Traditional transect survey methods for forest antelopes often underestimate density for common species and do not provide sufficient data for rarer species. The use of camera trapping as a survey tool for medium and large terrestrial mammals has become increasingly common, especially in forest habitats. Here, we applied the distance sampling method to images generated from camera-trap surveys in Dja Faunal Reserve, Cameroon, and used an estimate of the proportion of time animals are active to correct for negative bias in the density estimates from the 24-hour camera-trap survey datasets. We also used multiple covariate distance sampling with body weight as a covariate to estimate detection probabilities and densities of rarer species. These methods provide an effective tool for monitoring the status of individual species or a community of forest antelope species, information urgently needed for conservation planning and action.

Camera traps (CTs) are an increasingly popular tool for wildlife survey and monitoring. Estimating relative abundance in unmarked species is often done using detection rate as an index of relative abundance, which assumes that detection rate has a positive linear relationship with true abundance. This assumption may be violated if movement behavior varies with density, but the degree to which movement behavior is density-dependent across taxa is unclear. The potential confounding of population-level relative abundance indices by movement would depend on how regularly, and by what magnitude, movement rate and home-range size vary with density. We conducted a systematic review and meta-analysis to quantify relationships between movement rate, home-range size, and density, across terrestrial mammalian taxa. We then simulated animal movements and CT sampling to test the effect of contrasting movement scenarios on CT detection rate indices. Overall, movement rate and home-range size were negatively correlated with density and positively correlated with one another. The strength of the relationships varied significantly between taxa and populations. In simulations, detection rates were related to true abundance but underestimated change, particularly for slower moving species with small home ranges. In situations where animal space use changes markedly with density, we estimate that up to thirty percent of a true change in relative abundance may be missed due to the confounding effect of movement, making trend estimation more difficult. The common assumption that movement remains constant across densities is therefore violated across a wide range of mammal species. When studying unmarked species using CT detection rates, researchers and managers should explicitly consider that such indices of relative abundance reflect both density and movement. Practitioners interpreting changes in camera detection rates should be aware that observed differences may be biased low relative to true changes in abundance. Further information on animal movement, or methods that do not depend on assumptions of density-independent movement, may be required to make robust inferences on population trends.

Abundance is an important parameter used to estimate the population size of various wildlife species. With the growing application of camera-traps (movement or heat activated) to monitoring wildlife, the relative abundance index (RAI) has become one of the most popular indicators of population abundance for inventories and assessment. Despite a simple and convenient indicator of population size, RAI obtained from camera-trapping data can be greatly affected by many factors such as species traits, detection rates and environmental factors. Therefore, we need verify the correlation between RAI and population density prior to its general application. So far, several types of RAIs have been developed based on camera-trapping data, and it is critical to compare these RAI indices and their applications. In this paper, we summarized the methods calculating RAI with camera-trapping data and reviewed their applications in wildlife monitoring and inventories in China. Four main types of RAIs were identified including (1) the number of days when one animal is photographed, (2) the number of photographs of focal species per 100 trap days, (3) the number of photographs of focal species per trap day, and (4) the proportion of photos from the focal species compared to all photos of all animals. Among them, the second RAI type is the most widely used (72.5%) in wildlife monitoring and inventories in China, and the fourth RAI type is used to compare species components in communities. Consequently, we recommend the second RAI type for estimating population abundance in particular when camera-trapping data are used for broad-scale comparisons over different spatial and temporal scales.

多度是衡量物种种群数量的参数之一, 多度的动态及其影响因素是种群生态学研究的经典问题。物种相对多度指数(relative abundance index, RAI)作为一种简单、便利的指标, 广泛应用于动物本底清查中。但RAI易受物种自身特征、探测率和环境因素的影响, 需要结合其他物种数量分析方法, 以验证其与种群大小的相关性。随着红外相机技术在野生动物调查中的广泛应用, 用红外相机数据估计动物种群数量的研究越来越多。目前, 基于红外相机数据计算RAI的方法有多种, 不同计算方法和应用范围存在差异, 亟需对现有方法和应用进行梳理。本文综述了根据红外相机数据计算物种相对多度的4种主要方法: (1)拍摄一张有效照片所需要的天数; (2)基于单位调查强度的物种拍摄率; (3)每个位点每天的物种拍摄率; (4)某一物种的照片数占所有物种的比例。总结了我国野生动物监测调查中采用红外相机方法计算RAI的应用现状。国内的研究主要采用第2种和第4种计算方法, 其中约72.5%的研究论文应用第2种计算方法, 而第4种方法一般适用于群落中的物种组成比较。我们建议根据红外相机数据计算RAI时尽量使用第2种计算方法, 这有助于研究或管理人员对不同研究中的物种RAI进行比较分析。

