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Modelling the biological invasion of Prosopis juliflora using geostatistical-based bioclimatic variables under climate change in arid zones of southwestern Iran

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Journal of Arid Land 2022年第2期14卷 203-224页

作者： Mohadeseh AMIRI Mosfata TARKESH Mohammad SHAFIEZADEH Department of Natural Resources Isfahan University of TechnologyIsfahan ***Iran Natural Resources and Watershed Management Organization Khuzestan ***Iran

Invasive species have been the focus of ecologists due to their undesired impacts on the environment.The extent and rapid increase in invasive plant species is recognized as a natural cause of global-biodiversity loss and degrading ecosystem services.Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions.Understanding the impact of climate change on species invasion is crucial for sustainable biodiversity conservation.In this study,the possibility of mapping the distribution of invasive Prosopis juliflora(Swartz)DC.was shown using present background data in Khuzestan Province,Iran.After removing the spatial bias of background data by creating weighted sampling bias grids for the occurrence dataset,we applied six modelling algorithms(generalized additive model(GAM),classification tree analysis(CTA),random forest(RF),multivariate adaptive regression splines(MARS),maximum entropy(Max Ent)and ensemble model)to predict invasion distribution of the species under current and future climate conditions for both optimistic(RCP2.6)and pessimistic(RCP8.5)scenarios for the years 2050 and 2070,respectively.Predictor variables including weighted mean of CHELSA(climatologies at high resolution for the Earth’s land surface areas)-bioclimatic variables and geostatistical-based bioclimatic variables(1979–2020),physiographic variables extracted from shuttle radar topography mission(SRTM)and some human factors were used in modelling process.To avoid causing a biased selection of predictors or model coefficients,we resolved the spatial autocorrelation of presence points and multi-collinearity of the predictors.As in a conventional receiver operating characteristic(roc),the area under curve(AUC)is calculated using presence and absence observations to measure the probability and the two error components are weighted equally.All models were evaluated using partial roc at different thresholds and other statistical indices derived from confusion matrix.Sensitivity analysis showed that mean diurnal range

关键词： invasive species climate change scenarios partial roc ensemble forecast Kriging spatial bias

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