中国植物分布模拟研究现状

在过去的20年里,物种分布模型已广泛应用于动植物地理分布的模拟研究。该文以植物物种分布模拟为例,利用中国知网、维普网以及Web of Science文献数据库的检索与统计,分析了2000–2018年间,中国研究人员利用各种物种分布模型对植物物种分布模拟研究的发文量、模拟模型、物种类型、数据来源、研究目的等信息。最终共收集到366篇有效文献,分析表明2011年以来中国的物种分布模型应用发展迅速,且以最近5年最为迅猛,在生态学、中草药业、农业和林业等行业部门应用广泛。在使用的33种模型中,应用最广的为最大熵模型(MaxEnt)。有一半研究的环境数据仅包含气候数据,另一半研究不仅包含气候数据还包括地形与土壤等数据;环境及物种数据的来源多样,国际及本土数据库均得到使用。模拟涉及有明确清单的562个植物种,既有木本植物(52.7%),也有草本植物(41.8%),其中中草药、果树、园林植物、农作物等占比较高。研究目的主要集中在过去、现在和未来气候变化对植物种分布的影响及预测,以及物种分布评估与生物多样性评价(包括入侵植物风险评估)两大方面。预测物种潜在分布范围与气候变化影响等基础研究,与模拟物种适生区与推广种植等应用研究并重,物种分布模型在生态学与农业、林业和中草药业等多学科、多行业开展多种应用,多物种、多模型和多来源数据共同参与模拟与比较,开发新的机理性物种分布模型,拓展新的物种分布模拟应用领域,是今后研究的重点发展方向。     

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。     

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。     

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.     

中国植物分布模拟研究现状

在过去的20年里, 物种分布模型已广泛应用于动植物地理分布的模拟研究。该文以植物物种分布模拟为例, 利用中国知网、维普网以及Web of Science文献数据库的检索与统计, 分析了2000-2018年间, 中国研究人员利用各种物种分布模型对植物物种分布模拟研究的发文量、模拟模型、物种类型、数据来源、研究目的等信息。最终共收集到366篇有效文献, 分析表明2011年以来中国的物种分布模型应用发展迅速, 且以最近5年最为迅猛, 在生态学、中草药业、农业和林业等行业部门应用广泛。在使用的33种模型中, 应用最广的为最大熵模型(MaxEnt)。有一半研究的环境数据仅包含气候数据, 另一半研究不仅包含气候数据还包括地形与土壤等数据; 环境及物种数据的来源多样, 国际及本土数据库均得到使用。模拟涉及有明确清单的562个植物种, 既有木本植物(52.7%), 也有草本植物(41.8%), 其中中草药、果树、园林植物、农作物等占比较高。研究目的主要集中在过去、现在和未来气候变化对植物种分布的影响及预测, 以及物种分布评估与生物多样性评价(包括入侵植物风险评估)两大方面。预测物种潜在分布范围与气候变化影响等基础研究, 与模拟物种适生区与推广种植等应用研究并重, 物种分布模型在生态学与农业、林业和中草药业等多学科、多行业开展多种应用, 多物种、多模型和多来源数据共同参与模拟与比较, 开发新的机理性物种分布模型, 拓展新的物种分布模拟应用领域, 是今后研究的重点发展方向。     

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...     

基于BIOMOD的青海湖流域高原鼠兔分布模拟

随着统计模型及空间信息数据的不断发展和完善,物种分布模型已经成为全球变化背景下研究大尺度物种分布情况的重要工具。高原鼠兔(Ochotona curzoniae)是青藏高原特有的关键物种,在青藏高原生态系统中占有重要地位。通过采集高原鼠兔的分布点数据及环境变量数据,基于R语言中BIOMOD包中的7个模型对其在青海湖流域的分布进行了模拟。结果表明,高原鼠兔主要分布于青海湖西岸和北岸、天峻县周边及布哈河流域上游,影响高原鼠兔分布的主要环境因子为距道路距离、距居民点距离、最暖月最高气温、NDVI标准差、最冷季和最干季降水量。BIOMOD组合模型中,推进式回归树模型(GBM)和最大熵模型(MAXENT)的模拟效果最好,广义线性回归模型(GLM)结果较差。而优化后的结果显示,模拟结果的集成和筛选能有效提高模型的精度和效果。     

