Maxent模型复杂度对物种潜在分布区预测的影响

生态位模型在入侵生物学和保护生物学中具有广泛的应用, 其中Maxent模型最为流行, 被越来越多地应用在预测物种的现实分布和潜在分布的研究中。在Maxent模型中, 多数研究者采用默认参数来构建模型, 这些默认参数源自早期对266个物种的测试, 以预测物种的现实分布为目的。近期研究发现, Maxent模型采用复杂机械学习算法, 对采样偏差敏感, 易产生过度拟合, 模型转移能力仅在低阈值情况下较好。基于默认参数的Maxent模型不仅预测结果不可靠, 而且有时很难解释。在本研究中, 作者以入侵害虫茶翅蝽(Halyomorpha halys)为例, 采用经典模型构建方案(即构建本土模型然后将其转移至入侵地来评估), 利用ENMeval数据包来调整本土Maxent模型调控倍频和特征组合参数, 分析各种参数条件下模型的复杂度, 然后选取最低复杂度的模型参数(即为最优模型), 综合比较默认参数和调整参数后Maxent模型的响应曲线和预测结果, 探讨Maxent模型复杂度对预测结果的影响及Maxent模型构建时所需注意事项, 以期对物种潜在分布进行合理的预测, 促进Maxent模型在我国的合理运用和发展。作者认为, 环境变量的选择至关重要, 需要综合分析其对所模拟物种分布的限制作用和环境变量之间的空间相关性。构建Maxent模型前需对物种分布采样偏差及模型的构建区域进行合理地判断, 模型构建时需要比较不同参数下模型的预测结果和响应曲线, 选取复杂度较低的模型参数来最终建模。在茶翅蝽的分析中, Maxent模型的默认参数和最优模型参数不同, 与Maxent模型默认参数相比, 采用调整参数后所构建的模型预测效果较好, 响应曲线较为平滑, 模型转移能力较高, 能够较为合理反映物种对环境因子的响应和准确地模拟该物种的潜在分布。     

应用Maxent建立沙筛贝潜在生境模型

外来入侵物种沙筛贝适应能力强、繁殖率高,一旦入侵,将严重危害潮间带生物多样性。我国广东省部分沿海地区的潮间带和牡蛎增养殖区已被沙筛贝入侵,且污损情况较严重。为了了解沙筛贝目前在我国的潜在生境情况,本研究选用最大熵模型(Maxent)和地理信息系统相结合,建立了沙筛贝在我国和全球的潜在生境预测模型,并利用受试者工作特征曲线(ROC)和实地调查对结果进行验证。结果表明: 沙筛贝全球存在概率较高的地区分布于北美洲与南美洲之间、印度南部、斯里兰卡和我国长江以南沿海以及南半球的澳大利亚范迪门湾;沙筛贝在我国的适生区域主要分布在上海以南沿海省份。影响沙筛贝适生区域分布的主要环境变量包括水汽压、温度和太阳辐射,经ROC检测后训练集AUC值为0.996,预测结果达到优秀水平。研究结果可为沙筛贝入侵风险评估和治理提供理论依据, 补充我国外来入侵物种的潜在生境预测工作。     

中国外来入侵植物的分布格局及其与环境因子和人类活动的关系

认识区域尺度上外来入侵植物的分布格局及其成因对预测入侵的影响和入侵种的管理具有重要意义。该文采用聚类分析和排序的方法分析了我国外来入侵植物的空间格局,并利用多元线性逐步回归和典范对应分析探讨了自然环境因子和人类活动强度对中国32个省级空间单位(省市自治区,不包括香港和澳门)中外来入侵植物分布的影响。结果表明,中国各省外来入侵植物物种数从南到北逐渐减少,导致这一格局的主要因子为无霜期;各省外来入侵植物物种密度由东南海岸向内陆递减,造成这一趋势的主要影响因素为交通密度;纬度是解释中国各省外来入侵植物物种组成变异的主要因子,因此中国32个省区可归为低、中、高纬度区3大类型。在此基础上预测中国东南部地区有遭受更多外来植物入侵的可能;此外,交通发达的区域也将成为外来植物入侵的热点区,应该引起有关部门的重视。     

