Connecting species’ geographical distributions to environmental variables: range maps versus observed points of occurrence

Connecting the geographical occurrence of a species with underlying environmental variables is fundamental for many analyses of life history evolution and for modeling species distributions for both basic and practical ends. However, raw distributional information comes principally in two forms: points of occurrence (specific geographical coordinates where a species has been observed), and expert-prepared range maps. Each form has potential short-comings: range maps tend to overestimate the true occurrence of a species, whereas occurrence points (because of their frequent non-random spatial distribution) tend to underestimate it. Whereas previous comparisons of the two forms have focused on how they may differ when estimating species richness, less attention has been paid to the extent to which the two forms actually differ in their representation of a species’ environmental associations. We assess such differences using the globally distributed avian order Galliformes (294 species). For each species we overlaid range maps obtained from IUCN and point-of-occurrence data obtained from GBIF on global maps of four climate variables and elevation. Over all species, the median difference in distribution centroids was 234 km, and median values of all five environmental variables were highly correlated, although there were a few species outliers for each variable. We also acquired species’ elevational distribution mid-points (mid-point between minimum and maximum elevational extent) from the literature; median elevations from point occurrences and ranges were consistently lower (median −420 m) than mid-points. We concluded that in most cases occurrence points were likely to produce better estimates of underlying environmental variables than range maps, although differences were often slight. We also concluded that elevational range mid-points were biased high, and that elevation distributions based on either points or range maps provided better estimates.     

末次盛冰期以来观光木的潜在地理分布变迁

观光木(Tsoongiodendron odorum)是木兰科的古老残遗物种, 目前正面临严峻的生存威胁, 属于极小种群濒危植物。通过生态位模型(ENM)能够重建观光木地理分布格局的历史变迁, 探究气候变化对该物种分布的影响, 并了解其地理分布与气候需求间的关系, 从而为全球变暖背景下观光木的保护提供理论基础。该文基于96条现代分布记录和8个环境变量, 采用最大熵(MaxEnt)模型模拟观光木在末次盛冰期、全新世中期、现代和未来(2061-2080年, RCP 8.5)的潜在分布区, 利用SDM toolbox分析观光木的地理空间变化, 并综合贡献率、置换重要值和Jackknife检验来评估气候因子的重要性。研究结果表明: (1)观光木的高度适生区在南岭地区, 末次盛冰期时没有大尺度向南退缩, 很可能在山区避难所原地存活; (2)在全新世中期和未来两个增温的气候情境下, 观光木的分布区均表现为缩减, 其中未来分布的减幅更大, 表明气候变暖对观光木的生长有一定的负面影响; (3)总体上看, 观光木各个时期的地理分布范围相对稳定, 说明观光木对气候变化有一定的适应能力, 人为活动或自身繁育问题可能是致濒的重要原因, 并建议对广东和广西群体进行优先保护。     

城市群新冠疫情时空分布格局与分异机制的地理探测

自从2019年12月湖北武汉爆发新型冠状病毒（COVID-19）以来,疫情在全国范围内迅速传播并蔓延。城市群既是我国新型城镇化主体特定的空间组织形态,也是相互连接的城市网,集合了相当数量不同性质、类型和等级规模的城市,城市群经济发展程度较高、人口密度大,交通发达,是疫情传染扩散-关联的重点区域,因而跨区域感染风险高,联防联控难度大。以疫情集中地区城市群为研究对象,通过核密度分析聚焦重点研究的城市群,从多层空间尺度对疫情在数量、人口迁移和空间分布上进行测度,利用空间自相关分析疫情分布特征,运用地理探测器方法客观地测度城市群疫情发展的差异性主导因素,从景观格局中挖掘相关主导因子,为生态、安全的空间规划和治理提供科学依据和思路。结果显示：①全国范围感染人数核密度高值区的空间分布与长江中游城市群和三大沿海城市群范围耦合,首位核心城市确诊感染数在城市群中占比均较高,城市群疫情扩散分布呈现典型的由核心城市向外辐射的特征,湖北地区迁出的人数对迁往地区的疫情形势构成一定程度的影响;②全国范围的COVID-19感染人数和感染增长率分布存在显著空间集聚特征,城市群范围感染人数的空间聚集性从长江中游城市群向其他城市群依次减弱;③地理探测器识别出人口密度、城乡建设、交通、卫生、科技、生态绿地为疫情的主导因素,并且交互后所产生的共同作用增加了对疫情的解释力;④生态与建设用地景观格局对疫情的解释能力较强,其中建设用地的聚集性和生态用地的蔓延性为主要因子。     

福州市公园外来入侵植物初步调查与分析

[目的] 了解福州市公园的外来入侵植物种类组成,分析其原产地及入侵等级等情况,能够为福州外来入侵植物的扩散、防控以及生物多样性保护提供参考依据。[方法] 对福州市4个区49个公园的外来入侵植物种类、原产地、生活型、入侵频度、危害程度（入侵等级）等进行调查和分析。[结果] 在调查的福州市所有公园内均有入侵植物分布,目前有入侵植物66种,隶属25科,其中菊科、豆科、苋科3个科为优势科,共计33种（占50.00%）;从来源上看,大多数原产于美洲,其次是非洲、欧洲、亚洲;以草本植物为主,共有55种（占83.30%）;从入侵频度看,入侵频度超过50%的入侵植物有7种,其中小蓬草的频度最高,为87.76%;从入侵等级来看,其中恶性（1级）入侵植物16种,严重（2级）入侵植物14种,局部（3级）入侵植物12种,一般（4级）入侵植物11种,有待观察类（5级）13种。[结论] 当前福州市公园内的外来植物入侵现象比较严重,应当加强该区域的入侵植物监测与预警,防止入侵植物扩散从而造成重大生态危害。     

