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。     

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。     

亚热带常绿阔叶林群落物种多度分布格局对取样尺度的响应

为揭示物种多度格局随尺度的变化规律,探讨多度格局形成的机理及生态学过程,作者以古田山亚热带常绿阔叶林24 ha固定监测样地为背景.采用断棍模型(broken stick model)、对数正态模型(Iognormal distribution model)、生态位优先占领模型(preemption model)、Zipf模型(Zipf model)、Zipf-Mandelbrot模型(Zipf-Mandelbrot model)及中性理论模型(neutral model),对不同尺度下的物种多度分布格局进行拟合,并采用AIC检验和卡方检验选择最优拟合模型.结果表明,不同尺度上适合的物种一多度曲线模型不同;在取样边长为10 m和20 m时,除中性模型外的5个模型均不能被拒绝,它们均适合小尺度下的格局,这表明存小的尺度上生态位过程对物种一多度曲线的格局贡献较大;在取样边长为40 m时,最适合的模型为对数正态模型;取样边长为60 m和80 m时,Zipf-Mandelbrot模型为最优拟合模型;在取样边长为100 m时,尽管Zipf-Mandelbrot模型有最小的AIC值,但卡方检验拒绝了除中性模型外的5个模型;中性理论模型除了边长为10 m和20 m尺度以外,在其他尺度上均比前面5种模型的预测效果更好.因此在研究物种多度分布规律时必须注意空间尺度的影响.研究结果表明随着尺度的增加,中性过程成为决定物种一多度曲线格局的主要生态过程.     

使用大熊猫数据评估Biomod2和MaxEnt分布预测模型的表现

物种分布模型是物种研究和保护者常用的工具.不同模型的预测结果可能相差很大,对研究者选择模型造成一定的难度.本研究使用大熊猫的实际分布数据评估了两种常见物种分布模型Biomod2和最大熵模型(MaxEnt)的表现,运用ROC曲线下面积(area under the curve,AUC)、真实技巧统计值(true skill statistics,TSS)、KAPPA统计量3种指标综合评估了两种模型预测结果的准确度.结果表明: 当使用的物种分布数据和模拟重复次数足够多的时候,两者都能够给出相当准确的预测.相对于MaxEnt,Biomod2的预测准确度更高,尤其是在物种分布点稀少的情况下.然而,Biomod2使用难度较大,运行时间较长,数据处理能力有限.研究者应基于对预测结果的误差要求来选择模型.在误差要求明确且两个模型都能满足误差要求时,建议使用MaxEnt,否则应优先考虑使用Biomod2.     

最大熵原理及其在生态学研究中的应用

最大熵原理(the principle of maximum entropy)起源于信息论和统计力学,是基于有限的已知信息对未知分布进行无偏推断的一种数学方法.这一方法在很多领域都有成功应用,但只是近几年才被应用到生态学研究中,并且还存在很多争论.我们从基本概念和方法出发,用掷骰子的例子阐明了最大熵原理的概念,并提出运用最大熵原理解决问题需要遵从的步骤.最大熵原理在生态学中的应用主要包括以下方面:(1)用群落水平功能性状的平均值作为约束条件来预测群落物种相对多度的模型;(2)基于气候、海拔、植被等环境因子构建物种地理分布的生态位模型;(3)对物种多度分布、种一面积关系等宏生态学格局的推断;(4)对物种相互作用的推断;(5)对食物网度分布的研究等等.最后我们综合分析了最大熵原理在生态学应用中所存在的争议,包括相应模型的有效性、可靠性等方面,介绍了一些对最大熵原理预测能力及其局限性的检验结果,强调了生态学家应用最大熵原理需要注意的问题,比如先验分布的选择、约束条件的设置等等.在物种相互作用、宏生态学格局等方面对最大熵原理更广泛的讨论与应用可能会给生态学带来新的发展机会.     