物种分布模型在海洋潜在生境预测的应用研究进展

海洋生物的栖息分布与环境要素的关联性一直是海洋生态学研究的热点之一.近年来,物种分布模型被广泛应用于预测海洋物种分布、潜在适宜性生境评价等研究,为保护海洋生物多样性、防治外来物种入侵及制定渔业管理措施等提供了一条有效途径.物种分布模型主要包括生境适宜性指数模型、机理模型和统计模型.本文对物种分布模型的理论基础进行了归纳和总结,回顾了物种分布模型在预测海洋物种潜在地理分布研究中的开发与应用,重点介绍了不同类型统计模型在海洋物种潜在分布预测中的研究实例.比较各种选取变量和模型验证方法,认为赤池信息准则对于选取模型变量具有优势,Kappa系数和受试者操作特征曲线下面积在验证模型精度中应用最广泛.阐述了物种分布模型存在的问题及未来发展趋势,随着海洋生物生理机制研究的进一步深入,机理模型将是今后物种分布模型发展的重点.     

中国外来入侵生物的空间分布格局及其影响因素

不同地理区域影响生物分布格局的因子不同,对外来入侵物种也是如此。在区域尺度上分析外来入侵生物的空间分布格局及其影响因子对预测生物入侵的影响及入侵种的控制管理具有重要意义。本研究应用中国外来入侵物种数据库、自然环境数据库和社会人文环境数据库,分析了我国外来入侵动植物的空间格局;并运用主成分分析（PCA）和典范对应分析（CCA）探讨了自然环境和人类活动等因素对外来入侵动植物分布格局的影响;同时研究了外来入侵物种多样性与本地物种多样性之间的关系。结果表明,我国现有外来入侵动物138种、入侵植物384种,其数量和密度都呈现出由东南沿海向西北内陆减少的趋势,且入侵动物和入侵植物空间格局基本一致;降水（MAP）是决定我国外来入侵动植物分布格局的主要自然环境因子,国民生产总值（GDP）是主要社会经济影响因子。在全国尺度上,外来入侵物种多样性与本地物种多样性之间呈显著的正相关关系,但地域间存在较大差异。不同区域外来入侵物种与本土物种多样性的相关关系表现出不同,与研究尺度有密切关系。     

怒江河谷入侵植物与乡土植物丰富度的分布格局与影响因子

外来物种入侵严重威胁着乡土植物多样性并削弱了生态系统服务功能。本文基于滇西北怒江河谷植被调查的样方数据, 从群落水平研究了乡土和入侵植物多样性的空间分布格局, 以及地形、气候、人类干扰等因子对两种格局的影响。本研究共记录到外来入侵植物26种, 隶属于13科21属; 乡土植物1,145种, 分属于158科628属。沿着怒江河谷, 入侵植物物种丰富度随纬度与海拔的增加而减少; 乡土物种丰富度则随纬度增加而增加, 并在海拔梯度上呈单峰格局。运用广义线性模型分析公路边缘效应(反映生境干扰)、气候、地形和土壤等环境因素对物种丰富度分布格局的影响。等级方差分离的结果显示, 公路两侧的生境干扰对入侵种和乡土种的丰富度格局均具有首要影响。在自然环境因子中, 降水量是入侵植物丰富度的主要限制因子, 而乡土物种丰富度则主要受到地形因子尤其是坡向的影响。结构方程模型的分析结果也表明, 乡土植物和入侵植物丰富度之间的负相关关系反映了二者对环境响应的差异。本文结果支持物种入侵的资源可利用性限制假说, 并强调了人类活动对生物多样性的负面影响; 乡土植物或已较好地适应了干旱河谷气候, 但并没有显示出对外来物种入侵的抵抗作用。     

