基于Maxent模型的檵木分布格局模拟

应用最大熵模型（Maxent）模拟檵木（Loropetalum chinense）在末次间冰期（last inter glacial,LIG）、末次盛冰期（last glacial maximum,LGM）和当代（Present）等不同时期的中国潜在分布格局,分析影响其分布的主导生物气候因子。结果显示：（1）历史气候的变迁,檵木由末次间冰期经末次盛冰期进入当代,适生区面积呈现增加趋势。当代适生区面积占比最大,适生程度也较高;（2）训练数据和测试数据的AUC值分别为0.947和0.954,均达到了极高的精度;（3）刀切法检测表明,影响檵木分布的主导环境因子依次为bio14（最干月份平均降雨量）、bio17（极干季降雨量）、为bio19（极冷季平均降雨量）和bio15（湿度变化方差）,其适生值范围分别是23-93 mm,98-300 mm,110-350 mm和42%-65%;（4）较纬度而言,经度是影响我国檵木分布格局的主要因子;（5）我国檵木当代的潜在地理分布主要在北回归线以北的区域,总面积为162.55万km²,占国土总面积的16.93%,高适生区集中于广西北部、江西东部、以及湖南南部、福建东部等地区。本研究较为准确地模拟了檵木在3个不同时期的适生分布,为深入分析檵木现代分布格局的形成提供科学依据。     

濒危物种长柄扁桃的潜在分布与保护策略

长柄扁桃(Amygdalus pedunculata)在我国分布于内蒙古和陕西,是一种濒危灌木,其资源现状只有零星的文献记录,这限制了对其资源数量和保护现状的评估。为此,本文通过野外调查来确立其自然分布区范围。我们选择了8个环境因子,运用基于规则集的遗传算法(genetic algorithm for rule-set prediction,GARP)模型和最大熵(maximum entropy,Max Ent)模型进行模拟,预测了长柄扁桃在中国的潜在适宜分布区;同时基于4个模型精度评估指标(Kappa、真实技巧统计法、总精度和受试者工作特征曲线下的面积)对模型进行验证,采用刀切法评估了预测变量的重要性。结果表明,两种模型均能准确预测长柄扁桃的地理分布规律,但Max Ent模型的4个预测精度指标都大于GARP模型。根据模型结果可判断长柄扁桃的适宜分布区以内蒙古中部地区为主,东起大兴安岭南部,向西可至贺兰山、乌兰布和沙漠以东,涵盖了毛乌素沙地、库布齐沙漠和浑善达克沙地,以及乌拉山、大青山等土石山区。其低适宜分布区可辐射至辽宁、河北、山西、陕西等省部分地区,另外在宁夏和甘肃中部地区也有零星的低适宜分布区。变量重要性分析结果表明,与降水相关的变量是决定长柄扁桃地理分布的重要环境因素。     

中国梭梭属植物历史分布格局及其驱动机制

梭梭属(Haloxylon)植物是藜科的古老孑遗物种, 探究末次间冰期(last interglacial period, LIG)和末次盛冰期(last glacial maximum period, LGM)以来中国梭梭属植物的历史地理分布格局及其驱动机制, 对了解气候变化背景下旱生植物区系的发展与演化具有重要意义。本研究利用梭梭属85个自然分布点数据(60条梭梭(Haloxylon ammodendron)分布记录、25条白梭梭(H. persicum)分布记录)和2套环境因子数据, 整合GIS空间分析和9种物种分布模型, 分析了梭梭属末次间冰期以来的地理分布格局变化及其驱动机制。基于62个梭梭属种群的叶绿体基因测序数据, 利用最小成本路径方法, 模拟了末次间冰期以来梭梭属可能的扩散路径。利用R软件prcomp函数对影响梭梭属分布的环境变量进行主成分分析(principal component analysis, PCA), 评价了环境变量对梭梭属适宜分布的贡献, 并分析了关键变量与分布适宜性的相关性。结果表明: (1)集成模型的模拟精度较单一模型显著提升, 且对白梭梭的模拟精度高于梭梭; (2)末次间冰期以来, 梭梭属植物的分布均经历了显著收缩和冰后期扩张, 末次间冰期至末次盛冰期时期, 在准噶尔盆地、塔里木盆地西部广泛分布的梭梭大面积向西退缩至避难所(准噶尔盆地西北缘和塔里木盆地西北缘); 白梭梭从准噶尔盆地、塔里木盆地西端向南退缩至避难所(准噶尔盆地南缘); 末次盛冰期至今, 梭梭向东沿甘肃北部扩张直至内蒙古西部阿拉善荒漠, 白梭梭向东北方向小范围扩张, 占据了准噶尔盆地西部和南缘; (3)末次间冰期以来的气候波动对梭梭属植物的分布存在较大限制, 降水因子主导了梭梭属适宜分布面积的变化, 温度因子影响了梭梭属分布适宜性的高低。     

