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。     

提高生态位模型转移能力来模拟入侵物种 的潜在分布

生态位模型利用物种分布点所关联的环境变量去推算物种的生态需求, 模拟物种的分布。在模拟入侵物种分布时, 经典生态位模型包括模型构建于物种本土分布地, 然后将其转移并投射至另一地理区域, 来模拟入侵物种的潜在分布。然而在模型运用时, 出现了模型的转移能力较低、模拟的结果与物种的实际分布不相符的情况, 由此得出了生态位漂移等不恰当的结论。提高生态位模型的转移能力, 可以准确地模拟入侵物种的潜在分布, 为入侵种的风险评估提供参考。作者以入侵种茶翅蝽(Halyomorpha halys)和互花米草(Spartina alterniflora)为例, 从模型的构建材料(即物种分布点和环境变量)入手, 全面阐述提高模型转移能力的策略。在构建模型之前, 需要充分了解入侵物种的生物学特性、种群平衡状态、本土地理分布范围及物种的生物历史地理等方面的知识。在模型构建环节上, 物种分布点不仅要充分覆盖物种的地理分布和生态空间的范围, 同时要降低物种采样点偏差; 环境变量的选择要充分考虑其对物种分布的限制作用、各环境变量之间的空间相关性, 以及不同地理种群间生态空间是否一致, 同时要降低环境变量的空间维度; 模型构建区域要真实地反映物种的地理分布范围, 并考虑种群的平衡状态。作者认为, 在生态位保守的前提下, 如果模型是构建在一个合理方案的基础上, 生态位模型的转移能力是可以保证的, 在以模型转移能力较低的现象来阐述生态位分化时需要引起注意。     

入侵害虫菊方翅网蝽在中国的潜在分布预测

菊方翅网蝽Corythucha marmorata(Uhler,1878)是我国新近发现的外来入侵害虫,研究明确菊方翅网蝽在我国的潜在分布范围对其监测预警及科学防控具有重要意义。本研究根据菊方翅网蝽的地理分布数据及相关环境变量,运用Maxent生态位模型与ArcGIS预测了菊方翅网蝽在中国的潜在地理分布范围。预测结果表明:菊方翅网蝽在我国的适生区主要分布于100°～125°E,20°～40°N的亚热带、暖温带区域,其中高适生区主要集中在长江中下游地区,包括浙江、江苏、湖南、上海大部分地区、安徽南部、湖北南部、江西西部及南部、贵州东部、福建东部、广西北部、山东中部、河南南部以及重庆、台湾局部;此外,极端气温、平均气温、最干月份降雨量对菊方翅网蝽的潜在分布影响较大。菊方翅网蝽已在我国成功入侵并迅速蔓延成灾,应在疫区边缘地带加强监测,并采取措施防止其进一步扩散。     

栎方翅网蝽Corythucha arcuata(Say)在中国的潜在适生区预测

【目的】栎方翅网蝽是栎树上的一种重要害虫,自21世纪入侵意大利以来,迅速在欧洲地区暴发成灾。本文旨在探明栎方翅网蝽入侵中国的风险性。【方法】利用MaxEnt生态位模型预测栎方翅网蝽在中国的潜在适生区,分析栎方翅网蝽入侵、定殖和扩散的可能性。【结果】栎方翅网蝽入侵中国并在中国黄河长江中下游平原定殖和扩散的风险性极高,高度适生区包括重庆市、安徽省、湖北省、河南省、浙江省、湖南省、贵州省和四川省。通过分析环境因子重要性表明,最冷季度平均温度是影响栎方翅网蝽适应区的最关键环境变量;年平均温度、等温性和最干季度平均温度是影响栎方翅网蝽适应区分布的主要环境因素。【结论】栎方翅网蝽在我国定殖扩散风险高,建议植物检疫部门加强栎方翅网蝽的监控,并将栎方翅网蝽列入中华人民共和国进境植物检疫性有害生物名录。     

美国大陆外来入侵物种斑马纹贻贝(Dreissena polymorpha)潜在生境预测模型

防止外来生物入侵造成危害的重要手段是阻止可能造成入侵的物种进入适合其生存的地区.论文以1864个美国外来入侵物种斑马纹贻贝定点发生数据和开放式基础地理信息数据库Daymet的34个环境变量为主要信息源,采用逻辑斯蒂回归(LR)、分类与回归树模型(CART)、基于规则的遗传算法(GARP)、最大熵法(Maxent)4种途径,建立美国大陆部分潜在生境预测模型,从接受者运行特征曲线下面积(AUC)、Pearson相关系数、Kappa值3个方面来检验模型预测精度,在此基础上分析斑马纹贻贝的空间分布规律及其环境影响因素.研究结果表明:在3个评价指标中,4个生态位模型预测精度均达到优良水平,其中Maxent在物种现实生境模拟、主要生态环境因子筛选、环境因子对物种生境影响的定量描述方面都表现出了优越的性能;距水源距离、海拔高度、降水频率、太阳辐射是影响物种空间分布的主要环境因子.论文提出的研究方法对中国外来入侵物种生境预测具有较强的借鉴意义,研究结果对中国海洋外来入侵物种沙筛贝的预测与防治,具有一定的指导作用.     