环境DNA技术在贻贝入侵监测中的应用挑战

沼蛤(Limnoperna fortunei)和斑马贻贝(Dreissena polymorpha)是淡水系统中常见的入侵贻贝物种,对其种群规模的持续监测是入侵贻贝防治管控中的关键环节。随着分子生物学技术的发展,入侵物种监测中逐渐尝试利用环境DNA(eDNA)技术实现快速、灵敏检测。然而,在入侵物种引入-定植-扩散过程的监测中,eDNA技术的灵敏度及定量效果受到诸多因素的影响,给实际应用带来挑战。系统梳理了国内外学者利用eDNA技术监测沼蛤、斑马贻贝等入侵物种的研究进展;分析了eDNA技术的采样方案、引物设计、定量分析、质量保证、原位便携仪器设计等影响监测效率与准确率的关键环节;进一步探讨了eDNA技术在贻贝入侵监测中的优势和局限性,以及未来的改进方向。     

人类活动和气候因子对中国外来入侵物种分布的影响

全球变化引起的生物入侵已经严重地威胁了生物多样性分布和生态系统功能与服务.生物入侵分布格局及其影响因素的深入研究有助于更好地预测和管理外来入侵物种,从而有效地保护生物多样性和生态系统功能与服务.然而,目前尚未有研究系统地讨论不同时期人类活动与自然因子对中国外来入侵植物、外来入侵昆虫和外来入侵微生物分布格局的影响.本研究基于最近出版的外来入侵生物相关资料,首先描述了3个外来入侵类群在我国的省级分布格局.然后利用单因素普通最小二乘法模型和空间自回归模型探讨了不同时期人类活动和自然因素对这些分布格局的影响.结果表明, 3个外来入侵类群丰富度在我国各省份的分布呈现相似的格局,即东南偏多,西北偏少;相较于入侵植物和入侵昆虫,入侵微生物在东北和华北的丰富度也较高. 2000年的国内生产总值是影响3个分类群丰富度最大的人类活动指标,且均呈显著正相关关系.在自然因子中,年均温和年降水对入侵植物丰富度和入侵昆虫丰富度解释率也较高,且呈显著正相关关系,但对入侵微生物丰富度影响不显著.值得一提的是,即使在控制自然因子的影响后, 2000年的国内生产总值依然是对3个分类群丰富度影响最大的因子.研究结果表明,尽...     

气候变化对东北濒危动物驼鹿潜在生境的影响

气候变化是造成生物多样性下降和物种灭绝的主要因素之一。研究气候变化对物种生境,尤其是濒危物种生境影响对未来保护物种多样性和保持生态系统功能完整性具有重要意义。以驼鹿乌苏里亚种(Alces alces cameloides)为研究对象,选取了对驼鹿分布可能存在影响的22个环境因子,利用最大熵(Maxent)模型模拟了驼鹿基准气候条件下在我国东北的潜在生境分布,并预测了RCP4.5和RCP8.5两种气候变化情景下2041—2060年(2050s)、2061—2080年(2070s)驼鹿潜在分布,采用接收工作曲线下面积(AUC)对模型预测能力进行评估。研究结果表明:最大熵模型预测驼鹿潜在生境分布的精度较高(平均AUC值为0.845),22个环境因子中,年均温、最暖季均温、年降水、平均日较差是影响驼鹿生境分布的主要因子。基准气候条件下,驼鹿的潜在生境面积占研究区域总面积的36.4%,潜在生境分布区主要在大、小兴安岭。随着时间的推移,研究区内驼鹿当前潜在生境面积明显减少,而新增潜在生境面积较少,总面积呈现急剧减少的趋势,其中RCP8.5情景减少程度大于RCP4.5情景。至2050s阶段,当前潜在生境面积平均将减少62.3%,新增潜在分布面积平均仅为3.6%,总潜在生境面积最高将减少65.6%,平均将减少58.8%;至2070s阶段,当前潜在生境面积平均将减少75.8%,新增潜在分布面积平均仅为1.9%,总潜在生境面积最高将减少93.1%,平均减少73.9%。空间分布上,驼鹿的潜在生境的几何中心将先向西北移动,然后再向高纬度地区西南方向迁移,至2050s阶段,潜在分布生境的几何中心在RCP4.5和RCP8.5情景下的迁移距离分别为183.5 km和210.8 km;至2070s阶段,相应情景下的迁移距离将缩短至28.7 km和33.8 km。潜在生境分布整体呈现向高海拔、高纬度迁移的趋势。     

