太白山大熊猫的分布与保护

该文主要以1993年对太白山大熊猫资源调查和1996年对大熊猫资源调查为基础,对太白山大熊猫的分布与保护进行了分析研究,结果表明:太白山的大熊猫主要分布在秦岭南坡的海棠河、大干沟、小干沟、欠开营河、太白河、龙洞沟和太白山主峰拔仙台东南、秦岭北坡的万泉沟,栖息地面积14?898hm2.分布大熊猫为11只,栖息在海拔1?800～2?900m之间的落叶阔叶林带、桦木林带、巴山冷杉林带下,为我国大熊猫的一块新的栖息地,也是分布的最北界.这块栖息地目前正受到采伐、割竹、旅游、采药等人为活动的干扰和威胁,必须采取保护措施,阻止栖息地的进一步恶化和对大熊猫的干扰.     

四川青川县大熊猫种群分析

本文研究了青川县大熊猫冬春季对栖息地选择和 2 0世纪 70年代、80年代和 90年代栖息地分布和种群数量的变化 ,表明该地大熊猫冬春季对地形的选择主要为向阳的陡坡和斜坡或较干燥的阴坡 ,郁闭度 0 6以下 ,主要活动在海拔 2 2 0 0～ 2 80 0m区域 ;随着年代的递增 ,栖息地的面积日趋缩小 ,种群数量也逐步减少 ,分别为 114只、6 0只和47只 ;探讨了影响分布和数量的因素。     

秦岭大熊猫栖息地范围扩大

世界自然基金会（WWF）和陕西省林业厅日前联合发布（2006年秦岭大熊猫及其栖息地巡护监测报告》,认为“与1999到2003年的第3次全国大熊猫调查相比,目前秦岭大熊猫的栖息地范围扩大、质量提高”。[第一段]     

岷山北部竹类开花状况及对大熊猫的影响调查

2006年12月、2007年7～8月对岷山北部4县的竹类开花及大熊猫活动情况进行了调查.结果表明:(1)该区域竹种单一,仅有华西箭竹Fargesia nitida和缺苞箭竹F.denudata两种竹类,其中华西箭竹的分布面积最大;(2)该区域竹类从2004年开始出现开花现象,到2007年调查时开花过程已基本结束,开花竹面积达73 082 hm2,占竹类分布面积的51.36%;(3)在整个开花过程中,大熊猫实体被发现的频次增加,没有发现病、饿、死大熊猫的个体,现有大熊猫分布在竹类未开花的迭部县东部和舟曲县境内.此次竹类开花枯死,使该区域原有的大面积的大熊猫栖息地消失,因此需要加强对栖息地的保护和研究工作,使之尽快恢复成为大熊猫适合的栖息地.     

汶川地震对四川龙溪—虹口和唐家河自然保护区大熊猫栖息地利用格局的影响

2008年的汶川地震发生在大熊猫集中分布的区域,对大熊猫的栖息地造成了严重破坏.利用龙溪—虹口和唐家河两个国家级自然保护区地震前(2003～ 2007)和地震后(2008～ 2010)共计7年的大熊猫监测数据,就汶川地震对大熊猫栖息地利用格局的影响进行了分析.研究结果表明:(1)地震前5年,大熊猫对栖息地的利用格局在年间没有显著变化;(2)地震后的2年间,大熊猫对栖息地的利用格局在年间没有显著变化;(3)地震前后连续7年调查的样线中,大熊猫对栖息地的利用格局在年间没有显著变化;(4)在龙溪一虹口保护区,大熊猫对样线的利用与滑坡体的面积没有显著关系,即大熊猫对栖息地的空间利用没有受到滑坡体面积大小的显著影响;(5)虽然从现有资料发现地震对大熊猫栖息地的利用格局没有显著的影响,但仍然需要在震后加强对大熊猫及其栖息地的保护.     

