Predicting the potential distribution of Pseudomonas syringae pv. actinidiae in China using ensemble models

Pseudomonas syringae pv. actinidiae (Psa) is a causal agent of kiwifruit bacterial canker worldwide, which has affected kiwifruit vines in China since 1996 and has subsequently spread to the main cultivation areas. Based on occurrence of Psa and pseudo-absences randomly generated in China, the consensus-based modelling technique was used to estimate the spatial spread of Psa epidemics within China. Environmental variables that related to Psa development were identified, and their contributions to Psa development were evaluated. Three modelling algorithms, namely generalized boosting models (GBM), random forests (RF) and classification tree analysis (CTA) within the BIOMOD2 framework, were employed to construct the model. The ensemble models weighted by the true skill statistic (TSS) value were used to predict the current habitat suitability of Psa, and were projected using the four general circulation models (GCMs) to assess range shifts under two types of representative concentration pathways (RCP 4.5 and RCP 8.5) by 2050. The results indicated that precipitation in March and mean temperature of warmest quarter were the most important limiting factors for distribution of Psa. The predictive accuracy of the ensemble model showed acceptable predictive powers (TSS = 0.852). Under future climate conditions, substantial net loss of suitability for Psa was estimated to be 3.03–12.5% under RCP 4.5 (except one GCM), and 2.46–9.89% under RCP 8.5. Shrinkage of suitable habitats was detected mainly in the areas currently infected by Psa. Special attention should be given to recent infectious regions in south and southwest China, considering the locally expanding kiwifruit commercial plantations.     

Mapping the current and future distributions of Onosma species endemic to Iran

Climate change may cause shifts in the natural range of species especially for those that are geographically restricted and/or endemic species. In this study, the spatial distribution of five endemic and threatened species belonging to the genus Onosma (including O. asperrima, O. bisotunensis, O. kotschyi, O. platyphylla, and O. straussii) was investigated under present and future climate change scenarios: RCP2.6 (RCP, representative concentration pathway; optimistic scenario) and RCP8.5 (pessimistic scenario) for the years 2050 and 2080 in Iran. Analysis was conducted using the maximum entropy (MaxEnt) model to provide a basis for the protection and conservation of these species. Seven environmental variables including aspect, depth of soil, silt content, slope, annual precipitation, minimum temperature of the coldest month, and annual temperature range were used as main predictors in this study. The model output for the potential habitat suitability of the studied species showed acceptable performance for all species (i.e., the area under the curve (AUC)>0.800). According to the models generated by MaxEnt, the potential current patterns of the species were consistent with the observed areas of distributions. The projected climate maps under optimistic and pessimistic scenarios (RCP2.6 and RCP8.5, respectively) of 2050 and 2080 resulted in reductions and expansions as well as positive range changes for all species in comparison to their current predicted distributions. Among all species, O. bisotunensis showed the most significant and highest increase under the pessimistic scenario of 2050 and 2080. Finally, the results of this study revealed that the studied plant species have shown an acute adaptability to environmental changes. The results can provide useful information to managers to apply appropriate strategies for the management and conservation of these valuable Iranian medicinal and threatened plant species in the future.     

Ecological determinants and risk areas of Striga hermonthica infestation in western Kenya under changing climate

Striga hermonthica (Del.) Benth is a parasitic weed that is damaging major cereal crops in sub-Saharan Africa (SSA). Although Striga is recognised as an agricultural scourge, there is limited information available indicating the extent of its growth and spread as impacted by the changing climate in Kenya. This study investigated the impact of current climate conditions and projected future (2050) climate change on the infestation of Striga hermonthica in the western Kenya region. Specifically, the study aimed to predict Striga hermonthica habitat suitability in five counties in the western Kenya region through using the maximum entropy (MaxEnt) model and bioclimatic, soil, topographic and land use, and land cover (LULC) variables. Striga hermonthica geolocations were collected and collated and ecological niche models were developed to determine the habitat suitability. The results showed that approximately 1767 km² (10% of the total study area) is currently highly suitable for Striga hermonthica occurrence. The future projections showed a range between 2106 km² (19% of the total study area) and 2712 km² (53% of the total study area) at the minimum carbon (RCP 2.6) and the maximum carbon emission scenarios (RCP 8.5) respectively. Elevation, annual precipitation, LULC, temperature seasonality and soil type were determined to be the most influential ecological predictor variables for Striga hermonthica establishment. The study revealed the importance of using climate, soil, topographic and LULC variables when evaluating agricultural production constraints such as Striga's prevalence. The methodology used in this study should be tested in other Striga affected areas.     

