样点法在森林鸟类调查中的运用

样点法由于具有易于实施、更易做到随机化或系统化以及适合于复杂及斑块化生境等优点,已成为目前使用最为广泛的森林鸟类调查方法。本文在介绍样点法的假设条件、类型和影响鸟类调查准确性因素的基础上,重点介绍了样点法在调查森林鸟类需要考虑的样本大小、样点布设、调查时间、每点的停留时间和调查周期等。同时也分析了样点法的不足,并提出样点法与网捕法结合是提高调查结果准确性的有效途径。     

鸟类调查的样线法和样点法比较:以崇明东滩春季盐沼鸟类调查为例

为了探讨样线法和样点法对盐沼湿地鸟类调查的有效性及适用性, 我们于2008年3–5月采用固定距离样线法和固定半径样点法对崇明东滩盐沼鸟类进行了调查。5次调查共记录到鸟类24种, 其中样线法记录到19种, 样点法记录到21种, 两种调查方法平均每次记录到的鸟类种数没有显著差异, 表明两种方法对盐沼鸟类种类的发现能力相似。两种方法得到的鸟类密度之间有显著的相关性,说明两种方法在反映鸟类群落中每种鸟类的相对数量方面具有可比性。但除了仅在一种调查方法中记录到的种类, 样点法调查得到的每种鸟类密度和鸟类总密度均高于样线法的调查结果。因此, 在对鸟类种群或群落的时空特征进行比较时, 需要考虑所采用的调查方法对调查结果的影响。     

样线法和网捕法在机场鸟情调查中的应用比较

鸟击事件主要发生在机场范围内,对机场周边生境的鸟类组成进行调查,是制定鸟击防范对策的重要前提。国内多数机场已经开展了相关的鸟类组成调查,但是选用的调查方法却存在较大差异。以样线法和网捕法两种常用的调查方法对沈阳桃仙国际机场周边的迁徙期鸟类进行了研究,旨在比较两种调查方法在机场鸟类群落组成调查中的效果及调查的鸟类群落组成差异。结果表明：两种方法均适合机场鸟类调研,共记录到鸟类97种,网捕法记录的鸟类种数(83种vs. 57种)高于样线法,网捕法单独记录的鸟类种数(40种vs. 14种)也高于样线法。尽管两种方法每期(半个月)调查的物种数和目的数量无明显差别,但随调查强度(如调查次数)增加,网捕法累计记录到的鸟类种类明显高于样线法,且较晚达到渐近线。另外,两种方法调查记录的鸟类群落组成也具有显著的差别,如网捕法记录到较多鹌鹑和鸮形目等夜间迁徙或活动鸟类,而样线法可记录到鹭科和雁鸭类水鸟等。这说明两种调研方法在鸟类调查中有较好的互补性。因此,本研究认为机场鸟情调研时应该根据鸟情特点,选择合适的鸟类调研方法,综合采用多种调研方法可更有利于获得科学的鸟情信息。     

四川大学江安校区鸟类调查

2007年9月～2008年8月,采用样线法对位于四川省成都市双流县四川大学江安校区的鸟类进行了调查,共记录到鸟类74种,其中留鸟占44.6%,冬候鸟占20.3%,夏候鸟占18.9%,旅鸟占14.9%;家麻雀、普通鸬鹚等2种为成都市区的鸟类新纪录;国家二级保护动物有长耳鸮、普通鵟2种;省级重点保护动物有小、普通鸬鹚、苍鹭等10种;白头鹎、红头长尾山雀、白颊噪鹛、树麻雀等4种为江安校区鸟类优势种,小白鹭、珠颈斑鸠、家鸽、家燕等12种为常见种.对不同生境的鸟类种类进行分析发现,植被受人为干扰较少的待建荒地的鸟类多样性指数最高,人工绿化带如草坪、灌丛、树林等次之,建筑区的鸟类多样性指数最低.     

样线法在南方山地生态系统野生动物调查中的试点效果评价

样线法是全国第二次陆生野生动物资源调查中的首选方法,但已有文献表明该方法在南方山地森林中应用时存在诸多问题。为此,我们于2010年4月19–24日在广东车八岭国家级自然保护区对样线法的实际调查效果进行试点,在保护区中心区域划定5 km×10 km的范围,布设6条3 km长的理论样线。实际调查时,使用GPS轨迹记录功能精确标记调查样线和时间。调查时有2条样线未能达到理论长度,平均每条样线耗时5.3±1.4 h,调查速度小于600 m/h。该方法针对常规调查物种的发现概率偏低,调查到的物种总数占保护区常规调查物种总数的比例(0.22)小于调查到的总物种数占保护区总物种数的比例(0.37);在调查强度为0.75%时,可对区域内物种进行有效抽样,但在有效评估一个区域的具体物种数量上可能存在缺陷。为此,我们对比了全国第一次陆生野生动物调查及试点工作的结果,针对南方山地森林生态系统调查提出以下建议:(1)应用GPS轨迹记录功能进行实际样线的设置和记录;(2)调查速度从2–3 km/h适当降低至600 m/h左右;样线长度控制在3–5 km,确保1天内可完成2条样线调查;(3)在现有的财力和人力条件下,样区内各类群调查强度略高于1%是较为适宜的;(4)采用多种辅助手段来提升常规调查物种的发现概率;(5)在地形复杂的位置可考虑使用样方或样点法辅助调查,增加物种发现概率,但不宜限制其调查强度。     