毛乌素沙地天然植物多样性组成及区系特征分析

毛乌素沙地位于干旱、半干旱区向半湿润区过渡地带,该研究通过野外调查及数据统计,对毛乌素沙地天然植物科、属、种的组成及区系特征进行了分析,并与周围的沙地(沙漠)植物区系进行比较,以明确毛乌素沙地的珍稀植物及其植物多样性组成特征。结果表明:(1)毛乌素沙地天然植物共90科360属772种,区内植物类群相对丰富,植被群落类型多样,优势科明显,单种属、寡种属占到的比例达到73.45%。(2)毛乌素沙地天然植物90科划分为10个分布区类型及4个变型,360属划分为15个分布区类型及10个变型。(3)毛乌素沙地科的植物区系组成以世界分布为主,属的植物区系组成以温带分布为主,反映了该区的植物区系与所处的地理位置和气候具有相适应的特性。(4)毛乌素沙地植物区系成分与浑善达克沙地植物区系亲缘关系最近,与库布齐沙漠、乌兰布和沙漠、库姆塔格沙漠植物区系有一定差异,表明毛乌素沙地植物区系存在明显的从温带分布到地中海区、西亚至中亚分布的过渡性质。     

云南鹤庆盆地末次盛冰期的孢粉记录与古季风

通过研究相当于末次盛冰期鹤庆古湖泊沉积物4．6－9．0m段的孢粉记录,对该区末次盛冰期阶段的植被与古季风变迁模式进行了恢复。该区末次盛冰期冷湿的气候特点与同时东部干旱的草原植被、青藏、高原的荒漠植被和黄土高原区风尘堆积存在明显差异,而与滇池的气候记录有较好的一致性。冰期内部的气候波动与深海氧同位素记录有较好的可比性。冰期冷锋强度的增加,与北方冬季风的经常入侵和冰期青藏高原的冷源效应有关。     

小叶栎分布格局对末次盛冰期以来气候变化的响应

小叶栎(Quercus chenii)是华东植物区系的代表树种, 具有很高的生态、经济价值。为重建冰期以来小叶栎地理分布格局的变迁历史、了解环境因子对潜在地理分布的制约机制, 为小叶栎种质资源保护和管理提供科学依据, 该研究基于55条分布记录和8个环境变量, 利用MaxEnt模型模拟小叶栎在末次盛冰期、全新世中期、现代和2070年(温室气体排放情景为典型浓度目标8.5)的潜在分布区, 利用多元环境相似度面和最不相似变量分析探讨气候变迁过程中环境异常区域和引起潜在地理分布改变的关键因素, 综合应用贡献率及置换重要值比较、Jackknife检验评估制约现代地理分布的主要因子, 采用响应曲线确定环境变量的适宜区间。研究结果表明: MaxEnt模型的预测准确度极高, 受试者工作特征曲线下的面积(AUC值)达0.9869 ± 0.0045; 现代高度适宜区在安徽南部、浙江西部、江西东北部和湖北东部; 影响小叶栎地理分布的主要气候因子为气温和降水量, 气温更重要; 最干季平均气温可能是制约小叶栎向北分布的关键因素; 末次盛冰期时, 小叶栎高度适宜区位于东海大陆架内; 全新世中期适宜分布区轮廓已与现代近似; 2070年适宜分布区向北移, 高度适宜区面积增大, 与末次盛冰期、全新世中期和现代相比, 这一时期的气候异常程度最高。气温季节变化和降水季节变化可能是引起地理分布变迁的重要气候因素。     