基于MaxEnt模型的细足捷蚁在我国的适生区分析

细足捷蚁(Anoplolepis gracilipes)是新发现入侵我国南方地区的外来物种,对入侵地的生物多样性造成了严重威胁。为探究细足捷蚁的潜在扩散风险及其野生种群在我国的适生区范围,本文将细足捷蚁的分布点分为本土分布点和全球分布点,并分别构建了本土预测模型和全球预测模型,采用对细足捷蚁生存影响比较大的7个环境变量,通过调用ENMeval数据包调整MaxEnt模型参数,分别采用默认参数和优化参数并基于上述两种模型,对细足捷蚁在我国的适生区范围进行了预测,最后采用pROC方案对模型结果进行可信度检验。研究发现,在相同参数条件下,基于全球模型和本土模型的细足捷蚁适生区分布范围预测差异较大,而模型参数对模型预测的影响较小。综合4种情况的模型预测结果,发现细足捷蚁在我国云南、广西、广东、福建、海南和台湾均表现为高度适生,在湖南、贵州、江西和四川的部分地区表现为中度适生。此外,在世界范围内细足捷蚁于非洲中部和美洲中北部表现出高适生性。因此,作者认为,入侵昆虫细足捷蚁本土分布范围的界定对其在入侵地的预测结果有较大影响,也是影响模型预测结果准确性的重要因素。     

基于本土最优模型模拟入侵物种水盾草在中国的潜在分布

生态位模型预测存在不确定性, 不同模型预测结果差别较大。在生态位保守的前提下, 在本土区域构建经典生态位模型, 利用入侵地独立样本数据检验并选择最优模型, 具有独特优势, 可为入侵物种风险分析提供可靠参考。水盾草(Cabomba caroliniana)是一种恶性水生入侵杂草, 原产于南美洲, 已在我国多个省市建立种群, 本文基于本土最优模型预测其在我国的潜在分布, 以期为其风险分析和综合治理提供依据, 并通过水盾草案例探讨如何提高生态位模型预测准确性的方法。本文按时间顺序梳理了水盾草在我国的分布记录, 然后根据水盾草已有分布记录和其所关联的环境因子比较了不同地理种群所占有的气候生态空间, 测试水盾草在世界入侵过程中的现实生态位保守性。采用两组环境变量和5种算法在南美洲本土地区构建10种生态位模型, 并将其转移至我国, 基于最小遗漏率和记账错率, 利用我国(入侵地)的样本数据选择最优模型预测水盾草在我国的适宜生态空间和潜在分布。研究发现当前水盾草在我国的分布集中在东部水域充沛地区, 沿京杭运河和南水北调工程等向北扩散。生态空间比对中发现水盾草在亚洲与其他大洲所占有的生态空间具有一定的重叠, 其在我国的入侵过程中生态位是保守的。与本土空间相比, 水盾草在我国所占有的生态空间存在较大的生态位空缺, 表明水盾草在我国的潜在分布范围较大。生态位模型预测显示水盾草的适生区主要分布于我国的北京、上海、山东、浙江、江苏、安徽、湖北和湖南等省(市)。水盾草的潜在分布区多聚集在我国东南部, 该地区河流、湖泊、运河和渠道较为密集, 人类活动及自然天敌的缺乏容易助长其入侵趋势, 应在这些适宜地区开展调查, 及时发现疫情并采取相应措施。     

