基于红外相机的不可个体识别动物种群密度估算方法

种群密度估计对野生动物的保护和管理至关重要,也是动物生态学和保护生物学备受关注的研究热点,但对大中型兽类种群数量的准确估算一直面临挑战。红外相机是哺乳动物调查中普遍采用的工具,也是克服这一挑战的一种经济有效的方法。目前国际上已有多种方法采用红外相机数据估算不可个体识别动物的种群密度,但相关技术在我国的应用案例较少,本文旨在为国内研究者应用红外相机数据估算动物种群密度提供参考。首先,我们介绍了随机相遇模型(randomencounter model, REM)、随机相遇与停留时间(random encounter and staying time, REST)模型、相机前停留时间(time in front of the camera,TIFC)模型以及红外相机距离取样(cameratrapdistancesampling,CTDS)这四种模型的基本原理和假设;其次,描述了这些模型在野外调查中的技术要点,并给出数据处理与分析的建议;最后,总结了每个模型的数据需求、优点和缺点。虽然我国目前拥有估算种群密度的大量红外相机数据源,但有很多物种的数量尚未知晓,也没有一种方法对所有红外相机数据都是...     

用标志重捕法估算湖泊二龄河蟹种群数量

估算种群数量是动物生态学研究中非常重要的内容。标志重捕法估算鱼类种群数量有100多年的历史,且得到了广泛的应用。蟹类由于其独特的行为学规律,标志重捕法对蟹类数量估算结果不令人满意,国内外文献中较少应用。       

陆桥岛屿环境下社鼠种群数量的估算方法

根据2010年3月—11月在千岛湖地区2个岛屿上社鼠(Niviventer confucianus)的标志重捕数据,分别用Jolly-Seber法、修正Lincoln指数法、Schnabel法和MNA法计算两个岛屿上社鼠种群数量,并深入探讨在陆桥岛屿环境下估算社鼠种群数量的适用方法。研究结果显示,在满足Jolly-Seber法的条件下,通过该方法计算的结果与修正Lincoln指数法无显著差异。但在野外实验中,并不是所有的重捕数据都满足Jolly-Seber法的条件,而且该方法不能估算头尾两月的数量。因此,修正Lincoln指数法更适于估算陆桥岛屿环境下社鼠的种群数量。可为今后开展陆桥岛屿环境下鼠类种群生态学研究奠定基础。     

种群密度调查——标志重捕法的模拟实验

种群密度是生物种群最基本的数量特征．标志重捕法是估算动物种群密度的常用方法．在野生动物保护、农林业害虫、害兽的监控与防治、渔业资源的评估、利用与保护上有重要应用。鉴于在中学现有条件下。对兽鸟鱼虫进行实际的标志重捕实验难以展开,在新课程教学实践中,我们反复研究了几种版本中标志重捕法的说明,设计并实施了如下标志重捕法的模拟实验,教学效果反馈很好。     

虎豹及有蹄类猎物种群数量监测方法概述

虎(Panthera tigris)和豹(P. pardus)及有蹄类猎物的种群数量监测是虎豹保护的核心任务, 也是制定有效管理、保护和恢复措施的基础。近年来, 国内外用于虎豹种群数量监测的方法主要有: 信息网络收集法、基于标志重捕模型的红外相机调查法和非损伤采样粪便DNA分析技术; 有蹄类猎物的监测方法主要有: 样线法、样带法、大样方法、红外相机调查技术和非损伤性遗传标志重捕法。每种监测方法基于的假设前提和生态学原理不同, 监测结果的准确度也不同。由于监测物种的生物学特征、种群分布状况、监测目标和空间尺度或环境因素各异, 每种方法的适用性也不同。本文从野外调查设计、数据收集、处理分析等方面对虎豹及其有蹄类猎物数量监测方法的应用过程和统计原理进行了介绍, 分析了各种监测方法的优缺点, 并针对在虎豹监测中相机布设密度过大可能造成的伪重复抽样, 以及应用虎豹监测设计的自动相机监测替代猎物种群监测数量的评估等不科学的方面进行了探讨和建议。     

Jolly—Seber法中种群存活率估算的探讨

在Jolly-Seber法中,存活率为其核心参数之一。一些作者指出,采用Jolly-Seber法估算标志重捕数据有时候会出现存活率大于1的情形。本文就这一情形做了相应的分析,认为产生这一现象的原因为标志个体间不具有等捕性或等存活率所致。在设计标志重捕取样的野外调查中,应设法提高重捕率以增加估计精度。     