基于MaxEnt模型预测苹红缢管蚜在中国的适生区

为评估苹红缢管蚜Rhopalosiphum oxyacanthae入侵我国的潜在地理分布范围,基于其在全球的118个分布点和筛选的9个环境变量数据,利用MaxEnt模型和ArcGIS软件对苹红缢管蚜于当前和未来气候条件下在我国的潜在适生区进行预测。结果表明,基于苹红缢管蚜的全球分布数据,MaxEnt模型预测的平均AUC值为0.919,预测结果准确性较高。当前气候条件下,苹红缢管蚜在我国的总适生区面积占全国陆地面积的48.73%,其中高、中、低适生区的面积分别占全国陆地面积的5.92%、13.30%和29.51%,高适生区主要位于河南、江苏、安徽、湖北、湖南、贵州、江西、浙江、福建、广西、台湾等省区。未来RCP2.6和RCP8.5气候情景下,苹红缢管蚜在我国的总适生区面积增加并呈逐渐向东北方向迁移扩散的趋势,其中低适生区面积逐渐增加,高、中适生区面积逐渐减少,原先中南部的高适生区逐渐转变为低适生区或非适生区,但新疆维吾尔自治区北部、吉林省与辽宁省边界处的高适生区面积呈逐渐增加趋势。表明苹红缢管蚜在我国的适生范围极为广泛,具有较高的入侵风险,应加强进境口岸对该虫的检疫监管力度。     

基于MaxEnt模型的菜豆象和蚕豆象在中国的适生区预测

为减少外来入侵物种菜豆象Acanthoscelides obtectus和蚕豆象Bruchus rufimanus对中国造成的潜在威胁,收集这2种豆象的全球地理分布数据,采用Pearson相关性分析和主成分分析分别从19个环境变量中筛选关键环境变量,采用MaxEnt模型对历史气候条件下和未来气候情景下这2种豆象在中国的适生区进行预测,并对预测结果进行分析。结果显示,经Pearson相关性分析共筛选出4个关键环境变量用于菜豆象适生性区的模型构建,分别为最暖季度平均温度、最干月份降水量、年气温变化范围及最湿季度降水量,其对MaxEnt模型的累积贡献率分别为31.6%、28.4%、26.3%和13.7%;经Pearson相关性分析共筛选出4个主要关键环境变量用于蚕豆象适生性区的模型构建,分别为最冷季度平均温度、最干月份降水量、最热月份最高温度和最湿月份降水量,其对MaxEnt模型的累积贡献率分别为48.5%、39.5%、7.8%和4.2%。MaxEnt模型重复运行10次后,菜豆象训练数据的平均AUC值为0.938,蚕豆象训练数据的平均AUC值为0.963,均显著高于随机模型的AUC值,表明基于MaxEnt模型的菜豆象和蚕豆象在中国适生区的预测结果准确。未来气候情景下,这2种豆象在中国的适生区均呈现向北扩张的趋势,需加强对这2种豆象的检疫与防治,严防发生区域进一步扩大。     

Climate change in the Tianshan and northern Kunlun Mountains based on GCM simulation ensemble with Bayesian model averaging