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。     

石灰岩特有植物海南凤仙花潜在适宜生境分布模拟

模拟物种的潜在分布区是保护管理受威胁物种的重要手段。该研究对海南岛石灰岩特有种、濒危植物——海南凤仙花(Impatiens hainanensis)的潜在适宜生境分布进行预测, 旨在为海南凤仙花的有效保护及重引入工作提供基础的科学依据。研究基于海南凤仙花8个种群分布点和12个环境变量, 利用最大熵模型(MaxEnt)和GIS技术构建海南凤仙花适宜生境预测模型, 模拟了当前时期海南凤仙花在海南岛的潜在分布区; 同时基于5个实际分布数据和5个不存在数据, 采用受试者工作特征曲线下的面积(AUC)、Kappa系数、真实技巧统计值(TSS)及总体精度4个评估指标综合评价模型的预测精度。研究结果表明: 4个评估指标值均在0.9以上, 说明MaxEnt模型能够很好地预测海南凤仙花潜在适宜生境的分布。限制其分布的主要环境因子为坡度、最干季降水量、降水量季节性变异系数。当前, 海南凤仙花的最适宜生境占海南岛总面积的1.8%, 主要分布于白沙西部与南部、昌江中部和南部、东方东部、乐东东北部。海南凤仙花潜在适宜生境分布狭窄, 且破碎化严重, 迫切需要保护。因此建议: 收集海南凤仙花各种群种子, 建立种质资源库; 将东方天安乡、江边乡及乐东东北部(佳西保护区)等可能存在最适宜生境的地区, 作为今后野外深入调查的首选区域和重引入的重点区域。     

黄土残塬沟壑区流域次生植被物种分布的地形响应

研究流域次生植被物种对地形因子的响应规律,识别影响次生植被物种分布的主要地形因子,是流域近自然植被生态恢复和重建的基础。采用ArcGIS空间分析模块和地形分析模块TauDEM,并与统计软件SPLUS2000中的GRASP工具相结合,建立了位于黄土高原残垣沟壑区山西省吉县蔡家川流域次生植被各个物种分布基于地形因子的广义相加模型(GAM)。模型中的地形因子包括:海拔、坡向、坡度、平面曲率、坡位指数(SPI)、地形湿度指数(TWI)、单宽汇水面积(SCA)等。受试者操作特征曲线(ROC)测试中AUC值表明:大部分测试物种(约62%)拟合模型效果较好,且模型较为稳定。总体来看,研究流域次生植被物种分布体现了水分限制的空间分异特征:阴坡各物种分布概率较大,且随海拔升高而减小。影响研究流域次生植被物种空间分布的潜在重要因子为海拔和坡向,而单宽汇水面积(SCA)和地形湿度指数(TWI)虽然是多个物种响应模型的预测因子,但受高一级尺度海拔的影响,SCA与TWI对物种分布的影响作用较小;坡度影响作用最小。据此,在流域植被恢复和防护林建设目标区选择及立地条件划分时应首先以海拔和坡向为依据,单宽汇水面积(SCA)和地形湿度指数(TWI)则可以作为次一级立地分类依据,而坡度则仅能作为最后一级的分类依据。     

Canopy closure estimation in a temperate forest using airborne LiDAR and LANDSAT ETM+ data