基于标记-重捕模型开展野生动物红外相机种群监测的方法及案例

红外相机技术的广泛应用推动了动物种群生态学研究方法的发展和革新, 特别是基于标记-重捕模型框架通过非损伤取样方式对物种数量和密度等种群参数的可靠估计, 为保护濒危物种和评估保护成效提供了有力的科学依据。对于身体上具有独特天然标记的动物(如多数猫科动物), 可依据红外相机拍摄身体上的独特斑点或条纹鉴别个体, 再运用标记-重捕模型, 估计动物种群数量、密度等参数。本文概述了标记-重捕模型的基本原理、特点以及国内外的应用, 特别是近年来发展出的空间标记-重捕模型。总结了从相机布设到数据分析的具体流程、操作原则, 并以青城山家猫为实例, 展示了应用红外相机数据通过空间标记-重捕模型估计种群密度和数量的基本步骤。最后展望了该模型在种群动态、景观廊道设计、资源选择等方面的应用和发展趋势。     

Jolly - Seber 法估算长爪沙鼠种群参数的适用性探讨

以2000 年6 月～10 月群居性长爪沙鼠野生种群的标志重捕资料为依据, 采用Jolly - Seber 模型估算了该鼠种群参数, 结果表明, 长爪沙鼠个体间具等捕性(Leslie 法检验) , 研究期间取样个体的重捕率平均为89.7 %(77.4 % ～ 100 %) ; 参数估计结果具有合理的生物学意义, 认为采用该模型估算长爪沙鼠种群参数是适用的。     

三种方法对天山马鹿喀拉乌成山种群数量的比较

为了获得天山马鹿Cervuselaphus songaricus喀拉乌成山种群数量,并评估不同种群数量统计方法之间的差异。2010～2012年利用样线法、粪堆计数法以及非损伤性标记重捕法对喀拉乌成山的天山马鹿种群数量进行研究,发现样线法估计的种群数量最低,仅为1.316～1.656头/km2;而非损伤性标记重捕法获得的种群数量最高,为2.075～3.11头/km2;粪堆计数法获得的数据居中,为1.422～2.844头/km2,说明不同的方法之间很难具有可比性。     

大仓鼠种群年龄组存活率的估算

本文根据体重年龄组成数据和体重生长标准曲线,提出了一种估算鼠类各年龄组存活率的方法,其原理如下:设N_(i,k)为第k次取样时年龄组i的数量,DT_k为第k至k+1次间取样间隔,m为总取样次数,N_i和N_i分别为取样期年龄组i和j的总数量,T_i和T_j分别为年龄组i和j的发育历期,T_(i,j)为年龄组i到年龄组j的发育历期,φ_(i,j)为年龄组i到年龄组j的存活率,s_(i,j)为年龄组i到年龄组j的月均存活率,若DT_k相对的小,则有如下关系式:若DT_k恒定,即取样间隔一定,那么, φ_(i,j)=N_i/T_j/N_i/T_j s_(i,j)=e~[(30.Inφ_(i,j))/T_(i,j)] 根据夹捕和标志重捕资料,分别估算了1986和1988年河北省饶阳县大仓鼠(Cricetulustriton)种群各年龄组间月均存活率。在1986年,胎—Ⅱ,Ⅱ—Ⅲ,Ⅲ—Ⅳ和Ⅳ—Ⅴ月均存活率分别为0.6031,0.5874,0.7423和0.7426,平均为0.6689±0.0852;在1988年分别为0.5716,0.6417,0.5658和0.7437,平均为0.6307±0.0829。此外,还分别估算了大仓鼠雌、雄种群的月均存活率。     

Bayesian methods for multiple capture-recapture surveys

To estimate the total size of a closed population, a multiple capture-recapture sampling design can be used. This sampling design has been used traditionally to estimate the size of wildlife populations and is becoming more widely used to estimate the size of hard-to-count human populations. This paper presents Bayesian methods for obtaining point and interval estimates from data gathered from capture-recapture surveys. A numerical example involving the estimation of the size of a fish population is given to illustrate the methods.     