基于四种常用机器学习的梭梭(Haloxylon ammod endron (C.A.Mey) Bunge)适宜生境分布研究

梭梭Haloxylon ammodendron (C.A.Mey) Bunge是适应中亚荒漠生境的耐寒、抗旱、耐盐碱C4植物, 预测该物种地理分布范围以及未来不同气候变化情景下该物种适宜生境分布的变化, 对于我国荒漠区生态环境保护具有重要意义。利用收集的梭梭分布数据与33个环境因子, 基于四种常用的机器学习算法组成的组合模型对该物种适宜生境在我国西北干旱区的分布进行了预测。结果表明: (1)不同的模型算法的结果在空间上具有较高的一致性, 但其分布细节具有一定的差异性。(2)梭梭在我国的适宜生境面积约为0.91×106 km2, 主要分布在新疆的准噶尔盆地和塔里木盆地边缘, 甘肃河西走廊地区, 内蒙古巴丹吉林、乌兰布和、腾格里沙漠和库布齐沙漠的西部, 青海省柴达木盆地等地区。(3)未来气候变化对该物种适宜生境分布的影响取决于升温幅度, 但是在大部分的增温情景中, 该物种的适宜生境变化较小。(4)高适宜生境面积为0.19×106 km2, 应该作为梭梭原生产地保护以及人工种植的重点区域。(5)集成多模型结果的组合物种分布模型能够在一定程度上减少模型的不确定性, 增加模型的精度。在环境条件限制性较高的干旱区, 加入土壤因素能够有效提高物种分布模型的建模科学性和合理性。     

Modeling potential habitats for alien species Dreissena polymorpha in Continental USA

The effective measure to minimize the damage of invasive species is to block the potential invasive species to enter into suitable areas. 1864 occurrence points with GPS coordinates and 34 environmental variables from Daymet datasets were gathered, and 4 modeling methods, i.e., Logistic Regression (LR), Classification and Regression Trees (CART), Genetic Algorithm for Rule-Set Prediction (GARP), and maximum entropy method (Maxent), were introduced to generate potential geographic distributions for invasive species Dreissena polymorpha in Continental USA. Then 3 statistical criteria of the area under the Receiver Operating Characteristic curve (AUC), Pearson correlation (COR) and Kappa value were calculated to evaluate the performance of the models, followed by analyses on major contribution variables. Results showed that in terms of the 3 statistical criteria, the prediction results of the 4 ecological niche models were either excellent or outstanding, in which Maxent outperformed the others in 3 aspects of predicting current distribution habitats, selecting major contribution factors, and quantifying the influence of environmental variables on habitats. Distance to water, elevation, frequency of precipitation and solar radiation were 4 environmental forcing factors. The method suggested in the paper can have some reference meaning for modeling habitats of alien species in China and provide a direction to prevent Mytilopsis sallei on the Chinese coast line.     

Modeling potential habitats for alien species Dreissena polymorpha in Continental USA

The effective measure to minimize the damage of invasive species is to block the potential invasive species to enter into suitable areas. 1864 occurrence points with GPS coordinates and 34 environmental variables from Daymet datasets were gathered, and 4 modeling methods, i.e., Logistic Regression (LR), Classification and Regression Trees (CART), Genetic Algorithm for Rule-Set Prediction (GARP), and maximum entropy method (Maxent), were introduced to generate potential geographic distributions for invasive species Dreissena polymorpha in Continental USA. Then 3 statistical criteria of the area under the Receiver Operating Characteristic curve (AUC), Pearson correlation (COR) and Kappa value were calculated to evaluate the performance of the models, followed by analyses on major contribution variables. Results showed that in terms of the 3 statistical criteria, the prediction results of the 4 ecological niche models were either excellent or outstanding, in which Maxent outperformed the others in 3 aspects of predicting current distribution habitats, selecting major contribution factors, and quantifying the influence of environmental variables on habitats. Distance to water, elevation, frequency of precipitation and solar radiation were 4 environmental forcing factors. The method suggested in the paper can have some reference meaning for modeling habitats of alien species in China and provide a direction to prevent Mytilopsis sallei on the Chinese coast line.     

