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。     

严重急性呼吸综合征冠状病毒2变异体对全球疫情防控的影响分析

由严重急性呼吸综合征冠状病毒2（SARS-CoV-2）引起的新型冠状病毒肺炎（COVID-19，简称新冠肺炎）的出现，对国际公众健康构成了严重威胁，伴随COVID-19大流行而来的是SARS-CoV-2基因组的不断突变，尤其是受关注的变异体（variants of concern，VOCs）给全球COVID-19疫情防控带来了挑战。本文综述了SARS-CoV-2的突变情况和现阶段主要流行的VOCs的特征，总结了现有及潜在的COVID-19预防、诊断和治疗手段，并通过分析SARS-CoV-2变异体对全球COVID-19疫情防控措施的影响，提出合理的建议，以期为今后可能爆发的大范围流行病的防控提供理论依据。     

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...     

甘肃草地4种毒杂草潜在入侵区预测研究

针对4种著名的草原毒杂草:醉马草,黄花棘豆,狼毒和露蕊乌头,应用生态位模型分别研究其在甘肃的潜在扩散区域。首先,通过最近邻体距离法和相关性分析分别选取样本数据和环境变量,接着应用最大熵方法(Maxent)建立生态位模型,预测了4种毒杂草的潜在分布区。最后通过Matlab和ENMTools计算了地理分布重心、平均海拔、等级分布区比例、生态位宽度、生态位重合度和地理分布重合度。研究结果表明:4种毒杂草中醉马草和狼毒的环境适应能力较强,但醉马草的分布范围更为广泛,从祁连山脉一直延伸到甘南草原,扩散重心基本在祁连山西侧,而狼毒分布范围主要在甘肃南部,地理分布重心大致位于兰州地区。黄花棘豆的分布范围主要集中在祁连山脉,而露蕊乌头更偏向甘南草原地区。     

应用生态位模型研究外来入侵物种生态位漂移

由于基础生态位和实际生态位的改变,外来入侵物种在入侵地成功定殖、扩散后常会发生生态位漂移,而物种生态位漂移往往很难直接证明。生态位模型在假设入侵物种的生态位需求保守的前提下,以物种在其原产地的生态位需求为基础,预测其在入侵地的潜在分布,通过比较预测分布与实际分布的差异可以从一定程度上得到外来入侵物种的生态位是否发生漂移的间接证据。以我国入侵杂草胜红蓟在原产地的生态位需求为基础,应用生态位模型预测其在其他地区的潜在分布。研究结果表明,生态位模型可以很好地预测胜红蓟在亚太平洋地区和非洲地区的分布,但在我国,其预测分布与实际分布存在较大差别。胜红蓟在我国预测分布主要为云南、海南、台湾部分地区,而胜红蓟入侵我国后现已广泛分布于长江以南地区,其实际分布比预测分布广泛得多,由此推测胜红蓟在入侵我国后其生态位已经产生了漂移。     

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。     

植被冠层尺度生理生态模型的研究进展

随着人们对植物生命活动各个过程研究的不断深入,以植物生理过程、物理过程为基础的各种生理生态学模型逐渐发展起来,而植被冠层尺度生理生态学过程模型已成为生态系统模型的核心之一。目前植被冠层尺度的大叶模型、多层模型、二叶模型以其成熟的理论基础及对植被冠层的光合作用、蒸腾作用较为成功的模拟,得到了广泛的应用。3个模型都以光合作用-气孔导度-蒸腾作用耦合模型为基础,但又具有各自的特点。本文对3种模型的结构及特点进行了总结,并对其进行了比较,简要介绍了目前植被冠层尺度生理生态学模型的应用及存在的问题和发展状况。     

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。     

ROC曲线分析在评价入侵物种分布模型中的应用

生态位模型（ecological niche models,ENMs）已广泛应用于物种潜在分布区预测,ENMs的应用也为外来入侵物种的风险分析提供了重要的定量化分析工具,但如何评价不同模型之间的预测效果成了当今研究的热点问题。本文介绍了受试者工作特征（ROC）曲线分析在评价不同生态位模型预测效果中的应用原理和分析方法,并以一种植物病原线虫-相似穿孔线虫（Radopholus similis）为例,应用ROC曲线分析法对其5种模型（BIOCLIM,CLIMEX,DOMAIN,GARP,MAXENT）的预测结果进行了比较分析。5种模型的ROC曲线下面积AUC（Area Under Curve）值分别为0.810,0.758,0.921,0.903和0.950,以MAXENT模型的AUC值最大,表明其预测效果最好;方差分析结果表明,除GARP与DOMAIN模型之间AUC值差异不显著外,其余各模型之间差异显著。     

