佛坪自然保护区6种有蹄动物的活动痕迹监测

野生动物监测在保护管理自然资源以确保可持续利用上占有关键地位.然而,我国对大型哺乳动物的长期监测基本尚未纳入常规.佛坪自然保护区自20世纪90年代中期就大熊猫等珍稀动物及其栖息环境展开了长期监测.以该保护区2000～2006年监测数据为基础,探讨了该地有蹄类动物(除扭角羚外)的资源动态及在生境因子利用上的差异.结果表明该保护区内有蹄类动物资源在不同区域的分布并不均衡,以三官庙区域的痕迹密度最高而以龙潭区域为最低.监测期间有蹄类动物资源呈现出逐步下降的趋势.物种两两之间在海拔、坡度等生境因子的利用上大多存在显著差异,表现出不同的微生境利用模式.最后就监测对象的确定以及监测方法的选择进行了探讨,该保护区所采用的监测方法较为科学有效.     

GPS项圈系统在野生动物管理与监测中的应用

野生动物管理与监测是生物多样性保护工作的重要内容。而监测手段决定了保护工作中收集数据的质量和数量。收集动物活动位点数据是野生动物管理与监测中的一项重要工作,可通过样线法收集动物活动痕迹(实体)位点或采用无线电遥测获取动物的活动位点等,采用这些位点数据可以研究野生动物种群数量、家域、运动、栖息地利用和选择等。通过给野生动物佩戴GPS项圈,利用GPS项圈系统进行定位记录其活动位点,较前述常规方法更为便利,能高效地收集大量高质量位点,这种方法可以为野生动物的管理与生物多样性保护提供更为可靠的数据基础。     

驼鹿的圣地

汗马蕴育着我国独特的寒温带森林湿地动物群.在众多的物种中,以大型有蹄类动物为代表的物种多样性、丰富度和独特性令人惊叹! 有蹄类是动物界的重要组成部分,亦是现存最大群的大型陆生动物,是自然界食物网的主要环节;同时,作为动物界的一部分,有蹄类对生态系统有着重要的影响.有蹄类动物中,驼鹿是鹿科动物中的体型最大者.尽管驼鹿广泛分布于欧洲和北美,但我国驼鹿种群是亚洲最南部的种群.它是国家Ⅱ级保护野生动物,别名:犴、堪达犴.驼鹿这个鹿科动物中的巨人,也是对环境变化非常敏感的物种之一.     

野生大熊猫种群数量调查方法研究进展

大熊猫(Ailuropodamelanoleuca)是中国特有珍稀野生动物,被誉为野生动物保护领域的"旗舰物种"。大熊猫种群数量的变化可以实时反应大熊猫种群动态,提供大熊猫分布区域、栖息地质量等最直接的信息,是制定大熊猫保护方案的基础,也是有效实施大熊猫保护措施的前提。综述了几种传统野生大熊猫种群数量调查方法(包括直接计数法、数学模型法、距离-咬节分析法及分子生物学方法),以及近年来最新应用于野生动物种群数量调查的红外相机技术、足迹鉴定法,讨论了传统方法中可能存在的问题、分析新方法的应用前景,并针对今后的野生大熊猫种群数量调查提出了一些建议与展望。     

地理信息系统在野生动物研究中的应用

简要地介绍了地理信息系统的功能及其在动物生态学研究、野生动物保护和资源管理中的应用。它在动物迁徙研究、动物栖息地选择研究、动物栖息地利用研究、动物种群数量调查、动物群落研究、动物栖息地状况评估和监测、动物种群生存力分析、建立物种分布数据库等多方面都有极大的应用价值。     

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。     

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。     

东北虎豹生物多样性红外相机监测平台概述

东北虎豹生物多样性红外相机监测平台始建于2006年, 位于中国东北温带针阔混交林区, 覆盖老爷岭、张广才岭和完达山, 面积达1.5万多平方公里。平台的监测目标是从生态系统水平上对东北虎(Panthera tigris altaica)、东北豹(P. pardus orientalis)、有蹄类猎物及同域分布的其他哺乳动物、森林栖息生境、环境要素和人类活动等进行全面系统的调查和观测。截至2019年6月, 平台产生视频记录超过78.5万条, 有效相机工作日173.6万多天, 记录了28种野生兽类和32种野生鸟类。另外, 利用红外相机平台已经在野生动物多样性本底调查、虎豹种群分布、数量与扩散限制、同域食肉动物种间关系、动物生境利用等方面取得一些成果, 同时为东北虎豹国家公园生物多样性监测、评估和管理提供了科技支撑。     

