Trait mediation explains decadal distributional shifts for a wide range of insect taxa

Bourhis, Yoann; Milne, Alice E.; Shortall, Chris R.; Beckman, Björn; Blumgart, Dan; Edwards, Rowan; Evans, Luke C.; Foster, Chris W.; Fox, Richard; Botham, Marc S.; Rowland, Clare; Roberts, Stuart; Speight, Martin C.D.; Hassall, Chris; Kunin, William E.; Bell, James R.

Trait mediation explains decadal distributional shifts for a wide range of insect taxa

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Official URL: https://doi.org/10.1038/s41467-025-63093-y

Abstract/Summary

Shifts in insect distributions have been reported globally, largely attributed to climate and landscape changes. Communities are being reshaped, with species response traits mediating the effects of changing environments. Using a machine-learning approach we model 1252 insect occupancies across three decades in Great Britain. We combine independent models of nine insect groups (butterflies, moths, odonates, orthopterans, carabids, ladybirds, bees, wasps and hoverflies) to take a high-level view of the trends and key environmental drivers of insect occupancy, as well as to highlight the trait mediations underlying the resulting niches. Across this wide taxonomic range, we identify common trends in insect occupancies, showing no Great Britain-wide decline since 1990, but instead local declines and changes in community compositions. Known drivers of biodiversity loss appear to underlie those changes, notably urban sprawl and landscape simplification. Our approach also highlights the crucial roles of two response traits: habitat breadth, in mediating the effects of changing landscapes diversity and voltinism, in mediating the effects of increasing temperatures on insect life cycles.

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1038/s41467-025-63093-y

UKCEH and CEH Sections/Science Areas:

Biodiversity and Land Use (2025-)

ISSN:

2041-1723

Additional Information:

Open Access paper - full text available via Official URL link.

Additional Keywords:

biodiversity, conservation biology, machine learning, phenology