Climate change in mountainous regions has significant impacts on hydrological and ecological systems. This research studied the future temperature, precipitation and snowfall in the 21~(st) century for the Tianshan and northern Kunlun Mountains(TKM) based on the general circulation model(GCM) simulation ensemble from the coupled model intercomparison project phase 5(CMIP5) under the representative concentration pathway(RCP) lower emission scenario RCP4.5 and higher emission scenario RCP8.5 using the Bayesian model averaging(BMA) technique. Results show that(1) BMA significantly outperformed the simple ensemble analysis and BMA mean matches all the three observed climate variables;(2) at the end of the 21~(st) century(2070–2099) under RCP8.5, compared to the control period(1976–2005), annual mean temperature and mean annual precipitation will rise considerably by 4.8°C and 5.2%, respectively, while mean annual snowfall will dramatically decrease by 26.5%;(3) precipitation will increase in the northern Tianshan region while decrease in the Amu Darya Basin. Snowfall will significantly decrease in the western TKM. Mean annual snowfall fraction will also decrease from 0.56 of 1976–2005 to 0.42 of 2070–2099 under RCP8.5; and(4) snowfall shows a high sensitivity to temperature in autumn and spring while a low sensitivity in winter, with the highest sensitivity values occurring at the edge areas of TKM. The projections mean that flood risk will increase and solid water storage will decrease.     

基于MaxEnt模型预测气候变化下玉米根萤叶甲在中国的潜在地理分布

为评估不同气候条件下玉米根萤叶甲Diabrotica virgifera virgifera在我国的潜在地理分布情况及适生区的空间格局变化趋势,通过筛选影响该虫分布的关键环境变量并基于其在全球的分布数据,运用MaxEnt模型和ArcGIS软件预测其在历史和未来气候情景下的潜在地理分布范围和适生区空间格局变化。结果表明,所构建MaxEnt模型的受试者工作特征 （receiver operating characteristic, ROC）曲线下面积（area under curve, AUC）平均为0.960,说明模型预测结果为优秀,具有较高的可信度。关键气候变量中最冷月最低温对玉米根萤叶甲的潜在地理分布具有十分重要的影响,累积贡献率为44.5%。历史气候条件下,玉米根萤叶甲的总适生区面积占我国陆地总面积的23.78%,高适生区主要分布于我国河南、湖北、陕西、甘肃、重庆、四川和云南等省市。未来气候情景下,玉米根萤叶甲在我国的总适生区面积略有减少,整体上呈现出南部收缩、北部扩张的趋势,原中南部的中、高适生区逐步转变为低适生区或非适生区。玉米根萤叶甲在我国的适生区较为广泛,适生范围涵盖多个重要玉米产区,对玉米安全生产威胁较大,应给予足够的重视,严防该虫传入我国。     

气候变化下基于GIS的农田恶性杂草旱雀麦在中国的分布与发展趋势

为确定旱雀麦在我国的空间分布及其对气候变化的响应,以期进一步开展生态防控,本研究利用旱雀麦在中国的地理分布数据,结合当前气候数据和未来气候变化情景（RCP8.5情景下2050s,2070s）,建立最大熵模型（MaxEnt模型）,确定影响旱雀麦分布的主导环境因子。应用地理信息系统（GIS）对中国地区旱雀麦的适生区进行划分,以ROC曲线作为模拟的准确性评价指标。结果表明,MaxEnt模型模拟效果极好（AUC=0.965）;当前气候条件下,旱雀麦适生面积为2.5534×10⁶ km²,主要集中分布于青海省东北部、甘肃省与青海省接壤的地区、四川省的西北部,以及新疆的西北部;其中影响旱雀麦分布的主要环境因子为海拔、bio12（年降水量）、bio9（最干季度平均温度）和bio15（降水量季变异系数）,其贡献率分别为45.0%、17.5%、9.7%、9.7%,累计贡献率达81.9%;在RCP8.5情景下,未来2个时期,旱雀麦潜在高适生区分布面积与当前相比增加了12.2%~23.3%,但RCP8.5情景下2070s较RCP8.5情景下2050s旱雀麦的潜在高度适生区分布面积减少了8.9%。综上所述,气候变化情景下旱雀麦的潜在分布面积呈现出扩大趋势,且RCP8.5情景下2070s较RCP8.5情景下2050s的适生区分布面积有缩减趋势。     

