样本量与研究范围变化对MaxEnt模型准确度的影响——以黑白仰鼻猴为例

MaxEnt模型是过去几年最为流行的物种分布预测模型之一。针对一些濒危物种、入侵种和模拟数据的研究表明,MaxEnt模型均能在小样本的分布数据下得到较准确的预测结果。此外,研究范围的变化也会影响MaxEnt模型的构建。 然而,基于动物的实际分布数据来评估MaxEnt模型的研究甚少。 我们以黑白仰鼻猴 (Rhinopithecus bieti)为例,以11个猴群的分布数据为训练数据（样本量从1到10个猴群）,在不同研究范围内构建MaxEnt模型,通过其它5个的猴群分布数据验证,分析样本量和研究范围变化对模型准确度产生的影响。 结果表明,随样本量和研究范围增大,MaxEnt模型准确度及稳定性都有增加。 此外,研究范围变化对模型准确度有一定影响。 应用Maxent进行物种分布预测时,训练数据应尽可能涵盖该物种可能出现的全部环境梯度。构建模型所需的背景数据点选择,应与建模使用的物种出现点形成有效对照。     

基于最大熵模型的不同尺度物种分布概率优化热点分析: 以红色木莲为例

单一空间尺度构建的最大熵(maximum entropy, MaxEnt)模型是否具有代表性, 是MaxEnt模型应用与发展中面临的重要问题。本研究基于有效的地理分布位点数据, 利用最小凸多边形法(the minimum convex polygon method)在三江并流、云南省及全国3个空间尺度下分别识别了红色木莲(Manglietia insignis)的建模区域, 并进一步建立MaxEnt模型: 使用ROC曲线分析法与遗漏率(omission rate, OR)检验评估MaxEnt模型预测精度; 基于ArcGIS分析分布概率及其热点区域的分布趋势, 并通过分区统计工具Zonal识别潜在适宜分布区域的质心位置; 采用刀切法检验环境因子贡献率。结果表明: (1)不同尺度下红色木莲的MaxEnt模型都有良好的预测效果, 三江并流、云南省及全国尺度下的AUC值分别为0.936、0.887和0.930, OR值分别为0.18、0.15和0.20; (2)各尺度红色木莲的适生区格局呈现一致性分布趋势, 集中在独龙江、怒江和澜沧江3个流域; (3) 3个空间尺度下红色木莲的地理分布受不同环境因子影响, 存在着尺度依赖效应。由此可见, 红色木莲在不同空间尺度下的预测模型有着稳定的性能表现与良好的预测效果。此外, 我们建议在野外实地调查与野生生物资源保护中加强对普通物种的关注, 在预测物种地理分布的研究中将MaxEnt模型与热点分析结合使用。     

基于最大熵模型的森林-草原交错区野生东北马鹿分布区预测与参数优化研究

利用最大熵模型(MaxEnt)对内蒙古高格斯台罕乌拉国家级自然保护区内的东北马鹿Cervus elaphus xanthopygus潜在分布区和适宜生境进行预测,比较基于默认参数和优化参数的预测结果,探讨参数优化对东北马鹿种群潜在分布区和适宜生境预测结果的影响。研究结果表明：优化参数模型的特征组合选择线性、二次型、片段化、成积型和阈值型特征,调控倍率为4时,模型的拟合度和复杂度得到改善;优化参数建模下,温度季节性、距道路距离、距草地距离、最冷季平均温度、等温性、海拔和距河流距离是影响东北马鹿分布的主要环境因子;2种模型预测结果表明,东北马鹿均主要分布于保护区东北部,适宜生境面积分别为36.04 km²和126.67 km²,占总面积的3.5%和12.6%。研究表明,使用MaxEnt模型进行濒危珍稀物种潜在适宜生境预测时,需根据影响物种分布的关键环境因子选择最合理的参数设置。     

