Thermal niche predictors of alpine plant species
Abstract Within the context of species distribution models, scrutiny arises from the choice of meaningful environmental predictors. Thermal conditions are not the sole driver, but are the most widely acknowledged abiotic driver of plant life within alpine ecosystems. We linked long‐term measurements...
Published in: | Ecology |
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Main Authors: | , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Wiley
2019
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Subjects: | |
Online Access: | http://dx.doi.org/10.1002/ecy.2891 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fecy.2891 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecy.2891 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ecy.2891 https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecy.2891 |
Summary: | Abstract Within the context of species distribution models, scrutiny arises from the choice of meaningful environmental predictors. Thermal conditions are not the sole driver, but are the most widely acknowledged abiotic driver of plant life within alpine ecosystems. We linked long‐term measurements of direct, plant‐relevant, near‐surface temperatures to plant species frequency. Across 47 sites located along environmental gradients within the Scandinavian mountain chain, the thermal preferences of 26 focal species of vascular plants, lichens, and bryophytes were explored. Based on partial least‐squares regression, we applied a relative importance analysis to derive inductively the thermal variables that were best related to a species’ frequency. To discover potential seasonal variability of thermal controls, analyses were both differentiated according to meteorological season and integrated across the entire year. The pronounced interspecies and temporal variability of thermal constraints revealed the thermal niches were much more nuanced and variable than they have commonly been represented. This finding challenges us to present, interrogate, and interpret data representing these thermal niches, which seems to be required in order to move beyond purely probabilistic and correlative descriptions of species’ range limits. Thus, this information will help improve predictions of species distributions in complex arctic‐alpine landscapes. |
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