Earth and field observations underpin metapopulation dynamics in complex landscapes: Near-term study on carabids

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Abstract

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 numbers 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 in time, highlighting the features of the metapopulation dynamics of colonization and extinction, and their expected trends within verifiable timeframes.

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Giezendanner, J., Pasetto, D., Perez-Saez, J., Cerrato, C., Viterbi, R., Terzago, S., … Rinaldo, A. (2020). Earth and field observations underpin metapopulation dynamics in complex landscapes: Near-term study on carabids. Proceedings of the National Academy of Sciences of the United States of America, 117(23), 12877–12884. https://doi.org/10.1073/pnas.1919580117

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