基于MaxEnt模型的中国马尾松分布格局及未来变化

气候因素是影响物种分布的决定性因素之一。根据现有的马尾松分布数据和19个全球气候因子变量数据,依托QGIS 2.18.3和ArcGIS 10.1等软件,运用MaxEnt模型,模拟了马尾松的现分布区,并对其未来分布进行预测,同时对影响马尾松的气候变量进行了分析。结果表明:(1)影响马尾松分布的19个气候变量中,最干燥月的降水量(bio14)和最冷季度的平均温度(bio11)对马尾松分布的影响贡献率超过70%;(2)依托气候数据,对马尾松未来分布进行预测,其未来的分布面积增加,增比为35.82%;(3)使用QGIS 2.18.3软件对未来的气候因子变化进行预测,结果显示,气候变化情况与马尾松未来分布格局相吻合。研究表明,马尾松适应能力较强,未来的气候变化对其分布呈正向影响。     

气候变化下青藏高原全缘叶绿绒蒿的潜在分布变迁

青藏高原物种丰富且属于气候变化敏感区,研究气候变化对青藏高原物种的潜在分布影响,对于该区域物种多样性保护具有重要意义。该研究以一级濒危藏药植物全缘叶绿绒蒿为研究对象,利用加权平均算法(weighted average algorithm, WAA)构建随机森林(RF)、灵活判别分析(FDA)及人工神经网络(ANN)的集成模型,同时对比分析了WAA模型和不同生态位模型的预测精度。最后利用WAA模型预测了全缘叶绿绒蒿在当前(1970~2000年平均)和未来(2041~2060年平均)气候情景下的潜在分布,其中未来气候考虑了2种“共享社会经济路径”(SSP2-45和SSP5-85)。结果显示:(1) WAA模型的预测表明,基于RF、FDA和ANN的集成模型的AUC值为0.926,在AUC值最高RF模型的基础上提高了3%,在FDA和ANN模型的AUC值的基础上均提高了5%。(2) WAA模型确定,全缘叶绿绒蒿的潜在分布对年降水量和最暖季降水量最为敏感,其次是最热月份最高气温,同时对最湿月份降水量以及等温性表现出较低的敏感性。(3)当前全缘叶绿绒蒿潜在分布区主要分布在甘肃西南部、青海东部至南部、四川西部和西北部、云南西北部和东北部、西藏东部。(4)未来气候变化下青藏高原全缘叶绿绒蒿潜在分布预测表明,在2050年SSP2-45情景下,全缘叶绿绒蒿的潜在分布区大小与当前潜在分布区大小基本相同,但整体向西北方向高海拔高纬度地区迁移;在SSP5-85情景下,全缘叶绿绒蒿的潜在分布区明显收缩,且向西北高纬度高海拔地区延伸的趋势更加明显。     

广义模型及分类回归树在物种分布模拟中的应用与比较

比较3个应用较广的模拟物种地理分布模型:广义线性模型(GLM)、广义加法模型(GAM)与分类回归树(CART)对中国树种地理分布模拟的优劣,以提出更为合适的模拟物种地理分布模型,并用于预测气候变化对物种地理分布的影响。3个模型对中国15种树种地理分布的模拟研究表明:除对油松、辽东栎分布的模拟精度稍差外,对其余树种分布的模拟精度均较高,其中以GAM模型最好。结合地理信息系统(GIS),比较分析了这3个模型对青冈、木荷、红松和油松4种树种的地理分布模拟效果,结果亦表明:这3个模型均能很好模拟青冈和木荷的地理分布,而GLM模型对红松分布的模拟结果不太理想,3个模型对油松分布的模拟结果均不甚理想,其中以GLM模型最差。基于3个模型对未来气候变化下青冈与蒙古栎地理分布的预测表明:GLM模型与GAM模型对青冈分布的预测结果较为接近,青冈在未来气候变化情景下向西和向北扩展,而CART模型预测青冈在未来气候变化情景下除有向西、向北扩展趋势外,广东和广西南部的青冈分布区将消失;3个模型均预测蒙古栎在未来气候变化情景下向西扩展,扩展面积的大小为:模型的模拟面积>模型>模型。     