互花米草(Spartina alterniflora)潜在分布格局的空间尺度效应

了解不同空间尺度下外来入侵植物互花米草(Spartina alterniflora)的潜在分布格局,有助于制定科学的防治管理策略,维护滨海湿地的生物多样性。研究基于有效的地理分布点位和环境变量数据集,设置了3个研究区幅度(区域、国家、全球)和5种环境变量粒度(30″、1.0′、2.5′、5.0′、10′),应用最大熵(MaxEnt)模型预测互花米草在不同幅度和粒度下的潜在分布,探究互花米草分布格局及其环境影响因子对空间尺度响应。结果表明：(1)MaxEnt模型在不同空间尺度下的预测效果较好,各尺度下测试集的受试者曲线下面积(AUC)值均大于0.8,真实技巧统计值(TSS)值则超过0.56,但模型的预测精度对空间尺度变化较为敏感;(2)不同空间尺度下互花米草的潜在分布格局存在着显著的差异性,表现为适生区面积会随着空间范围扩大或环境变量分辨率降低而提高,且质心位置也在不断发生地带性转移;(3)空间尺度的变化会削弱主要环境变量的解释力。在大尺度范围和低分辨率环境变量图层中,气候因子的重要性较大,而在相反尺度下地形因子的影响度得到提升;(4)研究区范围与环境变量分辨率不匹配时,模型预测精度和物...     

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。     

物种分布模型在海洋潜在生境预测的应用研究进展

海洋生物的栖息分布与环境要素的关联性一直是海洋生态学研究的热点之一.近年来,物种分布模型被广泛应用于预测海洋物种分布、潜在适宜性生境评价等研究,为保护海洋生物多样性、防治外来物种入侵及制定渔业管理措施等提供了一条有效途径.物种分布模型主要包括生境适宜性指数模型、机理模型和统计模型.本文对物种分布模型的理论基础进行了归纳和总结,回顾了物种分布模型在预测海洋物种潜在地理分布研究中的开发与应用,重点介绍了不同类型统计模型在海洋物种潜在分布预测中的研究实例.比较各种选取变量和模型验证方法,认为赤池信息准则对于选取模型变量具有优势,Kappa系数和受试者操作特征曲线下面积在验证模型精度中应用最广泛.阐述了物种分布模型存在的问题及未来发展趋势,随着海洋生物生理机制研究的进一步深入,机理模型将是今后物种分布模型发展的重点.     

Spatial sampling bias and model complexity in stream‐based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin,USA

Leveraging existing presence records and geospatial datasets, species distribution modeling has been widely applied to informing species conservation and restoration efforts. Maxent is one of the most popular modeling algorithms, yet recent research has demonstrated Maxent models are vulnerable to prediction errors related to spatial sampling bias and model complexity. Despite elevated rates of biodiversity imperilment in stream ecosystems, the application of Maxent models to stream networks has lagged, as has the availability of tools to address potential sources of error and calculate model evaluation metrics when modeling in nonraster environments (such as stream networks). Herein, we use Maxent and customized R code to estimate the potential distribution of paddlefish (Polyodon spathula) at a stream‐segment level within the Arkansas River basin, USA, while accounting for potential spatial sampling bias and model complexity. Filtering the presence data appeared to adequately remove an eastward, large‐river sampling bias that was evident within the unfiltered presence dataset. In particular, our novel riverscape filter provided a repeatable means of obtaining a relatively even coverage of presence data among watersheds and streams of varying sizes. The greatest differences in estimated distributions were observed among models constructed with default versus AIC_C‐selected parameterization. Although all models had similarly high performance and evaluation metrics, the AIC_C‐selected models were more inclusive of westward‐situated and smaller, headwater streams. Overall, our results solidified the importance of accounting for model complexity and spatial sampling bias in SDMs constructed within stream networks and provided a roadmap for future paddlefish restoration efforts in the study area.     

The challenge of modeling niches and distributions for data‐poor species: a comprehensive approach to model complexity

Models of species ecological niches and geographic distributions now represent a widely used tool in ecology, evolution, and biogeography. However, the very common situation of species with few available occurrence localities presents major challenges for such modeling techniques, in particular regarding model complexity and evaluation. Here, we summarize the state of the field regarding these issues and provide a worked example using the technique Maxent for a small mammal endemic to Madagascar (the nesomyine rodent Eliurus majori). Two relevant model‐selection approaches exist in the literature (information criteria, specifically AICc; and performance predicting withheld data, via a jackknife), but AICc is not strictly applicable to machine‐learning algorithms like Maxent. We compare models chosen under each selection approach with those corresponding to Maxent default settings, both with and without spatial filtering of occurrence records to reduce the effects of sampling bias. Both selection approaches chose simpler models than those made using default settings. Furthermore, the approaches converged on a similar answer when sampling bias was taken into account, but differed markedly with the unfiltered occurrence data. Specifically, for that dataset, the models selected by AICc had substantially fewer parameters than those identified by performance on withheld data. Based on our knowledge of the study species, models chosen under both AICc and withheld‐data‐selection showed higher ecological plausibility when combined with spatial filtering. The results for this species intimate that AICc may consistently select models with fewer parameters and be more robust to sampling bias. To test these hypotheses and reach general conclusions, comprehensive research should be undertaken with a wide variety of real and simulated species. Meanwhile, we recommend that researchers assess the critical yet underappreciated issue of model complexity both via information criteria and performance on withheld data, comparing the results between the two approaches and taking into account ecological plausibility.     

Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation

Accurate modeling of geographic distributions of species is crucial to various applications in ecology and conservation. The best performing techniques often require some parameter tuning, which may be prohibitively time‐consuming to do separately for each species, or unreliable for small or biased datasets. Additionally, even with the abundance of good quality data, users interested in the application of species models need not have the statistical knowledge required for detailed tuning. In such cases, it is desirable to use “default settings”, tuned and validated on diverse datasets. Maxent is a recently introduced modeling technique, achieving high predictive accuracy and enjoying several additional attractive properties. The performance of Maxent is influenced by a moderate number of parameters. The first contribution of this paper is the empirical tuning of these parameters. Since many datasets lack information about species absence, we present a tuning method that uses presence‐only data. We evaluate our method on independently collected high‐quality presence‐absence data. In addition to tuning, we introduce several concepts that improve the predictive accuracy and running time of Maxent. We introduce “hinge features” that model more complex relationships in the training data; we describe a new logistic output format that gives an estimate of probability of presence; finally we explore “background sampling” strategies that cope with sample selection bias and decrease model‐building time. Our evaluation, based on a diverse dataset of 226 species from 6 regions, shows: 1) default settings tuned on presence‐only data achieve performance which is almost as good as if they had been tuned on the evaluation data itself; 2) hinge features substantially improve model performance; 3) logistic output improves model calibration, so that large differences in output values correspond better to large differences in suitability; 4) “target‐group” background sampling can give much better predictive performance than random background sampling; 5) random background sampling results in a dramatic decrease in running time, with no decrease in model performance.     

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.     

Using maximum entropy to predict the potential distribution of an invasive freshwater snail

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Collinearity in ecological niche modeling: Confusions and challenges

Ecological niche models are widely used in ecology and biogeography. Maxent is one of the most frequently used niche modeling tools, and many studies have aimed to optimize its performance. However, scholars have conflicting views on the treatment of predictor collinearity in Maxent modeling. Despite this lack of consensus, quantitative examinations of the effects of collinearity on Maxent modeling, especially in model transfer scenarios, are lacking. To address this knowledge gap, here we quantify the effects of collinearity under different scenarios of Maxent model training and projection. We separately examine the effects of predictor collinearity, collinearity shifts between training and testing data, and environmental novelty on model performance. We demonstrate that excluding highly correlated predictor variables does not significantly influence model performance. However, we find that collinearity shift and environmental novelty have significant negative effects on the performance of model transfer. We thus conclude that (a) Maxent is robust to predictor collinearity in model training; (b) the strategy of excluding highly correlated variables has little impact because Maxent accounts for redundant variables; and (c) collinearity shift and environmental novelty can negatively affect Maxent model transferability. We therefore recommend to quantify and report collinearity shift and environmental novelty to better infer model accuracy when models are spatially and/or temporally transferred.     

Wrong,but useful: regional species distribution models may not be improved by range‐wide data under biased sampling

Species distribution modeling (SDM) is an essential method in ecology and conservation. SDMs are often calibrated within one country's borders, typically along a limited environmental gradient with biased and incomplete data, making the quality of these models questionable. In this study, we evaluated how adequate are national presence‐only data for calibrating regional SDMs. We trained SDMs for Egyptian bat species at two different scales: only within Egypt and at a species‐specific global extent. We used two modeling algorithms: Maxent and elastic net, both under the point‐process modeling framework. For each modeling algorithm, we measured the congruence of the predictions of global and regional models for Egypt, assuming that the lower the congruence, the lower the appropriateness of the Egyptian dataset to describe the species' niche. We inspected the effect of incorporating predictions from global models as additional predictor (“prior”) to regional models, and quantified the improvement in terms of AUC and the congruence between regional models run with and without priors. Moreover, we analyzed predictive performance improvements after correction for sampling bias at both scales. On average, predictions from global and regional models in Egypt only weakly concur. Collectively, the use of priors did not lead to much improvement: similar AUC and high congruence between regional models calibrated with and without priors. Correction for sampling bias led to higher model performance, whatever prior used, making the use of priors less pronounced. Under biased and incomplete sampling, the use of global bats data did not improve regional model performance. Without enough bias‐free regional data, we cannot objectively identify the actual improvement of regional models after incorporating information from the global niche. However, we still believe in great potential for global model predictions to guide future surveys and improve regional sampling in data‐poor regions.