Using codispersion analysis to quantify and understand spatial patterns in species-environment relationships
© 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.
| Veröffentlicht in: | The New phytologist. - 1979. - 211(2016), 2 vom: 20. Juli, Seite 735-49 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2016
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| Zugriff auf das übergeordnete Werk: | The New phytologist |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. anisotropy bivariate environmental gradient forest dynamics plot spatial analysis species-environment variogram |
| Zusammenfassung: | © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust. The analysis of spatial patterns in species-environment relationships can provide new insights into the niche requirements and potential co-occurrence of species, but species abundance and environmental data are routinely collected at different spatial scales. Here, we investigate the use of codispersion analysis to measure and assess the scale, directionality and significance of complex relationships between plants and their environment in large forest plots. We applied codispersion analysis to both simulated and field data on spatially located tree species basal area and environmental variables. The significance of the observed bivariate spatial associations between the basal area of key species and underlying environmental variables was tested using three null models. Codispersion analysis reliably detected directionality (anisotropy) in bivariate species-environment relationships and identified relevant scales of effects. Null model-based significance tests applied to codispersion analyses of forest plot data enabled us to infer the extent to which environmental conditions, tree sizes and/or tree spatial positions underpinned the observed basal area-environment relationships, or whether relationships were a result of other unmeasured factors. Codispersion analysis, combined with appropriate null models, can be used to infer hypothesized ecological processes from spatial patterns, allowing us to start disentangling the possible drivers of plant species-environment relationships |
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| Beschreibung: | Date Completed 31.01.2018 Date Revised 30.09.2020 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1469-8137 |
| DOI: | 10.1111/nph.13934 |