西北干旱区白刺属植物适生环境的模拟分析

白刺属(Nitraria L.)植物是古地中海第三纪孑遗植物,对我国西北干旱、半干旱地区具有极高的生态效益。本研究基于MaxEnt模型和ArcGIS软件及其SDM工具箱预测了过去到未来不同时期下白刺属6个物种在我国的潜在适生区及其迁移路线,以期为该属植物的保护及资源利用方面提供一定的理论依据。结果表明:白刺属植物当代在我国的高适生区面积占我国国土总面积的6.32%,主要集中在宁夏北部、内蒙古南部和新疆西部地区。与过去相比,白刺属植物的潜在高适生区在当代减少了1.68%。与当代相比,白刺属植物高适生区预测面积在2070年(RCP4.5)气候情景下减少了0.13%,在2070年(RCP8.5)气候情景下增加了1.61%,主要集中在甘肃东南部和宁夏地区;白刺属植物在我国潜在适生区的主导环境因子是最冷月最低温(bio6);最冷季度的平均降水量(bio19)和年均温(bio1)是影响白刺属潜在地理分布的主要环境因子;白刺属植物在末次间冰期(LIG)、末次冰盛期(LGM)、当代及2070年(RCP4.5和RCP8.5)均有向青海等高海拔地区迁移的趋势。     

Simulating hydrological responses to climate change using dynamic and statistical downscaling methods: a case study in the Kaidu River Basin,Xinjiang, China

Climate change may affect water resources by altering various processes in natural ecosystems. Dynamic and statistical downscaling methods are commonly used to assess the impacts of climate change on water resources. Objectively, both methods have their own advantages and disadvantages. In the present study, we assessed the impacts of climate change on water resources during the future periods (2020-2029 and 2040-2049) in the upper reaches of the Kaidu River Basin, Xinjiang, China, and discussed the uncertainties in the research processes by integrating dynamic and statistical downscaling methods (regional climate models (RCMs) and general circulation modes (GCMs)) and utilizing these outputs. The reference period for this study is 1990-1999. The climate change trend is represented by three bias-corrected RCMs (i.e., Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA), Regional Climate Model version 4 (RegCM4), and Seoul National University Meso-scale Model version 5 (SUN-MM5)) and an ensemble of GCMs on the basis of delta change method under two future scenarios (RCP4.5 and RCP8.5). We applied the hydrological SWAT (Soil and Water Assessment Tool) model which uses the RCMs/GCMs outputs as input to analyze the impacts of climate change on the stream flow and peak flow of the upper reaches of the Kaidu River Basin. The simulation of climate factors under future scenarios indicates that both temperature and precipitation in the study area will increase in the future compared with the reference period, with the largest increase of annual mean temperature and largest percentage increase of mean annual precipitation being of 2.4°C and 38.4%, respectively. Based on the results from bias correction of climate model outputs, we conclude that the accuracy of RCM (regional climate model) simulation is much better for temperature than for precipitation. The percentage increase in precipitation simulated by the three RCMs is generally higher than that simulated by the ensemble of GCMs. As for the changes in seasonal precipitation, RCMs exhibit a large percentage increase in seasonal precipitation in the wet season, while the ensemble of GCMs shows a large percentage increase in the dry season. Most of the hydrological simulations indicate that the total stream flow will decrease in the future due to the increase of evaporation, and the maximum percentage decrease can reach up to 22.3%. The possibility of peak flow increasing in the future is expected to higher than 99%. These results indicate that less water is likely to be available in the upper reaches of the Kaidu River Basin in the future, and that the temporal distribution of flow may become more concentrated.     

基于优化的MaxEnt模型预测海灰翅夜蛾潜在地理分布区

海灰翅夜蛾Spodoptera littoralis是全球范围内热带和亚热带地区的重大农业入侵害虫,也是我国进境检疫性有害生物,其对欧洲、非洲和亚洲地区的农作物产生严重危害,并造成大量的经济损失。本研究基于海灰翅夜蛾的世界617个分布记录和8个生物气候因子,利用优化的MaxEnt模型预测其在全球和我国的潜在地理分布区,并进一步分析其在我国的传入、定殖和扩散风险。结果表明:气温是影响海灰翅夜蛾潜在地理分布的主导生物气候因子。其在全球的潜在地理分布区主要位于北美洲南部、南美洲、非洲、亚洲西部和东部、欧洲南部和大洋洲,在我国主要位于东部和南部地区。在我国,其潜在地理分布区范围内有着较多的贸易口岸和广泛的寄主分布,增大了其传入我国的风险,并为其定殖和扩散提供了有利的条件。建议海关口岸、农林行业部门加强对海灰翅夜蛾的检疫、调查和监测工作,严防海灰翅夜蛾传入。     