标记重捕法对模式产地霸王岭睑虎种群资源调查

霸王岭睑虎Goniurosaurus bawanglingensis是海南岛特有种,自2002年被命名以来,因种群数量小、分布区窄、栖息地破碎化等因素被《中国生物多样性红色名录》列为易危(VU)等级,种群资源现状尚不清楚。2018年7—8月,根据霸王岭睑虎种群分布,在模式产地霸王岭国家级自然保护区内选择了2个样区,首次采用植入电子标签的标记重捕法对种群密度、性比、窝卵数、成幼比等开展调查,并比较了雌雄个体的形态特征。结果显示:样区A种群密度为846只/hm^2,性比1.6∶1,成幼比7∶1;样区B种群密度为591只/hm 2,性比1.2∶1,成幼比10∶1。窝卵数为1~3枚,87%的窝卵数为2枚。身体量度特征(除吻长外)在两性间差异无统计学意义。研究结果进一步补充了霸王岭睑虎的基础生态学资料,可为资源状况评估和保护提供依据。此外,本研究证实了植入电子标签是对睑虎属Goniurosaurus物种野外标记研究的一种简单、有效的方法,可对动物进行长期标记。     

Abundance Estimation With a Transient Model Under the Robust Design

Abstract: A common situation in capture-mark-recapture (CMR) studies on birds and other organisms is to capture individuals not belonging to the studied population only present during the short time of the capture session. Presence of such transient individuals affects demographic parameter estimation from CMR data. Methods exist to reduce biases on survival estimates in the presence of transients and have been shown to be particularly efficient within the Robust Design framework (several secondary capture sessions within a short time interval during which the studied population can be assumed closed). We present a new model to estimate population size accounting for transients. We first used simulated data to show that the method reduces positive biases due to transients. In a second step, we applied the method to a real CMR dataset on a reed warbler (Acrocephalus scirpaceus) population. Population size estimates are reduced by up to 50% when correcting for the presence of transients. Many field studies on managed animal populations use capture-recapture methodology to obtain crucial parameters of the focal population demography. The resulting data sets are used either to estimate population size ignoring the presence of transients, or to estimate vital rates, accounting for transients but overlooking abundance estimation. Our method conciliates these 2 approaches.     

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction.     

北方冬季有蹄类动物4种数量调查方法的比较

科学的种群数量调查方法的探索一直是困扰北方有蹄类动物种群资源有效管理工作的重要问题。目前,北方野生有蹄类调查所采用的方法主要有样线法、样带法、大样方法和非损伤性CMR法4种。然而,不同的调查方法基于的统计学假设和生态学原理不同,调查结果往往会出现很大差异,迫切需要对北方冬季有蹄类动物的这4 种调查方法的有效性进行评估。以驼鹿种群数量调查为例,采用样线法、样带法、大样方法和非损伤性CMR调查法,于2012年3月和2012年12月对内蒙古汗马国家级自然保护区约120 km²的区域驼鹿种群数量进行了调查和评估。 结果显示,以上4 种方法得到的驼鹿种群数量分别为:样线法168(109-227) 只,样带法237(165-309) 只,大样方法37(23-50) 只,非损伤性CMR法55(43-68) 只,表明样线法和样带法的调查结果远大于大样方法和非损伤性CMR法,并探讨了不同调查方法应用的科学性、限制性和适用性,为北方冬季有蹄类动物种群资源调查方法的选择和应用提供了科学参考。     

Petersen and removal population size estimates: combining methods to adjust and interpret results when assumptions are violated

Synopsis We present ways to test the assumptions of the Petersen and removal methods of population size estimation and ways to adjust the estimates if violations of the assumptions are found. We were motivated by the facts that (1) results of using both methods are commonly reported without any reference to the testing of assumptions, (2) violations of the assumptions are more likely to occur than not to occur in natural populations, and (3) the estimates can be grossly in error if assumptions are violated. We recognize that in many cases two days in the field is the most time fish biologists can spend in obtaining a population estimate, so the use of alternative models of population estimation that require fewer assumptions is precluded. Hence, for biologists operating with these constraints and only these biologists, we describe and recommend a two-day technique that combines aspects of both capture-recapture and removal methods. We indicate how to test: most of the assumptions of both methods and how to adjust the population estimates obtained if violations of the assumptions occur. We also illustrate the use of this combined method with data from a field study. The results of this application further emphasize the importance of testing the assumptions of whatever method is used and making appropriate adjustments to the population size estimates for any violations identified.     

