物种潜在分布地预测系统PSDS的设计与实现

本研究设计并实现了一个基于地理信息系统(GIS)的仅用物种已知分布点数据预测物种潜在分布地的PSDS系统.在这一系统中,通过层次聚类算法对物种已知分布点数据进行处理,减少了异常值对预测结果的影响,从而解决了环境包络模型预测结果过于乐观的问题,在物种已知分布数据较少时也能取得较好的结果.该系统实现了数据加载与导出、图层浏览与显示、生态因子分析与分布地预测、结果展示等功能,操作方便,简单易用.本文以白冠长尾雉(Syrmaticus reevesii)为例,根据4个省的少量已知分布点数据对其在国内的潜在分布地进行了预测,获得了较理想的结果,可为该物种的保护提供科学依据.     

基于MaxEnt模型预测梅的潜在适生区分布

目的：确定影响梅分布的主要环境因子,并预测当前与未来条件下梅的适生区。方法：收集172份梅分布点的32个环境因子数据,构建MaxEnt模型,筛选影响梅生长的主导环境因子,结合地理信息系统（ArcGIS10.8）绘制梅目前与未来的适生区分布预测图。结果：影响梅分布的主要环境因子有5个（最冷月最低气温、年降水量、最暖季降水量、年温差与最干燥月降水量）;其中最冷月最低气温对梅生存概率影响最大,当最冷月最低气温约10.1℃时梅适生概率最大,达到71.47%。模拟当前气候环境下,梅的高适生区、中适生区和低适生区面积分别占全国总面积的7.78%、17.01%与7.73%。目前高适宜区主要分布在广东、广西、四川、云南、贵州、重庆、浙江与台湾等省,在SSP1-2.6与SSP5-8.5下,梅适宜面积（潜在高适生与中适生区）在2021至2060年期间,呈现波浪式增加和北移的趋势,分别为当前的101.85%和102.28%。结论：本研究结果可为梅资源的可持续利用,以及梅人工种植的合理布局与区划研究提供科学依据。     

蒙古黄芪潜在分布区预测的多模型比较

根据蒙古黄芪（Astragalus membranaceus（Fisch.）Bge.var.mongholicus（Bge.）Hsiao）123个样本点数据和19个环境数据,采用4种生态位模型对蒙古黄芪在中国的潜在适生区进行综合分析,并采用受试者工作特征曲线ROC和Kappa统计量,比较不同模型的预测效果。结果显示：4个模型预测精度良好,一致性显著。AUC值均达到0.8以上,Kappa值均达到0.6以上;其中DOMAIN模型的AUC值和Kappa值均最大,说明该模型的预测精度最佳,预测结果最稳定。潜在适生区的预测结果发现,GARP模型预测的最适宜区范围最广;MAXENT和BIOCLIM模型预测结果较为相似;DOMAIN模型预测结果比较分散。4个模型预测结果均表明西北一带可以作为蒙古黄芪栽培引种的主要产区。蒙古黄芪潜在适生区主要分布于中国北纬33°以北地区;最适宜区主要分布于甘肃、宁夏、陕西、山西、河北和内蒙古等地区。     

基于最大熵模型(MaxEnt)预测暗紫贝母的潜在分布

暗紫贝母(Fritillaria unibracteata)干燥的鳞茎是市场名贵药材"川贝"的主要来源,目前由于人们的过度挖采及环境的恶化,使得其野生资源已近枯竭,被列为国家三级濒危保护药材物种。该研究通过收集暗紫贝母的地理分布点经纬度,结合26项生态因子,运用最大熵模型(MaxEnt)并结合地理信息系统(ArcGIS),对其在中国的潜在分布区域进行了预测。结果表明:暗紫贝母的潜在适生区主要分布在四川西部和北部、青海南部、甘肃南部,其中四川阿坝藏族羌族自治州的理县、茂县、松潘县、红原县、黑水县等地区,青海省果洛藏族自治州的久治县、玛沁县、同德县、兴海县、河南县地区以及甘肃省甘南藏族自治州地区是暗紫贝母最佳适生区。此外,在西藏和云南也有零星的分布。对暗紫贝母的分布贡献率较大的主要生态因子有5个,分别是海拔(40.8%),年均降水量(28%),1月最高温度(7.1%),最干季平均温度(6.6%)和昼夜温差日均值(6.6%)。其中,海拔为2700~4 500m、年均降水量为400~1 400mm,是暗紫贝母最适宜生长的生态位参数。该研究结果为暗紫贝母的野生抚育和人工栽培提供了重要的科学依据。     

