蒙古黄芪潜在分布区预测的多模型比较

根据蒙古黄芪（Astragalus membranaceus（Fisch.）Bge.var.mongholicus（Bge.）Hsiao）123个样本点数据和19个环境数据,采用4种生态位模型对蒙古黄芪在中国的潜在适生区进行综合分析,并采用受试者工作特征曲线ROC和Kappa统计量,比较不同模型的预测效果。结果显示：4个模型预测精度良好,一致性显著。AUC值均达到0.8以上,Kappa值均达到0.6以上;其中DOMAIN模型的AUC值和Kappa值均最大,说明该模型的预测精度最佳,预测结果最稳定。潜在适生区的预测结果发现,GARP模型预测的最适宜区范围最广;MAXENT和BIOCLIM模型预测结果较为相似;DOMAIN模型预测结果比较分散。4个模型预测结果均表明西北一带可以作为蒙古黄芪栽培引种的主要产区。蒙古黄芪潜在适生区主要分布于中国北纬33°以北地区;最适宜区主要分布于甘肃、宁夏、陕西、山西、河北和内蒙古等地区。     

蒙古黄芪潜在分布区预测的多模型比较

根据蒙古黄芪(Astragalus membranaceus (Fisch.) Bge. var. mongholicus (Bge.) Hsiao) 123个样本点数据和19个环境数据,采用4种生态位模型对蒙古黄芪在中国的潜在适生区进行综合分析,并采用受试者工作特征曲线ROC和Kappa统计量,比较不同模型的预测效果。结果显示:4个模型预测精度良好,一致性显著。AUC值均达到0.8以上,Kappa值均达到0.6以上;其中DOMAIN模型的AUC值和Kappa值均最大,说明该模型的预测精度最佳,预测结果最稳定。潜在适生区的预测结果发现,GARP模型预测的最适宜区范围最广; MAXENT和BIOCLIM模型预测结果较为相似; DOMAIN模型预测结果比较分散。4个模型预测结果均表明西北一带可以作为蒙古黄芪栽培引种的主要产区。蒙古黄芪潜在适生区主要分布于中国北纬33°以北地区;最适宜区主要分布于甘肃、宁夏、陕西、山西、河北和内蒙古等地区。     

ROC曲线形状在生态位模型评价中的重要性——以美国白蛾为例

【目的】生态位模型在生物地理学、入侵生物学和保护生物学中具有广泛的应用,被越来越多地用于预测物种潜在分布和现实分布的研究中。本文以美国白蛾为例介绍pROC方案在生态位模型评价中的应用及其注意事项,以期对物种潜在分布预测进行合理的评价,促进生态位模型在我国的合理运用和发展。【方法】介绍ROC曲线和AUC值基本原理,总结其在生态位模型评价中的应用,从物种存在分布点和不存在分布点的可信度出发,分析AUC值用于模型评价的优点和不足,最后介绍局部受试者工作特征曲线的线下面积方案(pROC方案)来弥补传统AUC值的不足。【结果】AUC值虽独立于阈值,但因其综合灵敏度和特异度,而屏蔽这2个指标各自的特征,不能分别评估预测结果的灵敏度和特异度,同时对遗漏率和记账错率不能进行权衡,会误导使用者对模型的评价。与AUC值相比,ROC曲线的形状更具有价值,蕴含丰富的模型评价信息。【结论】模型评价需要将灵敏度和特异度区别对待,ROC曲线形状比AUC值在生态位模型评价中更为重要,pROC方案相对于传统AUC值具有优势,但容易对过度模拟做出不当判断。模型评价与作者研究目的密切相关:当以预测物种潜在分布为目的时(如入侵物种潜在分布、气候变化对物种分布的影响和谱系生物地理学),模型评价应当给予灵敏度(或者遗漏率)更多的权重;当以预测物种现实分布为目的时(如保护区界定和濒危物种引入),模型评价应当给予灵敏度和特异度同等的权重。     