气候变化对新疆意大利蝗潜在分布的影响

意大利蝗Calliptamus italicus(L.)是新疆草原主要优势蝗虫之一,每年给新疆畜牧业经济带来严重损失,气候变化对其潜在分布影响的预测对其科学防治有重要意义。本研究采用意大利蝗的分布数据和生物气候数据,结合MaxEnt模型和ArcGIS软件,预测了BCC_CSM1.1气候模式下政府间气候变化专门委员会第五次工作报告(IPCC AR5)采用的RCP2.6、RCP4.5和RCP8.5三种气候情景在2021-2040年(2030s)、2041-2060年(2050s)和2061-2080年(2070s)的意大利蝗新疆潜在适生区分布范围。结果表明:在BCC_CSM1.1的各情景下,意大利蝗适生区在北疆及天山一带分布格局基本保持不变,但高度适生区面积都有所增加,其中在天山和阿尔泰山地区,意大利蝗中、高度适生区范围将向更高海拔区域蔓延,在北疆阿勒泰地区高度适生区明显增加。极端水分条件和水热条件对意大利蝗在新疆潜在分布发挥主要作用,其中4月、10月、3月和11月降水量对意大利蝗在新疆潜在分布影响最大,因其直接影响土壤相对含水量和土壤温度,从而决定意大利蝗卵的存活量。     

基于物种分布模型对未来气候变化下云南松毛虫在四川省适生区的预测

为明确云南松毛虫Dendrolimus houi于未来气候变化下在四川省的分布情况,运用最大熵（maximum entropy,MaxEnt）模型中的刀切法和Pearson相关系数分析法对未来气候数据（2050年和2070年）、林地因子及人为因子进行权重划分,筛选出影响云南松毛虫潜在分布的重要且相关系数较低的环境因子,并结合openModeller中的人工神经网络（artificial neural network,ANN）模型、生物气候（BioClim）模型、气候空间（climate space,CS）模型、气候信封（envelope score,ES）模型、基于统计概率和规则集的遗传算法（genetic alorithm for rule-set production,GARP）模型、MaxEnt模型和支持向量机（support vector machine,SVM）模型对云南松毛虫未来的适生区进行预测,利用AUC（area under curve）评价模型的精确度。结果发现,最暖季降水量、人类足迹指数、最冷季降水量和海拔对云南松毛虫在未来气候条件下的潜在分布有较强影响,在2050年贡献率分别为27.2%、16.0%、2.0%和4.9%,在2070年贡献率分别为20.6%、16.8%、9.7%和4.9%。对比7种模型的预测结果,发现SVM模型在2050年和2070年对云南松毛虫适生区预测的AUC为0.93,预测精确度最高,具有较高的可信度;该模型预测结果显示,从2050年至2070年云南松毛虫在四川省的总适生面积增加了4 269.8 km²,其中,中、低适生区面积共减少了17 185.8 km²,高适生区面积增加了21 455.6 km²。     

基于MaxEnt模型预测玉米矮花叶病毒的潜在适生区

为明确玉米矮花叶病毒 （maize dwarf mosaic virus, MDMV）在未来气候条件下在全球以及我国的潜在适生区,通过整理现有文献记载的MDMV发生分布数据,运用MaxEnt模型与ArcGIS10.2软件预测MDMV在历史和未来2个时间区段不同气候条件下（低胁迫情景SSP126和高胁迫情景SSP585）的潜在地理分布。结果显示,所建MaxEnt模型的受试者工作特征（receiver operatingcharacteristic, ROC）曲线下面积 （area under curve, AUC）值为0.911,表明预测结果可靠性高。MDMV的潜在分布受最冷月最低温 （仅此变量时贡献率最高,为67.9%）和年降水量 （除此变量时贡献率最低,为2.1%）的影响最大。历史气候条件下, MDMV在北美洲、南美洲南部、欧洲、非洲南部及北部、中亚以及大洋洲南部广泛适生;在我国除东北北部、内蒙古自治区东北部、新疆维吾尔自治区南部、西藏自治区北部、四川省东部、广西壮族自治区、广东省和海南省以外的大部分地区适生;未来气候条件下, MDMV在世界范围内的分布呈现北半球向北、南半球向南的变化趋向,在我国的分布则呈现向北收缩趋势。     

