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。     

种间作用对物种分布模拟的影响及建模方法

物种分布模型(SDMs)通过量化物种分布和环境变量之间的关系,并将其外推到未知的景观单元,模拟、预测地理空间中生物的潜在分布,是生态学、生物地理学、保护生物学等研究领域的重要工具.然而,目前物种分布模型主要采用非生物因素作为预测变量,由于数据量化和建模表达困难,生物因素特别是种间作用在物种分布模型中常被忽略,将种间作用...     

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.     

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

To investigate the comparative abilities of six different bioclimatic models in an independent area, utilizing the distribution of eight different species available at a global scale and in Australia. Global scale and Australia. We tested a variety of bioclimatic models for eight different plant species employing five discriminatory correlative species distribution models (SDMs) including Generalized Linear Model (GLM), MaxEnt, Random Forest (RF), Boosted Regression Tree (BRT), Bioclim, together with CLIMEX (CL) as a mechanistic niche model. These models were fitted using a training dataset of available global data, but with the exclusion of Australian locations. The capabilities of these techniques in projecting suitable climate, based on independent records for these species in Australia, were compared. Thus, Australia is not used to calibrate the models and therefore it is as an independent area regarding geographic locations. To assess and compare performance, we utilized the area under the receiver operating characteristic (ROC) curves (AUC), true skill statistic (TSS), and fractional predicted areas for all SDMs. In addition, we assessed satisfactory agreements between the outputs of the six different bioclimatic models, for all eight species in Australia. The modeling method impacted on potential distribution predictions under current climate. However, the utilization of sensitivity and the fractional predicted areas showed that GLM, MaxEnt, Bioclim, and CL had the highest sensitivity for Australian climate conditions. Bioclim calculated the highest fractional predicted area of an independent area, while RF and BRT were poor. For many applications, it is difficult to decide which bioclimatic model to use. This research shows that variable results are obtained using different SDMs in an independent area. This research also shows that the SDMs produce different results for different species; for example, Bioclim may not be good for one species but works better for other species. Also, when projecting a “large” number of species into novel environments or in an independent area, the selection of the “best” model/technique is often less reliable than an ensemble modeling approach. In addition, it is vital to understand the accuracy of SDMs' predictions. Further, while TSS, together with fractional predicted areas, are appropriate tools for the measurement of accuracy between model results, particularly when undertaking projections on an independent area, AUC has been proved not to be. Our study highlights that each one of these models (CL, Bioclim, GLM, MaxEnt, BRT, and RF) provides slightly different results on projections and that it may be safer to use an ensemble of models.     

基于MAXENT模型预测齿裂大戟在中国的潜在分布区

【背景】齿裂大戟原产于北美,是近年来入侵我国的一种检疫性杂草。目前,该杂草已在我国的华北、华东和西南建立种群,并呈扩散蔓延趋势。由于该入侵植物具有极强的繁殖能力,一旦大面积扩散势必造成极大损失,急需通过风险评估明确其未来的扩散趋势,进而制定早期预警措施。【方法】使用MAXNET模型,运用齿裂大戟在原产地和中国的已知分布数据及筛选后的环境变量,结合地理信息系统(Geographic information system,GIS)及其生活史特征和环境适应特性,直观和定量地预测了该原产北美的植物在中国的适生范围,并采用受试者工作曲线(Receiver operator characteristic curve,ROC)分析方法对模型预测结果进行了检验。【结果】齿裂大戟在我国有较为广阔的潜在分布区,其中高风险区主要集中在地处33°~40°N,109°~119°E的北京、天津、河北南部、河南北部、山东中北部、山西南部和陕西西安等地。【结论与意义】结合齿裂大戟在我国的分布现状和传入扩散特性,划定了其在我国潜在的高风险区域,为制定预防和控制入侵植物进一步传入和扩散的早期预警和监测措施提供科学依据。     

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。     

Uncertainties in the projection of species distributions related to general circulation models

