Année de publication
2025

Journal

Global Change Biology
Volume
31
Ticket
6
DOI
10.1111/gcb.70256
Numéro ISSN
1354-1013, 1365-2486
Catégorie HCERES
ACL - Articles dans des revues internationales ou nationales avec comité de lecture répertoriées par l'HCERES ou dans les bases de données internationales
Résumé

To predict the spatial responses of biodiversity to climate change, studies typically rely on species‐specific approaches, such as species distribution models. In this study, we propose an alternative methodology that investigates the collective response of species groups by modelling biogeographical regions. Biogeographical regions are areas defined by homogeneous species compositions and separated by barriers to dispersal. When climate acts as such a barrier, species within the same region are expected to respond similar to changing climatic conditions, enabling the prediction of entire region shifts in response to future climate scenarios. We applied this approach to the Southern Ocean, which exhibits sharp climatic transitions known as oceanic fronts, focusing on the mesopelagic lanternfishes (family Myctophidae). We compiled occurrence data for 115 lanternfish species from 1950 onwards and employed a network‐based analysis to identify two major biogeographical regions: a southern and a subtropical region. These regions were found to be distinct, with minimal overlap in species distributions along the temperature gradient and a separation around 8°C, indicating that temperature likely acts as a climatic barrier. Using an ensemble modelling approach, we projected the response of these regions to future temperature changes under various climate scenarios. Our results suggest a circumpolar expansion of the subtropical region and a contraction of the southern region, with the Southern Ocean becoming a cul‐de‐sac for southern species. Ultimately, our results suggest that when support is found for the climatic barrier hypothesis, community‐level models from a ‘group first, then predict’ strategy may effectively predict future shifts in species assemblages.