“野生动物”的概念框架和术语定义

“野生动物”(wild animal)一词不止在我国, 在全球的英语使用者中也有不同的含义。通过梳理相关研究、国内法和国际法背景下的定义和适用范围, 结合人类对动物繁殖和生活条件的控制情况, 本文提出了“野生动物”的二维概念框架, 梳理了动物从“野生”到“驯化”的12个连续状态。以下状态即未经中长期人工选择的动物类群应被视为野生动物: (1)其在荒野自然或人工环境(如城市或乡村)中自由生存繁殖, 无论是否存在人工投喂、经救护或辅助生殖后被放归的个体; (2)被捕捉圈养在人工环境中生活, 或源自野外但在圈养条件下出生的个体; (3)直系血亲(《濒危野生动植物种国际贸易公约》解释为世系前四代)仍有野外来源的人工繁育后代; (4)放生、逃逸或引入到自然环境中的人工繁育个体。在野生动物物种保护的目标和语境之下, 经过长期人工选择的驯化动物, 无论其是否在人类控制下生活, 如家养猫狗、家禽家畜或模式实验动物, 以及流浪猫狗、放生禽畜和野化家养动物等都不是“野生动物”。但对于一些经过一定程度的人工选择, 所处人类控制情况和对野外种群的影响各异(如经过多代人工繁育的驯养动物、因人类活动导致的外来动物等), 其是否需被作为野生动物管理, 则需要根据生态安全、物种管理、立法目标等特别设定监管范围。《中华人民共和国野生动物保护法》的保护对象可以考虑为: 受到人类威胁濒临灭绝的, 或者具有重要生态作用的野生动物物种, 其状态可不限于是在野外还是人工控制条件下。其他动物的管理, 可根据遗传资源保护、疫病防控、动物福利和生态安全等需要, 另外设立《动物福利法》《生物安全法》等, 并和已有的法律法规如《动物防疫法》《渔业法》等做好衔接。本文还就《野生动物保护法》可能采用的“野生动物”定义提出建议。     

舟山群岛春秋季黄麂的栖息地特征及其利用的初步研究

栖息地选择是野生动物与其环境之间的基本关系,野生动物的栖息地特征研究对野生动物的保护和利用具有重要意义(骆颖等,2009).动物的食物资源具有季节性变化(蒋志刚,2004),在舟山群岛,春季有蹄类动物的可利用资源相对较为缺乏而到秋季相对丰富,有蹄类动物的栖息地特征由于不同季节间的环境可利用资源不同而产生差异(Sehmitz,1991).研究不同季节动物的柄息地特征及利用机制,可以深入了解动物是如何适应环境的(蒋志刚,2004).     

Demographic modeling and monitoring cycle in a long-lived endangered shrub

Demographic monitoring is a well established tool for conservation managers. But, monitoring programs typically do not offer methods to explore the relationship between projected population trends and the probability of trend detection. Moreover, conservation biologists need to evaluate and incorporate the effect of variability and habitat perturbation on the efficiency of monitoring programs.We studied population demography of an endangered shrub endemic to East Central Spain, Vella pseudocytisus subsp. paui in order to understand its present demographic performance, how it is affected by increasing variability and perturbation and how these two factors are related to monitoring and monitoring thresholds.Using Lefkovich demographic matrices on six years of data, we produced 19 different population projections, and we compared these modeled projections against observed projection. We designated three conceptually important detection thresholds: an early warning; an unequivocal signal; and, a quasi-extinction threshold. Based on calculated detection times of all models for every threshold, we produced an averaged monitoring cycle (minimum visit frequency) to provide managers with a tool to design a consistent monitoring program for this plant.Our results indicate a relatively stable current population along with large detection times for critical threshold changes of modeled populations when considering small management changes (low population variability and low perturbation intensity). In contrast, model combinations incorporating high population variance and high perturbation produce disproportionally short times to detection of a significant population trend. In terms of designing studies to maximise results while minimising monitoring effort, designing a monitoring program capable of providing an early warning system of potential population failure requires more effort than detecting an unequivocal signal of decline.Finally, we propose two alternatives when calculating monitoring cycle (MC) values. One of them implies demographic MC if population perturbation and variation are high. For that, an optimal monitoring cycle is estimated based on a range of possible scenarios for population trends. Alternatively, we propose environmental MC, when low values of perturbation and variation are expected.     

