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

P. Pradhan, A. Setyawan
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引用次数: 4

Abstract

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. WorldClim is one of the popular environmental datasets which hosts multi-resolution interpolated gridded climate raster surfaces and derived bioclimatic variables for both the immediate past, present and future scenarios. Bioclimatic variables along with other environmental factors like solar radiation, wind speed, water vapour pressure etc. have been used as primary set of explanatory variables for mapping and spatial modeling of many biological processes, including defining environmental niche of a species and identifying potential areas for its distribution through machine learning methods like Ecological Niche Modeling or Species Distribution Modeling or Habitat Suitability Modeling. However, the interpolated explanatory datasets are known to cause over-fitting of the models mainly due to multi-collinearity or redundancy within the variables. In the present study, 58 bioclimatic and environmental variables of Indonesian extent extracted from WorldClim 2.1 are screened to investigate the presence of multi-collinearity or redundancy. From the total 3364 variable pairs per raster resolution, 174 variable pairs were known to be affected by multicollinearity, from which temperature related bioclimatic variables, water vapour pressure and elevation associated variables were highly notable. For all the raster resolutions, bioclimatic variable 2, 3, 4, 15, 18 and 19, as well as slope, aspect, solar radiation for January, April, May, September, wind speed for August and November were found to be non-collinear. While, solar radiation for March and July were found to be non-collinear for 30s, 2.5m and 5m raster resolutions; Wind speed of July was non-collinear for 30s and 2.5m; Solar radiation for February and June were non-collinear for 10m; water vapour pressure for August for 2.5m and wind speed for January was non-collinear for 30s raster resolutions. The results of this study might serve as a convenient reference for investigators of the region for selection of bioclimatic and other environmental variables for conducting ecological niche modeling studies.
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从WorldClim 2.1的多分辨率栅格中筛选多重共线性预测变量,用于印度尼西亚环境下的生态位建模
摘要Pradhan P, Setyawan AD。2021. 从WorldClim 2.1的多分辨率栅格中筛选多重共线性预测变量,用于印度尼西亚环境下的生态位建模。亚洲J . 5: 111-122。WorldClim是一个流行的环境数据集,它包含多分辨率插值网格气候栅格表面和衍生的生物气候变量,包括过去、现在和未来的情景。生物气候变量与其他环境因子如太阳辐射、风速、水蒸气压力等已被用作许多生物过程制图和空间建模的主要解释变量集,包括通过生态位建模或物种分布建模或生境适宜性建模等机器学习方法定义物种的环境生态位并确定其潜在分布区域。然而,已知插值的解释数据集会导致模型的过度拟合,主要是由于变量内的多重共线性或冗余。在本研究中,筛选了从WorldClim 2.1中提取的58个印度尼西亚范围的生物气候和环境变量,以研究多重共线性或冗余的存在。在每栅格分辨率的3364个变量对中,已知174个变量对受多重共线性的影响,其中温度相关的生物气候变量、水汽压力和海拔相关的变量尤为显著。在所有栅格分辨率下,生物气候变量2、3、4、15、18和19以及坡度、坡向、1月、4月、5月、9月的太阳辐射、8月和11月的风速均呈非共线关系。而3月和7月的太阳辐射在30s、2.5m和5m栅格分辨率下呈非共线;7月风速30s、2.5m为非共线;2月和6月的太阳辐射在10m范围内呈非共线;8月水汽压为2.5m, 1月风速为非共线,栅格分辨率为30s。研究结果可为研究人员选择生物气候和其他环境变量进行生态位模型研究提供参考。
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