截线法对西藏盘羊种群数量的估计

１９８７～１９９０年间,采用截线抽样法对西藏盘羊的种群数量及分布进行了全面调查。结果在１５５０ｋｍ样线上遇见９９头盘羊。由此资料,以傅立叶级数表达其探测函数,估计出了盘羊在西藏分布区内的平均分布密度为０．０８２０±０．００９７头／ｋｍ２,即每１２ｋｍ２约有１头,而波动在０．０１２１～０．３６７１头／ｋｍ２之间。并依此确定的１０．５万ｋｍ２的栖息面积计算、西藏盘羊的种群数量为８６３０±１０２１头。目前,盘羊群平均数下降为５～６头／群,亟待保护。     

鸟类种群密度调查和估算方法初探

为探讨不同调查方法对鸟类种群密度估计的差异,于2010年12月15~20日,对云南省昆明市嵩明县嘉丽泽地区常见的四种鸟(白脊鹡鸰Motacilla alba、黑喉石駑(嶋)Saxicola torquata、白鹭Egretta garzetta、苍鹭Ardea cinerea)进行调查研究.之后分别采用无距离样线法、固定距离样线法以及可变距离样线法(Distance软件)分析,得出其相对丰富度、种群密度等相关信息.对比三种样线法在鸟类数量估计方面的优劣,认为无距离样线法不能真实地反映鸟类数量的多少,固定距离样线法结合样带计数法往往低估鸟类种群密度,呵变距离样线法结合Distance软件分析是相对更为先进和精准的种群密度估算方法.对如何进一步利用Distance软件的分析结果也进行了初步探讨.     

AI辅助识别的鸟类被动声学监测在城市湿地公园中的应用

为了探究基于AI识别的鸟类被动声学监测手段在城市湿地公园中的应用效果, 同时对比其与传统人工样线调查结果的差别, 本研究于2023年3-5月在广州市湾咀头湿地公园开展了为期3个月的同期监测。样线法为每月调查两次; 声学监测法通过安装两台声纹监测仪, 全天开启触发录制模式, 通过4G网络回传音频文件并使用以珠三角鸟类名录构建的AI识别模型进行鸟种识别, 再对结果进行置信度筛选和人工复核。样线法累计记录鸟类2,200只次; 声学监测法共采集音频96,848条, 筛选验证获得有效记录34,117条。两种方法共记录鸟类70种, 其中样线调查记录鸟类48种, 声学监测记录49种, 两种调查方法都记录到的鸟类有27种。两种调查方法重叠的物种比例不足总物种数的一半, 说明在此类湿地公园生境下这两种方法尚无法互相取代。样线调查结果相对准确、便于估算种群密度, 但对调查者的认鸟水平和工作量要求较高; 声学监测可自动化运行, 便于扩大监测规模, 但后期数据处理难度较大, 结合AI物种识别和人工校正可以提高数据处理效率。综上, 基于机器学习的AI识别技术的鸟类被动声学监测方法大大提高了数据处理效率, 但仍需要结合传统的样线调查方法, 两者结合将有更高的准确率和更广阔的应用前景。     

运用红外相机和样线法调查黑龙江双河自然保护区兽类资源

<正>双河国家级自然保护区位于黑龙江省大兴安岭北部(124°52′48″–125°32′03″E,52°54′25″–53°12′08″N),面积88,849 ha,属于森林、湿地生态系统类型自然保护区,也是我国最北的国家级自然保护区。保护区始建于2002年,并于2008年晋升为国家级自然保护区,保存着我国较大面积的原始兴安落叶松(Larix gmelinii)林和樟子松(Pinus sylvestris var.mongolica)林(程继臻,1996)。保护区野生动植物资源丰富,已记录高等维管植物416种,鸟类42科180     

秦皇岛雀形目鸟类环志数量变化及其影响因素

鸟类种群数量变动及其机制是种群生态学研究的重要内容.本文分析了2005-2019年河北秦皇岛雀形目鸟类环志数量的变化,并探讨了数量变化的影响因素,以期为当地鸟类及其栖息地的保护提供基础资料.我们选择了环志数量较多的食虫鸟柳莺科、鹟科、绣眼鸟科、山雀科以及食谷鸟鹀科和燕雀科等雀形目鸟类,研究其环志数量变动趋势.结果 表明...     

