秦巴山地NPP及对气候变化响应的多维地带性与暖温带-亚热带界线

秦巴山地位于我国的南北过渡带,对我国生态地理格局产生重要影响。为了探索秦巴山地植被净第一性生产力(NPP,Net Primary Productivity)的时空格局及其气候响应的多样性和复杂性,为我国暖温带-亚热带界线的具体分布提供新的佐证,基于2000—2015年的MOD17A3的地表植被NPP数据和秦巴山地93个气象站点数据,从经度、纬度、海拔、坡向多个维度研究了秦巴山地地表植被NPP的分布及与气候因子的关系。结果表明:从2000—2015年,(1)秦巴山地中低山地区,自北向南随纬度降低,地表植被多年平均NPP呈现增加的趋势,体现了纬度地带性;年均NPP与温度的关系由负相关变为正相关,转折点出现在汉江;与降水的相关性减弱。(2)自西向东多年平均NPP值先增加后减少,秦岭一线地表植被年均NPP与温度由正相关变为负相关,与降水主要呈正相关,相关性先增加后减少。(3)随高度的增加,秦巴山地多年NPP值及增长率均呈现先增加后减少的趋势。(4)秦岭和大巴山多年平均NPP均呈现增加趋势,但是秦岭增长较大巴山更明显;2000m以下,秦岭南坡增长率明显高于北坡,大巴山北坡增长率明显高于南坡;2000—3000m,秦岭南北坡差异较小,但是大巴山差异明显;中山地区(1000—2500m),秦岭年均NPP与气温呈负相关,而大巴山则呈现正相关或弱相关;秦岭地区年均NPP与降水的相关性整体强于大巴山地区。这就意味着全球变暖、气温升高对秦岭植被尤其是中低山地区的植被产生不利影响,但是对大巴山则有利,而前者植被生长主要与降水增加有关。这也说明了基于汉江为界的秦岭和大巴山无论是地表植被NPP的均值还是其南北坡差异以及对气候因子的响应呈现了明显的差异,而汉江作为中山地区植被NPP与气温相关性由正相关性到负相关的转折点,与降水的关系由弱相关到正相关的转折点,更合适作为南北分界线。     

大小兴安岭可培养细菌的资源多样性

细菌作为微生物中的重要组成部分, 在有机质的形成与分解、维持生态系统平衡、促进动植物发育等多方面都有着重要作用。2014-2017年, 我们采用常见培养基分离纯培养法及16S rRNA基因序列分析方法对大小兴安岭地区土壤可培养细菌的群落结构和多样性进行了调查研究。结果表明: 从大小兴安岭地区的17个自然保护区内不同生境的土壤中分离获得3,180个菌株, 隶属于24属120种。其中, 芽孢杆菌属(Bacillus)的种数和株数最多, 分别为38种和2,419株, 是大小兴安岭地区可培养细菌的绝对优势类群(占总株数的76.1%); 其次是短杆菌属(Brevibacterium)(13.0%)。大兴安岭地区的物种数、Simpson多样性指数和Shannon-Wiener指数高于小兴安岭地区。优势类群芽孢杆菌属的枯草芽孢杆菌(B. subtilis)、苏云金芽孢杆菌(B. thuringiensis)、巨大芽孢杆菌(B. megaterium)等有重要的生产及科学研究价值。     

天山冻土产低温脂肪酶菌株的筛选及其多样性分析

【目的】通过天山冻土细菌的分离和产低温脂肪酶菌株的筛选,了解天山冻土微生物的物种多样性和产脂肪酶菌株的系统发育多样性,为高效低温脂肪酶生物技术奠定基础。【方法】采用稀浓度的R2A、TSB平板涂布分离天山冻土中可培养细菌,通过选择性培养基筛选产低温脂肪酶的菌株。采用细菌常规生理生化实验、最适生长温度、耐盐性、产酶性能对分离菌株的生理学进行研究,通过16S rRNA基因序列分析确定产脂肪酶菌种的系统进化地位,通过BOX-PCR指纹技术对16S rRNA基因高度同源性的菌株进一步区分。【结果】分离筛选到78株可培养低温菌,选择培养基显示有17株可产低温脂肪酶,其中8株在两种选择培养基中均可产脂肪酶和酯酶。17株产酶菌分别隶属于5个系统发育类群、6个属,其中假单胞菌属(Pseudomonas)占大多数(58.9%)。【结论】天山冻土中产低温脂肪酶的细菌具有较丰富的系统发育多样性,依据生长温度,均属于耐冷菌。     

