Filtering multi-collinear predictor variables from multi-resolution rasters of WorldClim 2.1 for Ecological Niche Modeling in Indonesian context

نویسندگان

چکیده

Abstract. Pradhan P, Setyawan AD. 2021. Filtering multi-collinear predictor variables from multi-resolution rasters of WorldClim 2.1 for Ecological Niche Modeling in Indonesian context. Asian J For 5: 111-122. is one the popular environmental datasets which hosts interpolated gridded climate raster surfaces and derived bioclimatic both immediate past, present future scenarios. Bioclimatic along with other factors like solar radiation, wind speed, water vapour pressure etc. have been used as primary set explanatory mapping spatial modeling many biological processes, including defining niche a species identifying potential areas its distribution through machine learning methods or Species Distribution Habitat Suitability Modeling. However, are known to cause over-fitting models mainly due multi-collinearity redundancy within variables. In study, 58 extent extracted screened investigate presence redundancy. From total 3364 variable pairs per resolution, 174 were be affected by multicollinearity, temperature related variables, elevation associated highly notable. all resolutions, 2, 3, 4, 15, 18 19, well slope, aspect, radiation January, April, May, September, speed August November found non-collinear. While, March July non-collinear 30s, 2.5m 5m resolutions; Wind was 30s 2.5m; Solar February June 10m; January resolutions. The results this study might serve convenient reference investigators region selection conducting ecological studies.

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ژورنال

عنوان ژورنال: Asian journal of forestry

سال: 2021

ISSN: ['2580-2844']

DOI: https://doi.org/10.13057/asianjfor/r050207