The random encounter model (REM) is a novel method for estimating animal density from camera trap data without the need for individual recognition. It has never been used to estimate the density of large carnivore species, despite these being the focus of most camera trap studies worldwide. In this context, we applied the REM to estimate the density of female lions () from camera traps implemented in Serengeti National Park, Tanzania, comparing estimates to reference values derived from pride census data. More specifically, we attempted to account for bias resulting from non-random camera placement at lion resting sites under isolated trees by comparing estimates derived from night versus day photographs, between dry and wet seasons, and between habitats that differ in their amount of tree cover. Overall, we recorded 169 and 163 independent photographic events of female lions from 7,608 and 12,137 camera trap days carried out in the dry season of 2010 and the wet season of 2011, respectively. Although all REM models considered over-estimated female lion density, models that considered only night-time events resulted in estimates that were much less biased relative to those based on all photographic events. We conclude that restricting REM estimation to periods and habitats in which animal movement is more likely to be random with respect to cameras can help reduce bias in estimates of density for female Serengeti lions. We highlight that accurate REM estimates will nonetheless be dependent on reliable measures of average speed of animal movement and camera detection zone dimensions. © 2015 The Authors. published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.

This paper describes and explains design patterns for software that supports how analysts can efficiently inspect and classify camera trap images for wildlife-related ecological attributes. Broadly speaking, a design pattern identifies a commonly occurring problem and a general reusable design approach to solve that problem. A developer can then use that design approach to create a specific software solution appropriate to the particular situation under consideration. In particular, design patterns for camera trap image analysis by wildlife biologists address solutions to commonly occurring problems they face while inspecting a large number of images and entering ecological data describing image attributes. We developed design patterns for image classification based on our understanding of biologists' needs that we acquired over 8 years during development and application of the freely available Timelapse image analysis system. For each design pattern presented, we describe the problem, a design approach that solves that problem, and a concrete example of how Timelapse addresses the design pattern. Our design patterns offer both general and specific solutions related to: maintaining data consistency, efficiencies in image inspection, methods for navigating between images, efficiencies in data entry including highly repetitious data entry, and sorting and filtering image into sequences, episodes, and subsets. These design patterns can inform the design of other camera trap systems and can help biologists assess how competing software products address their project-specific needs along with determining an efficient workflow.

With most of the world's Caprinae taxa threatened with extinction, the IUCN appeals to the development of simple and affordable sampling methods that will produce credible abundance and distribution data for helping conserve these species inhabiting remote areas. Traditional sampling approaches, like aerial sampling or mark-capture-recapture, can generate bias by failing to meet sampling assumptions, or by incurring too much cost and logistical burden for most projects to address them. Therefore, we met the IUCN's challenge by testing a sampling technique that leverages imagery from camera traps with conventional distance sampling, validating its operability in mountainous topography by comparing results to known abundances. Our project occurred within a captive facility housing a wild population of desert bighorn sheep (Ovis canadensis) in the Chihuahuan desert of New Mexico, which is censused yearly. True abundance was always within our 90% confidence bounds, and the mean abundance estimates were within 4.9 individuals (average) of the census values. By demonstrating the veracity of this straightforward and inexpensive sampling method, we provide confidence in its operability, urging its use to fill conservation voids for Caprinae and other data-deficient species inhabiting rugged or heavily vegetated terrain.