祁连山北坡退化林地植被群落的自然恢复过程及土壤特征变化

采用长期定位观测的方法,研究了祁连山北坡退化林地人工抚育下2001-2008年间植被群落的自然恢复过程和土壤特征变化。结果表明:人为干扰消除后,退化林地群落环境逐渐优化,群落的科、属、种均明显增加,物种成员更替频繁;灌木和乔木物种出现后,群落垂直高度增大,群落结构出现成层现象;群落总体多样性指数呈不断增大的趋势,在空间结构上,Patrick丰富度指数、Shannon-Wiener多样性指数和Simpson优势度指数表现出:草本层>灌木层>乔木层的规律,而Pielou均匀度指数变化相反;土壤含水量、土壤有机碳和全氮含量随植被恢复均不断增加。在实施封育禁牧措施后,退化林地实现了由草本群落-灌木群落-乔木群落方向的快速演替,当恢复到早期的先锋乔灌混交阶段时,群落的物种组成、结构和多样性趋于复杂化,土壤性状也得到一定改善,显示出相对较好的适应性和恢复效果。     

基于红外相机的四川亚丁国家级自然保护区鸟兽多样性初报

2017年5～11月,我们在四川亚丁国家级自然保护区内共布设64台红外相机,对保护区内的兽类和鸟类多样性进行了调查。经过8 394个相机工作日的调查,我们共鉴定出分属9目26科共56种的野生兽类和鸟类,其中国家Ⅰ级、Ⅱ级重点保护野生动物分别有5种和13种,被IUCN红色名录评估为濒危(EN)、易危(VU)、近危(NT)的野生动物分别有2种、3种和6种。相对多度指数居前三位的兽类和鸟类分别是毛冠鹿(Elaphodus cephalophus)、珀氏长吻松鼠(Dremomys pernyi)、猕猴(Macaca mulatta)和血雉(Ithaginis cruentus)、大噪鹛(Garrulax maximus)、雉鹑(Tetraophasis obscurus)。本次调查初步了解了亚丁保护区内鸟兽的种类、丰富度、分布以及人为干扰情况,是亚丁国家级自然保护区第一次开展鸟兽的本底资源调查和研究。我们的调查结果对掌握亚丁国家级自然保护区的鸟兽种类和分布现状等本底资料具有重要意义,同时也为保护区今后的科研工作及开展野生动物的保护管理和长期监测提供了数据支持和指导。     

Current biogeographical roles of the Kunlun Mountains

Large‐scale patterns of biodiversity and formation have garnered increasing attention in biogeography and macroecology. The Qinghai‐Tibet Plateau (QTP) is an ideal area for exploring these issues. However, the QTP consists of multiple geographic subunits, which are understudied. The Kunlun Mountains is a geographical subunit situated in the northern edge of the QTP, in northwest China. The diversity pattern, community phylogenetic structures, and biogeographical roles of the current flora of the Kunlun Mountains were analyzed by collecting and integrating plant distribution, regional geological evolution, and phylogeography. A total of 1911 species, 397 genera, and 75 families present on the Kunlun Mountains, of which 29.8% of the seed plants were endemic to China. The mean divergence time (MDT) of the Kunlun Mountains flora was in the early Miocene (19.40 Ma). Analysis of plant diversity and MDT indicated that the eastern regions of the Kunlun Mountains were the center of species richness, endemic taxa, and ancient taxa. Geographical origins analysis showed that the Kunlun Mountains flora was diverse and that numerous clades were from East Asia and Tethyan. Analysis of geographical origins and geological history together highlighted that the extant biodiversity on the Kunlun Mountains appeared through species recolonization after climatic fluctuations and glaciations during the Quaternary. The nearest taxon index speculated that habitat filtering was the most important driving force for biodiversity patterns. These results suggest that the biogeographical roles of the Kunlun Mountains are corridor and sink, and the corresponding key processes are species extinction and immigration. The Kunlun Mountains also form a barrier, representing a boundary among multiple floras, and convert the Qinghai‐Tibet Plateau into a relatively closed geographical unit.     