气候变化对物种分布影响模拟中的不确定性组分分割与制图——以油松为例

物种分布模型是预测评估气候变化对物种分布影响的主要工具。为了降低物种分布模型在预测过程中的不确定性,近期有学者提出了采用组合预测的新方法,即采用多套建模数据、模型技术,模型参数,以及环境情景数据对物种分布进行预测,构成物种分布预测集合。但是,组合预测中各组分对变异的贡献还知之甚少,因此有必要把变异组分来源进行分割,以更有效地利用组合预测方法来降低模型预测中的不确定性。以油松为例,采用8个生态位模型,9套模型训练数据,3个GCM模型和一个SRES(A2)排放情景,模型分析了油松当前(1961-1990年)和未来气候条件下3个时间段(2010-2039年,2040-2069年,2070-2099年)的潜在分布。共计得到当前分布预测数据72套,未来每个时间段分布数据216套。采用开发的ClimateChina软件进行当前和未来气候数据的降尺度处理。采用Kappa、真实技巧统计方法(TSS)和接收机工作特征曲线下的面积(AUC)对模型预测能力进行评估。结果表明,随机森林(RF)、广义线性模型(GLM),广义加法模型(GAM)、多元自适应样条函数(MARS)以及助推法(GBM)预测效果较好,几乎不受建模数据之间差异的影响。混合判别分析模型(MDA)对建模数据之间的差异非常敏感,甚至出现建模失败现象。采用三因素方差分析方法对组合预测中的不确定性来源进行变异分割,结果表明,模型之间的差异对模拟预测结果不确定性的贡献最大且所占比例极高,而建模数据之间的差异贡献最小,GCM贡献居中。研究将有助于加深对物种分布模拟预测中不确定性的认识。     

气候变化下青藏高原两种云杉植物的潜在适生区预测

为了预测未来气候变化下云杉属植物的适宜生境,选择青藏高原暗针叶林的两种重要建群植物丽江云杉（Picea likiangensis）和紫果云杉（Picea purpurea）作为研究对象,采用MaxEnt模型预测21世纪50年代（2050s）和70年代（2070s）两物种在未来气候情景下的潜在分布,并结合ArcGIS计算物种分布面积和空间格局变化。结果表明：（1）丽江云杉的潜在适宜分布区主要集中在四川西南部和西藏东部。紫果云杉潜在适宜分布区主要集中在四川西北部、甘肃南部、青海东南部,以及西藏东部地区。（2）在未来两个时期丽江云杉的分布面积总体呈增加趋势,紫果云杉呈先增加后减少的趋势,但与其现代分布面积相比,两种云杉的总适生区面积都有不同程度的增加。（3）丽江云杉适宜生境未来可能会向北迁移,而紫果云杉可能会向西迁移。（4）影响丽江云杉和紫果云杉潜在地理分布的主要气候因子为最暖季降水量和最暖季均温。研究结果可为丽江云杉和紫果云杉在未来气候变化情景下的可持续管理提供一定的理论依据和参考价值。     

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。     

气候变化条件下山西翅果油树适宜分布区的空间迁移预测

气候变化对生物多样性的影响及其适应性直接关系着生物多样性保护的成效,预测未来气候变化条件下受威胁物种适宜生境的空间变化趋势对生物多样性保护具有重要的理论和实践意义.本文选取我国特有濒危植物翅果油树为研究对象,在区域尺度上预测气候变化条件下的物种适宜分布区,进而通过空间分析模拟不同气候变化情景下其适宜分布区的空间变化和迁移趋势.最大熵(Maxent)物种分布模型预测结果显示: 翅果油树的两个适宜分布区在未来气候变化情景下呈现不同的迁移趋势,吕梁山适生区呈现出纬度方向上的轻微波动,而中条山适生区则呈现出向高海拔地区迁移的趋势.适生区空间格局变化分析表明,翅果油树当前适生区的边界存在明显变化区域,包括新增适生区(零星分布在两个适生区的边缘地带,新增率为9.1%～20.9%)和丧失适生区(集中分布在吕梁山适生区北缘和中条山适生区东南部,丧失率为16.4%～31.2%),且两者对气候变化的响应较为敏感.利用分类统计工具Zonal计算得出,在未来气候变化条件下吕梁山适生区的中心点呈现向南迁移的趋势,最大迁移距离为7.451 km;中条山适生区的中心点则呈现出向西北迁移的趋势,最大迁移距离为8.284 km.表明山西翅果油树的分布对气候变化的响应较为剧烈.     

