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Using codispersion analysis to quantify and understand spatial patterns in species-environment relationships.

Hannah L Buckley1,2, Bradley S Case2,3, Jess K Zimmerman4

  • 1Department of Ecology, Lincoln University, Lincoln, 7647, New Zealand.

The New Phytologist
|April 3, 2016
PubMed
Summary
This summary is machine-generated.

Codispersion analysis reveals spatial patterns in plant-environment links, identifying key scales and directions. This method, using null models, helps infer ecological drivers behind observed species-environment relationships in forests.

Keywords:
anisotropybivariateenvironmental gradientforest dynamics plotspatial analysisspecies-environmentvariogram

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Area of Science:

  • Ecology
  • Spatial Analysis
  • Forest Science

Background:

  • Species-environment relationships are crucial for understanding ecological niches and co-occurrence.
  • Spatial data for species abundance and environmental factors are often collected at mismatched scales.
  • Analyzing spatial patterns requires methods that can handle complex, multi-scale relationships.

Purpose of the Study:

  • To investigate codispersion analysis for measuring scale, directionality, and significance of plant-environment relationships.
  • To assess the utility of codispersion analysis in large forest plots using simulated and field data.
  • To differentiate between direct environmental drivers and other factors influencing species-environment associations.

Main Methods:

  • Applied codispersion analysis to spatially located tree species basal area and environmental variables.
  • Utilized simulated and field data from large forest plots.
  • Employed three null models to test the significance of bivariate spatial associations.

Main Results:

  • Codispersion analysis successfully detected directionality (anisotropy) in species-environment relationships.
  • The analysis identified relevant scales at which these relationships operate.
  • Null model tests helped infer whether environmental conditions, tree characteristics, or unmeasured factors drove the observed patterns.

Conclusions:

  • Codispersion analysis is a reliable tool for assessing spatial relationships between species and their environment.
  • Combined with null models, it can help disentangle ecological processes driving observed spatial patterns.
  • This approach offers insights into niche requirements and potential co-occurrence of species in forest ecosystems.