地形对七姊妹山自然保护区植物丰富度及分布格局的影响

该研究以七姊妹山自然保护区40个(20×20m2)植物群落调查样方为基础,并采用回归分析和典型对应分析(CCA)的方法研究该区地形对植物物种丰富度及植物分布格局的影响,以明确海拔、坡度、坡向、坡位等地形因子的相对重要性,为该区植物多样性的保护和管理提供理论依据。结果表明:(1)七姊妹山自然保护区40个调查样地共有植物633种,隶属133科,316属,其中乔木118种,灌木150种,草本365种。(2)曲线回归方程拟合结果显示,七姊妹山自然保护区植物物种丰富度分别与海拔、坡度具有显著相关性,物种丰富度沿海拔梯度升高而增大,沿坡度梯度先减少后增大之后又减小。(3)从植物的生活型来看,在所有海拔段,乔木物种丰富度始终低于灌木和草本植物;在低、中低海拔地带,灌木物种丰富度均高于乔木和草本植物;而在中、高海拔地带草本植物物种丰富度较大且高于乔木和灌木。(4)CCA排序结果表明,地形因子对植物物种的分布具有显著影响按其影响强度排序为海拔坡度坡位坡向,说明海拔是影响该区植物物种分布最重要的地形因子。     

Hazards Affecting Grizzly Bear Survival in the Greater Yellowstone Ecosystem

Abstract: During the past 2 decades, the grizzly bear (Ursus arctos) population in the Greater Yellowstone Ecosystem (GYE) has increased in numbers and expanded its range. Early efforts to model grizzly bear mortality were principally focused within the United States Fish and Wildlife Service Grizzly Bear Recovery Zone, which currently represents only about 61% of known bear distribution in the GYE. A more recent analysis that explored one spatial covariate that encompassed the entire GYE suggested that grizzly bear survival was highest in Yellowstone National Park, followed by areas in the grizzly bear Recovery Zone outside the park, and lowest outside the Recovery Zone. Although management differences within these areas partially explained differences in grizzly bear survival, these simple spatial covariates did not capture site-specific reasons why bears die at higher rates outside the Recovery Zone. Here, we model annual survival of grizzly bears in the GYE to 1) identify landscape features (i.e., foods, land management policies, or human disturbances factors) that best describe spatial heterogeneity among bear mortalities, 2) spatially depict the differences in grizzly bear survival across the GYE, and 3) demonstrate how our spatially explicit model of survival can be linked with demographic parameters to identify source and sink habitats. We used recent data from radiomarked bears to estimate survival (1983–2003) using the known-fate data type in Program MARK. Our top models suggested that survival of independent (age ≥ 2 yr) grizzly bears was best explained by the level of human development of the landscape within the home ranges of bears. Survival improved as secure habitat and elevation increased but declined as road density, number of homes, and site developments increased. Bears living in areas open to fall ungulate hunting suffered higher rates of mortality than bears living in areas closed to hunting. Our top model strongly supported previous research that identified roads and developed sites as hazards to grizzly bear survival. We also demonstrated that rural homes and ungulate hunting negatively affected survival, both new findings. We illustrate how our survival model, when linked with estimates of reproduction and survival of dependent young, can be used to identify demographically the source and sink habitats in the GYE. Finally, we discuss how this demographic model constitutes one component of a habitat-based framework for grizzly bear conservation. Such a framework can spatially depict the areas of risk in otherwise good habitat, providing a focus for resource management in the GYE.     

GLM versus CCA spatial modeling of plant species distribution

Despite the variety of statistical methods available for static modeling of plant distribution, few studies directly compare methods on a common data set. In this paper, the predictive power of Generalized Linear Models (GLM) versus Canonical Correspondence Analysis (CCA) models of plant distribution in the Spring Mountains of Nevada, USA, are compared. Results show that GLM models give better predictions than CCA models because a species-specific subset of explanatory variables can be selected in GLM, while in CCA, all species are modeled using the same set of composite environmental variables (axes). Although both techniques can be readily ported to a Geographical Information System (GIS), CCA models are more readily implemented for many species at once. Predictions from both techniques rank the species models in the same order of quality; i.e. a species whose distribution is well modeled by GLM is also well modeled by CCA and vice-versa. In both cases, species for which model predictions have the poorest accuracy are either disturbance or fire related, or species for which too few observations were available to calibrate and evaluate the model. Each technique has its advantages and drawbacks. In general GLM will provide better species specific-models, but CCA will provide a broader overview of multiple species, diversity, and plant communities.