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Open AccessArticle

Effects of Temperature Rise on Multi-Taxa Distributions in Mountain Ecosystems

1
Biodiversity Monitoring Office, Gran Paradiso National Park, Via Pio VII 9, 10135 Turin, Italy
2
Management and Conservation of Natural Resources Office, Ossola Protected Areas, Viale Pieri 27, 28868 Varzo (VB), Italy
3
Institute of Geosciences and Earth Resources, National Research Council, Via Moruzzi 1, 56124 Pisa, Italy
*
Author to whom correspondence should be addressed.
Diversity 2020, 12(6), 210; https://doi.org/10.3390/d12060210
Received: 22 April 2020 / Revised: 18 May 2020 / Accepted: 21 May 2020 / Published: 26 May 2020
(This article belongs to the Special Issue Biodiversity of Insect)
Mountain biodiversity is associated with rare and fragile biota that are highly sensitive to climate change. To estimate the vulnerability of biodiversity to temperature rise, long-term field data are crucial. Species distribution models are an essential tool, in particular for invertebrates, for which detailed information on spatial and temporal distributions is largely missing. We applied presence-only distribution models to field data obtained from a systematic survey of 5 taxa (birds, butterflies, carabids, spiders, staphylinids), monitored in the northwestern Italian Alps. We estimated the effects of a moderate temperature increase on the multi-taxa distributions. Only small changes in the overall biodiversity patterns emerged, but we observed significant differences between groups of species and along the altitudinal gradient. The effects of temperature increase could be more pronounced for spiders and butterflies, and particularly detrimental for high-altitude species. We observed significant changes in community composition and species richness, especially in the alpine belt, but a clear separation between vegetation levels was retained also in the warming scenarios. Our conservative approach suggests that even a moderate temperature increase (about 1 °C) could influence animal biodiversity in mountain ecosystems: only long-term field data can provide the information to improve quantitative predictions, allowing us to readily identify the most informative signals of forthcoming changes. View Full-Text
Keywords: predictive models; altitudinal gradient; community composition; species richness predictive models; altitudinal gradient; community composition; species richness
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MDPI and ACS Style

Viterbi, R.; Cerrato, C.; Bionda, R.; Provenzale, A. Effects of Temperature Rise on Multi-Taxa Distributions in Mountain Ecosystems. Diversity 2020, 12, 210.

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