黑嘴鸥繁殖栖息地动态遥感监测

以江苏盐城国家级自然保护区核心区为研究区,结合黑嘴鸥巢址GPS点,利用1992、2002和2007年Landsat TM遥感影像作为主要信息源,借助地理信息空间分析技术,在获取栖息地植被类型信息的基础上,采用非等密度亚像元模型反演碱蓬植被覆盖度信息,提取研究区黑嘴鸥适宜繁殖栖息地动态变化情况,并进一步对其进行预测.结果表明,15a间,在自然和人为的双重干扰下,研究区内碱蓬滩面积由4772.3 hm2减少到4062.2hm2,年变化率为-47.34 hm2/a.碱蓬植被覆盖度均值由1992年的(27.6±3.8)%增长到2002年的(35.4±2.0)%,至2007年达到(53.1±4.5)%.同时,利用2002、2007年黑嘴鸥巢址GPS位点,提取对应年份黑嘴鸥繁殖栖息地碱蓬群落覆盖度,其均值分别为(35.08±4.8)%和(37.58±7.6)%,无差异显著性(P=0.066).结合黑嘴鸥繁殖栖息地选择的水源和干扰距离等因子,叠加分析表明核心区黑嘴鸥适宜繁殖栖息地面积由1992年的2063.1 hm2小幅增加到2002年的2158.0 hm2(4.6%),到2007年间大幅减少到1723.1 hm2(-20.2%),年降幅高达86.97 hm2/a.按照2002～2007年碱蓬覆盖度变化率水平计算,盐城保护区核心区黑嘴鸥适宜繁殖栖息地将在19.8a后全部消失.有关研究成果,可为保护区黑嘴鸥及其繁殖栖息地的保护、管理与恢复提供科学依据,同时也为特定栖息地的监测方法提供参考.     

四川青川县大熊猫种群分布

本文研究了青川县大熊猫冬季对栖息地选择和20世纪70年代、80年代和90年代栖息地分布和种群数量的变化,表明该地大熊猫冬春季对地形的选择主要为向阳的陡坡和斜坡或较干燥的阴坡,郁闭度0.6以下,主要活动在海拔2200-2800m区域;随着年代的递增,栖息地的面积日趋缩小,种群数量也逐步减少,分别为114只、60只和47只;探讨了影响分布和数量的因素。     

太白山巴山冷杉林主要树种与开花秦岭箭竹的空间点格局分析

采用单变量和双变量O-ring函数对太白山大熊猫栖息地巴山冷杉林主要树种的空间分布格局、种间关联性及其与林下开花箭竹的空间关联性进行了多尺度分析.结果表明: 巴山冷杉林中,巴山冷杉数量最多,但种群结构衰退,白桦种群相对年轻,种群结构稳定,红桦种群也呈衰退趋势;3个主要树种在小尺度上呈聚集分布,随尺度增加,逐渐表现为随机分布.3个树种的空间关联性主要表现在小尺度范围内,随尺度增加,空间分布格局逐渐表现为不关联;巴山冷杉和白桦与开花秦岭箭竹在大、中尺度内呈现正相关,而红桦与开花秦岭箭竹在大、中尺度上表现出负相关.大熊猫栖息地内乔木和林下秦岭箭竹共同推动森林的动态演替和发展,进而影响秦岭大熊猫栖息地的环境变化.     

中国大熊猫保护区发展历史、现状及前瞻

大熊猫是我国的特产动物,古时被列为贡品,在近代被视为国宝。新中国成立以前,由于乱捕滥猎等因素,该物种已处于濒危状态。解放以后,我国政府于20 世纪50 年代开始了自然保护区建设,并于20 世纪60 年建立了卧龙等5 个大熊猫自然保护区,并将大熊猫列为禁猎动物。20 世纪70 年代我国进行了第一次全国大熊猫资源调查,基于调查结果新建了佛坪、唐家河、蜂桶寨等自然保护区,使有关大熊猫的自然保护区总数达到了13 个。1985 -1988 年全国第二次大熊猫资源调查结果显示,野生大熊猫种群数量较第一次减少了54 ﹪。进入90 年代,全国大熊猫保护区增至36 个,迄今共建立了63 个与保护大熊猫这一珍稀物种相关的自然保护区。全国大熊猫栖息地已由80 年代衰落期时22 220 km2 增加至34 000 km2 ,其中分布在保护区内的栖息地面积达29 000 km2 ,有效地保护了约85% 的大熊猫栖息地总面积以及约50% 的野生大熊猫种群数量。现存野生大熊猫种群数量估计约2 000只,因密度稀疏,发展空间大。目前,国家主管部门已出台了各种就地与迁地保护措施,只需各方通力合作,野生大熊猫的未来将是光明的。     

