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Abstract

Most insect pollinators are ectothermic and rely on external heat sources for temperature regulation. Forests, with their diverse canopy structures and sunlight penetration levels, create a mosaic of microclimates influencing these insects' behaviour. This study examined how macro- and microclimatic temperatures, precipitation, forest structure, and flower resources affect pollinator activity in the understory of two Swedish mixed forests using a combination of artificial flower stations, time-lapse pictures, and high-resolution climate data. Diptera was the most abundant order of flower visitors, with Muscidae, Phoridae and Syrphidae being the most recorded families. Our results show that macroclimate was the main driver of flower visitation rates, while microclimatic conditions better predicted foraging time. Based on our models, forest fly pollinators increase their flower visitation rates within a narrow temperature window. Thus, we anticipate that the effects of rising temperatures due to climate change on flower visitation rates by insects will largely depend on baseline temperature. Additionally, temperature appears more important than changes in other macroclimatic variables such as rain. Precipitation reduced pollinator visits, and pollinators increased their activity with time since the last rainfall. Forest density negatively impacted pollinator presence, with effects varying by pollinator group and species, but suggesting that increasing canopy openness could enhance pollinator habitats and support biodiversity. Higher flower species richness decreased visitation rates but extended pollinator foraging time in some cases. Conversely, abundant wildflowers increased pollinator visitation rates but reduced their overall foraging time, probably due to competition. Our study highlights the complex relationships that occur between forest structure, climate, flower resources, and pollinator activity. Understanding species-specific responses to forest composition and understory vegetation can guide tree species selection in afforestation or reforestation projects to support diverse pollinator communities, ultimately informing effective conservation and forest management practices to maintain healthy pollinator populations and ecosystem resilience under a changing climate.

Keywords

Canopy openness; climate change; climatic refugia; Convolutional Neural Network; deep learning; diptera; forest composition; pollinator conservation; YOLOv5

Published in

Oikos
2025
Publisher: WILEY

SLU Authors

UKÄ Subject classification

Ecology
Forest Science

Publication identifier

  • DOI: https://doi.org/10.1002/oik.11405

Permanent link to this page (URI)

https://res.slu.se/id/publ/143960