最大熵模型在物种分布预测中的优化

最大熵模型在物种分布的预测研究中得到广泛应用,但未经优化的模型的预测结果可能存在严重的拟合偏差.本文汇总了最大熵模型在取样偏差修正、模型复杂性调整、物种分布判定阈值选择以及模型检验过程中的若干优化方法.在取样偏差的修正中,空间筛除法的修正效果最好,而背景限制法表现不佳.模型复杂性受建模变量的数量、函数模式和调控系数的影响.在样本量小于建模变量的数量时需进行变量筛选,筛选标准应侧重其生态学意义,而非变量间的相关性;函数模式对模型表现影响不大,在预测结果相近情况下应选择简单模型;建模时需要调整调控系数以控制过度拟合,一般最优模型调控系数高于默认值.判定物种出现阈值应遵从客观性、等效性和判别力3个原则,敏感度和特异性加和最大是良好的阈值判定标准.模型检验可分为不依赖阈值的检验和依赖阈值的检验,在不依赖阈值的模型评估方法中,基于信息标准选择的模型表现优于基于AUC或相关系数(COR)选择的模型;在基于阈值的模型评估方法中,真实技能统计能够兼顾模型遗漏误差和错判误差,不受假设缺失影响,且受物种流行度的影响较小.     

中国植物分布模拟研究现状

在过去的20年里, 物种分布模型已广泛应用于动植物地理分布的模拟研究。该文以植物物种分布模拟为例, 利用中国知网、维普网以及Web of Science文献数据库的检索与统计, 分析了2000-2018年间, 中国研究人员利用各种物种分布模型对植物物种分布模拟研究的发文量、模拟模型、物种类型、数据来源、研究目的等信息。最终共收集到366篇有效文献, 分析表明2011年以来中国的物种分布模型应用发展迅速, 且以最近5年最为迅猛, 在生态学、中草药业、农业和林业等行业部门应用广泛。在使用的33种模型中, 应用最广的为最大熵模型(MaxEnt)。有一半研究的环境数据仅包含气候数据, 另一半研究不仅包含气候数据还包括地形与土壤等数据; 环境及物种数据的来源多样, 国际及本土数据库均得到使用。模拟涉及有明确清单的562个植物种, 既有木本植物(52.7%), 也有草本植物(41.8%), 其中中草药、果树、园林植物、农作物等占比较高。研究目的主要集中在过去、现在和未来气候变化对植物种分布的影响及预测, 以及物种分布评估与生物多样性评价(包括入侵植物风险评估)两大方面。预测物种潜在分布范围与气候变化影响等基础研究, 与模拟物种适生区与推广种植等应用研究并重, 物种分布模型在生态学与农业、林业和中草药业等多学科、多行业开展多种应用, 多物种、多模型和多来源数据共同参与模拟与比较, 开发新的机理性物种分布模型, 拓展新的物种分布模拟应用领域, 是今后研究的重点发展方向。     

基于生态位模型预测野生油茶的潜在分布

油茶(Camellia oleifera)是我国第一大木本油料作物, 野生油茶是油茶育种的宝贵遗传资源。本研究从中国数字植物标本馆(CVH, http://www.cvh.org.cn/)获得可靠的野生油茶分布点数据, 结合气象和土壤数据, 分别应用最大熵(MaxEnt)模型和规则集遗传算法(GARP)模型构建了野生油茶的生态位模型, 预测了野生油茶的潜在分布区, 并分析了影响野生油茶分布的主要环境变量。根据生态位模型预测的分布概率值, 对野生油茶的潜在分布区划分适生等级, 并与主要油茶产地的实际分布数据进行比较, 以验证适生等级划分的可靠性。结果表明, 两种模型的预测结果均能较好地反映油茶的分布情况。GARP模型预测的潜在分布区更广, 而MaxEnt模型的预测结果更精确。两种模型的预测结果均显示, 野生油茶的潜在分布区大部分位于中国, 但在中南半岛也有部分分布。MaxEnt模型预测的野生油茶在中国的潜在分布区与我国亚热带常绿阔叶林的分布区基本吻合, 高适生区主要可以分为3大区域: (1)东北-西南走向的武夷山脉及附近的群山区域; (2)东西走向的南岭山脉及附近的群山区域; (3)东北-西南走向的武陵山脉及附近的群山区域。MaxEnt模型分析显示, 影响野生油茶分布的主要环境变量是昼夜温差月均值、最干季降水量与最暖季降水量。油茶生长面积较大的地区绝大部分都位于MaxEnt模型预测的中、高适生区, 说明适生等级的划分较可靠。实地考察显示, 生态位模型的预测结果对于寻找野生油茶资源具有较高的参考价值。此外, 本研究也充分显示, 利用中国数字植物标本馆的植物分布数据, 结合相应的环境数据构建生态位模型, 有助于了解作物野生近缘种的地理分布。     

