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Functional Response Trait Analysis Improves Climate Sensitivity Estimation in Beech Forests at a Trailing Edge

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Functional response traits influence the ability of species to colonize and thrive in a habitat and to persist under environmental challenges. Functional traits can be used to evaluate environment-related processes… Click to show full abstract

Functional response traits influence the ability of species to colonize and thrive in a habitat and to persist under environmental challenges. Functional traits can be used to evaluate environment-related processes and phenomena. They also help to interpret distribution patterns, especially under limiting ecological conditions. In this study, we investigate landscape-scale functional distribution responses of beech forests in a climatic transitional zone in Europe. We construct empirical density distribution responses for beech forests by applying coping-resilience-failure climatic traits based on 27 bioclimatic variables, resulting in prevalence-decay-exclusion distribution response patterns. We also perform multivariate exploratory cluster analysis to reveal significant sets of response patterns from the resilience and adaptation aspects. Temperature-related distribution responses presented a prevalence-dominated functional pattern, with Annual mean temperature indicating the most favorable adaptation function. Precipitation indices showed climate-limited response patterns with the dominance of extinction function. Considering regional site-specific climate change projections, these continental beech forests could regress moderately due to temperature increase in the near future. Our results also suggest that both summer and winter precipitation could play a pivotal role in successful resilience. Functions and variables that indicate climate sensitivity can serve as a useful starting point to develop adaptation measures for regional forest management.

Keywords: response; analysis; beech forests; functional response; climate sensitivity; distribution

Journal Title: Forests
Year Published: 2017

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