中国北方5种栎属树木多度分布及其对未来气候变化的响应

植物分布与气候之间的关系是预估未来气候变化对生态系统影响的实现基础。以往的物种分布模型通常以物种的分布区或者分布点的物种存在数据作为物种分布的响应变量。相较于物种存在数据, 多度反映了一个物种占用资源并把资源分配给个体的能力, 更能衡量物种对区域生态系统的影响。该研究通过野外调查获取了华北及周边地区1 045个样方的栎属树木多度, 利用广义线性模型、广义加性模型和随机森林模型模拟栓皮栎(Quercus variabilis)、麻栎(Q. acutissima)、槲栎(Q. aliena)、锐齿槲栎(Q. aliena var. acuteserrata)和蒙古栎(Q. mongolica) 5个树种多度的地理分布及未来2个不同时期(2050年和2070年)的潜在分布。结果表明: 随机森林模型对5个栎属树种的多度的拟合结果要优于广义线性模型和广义加性模型; 典型浓度路径(RCP) 8.5下的5个栎属树种在未来两个时期的多度变化幅度都要大于RCP 2.6下的变化, 在超过一半面积的区域中麻栎、槲栎、锐齿槲栎和蒙古栎的多度减少, 其中内蒙古东北部和黑龙江北部地区是5种栎属植物多度减少的集中分布地区。未来气候变化背景下, 需要加强对这几个区域的监测与物种保护。     

四川省岷江冷杉对气候变化的响应及其潜在分布格局

四川省是我国气候变化的敏感区之一,区域气候的暖干化趋势严重影响植物物种组成与分布。岷江冷杉(Abies faxoniana)作为我国特有种,其分布的动态变化对气候变化具有十分重要的指示作用。基于现有岷江冷杉分布数据、气候、土壤、地形等环境因子,运用最大熵模型(MaxEnt)预测当代气候条件下岷江冷杉潜在分布区,并分析未来时期(2050s和2070s)不同气候变化情景下(RCP2.6、RCP4.5和RCP8.5)岷江冷杉潜在适生分布区,筛选影响岷江冷杉分布的主导环境因子及阈值,探讨岷江冷杉分布对气候变化的响应机制。结果表明：(1)当前岷江冷杉的高适生区集中分布于岷江流域上游地区,在未来两个时期岷江冷杉潜在中、高适生区的面积较当代气候条件下适生区面积均有所增加,且适生区总体向四川南部扩张,北部适宜生境丧失。(2)岷江冷杉潜在中适生区在低排放浓度下(RCP2.6)面积占比最高,而潜在高适生区在高排放浓度下(RCP8.5)的面积占比最高。(3)影响岷江冷杉分布的主要环境因子分别是：降水季节性变异系数、气温年变化幅度、年降水量和海拔(累计贡献>70%)。适宜岷江冷杉潜在分布的环境条件是气温...     

孑遗植物长苞铁杉(Tsuga longibracteata)分布格局对未来气候变化的响应

长苞铁杉(Tsuga longibracteata)是中国特有的珍贵树种,不仅对研究裸子植物的系统发育、古生态和古气候具有重要作用,而且该树种具有造林、用材和药用等方面的较高价值。研究长苞铁杉在气候变化下的分布格局变化是制定其保护和可持续利用的重要基础。采用最大熵模型(MaxEnt),结合不同时期(当前、2050年和2070年)和不同二氧化碳排放情境下(RCP2.6和RCP8.5)的气候因子变量,探讨气候变化与物种地理分布格局的关系,预测长苞铁杉的潜在分布区变迁。本研究考虑了空间约束对物种分布的限制作用,构建了气候因子预测模型(C)和气候+空间约束因子预测模型(C+S)分别进行潜在分布区预测,比较其结果差异。结果显示,最干月降水量和温度年较差是影响长苞铁杉地理分布的主导气候因子,空间约束因子对长苞铁杉未来的地理分布有重要影响。随时间年限增加,长苞铁杉总潜在适生区面积降低,特别是中高等级的适生区面积有不同程度地减少,分布范围总体向北移动,这些变化趋势在RCP8.5情境下更加突出。这一结果表明未来气候变化会导致长苞铁杉种群分布范围收缩和生境适宜度下降,加剧其受胁程度。加入空间约束因子后,C+S模型的预测精度更高,结果更符合长苞铁杉的迁移、扩散特性。长苞铁杉未来的核心分布区仍位于现存的湘、桂、黔结合部,表明其具有"原地避难"的特性,应进一步加强对现有野生资源的保护。渝、川、鄂结合部的大巴山等地区是未来气候变化下长苞铁杉的理论分布区域,可作为长苞铁杉应对未来气候变化的引种地区,应提早进行人工引种、栽培等前期研究。研究结果可为气候变化背景下长苞铁杉的保护、物种迁地保存和可持续管理提供科学依据,也可为准确预测濒危、珍稀植物的地理分布范围提供方法参考。     

A niche for biology in species distribution models

Why species are found where they are is a central question in biogeography. The most widely used tool for understanding the controls on distribution is species distribution modelling. Species distribution modelling is now a well‐established method in both the theoretical and applied ecological literature. In this special issue we examine the current state of the art in species distribution modelling and explore avenues for including more biological processes in such models. In particular we focus on physiological, demographic, dispersal, competitive and ecological‐modulation processes. This overview highlights opportunities for new species distribution model concepts and developments, as well as a statistical agenda for implementing such models.     