秦岭大熊猫冬春季节对巴山木竹竹林生长指标的选择

2004～2005年冬季的12月、1月和春季的4月、5月，在秦岭山系南坡佛坪自然保护区内就大熊猫对构成其主要栖息地和食物来源的巴山木竹竹林的选择进行研究后的结果表明：在冬季，大熊猫栖息地多为幼竹与死竹比例基本持平、密度稳定的巴山木竹竹林，主要取食老竹；在春季，大熊猫更偏好选择竹子密度和盖度略低、竹子粗壮高大、幼竹与竹笋比例高的竹林活动，主要取食幼竹。大熊猫春季栖息地幼竹比例通常高过死竹比例，竹林密度处于增长期。竹林进入发笋期后，稀疏、高大的竹林下的粗大竹笋成为大熊猫的主要食物来源。     

Impact of road construction on giant panda’s habitat and its carrying capacity in Qinling Mountains

The Qinling giant panda (Ailuropoda melanoleuca) is an endangered endemic species to China. Despite ongoing efforts to ensure its conservation, concerns about maintaining its populations persist. We used GIS fed with data on land use including road network of 2001, third national giant panda survey, and a digital elevation model (DEM) to assess the impact of road construction on giant panda habitat, and estimate the carrying capacity of the Qinling Mountain area. We assessed habitat suitability with a mechanistic model, and conducted correlation analysis to evaluate relationship between the extent of giant panda habitat and amount of sites occupied by pandas within of 5 km × 5 km grid. We also estimated the carrying capacity of the Qinling Mountainous Area.
Our results revealed a significant correlation (R² = 0.447, P < 0.01) between the number of sites with signs left by giant panda and the extent of habitat within of 5 km × 5 km grid. The minimum habitat area that can support one panda was 10 km². Before the road network construction, the area of habitat suitable for the panda amounted about 1561 km² and that of marginally suitable habitat about 1499 km². The corresponding carrying capacity represented about 240 individuals. After the road network construction, the suitable habitat area was reduced by nearly 30% to 1093 km². Marginally suitable habitat and unsuitable habitat have both increased by 17% and 1%, respectively. As a result, the potential population size which the habitat could support was reduced to 217 individuals. The study results also suggested that most impacts on habitat from road construction took place in the high elevation areas above 1500 m. However, regarding the impact on the giant panda habitat, road networks developed much more inside the current nature reserves than outside of them.     

Impact of road construction on giant panda’s habitat and its carrying capacity in Qinling Mountains

The Qinling giant panda (Ailuropoda melanoleuca) is an endangered endemic species to China. Despite ongoing efforts to ensure its conservation, concerns about maintaining its populations persist. We used GIS fed with data on land use including road network of 2001, third national giant panda survey, and a digital elevation model (DEM) to assess the impact of road construction on giant panda habitat, and estimate the carrying capacity of the Qinling Mountain area. We assessed habitat suitability with a mechanistic model, and conducted correlation analysis to evaluate relationship between the extent of giant panda habitat and amount of sites occupied by pandas within of 5 km × 5 km grid. We also estimated the carrying capacity of the Qinling Mountainous Area.
Our results revealed a significant correlation (R² = 0.447, P < 0.01) between the number of sites with signs left by giant panda and the extent of habitat within of 5 km × 5 km grid. The minimum habitat area that can support one panda was 10 km². Before the road network construction, the area of habitat suitable for the panda amounted about 1561 km² and that of marginally suitable habitat about 1499 km². The corresponding carrying capacity represented about 240 individuals. After the road network construction, the suitable habitat area was reduced by nearly 30% to 1093 km². Marginally suitable habitat and unsuitable habitat have both increased by 17% and 1%, respectively. As a result, the potential population size which the habitat could support was reduced to 217 individuals. The study results also suggested that most impacts on habitat from road construction took place in the high elevation areas above 1500 m. However, regarding the impact on the giant panda habitat, road networks developed much more inside the current nature reserves than outside of them.     

