ... |
author2 |
Bundesamt für Naturschutz Deutsche Forschungsgemeinschaft Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz |
format |
Article in Journal/Newspaper |
author |
Planillo, Aimara Wenzler‐Meya, Moritz Reinhardt, Ilka Kluth, Gesa Michler, Frank‐Uwe Stier, Norman Louvrier, Julie Steyer, Katharina Gillich, Benjamin Rieger, Siegfried Knauer, Felix Kuemmerle, Tobias Kramer‐Schadt, Stephanie |
spellingShingle |
Planillo, Aimara Wenzler‐Meya, Moritz Reinhardt, Ilka Kluth, Gesa Michler, Frank‐Uwe Stier, Norman Louvrier, Julie Steyer, Katharina Gillich, Benjamin Rieger, Siegfried Knauer, Felix Kuemmerle, Tobias Kramer‐Schadt, Stephanie Understanding habitat selection of range‐expanding populations of large carnivores: 20 years of grey wolves ( Canis lupus) recolonizing Germany |
author_facet |
Planillo, Aimara Wenzler‐Meya, Moritz Reinhardt, Ilka Kluth, Gesa Michler, Frank‐Uwe Stier, Norman Louvrier, Julie Steyer, Katharina Gillich, Benjamin Rieger, Siegfried Knauer, Felix Kuemmerle, Tobias Kramer‐Schadt, Stephanie |
author_sort |
Planillo, Aimara |
title |
Understanding habitat selection of range‐expanding populations of large carnivores: 20 years of grey wolves ( Canis lupus) recolonizing Germany |
title_short |
Understanding habitat selection of range‐expanding populations of large carnivores: 20 years of grey wolves ( Canis lupus) recolonizing Germany |
title_full |
Understanding habitat selection of range‐expanding populations of large carnivores: 20 years of grey wolves ( Canis lupus) recolonizing Germany |
title_fullStr |
Understanding habitat selection of range‐expanding populations of large carnivores: 20 years of grey wolves ( Canis lupus) recolonizing Germany |
title_full_unstemmed |
Understanding habitat selection of range‐expanding populations of large carnivores: 20 years of grey wolves ( Canis lupus) recolonizing Germany |
title_sort |
understanding habitat selection of range‐expanding populations of large carnivores: 20 years of grey wolves ( canis lupus) recolonizing germany |
publisher |
Wiley |
publishDate |
2023 |
url |
http://dx.doi.org/10.1111/ddi.13789 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ddi.13789 |
genre |
Canis lupus |
genre_facet |
Canis lupus |
op_source |
Diversity and Distributions volume 30, issue 1, page 71-86 ISSN 1366-9516 1472-4642 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1111/ddi.13789 |
container_title |
PLOS ONE |
container_volume |
17 |
container_issue |
10 |
container_start_page |
e0265293 |
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1812812721568088064 |