中国植物分布模拟研究现状

在过去的20年里,物种分布模型已广泛应用于动植物地理分布的模拟研究。该文以植物物种分布模拟为例,利用中国知网、维普网以及Web of Science文献数据库的检索与统计,分析了2000–2018年间,中国研究人员利用各种物种分布模型对植物物种分布模拟研究的发文量、模拟模型、物种类型、数据来源、研究目的等信息。最终共收集到366篇有效文献,分析表明2011年以来中国的物种分布模型应用发展迅速,且以最近5年最为迅猛,在生态学、中草药业、农业和林业等行业部门应用广泛。在使用的33种模型中,应用最广的为最大熵模型(MaxEnt)。有一半研究的环境数据仅包含气候数据,另一半研究不仅包含气候数据还包括地形与土壤等数据;环境及物种数据的来源多样,国际及本土数据库均得到使用。模拟涉及有明确清单的562个植物种,既有木本植物(52.7%),也有草本植物(41.8%),其中中草药、果树、园林植物、农作物等占比较高。研究目的主要集中在过去、现在和未来气候变化对植物种分布的影响及预测,以及物种分布评估与生物多样性评价(包括入侵植物风险评估)两大方面。预测物种潜在分布范围与气候变化影响等基础研究,与模拟物种适生区与推广种植等应用研究并重,物种分布模型在生态学与农业、林业和中草药业等多学科、多行业开展多种应用,多物种、多模型和多来源数据共同参与模拟与比较,开发新的机理性物种分布模型,拓展新的物种分布模拟应用领域,是今后研究的重点发展方向。     

生境概率预测值转换为二元值过程中4个阈值选择方法的比较评估——以珙桐和杉木生境预估为例

物种生境模型预测结果通常是概率性的,然而在具体的保护管理等实践应用过程中通常需要基于二元值(存在/不存在)的分布图,此时就需要把概率性的预测结果转化为二元值,在此转化过程中就涉及阈值选择问题。此外,在评估模型预测准确度的时候,多数评估指标也需要选择一个阈值用于转化概率预测结果,这个阈值选择对于模型预测准确度也会有极大的影响。然而阈值选择却是物种生境模拟不确定性研究中较少涉及的领域。"随机森林"既可以生成物种生境概率分布图(回归算法)也可以生成二元分布图(分类算法),然而还未见对两种预测方式的比较研究。该文以珙桐(Davidia involucrata)和杉木(Cunninghamia lanceolata)为例,分别采用"随机森林"的分类算法和回归算法预测其生境二元分布图和概率分布图,通过4个不同阈值选择方法(默认值0.5、MaxKappa、MaxTSS和MaxACC)把概率预测图转换为二元分布图,进而比较分析转换结果对模型预估的影响。珙桐不同阈值选择方法所确立的阈值之间存在显著差异,而杉木没有显著差异;两物种模型准确度之间没有显著差异;在预测两物种未来气候条件下的生境面积变化、生境分布区迁移方向和距离以及最适宜海拔分布高度变化时,二元值转换后的回归算法与分类算法之间存在显著差异,但回归算法中各阈值选择方法之间没有显著差异。空间生境分布图的相似性分析表明MaxKappa和MaxTSS法具有最大相似性,分类算法与4种阈值选择方法之间具有最大差异。     

利用分布有/无数据预测物种空间分布的研究方法综述

详细的物种地理分布信息是生态学研究和制定保护策略的基础。相比较于直接估测种群数量,获取物种分布的有/无数据更为实用。因此,利用分布有/无数据并结合环境变量建立模型预测物种空间分布的方法在近年来得到了长足发展,并被广泛应用。利用分布有/无数据预测物种分布,关键的步骤包括：1)构建总体概念模型,2)收集物种分布有/无数据,并准备环境变量图层;3)选择合适的统计模型和算法,以及4)对模型进行评估。概念模型提出研究假设,并确定数据收集及模型方法。收集物种分布数据有系统调查及非系统调查方法。筛选并准备与物种分布相关的环境变量,利用GIS工具处理,使之成为符合模型条件的具有合适的空间尺度的数字化图层。利用环境变量和物种分布有/无的数据,选择合适的方法及软件建立模型,并对模型进行检验和评估。我们总结了用于构建物种分布模型的不同算法和软件。本文将针对以上各个环节,阐述利用物种分布有/无数据进行研究所需要的技术细节,以期望为读者提供借鉴。     