Impacts of temperature on giant panda habitat in the north Minshan Mountains

Understanding the impacts of meteorological factors on giant pandas is necessary for future conservation measures in response to global climate change. We integrated temperature data with three main habitat parameters (elevation, vegetation type, and bamboo species) to evaluate the influence of climate change on giant panda habitat in the northern Minshan Mountains using a habitat assessment model. Our study shows that temperature (relative importance = 25.1%) was the second most important variable influencing giant panda habitat excepting the elevation. There was a significant negative correlation between temperature and panda presence (ρ = −0.133, P < 0.05), and the temperature range preferred by giant pandas within the study area was 18–21°C, followed by 15–17°C and 22–24°C. The overall suitability of giant panda habitats will increase by 2.7%, however, it showed a opposite variation patterns between the eastern and northwestern region of the study area. Suitable and subsuitable habitats in the northwestern region of the study area, which is characterized by higher elevation and latitude, will increase by 18007.8 hm² (9.8% habitat suitability), while the eastern region will suffer a decrease of 9543.5 hm² (7.1% habitat suitability). Our results suggest that increasing areas of suitable giant panda habitat will support future giant panda expansion, and food shortage and insufficient living space will not arise as problems in the northwest Minshan Mountains, which means that giant pandas can adapt to climate change, and therefore may be resilient to climate change. Thus, for the safety and survival of giant pandas in the Baishuijiang Reserve, we propose strengthening the giant panda monitoring program in the west and improving the integrity of habitats to promote population dispersal with adjacent populations in the east.     

Characterizing the spatial distribution of giant pandas (Ailuropoda melanoleuca) in fragmented forest landscapes

Aim To examine the effects of forest fragmentation on the distribution of the entire wild giant panda (Ailuropoda melanoleuca) population, and to propose a modelling approach for monitoring the spatial distribution and habitat of pandas at the landscape scale using Moderate Resolution Imaging Spectro‐radiometer (MODIS) enhanced vegetation index (EVI) time‐series data. Location Five mountain ranges in south‐western China (Qinling, Minshan, Qionglai, Xiangling and Liangshan). Methods Giant panda pseudo‐absence data were generated from data on panda occurrences obtained from the third national giant panda survey. To quantify the fragmentation of forests, 26 fragmentation metrics were derived from 16‐day composite MODIS 250‐m EVI multi‐temporal data and eight of these metrics were selected following factor analysis. The differences between panda presence and panda absence were examined by applying significance testing. A forward stepwise logistic regression was then applied to explore the relationship between panda distribution and forest fragmentation. Results Forest patch size, edge density and patch aggregation were found to have significant roles in determining the distribution of pandas. Patches of dense forest occupied by giant pandas were significantly larger, closer together and more contiguous than patches where giant pandas were not recorded. Forest fragmentation is least in the Qinling Mountains, while the Xiangling and Liangshan regions have most fragmentation. Using the selected landscape metrics, the logistic regression model predicted the distribution of giant pandas with an overall accuracy of 72.5% (κ = 0.45). However, when a knowledge‐based control for elevation and slope was applied to the regression, the overall accuracy of the model improved to 77.6% (κ = 0.55). Main conclusions Giant pandas appear sensitive to patch size and isolation effects associated with fragmentation of dense forest, implying that the design of effective conservation areas for wild giant pandas must include large and dense forest patches that are adjacent to other similar patches. The approach developed here is applicable for analysing the spatial distribution of the giant panda from multi‐temporal MODIS 250‐m EVI data and landscape metrics at the landscape scale.     