Prediction of plant species distribution in lowland river valleys in Belgium: modelling species response to site conditions

In ecological modelling, limitations in data and their applicability for predictive modelling are more rule than exception. Often modelling has to be performed on sub-optimal data, as explicit and controlled collection of (more) appropriate data would not be feasible. An example of predictive ecological modelling is given with application of generalized additive and generalized linear models fitted to presence–absence records of plant species and site condition data from four nutrient-poor Flemish lowland valleys. Standard regression procedures are used for modelling, although explanatory and response data do not meet all the assumptions implicit in these procedures. Data were non-randomly collected and are spatially autocorrelated; model residuals retain part of that correlation. The scale of most site-condition records does not match the scale of the response variable (species distribution). Hence, interpolated and up-scaled explanatory variables are used. Data are aggregated from distinct phytogeographical regions to allow for generalized models, applicable to a wider population of river valleys in the same region. Nevertheless, ecologically sound models are obtained, which predict well the distribution of most plant species for the Flemish river valleys considered.     

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.     

Correlation and process in species distribution models: bridging a dichotomy

Within the field of species distribution modelling an apparent dichotomy exists between process‐based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlative–process spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the process–correlation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process‐based approaches to species distribution modelling lags far behind more correlative (process‐implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process‐explicit species distribution models and how they may complement current approaches to study species distributions.     

A critical assessment of two species distribution models: a case study of the vervet monkey (Cercopithecus aethiops)

Aim Species distribution models are invaluable tools in biogeographical, ecological and applied biological research, but specific concerns have been raised in relation to different modelling techniques in terms of their validity. Here we compare two fundamentally different approaches to species distribution modelling, one based on simple occurrence data where the lack of an ecological framework has been criticized, and the other firmly based in socio‐ecological theory but requiring highly detailed behavioural information that is often limited in availability. Location (Sub‐Saharan) Africa. Methods We used two distinct techniques to predict the realized distribution of a model species, the vervet monkey (Cercopithecus aethiops Linnaeus, 1758). A maximum entropy model was produced taking 13 environmental variables and presence‐only data from 174 sites throughout Africa as input, with an additional 58 sites retained to test the model. A time‐budget model considering the same environmental variables was constructed from detailed behavioural data on 20 groups representing 14 populations, with presence‐only data from the remaining 218 sites reserved to test model predictions on vervet monkey occurrence. Both models were further validated against a reference species distribution map as drawn up by the African Mammals Databank. Results Both models performed well, with the time budget and maximum entropy algorithms correctly predicting vervet monkey presence at 78.4% and 91.4% of their respective test sites. Similarly, the time‐budget model correctly predicted presence and absence at 87.4% of map pixels against the reference distribution map, and the maximum entropy model achieved a success rate of 81.8%. Finally, there was a high level of agreement (81.6%) between the presence–absence maps produced by the two models, and the environmental variables identified as most strongly driving vervet monkey distribution were the same in both models. Main conclusions The time‐budget and maximum entropy models produced accurate and remarkably similar species distribution maps, despite fundamental differences in their conceptual and methodological approaches. Such strong convergence not only provides support for the credibility of current results, but also relieves concerns about the validity of the two modelling approaches.     

A physiological analogy of the niche for projecting the potential distribution of plants

Aim  To develop a physiologically based model of the plant niche for use in species distribution modelling. Location  Europe. Methods  We link the Thornley transport resistance (TTR) model with functions which describe how the TTR’s model parameters are influenced by abiotic environmental factors. The TTR model considers how carbon and nutrient uptake, and the allocation of these assimilates, influence growth. We use indirect statistical methods to estimate the model parameters from a high resolution data set on tree distribution for 22 European tree species. Results  We infer, from distribution data and abiotic forcing data, the physiological niche dimensions of 22 European tree species. We found that the model fits were reasonable (AUC: 0.79–0.964). The projected distributions were characterized by a false positive rate of 0.19 and a false negative rate 0.12. The fitted models are used to generate projections of the environmental factors that limit the range boundaries of the study species. Main conclusions  We show that physiological models can be used to derive physiological niche dimensions from species distribution data. Future work should focus on including prior information on physiological rates into the parameter estimation process. Application of the TTR model to species distribution modelling suggests new avenues for establishing explicit links between distribution and physiology, and for generating hypotheses about how ecophysiological processes influence the distribution of plants.     

