Differences in trait–environment relationships: Implications for community weighted means tests
One of J.P. Grime's greatest achievements was demonstrating the importance of the relationship between the environment and plant functional traits for understanding community assembly processes and the effects of biodiversity on ecosystem functioning. A popular approach assessing trait–environment relationships is the community weighted means (CWMs) method, which evaluates changes in communities' average trait values along gradients, with Grime being among its first practitioners. Today the CWM method is well-established but some scholars have criticized it for inflated Type I errors. That is, in some scenarios of compositional turnover along a gradient, CWM tests can provide significant results even for randomly generated traits. Null models have been proposed to correct for such effects by randomizing trait values across species (CWM-sp). We review different approaches relating traits to the environment within the framework of the accepted dichotomy between species-level (observations are species) versus community-level (observations are community parameters) analyses. Between these families of analyses and their combinations, a great variety of methods exist that test different trait–environment relationships, each with different null hypotheses and ecological questions. In classic CWM tests, the null hypothesis focuses on characteristics of trait distributions at the community level along gradients. The Type I error rate should not be a priori considered inflated when this test is used to identify changes in community trait structure affecting the functioning of communities. Trait changes observed with CWM tests may be accurate, but the interpretation that a specific trait drives turnover may be fallacious. Approaches like CWM-sp may be more appropriate for testing other ecological hypotheses, such as whether trait–environment relationships are widespread across species. In effect, this moves the ecological focus towards species-level analyses, that is on the adaptive value of traits and their relation to species niches. Synthesis. There is no single trait–environment relationship. Species-level and community-level analyses, including variants within them, test different relationships with different null hypotheses, such that the potential for inflated error rates can be misleading. Using a spectrum of methods provides a comprehensive picture of the diversity of trait–environment relationships.
| Main Authors: | , |
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| Other Authors: | |
| Format: | artículo de revisión biblioteca |
| Language: | English |
| Published: |
British Ecological Society
2023-11
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| Subjects: | Adaptation, Community assembly, Environmental gradient, Functional traits, Null models, Phylogeny, Plant development and life-history traits, Response and effect traits, |
| Online Access: | http://hdl.handle.net/10261/350609 http://dx.doi.org/10.13039/501100011033 |
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| Summary: | One of J.P. Grime's greatest achievements was demonstrating the importance of the relationship between the environment and plant functional traits for understanding community assembly processes and the effects of biodiversity on ecosystem functioning. A popular approach assessing trait–environment relationships is the community weighted means (CWMs) method, which evaluates changes in communities' average trait values along gradients, with Grime being among its first practitioners.
Today the CWM method is well-established but some scholars have criticized it for inflated Type I errors. That is, in some scenarios of compositional turnover along a gradient, CWM tests can provide significant results even for randomly generated traits. Null models have been proposed to correct for such effects by randomizing trait values across species (CWM-sp). We review different approaches relating traits to the environment within the framework of the accepted dichotomy between species-level (observations are species) versus community-level (observations are community parameters) analyses. Between these families of analyses and their combinations, a great variety of methods exist that test different trait–environment relationships, each with different null hypotheses and ecological questions.
In classic CWM tests, the null hypothesis focuses on characteristics of trait distributions at the community level along gradients. The Type I error rate should not be a priori considered inflated when this test is used to identify changes in community trait structure affecting the functioning of communities. Trait changes observed with CWM tests may be accurate, but the interpretation that a specific trait drives turnover may be fallacious. Approaches like CWM-sp may be more appropriate for testing other ecological hypotheses, such as whether trait–environment relationships are widespread across species. In effect, this moves the ecological focus towards species-level analyses, that is on the adaptive value of traits and their relation to species niches.
Synthesis. There is no single trait–environment relationship. Species-level and community-level analyses, including variants within them, test different relationships with different null hypotheses, such that the potential for inflated error rates can be misleading. Using a spectrum of methods provides a comprehensive picture of the diversity of trait–environment relationships. |
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