Assessing vulnerability of giant pandas to climate change in the Qinling Mountains of China

Climate change might pose an additional threat to the already vulnerable giant panda (Ailuropoda melanoleuca). Effective conservation efforts require projections of vulnerability of the giant panda in facing climate change and proactive strategies to reduce emerging climate‐related threats. We used the maximum entropy model to assess the vulnerability of giant panda to climate change in the Qinling Mountains of China. The results of modeling included the following findings: (1) the area of suitable habitat for giant pandas was projected to decrease by 281 km² from climate change by the 2050s; (2) the mean elevation of suitable habitat of giant panda was predicted to shift 30 m higher due to climate change over this period; (3) the network of nature reserves protect 61.73% of current suitable habitat for the species, and 59.23% of future suitable habitat; (4) current suitable habitat mainly located in Chenggu, Taibai, and Yangxian counties (with a total area of 987 km²) was predicted to be vulnerable. Assessing the vulnerability of giant panda provided adaptive strategies for conservation programs and national park construction. We proposed adaptation strategies to ameliorate the predicted impacts of climate change on giant panda, including establishing and adjusting reserves, establishing habitat corridors, improving adaptive capacity to climate change, and strengthening monitoring of giant panda.     

圈养大熊猫野化培训期的生境选择特征

大熊猫(Ailuropoda melanoleuca)是我国特有的珍稀物种,也是世界上最濒危的野生动物之一。为了将人工繁育的部分大熊猫个体重引入其历史分布区或复壮野生种群,中国保护大熊猫研究中心从2003年开始进行圈养大熊猫的野外放归工作,通过野化培训以提高圈养大熊猫适应和选择野外环境的能力。对野化培训大熊猫"淘淘"的生境选择研究表明:该野化培训大熊猫幼仔经常活动于新笋密度较大的区域[生境与对照:(2.68±1.14)对(1.58±0.66)],却避开成竹密度过大[(9.91±2.51)对(12.18±4.68)]、竹子较高[(4.57±1.09) m对(4.98±0.66) m]以及枯死竹过多[(2.52±0.86)对(3.39±1.33)]的区域;喜欢活动于离水源[(1.59±0.67)对(2.19±0.87)]和隐蔽场所较近[(5.37±2.14) m对(8.35±7.76)m],以及距离乔木较远[(3.09±0.69) m对(2.70±0.42) m]和郁闭度较低[(1.85±0.57)对(2.10±0.47)]的区域(P < 0.05),新笋密度大小是该栖息地在整个野化培训期间是否被利用的最重要因素。该野化培训大熊猫幼仔保持着与带仔母兽相近的生境选择特征,对竹子环境的选择也与卧龙野生大熊猫相似,野化培训对该大熊猫幼仔产生了积极的作用。野化培训大熊猫幼仔形成的家域和核域面积分别为9.21 hm² 和1.93 hm²,占野化培训圈面积的51.95%和10.89%,其中家域面积仅有卧龙野生大熊猫的1.4%-2.4%,所以在以后的野化培训过程中需要采取增加野化培训圈中环境丰富度等方式,促进野化培训大熊猫形成较大的家域面积。     

Habitat Assessment for Giant Pandas in the Qinling Mountain Region of China

ABSTRACT Because habitat loss and fragmentation threaten giant pandas (Ailuropoda melanoleuca), habitat protection and restoration are important conservation measures for this endangered species. However, distribution and value of potential habitat to giant pandas on a regional scale are not fully known. Therefore, we identified and ranked giant panda habitat in Foping Nature Reserve, Guanyinshan Nature Reserve, and adjacent areas in the Qinling Mountains of China. We used Mahalanobis distance and 11 digital habitat layers to develop a multivariate habitat signature associated with 247 surveyed giant panda locations, which we then applied to the study region. We identified approximately 128 km²of giant panda habitat in Foping Nature Reserve (43.6% of the reserve) and 49 km²in Guanyinshan Nature Reserve (33.6% of the reserve). We defined core habitat areas by incorporating a minimum patch-size criterion (5.5 km²) based on home-range size. Percentage of core habitat area was higher in Foping Nature Reserve (41.8% of the reserve) than Guanyinshan Nature Reserve (26.3% of the reserve). Within the larger analysis region, Foping Nature Reserve contained 32.7% of all core habitat areas we identified, indicating regional importance of the reserve. We observed a negative relationship between distribution of core areas and presence of roads and small villages. Protection of giant panda habitat at lower elevations and improvement of habitat linkages among core habitat areas are important in a regional approach to giant panda conservation.     