Using simple species lists to monitor trends in animal populations: new methods and a comparison with independent data

There is an urgent need to develop simple and inexpensive methods for monitoring wildlife populations in resource-poor countries. List-based methods have been advocated as simple yet potentially useful biodiversity monitoring tools, and systems have recently been launched in a number of countries to collect species lists. We attempt to advance the use of systematic list-based monitoring by (1) suggesting improvements to the way in which list reporting rates are calculated; (2) assessing the extent to which degrading effort-corrected measures of abundance into simple species lists results in loss of information on population trends; (3) comparing long-term trends in list reporting rates with population trends from a wholly independent monitoring scheme. Daily species lists of birds were derived from regular trapping at a nature reserve in southern England. Most species showed a strong correlation across years between the proportion of lists on which they occurred, adjusted for list length (adjusted list reporting rate; ALRR), and an effort-corrected measure of abundance (captures per unit effort; CPUE). ALRR revealed almost as much about annual variation in abundance as CPUE for all but the most frequently captured species. Long-term (>20 years) trends in ALRRs at the nature reserve were positively correlated with UK national population trends recorded over the same period by an independent, labour-intensive monitoring scheme that counted birds at a large number of widely spread sites. Our results support previous claims that simple species lists could generate data useful for monitoring long-term population trends, particularly where such lists are collected systematically. However, further research on the efficiency of list reporting rates relative to more sophisticated methods is necessary, before list-based methods can be advocated for dedicated monitoring schemes in resource-poor regions.     

Confidence in ecological indicators: A framework for quantifying uncertainty components from monitoring data

The value of an ecological indicator is no better than the uncertainty associated with its estimate. Nevertheless, indicator uncertainty is seldom estimated, even though legislative frameworks such as the European Water Framework Directive stress that the confidence of an assessment should be quantified. We introduce a general framework for quantifying uncertainties associated with indicators employed to assess ecological status in waterbodies. The framework is illustrated with two examples: eelgrass shoot density and chlorophyll a in coastal ecosystems. Aquatic monitoring data vary over time and space; variations that can only partially be described using fixed parameters, and remaining variations are deemed random. These spatial and temporal variations can be partitioned into uncertainty components operating at different scales. Furthermore, different methods of sampling and analysis as well as people involved in the monitoring introduce additional uncertainty. We have outlined 18 different sources of variation that affect monitoring data to a varying degree and are relevant to consider when quantifying the uncertainty of an indicator calculated from monitoring data. However, in most cases it is not possible to estimate all relevant sources of uncertainty from monitoring data from a single ecosystem, and those uncertainty components that can be quantified will not be well determined due to the lack of replication at different levels of the random variations (e.g. number of stations, number of years, and number of people). For example, spatial variations cannot be determined from datasets with just one station. Therefore, we recommend that random variations are estimated from a larger dataset, by pooling observations from multiple ecosystems with similar characteristics. We also recommend accounting for predictable patterns in time and space using parametric approaches in order to reduce the magnitude of the unpredictable random components and reduce potential bias introduced by heterogeneous monitoring across time. We propose to use robust parameter estimates for both fixed and random variations, determined from a large pooled dataset and assumed common across the range of ecosystems, and estimate a limited subset of parameters from ecosystem-specific data. Partitioning the random variation onto multiple uncertainty components is important to obtain correct estimates of the ecological indicator variance, and the magnitude of the different components provide useful information for improving methods applied and design of monitoring programs. The proposed framework allows comparing different indicators based on their precision relative to the cost of monitoring.     