Aims　Forest canopy closure is one of the essential factors in forest survey, and plays an important role in forest ecosystem management. It is of great significance to study how to apply LiDAR (light detection and ranging) data efficiently in remote sensing estimation of forest canopy closure. LiDAR can be used to obtain data fast and accurately and therefore be used as training and validation data to estimate forest canopy closure in large spatial scale. It can compensate for the insufficiency (e.g. labor-intensive, time-consuming) of conventional ground survey, and provide foundations to forest inventory.Methods　In this study, we estimated canopy closure of a temperate forest in Genhe forest of Da Hinggan Ling area, Nei Mongol, China, using LiDAR and LANDSAT ETM+ data. Firstly, we calculated the canopy closure from ALS (Airborne Laser Scanning) high density point cloud data. Then, the estimated canopy closure from ALS data was used as training and validation data to modeling and inversion from eight vegetation indices computed from LANDSAT ETM+ data. Three approaches, multi-variable stepwise regression (MSR), random forest (RF) and Cubist, were developed and tested to estimate canopy closure from these vegetation indices, respectively.Important findings The validation results showed that the Cubist model yielded the highest accuracy compared to the other two models (determination coefficient (R²) = 0.722, root mean square error (RMSE) = 0.126, relative root mean square error (rRMSE) = 0.209, estimation accuracy (EA) = 79.883%). The combination of LiDAR data and LANDSAT ETM+ showed great potential to accurately estimate the canopy closure of the temperate forest. However, the model prediction capability needs to be further improved in order to be applied in larger spatial scale. More independent variables from other remotely sensed datasets, e.g. topographic data, texture information from high-resolution imagery, should be added into the model. These variables can help to reduce the influence of optical image, vegetation indices, terrain and shadow and so on. Moreover, the accuracy of the LiDAR-derived canopy closure needs to be further validated in future studies.     

Above-ground biomass estimation using airborne discrete-return and full-waveform LiDAR data in a coniferous forest

The estimation of forest aboveground biomass (AGB) is critical for quantifying carbon stocks and essential for evaluating global carbon cycle. Many previous studies have estimated forest AGB using airborne discrete-return Light Detection and Ranging (LiDAR) data, while fewer studies predicted forest AGB using airborne full-waveform LiDAR data. The objective of this work was to evaluate the utility of airborne discrete-return and full-waveform LiDAR data in estimating forest AGB. To fulfill the objective, airborne discrete-return LiDAR-derived metrics (DR-metrics), full-waveform LiDAR-derived metrics (FW-metrics) and structure parameters (combining height metrics and canopy cover) were used to estimate forest AGB. Additionally, the combined use of DR- and FW-metrics through a nonlinear way was also evaluated for AGB estimation in a coniferous forest in Dayekou, Gansu province of China. Results indicated that both height metrics derived from discrete-return and full-waveform LiDAR data were stronger predictors of forest AGB compared with other LiDAR-derived metrics. Canopy cover derived from discrete-return LiDAR data was not sensitive to forest AGB, while canopy cover estimated by full-waveform LiDAR data (CC_WF) showed moderate correlation with forest AGB. Structure parameters derived from full-waveform LiDAR data, such as H75_FW * CC_FW, were closely related to forest AGB. In contrast, structure parameters derived from discrete-return LiDAR data were not suitable for estimating forest AGB due to the less sensitivity of canopy cover CC_DR2 to forest AGB. This research also concluded that the synergistic use of DR- and FW-metrics can provide better AGB estimates in coniferous forest.     

Estimation of FPAR and FPAR profile for maize canopies using airborne LiDAR

FPAR (fraction of photosynthetically active radiation) and FPAR profile (vertical FPAR distribution) are important parameters for characterizing the vegetation growth status and studying global climate change. Few studies have been carried out to estimate FPAR and FPAR profile using waveform LiDAR data. This research explored the potential of airborne small-footprint full-waveform LiDAR in the estimation of FPAR and FPAR profile of the maize canopy in Huailai County of Hebei Province, China. First, the maize growing area was identified by a simple decision tree model. Second, raw waveform data were processed to extract LiDAR-derived energy ratio and energy ratio profile. Third, FPAR and FPAR profile were estimated from LiDAR-derived metrics. Finally, we analyzed the FPAR and FPAR profile estimation results and assessed the model validity using the leave-one-out cross-validation (LOOCV) method. The comparative analyses found that the LiDAR-derived energy ratio profile and field-measured FPAR profile had the same trend and similar change rate for all maize layers. The accuracy assessments indicated that the FPAR and FPAR profile were estimated well by the LiDAR waveform data, with the high R² (0.90 for the whole canopy, and 0.95, 0.90, 0.93, 0.92, and 0.97 for layers 1–5) and low RMSEs (0.042 for the whole canopy, and 0.033, 0.035, 0.039, 0.043, and 0.044 for layers 1–5). The spatial distribution map of FPAR was produced to describe the maize growth status of the whole study area, and the map showed that the FPAR distributed relatively uniformly. This study suggested that airborne small-footprint full-waveform LiDAR was useful in accurately measuring FPAR and FPAR profile of the maize canopy and in effectively mapping the maize FPAR spatial distribution.     