木本能源植物文冠果的生态特征及区划

利用文冠果(Xanthoceras sorbifolia Bunge)160个分布点和19个生态因子数据,采用BIOCLIM、DOMAIN、MAXENT和GARP四种模型预测文冠果的生态适宜区,分析其生态特征并以受试者工作特征曲线ROC对各模型进行评价。结果显示,文冠果最适宜生态区主要分布于西北黄土高原一带,如陕西、山西、宁夏、河北、内蒙古等省区。模型评估结果显示上述4个模型的AUC值均达到0.75以上,表明预测精度良好,均可用来预测文冠果的生态适宜区。文冠果的生态特征为:年平均温度1.9~16℃,昼夜温差月平均8.3~16℃,昼夜温差与年温差比值为22~33,温度变化方差值为7523~14198;最热月份最高温度为20.1~33.5℃;年平均降水量37~952 mm,最湿月份降水量11~219 mm,最干月份降水量0~29 mm,雨量变化方差值为40~137。研究结果表明文冠果适宜生态区包括我国西部的黄土高原地区和北方土石山区以及新疆北部地区。     

基于生态位模型预测天麻全球潜在适生区

目前对药用植物天麻(Gastrodia elata)的全球潜在适生区研究较少,基于多个生态位模型预测天麻在全球范围内的潜在适生区,对天麻人工引种栽培及其产业发展具有重要意义。该文收集220个天麻全球分布点和19个生态因子数据,最终筛选出8个环境变量参与模型训练,基于3个生态位模型(BIOCLIM、DOMAIN和MAXENT)预测天麻全球潜在适生区,并采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:3个模型的预测结果基本一致,天麻全球潜在适生区主要分布在20°–50°N的亚洲地区,其中中国、日本和韩国是集中分布地,此外,印度、尼泊尔以及欧洲地中海附近有少量适生区。其中最适宜区主要分布在:中国四川盆地附近的省区以及中东部;韩国中东部的忠清北道、庆尚北道和庆尚南道;日本本州岛、九州岛以及四国岛,因此中国、日本和韩国是天麻的主要产区。3个模型的受试者工作特征曲线下面积(AUC值)平均值均达到0.9以上,Kappa平均值均达到0.65以上,能较好地预测天麻的潜在适生区,其中MAXENT模型的精度较高,其次是DOMAIN和BIOCLIM模型。     

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.     

珍稀濒危植物野生莲的适生分布区预测

基于野生莲（Nelumbo nucifera Gaertn.）136个分布点的数据和14个环境因子参数,运用规则集遗传算法（GARP）和最大熵（MaxEnt）两个生态位模型对他们在我国的适生分布区进行预测。结果显示：根据GARP和MaxEnt模型计算得到的ROC曲线下面积的AUC均值分别为0.861和0.964,其中MaxEnt模型的AUC值更大,预测结果更精准。MaxEnt模型预测结果表明,莲的最适分布区主要集中在四川、湖北、湖南等地的大部分地区,江西北部,以及黑龙江、辽宁、浙江、广东等地的小部分地区。刀切法（Jackknife）检测结果表明,影响莲适生分布区的主要环境因子包括：水汽压、海拔、年平均气温、多年平均降水量、最热季节平均温度、最冷季节平均温度、最干月降水量、最冷月最低温和最热月最高温等。适生区环境因子的统计分析结果显示,野生莲最适宜生长在海拔1~2216 m、年降水量丰富（1202.50 mm）、年均温约为16.19℃、最热月温度范围在24.60℃~35.10℃、最冷月均温不低于-0.53℃的地区。研究结果可为有效保护中国野生莲资源提供有利依据。     

基于MAXENT模型的黑龙江大兴安岭森林雷击火火险预测

黑龙江大兴安岭是森林雷击火的高发地区,急需研发精确的火险预测模型对该区森林火灾进行预测.本文基于大兴安岭地区森林雷击火灾数据及环境变量数据,采用MAXENT模型进行森林雷击火的火险预测.首先对各环境变量进行共线性诊断,再利用累积正则化增益法和Jackknife方法评价了环境变量的重要性,最后采用最大Kappa值和AUC值检测了MAXENT模型的预测精度.结果表明: 闪电能量和中和电荷量的方差膨胀因子（VIF）值分别为5.012和6.230,与其他变量之间存在共线性,不能用于模型训练.日降雨量、云地闪电数量及云地闪回击电流强度是影响森林雷击火发生的3个最重要因素,日平均风速和坡向的影响较小.随着建模数据比例的增加,最大Kappa值和AUC值均有增大趋势.最大Kappa值都大于0.75,平均值为0.772; AUC值都大于0.5,平均值为0.859.MAXENT模型的预测精度达到中等精度,可应用于大兴安岭地区的森林雷击火火险预测.     

