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Detecting local diversity-dependence in diversification.

Liang Xu1, Rampal S Etienne1

  • 1Groningen Institute for Evolutionary Life Sciences, University of Groningen, PO Box 11103, Groningen, 9700 CC, The Netherlands.

Evolution; International Journal of Organic Evolution
|April 8, 2018
PubMed
Summary
This summary is machine-generated.

A new study shows that a common method for detecting diversity-dependent (DD) speciation can accurately identify local ecological limits. This approach works well even when species interactions are geographically restricted, suggesting broader applicability for understanding species diversification.

Keywords:
Diversity-dependencemacroevolutionparametric bootstrapphylogenysimulations

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Area of Science:

  • Ecology
  • Evolutionary Biology
  • Phylogenetics

Background:

  • The existence of ecological limits to species diversification is a key question in evolutionary biology.
  • Molecular phylogenies suggest speciation rates may decrease as species diversity increases (diversity-dependence).
  • Existing methods for detecting diversity-dependence often assume global interactions, overlooking local species dynamics.

Purpose of the Study:

  • To investigate if a nonspatial maximum-likelihood (ML) method can detect local diversity-dependent (DD) speciation.
  • To assess the performance of the nonspatial ML method using phylogenies simulated with a spatial model incorporating local DD speciation, extinction, and dispersal.

Main Methods:

  • Simulated phylogenies using a spatial stochastic model with local diversity-dependence, speciation, extinction, and dispersal between two communities.
  • Applied a nonspatial maximum-likelihood (ML) method, typically used for global diversity-dependence, to the simulated phylogenies.
  • Evaluated the method's type I error rate and power to detect local diversity-dependence under varying dispersal rates.

Main Results:

  • The nonspatial ML method demonstrated low type I errors and high power to detect local diversity-dependence when dispersal rates were not excessively low.
  • Detection power was even higher than predicted by the nonspatial model when dispersal rates were high.
  • Estimates of intrinsic speciation rate, extinction rate, and ecological limits were sensitive to dispersal rates.

Conclusions:

  • The nonspatial diversity-dependence (DD) approach is suitable for detecting local diversity-dependence in species clades inhabiting connected areas.
  • Parameter estimates derived from the nonspatial model require cautious interpretation, especially concerning the influence of dispersal rates.
  • This study validates the use of existing nonspatial models for analyzing local ecological dynamics in evolutionary diversification.