Novelty predictors for shrub (and climbers) ecological niche modeling,based on their successional stage

The identification of species' environmental predictors constitute a key challenge for decision making, especially when using ecological niche modeling based on these drivers and when presence points are limited. More specifically, shrub species are affected by ecosystem dynamics, and appear in degraded formations, in dense mid-stage vegetation formations, or under late climax-forest canopy. In this study, we tested novelty predictors to understand the drivers that affect the selected species distribution in the Mediterranean biome, targeting different vegetation successional stages, and further improve ecological models' performance, when presence points are limited. Land surface temperature (LST) in association with temperature related predictors, allowed differentiating between species thriving in the understory of the forest canopy, from those that are co-dominant with dense vegetation cover and a third group/species, thriving in degraded vegetation. In addition, the Normalized Difference Vegetation Level Index (NDVI) played a key role in the models for species growing in highly degraded ecological niches such as Spartium junceum, Calicotome villosa, but also forest-fringe vegetation like the climber Hedera helix. Our study highlights the importance of integrating remote sensed predictors, combined with appropriate climate drivers, when using ecological niche modeling.     

Ecological niche modeling re‐examined: A case study with the Darwin's fox

Many previous studies have attempted to assess ecological niche modeling performance using receiver operating characteristic (ROC) approaches, even though diverse problems with this metric have been pointed out in the literature. We explored different evaluation metrics based on independent testing data using the Darwin's Fox (Lycalopex fulvipes) as a detailed case in point. Six ecological niche models (ENMs; generalized linear models, boosted regression trees, Maxent, GARP, multivariable kernel density estimation, and NicheA) were explored and tested using six evaluation metrics (partial ROC, Akaike information criterion, omission rate, cumulative binomial probability), including two novel metrics to quantify model extrapolation versus interpolation (E‐space index I) and extent of extrapolation versus Jaccard similarity (E‐space index II). Different ENMs showed diverse and mixed performance, depending on the evaluation metric used. Because ENMs performed differently according to the evaluation metric employed, model selection should be based on the data available, assumptions necessary, and the particular research question. The typical ROC AUC evaluation approach should be discontinued when only presence data are available, and evaluations in environmental dimensions should be adopted as part of the toolkit of ENM researchers. Our results suggest that selecting Maxent ENM based solely on previous reports of its performance is a questionable practice. Instead, model comparisons, including diverse algorithms and parameterizations, should be the sine qua non for every study using ecological niche modeling. ENM evaluations should be developed using metrics that assess desired model characteristics instead of single measurement of fit between model and data. The metrics proposed herein that assess model performance in environmental space (i.e., E‐space indices I and II) may complement current methods for ENM evaluation.     

Ecological modeling and distribution analysis of digger scorpions: Odontobuthus doriae,Odonthubutus bidentatus (Scorpiones: Buthidae) and Scorpio maurus (Scorpiones: Scorpionidae) in Iran using the maximum entropy method

Prediction models are essential for the potential geographic distribution of scorpions, prevention of scorpion stings and diverse applications in conservation biology. There is limited information about habitat suitability and the factors affecting the distribution of Iranian digger scorpions. This study was undertaken to model the distribution of three types of digger scorpion in Iran, Odontobuthus doriae Thorell, Odonthubutus bidentatus Lourenco (Scorpiones: Buthidae) and Scorpio maurus Pocockin (Scorpiones: Scorpionidae), and investigate the factors affecting its distribution using the maximum entropy method. A total of 20 environmental and climate variables were used for modeling and evaluation of the ecological niche. The similarities and differences between the ecological overlap of the digger scorpions were evaluated using comparative environmental niche model (ENM Tools software). The results showed that the main factors for habitat suitability of O. doriae were soil type, mean temperature of the wettest quarter and slope. The variables for S. maurus were soil type, precipitation of the coldest quarter and slope. Annual temperature range, mean temperature of the driest quarter and land use had the greatest influence on the distribution of O. bidentatus. The ecological niches for O. doriae and O. bidentatus overlapped. The niche of these species differed from the niche of S. maurus. This approach could be helpful for the prediction of the potential distribution of three digger scorpion species and this model can be an effective for the promotion of health.