利用最大熵模型和规则集遗传算法模型预测孑遗植物裸果木的潜在地理分布及格局

利用野外调查的16个居群分布点和7个环境因子图层,选择最大熵模型(MAXENT)和规则集遗传算法模型(GARP),在地理和环境空间上模拟了第三纪孑遗植物裸果木(Gymnocarpos przewalskii)在中国西北地区的潜在分布。结果表明:(1)裸果木的潜在适生区全部集中在西北荒漠区,其中最佳适生区主要集中在3个区域,一是河西走廊中部和玉门以西、宁夏北部及内蒙古乌拉特后旗;二是塔里木盆地西北缘;三是柴达木盆地西北缘两片极小的高度适生区。裸果木的生态位被确定在一个较广的干旱环境空间:适生区极端最高气温基本上在29.2–36.8℃之间,极端最低气温在–18.3至–13.4℃之间;年平均降水量40–200mm;潜在蒸发率在3–15之间。(2)MAXENT和GARP模型都较好地预测了裸果木的潜在分布,但GARP产生了相对较大、较连续的潜在分布区,部分过预测了破碎化生境;而MAXENT预测到的潜在分布区,在不同区域具有不同的环境适生性指数,而且成功地排除了不合理的破碎化分布,从而更直观地展示了裸果木的潜在分布格局和生态位要求。     

新疆典型蝗虫适生区分布预测

新疆草原面积广阔,农牧业地位突出,蝗灾对当地经济、生态威胁很大,近年新疆极端天气日渐频发,蝗灾监测与防治任务艰巨。以意大利蝗、西伯利亚蝗和亚洲飞蝗为代表的蝗虫数据为基础,综合考虑对蝗虫各生命周期有重要影响的环境因素,运用BIOCLIM模型、领域模型（DOMAIN）、马氏距离模型（MAHAL）、广义线性模型（GLM）、随机森林模型（RF）、提升回归树模型（BRT）、支持向量机模型（SVM）、最大熵模型（MaxEnt）等八种经典物种分布模型及集成模型对新疆典型蝗虫适生区展开了预测。结果表明：（1）不同模型对新疆典型蝗虫适生区预测存在差异,其中DOMAIN最差（曲线下面积（AUC）=0.688,真实技巧统计（TSS）=0.301）,而提升回归树（BRT）最佳（AUC=0.920,TSS=0.910）,基于BRT、SVM和MaxEnt 3个集成模型预测的新疆蝗虫适生区可靠性更高;（2）新疆典型蝗虫不同等级适生区面积约56.844万km²,占新疆总面积的36%,其中高适生区面积16.568万km²;（3）新疆典型蝗虫适生区主要集中于北疆阿勒泰、塔城地区,此外东部哈密地区及南疆绿洲边缘地带亦有分布。研究可为新疆草原工作部门推进蝗虫监测防治工作提供支持。     

The Predictive Performance and Stability of Six Species Distribution Models

Background

Predicting species’ potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs.

Methodology

We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values.

Results

The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p<0.05), while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05), and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points).

Conclusions

According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.     

Does the interpolation accuracy of species distribution models come at the expense of transferability?

Model transferability (extrapolative accuracy) is one important feature in species distribution models, required in several ecological and conservation biological applications. This study uses 10 modelling techniques and nationwide data on both (1) species distribution of birds, butterflies, and plants and (2) climate and land cover in Finland to investigate whether good interpolative prediction accuracy for models comes at the expense of transferability – i.e. markedly worse performance in new areas. Models’ interpolation and extrapolation performance was primarily assessed using AUC (the area under the curve of a receiver characteristic plot) and Kappa statistics, with supplementary comparisons examining model sensitivity and specificity values. Our AUC and Kappa results show that extrapolation to new areas is a greater challenge for all included modelling techniques than simple filling of gaps in a well‐sampled area, but there are also differences among the techniques in the degree of transferability. Among the machine‐learning modelling techniques, MAXENT, generalized boosting methods (GBM), and artificial neural networks (ANN) showed good transferability while the performance of GARP and random forest (RF) decreased notably in extrapolation. Among the regression‐based methods, generalized additive models (GAM) and generalized linear models (GLM) showed good transferability. A desirable combination of good prediction accuracy and good transferability was evident for three modelling techniques: MAXENT, GBM, and GAM. However, examination of model sensitivity and specificity revealed that model types may differ in their tendencies to either increased over‐prediction of presences or absences in extrapolation, and some of the methods show contrasting changes in sensitivity vs specificity (e.g. ANN and GARP). Among the three species groups, the best transferability was seen with birds, followed closely by butterflies, whereas reliable extrapolation for plant species distribution models appears to be a major challenge at least at this scale. Overall, detailed knowledge of the behaviour of different techniques in various study settings and with different species groups is of utmost importance in predictive modelling.     