Nonparametric Estimation of Population Density for Line Transect Sampling Using FOURIER Series

A nonparametric, robust density estimation method is explored for the analysis of right-angle distances from a transect line to the objects sighted. The method is based on the FOURIER series expansion of a probability density function over an interval. With only mild assumptions, a general population density estimator of wide applicability is obtained.     

Censusing primates by transect in a forest of known primate density

A series of four transect or strip censuses of primates was carried out in a forest in eastern Colombia where the absolute density of the three diurnal species had been previously calculated during long-term studies. The diurnal species were Alouatta seniculus, Callicebus torquatus,and Cebus apella.Techniques were identical, except for a few variables tested for their effect on the experimental results;these included three different types of detection distances. Variable results were obtained for the two species having the smallest home ranges (A. seniculusand C. torquatus)with the most consistently accurate densities being calculated using the drop in the frequency distribution of sighting distances as a measure of the detection distance. Grossly inflated results were obtained for almost all calculations of C. apelladensity. The stimulus leading to the detection of particular primate groups was usually their vocalizations. Counts of recognized group members were inaccurate and short of the actual number of individuals in each group. Problems of censusing primates are discussed and some recommendations are made with respect to the use of the transect census method.     

Some Mathematical Models for Line Transect Sampling

ANDERSON and POSPAHALA (1970) investigated the estimation of wildlife population size using the belt or line transect sampling method and devised a correction for bias, thus leading to an estimator with interesting characteristics. This work was given a uniform mathematical framework in BURNHAM and ANDERSON (1976). In this paper we extend that mathematical framework to several different sampling models, and a number of interesting discrete probability distributions emerge.     

The line transect method for estimating densities of large mammals in a tropical deciduous forest: An evaluation of models and field experiments

We have evaluated techniques of estimating animal density through direct counts using line transects during 1988–92 in the tropical deciduous forests of Mudumalui Sanctuary in southern India for four species of large herbivorous mammals, namely, chital (Axis axis). sambar (Cervus unicolor). Asian elephant (Elephas maximus) and gaur (Bos gaurus) Density estimates derived from the Fourier Series and the Half-Normal models consistently had the lowest coefficient of variation. These two models also generated similar mean density estimates. For the Fourier Series estimator, appropriate cut-off widths for analyzing line transect data for the four species are suggested. Grouping data into various distance classes did not produce any appreciable differences in estimates of mean density or their variances, although model fit is generally better when data arc placed in fewer groups. The sampling effort needed to achieve a desired precision (coefficient of variation) in the density estimate is derived. A sampling effort of 800 km of transects returned a 10% coefficient of variation on estimate for ehital; for the other species a higher effort was needed to achieve this level of precision. There was no statistically significant relationship between detectability of a group and the size of the group for any species. Density estimates along roads were generally significantly different from those in the interior of the forest, indicating that road-side counts many not be appropriate for most species.     

Review of Some Important Techniques in Wildlife Sampling and Sampling Errors

This paper reviews some of the important methods for estimating animal numbers or densities based on (i) direct counts of population units as used in quadrat, strip, line-transect and line-intercept sampling and (ii) indirect counts and indices, such as capture-mark recapture, change-in-ratio, catch-effort methods and indices based on track counts, call, roadside and pellet-group counts, etc.     

Sampling tiger ungulate prey by the distance method: lessons learned in Bardia National Park, Nepal

Because tiger Panthera tigris numbers are regulated by their prey base, prey abundance needs to monitored and estimated reliably. Recently, distance sampling has been adopted as the most appropriate method and is now becoming the standard monitoring protocol in all tiger range countries in south Asia. However, the accuracy of the density estimates generated by this method has not been assessed. From total counts within habitat blocks, we obtained accurate density estimates of ungulates within three main habitats in Bardia National Park, Nepal. We then applied the distance sampling method in the same habitats and compared the results. Distance sampling on foot in dense habitats (riverine forest and tallgrass floodplain) violated method assumptions, and sampling from vehicle along roads gave biased estimates. Sampling from elephant back worked well in all habitat types, but owing to their behaviour, the density of barking deer Muntiacus muntjak was underestimated. The accuracy of the estimates varied with sampling effort; for the very abundant chital deer Axis axis , estimates varied markedly at <200 animal observations, but converged at larger sample sizes to a similar point estimate as intensive block counts when approaching 300 observations. For the less abundant species, with <20 observations along >100 km of transect lines, the confidence intervals were quite high, and, hence, of limited value for detecting short-term populations trends. It is therefore difficult to obtain accurate density estimates of rare species by the distance method. In areas consisting of dense habitats, we recommend that the food base of tiger be estimated by distance sampling from elephant back, not on foot, directed at the main and most abundant prey species. For rare species, encounter rates obtained simultaneously may then serve as indices of relative abundances.