Ecological Niche Models (ENMs) are increasingly used by ecologists to project species potential future distribution. However, the application of such models may be challenging, and some caveats have already been identified. While studies have generally shown that projections may be sensitive to the ENM applied or the emission scenario, to name just a few, the sensitivity of ENM‐based scenarios to General Circulation Models (GCMs) has been often underappreciated. Here, using a multi‐GCM and multi‐emission scenario approach, we evaluated the variability in projected distributions under future climate conditions. We modeled the ecological realized niche (sensu Hutchinson) and predicted the baseline distribution of species with contrasting spatial patterns and representative of two major functional groups of European trees: the dwarf birch and the sweet chestnut. Their future distributions were then projected onto future climatic conditions derived from seven GCMs and four emissions scenarios using the new Representative Concentration Pathways (RCPs) developed for the Intergovernmental Panel on Climate Change (IPCC) AR5 report. Uncertainties arising from GCMs and those resulting from emissions scenarios were quantified and compared. Our study reveals that scenarios of future species distribution exhibit broad differences, depending not only on emissions scenarios but also on GCMs. We found that the between‐GCM variability was greater than the between‐RCP variability for the next decades and both types of variability reached a similar level at the end of this century. Our result highlights that a combined multi‐GCM and multi‐RCP approach is needed to better consider potential trajectories and uncertainties in future species distributions. In all cases, between‐GCM variability increases with the level of warming, and if nothing is done to alleviate global warming, future species spatial distribution may become more and more difficult to anticipate. When future species spatial distributions are examined, we propose to use a large number of GCMs and RCPs to better anticipate potential trajectories and quantify uncertainties.     

Incorporating uncertainty in predictive species distribution modelling

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.     

Climate controls the distribution of a widespread invasive species: implications for future range expansion

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未来气候变化对四种木姜子地理分布的影响

该文利用最大墒模型(Maxent)和地理信息系统(ArcGIS 10.3)软件对中国木姜子属(Litsea)四种资源植物在我国当代、未来(2061年—2080年)气候条件下的潜在分布区进行预测,并对其适宜区进行分析和划分。结果表明:山鸡椒(Litsea cubeba)适宜区广泛分布在长江以南区域,在未来时段2061年—2080年两种(RCP2.6、RCP8.5)二氧化碳浓度情景下适宜区面积分别减少4.9%和0.5%;毛豹皮樟(L. coreana)适宜区主要分布在中亚热带及北亚热带区域,分布相对偏北,其在未来2061年—2080年两种二氧化碳浓度情景下适宜区面积分别增加5.6%和4.5%;华南木姜子(L. greenmaniana)适宜区主要分布在我国南亚热带区域;毛叶木姜子(L. mollis)适宜区广泛分布在亚热带区域。这两种树种在未来气候RCP2.6情景下适生面积减少1.0%和3.3%,在RCP8.5情景下减少5.6%和8.3%。上述结果说明木姜子属不同种由于生态习性差异对未来的气候变化的响应不尽相同,对这些植物引种栽培须考虑气候变化的影响。     

Tackling intraspecific genetic structure in distribution models better reflects species geographical range

Genetic diversity provides insight into heterogeneous demographic and adaptive history across organisms’ distribution ranges. For this reason, decomposing single species into genetic units may represent a powerful tool to better understand biogeographical patterns as well as improve predictions of the effects of GCC (global climate change) on biodiversity loss. Using 279 georeferenced Iberian accessions, we used classes of three intraspecific genetic units of the annual plant Arabidopsis thaliana obtained from the genetic analyses of nuclear SNPs (single nucleotide polymorphisms), chloroplast SNPs, and the vernalization requirement for flowering. We used SDM (species distribution models), including climate, vegetation, and soil data, at the whole‐species and genetic‐unit levels. We compared model outputs for present environmental conditions and with a particularly severe GCC scenario. SDM accuracy was high for genetic units with smaller distribution ranges. Kernel density plots identified the environmental variables underpinning potential distribution ranges of genetic units. Combinations of environmental variables accounted for potential distribution ranges of genetic units, which shrank dramatically with GCC at almost all levels. Only two genetic clusters increased their potential distribution ranges with GCC. The application of SDM to intraspecific genetic units provides a detailed picture on the biogeographical patterns of distinct genetic groups based on different genetic criteria. Our approach also allowed us to pinpoint the genetic changes, in terms of genetic background and physiological requirements for flowering, that Iberian A. thaliana may experience with a GCC scenario applying SDM to intraspecific genetic units.     

Effects of sample size on the performance of species distribution models

A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to predict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence–absence data from multiple species and regions. We evaluated predictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between predictor variables and complex response shapes (i.e. GBM, MARS-INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best predictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM-GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm predicted consistently well with small sample size ( n  < 30) and this should encourage highly conservative use of predictions based on small sample size and restrict their use to exploratory modelling.     