外来入侵植物假高粱在我国的潜在分布区分析

［目的］ 预测假高粱在我国的潜在分布区,为控制其在我国进一步传播扩散提供依据。［方法］ 基于采样后的假高粱(Sorghum halepense)全球分布数据,以19个生物气候因子作为预测的环境因子,MaxEnt模型作为物种适生性预测模型,同时结合假高粱的生态学参数,预测假高粱在我国的潜在分布。［结果］ 假高粱在我国有广泛的潜在分布区,目前的实际分布远没有达到其最大潜在分布范围,因此仍有可能继续扩散。最冷季节平均气温、年平均气温、最干季节降水量和最冷季节降水量等环境要素对假高粱的生长和分布具有重要影响。［结论］ 必须采取有效措施阻止假高粱的扩散。     

Effects of native and invasive Prosopis species on topsoil physiochemical properties in an arid riparian forest of Hormozgan Province,Iran

Biological invasions can alter soil properties within the range of their introduced, leading to impacts on ecosystem services, ecosystem functions, and biodiversity. To better understand the impacts of biological invasions on soil, we compared topsoil physiochemical properties at sites with invasive alien tree species (Prosopis juliflora), native tree species (Prosopis cineraria, Acacia tortilis, and Acacia ehrenbergiana), and mixed tree species in Hormozgan Province of Iran in May 2018. In this study, we collected 40 soil samples at a depth of 10 cm under single tree species, including P. juliflora, P. cineraria, A. tortilis, and A. ehrenbergiana, as well as under mixed tree species. The results showed that organic matter, moisture, potassium, calcium, nitrogen, and magnesium in topsoil at sites with A. tortilis and A. ehrenbergiana growing in combination with P. cineraria were higher than that at sites where P. juliflora was present (P<0.05). Sodium at sites with A. tortilis and A. ehrenbergiana growing in combination with P. cineraria and P. juliflora was lower as compared to that at sites with just A. tortilis and A. ehrenbergiana. Electrical conductivity was lower at sites with A. tortilis and A. ehrenbergiana growing in combination with P. cineraria, and it was higher at sites with mixed Acacia and P. juliflora trees. Based on the generally more positive effect of native Acacia and P. cineraria on topsoil physiochemical properties as compared to the P. julifora, afforestation with native tree species is preferable for soil restoration. In addition, due to the negative effects of P. julifora on soil properties, P. julifora spread should be better managed.     

基于MaxEnt模型预测外来入侵植物刺果瓜在中国的潜在地理分布

为明确外来入侵植物刺果瓜 Sicyos angulatus在我国的潜在地理分布,基于MaxEnt模型、ArcGIS软件及全球分布数据,在历史气候条件及未来气候条件下 （低强迫情景SSP126和高强迫情景SSP585）预测刺果瓜在中国的潜在地理分布。结果显示, MaxEnt模型的曲线下面积（area undercurve, AUC）值为0.977,表明模型具有较高的可靠性;气温季节性变化、最热月的最高温、最干月降水量和最湿季节的降水量是影响刺果瓜在中国潜在地理分布的4个关键环境变量;历史气候条件下刺果瓜主要发生在中国东部、中部和西部,适生区总面积占中国陆地总面积的23.29%;未来气候条件下,刺果瓜在中国的适生区范围有所减少, SSP126和SSP585情景下刺果瓜在中国的适生区总面积分别为186.10×10⁴ km²和162.68×10⁴ km²,分别占中国陆地总面积的19.35%和16.91%,但还是主要覆盖黄淮海平原夏播玉米区和南方丘陵玉米区等产区,质心由南向北移动。     

Scenario simulation of water retention services under land use/cover and climate changes: a case study of the Loess Plateau,China

