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Community structure informs species geographic distributions.

Alicia Montesinos-Navarro1,2, Alba Estrada3, Xavier Font4

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Summary

Incorporating local community structure into species distribution models (SDMs) using Bayesian Network Inference (BNI) moderately improved predictions. This approach enhances understanding of species distributions by including small-scale ecological processes.

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Area of Science:

  • Ecology
  • Biodiversity Science
  • Computational Biology

Background:

  • Species' geographic distributions are vital for biodiversity assessment under global change.
  • Current distribution models often lack fine-scale ecological process data.
  • Community structure, reflecting local assembly, offers potential for improved distribution predictions.

Purpose of the Study:

  • To test if incorporating community structure improves species distribution models (SDMs).
  • To demonstrate the utility of Bayesian Network Inference (BNI) for integrating community data into SDMs.
  • To investigate the relationship between species co-occurrences and distribution predictions.

Main Methods:

  • Applied Bayesian Network Inference (BNI) to 1570 Mediterranean woody plant assemblages.
  • Integrated community structure data (relative abundance) with environmental data in SDMs.
  • Assessed the impact of species associations on SDM predictive performance.

Main Results:

  • Species association data moderately improved SDM predictions for community structure and species distributions.
  • Predictive power increased by up to 15% for some habitat specialists.
  • 95% of observed species associations were positive, often between ecologically similar species.

Conclusions:

  • Bayesian Networks effectively integrate local community data into large-scale distribution models.
  • Species co-occurrences serve as a proxy for local ecological processes, enhancing SDM accuracy.
  • This method improves predictions of species distributions, aiding biodiversity conservation efforts.