Using a sub-pixel mapping model to improve the accuracy of landscape pattern indices

The assessment of landscape spatial patterns is a key issue in landscape management. Landscape pattern indices (LPIs) are tools appropriate for analyzing landscape spatial patterns. LPIs are often derived from raster land cover maps that are extracted from remotely sensed data through hard classification. However, pixel-based hard classification methods suffer from the mixed pixel problem (in which pixels contain more than one land cover class), making for inaccurate classification maps and LPIs. In addition, LPIs generated by hard classification methods are characterized by grain sizes (the sampling unit sizes) that limit the derived landscape pattern to a certain scale. Sub-pixel mapping (SPM) models can enable fine-scale estimation of the spatial patterns of land cover classes without requiring additional data; hence, this is an appropriate downscaling method for land cover mapping. The fraction images generated by soft classification estimate the area proportion of each land cover class within each pixel, and using these images as input enables SPM models to alleviate the mixed pixel problem. At the same time, by transforming fraction images into a finer-scaled hard classification map, SPM models can minimize the influence of grain size on LPIs calculation. In this research, simulated landscape thematic patterns that can provide different landscape spatial patterns, eight commonly used LPIs and a SPM model that maximizes the spatial dependence between neighbouring sub-pixels were applied to assess the efficiency of deriving LPIs from sub-pixel model maps. Results showed that the SPM model can more precisely characterize landscape patterns than hard classification methods can. Landscape fragmentation, class abundance, the uncertainty in SPM, and the spatial resolution of the remotely sensed data influenced LPIs derived from sub-pixel maps. The largest patch index, landscape division, and patch cohesion derived from remotely sensed data with different spatial resolutions through the SPM model were suitable for inter-comparison, whereas the patch density, mean patch area, edge density, landscape shape index, and area-weighted mean shape index derived from the sub-pixel maps were sensitive to the spatial resolution of the remotely sensed data.     

Estimation of the size of an open population from capture-recapture data using weighted martingale methods

A weighted martingale method, akin to a moving average, is proposed to allow the use of modified closed-population methods in the estimation of the size of a smoothly changing open population when there are frequent capture occasions. We concentrate here on modifications to martingale estimating functions for model Mt, but a wide range of closed-population estimators may be modified in this fashion. The method is motivated by and applied to weekly capture-recapture data from the Mai Po bird sanctuary in Hong Kong. Simulations show that the weighted martingale estimator compared well with the Jolly-Seber estimator when the conditions for the latter to be valid are met, and it performed far better when individuals were allowed to leave and reenter the population. Expressions are derived for the asymptotic bias and variance of the estimator in an appendix.     

Multilist population estimation with incomplete and partial stratification

Multilist capture-recapture methods are commonly used to estimate the size of elusive populations. In many situations, lists are stratified by distinguishing features, such as age or sex. Stratification has often been used to reduce biases caused by heterogeneity in the probability of list membership among members of the population; however, it is increasingly common to find lists that are structurally not active in all strata. We develop a general method to deal with cases when not all lists are active in all strata using an expectation maximization (EM) algorithm. We use a flexible log-linear modeling framework that allows for list dependencies and differential probabilities of ascertainment in each list. Finally, we apply our method of estimating population size to two examples.     

Hidden population size estimation and diagnostics using two respondent-driven samples with applications in Armenia

Estimating the size of hidden populations is essential to understand the magnitude of social and healthcare needs, risk behaviors, and disease burden. However, due to the hidden nature of these populations, they are difficult to survey, and there are no gold standard size estimation methods. Many different methods and variations exist, and diagnostic tools are needed to help researchers assess method-specific assumptions as well as compare between methods. Further, because many necessary mathematical assumptions are unrealistic for real survey implementation, assessment of how robust methods are to deviations from the stated assumptions is essential. We describe diagnostics and assess the performance of a new population size estimation method, capture–recapture with successive sampling population size estimation (CR-SS-PSE), which we apply to data from 3 years of studies from three cities and three hidden populations in Armenia. CR-SS-PSE relies on data from two sequential respondent-driven sampling surveys and extends the successive sampling population size estimation (SS-PSE) framework by using the number of individuals in the overlap between the two surveys and a model for the successive sampling process to estimate population size. We demonstrate that CR-SS-PSE is more robust to violations of successive sampling assumptions than SS-PSE. Further, we compare the CR-SS-PSE estimates to population size estimations using other common methods, including unique object and service multipliers, wisdom of the crowd, and two-source capture–recapture to illustrate volatility across estimation methods.