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Vegetation heterogeneity as an indicator of plant functional traits.

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Title: Vegetation heterogeneity as an indicator of plant functional traits.
Authors: Wan, Ji-Zhong1 (AUTHOR), Wang, Chun-Jing2,3 (AUTHOR) wangchunjing00@163.com, Wang, Xiaodan1 (AUTHOR)
Source: Progress in Physical Geography: Earth & Environment. Dec2025, Vol. 49 Issue 6, p668-687. 20p.
Subjects: Plant variation, Plant indicators, Statistical models, Plant health, Statistical measurement, Climate change, Ecosystem services
Geographic Terms: Americas
Abstract: Plant functional traits have been shown to be directly associated with vegetation variation. However, although previous studies have determined that environmental factors (i.e. climate and soil) are the main indicators of plant functional traits, trait–environment relationships have been found to be weak at large spatial scales. Hence, it is necessary to evaluate other factors that may contribute to the variation in plant functional traits at these broad scales. In this regard, although the enhanced vegetation index (EVI) is known to be an informative indicator of vegetation heterogeneity, few previous studies have provided evidence that EVI serves as a large-scale indicator of plant functional composition. We used data comprised of the textural features of EVI imagery at fine resolution for vegetation heterogeneity and the Botanical Information and Ecology Network (BIEN) for functional trait data of the Americas. We used abundance-weighted trait moments (i.e. community-weighted mean (CWM) and community-weighted variance (CWV)) to quantify variation in plant functional traits. Accordingly, we found that vegetation heterogeneity was significantly associated with the community-weighted mean and variance in the Americas (p <.05 for most trait–EVI relationships). Furthermore, we found that there were spatial non-stationary relationships of vegetation heterogeneity with CWM and CWV based on the results of geographically weighted regression. We also detected strong trait–EVI relationships in deserts, xeric shrublands, and tropical and subtropical moist broadleaf forests. Collectively, the findings of this study provide new insights into trait–EVI relationships across large spatial scales. Accordingly, we propose the use of the vegetation heterogeneity to predict how ecosystem functions and services respond to rapid changes in the global environment. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:Plant functional traits have been shown to be directly associated with vegetation variation. However, although previous studies have determined that environmental factors (i.e. climate and soil) are the main indicators of plant functional traits, trait–environment relationships have been found to be weak at large spatial scales. Hence, it is necessary to evaluate other factors that may contribute to the variation in plant functional traits at these broad scales. In this regard, although the enhanced vegetation index (EVI) is known to be an informative indicator of vegetation heterogeneity, few previous studies have provided evidence that EVI serves as a large-scale indicator of plant functional composition. We used data comprised of the textural features of EVI imagery at fine resolution for vegetation heterogeneity and the Botanical Information and Ecology Network (BIEN) for functional trait data of the Americas. We used abundance-weighted trait moments (i.e. community-weighted mean (CWM) and community-weighted variance (CWV)) to quantify variation in plant functional traits. Accordingly, we found that vegetation heterogeneity was significantly associated with the community-weighted mean and variance in the Americas (p <.05 for most trait–EVI relationships). Furthermore, we found that there were spatial non-stationary relationships of vegetation heterogeneity with CWM and CWV based on the results of geographically weighted regression. We also detected strong trait–EVI relationships in deserts, xeric shrublands, and tropical and subtropical moist broadleaf forests. Collectively, the findings of this study provide new insights into trait–EVI relationships across large spatial scales. Accordingly, we propose the use of the vegetation heterogeneity to predict how ecosystem functions and services respond to rapid changes in the global environment. [ABSTRACT FROM AUTHOR]
ISSN:03091333
DOI:10.1177/03091333251365834