Density is a key trait of populations and an essential parameter in ecological research, wildlife conservation and management. Several models have been developed to estimate population density based on camera trapping data, including the random encounter model (REM) and camera trap distance sampling (CTDS). Both models need to account for variation in animal behavior that depends, for example, on the species and sex of the animals along with temporally varying environmental factors. We examined whether the density estimates of REM and CTDS can be improved for Europe’s most numerous deer species, by adjusting the behavior-related model parameters per species and accounting for differences in movement speeds between sexes, seasons, and years. Our results showed that bias through inadequate consideration of animal behavior was exceeded by the uncertainty of the density estimates, which was mainly influenced by variation in the number of independent observations between camera trap locations. The neglection of seasonal and annual differences in movement speed estimates for REM overestimated densities of red deer in autumn and spring by ca. 14%. This GPS telemetry-derived parameter was found to be most problematic for roe deer females in summer and spring when movement behavior was characterized by small-scale displacements relative to the intervals of the GPS fixes. In CTDS, density estimates of red deer improved foremost through the consideration of behavioral reactions to the camera traps (avoiding bias of max. 19%), while species-specific delays between photos had a larger effect for roe deer. In general, the applicability of both REM and CTDS would profit profoundly from improvements in their precision along with the reduction in bias achieved by exploiting the available information on animal behavior in the camera trap data.

Obtaining reliable species observations is of great importance in animal ecology and wildlife conservation. An increasing number of studies use camera traps (CTs) to study wildlife communities, and an increasing effort is made to make better use and reuse of the large amounts of data that are produced. It is in these circumstances that it becomes paramount to correct for the species- and study-specific variation in imperfect detection within CTs. We reviewed the literature and used our own experience to compile a list of factors that affect CT detection of animals. We did this within a conceptual framework of six distinct scales separating out the influences of (a) animal characteristics, (b) CT specifications, (c) CT set-up protocols, and (d) environmental variables. We identified 40 factors that can potentially influence the detection of animals by CTs at these six scales. Many of these factors were related to only a few overarching parameters. Most of the animal characteristics scale with body mass and diet type, and most environmental characteristics differ with season or latitude such that remote sensing products like NDVI could be used as a proxy index to capture this variation. Factors that influence detection at the microsite and camera scales are probably the most important in determining CT detection of animals. The type of study and specific research question will determine which factors should be corrected. Corrections can be done by directly adjusting the CT metric of interest or by using covariates in a statistical framework. Our conceptual framework can be used to design better CT studies and help when analyzing CT data. Furthermore, it provides an overview of which factors should be reported in CT studies to make them repeatable, comparable, and their data reusable. This should greatly improve the possibilities for global scale analyses of (reused) CT data.

Distance sampling (DS) is a widely used framework for estimating animal abundance. DS models assume that observations of distances to animals are independent. Non-independent observations introduce overdispersion, causing model selection criteria such as AIC or AIC(c) to favour overly complex models, with adverse effects on accuracy and precision. We describe, and evaluate via simulation and with real data, estimators of an overdispersion factor (c), and associated adjusted model selection criteria (QAIC) for use with overdispersed DS data. In other contexts, a single value of c<^> is calculated from the "global" model, that is the most highly parameterised model in the candidate set, and used to calculate QAIC for all models in the set; the resulting QAIC values, and associated Delta QAIC values and QAIC weights, are comparable across the entire set. Candidate models of the DS detection function include models with different general forms (e.g. half-normal, hazard rate, uniform), so it may not be possible to identify a single global model. We therefore propose a two-step model selection procedure by which QAIC is used to select among models with the same general form, and then a goodness-of-fit statistic is used to select among models with different forms. A drawback of this approach is that QAIC values are not comparable across all models in the candidate set. Relative to AIC, QAIC and the two-step model selection procedure avoided overfitting and improved the accuracy and precision of densities estimated from simulated data. When applied to six real datasets, adjusted criteria and procedures selected either the same model as AIC or a model that yielded a more accurate density estimate in five cases, and a model that yielded a less accurate estimate in one case. Many DS surveys yield overdispersed data, including cue counting surveys of songbirds and cetaceans, surveys of social species including primates, and camera-trapping surveys. Methods that adjust for overdispersion during the model selection stage of DS analyses therefore address a conspicuous gap in the DS analytical framework as applied to species of conservation concern.