MAXENT最大熵模型在预测物种潜在分布范围方面的应用

MAXENT最大熵模型是以最大熵理论为基础的物种地理尺度空间分布模型。该模型自提出之后在国内外迅速得到广泛应用,被越来越多地应用于入侵生物学、保护生物学、全球气候变化对物种分布影响,以及进化生物学等领域的研究。主要从MAXENT模型在入侵生物潜在分布区预测、濒危物种及有经济价值物种的适生区预测,以及全球气候变化对物种分布的影响预测等3个方面,对其应用现状进行综述,分析应用该模型时应该注意的一些问题。     

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。     

跨期决策是否基于“折扣计算”:来自双分离范式的证据

跨期决策指对发生在不同时间点上的结果之间进行的权衡.跨期决策主要理论可依据跨期决策是否基于折扣计算分为两类:折扣模型和非折扣模型,但目前学界却缺乏研究直接检验跨期决策的折扣计算假设是否成立.因此,本研究通过设计新的实验范式检验了跨期决策的"折扣计算"假设.该范式对比了符合折扣计算假设的基线折扣任务和被试自主进行跨期决策的自主跨期任务的任务表现和分层贝叶斯模型拟合结果,并基于双分离逻辑,分别选择了仅影响基线折扣任务或者自主跨期任务的操作变量(计算难度或选项的结果大小)与调节变量(计算能力或认知反思风格).研究结果发现,基线折扣任务的任务表现符合折扣计算假设的预测:其反应时随计算难度上升而增加,且其正确率受到计算难度和计算能力的影响;自主跨期决策任务的任务表现与基线折扣任务不同:其反应时短于基线折扣任务,且当选项结果越大,其选择SS选项的比例越少;调节变量和模型拟合出现了双分离效应,计算能力只影响基线折扣任务中的选择,认知反思风格只影响自主跨期任务偏好选择;计算难度只影响基线折扣任务的模型拟合度,而选项结果大小只影响自主跨期任务的模型拟合度.本研究结果不仅证明了新实验范式在检验折扣计算假设中的有效性,且从任务表现、调节变量和模型拟合层面看,自主跨期任务可能并不符合折扣计算假设的预测.本研究为更好地检验跨期决策的过程提供了新的方法学上的有益探索.     

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。     

吉林蛟河阔叶红松林物种多度分布模型研究

以吉林蛟河阔叶红松林的木本植物为研究对象,将30hm²的样地面积划分为5m×5m,10m×10m,20m×20m,25m×25m的连续取样单元,在4个不同尺度下分别统计各物种在每个取样单元中的有无,得到每个物种在不同尺度下的取样单元数。利用随机分布模型和负二项分布模型分析物种的多度分布,对比预测多度与观测多度讨论两个模型的科学性与实用性。结果表明：对于阔叶红松林而言,负二项分布模型在所有研究尺度上的预测精度都要优于随机分布模型。随机分布和负二项分布的模型预测误差随着研究尺度的增大而增大,因此选取较小的取样单元可以切实提高物种多度的预测精度。利用随机分布和负二项分布模型对多度较小的物种进行预测的效果要优于多度较大的物种。负二项分布模型适合用来模拟阔叶红松林的物种多度分布格局,并且模型的拟合效果受取样单元大小影响。     

MaxEnt模型参数设置对其所模拟物种地理分布和生态位的影响——以茶翅蝽为例

【目的】生态位模型被广泛应用于入侵生物学和保护生物学研究,现有建模工具中,MaxEnt是最流行和运用最广泛的生态位模型。然而最近研究表明,基于MaxEnt模型的默认参数构建模型时,模型倾向于过度拟合,并非一定为最佳模型,尤其是在处理一些分布点较少的物种。【方法】以茶翅蝽为例,通过设置不同的特征参数、调控倍频以及背景拟不存在点数分别构建茶翅蝽的本土模型,然后将其转入入侵地来验证和比较模型,通过检测模型预测的物种对环境因子的响应曲线、潜在分布在生态空间中的生态位映射以及潜在分布的空间差异性,探讨3种参数设置对MaxEnt模型模拟物种分布和生态位的影响。【结果】在茶翅蝽的案例分析中,特征参数的设置对MaxEnt模型所模拟的潜在分布和生态位的影响最大,调控倍频的影响次之,背景拟不存在点数的影响最小。与其他特征相比,基于特征H和T的模型其响应曲线较为曲折;随着调控倍频的增加,响应曲线变得圆滑。【结论】在构建MaxEnt模型时,需要从生态空间中考虑物种的生态需求,分析模型参数对预测物种分布和生态位可能造成的影响。     

Improved species‐occurrence predictions in data‐poor regions: using large‐scale data and bias correction with down‐weighted Poisson regression and Maxent

