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Powney GD, Cham SS, Smallshire D, Isaac NJB.2014. Trait correlates of distribution trends in the Odonata of Britain and Ireland: Southern species benefit from climate warming. PeerJ PrePrints2:e648v1https://doi.org/10.7287/peerj.preprints.648v1
A major challenge in ecology is understanding what enables certain species to persist, while others decline, in response to environmental change. Trait-based comparative analyses are useful in this regard as they can help identify the key drivers of decline, and highlight traits that promote resistance to change. Despite their popularity trait-based comparative analyses tend to focus on explaining variation in range shift and extinction risk, seldom being applied to actual measures of species decline. Furthermore they have tended to be taxonomically restricted to birds, mammals, plants and butterflies. Here we utilise a novel approach to estimate trends for the Odonata in Britain and Ireland, and examine trait correlates of these trends using a recently available trait dataset. We found the dragonfly fauna in Britain and Ireland has undergone considerable change between 1980 and 2012, with 33 and 39% of species showing significant declines and increases respectively. Distribution type was the key trait associated with these trends, where southern species showed significantly higher trends than widespread and northern species. We believe this reflects the impact of climate change as the increased ambient temperature in Britain and Ireland better suits species that are adapted to warmer conditions. We conclude that northern species are particularly vulnerable to climate change due to the combined pressures of a decline in climate suitability, and competition from species that were previously limited by lower thermal tolerance.
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Appendix 1 The top subset of models for each of the alternative modelling approaches and response variables. A) Estimate linear non-phylogenetic regression, B) Weighted estimate non-phylogenetic regression, C) z-score PGLS, D) z-score linear model. Appendix 2 The model averaged coefficients for trait that was in the top subset of models. Each graph represents a different modelling approach/response variable combination. A) Estimate non-phylogenetic regression, B) weighted estimate non-phylogenetic regression, C) z-score PGLS, D) z-score non-phylogenetic regression. The reference distribution type was “southern”, which has a parameter estimate set to 0. Appendix 3 A comparison of the importance scores for each trait across each of the modelling and response variable combinations.