Scaling down distribution maps from atlas data: a test of different approaches with virtual species

Aim Conservation managers are increasingly looking for modelled projections of species distributions to inform management strategies; however, the coarse resolution of available data usually compromises their helpfulness. The aim of this paper is to delineate and test different approaches for converting coarse‐grain occurrence data into high‐resolution predictions, and to clarify the conceptual circumstances affecting the accuracy of downscaled models. Location We used environmental data from a real landscape, southern Africa, and simulated species distributions within this landscape. Methods We built 10 virtual species at a resolution of 5 arcmin, and for each species we simulated atlas range maps at four decreasing resolutions (15, 30, 60, 120 arcmin). We tested the ability of three downscaling strategies to produce high‐resolution predictions using two modelling techniques: generalized linear models and generalized boosted models. We calibrated reference models with high‐resolution data and we compared the relative reduction of predictive performance in the downscaled models by using a null model approach. We also estimated the applicability of downscaling procedures to different situations by using distribution data for Mediterranean reptiles. Results All reference models achieved high performance measures. For all strategies, we observed a reduction of predictive performance proportional to the degree of downscaling. The differences in evaluation indices between reference models and downscaled projections obtained from atlases at 15 and 30 arcmin were never statistically significant. The accuracy of projections scaled down from 60 arcmin largely depended on the combination of approach and algorithm adopted. Projections scaled down from 120 arcmin gave misleading results in all cases. Main conclusions Moderate levels of downscaling allow for reasonably accurate results, regardless of the technique used. The most general effect of scaling down coarse‐grain data is the reduction of model specificity. The models can successfully delineate a species’ environmental association up until a 12‐fold downscaling, although with an increasing approximation that causes the overestimation of true distributions. We suggest appropriate procedures to mitigate the commission error introduced by downscaling at intermediate levels (approximately 12‐fold). Reductions of grain size > 12‐fold are discouraged.     

Integrating mechanistic and empirical model projections to assess climate impacts on tree species distributions in northwestern North America

Empirical and mechanistic models have both been used to assess the potential impacts of climate change on species distributions, and each modeling approach has its strengths and weaknesses. Here, we demonstrate an approach to projecting climate‐driven changes in species distributions that draws on both empirical and mechanistic models. We combined projections from a dynamic global vegetation model (DGVM) that simulates the distributions of biomes based on basic plant functional types with projections from empirical climatic niche models for six tree species in northwestern North America. These integrated model outputs incorporate important biological processes, such as competition, physiological responses of plants to changes in atmospheric CO₂ concentrations, and fire, as well as what are likely to be species‐specific climatic constraints. We compared the integrated projections to projections from the empirical climatic niche models alone. Overall, our integrated model outputs projected a greater climate‐driven loss of potentially suitable environmental space than did the empirical climatic niche model outputs alone for the majority of modeled species. Our results also show that refining species distributions with DGVM outputs had large effects on the geographic locations of suitable habitat. We demonstrate one approach to integrating the outputs of mechanistic and empirical niche models to produce bioclimatic projections. But perhaps more importantly, our study reveals the potential for empirical climatic niche models to over‐predict suitable environmental space under future climatic conditions.     

基于MaxEnt模型不同气候变化情景下的豆梨潜在地理分布

针对豆梨的原生境保护和资源利用问题,本研究基于豆梨全球236个分布点和19个环境因子,利用最大熵模型(MaxEnt)和地理信息系统(GIS)预测了豆梨在不同气候条件下的全球生态适宜区.结果表明: 豆梨的生态适宜区主要集中在北美洲、亚洲等地区,面积共约1.6×10⁷ km².其中,中国生态适宜度较高的地区主要分布在湖南省、湖北省、安徽省、江西省、江苏省、浙江省、福建省等地.影响豆梨地理分布的主要气候因子是年平均气温和年降水量,气温季节性变化次之.由模型预测可知,在不同的气候背景下,豆梨适宜生境和低适宜生境的面积有所不同.在空间分布上,豆梨适宜生境和低适宜生境的范围和几何中心都由东部向西部地区扩散,北美洲的适宜生境增长较快,而欧洲地区的低适宜生境增长较快.     

PSDS 2.0: 一个基于GIS和多个模型的生物潜在分布地预测系统

根据对生物分布地预测模型和软件发展现状的分析和总结, 本研究在PSDS 1.0的基础上提出并实现一个基于GIS且具有多个代表性模型的生物分布地预测系统(PSDS 2.0)。PSDS 2.0系统继承了1.0的环境包络和聚类包络模型, 进一步引入了限制因子包络、马氏距离、支持向量机等新模型, 并针对本领域中模型比较与选择的难点增加了迭代交叉验证的多模型选择功能。系统还实现了灵活定制和评估伪负样本的功能, 通过用只需要正样本的I类模型预测的结果对随机产生的伪负样本进行评估, 减小其落入适宜地区的概率, 进一步提高需要正负样本的II类模型的准确率。GIS功能在PSDS 2.0中也得到加强, 被应用于数据准备及结果分析等重要环节。文章最后以白冠长尾雉(Syrmaticus reevesii)为例, 运用PSDS 2.0系统预测其在中国范围内的潜在分布地, 并对各种模型的预测结果进行评估和比较。