应用多个生态位模型预测福寿螺在中国的潜在适生区

高危性外来入侵种福寿螺严重危害我国的农业生产、生态系统完整性和人体健康.为制定有效的防控策略提供科学依据,本研究通过选取最适的生态位模型以预测福寿螺在我国的潜在适生区.结合福寿螺在我国的337条分布记录和年均温、年降水量等19个生物气候变量数据,本文采用MaxEnt、GARP、BIOCLIM和DOMAIN等4种生态位模型分别模拟预测了福寿螺在我国的潜在适生区,并利用受试者工作特征曲线(ROC)和Kappa统计量分析比较不同模型的预测效果.结果表明: 4种模型均能较好地模拟福寿螺在我国的分布,其中MaxEnt模型的模拟准确度最高(受试者工作特征曲线下的面积AUC=0.955±0.004,Kappa=0.845±0.017),其次是GARP和DOMAIN,准确度相对较小的是BIOCLIM,但其平均AUC也达0.898±0.017,平均Kappa值为0.771±0.025.MaxEnt模型的预测结果显示,福寿螺的潜在适生区主要分布在30° N以南地区,但其中也有部分地区地处30°N以北.适生区面积占国土面积的13.2%,广东、广西、湖南、重庆、浙江和福建沿海地区具有高度潜在入侵风险.本研究可以为福寿螺的科学防控提供参考,并且对大尺度上外来水生生物的适生区预测具有一定的借鉴意义.     

The effect of positional error on fine scale species distribution models increases for specialist species

Species occurrences inherently include positional error. Such error can be problematic for species distribution models (SDMs), especially those based on fine-resolution environmental data. It has been suggested that there could be a link between the influence of positional error and the width of the species ecological niche. Although positional errors in species occurrence data may imply serious limitations, especially for modelling species with narrow ecological niche, it has never been thoroughly explored. We used a virtual species approach to assess the effects of the positional error on fine-scale SDMs for species with environmental niches of different widths. We simulated three virtual species with varying niche breadth, from specialist to generalist. The true distribution of these virtual species was then altered by introducing different levels of positional error (from 5 to 500 m). We built generalized linear models and MaxEnt models using the distribution of the three virtual species (unaltered and altered) and a combination of environmental data at 5 m resolution. The models’ performance and niche overlap were compared to assess the effect of positional error with varying niche breadth in the geographical and environmental space. The positional error negatively impacted performance and niche overlap metrics. The amplitude of the influence of positional error depended on the species niche, with models for specialist species being more affected than those for generalist species. The positional error had the same effect on both modelling techniques. Finally, increasing sample size did not mitigate the negative influence of positional error. We showed that fine-scale SDMs are considerably affected by positional error, even when such error is low. Therefore, where new surveys are undertaken, we recommend paying attention to data collection techniques to minimize the positional error in occurrence data and thus to avoid its negative effect on SDMs, especially when studying specialist species.     

Spatial autocorrelation in predictors reduces the impact of positional uncertainty in occurrence data on species distribution modelling

Aim To investigate the impact of positional uncertainty in species occurrences on the predictions of seven commonly used species distribution models (SDMs), and explore its interaction with spatial autocorrelation in predictors. Methods A series of artificial datasets covering 155 scenarios including different combinations of five positional uncertainty scenarios and 31 spatial autocorrelation scenarios were simulated. The level of positional uncertainty was defined by the standard deviation of a normally distributed zero‐mean random variable. Each dataset included two environmental gradients (predictor variables) and one set of species occurrence sample points (response variable). Seven commonly used models were selected to develop SDMs: generalized linear models, generalized additive models, boosted regression trees, multivariate adaptive regression spline, random forests, genetic algorithm for rule‐set production and maximum entropy. A probabilistic approach was employed to model and simulate five levels of error in the species locations. To analyse the propagation of positional uncertainty, Monte Carlo simulation was applied to each scenario for each SDM. The models were evaluated for performance using simulated independent test data with Cohen’s Kappa and the area under the receiver operating characteristic curve. Results Positional uncertainty in species location led to a reduction in prediction accuracy for all SDMs, although the magnitude of the reduction varied between SDMs. In all cases the magnitude of this impact varied according to the degree of spatial autocorrelation in predictors and the levels of positional uncertainty. It was shown that when the range of spatial autocorrelation in the predictors was less than or equal to three times the standard deviation of the positional error, the models were less affected by error and, consequently, had smaller decreases in prediction accuracy. When the range of spatial autocorrelation in predictors was larger than three times the standard deviation of positional error, the prediction accuracy was low for all scenarios. Main conclusions The potential impact of positional uncertainty in species occurrences on the predictions of SDMs can be understood by comparing it with the spatial autocorrelation range in predictor variables.     

