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Please use this identifier to cite or link to this item:
https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/124494
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| Title: | Probabilistic approaches for investigating species co-occurrence from presence-absence maps |
| Authors: | Chang, Ya-Mei;Suman Rakshit, Chun-Hung Huang, Wen-Hsuan Wu |
| Keywords: | Chi-squared;Binomial;Poisson;Pairwise patterns |
| Date: | 2023-09-12 |
| Issue Date: | 2023-09-14 12:05:20 (UTC+8) |
| Publisher: | PeerJ, Ltd. |
| 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. |
| Relation: | PeerJ 11 |
| DOI: | 10.7717/peerj.15907  全文 |
| Appears in Collections: | [統計學系暨研究所] 期刊論文
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