基于MaxEnt模型的广东省红树林潜在适生区和保护空缺分析

红树林是分布在热带、亚热带海岸潮间带的木本群落,具有重要的生态功能。广东省是中国红树林分布面积最大的省份,在红树林保护方面具有重要地位。最大熵(MaxEnt)模型是以生态位理论为基础的物种分布模型,目前被广泛应用于物种潜在适生区预测。本研究应用MaxEnt模型,根据广东省红树林分布数据和环境变量数据,预测了该省红树林的潜在适生区分布,分析了影响红树林分布的关键环境变量及其适生区间,识别了广东省红树林保护与修复空缺区域。结果表明,在气候、地形、底质类型、海表盐度、海表温度5组变量中,影响广东省红树林分布的最主要环境变量为气温、海表温度和降水。关键环境变量的最佳适生区间为年平均温度22.37～23.58℃、最冷季平均海表温度23.15～23.34℃、年平均降水量1647.14～1809.61 mm、最干月降水量23.6～27.2 mm。广东省红树林的高适生区域主要集中于珠江口-大亚湾沿岸和雷州半岛-阳江沿岸,保护空缺主要出现在阳江港、镇海湾、珠江口、红海湾等地。本研究结果可为今后广东省红树林保护和修复行动与规划的空间布局提供科学依据。     

基于Maxent生态位模型的互花米草在我国沿海的潜在分布

以分布在我国沿海滩涂的96个互花米草分布记录点及覆盖东部沿海区域的海洋环境数据和气候环境数据为材料,利用Maxent生态位模型,研究外来物种互花米草在我国沿海的潜在分布情况.结果表明:互花米草适宜分布区占我国沿海区域的85%,其中高度适宜分布区占18%,中度适宜分布区占34%,低度适宜分布区占33%,不适宜分布区仅为15%.互花米草在我国沿海的地理分布主要受到年均最低海水温度、年均海水温度、年平均气温和1月最低气温4个环境因素的影响,而年均降水量、年均日较差、海水盐度、最高海水温度、7月最高气温及海流速度对互花米草地理分布的影响较小.互花米草高度适宜分布区的最低海水温度为0.62~24.81℃,平均海水温度为10.46~27.29℃,年均气温为9~25℃,1月最低气温为-13.5~16.7℃.互花米草地理分布概率在我国北部沿海区域达到20%以上,互花米草存在向我国北部进一步入侵的趋势,特别是在渤海湾地区,互花米草入侵潜力较大.互花米草在我国不适宜分布区主要集中在海南中部和南部海岸,以及台湾省大部分区域,依据当前我国分布记录及气候数据,这些区域互花米草入侵风险较小,但不排除未来入侵的可能性.     

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。     

基于4种生态位模型的金钱松潜在适生区预测

金钱松（Pseudolarix amabilis）是我国特有孑遗植物,为国家II级保护植物。基于4种生态位模型（GARP、Bioclim、Domain和Maxent）预测金钱松潜在适生区,采用受试者工作特征曲线（Receiver Operating Characteristic,ROC）和Kappa统计量检验模型的预测效果。预测结果表明金钱松在浙江西北部、安徽南部、湖北南部、湖南北部以及江西北部表现为高度适生,并以这些地带为中心向外延伸至北纬24.43°-33.35°和东经106.41°-123.42°之间,4种模型预测结果的受试者工作特征曲线下面积（Area under recriver operating characteristic curve,AUC）平均值均大于0.9,Kappa平均值亦大于0.75,精度较高。通过"刀切法"分析得出年均温是预测金钱松潜在适生区的关键影响因子,可能为当前金钱松分布格局形成的决定因素。模拟金钱松在末次盛冰期和2070年气候条件下的分布,结果表明其分布格局随气候变化由"南扩北缩"变为"南缩北扩",未来分布面积将大幅减小,气候变化是导致其"南缩北扩"的主要驱动因子。建议在当前金钱松高适分布区域内（江西铜鼓县、湖南张家界和衡阳）建立自然保护区或种子园,并在未来气候条件下高适分布区域内（如安徽北部、河南南部、湖北东南部等地）通过人工引种辅助金钱松的北向迁移。     

基于集合模型的草地贪夜蛾的潜在分布预测

[目的]探讨世界范围和我国草地贪夜蛾对玉米等作物的危害.[方法]采用生态位模型集合预测方案,结合基于中分辨率成像光谱仪(MODIS)的玉米种植密度模拟,对世界范围和我国草地贪夜蛾潜在分布进行了分析.[结果]世界范围内玉米的密集种植区已完全被草地贪夜蛾的潜在分布区所覆盖,这些适生区内的玉米受到草地贪夜蛾的威胁,需重点防控...     

