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• W2319326018 abstract "Summary 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." @default.
• W2319326018 created "2016-06-24" @default.
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• W2319326018 date "2016-04-01" @default.
• W2319326018 modified "2023-10-10" @default.
• W2319326018 title "Using codispersion analysis to quantify and understand spatial patterns in species–environment relationships" @default.
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• W2319326018 doi "https://doi.org/10.1111/nph.13934" @default.
• W2319326018 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27037819" @default.
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