预测物种潜在分布区——比较SVM与GARP

 物种分布与环境因子之间存在着紧密的联系,因此利用环境因子作为预测物种分布模型的变量是当前最普遍的建模思路,但是绝大多数物种分 布预测模型都遇到了难以解决的“高维小样本"问题。该研究通过理论和实践证明,基于结构风险最小化原理的支持向量机(Support vector machine, SVM)算法非常适合“高维小样本"的分类问题。以20种杜鹃花属(Rhododendron)中国特有种为检验对象,利用标本数据和11个1 km×1 km的栅格环境数据层作为模型变量,预测其在中国的潜在分布区,并通过全面的模型评估——专家评估,受试者工作特征(Receiver operator characteristic, ROC)曲线和曲线下方面积(Area under the curve, AUC)——来比较模型的性能。我们实现了以SVM为核心的物种分布预测 系统,并且通过试验证明其无论在计算速度还是预测效果上都远远优于当前广泛使用的规则集合预测的遗传算法(Algorithm for rule-set prediction, GARP)预测系统。     

Modelling the potential geographic distribution of invasive ant species in New Zealand

Despite their economic and environmental impacts, there have been relatively few attempts to model the distribution of invasive ant species. In this study, the potential distribution of six invasive ant species in New Zealand are modelled using three fundamentally different methods (BIOCLIM, DOMAIN, MAXENT). Species records were obtained from museum collections in New Zealand. There was a significant relationship between the length of time an exotic species had been present in New Zealand and its geographic range. This is the first time such a time lag has been described for exotic ant species, and shows there is a considerable time lag in their spread. For example, it has taken many species several decades (40–60 years) to obtain a distribution of 17–25% of New Zealand regions. For all six species, BIOCLIM performed poorly compared to the other two modelling methods. BIOCLIM had lower AUC scores and higher omission error, suggesting BIOCLIM models under-predicted the potential distribution of each species. Omission error was significantly higher between models fitted with all 19 climate variables compared to those models with fewer climate variables for BIOCLIM, but not DOMAIN or MAXENT. Widespread species had a greater commission error. A number of regions in New Zealand are predicted to be climatically suitable for the six species modelled, particularly coastal and lowland areas of both the North and South Islands.     

Modeling potential invasion range of alien invasive species,Hyptis suaveolens (L.) Poit. in India: Comparison of MaxEnt and GARP

Bushmint (Hyptis suaveolens (L.) Poit.) is one among the world's most noxious weeds. Bushmint is rapidly invading tropical ecosystems across the world, including India, and is major threat to native biodiversity, ecosystems and livelihoods. Knowledge about the likely areas under bushmint invasion has immense importance for taking rapid response and mitigation measures. In the present study, we model the potential invasion range of bushmint in India and investigate prediction capabilities of two popular species distribution models (SDM) viz., MaxEnt (Maximum Entropy) and GARP (Genetic Algorithm for Rule-Set Production). We compiled spatial layers on 22 climatic and non-climatic (soil type and land use land cover) environmental variables at India level and selected least correlated 14 predictor variables. 530 locations of bushmint along with 14 predictor variables were used to predict bushmint distribution using MaxEnt and GARP. We demonstrate the relative contribution of predictor variables and species-environmental linkages in modeling bushmint distribution. A receiver operating characteristic (ROC) curve was used to assess each model's performance and robustness. GARP had a relatively lower area under curve (AUC) score (AUC: 0.75), suggesting its lower ability in discriminating the suitable/unsuitable sites. Relative to GARP, MaxEnt performed better with an AUC value of 0.86. Overall the outputs of MaxEnt and GARP matched in terms of geographic regions predicted as suitable/unsuitable for bushmint in India, however, predictions were closer in the spatial extent in Central India and Western Himalayan foothills compared to North-East India, Chottanagpur and Vidhayans and Deccan Plateau in India.