基于MaxEnt模型的贵州省楠木潜在适生区预测

为揭示楠木(Phoebe zhennan)在贵州省潜在分布特征及其对环境因子的响应模式,该研究基于楠木在贵州省的地理分布点,运用最大熵模型(MaxEnt)与地理信息系统(ArcGIS)方法,结合气候、土壤及地形等30个环境因子,预测楠木在贵州省的潜在适生区,并分析了影响楠木生长的主要环境因子。结果表明：(1) MaxEnt模型AUC平均值为0.843,对贵州省楠木地理分布预测结果良好;楠木潜在适生区呈现以贵州省东北为重点区,从北到南、由东向西适生等级依次降低的趋势,高适生区主要在黔东北铜仁市、黔北遵义市中东部。(2)楠木在贵州省的潜在分布面积为80 013.47 km²,占全省总面积的45.4%,其中高适生区面积占全省总面积的17.4%。(3)等温性(Bio3)、最暖季度降水量(Bio18)、最湿月降水量(Bio13)、最干月降水量(Bio14)、最冷月最低温(Bio6)和温度季节性变动系数(Bio4)等是影响楠木在贵州省潜在分布的重要环境因子。该研究结果为贵州省楠木资源保护区划、种苗扩繁、造林推广与开发利用提供了科学依据。     

The effect of sample size on the accuracy of species distribution models: considering both presences and pseudo‐absences or background sites

Most high‐performing species distribution modelling techniques require both presences, and either absences or pseudo‐absences or background points. In this paper, we explore the effect of sample size, towards developing improved strategies for modelling. We generated 1800 virtual species with three levels of prevalence using ten modelling techniques, while varying the number of training presences (NTP) and the number of random points (NRP representing pseudo‐absences or background sites). For five of the ten modelling techniques we built two versions of models: one with an equal total weight (ETW) setting where the total weight for pseudo‐absence is equivalent to the total weight for presence, and another with an unequal total weight (UTW) setting where the total weight for pseudo‐absence is not required to be equal to the total weight for presence. We compared two strategies for NRP: a small multiplier strategy (i.e. setting NRP at a few times as large as NTP), and a large number strategy (i.e. using numerous random points). We produced ensemble models (by averaging the predictions from 30 models built with the same set of training presences and different sets of random points in equivalent numbers) for three NTP magnitudes and two NRP strategies. We found that model accuracy altered as NRP increased with four distinct patterns of performance: increasing, decreasing, arch‐shaped and horizontal. In most cases ETW improved model performance. Ensemble models had higher accuracy than the corresponding single models, and this improvement was pronounced when NTP was low. We conclude that a large NRP is not always an appropriate strategy. The best choice for NRP will depend on the modelling techniques used, species prevalence and NTP. We recommend building ensemble models instead of single models, using the small multiplier strategy for NRP with ETW, especially when only a small number of species presence records are available.     

Developing an approach to defining the potential distributions of invasive plant species: a case study of Hakea species in South Africa

Aim  Models of the potential distributions of invading species have to deal with a number of issues. The key one is the high likelihood that the absence of an invading species in an area is a false absence because it may not have invaded that area yet, or that it may not have been detected. This paper develops an approach for screening pseudo-absences in a way that is logical and defensible.
Innovation  The step-wise approach involves: (1) screening environmental variables to identify those most likely to indicate conditions where the species cannot invade; (2) identifying and selecting the most likely limiting variables; (3) using these to define the limits of its invasion potential; and (4) selecting points outside these limits as true absence records for input into species distribution models.
This approach was adopted and used for the study of three prominent Hakea species in South Africa. Models with and without the false absence records were compared. Two rainfall variables and the mean minimum temperature of the coldest month were the strongest predictors of potential distributions. Models which excluded false absences predicted that more of the potential distribution would have a high invasion potential than those which included them.
Main conclusions  The approach of applying a priori knowledge can be useful in refining the potential distribution of a species by excluding pseudo-absence records which are likely to be due to the species not having invaded an area yet or being undetected. The differences between the potential distributions predicted by the different models convey more information than making a single prediction, albeit a consensus model. The robustness of this approach depends strongly on an adequate knowledge of the ecology, invasion history and current distribution of that species.     