Biologists have repeatedly rediscovered classical models from physics predicting collision rates in an ideal gas. These models, and their two-dimensional analogues, have been used to predict rates and durations of encounters among animals or social groups that move randomly and independently, given population density, velocity, and distance at which an encounter occurs. They have helped to separate cases of mixed-species association based on behavioural attraction from those that simply reflect high population densities, and to detect cases of attraction or avoidance among conspecifics. They have been used to estimate the impact of population density, speeds of movement and size on rates of encounter between members of the opposite sex, between gametes, between predators and prey, and between observers and the individuals that they are counting. One limitation of published models has been that they predict rates of encounter, but give no means of determining whether observations differ significantly from predictions. Another uncertainty is the robustness of the predictions when animal movements deviate from the model's assumptions in specific, biologically relevant ways. Here, we review applications of the ideal gas model, derive extensions of the model to cover some more realistic movement patterns, correct several errors that have arisen in the literature, and show how to generate confidence limits for expected rates of encounter among independently moving individuals. We illustrate these results using data from mangabey monkeys originally used along with the ideal gas model to argue that groups avoid each other. Although agent-based simulations provide a more flexible alternative approach, the ideal gas model remains both a valuable null model and a useful, less onerous, approximation to biological reality.

During the last two decades, infrared-triggered camera-trapping has been widely used in wildlife and biodiversity research and conservation. In the areas of wildlife ecology research, animal species inventory, biodiversity monitoring and protected area management in China, considerable outputs have been produced by scientific research and conservation applications based on camera-trapping. This technique has been successfully used to detect rare or elusive species, conduct biodiversity inventory, study animal behavior, estimate population parameters, and evaluate the effectiveness of protected area management. Along with the rapid development of modern ecological analysis and modeling tools, camera-trapping will play a more important role in wildlife research at various levels. Meanwhile, along with improvements in techniques, decreasing cost and increasing application interests, camera-trapping will be adopted by more researchers, wildlife managers and protected areas, and can be used for systematic wildlife monitoring using standard protocols. Efforts devoted to its future development and applications should focus on establishing systematically-designed monitoring networks and data-sharing protocols, and developing new analytical approaches and statistical models specifically based on camera-trapping data.

20年来, 红外相机技术在国内外野生动物研究、监测与保护中得到了广泛应用。基于红外相机技术, 我国在野生动物生态学研究、动物行为学研究、稀有物种的探测与记录、动物本底资源调查、生物多样性监测及保护地管理与保护评价等领域取得了众多成果。目前, 数学模型、统计分析方法和新的概念正在促进红外相机技术在野生动物监测研究与保护管理中的发展和推广应用。同时, 随着红外相机技术的成熟、成本降低和应用普及, 这一技术也将会被更多的野生动物研究人员、管理人员和自然保护区管理者所采用, 并成为全国各级保护地和区域生物多样性监测研究的关键技术和方法。今后, 建立并完善系统化的监测网络和数据共享平台、开发新一代的数据分析方法与模型, 将是此项技术进一步发展和应用的主要方向。

Context: Estimating the population size of large and medium-sized mammals is a fundamental issue in animal ecology, attracting great attention from researchers, managers, and the public. However, despite the fact that it has been explored from the mid-20th century to now, the population sizes of numerous species worldwide are unknown. In China, the research targeting large and medium-sized mammals have been explored since 1980s. Although it has made great progress, the population size of many species in China are still unknown. Aims: We aim to establish a framework to categorize existing estimation methods and further summarize the research development of population size estimation in China while highlighting strengths and trends under this framework. Results & Conclusions: First, we establish a concise hierarchical framework according to the estimation theory, data resources, and models used. This framework indicates that there are four classes of methods including distance sampling method, capture-recapture method, encounter-based method, and direct count method from remotely sensed imagery according to estimation theory. Then for each of the four methods, we illustrate the basic model and its assumptions, explaining how existing data resources (including insight, camera trap, DNA microsatellite, satellite tracking, acoustic monitor, and remote sensing data) realize each theory respectively. We summarize unique features, advantages, and disadvantages of each method and compare size or density estimation resulted from different methods. Secondly, we summarize the development of population size estimation methods in China in the context of this framework while highlighting trends and strengths. Numerous data obtained from infrared cameras in many study areas during the last decade can be used to estimate the population size by employing distance sampling, capture-recapture models, and encounter-based methods. Meanwhile, the pellet distance sampling method, fecal-DNA capture-recapture method and direct count method from remotely sensed imagery are suggested to be developed. Finally, a guide to select the estimation methods appropriate for their studies is provided as a reference for future researchers.