秦岭火地塘林区油松和华山松林乔木层净生产力与气候因子的关系

以秦岭火地塘林区油松(Pinus tabulaeformis)和华山松(Pinus armandi)林为研究对象,以其生物量及树高-胸径模型为基础,运用树木年轮宽度方法推算出1973年至2011年生物量和生产力年际动态,并通过相关分析和多元逐步回归分析探讨了油松和华山松林乔木层净生产力与温度、降水之间的关系。结果显示,该林区油松林和华山松林乔木层生物量39a间增长迅速,分别从1973年的15.32 t/hm2和7.53 t/hm2增长到2011年的175.97 t/hm2和130.98 t/hm2,平均年净生产力分别为4.18 t hm-2a-1和3.20 t hm-2a-1,油松林乔木层生物量和生产力均高于华山松林;气候分析表明年净生产力与降水关系不明显,与温度关系较为密切,随气温升高呈波动上升趋势:单月气候因子中上年7月温度、当年7月温度与油松林乔木层净生产力显著正相关,上年7月温度与华山松林乔木层净生产力显著正相关;油松林乔木层净生产力动态变化主要受1—7月平均温度影响,华山松林主要受5—7月平均温度影响;油松林生产力与温度因子的相关性高于华山松林。两种林型的生物量和生产力差异是由油松和华山松生物学特性所致。     

气候变化背景下秦岭地区羚牛生境脆弱性评估及适应性保护对策

脆弱性是指物种受气候变化影响的程度,开展脆弱性评估工作有助于人类认识气候变化对野生动物的影响,为制定野生动物适应气候变化的保护对策提供科学依据。采用最大熵模型评估气候变化背景下秦岭地区羚牛（Budorcas taxicolor bedfordi）生境脆弱性。结果表明：（1）当前秦岭地区羚牛适宜生境总面积为6473 km²,到2050s年,预测秦岭地区羚牛适宜生境总面积为4217 km²,减少34.85%,羚牛适宜生境将向更高海拔地区转移,转移约210 m;（2）已建保护区覆盖49.82%当前羚牛适宜生境,尚有3248 km²的适宜生境处于保护区之外;到2050s年,保护区覆盖了43.87%适宜生境,尚有2367 km²的适宜生境未被保护;（3）到2050s年,当前分布在太白县、佛坪县、洋县和宁陕县等地区的3490 km²羚牛适宜生境将会成为生境脆弱区域,丧失53.92%;（4）分布在秦岭核心区域的2983 km²当前和2050s年保持不变适宜生境,将成为羚牛躲避气候变化的庇护所。基于研究结果,就未来羚牛应对气候变化的适应性保护对策提出以下几点建议：考虑将当前羚牛适宜生境纳入国家公园范围、构建适应性生态廊道、加强野生动物监测。     

Application of ecological-niche factor analysis in habitat assessment of giant pandas

Ecological-niche factor analysis (ENFA) is a multivariate approach to study geographic distribution of species on a large scale with only “presence” data. It has been widely applied in many fields including wildlife management, habitat assessment and habitat prediction. In this paper, this approach was applied in habitat suitability assessment for giant pandas in Pingwu County, Sichuan Province, China. With “presence” data of giant pandas and remote sensing data, habitat suitability of pandas in this county was evaluated based on ENFA model, and spatial distribution pattern of nature reserves and conservation gaps were then evaluated. The results show that giant pandas in this county prefer high-elevation zones (> 2128 m) dominated by coniferous forest, and mixed coniferous and deciduous broadleaf forest, and avoid deciduous broadleaf forest and shrubs. Pandas avoid staying at habitats with human disturbances. Farmland is a major factor threatening panda habitat. Panda habitat is mainly distributed in north and west of Pingwu with a total area of 234033 hm², 106345hm² for suitable habitat and 127688 hm² for marginally suitable habitat). 3 nature reserves were located in Pingwu, covering over 49.2% of total suitable habitat and 45.6% of total marginally suitable habitat. Although 47.2% of panda habitat was in reserves under protection, connectivity between reserves was weak and a conservation gap existed in the north part of Pingwu. Thus, a new nature reserve in Baima and Mupi should be established to link the isolated habitats.