Predicting the global areas for potential distribution of Gastrodia elata based on ecological niche models

Aims Previous studies on the globally suitable areas for growing the medicinal plant Gastrodia elata is lacking. This study aims to predict the global areas for potential distribution of this plant based on multiple ecological niche models. Methods A total of 220 global distribution points of G. elata and 19 ecological variables were compiled and eight environmental variables were selected for the model training. Three ecological niche models, including BIOCLIM, DOMAIN, and MAXENT, were used to predict the global areas for potential distribution of G. elata. The resulting data of different models were analyzed and compared with two statistical criteria: the area under the receiver operating characteristic curve (AUC) and Kappa value. Important findings The predictions of the three models are basically identical, showing that the global areas for potential distribution of G. elata are predominantly in the range of 20° N to 50° N in Asia, mainly in China, South Korea and Japan. A small proportion of the suitable areas occur in India, Nepal and the European countries near Mediterranean. The most suitable areas distribute in provinces close to the Sichuan Basin and the central East China, the mid-eastern parts of South Korea such as Chungcheongbuk-do, Gyeongsangbuk-do and Gyeongsangnam- do, and the Kyushu region and the Shikoku region on Japan’s main island. Therefore, these three countries can be used as the main production areas of G. elata for its commercial development. The AUC average values of the three models are all above 0.9 and the Kappa average values all above 0.65, justifying their applications for predicting the potential areas of G. elata. Among them, the MAXENT model appears to perform the best, followed by DOMAIN and BIOCLIM.     

物种分布模型理论研究进展

利用物种分布模型估计物种的真实和潜在分布区,已成为区域生态学与生物地理学中非常活跃的研究领域。然而,到目前为止,这项技术的理论基础仍然存在不足之处,一些关键的生态过程未能被有效纳入到物种分布模型的理论框架中,从而为解释物种分布模型预测的结果带来了诸多困惑。鉴于此,总结了物种分布模型的理论基础;系统探讨了物种分布模型与物种分布区的关系;特别指出了物种分布模型研究中存在的理论问题;重点阐述了物种分布模型未来的发展方向。研究认为,物种分布模型与生态位理论、源-库理论、种群动态理论、集合种群理论、进化理论等具有重要的联系;正确理解物种分布模型的预测结果与物种分布区的关系,有赖于对影响物种分布的3个主要因素(环境条件、物种相互作用与物种迁移能力)做出定量的分离;目前物种分布模型主要存在的问题是未能将物种的相互作用和物种的迁移能力有效纳入到模型的构建过程中;未来物种分布模型的发展应该加强模型背后理论框架的研究,并进一步加强整合物种相互作用过程、种群动态过程、迁移过程和物种进化过程等内容。研究还认为,从更高的理论层次模拟功能群和群落结构将是未来物种分布模型的重要发展方向。     

Using avatar species to model the potential distribution of emerging invaders

Aim Anticipating the potential distributions of emerging invasive species is complicated by the tendency for species distribution models to perform better when both native and invasive range data are available for model development. If invasive range data are lacking, species models are liable to under‐estimate distributions for emerging invaders, particularly for species that are not at equilibrium with their native range environment due to historical factors, dispersal limitation and/or ecological interactions. We demonstrate the potential to use well‐quantified niche shifts from established ‘avatar’ (i.e. the remote or virtual manifestation of an entity) invaders to develop plausible distributions for data‐poor emerging invaders contingent on niche shifts of similar magnitude or character. Location Global. Methods Using the globally invasive crayfishes Pacifastacus leniusculus and Procambarus clarkii as our avatar invaders, we quantify how niche position, size and structure differs between native and total ranges using Mahalanobis distance (a measure of multivariate similarity) and the climate predictors of annual minimum and maximum air temperature. We then generalize patterns of niche shift from these species to the emerging crayfish invader Cherax quadricarinatus. Results Some patterns of niche shifts were similar for Pacifastacus leniusculus and Procambarus clarkii, but niche shifts were of considerably greater magnitude for P. clarkii. When a native range model for C. quadricarinatus was modified with generalized niche shifts similar to Pacifastacus leniusculus and Procambarus clarkii, the potential global distribution for this species increased considerably, including many areas not identified by the native range model. Main conclusions We illustrate the potential to use avatar invaders to provide cautionary, niche shift‐assuming species distribution models for emerging invaders. Many theoretical and applied implications of the avatar species concept require additional investigation, including the development of frameworks to select appropriate avatar species and evaluate the performance of avatar‐derived models for emerging invaders. Despite these research needs, we believe this concept will have considerable utility for predicting vulnerability to invasion by data‐poor species; this is a critical management need because shifting pathways of introduction and climate change will produce many novel, emerging invasive species in the future.     