大中型兽类种群数量的估算是动物生态学中重要的基本问题, 受到研究者、管理者和公众的共同关注。国际上从20世纪中期开始研究该问题, 已出现了多种研究方法和相应案例, 且还在快速发展, 但世界各地仍有很多物种的种群数量尚未知晓。在我国, 从20世纪80年代开始调查大中型兽类种群数量, 取得了重要进展, 也还有很多物种的种群数量尚不清楚。因此, 有必要归纳国际上种群数量估算的研究进展, 同时, 总结国内研究的现状、优势和趋势, 供研究者参考。本文首先选择估算大中型兽类种群数量的原理、数据来源和模型这3个要素归纳出简明的研究框架, 将现有的多种方法置于其中予以阐述。在该框架下, 根据估算原理分为4大类方法, 为距离取样法、标志重捕法、基于遇见率法和遥感影像直接计数法。针对每一大类方法, 论述其基本原理模型和模型假设, 说明能实现该原理的相应数据来源(视觉观测、红外相机拍摄、DNA微卫星识别、卫星定位跟踪、声音监测或遥感影像)的特点及如何实现该原理, 评价其适用性及优缺点, 并选择其中具有可比性的方法予以比较评价。其次, 参照该研究框架, 总结我国的研究现状, 分析未来发展的优势和趋势: 我国的红外相机数据积累充分, 可以发展以此为数据源的距离取样法、标志重捕法和基于遇见率法; 发展以粪便样品为数据来源的距离取样法和粪便DNA标志重捕法; 相比地面调查数据, 获取高分辨率遥感影像数据更容易, 尽量以此估算符合适用条件的大中型兽类的种群数量。最后, 本文提出了适用于我国大中型兽类种群数量的估算方法的选择流程, 供研究者参考。

Wildlife monitoring technology is advancing rapidly and the use of remote sensors such as camera traps and acoustic detectors is becoming common in both the terrestrial and marine environments. Current methods to estimate abundance or density require individual recognition of animals or knowing the distance of the animal from the sensor, which is often difficult. A method without these requirements, the random encounter model (REM), has been successfully applied to estimate animal densities from count data generated from camera traps. However, count data from acoustic detectors do not fit the assumptions of the REM due to the directionality of animal signals.We developed a generalised REM (gREM), to estimate absolute animal density from count data from both camera traps and acoustic detectors. We derived the gREM for different combinations of sensor detection widths and animal signal widths (a measure of directionality). We tested the accuracy and precision of this model using simulations of different combinations of sensor detection widths and animal signal widths, number of captures and models of animal movement.We find that the gREM produces accurate estimates of absolute animal density for all combinations of sensor detection widths and animal signal widths. However, larger sensor detection and animal signal widths were found to be more precise. While the model is accurate for all capture efforts tested, the precision of the estimate increases with the number of captures. We found no effect of different animal movement models on the accuracy and precision of the gREM.We conclude that the gREM provides an effective method to estimate absolute animal densities from remote sensor count data over a range of sensor and animal signal widths. The gREM is applicable for count data obtained in both marine and terrestrial environments, visually or acoustically (e.g. big cats, sharks, birds, echolocating bats and cetaceans). As sensors such as camera traps and acoustic detectors become more ubiquitous, the gREM will be increasingly useful for monitoring unmarked animal populations across broad spatial, temporal and taxonomic scales.

