Binomial transformation applied to presence-absence community data

Conde, Anxo; Leandro, Sérgio; Maranhão, Paulo; Domínguez, Jorge

http://hdl.handle.net/10400.8/8706

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Abstract(s)

Community data is often transformed or standardized to meet the requirements and assumptions of multivariate analysis. While these methods are usually appropriate for abundance data, they are seldom applied to presence absence data. Here, a method of transforming a binary matrix using the binomial probability is described. Number of trials (n), number of successes (x) and probability of success (p) are necessary to compute the binomial probability. Successes were defined as the number of sites where the species occurrence can be considered; trials were equal and greater than the number of successes. The actual occurrence of each species along the gradient was considered the probability of success. The Mantel statistic associated with the binomially transformed distance matrix and the distance matrix based on binary data were used to choose an appropriate binomial transformation. The chosen binomial transformation gave greater value to species indicating habitat typologies. Binomially transformed data rendered results closer to expectations.

Description

MARE multipolar RD&I Centre was funded by the Fundaç˜ao para a Ciência e Tecnologia (FCT) through the strategic project UID/MAR/04292/2019. AC was funded by the Integrated Programme of SR&TD “Smart Valorization of Endogenous Marine Biological Resources Under a Changing Climate” (reference Centro-01-0145-FEDER-000018), cofunded by Centro 2020 program, Portugal 2020, European Union, through the European Regional Development Fund.

Keywords

Distance matrices Mantel test Multivariate analysis Ordinations Indicator Species Ecological gradients

URI

http://hdl.handle.net/10400.8/8706

Citation

Anxo Conde, Sérgio Leandro, Paulo Maranhão, Jorge Domínguez, Binomial transformation applied to presence-absence community data, Ecological Informatics, Volume 70, 2022, 101753, ISSN 1574-9541, https://doi.org/10.1016/j.ecoinf.2022.101753.