基于物种分布模型评价土壤因子对我国毛竹潜在分布的影响

基于单类别支持向量机方法的物种分布模型, 利用政府间气候变化专门委员会(IPCC)气候情景模式和联合国粮食与农业组织(FAO)的全球土壤数据, 模拟1981-2099年我国毛竹(Phyllostachys edulis)的潜在空间分布及变化趋势, 比较考虑土壤因子前后模拟结果的差异, 旨在探究土壤因子对毛竹潜在空间分布模拟结果的影响。结果表明, 仅以气候因子为模拟变量和同时考虑气候与土壤因子为模拟变量的毛竹潜在空间分布模拟均具有较高精度, 毛竹潜在分布区表现为面积增加并向北扩张。模拟因子重要性分析表明表征温暖程度的气候因子在毛竹潜在分布模拟中起主导作用, 而表征土壤质地和酸碱性的土壤因子以限制性作用为主。同时考虑气候与土壤因子的模拟结果具有较高的模拟效率, 且在未来气候变化情景模式下毛竹潜在分布区面积增幅与向北迁移幅度均小于仅使用气候因子的模拟, 表明土壤要素对毛竹潜在分布具有明显的限制作用, 该结果对现在的毛竹潜在分布模拟研究具有重要的补充作用。     

In search of relevant predictors for marine species distribution modelling using the MarineSPEED benchmark dataset

Aim

Ideally, datasets for species distribution modelling (SDM) contain evenly sampled records covering the entire distribution of the species, confirmed absences and auxiliary ecophysiological data allowing informed decisions on relevant predictors. Unfortunately, these criteria are rarely met for marine organisms for which distributions are too often only scantly characterized and absences generally not recorded. Here, we investigate predictor relevance as a function of modelling algorithms and settings for a global dataset of marine species.

Location

Global marine.

Methods

We selected well‐studied and identifiable species from all major marine taxonomic groups. Distribution records were compiled from public sources (e.g., OBIS, GBIF, Reef Life Survey) and linked to environmental data from Bio‐ORACLE and MARSPEC. Using this dataset, predictor relevance was analysed under different variations of modelling algorithms, numbers of predictor variables, cross‐validation strategies, sampling bias mitigation methods, evaluation methods and ranking methods. SDMs for all combinations of predictors from eight correlation groups were fitted and ranked, from which the top five predictors were selected as the most relevant.

Results

We collected two million distribution records from 514 species across 18 phyla. Mean sea surface temperature and calcite are, respectively, the most relevant and irrelevant predictors. A less clear pattern was derived from the other predictors. The biggest differences in predictor relevance were induced by varying the number of predictors, the modelling algorithm and the sample selection bias correction. The distribution data and associated environmental data are made available through the R package marinespeed and at http://marinespeed.org .

Main conclusions

While temperature is a relevant predictor of global marine species distributions, considerable variation in predictor relevance is linked to the SDM set‐up. We promote the usage of a standardized benchmark dataset (MarineSPEED) for methodological SDM studies.

     

A problem with variable selection in a comparison of correlative and process‐based species distribution models: Comments on Higgins et al., 2020

Comments are presented on an article published in October 2020 in Ecology and Evolution (“Predictive ability of a process‐based versus a correlative species distribution model”) by Higgins et al. This analyzed natural distributions of Australian eucalypt and acacia species and assessed the adventive range of selected species outside Australia. Unfortunately, inappropriate variables were used with the MaxEnt species distribution model outside Australia, so that large climatically suitable areas in the Northern Hemisphere were not identified. Examples from a previous analysis and from the use of the freely available spatial portal of the Atlas of Living Australia are provided to illustrate how the problem can be overcome. The comparison of methods described in the Higgins et al. paper is worthwhile, and it is hoped that the authors will be able to repeat their analyses using appropriate variables with the correlative model.