Probabilistic approaches for investigating species co-occurrence from presence-absence maps

Ya-Mei Chang; Suman Rakshit; Chun-Hung Huang; Wen-Hsuan Wu

doi:10.7717/peerj.15907

Probabilistic approaches for investigating species co-occurrence from presence-absence maps

PeerJ. 2023 Sep 12:11:e15907. doi: 10.7717/peerj.15907. eCollection 2023.

Authors

Ya-Mei Chang¹, Suman Rakshit^{2

3}, Chun-Hung Huang¹, Wen-Hsuan Wu¹

Affiliations

¹ Department of Statistics, Tamkang University, New Taipei City, Taiwan.
² School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, Western Australia, Australia.
³ Curtin Biometry and Agriculture Data Analytics, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia, Australia.

Abstract

Background: In this research, we propose probabilistic approaches to identify pairwise patterns of species co-occurrence by using presence-absence maps only. In particular, the two-by-two contingency table constructed from a presence-absence map of two species would be sufficient to compute the test statistics and perform the statistical tests proposed in this article. Some previous studies have investigated species co-occurrence through incidence data of different survey sites. We focus on using presence-absence maps for a specific study plot instead. The proposed methods are assessed by a thorough simulation study.

Methods: A Chi-squared test is used to determine whether the distributions of two species are independent. If the null hypothesis of independence is rejected, the Chi-squared method can not distinguish positive or negative association between two species. We propose six different approaches based on either the binomial or Poisson distribution to obtain p-values for testing the positive (or negative) association between two species. When we test to investigate a positive (or negative) association, if the p-value is below the predetermined level of significance, then we have enough evidence to support that the two species are positively (or negatively) associated.

Results: A simulation study is conducted to demonstrate the type-I errors and the testing powers of our approaches. The probabilistic approach proposed by Veech (2013) is served as a benchmark for comparison. The results show that the type-I error of the Chi-squared test is close to the significance level when the presence rate is between 40% and 80%. For extremely low or high presence rate data, one of our approaches outperforms Veech (2013)'s in terms of the testing power and type-I error rate. The proposed methods are applied to a tree data of Barro Colorado Island in Panama and a tree data of Lansing Woods in USA. Both positive and negative associations are found among some species in these two real data.

Keywords: Chi-squared; Binomial; Pairwise patterns; Poisson.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Benchmarking*
Colorado
Computer Simulation
Interior Design and Furnishings*
Panama

Grants and funding

This work was supported by the National Science and Technology Council of Taiwan (No. 111-2635-M-032-001 and 107-2118-M-032-007). The BCI forest dynamics research project was made possible by National Science Foundation grants to Stephen P. Hubbell: DEB-0640386, DEB-0425651, DEB-0346488, DEB-0129874, DEB-00753102, DEB-9909347, DEB-9615226, DEB-9615226, DEB-9405933, DEB-9221033, DEB-9100058, DEB-8906869, DEB-8605042, DEB-8206992, DEB-7922197, support from the Forest Global Earth Observatory (ForestGEO), the Smithsonian Tropical Research Institute, the John D. and Catherine T. MacArthur Foundation, the Mellon Foundation, and the Small World Institute Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.