末次盛冰期以来气候变化对中国山荆子分布格局的影响

山荆子(Malus baccata (L.) Borkh.)具有较高的观赏、经济价值,是苹果属(Malus)植物的重要种质资源。本文利用ENMeval数据包调整Max Ent模型的调控倍频和特征组合参数,根据602条现代分布记录和筛选的8个生物气候变量,模拟预测山荆子在末次盛冰期、中全新世、现代、2070年(RCP 8.5) 4个时期的潜在分布区。采用贡献率、置换重要值比较和刀切法进行检验,综合分析各环境变量对山荆子潜在地理分布的影响。结果显示,当RM值为2、FC为LQHPT时,Max Ent模型的复杂度和过拟合程度较低,该参数下AUC值的均值为0.9272±0.0019,表明模型预测极准确;山荆子现代高度适生区为山西的太行山、管涔山和吕梁山,吉林、辽宁东北部,陕甘宁交界处,河北北部,鲁中南地区;末次盛冰期山荆子适生区整体上显著向东南偏移,北方的高度适生区消失;中全新世适生区轮廓与现代基本一致,但略微有向高海拔地区收缩的趋势; 2070年山荆子在国内的适生区将向高海拔地区急剧收缩,中度、高度适生区面积急剧减少;山荆子现代潜在地理分布受温度和降水因子的共同影响,但后者的影响更大。本研究预测气候变化对山荆子分布范围的影响,将对其种质资源保护和管理提供重要的参考价值。     

多星韭分布格局对末次盛冰期以来气候的响应

该研究以分布区主要在横断山脉的多星韭为对象,基于最大熵模型(MaxEnt)和地理信息系统(ArcGIS)模拟了多星韭在末次盛冰期、全新世中期、当前以及未来的分布格局,以探讨多星韭对末次盛冰期以来气候变化的响应。结果显示:(1)多星韭当前的分布区主要受到最暖季度降水量、年均温变化范围和温度季节性变化标准差3个气候因子的影响;海拔对多星韭的当前分布也有着较大的影响。(2)最大熵模型的模拟精度较高(AUC=0.98)。(3)根据多星韭各个时期分布面积的变化得出多星韭与部分高山植物相似,相比当前的分布,多星韭末次盛冰期的分布区发生了较为明显的扩张。研究推测,未来多星韭的分布区将向西移动。     

未来气候变化下西北干旱区4种扁桃亚属植物潜在适生区分析

为研究气候变化对4种扁桃亚属植物在中国潜在适生分布区的影响,本文收集4种扁桃亚属植物的193个地理分布坐标和19个生物气候变量,利用Max Ent生态位模型预测扁桃(Amygdalus communis L.)、矮扁桃(Amygdalus nana L.)、蒙古扁桃(Amygdalus mongolica (Maxim.) Ricker.)和西康扁桃(Amygdalus tangutica (Batal.) Korsh.)在中国过去(末次间冰期和末次冰盛期)、当代和未来(2050年) 4个时期气候条件下的潜在适生区。结果表明:当代气候条件下,扁桃主要分布于中国新疆裕民县、巩留县、莎车县、英吉沙县、和田县、乌鲁木齐等地区,与过去相比,乌鲁木齐地区的低适生区面积有所减少;与当代相比,未来气候条件下扁桃的适生区面积增加0.17%,甘肃省酒泉市境内低适生区面积增加。当代气候条件下,矮扁桃主要分布于新疆布尔津县、额敏县和裕民县等;与过去相比,矮扁桃总分布面积主要在布尔津县和额敏县等地区,先减小再增加;与当代相比,未来气候条件下总分布面积减少0.85%;当代气候条件下,蒙古扁桃主要分布于东戈壁、呼和浩特和阿拉善戈壁、鄂尔多斯市等地区;与过去相比,蒙古扁桃的适生区分布面积先减小再增加;与当代相比,未来气候条件下分布面积减少3.39%;当代气候条件下,西康扁桃主要分布于四川省马尔康县、茂县等地区;与过去相比,西康扁桃适生区分布面积呈现出增长趋势;与当代相比,未来气候条件下总面积增加0.25%,低适生区面积有所增加。年均降水量是影响扁桃地理分布的主要环境因子;降水量变异系数是影响矮扁桃的主要环境因子;温度季节性变化标准差是影响蒙古扁桃和西康扁桃的主要环境因子。     

气候变化对蒙古扁桃适宜分布范围和空间格局的影响

为模拟、预测气候变化对孑遗、濒危植物蒙古扁桃(Amygdalus mongolica)潜在分布的影响, 利用最大熵(MAXENT)模型模拟、预测、对比、分析、揭示蒙古扁桃在最大冰期(CCSM及MIROC模型)、历史气候(1961-1990年)及未来气候(2020年、2050年和2080年, 政府间气候变化专门委员会排放情景特别报告的A2A情景)条件下的适宜分布范围和空间格局的变化。结果表明: (1)蒙古扁桃在历史气候条件下的潜在分布区集中在蒙古的南戈壁省及东戈壁省, 我国内蒙古巴彦淖尔市、阿拉善左旗、鄂尔多斯市、锡林郭勒盟西部, 河西走廊中部及东部, 宁夏北部及陕西北部, 以及河北北部的部分地区; (2)与历史气候条件下的潜在分布相比, 蒙古扁桃在最大冰期CCSM气候情景下的分布经历了明显的、大范围的向南迁移和范围缩小; (3)未来A2A气候情景下, 其潜在分布范围表现出在2020年明显扩大, 在2050年减小, 到2080年又略有增大的趋势。分布格局表现出不断向我国河北及内蒙古东部, 蒙古东部、北部及西部大幅度扩散、迁移的趋势。     