揭示群落结构及其环境响应的联合物种分布模型的研究进展

物种分布模型通常用于基础生态和应用生态研究,用来确定影响生物分布和物种丰富度的因素,量化物种与非生物条件的关系,预测物种对土地利用和气候变化的反应,并确定潜在的保护区.在传统的物种分布模型中,生物的相互作用很少被纳入,而联合物种分布模型(JSDMs)作为近年提出的一种新的可行方法,可以同时考虑环境因素和生物交互作用,因而成为分析生物群落结构和种间相互作用过程的有力工具.JSDMs以物种分布模型(SDMs)为基础,通常采用广义线性回归模型建立物种对环境变量的多变量响应,以随机效应的形式获取物种间的关联,同时结合隐变量模型(LVMs),并基于Laplace近似和马尔科夫蒙脱卡罗模拟的最大似然估计或贝叶斯方法来估算模型参数.本文对JSDMs的产生及理论基础进行归纳总结,重点介绍了不同类型JSDMs的特点及其在现代生态学中的应用,阐述了JSDMs的应用前景、使用过程中存在的问题及发展方向.随着对环境因素与多物种种间关系研究的深入,JSDMs将是今后物种分布模型研究的重点.     

Ecological niche models display nonlinear relationships with abundance and demographic performance across the latitudinal distribution of Astragalus utahensis (Fabaceae)

The potential for ecological niche models (ENMs) to accurately predict species' abundance and demographic performance throughout their geographic distributions remains a topic of substantial debate in ecology and biogeography. Few studies simultaneously examine the relationship between ENM predictions of environmental suitability and both a species' abundance and its demographic performance, particularly across its entire geographic distribution. Yet, studies of this type are essential for understanding the extent to which ENMs are a viable tool for identifying areas that may promote high abundance or performance of a species or how species might respond to future climate conditions. In this study, we used an ensemble ecological niche model to predict climatic suitability for the perennial forb Astragalus utahensis across its geographic distribution. We then examined relationships between projected climatic suitability and field‐based measures of abundance, demographic performance, and forecasted stochastic population growth (λ_s). Predicted climatic suitability showed a J‐shaped relationship with A. utahensis abundance, where low‐abundance populations were associated with low‐to‐intermediate suitability scores and abundance increased sharply in areas of high predicted climatic suitability. A similar relationship existed between climatic suitability and λ_s from the center to the northern edge of the latitudinal distribution. Patterns such as these, where density or demographic performance only increases appreciably beyond some threshold of climatic suitability, support the contention that ENM‐predicted climatic suitability does not necessarily represent a reliable predictor of abundance or performance across large geographic regions.     

Characterizing and predicting species distributions across environments and scales: Argentine ant occurrences in the eye of the beholder

Aim Species distribution models (SDMs) or, more specifically, ecological niche models (ENMs) are a useful and rapidly proliferating tool in ecology and global change biology. ENMs attempt to capture associations between a species and its environment and are often used to draw biological inferences, to predict potential occurrences in unoccupied regions and to forecast future distributions under environmental change. The accuracy of ENMs, however, hinges critically on the quality of occurrence data. ENMs often use haphazardly collected data rather than data collected across the full spectrum of existing environmental conditions. Moreover, it remains unclear how processes affecting ENM predictions operate at different spatial scales. The scale (i.e. grain size) of analysis may be dictated more by the sampling regime than by biologically meaningful processes. The aim of our study is to jointly quantify how issues relating to region and scale affect ENM predictions using an economically important and ecologically damaging invasive species, the Argentine ant (Linepithema humile). Location California, USA. Methods We analysed the relationship between sampling sufficiency, regional differences in environmental parameter space and cell size of analysis and resampling environmental layers using two independently collected sets of presence/absence data. Differences in variable importance were determined using model averaging and logistic regression. Model accuracy was measured with area under the curve (AUC) and Cohen's kappa. Results We first demonstrate that insufficient sampling of environmental parameter space can cause large errors in predicted distributions and biological interpretation. Models performed best when they were parametrized with data that sufficiently sampled environmental parameter space. Second, we show that altering the spatial grain of analysis changes the relative importance of different environmental variables. These changes apparently result from how environmental constraints and the sampling distributions of environmental variables change with spatial grain. Conclusions These findings have clear relevance for biological inference. Taken together, our results illustrate potentially general limitations for ENMs, especially when such models are used to predict species occurrences in novel environments. We offer basic methodological and conceptual guidelines for appropriate sampling and scale matching.     