Mapping Current and Potential Distribution of Non-Native Prosopis juliflora in the Afar Region of Ethiopia

We used correlative models with species occurrence points, Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, and topo-climatic predictors to map the current distribution and potential habitat of invasive Prosopis juliflora in Afar, Ethiopia. Time-series of MODIS Enhanced Vegetation Indices (EVI) and Normalized Difference Vegetation Indices (NDVI) with 250 m² spatial resolution were selected as remote sensing predictors for mapping distributions, while WorldClim bioclimatic products and generated topographic variables from the Shuttle Radar Topography Mission product (SRTM) were used to predict potential infestations. We ran Maxent models using non-correlated variables and the 143 species- occurrence points. Maxent generated probability surfaces were converted into binary maps using the 10-percentile logistic threshold values. Performances of models were evaluated using area under the receiver-operating characteristic (ROC) curve (AUC). Our results indicate that the extent of P. juliflora invasion is approximately 3,605 km² in the Afar region (AUC  = 0.94), while the potential habitat for future infestations is 5,024 km² (AUC  = 0.95). Our analyses demonstrate that time-series of MODIS vegetation indices and species occurrence points can be used with Maxent modeling software to map the current distribution of P. juliflora, while topo-climatic variables are good predictors of potential habitat in Ethiopia. Our results can quantify current and future infestations, and inform management and policy decisions for containing P. juliflora. Our methods can also be replicated for managing invasive species in other East African countries.     

Habitat suitability of Gymnocladus assamicus - A critically endangered plant of Arunachal Pradesh,India using machine learning and statistical modeling

The present study sought to identify the potential distribution range of critically endangered Gymnocladus assamicus in Arunachal Pradesh based on published data and field collection. We used the Maxent model to estimate the range of distribution and the result was then compared with three other models, i.e., the Generalized Linear Model (GLM), the Bioclim and the Random Forest model to assess the species' habitat suitability. A total of 23 different environmental variables were used, including bioclimatic ones, monthly minimum and maximum temperature, monthly precipitation and elevation data. The Maxent output listed 12 variables explaining 99.9% variation in the model. In comparison, Maxent showed the maximum region under habitat suitability criteria (1884.48 km²), followed by Random Forest (70.73 km²) and Bioclim (11.62 km²) model. Except for the Maxent model, suitable habitats predicted by other models are highly restricted within and across the study species' current distribution range. The average model prediction shows an expanded distribution range for the species up to Tawang which is the closest district of currently known distribution of the species in the state. Thus, the present study recognizes the importance of the geographic range of G. assamicus, a critically endangered species with very limited spatial distribution range and also provides some specific details to explore possible habitats for the species in new areas of potential occurrence in Arunachal Pradesh, India.     

Validating distribution models for twelve endemic bird species of tropical dry forest in western Mexico

Considering the high biodiversity and conservation concerns of the tropical dry forest, this study aim is to predict and evaluate the potential and current distributions of twelve species of endemic birds which distribute along the western slope of Mexico. The main goal is to evaluate altogether different methods for predicting actual species distribution models (ADMs) of the twelve species including the identification of key environmental potential limiting factors. ADMs for twelve endemic Mexican birds were generated and validated by means of applying: (1) three widely used species niche modeling approaches (ENFA, Garp, and Maxent); (2) two thresholding methods, based on ROC curves and Kappa Index, for transforming continuous models to presence/absence (binary) models; (3) documented habitat–species associations for reducing species potential distribution models (PDMs); and (4) field occurrence data for validating final ADMs. Binary PDMs' predicted areas seemed overestimated, while ADMs looked drastically reduced and fragmented because of the approach taken for eliminating those predicted areas which were documented as unsuitable habitat types for individual species. Results indicated that both thresholding methods generated similar threshold values for species modeled by each of the three species distribution modeling algorithms (SDMAs). A Wilcoxon signed‐rank test, however, showed that Kappa values were generally higher than ROC curve for species modeled by ENFA and Maxent, while for Garp models there were no significant differences. Prediction success (e.g., true presences percentage) obtained from field occurrence data revealed a range of 50%–82% among the 12 species. The three modeling approaches applied enabled to test the application of two thresholding methods for transforming continuous to binary (presence/absence) models. The use of documented habitat preferences resulted in drastic reductions and fragmentation of PDMs. However, ADMs predictive success rate, tested using field species occurrence data, varied between 50 and 82%.     

