Binomial transformation applied to presence-absence community data |
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| Institution: | 1. MARE – Marine and Environmental Sciences Centre, ESTM, Politécnico de Leiria, 2520-630 Peniche, Portugal;2. Universidade de Vigo, Grupo de Ecoloxía Animal (GEA), Vigo, E-36310 Pontevedra, Spain;1. School of Land Science and Technology, China University of Geosciences, Beijing 100083, China;2. Technology innovation center of land engineering, MNR, Beijing 100035, China;3. Key Laboratory of Land Consolidation, MNR, Beijing 100035, China;4. Ningbo Natural Resources & Planning Research Center, Ningbo 315042, China;1. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China;2. Key Laboratory of 3D Information Acquisition and Application of Ministry, Capital Normal University, Beijing 100048, China;3. School of Soil and Water Conservation, Beijing Forestry University, Beijing 100038, China;4. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;5. University of Chinese Academy of Sciences, Beijing 100149, China;6. School of Land Science and Technology, China University of Geosciences, Beijing 100083, China |
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| Abstract: | 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. |
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