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1. Climate change is rapidly altering habitats, forcing many plant species to shift their distribution. However, slow dispersal rates and habitat fragmentation hinder their ability to track these changes, risking local extinctions and reduced ecosystem functioning. Current management strategies may not suffice to address these challenges. 2. We propose functional assisted migration (FAM) as a novel strategy to sustain ecosystem functionality under climate change by translocating non-native plant species capable of filling functional gaps in vulnerable ecosystems. By aligning plant communities with future climate conditions, FAM further enhances ecosystem resilience to withstand additional stressors. 3. To operationalize FAM, we outline key criteria and a data-driven workflow for species selection. Species selected for FAM should meet four key criteria: adaptation to the future climate, adaptation to edaphic conditions, the ability to fill functional gaps, and a low risk of invasiveness. The structured workflow, integrating climate analogue analyses, species distribution models, and functional trait assessments, provides a data-driven backbone for selecting non-native plant species suitable for FAM. 4. Synthesis and applications: By prioritizing ecosystem functionality and resilience, FAM offers a forward-thinking solution to one of conservation science's most pressing challenges. FAM complements traditional conservation efforts by targeting regions where natural dispersal and conventional strategies fall short, but empirical research remains essential to validate its ecological impacts and contributions.
https://doi.org/10.32942/X2PS61
Ecology and Evolutionary Biology, Life Sciences
Assisted colonization, biodiversity conservation, Climate change adaptation, Ecosystem functioning, functional traits, translocation
Published: 2025-02-21 14:33
Last Updated: 2025-11-14 19:18
CC-BY Attribution-NonCommercial 4.0 International
Data and Code Availability Statement:
Only freely available data were used in this study
Language:
English
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