Modeling habitat distribution of Cornus officinalis with Maxent modeling and fuzzy logics in China

Aims Predicting suitable habitat distribution is an effective way to protect rare or endangered medicinal plants. Cornus officinalis is a perennial tree growing in forest edge and its air-dried pericarp is one of the traditional Chinese medicines (TCM) with significant medicinal values. In recent years, C. officinalis has undergone severe degeneration of its natural habitat owing to growing market demands and unprecedented damage to the forests. Moreover, the degeneration of suitable habitat has threatened the supply of medicinal materials, and even led to the extinction of some engendered medicinal plant species. In this case, there is a great risk to introduce and cultivate medicinal plants if planners determine the suitable cultivation regions based on personal subjective experience alone. Therefore, predicting suitable potential habitat distribution of medicinal plants (e.g. C. officinalis) and revealing the environmental factors determining such distribution patterns are important to habitat conservation and environmental restoration.Methods In this article, we report the results of a study on the habitat distribution of C. officinalis using maximum entropy (Maxent) modeling and fuzzy logics together with loganin content and environmental variables. The localities of 106 C. officinalis in China were collected by our group and other researchers and used as occurrence data. The loganin content of 234 C. officinalis germplasm resources were tested by high-performance liquid chromatography (HPLC) and used as content data. 79 environmental variables were selected and processed with multicollinearity test by using Pearson Correlation Coefficient (r) to determine a set of independent variables. The chosen variables were then processed in the fuzzy linear model according to the cell values (maximum, minimum) of localities with estimated loganin content. The SDMtoolbox was used to spatially rarefy occurrence data and prepare bias files. Furthermore, combined Maxent modeling and fuzzy logics were used to predict the suitable habitat of C. officinalis. The modeling result was validated using null-model method.Important findings As a result, six environmental factors including tmin3, prec3, bio4, alt, bio12 and bio3 were determined as key influential factors that mostly affected both the habitat suitability and active ingredient of C. officinalis. The highly suitable regions of C. officinalis mainly distribute in a 'core distribution zone' of the east-central China. The statistically significant AUC value indicated that combined Maxent modeling and fuzzy logics could be used to predict the suitable habitat distribution of medicinal plants. Furthermore, our results confirm that ecological factors played critical roles in assessing suitable geographical regions as well as active ingredient of plants, highlighting the need for effective habitat rehabilitation and resource conservation.     

Dealing with vagueness in complex forest landscapes: A soft classification approach through a niche-based distribution model

The increasing interest in biodiversity conservation has led to the development of new approaches to facilitate ecologically based conservation policies and management plans. In this context, the development of effective methods for the classification of forest types constitutes a crucial issue as forests represent the most widespread vegetation structure and play a key role in ecosystem functioning. In this study a maximum entropy approach (Maxent) to forest type classification in a complex Mediterranean area, has been investigated. Maxent, a niche-based model of species/habitat distribution, allowed researchers to estimate the potential distribution of four forest types: Holm oak, Mixed oak, Mixed broadleaved and Riparian forests. The Maxent model's internal tests have proved a powerful tool for estimating the model's accuracy and analyzing the effects of the most important variables in the produced models. Moreover the comparison with a spectral response-based fuzzy classification, showed a higher accuracy in the Maxent outputs, demonstrating how the use of environmental variables, combined with spectral information in the classification of natural or semi-natural land cover classes, improves map accuracies. The modeling approach followed by this study, taking into account the uncertainty proper of the natural ecosystems and the use of environmental variables in land cover classification, can represent a useful approach to making more efficient and effective field inventories and to developing effective conservation policies.     