Responses of the distribution pattern of Quercus chenii to climate change following the Last Glacial Maximum

Aims Quercus chenii is a representative species of the flora in East China, with high ecological and economic values. Here, we aim to simulate the changes in the distribution pattern of this tree species following the Last Glacial Maximum (LGM) and to explore how climatic factors constrain the potential distribution, so as to provide scientific basis for protection and management of the germplasm resources in Q. chenii.
Methods Based on 55 presence point records and data on eight environmental variables, we simulated the potential distribution of Q. chenii during the Last Glacial Maximum, mid-Holocene, present and the year 2070 (the scenario of greenhouse gas emission is Representative Concentration Pathway 8.5) with MaxEnt model. The novel climate area and main factors influencing the changes in distribution pattern were evaluated by multivariate environmental similarity surface analysis and the most dissimilar variable analysis. The importance of environmental variables was evaluated by percent contribution, permutation importance and Jackknife test. Response curves were used to estimate the suitable value range of each variable.
Important findings The accuracy of MaxEnt model is very high, as indicated by the value of the area under the receiver operator characteristic curve of 0.9869 ± 0.0045. The highly suitable region for the present distribution covers southern Anhui, western Zhejiang, northeastern Jiangxi and eastern Hubei. The main factors affecting the potential distribution of Q. chenii are temperature and precipitation, with the former being more important. Mean temperature of the driest quarter is likely the main factor restricting Q. chenii growing in the north. During the LGM, the East China Sea Shelf occurs as the highly suitable region for the distribution of Q. chenii. In the mid-Holocene, the outline of the suitable area for the distribution of Q. chenii is similar to the present. The potential distribution region will likely move northward and experience an area expansion under the climate condition in 2070. At that time, climate anomaly will also be most severe compared to the LGM, mid-Holocene and present. Temperature seasonality and precipitation seasonality may be the main climatic factors promoting changes in the distribution pattern of Q. chenii.     

The recolonization of Europe by brown bears Ursus arctos Linnaeus, 1758 after the Last Glacial Maximum

1. At the end of the Last Glacial Maximum brown bears Ursus arctos recolonized the glacial landscape of Central and Northern Europe faster than all other carnivorous mammal species of the Holocene fauna. Ursus arctos was recorded in Northern Europe from the beginning of the Late-Glacial. The recolonization of northern Central Europe may have taken place directly after the maximum glaciation. The distribution of the brown bear was restricted to glacial refugia only during the Last Glacial Maximum, for probably no more than 10 000 years. 2. Genetic analyses have suggested three glacial refugia for the brown bear: the Iberian Peninsula, the Italian Peninsula and the Balkans. Subfossil records of Ursus arctos from north-western Moldova as well as reconstructed environmental conditions during the Last Glacial Maximum in this area suggest to us a fourth glacial refuge for the brown bear. Because of its connection to the Carpathians, we designate this as the ‘Carpathian refuge’. 3. Due to the low genetic distance between brown bears of northern Norway, Finland, Estonia, north-eastern Russia and the northern Carpathians (the so-called eastern lineage), the Carpathians were considered the geographical origin of the recolonization of these regions. During the recolonization of northern Europe the brown bear probably reached these areas rapidly from the putative Carpathian refuge.     

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。     

Spatial genetic structure and historical demography of East Asian wild boar

Pleistocene climatic fluctuations may have had a profound impact on the evolutionary history of many species. The geographical pattern of European wild boar (Sus scrofa) is clearly studied, and it was greatly influenced by ancient climatic events, especially the Last Glacial Maximum. Previous research on genetic variation has mainly focused on the origin and distribution histories of domestic pigs. However, some questions have not been answered, including those concerning the genetic diversity, geographical pattern and possible historic influence of climate on East Asian wild boar (EAWB). Employing the control region of mtDNA (511 bp), we investigated the contributions of historic climate, which possibly shaped the genetic pattern of wild boar. Given that the level of genetic diversity of wild boars is higher in East Asia than in Europe, 172 haplotypes were detected from 680 individuals. Phylogenetic analysis demonstrated the complex phylogeographic structure of EAWB. Mismatch analysis, neutrality tests and the Bayesian Skyline Plot results all retrieved signals of a rapid population expansion, which might have played an important role in driving the formation of complex spatial genetic structure. Genetic data and species distribution modelling showed that the Last Glacial Maximum had weak effect on the distribution of the EAWB. We suggest that, in shaping spatial genetic structure in East Asian, long-term gene flow and population history played more important roles than Pleistocene climate fluctuations.     

