Species ranges are changing in response to human-related disturbances and often management and conservation decisions must be based on incomplete information. In this context, species distribution models (SDMs) represent the most widely used tool, but they often lack any reference to demographic performance of the population under study, spatial structure of the habitat patches, or connectivity at the landscape level. Combining a multi-state SDM with a landscape pattern analysis and a mortality model, we developed a spatially-explicit, integrated model to assist and inform conservation planning for the Apennine brown bear in central Italy. We identified 15 critical habitat areas, potentially hosting 76 adult female bears. Many of these areas are, however, characterized by high levels of human-related mortality, making them attractive sink-like areas. Structural connectivity was higher in the northern part of the study area while only limited connectivity characterizes the core area, where most of the bears currently live. Our integrated model indicates that the conservation of this relict and isolated bear population is a realistic conservation goal, as we estimated that 192–270 bears could live across the Apennines. Our modelling framework enhances the biological realism of traditional SDMs and provides a conservation planning tool that integrates habitat suitability, mortality risk (as a component of the total demographic performance) and structural connectivity among habitat patches at the landscape scale. It is particularly suited for endangered species living in a human-modified landscapes where establishing a realistic and spatially explicit conservation goal would facilitate pro-active management.
Combining multi-state species distribution models, mortality estimates, and landscape connectivity to model potential species distribution for endangered species in human dominated landscapes / Maiorano, Luigi; Chiaverini, Luca; Falco, Matteo; Ciucci, Paolo. - In: BIOLOGICAL CONSERVATION. - ISSN 0006-3207. - 237:(2019), pp. 19-27. [10.1016/j.biocon.2019.06.014]
Combining multi-state species distribution models, mortality estimates, and landscape connectivity to model potential species distribution for endangered species in human dominated landscapes
Maiorano, Luigi
;Chiaverini, Luca;Falco, Matteo;Ciucci, Paolo
2019
Abstract
Species ranges are changing in response to human-related disturbances and often management and conservation decisions must be based on incomplete information. In this context, species distribution models (SDMs) represent the most widely used tool, but they often lack any reference to demographic performance of the population under study, spatial structure of the habitat patches, or connectivity at the landscape level. Combining a multi-state SDM with a landscape pattern analysis and a mortality model, we developed a spatially-explicit, integrated model to assist and inform conservation planning for the Apennine brown bear in central Italy. We identified 15 critical habitat areas, potentially hosting 76 adult female bears. Many of these areas are, however, characterized by high levels of human-related mortality, making them attractive sink-like areas. Structural connectivity was higher in the northern part of the study area while only limited connectivity characterizes the core area, where most of the bears currently live. Our integrated model indicates that the conservation of this relict and isolated bear population is a realistic conservation goal, as we estimated that 192–270 bears could live across the Apennines. Our modelling framework enhances the biological realism of traditional SDMs and provides a conservation planning tool that integrates habitat suitability, mortality risk (as a component of the total demographic performance) and structural connectivity among habitat patches at the landscape scale. It is particularly suited for endangered species living in a human-modified landscapes where establishing a realistic and spatially explicit conservation goal would facilitate pro-active management.File | Dimensione | Formato | |
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