Throughout the Himalaya, mountain ungulates are threatened by hunting for meat and body parts, habitat loss, and competition with livestock. Accurate population estimates are important for conservation management but most of the available methods to estimate ungulate densities are difficult to implement in mountainous terrain. Here, we tested the efficacy of the recent extension of the point transect method, using camera traps for estimating density of two mountain ungulates: the group-living Himalayan blue sheep or bharal Pseudois nayaur and the solitary Himalayan musk deer Moschus leucogaster. We deployed camera traps in 2017–2018 for the bharal (summer: 21 locations; winter: 25) in the trans-Himalayan region (3,000–5,000 m) and in 2018–2019 for the musk deer (summer: 30 locations; winter: 28) in subalpine habitats (2,500–3,500 m) in the Upper Bhagirathi basin, Uttarakhand, India. Using distance sampling with camera traps, we estimated the bharal population to be 0.51 ± SE 0.1 individuals/km2 (CV = 0.31) in summer and 0.64 ± SE 0.2 individuals/km2 (CV = 0.37) in winter. For musk deer, the estimated density was 0.4 ± SE 0.1 individuals/km2 (CV = 0.34) in summer and 0.1 ± SE 0.05 individuals/km2 (CV = 0.48) in winter. The high variability in these estimates is probably a result of the topography of the landscape and the biology of the species. We discuss the potential application of distance sampling with camera traps to estimate the density of mountain ungulates in remote and rugged terrain, and the limitations of this method.

Day range (DR), the distance travelled by an individual during the day, is an important metric in movement ecology that recently gained interest by its relevance for estimating population density through the random encounter model (REM). Traditionally, DR has been estimated using GPS technology and considering raw straight-line distances between consecutive locations, which is an underestimation of the true path distance. In this work, we tested the accuracy of a new approach based on camera-trap data for the estimation of DR taking into account the animals' behaviour. For this purpose, we considered wild boar (Sus scrofa) as a model species. We tagged 18 individuals with telemetry devices and then monitored the population with camera-traps (photo and video mode) to estimate the DR. In the case of telemetry, a straight-line DR was estimated and rescaled with a tortuosity-related correction factor. Using this camera-trap data, we revisited the procedure described by Rowcliffe et al. (Remote Sens. Ecol. Conserv., 2, 2016, 84) to estimate the DR from the speed and activity information obtained from camera-trapsping. A new derivation of this approach was then developed, in which different animal behaviours were weighted to estimate the DR. The analysis showed no significant differences between the DR values obtained using telemetry data (corrected by the tortuosity-related correction factor) and those attained with the weighted approach. However, the original approach used to estimate the DR based on camera-trap data underestimated this parameter. The DR estimated with the weighted approach was 12.74 km center dot day(-1) +/- (se) 1.89. Here, we showed that animals' behaviour should be taken into account to estimate the DR when working with species that behave differently in front of cameras. These results may be relevant not only for REM, but also for movement ecology, disease dynamics and population monitoring methods.

The use of camera traps in ecology helps affordably address questions about the distribution and density of cryptic and mobile species. The random encounter model (REM) is a camera-trap method that has been developed to estimate population densities using unmarked individuals. However, few studies have evaluated its reliability in the field, especially considering that this method relies on parameters obtained from collared animals (., average speed, in km/h), which can be difficult to acquire at low cost and effort. Our objectives were to (1) assess the reliability of this camera-trap method and (2) evaluate the influence of parameters coming from different populations on density estimates. We estimated a reference density of black bears () in Forillon National Park (Québec, Canada) using a spatial capture-recapture estimator based on hair-snag stations. We calculated average speed using telemetry data acquired from four different bear populations located outside our study area and estimated densities using the REM. The reference density, determined with a Bayesian spatial capture-recapture model, was 2.87 individuals/10km [95% CI: 2.41-3.45], which was slightly lower (although not significatively different) than the different densities estimated using REM (ranging from 4.06-5.38 bears/10km depending on the average speed value used). Average speed values obtained from different populations had minor impacts on REM estimates when the difference in average speed between populations was low. Bias in speed values for slow-moving species had more influence on REM density estimates than for fast-moving species. We pointed out that a potential overestimation of density occurs when average speed is underestimated, that is, using GPS telemetry locations with large fix-rate intervals. Our study suggests that REM could be an affordable alternative to conventional spatial capture-recapture, but highlights the need for further research to control for potential bias associated with speed values determined using GPS telemetry data.© 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Spatial replication is a common theme in count surveys of animals. Such surveys often generate sparse count data from which it is difficult to estimate population size while formally accounting for detection probability. In this article, I describe a class of models (N-mixture models) which allow for estimation of population size from such data. The key idea is to view site-specific population sizes, N, as independent random variables distributed according to some mixing distribution (e.g., Poisson). Prior parameters are estimated from the marginal likelihood of the data, having integrated over the prior distribution for N. Carroll and Lombard (1985, Journal of American Statistical Association 80, 423-426) proposed a class of estimators based on mixing over a prior distribution for detection probability. Their estimator can be applied in limited settings, but is sensitive to prior parameter values that are fixed a priori. Spatial replication provides additional information regarding the parameters of the prior distribution on N that is exploited by the N-mixture models and which leads to reasonable estimates of abundance from sparse data. A simulation study demonstrates superior operating characteristics (bias, confidence interval coverage) of the N-mixture estimator compared to the Caroll and Lombard estimator. Both estimators are applied to point count data on six species of birds illustrating the sensitivity to choice of prior on p and substantially different estimates of abundance as a consequence.