After Last Glacial Maximum: The third migration

A critical analysis of “Real World” data concerning the genetic origins of people, archaeology and palaeoclimatic conditions, demonstrates possibilities of population migration for third time in ancient history, from East to West after Last Glacial Maximum (LGM), which gave the foundations of modern human civilization.     

Tracking the ice: Subterranean harvestmen distribution matches ancient glacier margins

Biogeographic studies often underline the role of glacial dynamism during Pleistocene (1.806–0.011 Mya) in shaping the distribution of subterranean species. Accordingly, it is presumed that present‐day distribution of most specialized cold‐adapted (cryophilic) cave‐dwelling species should bear the signatures of past climatic events. To test this idea, we modelled the distribution of specialized cold‐adapted subterranean alpine harvestmen (Arachnida: Opiliones: Ischyropsalididae: Ischyropsalis). We found that the distance from the glacier margins during Last Glacial Maximum (LGM; about 22,000 years ago) was the most important predictor of their present‐day distribution. In particular, the peak in the probability of occurrence of alpine subterranean Ischyropsalis was found to be in close proximity to the LGM glacier, with a sharp drop at a distance of 30 km from the ice margin. In light of the role played by past climatic events in determining the species current range, we briefly discuss their biogeographic history and the role played by glacial refugia dynamics in determining the current distribution of these species. We argue that low dispersal harvestmen such as our model species can be used as biological indicators for tracking past glaciations and other similar biogeographic events.     

Prokaryotes in the WAIS Divide ice core reflect source and transport changes between Last Glacial Maximum and the early Holocene

We present the first long‐term, highly resolved prokaryotic cell concentration record obtained from a polar ice core. This record, obtained from the West Antarctic Ice Sheet (WAIS) Divide (WD) ice core, spanned from the Last Glacial Maximum (LGM) to the early Holocene (EH) and showed distinct fluctuations in prokaryotic cell concentration coincident with major climatic states. The time series also revealed a ~1,500‐year periodicity with greater amplitude during the Last Deglaciation (LDG). Higher prokaryotic cell concentration and lower variability occurred during the LGM and EH than during the LDG. A sevenfold decrease in prokaryotic cell concentration coincided with the LGM/LDG transition and the global 19 ka meltwater pulse. Statistical models revealed significant relationships between the prokaryotic cell record and tracers of both marine (sea‐salt sodium [ssNa]) and burning emissions (black carbon [BC]). Collectively, these models, together with visual observations and methanosulfidic acid (MSA) measurements, indicated that the temporal variability in concentration of airborne prokaryotic cells reflected changes in marine/sea‐ice regional environments of the WAIS. Our data revealed that variations in source and transport were the most likely processes producing the significant temporal variations in WD prokaryotic cell concentrations. This record provided strong evidence that airborne prokaryotic cell deposition differed during the LGM, LDG, and EH, and that these changes in cell densities could be explained by different environmental conditions during each of these climatic periods. Our observations provide the first ice‐core time series evidence for a prokaryotic response to long‐term climatic and environmental processes.     

The crested newt Triturus cristatus recolonized temperate Eurasia from an extra‐Mediterranean glacial refugium

We assess the role of the Carpathians as an extra‐Mediterranean glacial refugium for the crested newt Triturus cristatus. We combine a multilocus phylogeography (one mitochondrial protein‐coding gene, three nuclear introns, and one major histocompatibility complex gene) with species distribution modelling (projected on current and Last Glacial Maximum climate layers). All genetic markers consistently show extensive genetic variation within and genetic depletion outside the Carpathians. The species distribution model suggests that most of the current range was unsuitable at the Last Glacial Maximum, but a small suitable area remained in the Carpathians. Triturus cristatus dramatically expanded its postglacial range, colonizing much of temperate Eurasia from a glacial refugium in the Carpathians. Within the Carpathians, T. cristatus persisted in multiple geographically discrete regions, providing further support for a Carpathian ‘refugia within refugia’ scenario. © 2015 The Linnean Society of London, Biological Journal of the Linnean Society, 2015, 114 , 574–587.