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How to quantify immigration from community abundance data using the neutral community model.

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Area of Science:

  • Ecology
  • Computational Biology
  • Bioinformatics

Background:

  • Biological communities are interconnected via dispersal, influencing local and regional diversity.
  • Direct measurement of dispersal is challenging, hindering understanding of its ecological impact.
  • The Neutral Community Model (NCM) offers a way to estimate immigration rates (NTm) using abundance data.

Purpose of the Study:

  • To evaluate the accuracy of NCM-based immigration rate (NTm) estimation.
  • To introduce and compare novel inference methods for NTm.
  • To determine optimal sampling strategies for accurate NTm estimation in diverse communities.

Main Methods:

  • Introduced two new inference methods: variance-based and Dirichlet-multinomial log-likelihood (DM-LL).
  • Complemented these with the established occupancy-based inference method.
  • Validated methods using simulations of activated sludge microbiomes and real-world datasets (wastewater, tropical trees, coral reefs).

Main Results:

  • All tested methods estimated NTm within 10% of ground-truth values in simulations.
  • Variance-based and DM-LL methods required less sampling effort for accurate estimates.
  • Accurate NTm inference necessitates read depths exceeding the immigration rate per sample.

Conclusions:

  • Developed and validated robust methods for quantifying community immigration using NCM.
  • Identified key sampling requirements (read depth) for reliable NTm estimation.
  • Provided practical guidelines for studying dispersal in complex, diverse biological communities.