Pitch the niche – taking responsibility for the concepts we use in ecology and species distribution modelling

In a discussion it is often easier to staunchly reject or offer resolute support for an idea. This third paper on the niche concept aims to develop a balanced argument by exploring general principles for determining an appropriate level for pitching the niche concept that will guide better use and less abuse of niche concepts. To do this we first have to accept that niche concepts are not necessarily essential for ecology. Rather than to improve niche concepts, our aim should then be to pitch the niche in terms of ecology. This aim helps us develop an ‘ultimate goal of the niche’ by which we can evaluate the concepts we use. For species distribution modelling, there has been a focus on the niche as an equilibrium outcome that perhaps has less relevance for disequilibrium situations (e.g. climate change projections). As is the case for much of ecology, more causal explanations of species' distributions use alternative terminologies and less frequently use the word ‘niche’. We suggest that niche concepts that are better aligned with the rest of ecology could arise from taking more responsibility for our own implementations, and by explaining our models with terms other than niche. A general, holistic niche concept promotes this view and promotes practical thinking about what we are modelling and how we interpret those models, which in turn should help inspire and support innovative modelling approaches in species distribution modelling.     

Uncertainty in assessing the impacts of global change with coupled dynamic species distribution and population models

Concern over rapid global changes and the potential for interactions among multiple threats are prompting scientists to combine multiple modelling approaches to understand impacts on biodiversity. A relatively recent development is the combination of species distribution models, land‐use change predictions, and dynamic population models to predict the relative and combined impacts of climate change, land‐use change, and altered disturbance regimes on species' extinction risk. Each modelling component introduces its own source of uncertainty through different parameters and assumptions, which, when combined, can result in compounded uncertainty that can have major implications for management. Although some uncertainty analyses have been conducted separately on various model components – such as climate predictions, species distribution models, land‐use change predictions, and population models – a unified sensitivity analysis comparing various sources of uncertainty in combined modelling approaches is needed to identify the most influential and problematic assumptions. We estimated the sensitivities of long‐run population predictions to different ecological assumptions and parameter settings for a rare and endangered annual plant species (Acanthomintha ilicifolia, or San Diego thornmint). Uncertainty about habitat suitability predictions, due to the choice of species distribution model, contributed most to variation in predictions about long‐run populations.     

Assessing the accuracy of species distribution models to predict amphibian species richness patterns

1. Evaluating the distribution of species richness where biodiversity is high but has been insufficiently sampled is not an easy task. Species distribution modelling has become a useful approach for predicting their ranges, based on the relationships between species records and environmental variables. Overlapping predictions of individual distributions could be a useful strategy for obtaining estimates of species richness and composition in a region, but these estimates should be evaluated using a proper validation process, which compares the predicted richness values and composition with accurate data from independent sources. 2. In this study, we propose a simple approach to estimate model performance for several distributional predictions generated simultaneously. This approach is particularly suitable when species distribution modelling techniques that require only presence data are used. 3. The individual distributions for the 370 known amphibian species of Mexico were predicted using maxent to model data on their known presence (66,113 presence-only records). Distributions were subsequently overlapped to obtain a prediction of species richness. Accuracy was assessed by comparing the overall species richness values predicted for the region with observed and predicted values from 118 well-surveyed sites, each with an area of c. 100 km(2), which were identified using species accumulation curves and nonparametric estimators. 4. The derived models revealed a remarkable heterogeneity of species richness across the country, provided information about species composition per site and allowed us to obtain a measure of the spatial distribution of prediction errors. Examining the magnitude and location of model inaccuracies, as well as separately assessing errors of both commission and omission, highlights the inaccuracy of the predictions of species distribution models and the need to provide measures of uncertainty along with the model results. 5. The combination of a species distribution modelling method like maxent and species richness estimators offers a useful tool for identifying when the overall pattern provided by all model predictions might be representing the geographical patterns of species richness and composition, regardless of the particular quality or accuracy of the predictions for each individual species.     

Five (or so) challenges for species distribution modelling

Species distribution modelling is central to both fundamental and applied research in biogeography. Despite widespread use of models, there are still important conceptual ambiguities as well as biotic and algorithmic uncertainties that need to be investigated in order to increase confidence in model results. We identify and discuss five areas of enquiry that are of high importance for species distribution modelling: (1) clarification of the niche concept; (2) improved designs for sampling data for building models; (3) improved parameterization; (4) improved model selection and predictor contribution; and (5) improved model evaluation. The challenges discussed in this essay do not preclude the need for developments of other areas of research in this field. However, they are critical for allowing the science of species distribution modelling to move forward.