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Species groups can be transferred across different scales.

Petr Petřík1, Helge Bruelheide2

  • 1Institute of Botany, Academy of Sciences of the Czech Republic, Průhonice, Czech Republic.

Journal of Biogeography
|April 22, 2020
PubMed
Summary

Species groups identified at fine scales are more transferable to broader scales. This finding aids in predicting missing species in large-scale surveys using detailed vegetation data.

Keywords:
COCKTAIL methodCzech RepublicEllenberg indicator valuesdistributiongrid mappingmultivariate analysissampling biasscalingvascular plantsvegetation

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Area of Science:

  • Ecology
  • Biogeography
  • Plant Community Ecology

Background:

  • Species assemblages, or groups, are fundamental units in ecological studies.
  • Understanding how these groups behave across different spatial scales is crucial for ecological generalization.

Purpose of the Study:

  • To assess the transferability of statistically derived species groups between broad, medium, and fine spatial scales.
  • To identify environmental factors driving species group composition at different scales.

Main Methods:

  • Utilized the COCKTAIL algorithm to identify species groups from floristic and vegetation data across three scales.
  • Performed cross-scale transferability analysis of identified species groups.
  • Employed canonical correspondence analysis (CCA) to determine driving factors for species group distribution.

Main Results:

  • Six out of twelve initially identified species groups were consistent across all three spatial scales.
  • Species groups derived from fine scales showed higher correspondence when applied to broader scales than vice versa.
  • Forest cover, altitude, and bedrock type were key drivers of species group variation across scales.

Conclusions:

  • Species group transferability is scale-dependent, with fine-scale groups being more robust for broader-scale applications.
  • This research provides a method for predicting species occurrences in large-scale surveys using fine-scale vegetation data.