Using maximum entropy to predict the potential distribution of an invasive freshwater snail

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Evaluating models to assess the distribution of Buxus balearica in southern Spain

Question: Which is the best model to predict the habitat distribution of Buxus balearica Lam. in southern Spain? Location: Málaga and Granada, Spain, across an area of 38 180 km². Methods: Prediction models based on 17 environmental variables were tested. Six methods were compared: multivariate adaptive regression spline (MARS), maximum entropy approach to modelling species' distributions (Maxent), two generic algorithms based on environmental metrics dissimilarity (BIOCLIM and DOMAIN), Genetic Algorithm for Rule‐set Prediction (GARP), and supervised learning methods based on generalized linear classifiers (support vector machines, SVMs). To test the predictive power of the models we used the Kappa index. Results: Maxent most accurately predicted the habitat distribution of B. balearica, followed by MARS models. The other models tested yielded lower accuracy values. A comparison of the predictive power of the models revealed that climate variables made the highest contributions among the environmental variables studied. The variables that made the lowest contributions were the insolation models. To examine the sensitivity of the models to a reduction in the number of variables, a test showed that accuracy of over 0.90 was maintained by applying just three climatic variables (spring rainfall, mean temperature of the warmest month, and mean temperature of the coldest month). Maps derived from the algorithms of all models tested coincided well with the known distribution of the species. Conclusions: Model habitat prediction is a preliminary step towards highlighting areas of high habitat suitability of B. balearica. These data support the results of previous research, which show that MaxEnt is the best technique for modelling species distributions with small sample sizes.     

基于最大熵值法生态位模型（Maxent）的三种实蝇潜在适生性分布预测

本研究首先对3种重要生态位模型BIOCLIM, DOMAIN和Maxent（基于最大熵值原理模型）的分布预测精确度进行了分析和比较, 再结合分布点记录以及一系列环境数据图层对3种重要外来入侵性检疫害虫（葫芦寡鬃实蝇Dacus bivittatus、埃塞俄比亚寡鬃实蝇D. ciliatus和西瓜寡鬃实蝇D. vertebratus）的潜在适生性分布区域进行了预测和分析。在模型预测精确度的比较过程中, 3种评估指标（ROC/AUC, Kappa, TSS）均显示Maxent拥有最好的预测结果和最好的运行性能。由Maxent对葫芦寡鬃实蝇、埃塞俄比亚寡鬃实蝇和西瓜寡鬃实蝇的预测结果显示, 这3种实蝇在中美洲、南美洲、东南亚和澳大利亚沿岸的广大地区在总体上具有相似的分布区域。相对而言, 埃塞俄比亚寡鬃实蝇在全球范围具有最为广泛的分布区域, 除前述地区外, 其潜在适生区还包括地中海沿岸、沙特阿拉伯、也门、安曼和伊朗南部的大片地区, 这也意味着在3种寡鬃实蝇中, 它能忍受变化幅度最广的生态、环境条件。在中国, 云南和海南都极适宜于3种实蝇的生存, 同时广东南部及台湾的部分地区也是它们的潜在适生区。基于Maxent的预测结果显示, 相对而言, 埃塞俄比亚寡鬃实蝇在中国范围也具有最为广泛的分布区域, 除前述省份和地区外, 四川、贵州和西藏的南部部分地区以及中国南部的部分沿海地区, 也都是它的潜在适生区。综合所得出的预测结果, 3种寡鬃实蝇从境外传入广东并在此定殖的风险可能性是实际存在的。Jackknife分析显示, 温度以及与此有关的环境因子对于3种实蝇在全球和局部地区的分布模式和分布情况都有极大的影响, 并需要进一步的研究。