基于感染力与免疫作用的新型冠状病毒肺炎疫情传播模型

目的 新型冠状病毒（SARS-CoV-2）变体往往具有更强的感染力与免疫逃逸能力，目前出现的SARS-CoV-2变体种类繁多，疫情评估与防控形势严峻。本文希望通过建立模拟病毒传染的理论模型，对SARS-CoV-2及其变体引起的疫情进行追踪与预测，并对它们的综合传染性进行评估。方法 根据方格传染病模型，对传染持续时间和群体免疫作用的相互关系进行推导，并在此基础上建立了新型冠状病毒肺炎（COVID-19）疫情感染传播的普遍理论模型，提出感染力参数A和免疫作用参数B，将传染时间与感染人数的复杂关系公式化，用于预测感染日变曲线。还引入了突变株综合传染性参数，用以定量比较各突变株的综合传染能力，并对感染参数A和B不与地域因素相关的猜想进行了验证。结果 通过COVID-19疫情传播的理论模型，对病毒步行次数与传染时间做出了较为精准的预测。通过对突变株感染能力与电性变化的分析，指出了突变株传染性和突变残基电性变化的内在联系。分析了突变株的参数变化，定量比较了各突变株的综合传染能力，得出了综合传染性排行。还验证了参数A和B只与病毒自身性质、病毒与人体共存的性质相关，而与地域无关的猜想，并对各爆发地域的防疫水平进行了评估与比较。结论 本文建立了COVID-19疫情传播的理论模型，在预测疫情持续时间、每日新增感染人数与评估病毒感染力、免疫逃逸能力、综合传染性、地域防疫水平方面具有一定作用，还根据病毒变异可能导致的参数变化给出了防疫注意事项与相关对策的建议。     

Universal risk phenotype of US counties for flu-like transmission to improve county-specific COVID-19 incidence forecasts

The spread of a communicable disease is a complex spatio-temporal process shaped by the specific transmission mechanism, and diverse factors including the behavior, socio-economic and demographic properties of the host population. While the key factors shaping transmission of influenza and COVID-19 are beginning to be broadly understood, making precise forecasts on case count and mortality is still difficult. In this study we introduce the concept of a universal geospatial risk phenotype of individual US counties facilitating flu-like transmission mechanisms. We call this the Universal Influenza-like Transmission (UnIT) score, which is computed as an information-theoretic divergence of the local incidence time series from an high-risk process of epidemic initiation, inferred from almost a decade of flu season incidence data gleaned from the diagnostic history of nearly a third of the US population. Despite being computed from the past seasonal flu incidence records, the UnIT score emerges as the dominant factor explaining incidence trends for the COVID-19 pandemic over putative demographic and socio-economic factors. The predictive ability of the UnIT score is further demonstrated via county-specific weekly case count forecasts which consistently outperform the state of the art models throughout the time-line of the COVID-19 pandemic. This study demonstrates that knowledge of past epidemics may be used to chart the course of future ones, if transmission mechanisms are broadly similar, despite distinct disease processes and causative pathogens.     

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.     

Accuracy of models for the 2001 foot-and-mouth epidemic

Since 2001 models of the spread of foot-and-mouth disease, supported by the data from the UK epidemic, have been expounded as some of the best examples of problem-driven epidemic models. These claims are generally based on a comparison between model results and epidemic data at fairly coarse spatio-temporal resolution. Here, we focus on a comparison between model and data at the individual farm level, assessing the potential of the model to predict the infectious status of farms in both the short and long terms. Although the accuracy with which the model predicts farms reporting infection is between 5 and 15%, these low levels are attributable to the expected level of variation between epidemics, and are comparable to the agreement between two independent model simulations. By contrast, while the accuracy of predicting culls is higher (20-30%), this is lower than expected from the comparison between model epidemics. These results generally support the contention that the type of the model used in 2001 was a reliable representation of the epidemic process, but highlight the difficulties of predicting the complex human response, in terms of control strategies to the perceived epidemic risk.     