Camera traps have become a ubiquitous tool in ecology and conservation. They are routinely deployed in wildlife survey and monitoring work, and are being advocated as a tool for planetary-scale biodiversity monitoring. The camera trap's widespread adoption is predicated on the assumption of its effectiveness, but the evidence base for this is lacking. Using 104 past studies, we recorded the qualitative overall recommendations made by study authors (for or against camera traps, or ambiguous), together with quantitative data on the effectiveness of camera traps (e.g. number of species detected or detection probabilities) relative to 22 other methods. Most studies recommended the use of camera traps overall and they were 39% more effective based on the quantitative data. They were significantly more effective compared with live traps (88%) and were otherwise comparable in effectiveness to other methods. Camera traps were significantly more effective than other methods at detecting a large number of species (31% more) and for generating detections of species (91% more). This makes camera traps particularly suitable for broad-spectrum biodiversity surveys. Film camera traps were found to be far less effective than digital models, which has led to an increase in camera trap effectiveness over time. There was also evidence from the authors that the use of attractants with camera traps reduced their effectiveness (counter to their intended effect), while the quantitative data indicated that camera traps were more effective in closed than open habitats. Camera traps are a highly effective wildlife survey tool and their performance will only improve with future technological advances. The images they produce also have a range of other benefits, for example as digital voucher specimens and as visual aids for outreach. The evidence-base supports the increasing use of camera traps and underlines their suitability for meeting the challenges of global-scale biodiversity monitoring.

As an apex predator the Amur tiger (Panthera tigris altaica) could play a pivotal role in maintaining the integrity of forest ecosystems in Northeast Asia. Due to habitat loss and harvest over the past century, tigers rapidly declined in China and are now restricted to the Russian Far East and bordering habitat in nearby China. To facilitate restoration of the tiger in its historical range, reliable estimates of population size are essential to assess effectiveness of conservation interventions. Here we used camera trap data collected in Hunchun National Nature Reserve from April to June 2013 and 2014 to estimate tiger density and abundance using both maximum likelihood and Bayesian spatially explicit capture-recapture (SECR) methods. A minimum of 8 individuals were detected in both sample periods and the documentation of marking behavior and reproduction suggests the presence of a resident population. Using Bayesian SECR modeling within the 11 400 km(2) state space, density estimates were 0.33 and 0.40 individuals/100 km(2) in 2013 and 2014, respectively, corresponding to an estimated abundance of 38 and 45 animals for this transboundary Sino-Russian population. In a maximum likelihood framework, we estimated densities of 0.30 and 0.24 individuals/100 km(2) corresponding to abundances of 34 and 27, in 2013 and 2014, respectively. These density estimates are comparable to other published estimates for resident Amur tiger populations in the Russian Far East. This study reveals promising signs of tiger recovery in Northeast China, and demonstrates the importance of connectivity between the Russian and Chinese populations for recovering tigers in Northeast China. © 2016 International Society of Zoological Sciences, Institute of Zoology/Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.