Species distribution modelling (SDM) has become an essential method in ecology and conservation. In the absence of survey data, the majority of SDMs are calibrated with opportunistic presence‐only data, incurring substantial sampling bias. We address the challenge of correcting for sampling bias in the data‐sparse situations. We modelled the relative intensity of bat records in their entire range using three modelling algorithms under the point‐process modelling framework (GLMs with subset selection, GLMs fitted with an elastic‐net penalty, and Maxent). To correct for sampling bias, we applied model‐based bias correction by incorporating spatial information on site accessibility or sampling efforts. We evaluated the effect of bias correction on the models’ predictive performance (AUC and TSS), calculated on spatial‐block cross‐validation and a holdout data set. When evaluated with independent, but also sampling‐biased test data, correction for sampling bias led to improved predictions. The predictive performance of the three modelling algorithms was very similar. Elastic‐net models have intermediate performance, with slight advantage for GLMs on cross‐validation and Maxent on hold‐out evaluation. Model‐based bias correction is very useful in data‐sparse situations, where detailed data are not available to apply other bias correction methods. However, bias correction success depends on how well the selected bias variables describe the sources of bias. In this study, accessibility covariates described bias in our data better than the effort covariate, and their use led to larger changes in predictive performance. Objectively evaluating bias correction requires bias‐free presence–absence test data, and without them the real improvement for describing a species’ environmental niche cannot be assessed.     

A statistical explanation of MaxEnt for ecologists

MaxEnt is a program for modelling species distributions from presence‐only species records. This paper is written for ecologists and describes the MaxEnt model from a statistical perspective, making explicit links between the structure of the model, decisions required in producing a modelled distribution, and knowledge about the species and the data that might affect those decisions. To begin we discuss the characteristics of presence‐only data, highlighting implications for modelling distributions. We particularly focus on the problems of sample bias and lack of information on species prevalence. The keystone of the paper is a new statistical explanation of MaxEnt which shows that the model minimizes the relative entropy between two probability densities (one estimated from the presence data and one, from the landscape) defined in covariate space. For many users, this viewpoint is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts. We then step through a detailed explanation of MaxEnt describing key components (e.g. covariates and features, and definition of the landscape extent), the mechanics of model fitting (e.g. feature selection, constraints and regularization) and outputs. Using case studies for a Banksia species native to south‐west Australia and a riverine fish, we fit models and interpret them, exploring why certain choices affect the result and what this means. The fish example illustrates use of the model with vector data for linear river segments rather than raster (gridded) data. Appropriate treatments for survey bias, unprojected data, locally restricted species, and predicting to environments outside the range of the training data are demonstrated, and new capabilities discussed. Online appendices include additional details of the model and the mathematical links between previous explanations and this one, example code and data, and further information on the case studies.     

Performance tradeoffs in target‐group bias correction for species distribution models

Species distribution models (SDMs) are often calibrated using presence‐only datasets plagued with environmental sampling bias, which leads to a decrease of model accuracy. In order to compensate for this bias, it has been suggested that background data (or pseudoabsences) should represent the area that has been sampled. However, spatially‐explicit knowledge of sampling effort is rarely available. In multi‐species studies, sampling effort has been inferred following the target‐group (TG) approach, where aggregated occurrence of TG species informs the selection of background data. However, little is known about the species‐ specific response to this type of bias correction. The present study aims at evaluating the impacts of sampling bias and bias correction on SDM performance. To this end, we designed a realistic system of sampling bias and virtual species based on 92 terrestrial mammal species occurring in the Mediterranean basin. We manipulated presence and background data selection to calibrate four SDM types. Unbiased (unbiased presence data) and biased (biased presence data) SDMs were calibrated using randomly distributed background data. We used real and TG‐estimated sampling efforts in background selection to correct for sampling bias in presence data. Overall, environmental sampling bias had a deleterious effect on SDM performance. In addition, bias correction improved model accuracy, and especially when based on spatially‐explicit knowledge of sampling effort. However, our results highlight important species‐specific variations in susceptibility to sampling bias, which were largely explained by range size: widely‐distributed species were most vulnerable to sampling bias and bias correction was even detrimental for narrow‐ranging species. Furthermore, spatial discrepancies in SDM predictions suggest that bias correction effectively replaces an underestimation bias with an overestimation bias, particularly in areas of low sampling intensity. Thus, our results call for a better estimation of sampling effort in multispecies system, and cautions the uninformed and automatic application of TG bias correction.     