物种分布模型的发展及评价方法

物种分布模型已被广泛地应用于以保护区规划、气候变化对物种分布的影响等为目的的研究。回顾了已经得到广泛应用的多种物种分布模型,总结了评价模型性能的方法。基于物种分布模型的发展和应用以及性能评价中尚存在的问题,本文认为:在物种分布模型中集成样本选择模块能够避免模型预测过程中的过度拟合及欠拟合,增加变量选择模块可评估和降低变量之间自相关性的影响,增加生物因子以及将物种对环境的适应性机制(及扩散行为特征)和潜在分布模型进行结合,是提高模型预测性能的可行方案;在模型性能的评价方面,采用赤池信息量可对模型的预测性能进行客观评价。相关建议可为物种分布建模提供参考。     

Limited transferability of stream‐fish distribution models among river catchments: reasons and implications

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A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area

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NOO3D: A procedure to perform 3D species distribution models

There is consensus surrounding the need to include a third dimension when estimating Species Distribution Models (SDMs), which is of special interest for marine species. Application of the third dimension is, however, rarely available, thus users are obliged to manually combine 2D SDM outputs (i.e., suitability or presence/absence maps) for 3D distribution generation. Herein, the Niche of Occurrence 3D (NOO3D) is presented, which is a new, simple modelling procedure that provides 3D distributions using both 3D occurrence samples and environmental datasets that consist of one layer per depth value. NOO3D performance was evaluated using five virtual marine species to avoid errors associated with real data sets (three pelagic species, with wide, medium, and narrow distributions, respectively, a mesopelagic species and an abyssal species). These virtual species are distributed across the North Atlantic Ocean and were built to a 0.5° x 0.5° resolution and considering 49 depth levels (from 0.43 m to an undersea depth of 5274.7 m). NOO3D results were also compared to those provided by 3D Alpha Shapes and Maximum Entropy (MaxEnt). The True Positive Rate (TPR), or sensitivity, True Negative Rate (TNR), or specificity, False Positive Rate (FPR), or commission error, and False Negative Rate (FNR), or omission error, were employed in order to facilitate comparison between methods. MaxEnt performed best for TPR, TSS and FNR, and Alpha Shape 3D performed best for FPR and TNR. NOO3D was always the second-ranked method for all metrics considered, which indicates that it was the most suitable method. The provided results indicate that NOO3D can be considered a viable alternative in achieving three-dimensional species distribution models.     

Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment

Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted “punctual models”). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure (“ablation experiments”). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.     

Where is positional uncertainty a problem for species distribution modelling?

Species data held in museum and herbaria, survey data and opportunistically observed data are a substantial information resource. A key challenge in using these data is the uncertainty about where an observation is located. This is important when the data are used for species distribution modelling (SDM), because the coordinates are used to extract the environmental variables and thus, positional error may lead to inaccurate estimation of the species–environment relationship. The magnitude of this effect is related to the level of spatial autocorrelation in the environmental variables. Using local spatial association can be relevant because it can lead to the identification of the specific occurrence records that cause the largest drop in SDM accuracy. Therefore, in this study, we tested whether the SDM predictions are more affected by positional uncertainty originating from locations that have lower local spatial association in their predictors. We performed this experiment for Spain and the Netherlands, using simulated datasets derived from well known species distribution models (SDMs). We used the K statistic to quantify the local spatial association in the predictors at each species occurrence location. A probabilistic approach using Monte Carlo simulations was employed to introduce the error in the species locations. The results revealed that positional uncertainty in species occurrence data at locations with low local spatial association in predictors reduced the prediction accuracy of the SDMs. We propose that local spatial association is a way to identify the species occurrence records that require treatment for positional uncertainty. We also developed and present a tool in the R environment to target observations that are likely to create error in the output from SDMs as a result of positional uncertainty.     

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