分类： 环境科学技术及资源科学技术 >> 环境学 提交时间： 2022-03-15 合作期刊: 《干旱区科学》
摘要： 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 (MaxEnt) 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 (19792020), 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 (Bio2) and annual precipitation (Bio12) explained more than 50%of the changes in the invasion distribution and played a pivotal role in mapping habitat suitability of P. juliflora. At all thresholds, the ensemble model showed a significant difference in comparison with single model. However, MaxEnt and RF outperformed the others models. Under climate change scenarios, it is predicted that suitable areas for this invasive species will increase in Khuzestan Province, and increasing climatically suitable areas for the species in future will facilitate its future distribution. These findings can support the conservation planning and management efforts in ecological engineering and be used in formulating preventive measures.
分类： 地球科学 >> 地球科学史 提交时间： 2018-10-29 合作期刊: 《干旱区科学》
摘要： Taking an area of about 2.3×104 km2 of southeastern Iran, this study aims to detect and predict regional-scale salt-affected lands. Three sets of Landsat images, each set containing 4 images for 1986, 2000, and 2015 were acquired as the main source of data. Radiometric, atmospheric and cutline blending methods were used to improve the quality of images and help better classify salinized land areas under the support vector machine method. A set of landscape metrics was also employed to detect the spatial pattern of salinized land expansion from 1986 to 2015. Four factors including distance to sea, distance to sea water channels, slope, and elevation were identified as the main contributing factors to land salinization. These factors were then integrated using the multi-criteria evaluation (MCE) procedure to generate land sensitivity map to salinization and also to calibrate the cellular-automata (CA) Markov chain (CA-Markov) model for simulation of salt-affected lands up to 2030, 2040 and 2050. The results of this study showed a dramatic dispersive expansion of salinized land from 7.7 % to 12.7% of the total study area from 1986 to 2015. The majority of areas prone to salinization and the highest sensitivity of land to salinization was found to be in the southeastern parts of the region. The result of the MCE-informed CA-Markov model revealed that 20.3% of the study area is likely to be converted to salinized lands by 2050. The findings of this research provided a view of the magnitude and direction of salinized land expansion in a past-to-future time period which should be considered in future land development strategies.
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