Bunching up the background betters bias in species distribution models

Sets of presence records used to model species’ distributions typically consist of observations collected opportunistically rather than systematically. As a result, sampling probability is geographically uneven, which may confound the model's characterization of the species’ distribution. Modelers frequently address sampling bias by manipulating training data: either subsampling presence data or creating a similar spatial bias in non‐presence background data. We tested a new method, which we call ‘background thickening’, in the latter category. Background thickening entails concentrating background locations around presence locations in proportion to presence location density. We compared background thickening to two established sampling bias correction methods – target group background selection and presence thinning – using simulated data and data from a case study. In the case study, background thickening and presence thinning performed similarly well, both producing better model discrimination than target group background selection, and better model calibration than models without correction. In the simulation, background thickening performed better than presence thinning when the number of simulated presence locations was low, and vice versa. We discuss drawbacks to target group background selection, why background thickening and presence thinning are conservative but robust sampling bias correction methods, and why background thickening is better than presence thinning for small sample sizes. Particularly, background thickening is advantageous for treating sampling bias when data are scarce because it avoids discarding presence records.     

Akaike information criterion should not be a “test” of geographical prediction accuracy in ecological niche modelling

Model complexity in ecological niche modelling has been recently considered as an important issue that might affect model performance. New methodological developments have implemented the Akaike information criterion (AIC) to capture model complexity in the Maxent algorithm model. AIC is calculated based on the number of parameters and likelihoods of continuous raw outputs. ENMeval R package allows users to perform a species-specific tuning of Maxent settings running models with different combinations of regularization multiplier and feature classes and finally, all these models are compared using AIC corrected for small sample size. This approach is focused to find the “best” model parametrization and it is thought to maximize the model complexity and therefore, its predictability. We found that most niche modelling studies examined by us (68%) tend to consider AIC as a criterion of predictive accuracy in geographical distribution. In other words, AIC is used as a criterion to choose those models with the highest capacity to discriminate between presences and absences. However, the link between AIC and geographical predictive accuracy has not been tested so far. Here, we evaluated this relationship using a set of simulated (virtual) species. We created a set of nine virtual species with different ecological and geographical traits (e.g., niche position, niche breadth, range size) and generated different sets of true presences and absences data across geography. We built a set of models using Maxent algorithm with different regularization values and features schemes and calculated AIC values for each model. For each model, we obtained binary predictions using different threshold criteria and validated using independent presence and absences data. We correlated AIC values against standard validation metrics (e.g., Kappa, TSS) and the number of pixels correctly predicted as presences and absences. We did not find a correlation between AIC values and predictive accuracy from validation metrics. In general, those models with the lowest AIC values tend to generate geographical predictions with high commission and omission errors. The results were consistent across all species simulated. Finally, we suggest that AIC should not be used if users are interested in prediction more than explanation in ecological niche modelling.     

Predicting potential current distribution of Lycorma delicatula (Hemiptera: Fulgoridae) using MaxEnt model in South Korea

Lycorma delicatula (Hemiptera: Fulgoridae) is an invasive insect in Korea which causes plant damages by sucking and sooty molds. Lycorma delicatula was first detected in South Korea in 2004, where its introduction and spreading possibly were affected by human activity-related factors. Here, we used MaxEnt to describe current distribution of L. delicatula in Korea and tried to find out the impact of human influences on distribution. We used 143 sites of occurrence data, 19 bioclimatic variables, duration of temperature below −11 °C, average daily minimum temperature in January, cumulative thermal unit variable, the distribution of grape orchard variable and human footprint to create models. These models were estimated by two sets of 24 candidates with feature combinations and regularization multipliers. In addition, these two sets were created as models with and without footprint to assess human influence on distribution. Model selection for optimal model was performed by selecting a model with a lowest sum of each rank in small sample-size corrected Akaike’s information criterion and difference between training and test AUC. Model of LQ10 parameter combinations was selected as optimal models for both model sets. Consequently, both of distribution maps from these models showed similar patterns of presence probability for L. delicatula. Both models expected that low altitude regions were relatively more suitable for L. delicatula than mountain areas in Korea. Footprint might be limited for the distribution and L. delicatula might already occupy most of available habitats. Human-related factors might contribute to spread of L. delicatula to uninfected areas.     