Population dynamics can be more important than physiological limits for determining range shifts under climate change

Evidence is accumulating that species' responses to climate changes are best predicted by modelling the interaction of physiological limits, biotic processes and the effects of dispersal‐limitation. Using commercially harvested blacklip (Haliotis rubra) and greenlip abalone (Haliotis laevigata) as case studies, we determine the relative importance of accounting for interactions among physiology, metapopulation dynamics and exploitation in predictions of range (geographical occupancy) and abundance (spatially explicit density) under various climate change scenarios. Traditional correlative ecological niche models (ENM) predict that climate change will benefit the commercial exploitation of abalone by promoting increased abundances without any reduction in range size. However, models that account simultaneously for demographic processes and physiological responses to climate‐related factors result in future (and present) estimates of area of occupancy (AOO) and abundance that differ from those generated by ENMs alone. Range expansion and population growth are unlikely for blacklip abalone because of important interactions between climate‐dependent mortality and metapopulation processes; in contrast, greenlip abalone should increase in abundance despite a contraction in AOO. The strongly non‐linear relationship between abalone population size and AOO has important ramifications for the use of ENM predictions that rely on metrics describing change in habitat area as proxies for extinction risk. These results show that predicting species' responses to climate change often require physiological information to understand climatic range determinants, and a metapopulation model that can make full use of this data to more realistically account for processes such as local extirpation, demographic rescue, source‐sink dynamics and dispersal‐limitation.     

Structural flexibility in proteins: impact of the crystal environment

MOTIVATION: In the study of the structural flexibility of proteins, crystallographic Debye-Waller factors are the most important experimental information used in the calibration and validation of computational models, such as the very successful elastic network models (ENMs). However, these models are applied to single protein molecules, whereas the experiments are performed on crystals. Moreover, the energy scale in standard ENMs is undefined and must be obtained by fitting to the same data that the ENM is trying to predict, reducing the predictive power of the model. RESULTS: We develop an elastic network model for the whole protein crystal in order to study the influence of crystal packing and lattice vibrations on the thermal fluctuations of the atom positions. We use experimental values for the compressibility of the crystal to establish the energy scale of our model. We predict the elastic constants of the crystal and compare with experimental data. Our main findings are (1) crystal packing modifies the atomic fluctuations considerably and (2) thermal fluctuations are not the dominant contribution to crystallographic Debye-Waller factors. AVAILABILITY: The programs developed for this work are available as supplementary material at Bioinformatics Online.     

Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling

The availability of user-friendly software and publicly available biodiversity databases has led to a rapid increase in the use of ecological niche modelling to predict species distributions. A potential source of error in publicly available data that may affect the accuracy of ecological niche models (ENMs), and one that is difficult to correct for, is incorrect (or incomplete) taxonomy. Here we remind researchers of the need for careful evaluation of database records prior to use in modelling, especially when the presence of cryptic species is suspected or many records are based on indirect evidence. To draw attention to this potential problem, we construct ENMs for the North American Sasquatch (i.e. Bigfoot). Specifically, we use a large database of georeferenced putative sightings and footprints for Sasquatch in western North America, demonstrating how convincing environmentally predicted distributions of a taxon's potential range can be generated from questionable site-occurrence data. We compare the distribution of Bigfoot with an ENM for the black bear, Ursus americanus , and suggest that many sightings of this cryptozoid may be cases of mistaken identity.     

Field validation supports novel niche modeling strategies in a cryptic endangered amphibian

Many studies employ ecological niche models (ENMs) to predict species’ occurrences in undersampled regions, generally without field confirmation. Here, we use field surveys to test the relative utility of four potential refinements to the standard ENM approach: 1) altering model complexity based on AIC_c, 2) selecting background points from a biologically informed region, 3) using target‐group background to account for sampling bias in existing localities, and 4) using many rangewide localities (global model) versus fewer proximal localities (local model) to construct geographically restricted range predictions. We used Maxent to predict new localities for the California tiger salamander Ambystoma californiense, an endangered species that often goes undocumented due to its cryptic lifestyle. We followed this with a field survey of 260 previously unsampled potential breeding sites in Solano County, CA and used the resulting presence/absence data to compare all factorial combinations of the four model refinements using a new application of the Kruskal–Wallis test for ENM outputs. Our field surveys led to the discovery of 81 previously undocumented breeding localities for the California tiger salamander and demonstrated that ENMs could be significantly improved by utilizing target‐group background to account for spatial sampling bias and local models to focus model output on the subregion of the range being surveyed. Our results clearly demonstrate the potential for local models to outperform global models, and we recommend supplementing traditional Maxent global models that utilize all known localities with local models, particularly when species occupy geographically structured, heterogeneous habitat types. We also recommend using target‐group background since the improvement we observed when including it in our models was significant and very similar to that documented by previous studies. Most importantly, we emphasize the importance of field verification to enable rigorous statistical comparisons among models.