Australian wetlands: the monitoring challenge

Management of Australian wetlands is confounded by an absence of adequate environmental monitoring data. To a large extent we are still not sure of the exact location and extent of many wetlands. Inventory and monitoring information are intricately linked and at the heart of successful wetland management. Monitoring addresses the extent of change in the environment, yet, for wetlands, it is rarely based on valid scientific principles. Monitoring is also a research tool, but has not, until relatively recently, been accepted as integral to the research effort in environmental management, in part because the questions that may be answered have not been well defined or articulated. The absence of rigorous scientific input to monitoring design has contributed to the devaluing of past long-term monitoring effort. To turn this around and to ensure that wetland managers receive adequate and timely data we need to enhance the scientific rigor applied to monitoring. This requires long-term and strategic support. Management is a complex process and involves many sectors of society. Involving these sectors in monitoring has recently received some attention, but this is not universal and is not necessarily well supported. The challenge is to correct this situation and take advantage of community interest in wetland conservation that may provide mechanisms for more cost effective monitoring. A protocol for monitoring Australian wetlands is presented. This is a series of logical steps to assist in the development and implementation of monitoring programs that are effectively linked to management procedures (such as a formal management plan). The protocol includes a review process that could result in the termination of a program that has achieved its goals, or the abandonment or revamping of one that has failed.     

Population size influences amphibian detection probability: implications for biodiversity monitoring programs

Monitoring is an integral part of species conservation. Monitoring programs must take imperfect detection of species into account in order to be reliable. Theory suggests that detection probability may be determined by population size but this relationship has not yet been assessed empirically. Population size is particularly important because it may induce heterogeneity in detection probability and thereby cause bias in estimates of biodiversity. We used a site occupancy model to analyse data from a volunteer-based amphibian monitoring program to assess how well different variables explain variation in detection probability. An index to population size best explained detection probabilities for four out of six species (to avoid circular reasoning, we used the count of individuals at a previous site visit as an index to current population size). The relationship between the population index and detection probability was positive. Commonly used weather variables best explained detection probabilities for two out of six species. Estimates of site occupancy probabilities differed depending on whether the population index was or was not used to model detection probability. The relationship between the population index and detectability has implications for the design of monitoring and species conservation. Most importantly, because many small populations are likely to be overlooked, monitoring programs should be designed in such a way that small populations are not overlooked. The results also imply that methods cannot be standardized in such a way that detection probabilities are constant. As we have shown here, one can easily account for variation in population size in the analysis of data from long-term monitoring programs by using counts of individuals from surveys at the same site in previous years. Accounting for variation in population size is important because it can affect the results of long-term monitoring programs and ultimately the conservation of imperiled species.     

Modeling the effects of varying data quality on trend detection in environmental monitoring

Detection of changes in ecosystem characteristics is a principal tool for identifying and understanding the effects of anthropogenic activities on the condition and functioning of ecosystems. It is widely known that temporal trends can be blurred by the imprecision of the data. Research program managers are aware of the difficulties surrounding representative sampling and therefore enforce strict sampling protocols. Standardized sampling can be so effective that the initially much smaller uncertainty in the instrumental analysis becomes substantial. However, until now the effect of the quality of the instrumental analysis on the time required for trend detection has only rarely been quantified. In this paper, we present a novel technique and theoretical computations for the detection of trends in single and combined indices. The theory is clarified with examples from the International Co-operative Programme on Assessment and Monitoring of Air Pollution on Forests (ICP Forests). Moreover, the theoretical computations were made for normalized or scaled distributions and are therefore equally valid outside the field of environmental monitoring. The results show that, when sampling protocols largely reduce the variability of representative sampling, poor quality of the instrumental analysis blurs the data such that environmental monitoring or long-term ecological research programs can lose the ability to detect trends by causing up to three decades-long delay in detecting changes. We can thus conclude that high quality of the instrumental analysis is a prerequisite for a sensitive monitoring program.     