     

Division within the North American boreal forest: Ecological niche divergence between the Bicknell's Thrush (Catharus bicknelli) and Gray‐cheeked Thrush (C. minimus)

Sister species that diverged in allopatry in similar environments are expected to exhibit niche conservatism. Using ecological niche modeling and a multivariate analysis of climate and habitat data, I test the hypothesis that the Bicknell's Thrush (Catharus bicknelli) and Gray‐cheeked Thrush (C. mimimus), sister species that breed in the North American boreal forest, show niche conservatism. Three tree species that are important components of breeding territories of both thrush species were combined with climatic variables to create niche models consisting of abiotic and biotic components. Abiotic‐only, abiotic+biotic, and biotic‐only models were evaluated using the area under the curve (AUC) criterion. Abiotic+biotic models had higher AUC scores and did not over‐project thrush distributions compared to abiotic‐only or biotic‐only models. From the abiotic+biotic models, I tested for niche conservatism or divergence by accounting for the differences in the availability of niche components by calculating (1) niche overlap from ecological niche models and (2) mean niche differences of environmental values at occurrence points. Niche background similarity tests revealed significant niche divergence in 10 of 12 comparisons, and multivariate tests revealed niche divergence along 2 of 3 niche axes. The Bicknell's Thrush breeds in warmer and wetter regions with a high abundance of balsam fir (Abies balsamea), whereas Gray‐cheeked Thrush often co‐occurs with black spruce (Picea mariana). Niche divergence, rather than conservatism, was the predominant pattern for these species, suggesting that ecological divergence has played a role in the speciation of the Bicknell's Thrush and Gray‐cheeked Thrush. Furthermore, because niche models were improved by the incorporation of biotic variables, this study validates the inclusion of relevant biotic factors in ecological niche modeling to increase model accuracy.     

An assessment of the effect of data partitioning on the performance of modelling algorithms for habitat suitability for ticks

A comparison of the performance of five modelling methods using presence/absence (generalized additive models, discriminant analysis) or presence-only (genetic algorithm for rule-set prediction, ecological niche factor analysis, Gower distance) data for modelling the distribution of the tick species Boophilus decoloratus (Koch, 1844) (Acarina: Ixodidae) at a continental scale (Africa) using climate data was conducted. This work explicitly addressed the usefulness of clustering using the normalized difference vegetation index (NDVI) to split original records and build partial models for each region (cluster) as a method of improving model performance. Models without clustering have a consistently lower performance (as measured by sensitivity and area under the curve [AUC]), although presence/absence models perform better than presence-only models. Two cluster-related variables, namely, prevalence (commonness of tick records in the cluster) and marginality (the relative position of the climate niche occupied by the tick in relation to that available in the cluster) greatly affect the performance of each model (P < 0.05). Both sensitivity and AUC are better for NDVI-derived clusters where the tick is more prevalent or its marginality is low. However, the total size of the cluster or its fragmentation (measured by Shannon's evenness index) did not affect the performance of models. Models derived separately for each cluster produced the best output but resulted in a patchy distribution of predicted occurrence. The use of such a method together with weighting procedures based on prevalence and marginality as derived from populations at each cluster produced a slightly lower predictive performance but a better estimation of the continental distribution of the tick. Therefore, cluster-derived models are able to effectively capture restricting conditions for different tick populations at a regional level. It is concluded that data partitioning is a powerful method with which to describe the climate niche of populations of a tick species, as adapted to local conditions. The use of this methodology greatly improves the performance of climate suitability models.     

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。