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Carabids data of Pterostichus flavofemoratus and Carabus depressus in the Gran Paradiso National Park (2006, 2007, 2012, 2013) - MaRDI portal

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Carabids data of Pterostichus flavofemoratus and Carabus depressus in the Gran Paradiso National Park (2006, 2007, 2012, 2013) (Q6685708)

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Dataset published at Zenodo repository.
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English
Carabids data of Pterostichus flavofemoratus and Carabus depressus in the Gran Paradiso National Park (2006, 2007, 2012, 2013)
Dataset published at Zenodo repository.

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    Understanding risks to biodiversity requires predictions of the spatial distribution of species adapting to changing ecosystems and, to that end, earth observations integrating field surveys prove essential as they provide key figures for assessing landscape-wide biodiversity scenarios. Here, we develop, and apply to a relevant case study, a method suited to merge earth/field observations with spatially explicit stochastic metapopulation models to study the near-term ecological dynamics of target species in complex terrains. Our framework incorporates the use of species distribution models for a reasoned estimation of the initial presence of the target species, and accounts for imperfect and incomplete detection of the species presence in the study area. It also uses a metapopulation fitness function derived from earth observation data subsuming the ecological niche of the target species. This framework is applied to contrast occupancy of two species of carabids (Pterostichus flavofemoratus, Carabus depressus) observed  in the context of a large ecological monitoring program carried out within the Gran Paradiso National Park (GPNP, Italy). Results suggest that the proposed framework may indeed exploit the hallmarks of spatially explicit ecological approaches and of remote Earth observations. The model reproduces well the observed in-situ data. Moreover, it projects in the near-term the two species' presence both in space and time, highlighting the features of the metapopulation dynamics of colonization and extinction, and their expected trends within verifiable timeframes.
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    27 May 2020
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