Analysis and Portrayal of Uncertainty in a Food-Web Exposure Model

The results of quantitative risk assessments are key factors in a risk manager's decision of the necessity to implement actions to reduce risk. The extent of the uncertainty in the assessment will play a large part in the degree of confidence a risk manager has in the reported significance and probability of a given risk. The two main sources of uncertainty in such risk assessments are variability and incertitude. In this paper we use two methods, a second-order two-dimensional Monte Carlo analysis and probability bounds analysis, to investigate the impact of both types of uncertainty on the results of a food-web exposure model. We demonstrate how the full extent of uncertainty in a risk estimate can be fully portrayed in a way that is useful to risk managers. We show that probability bounds analysis is a useful tool for identifying the parameters that contribute the most to uncertainty in a risk estimate and how it can be used to complement established practices in risk assessment. We conclude by promoting the use of probability analysis in conjunction with Monte Carlo analyses as a method for checking how plausible Monte Carlo results are in the full context of uncertainty.     

Hybrid Framework for Managing Uncertainty in Life Cycle Inventories

Life cycle assessment (LCA) is increasingly being used to inform decisions related to environmental technologies and polices, such as carbon footprinting and labeling, national emission inventories, and appliance standards. However, LCA studies of the same product or service often yield very different results, affecting the perception of LCA as a reliable decision tool. This does not imply that LCA is intrinsically unreliable; we argue instead that future development of LCA requires that much more attention be paid to assessing and managing uncertainties. In this article we review past efforts to manage uncertainty and propose a hybrid approach combining process and economic input–output (I‐O) approaches to uncertainty analysis of life cycle inventories (LCI). Different categories of uncertainty are sometimes not tractable to analysis within a given model framework but can be estimated from another perspective. For instance, cutoff or truncation error induced by some processes not being included in a bottom‐up process model can be estimated via a top‐down approach such as the economic I‐O model. A categorization of uncertainty types is presented (data, cutoff, aggregation, temporal, geographic) with a quantitative discussion of methods for evaluation, particularly for assessing temporal uncertainty. A long‐term vision for LCI is proposed in which hybrid methods are employed to quantitatively estimate different uncertainty types, which are then reduced through an iterative refinement of the hybrid LCI method.     

三江源国家级自然保护区内云塔村雪豹种群动态

种群监测可为物种研究和保护提供关键信息和依据。雪豹(Panthera uncia)作为亚洲高山生态系统的顶级捕食者和旗舰种, 一直是研究和保护的重点, 但其难以到达的栖息地、隐秘的行踪和广阔的家域使其监测工作开展难度较大, 雪豹种群动态研究较为匮乏。本研究在2013年10月至2019年1月期间, 使用当地社区维护的红外相机, 监测三江源国家级自然保护区通天河沿保护分区内青海省玉树州哈秀乡云塔村雪豹种群的密度和动态, 共识别出35只雪豹个体。基于数据质量较好的2015、2016、2017年连续3年的红外相机数据各年截取3个月数据, 使用空间标记-重捕模型估算种群数量和密度, 发现当地雪豹种群和成年个体密度基本维持稳定, 种群增长率为1.02, 但监测期间雪豹个体更替明显, 平均个体更替率为0.44, 并且围绕两片雪豹核心利用区域发生了领域取代。推测雪豹种群具有较多个体更替和领域取代是因为种群处在雪豹潜在扩散通道上, 或调查范围未覆盖完整种群。本研究是国内首次对雪豹进行较为长期的种群动态监测和分析, 研究结果体现了动态监测的重要性, 也显示出以当地社区为主体监测哺乳动物种群的可能性。     

Precautionary Defaults—A New Strategy for Chemical Risk Management

In order to give adequate support to risk managers, new risk assessment methods should be developed that are (1) scientifically sound, (2) simplified, and (3) suited for precautionary risk management. In this Perspective we propose that the notion of a precautionary default can be a useful tool in the development of such methods. A precautionary default is a cautious or pessimistic assumption that is used in the absence of adequate information and that should be replaced when such information is obtained. Furthermore, we point out some promising research areas for the development of such indicators, viz. connections between chemical characteristics such as persistence and effect parameters, monitoring of contaminants in polar regions, monitoring of contaminants in breast milk, application of results from (human) toxicology in ecotoxicology and vice versa, (eco)toxicological test systems that are sensitive to effects on reproduction, and the application of bioinformatic methods to complex data, both in genomic research and in ecotoxicology. We conclude that precautionary decision-making does not require less science, but to the contrary it requires more science and improved communication between scientists and risk managers.