基于CLIMEX预测黑角负泥虫在中国的潜在地理分布

【目的】研究黑角负泥虫在我国的潜在地理分布及其入侵风险程度。【方法】运用适生性分析软件CLIMEX 4. 0对黑角负泥虫在我国的潜在地理分布进行模拟,将模拟结果在地理信息系统软件Arc GIS10.2下进行插值分析,并绘制黑角负泥虫的潜在地理分布示意图。【结果】黑角负泥虫高度适生区主要集中在我国的华北平原、黄土高原南部及云贵高原北部;中度适生区主要集中在华北平原北部、黄土高原北部及东北平原地区;低度适生区则多分布在中高度适生区的过渡区域及我国新疆天山南北两侧地区。限制黑角负泥虫在我国分布的主要胁迫因素为干胁迫和冷胁迫,且其影响的区域主要分布在我国的西北和东北地区。【结论】黑角负泥虫在我国潜在地理分布范围广,适生程度高。目前,黑角负泥虫已经传播到我国的新疆和内蒙古等省(自治区),但由于地理隔离的存在和自身较弱的扩散条件,并没有传入气候适宜度较高的东部地区。随着东西部经济交流的不断增多,黑角负泥虫随人为因素进一步扩散的可能性将不断增加。因此,建议检疫部门做好检疫工作,严防黑角负泥虫的传播扩散。     

中老铁路经济廊带生态质量及其与人类活动的关系

区域生态质量很大程度上受人类活动强度影响,制约着区域经济可持续发展。以中老铁路经济廊带为对象,重点研究区内人类活动强度对区域生态环境的影响。选取1999、2009、2019年遥感影像计算遥感生态指数(RSEI),采用空间自相关及局域G统计量探测研究区生态质量的时空演变规律,并结合同期人口密度构建地理加权回归模型,定量分析区内人类活动强度引起的生态效应。研究表明: 1999—2019年,区内生态质量经历了先升后降的变化,RSEI均值由1999年的0.645升为2009年的0.738,随后又降为2019年的0.721,中部地区生态质量波动较大;基于地理加权回归模型(GWR)拟合人口密度与生态质量的效果明显好于最小二乘法,基于GWR的不同时期R²均高于0.7且拟合效果稳定,2019年拟合度最好(R²为0.785),1999、2009年R²分别为0.726、0.754;中老铁路沿线南部万象一带的生态质量对人类活动较敏感,以中度敏感区为主。对高、中、低度敏感区而言,人口密度每增长10、100、1000倍将相应导致RSEI均值降低0.2、0.4、0.6。经济廊带发展会导致人口密度增加,在规划布局过程中应控制人类活动,防止潜在敏感区及现有敏感区生态质量进一步恶化。     

基于地理探测器的中国农业生态效率时空分异及其影响因素

研究农业生态效率的时空分异及其影响因素对实现中国农业生态高质量发展具有重要意义。基于2000—2018年中国30个省/区/市的面板数据,采用超效率SBM模型测算省际农业生态效率,在时间序列、空间可视化及趋势面分析揭示农业生态效率时空演变规律的基础上,进一步利用地理探测器模型识别影响农业生态效率空间分异的主导因子及其交互作用。结果表明: 2000—2018年,中国农业生态效率整体呈现稳定上升的趋势,但仍然处于较低水平,存在较大提升空间;中国农业生态效率具有显著的空间分异特征,总体上呈现出东西部地区较高、而中部地区较低的空间分布格局;中国农业生态效率的空间分异受到农业资源禀赋、社会经济、自然生态环境等多种因素的影响,不同因子对农业生态效率空间分异的影响存在明显差异且因子间交互作用会增强其空间分异。综上,要关注农业生态效率时空分异的主导因子,并注重区域间的协同合作,以实现农业的高质量发展。     

Studio comparativo sulla flora urbana in Italia

Abstract

Lo studio è basato sul confronto tra la flora di 5 città localizzate nelle principali regioni fitoclimatiche italiane: Milano, Ancona, Roma, Cagliari e Palermo. L'analisi di 684 specie rilevate in 50 aree campione ha evidenziato l'importanza delle apofite nella flora urbana in Italia e il ruolo delle terofite che provengono direttamente dalla vegetazione mediterranea. Il confronto ha inoltre mostrato la dominanza dei fattori biogeografici nella costituzione della copertura vegetale delle 5 città. Questi risultati contrastano con l'elevata percentuale di specie esotiche e con l'uniformità floristica osservate nelle aree urbane dell'Europa Centrale.     

Mantel test in population genetics

The comparison of genetic divergence or genetic distances, estimated by pairwise F_ST and related statistics, with geographical distances by Mantel test is one of the most popular approaches to evaluate spatial processes driving population structure. There have been, however, recent criticisms and discussions on the statistical performance of the Mantel test. Simultaneously, alternative frameworks for data analyses are being proposed. Here, we review the Mantel test and its variations, including Mantel correlograms and partial correlations and regressions. For illustrative purposes, we studied spatial genetic divergence among 25 populations of Dipteryx alata (“Baru”), a tree species endemic to the Cerrado, the Brazilian savannas, based on 8 microsatellite loci. We also applied alternative methods to analyze spatial patterns in this dataset, especially a multivariate generalization of Spatial Eigenfunction Analysis based on redundancy analysis. The different approaches resulted in similar estimates of the magnitude of spatial structure in the genetic data. Furthermore, the results were expected based on previous knowledge of the ecological and evolutionary processes underlying genetic variation in this species. Our review shows that a careful application and interpretation of Mantel tests, especially Mantel correlograms, can overcome some potential statistical problems and provide a simple and useful tool for multivariate analysis of spatial patterns of genetic divergence.