The Influence of Data Resolution on Predicted Distribution and Estimates of Extent of Current Protection of Three ‘Listed’ Deep-Sea Habitats

Modelling approaches have the potential to significantly contribute to the spatial management of the deep-sea ecosystem in a cost effective manner. However, we currently have little understanding of the accuracy of such models, developed using limited data, of varying resolution. The aim of this study was to investigate the performance of predictive models constructed using non-simulated (real world) data of different resolution. Predicted distribution maps for three deep-sea habitats were constructed using MaxEnt modelling methods using high resolution multibeam bathymetric data and associated terrain derived variables as predictors. Model performance was evaluated using repeated 75/25 training/test data partitions using AUC and threshold-dependent assessment methods. The overall extent and distribution of each habitat, and the percentage contained within an existing MPA network were quantified and compared to results from low resolution GEBCO models. Predicted spatial extent for scleractinian coral reef and Syringammina fragilissima aggregations decreased with an increase in model resolution, whereas Pheronema carpenteri total suitable area increased. Distinct differences in predicted habitat distribution were observed for all three habitats. Estimates of habitat extent contained within the MPA network all increased when modelled at fine scale. High resolution models performed better than low resolution models according to threshold-dependent evaluation. We recommend the use of high resolution multibeam bathymetry data over low resolution bathymetry data for use in modelling approaches. We do not recommend the use of predictive models to produce absolute values of habitat extent, but likely areas of suitable habitat. Assessments of MPA network effectiveness based on calculations of percentage area protection (policy driven conservation targets) from low resolution models are likely to be fit for purpose.     

Opportunities for improved distribution modelling practice via a strict maximum likelihood interpretation of MaxEnt

Maximum entropy (MaxEnt) modelling, as implemented in the Maxent software, has rapidly become one of the most popular methods for distribution modelling. Originally, MaxEnt was described as a machine‐learning method. More recently, it has been explained from principles of Bayesian estimation. MaxEnt offers numerous options (variants of the method) and settings (tuning of parameters) to the users. A widespread practice of accepting the Maxent software's default options and settings has been established, most likely because of ecologists’ lack of familiarity with machine‐learning and Bayesian statistical concepts and the ease by which the default models are obtained in Maxent. However, these defaults have been shown, in many cases, to be suboptimal and exploration of alternatives has repeatedly been called for. In this paper, we derive MaxEnt from strict maximum likelihood principles, and point out parallels between MaxEnt and standard modelling tools like generalised linear models (GLM). Furthermore, we describe several new options opened by this new derivation of MaxEnt, which may improve MaxEnt practice. The most important of these is the option for selecting variables by subset selection methods instead of the ?₁‐regularisation method, which currently is the Maxent software default. Other new options include: incorporation of new transformations of explanatory variables and user control of the transformation process; improved variable contribution measures and options for variation partitioning; and improved output prediction formats. The new options are exemplified for a data set for the plant species Scorzonera humilis in SE Norway, which was analysed by the standard MaxEnt procedure in a previously published paper. We recommend that thorough comparisons between the proposed alternative options and default procedures and variants thereof be carried out.     

A practical guide to MaxEnt for modeling species’ distributions: what it does,and why inputs and settings matter

The MaxEnt software package is one of the most popular tools for species distribution and environmental niche modeling, with over 1000 published applications since 2006. Its popularity is likely for two reasons: 1) MaxEnt typically outperforms other methods based on predictive accuracy and 2) the software is particularly easy to use. MaxEnt users must make a number of decisions about how they should select their input data and choose from a wide variety of settings in the software package to build models from these data. The underlying basis for making these decisions is unclear in many studies, and default settings are apparently chosen, even though alternative settings are often more appropriate. In this paper, we provide a detailed explanation of how MaxEnt works and a prospectus on modeling options to enable users to make informed decisions when preparing data, choosing settings and interpreting output. We explain how the choice of background samples reflects prior assumptions, how nonlinear functions of environmental variables (features) are created and selected, how to account for environmentally biased sampling, the interpretation of the various types of model output and the challenges for model evaluation. We demonstrate MaxEnt’s calculations using both simplified simulated data and occurrence data from South Africa on species of the flowering plant family Proteaceae. Throughout, we show how MaxEnt’s outputs vary in response to different settings to highlight the need for making biologically motivated modeling decisions.