Comprehensive assessments of ecosystem services in environments under the influences of human activities and climate change are critical for sustainable regional ecosystem management. Therefore, integrated interdisciplinary modelling has become a major focus of ecosystem service assessment. In this study, we established a model that integrates land use/cover change (LUCC), climate change, and water retention services to evaluate the spatial and temporal variations of water retention services in the Loess Plateau of China in the historical period (2000-2015) and in the future (2020-2050). An improved Markov-Cellular Automata (Markov-CA) model was used to simulate land use/land cover patterns, and ArcGIS 10.2 software was used to simulate and assess water retention services from 2000 to 2050 under six combined scenarios, including three land use/land cover scenarios (historical scenario (HS), ecological protection scenario (EPS), and urban expansion scenario (UES)) and two climate change scenarios (RCP4.5 and RCP8.5, where RCP is the representative concentration pathway). LUCCs in the historical period (2000-2015) and in the future (2020-2050) are dominated by transformations among agricultural land, urban land and grassland. Urban land under UES increased significantly by 0.63×10³ km²/a, which was higher than the increase of urban land under HS and EPS. In the Loess Plateau, water yield decreased by 17.20×10⁶ mm and water retention increased by 0.09×10⁶ mm in the historical period (2000-2015), especially in the Interior drainage zone and its surrounding areas. In the future (2020-2050), the pixel means of water yield is higher under RCP4.5 scenario (96.63 mm) than under RCP8.5 scenario (95.46 mm), and the pixel means of water retention is higher under RCP4.5 scenario (1.95 mm) than under RCP8.5 scenario (1.38 mm). RCP4.5-EPS shows the highest total water retention capacity on the plateau scale among the six combined scenarios, with the value of 1.27×10⁶ mm. Ecological restoration projects in the Loess Plateau have enhanced soil and water retention. However, more attention needs to be paid not only to the simultaneous increase in water retention services and evapotranspiration but also to the type and layout of restored vegetation. Furthermore, urbanization needs to be controlled to prevent uncontrollable LUCCs and climate change. Our findings provide reference data for the regional water and land resources management and the sustainable development of socio-ecological systems in the Loess Plateau under LUCC and climate change scenarios.     

The current and future potential distribution of Melaleuca quinquenervia

Melaleuca quinquenervia (broad-leaved paperbark) is a tree native to Australia, Papua New Guinea and New Caledonia that has become moderately to highly invasive in a number of countries over the last century. Although a synoptic view of the invasion would be of considerable use in devising appropriate control strategies, little effort has been applied to investigate the potential distribution of this invasive species. A climate suitability analysis was undertaken in this study, to assess the risk of geographic range expansion of M . quinquenervia under current climate and how this distribution will be modified by climate change during the 2080s. CLIMEX™ was used to infer the climatic requirements of M . quinquenervia from observations in both the native and exotic range. The accuracy of the developed model was assessed using independent naturalised observations of the species in Asia, Africa, Oceania and the Caribbean. Projections of climatic suitability indicate considerable scope for further invasion, with the most suitable areas occurring adjacent to existing populations in south-east Asia, the Caribbean, Central and South America and the Gulf coast in southern USA. Under future climates, model projections indicate there will be a marked contraction of suitable habitat in most regions and a slight poleward range expansion within south-eastern China, southern Europe and northern New Zealand.     

基于国际贸易和环境气候的生物入侵风险评估综合模型研究初探

国际贸易所带来的外来生物入侵已经成为各个国家面临的重要问题,对其进行风险评估迫在眉睫。为了能够对跨国运输带来的生物入侵风险进行量化评估,我们以联合国粮食及农业组织(FAO)的国际贸易、WorldClim的环境气候因子、全球生物多样性信息网络(GBIF)的物种分布和国内交通运输等数据为基础,以 R 语言为主要计算工具,建立综合分析外来生物入侵风险的评估方法。以苹果蠹蛾Cydia pomonella 和番茄潜叶蛾Tuta absoluta 为例计算其引入风险和定殖可能性,综合量化评估国际贸易带来的物种入侵风险。借此结果对国际进口贸易从入侵风险角度有针对性地提出合理建议。