红外相机技术在我国野生动物监测研究中的应用

<正>长期以来,野生动物(特别是兽类)监测面临着极大困难,具体表现在:(1)许多野生动物种群数量日渐减少,甚至濒临灭绝;(2)未经许可,许多珍稀种类禁止采集实体样本;(3)许多动物昼伏夜出,活动隐秘,很难观察到实体,甚至很难发现痕迹;(4)许多动物仅分布在人稀罕至的森林或其他生境中,监测难度大、成本高;(5)野生动物行为习性和生存空间多种多样,难以形成统一的监测方法和技术标准。20世纪90年代以来,"3S"技术、分子生物学技术、数码影像技术(如自动相机技术或红外相机技术     

我国森林动态监测样地的野生动物红外相机监测

<正>自2004年以来的10年中,中国森林生物多样性监测网络(Chinese Forest Biodiversity Monitoring Network,CForBio)参照世界热带森林研究中心(Center for Tropical Forest Science,CTFS)的监测规范和标准在全国陆续建立了至少12个森林动态监测样地(www.cfbiodiv.org/)。这些样地涵盖了我国不同纬度带的主要森林植被类型,已成为我国生物多样性长期监测与研究的重要平台。野生动物多样性是各个样地的重要监测内容,2009–2013年陆续有7个样地采用红外相机技术来监测兽类和地面活动鸟类的多样性。本专辑内以"森林动态监测样地"专题来     

Overcoming the distance estimation bottleneck in estimating animal abundance with camera traps

The biodiversity crisis is still accelerating, despite increasing efforts by the international community. Estimating animal abundance is of critical importance to assess, for example, the consequences of land-use change and invasive species on community composition, or the effectiveness of conservation interventions. Various approaches have been developed to estimate abundance of unmarked animal populations. Whereas these approaches differ in methodological details, they all require the estimation of the effective area surveyed in front of a camera trap. Until now camera-to-animal distance measurements are derived by laborious, manual and subjective estimation methods. To overcome this distance estimation bottleneck, this study proposes an automatized pipeline utilizing monocular depth estimation and depth image calibration methods. We are able to reduce the manual effort required by a factor greater than 21. Our system is available.¹     

Recommended guiding principles for reporting on camera trapping research

Camera traps are used by scientists and natural resource managers to acquire ecological data, and the rapidly increasing camera trapping literature highlights how popular this technique has become. Nevertheless, the methodological information reported in camera trap publications can vary widely, making replication of the study difficult. Here we propose a series of guiding principles for reporting methods and results obtained using camera traps. Attributes of camera trapping we cover include: (i) specifying the model(s) of camera traps(s) used, (ii) mode of deployment, (iii) camera settings, and (iv) study design. In addition to suggestions regarding best practice data coding and analysis, we present minimum principles for standardizing information that we believe should be reported in all peer-reviewed papers. Standardised reporting enables more robust comparisons among studies, facilitates national and global reviews, enables greater ease of study replication, and leads to improved wildlife research and management outcomes.     

相机陷阱在野生动物种群生态学中的应用

种群参数估计及空间分布格局是动物生态学和保护生物学领域的重要目标之一.最近十几年来, 相机陷阱(camera trap)作为野外调查的一种非损伤性技术手段,在传统调查方法难以实现的情况下表现出极大优势,被广泛应用于野生动物生态学和保护学研究中.相机陷阱所获取的动物出现数据为野生动物种群提供了极其重要的定量信息.本文从相机陷阱工作原理出发,主要阐述了目前在种群生态学中较为成熟的两类针对具有或不具有天然个体标志物种的模型原理及应用: 1)种群密度和种群数量估计; 2)空间占据率估计.论文特别关注了模型发展的逻辑过程、依赖的假定、使用范围、仍然存在的问题以及未来发展方向.最后, 本文综合分析了相机陷阱在种群参数估计应用中还需注意的问题, 以及其在种群动态和生物多样性研究等方面的发展潜力.     

Hierarchical multi-scale occupancy estimation for monitoring wildlife populations

Occupancy estimation is an effective analytic framework, but requires repeated surveys of a sample unit to estimate the probability of detection. Detection rates can be estimated from spatially replicated rather than temporally replicated surveys, but this may violate the closure assumption and result in biased estimates of occupancy. We present a new application of a multi-scale occupancy model that permits the simultaneous use of presence–absence data collected at 2 spatial scales and uses a removal design to estimate the probability of detection. Occupancy at the small scale corresponds to local territory occupancy, whereas occupancy at the large scale corresponds to regional occupancy of the sample units. Small-scale occupancy also corresponds to a spatial availability or coverage parameter where a species may be unavailable for sampling at a fraction of the survey stations. We applied the multi-scale occupancy model to a hierarchical sample design for 2 bird species in the Black Hills National Forest: brown creeper (Certhia americana) and lark sparrow (Chondestes grammacus). Our application of the multi-scale occupancy model is particularly well suited for hierarchical sample designs, such as spatially replicated survey stations within sample units that are typical of avian monitoring programs. The model appropriately accounts for the non-independence of the spatially replicated survey stations, addresses the closure assumption for the spatially replicated survey stations, and is useful for decomposing the observation process into detection and availability parameters. This analytic approach is likely to be useful for monitoring at local and regional scales, modeling multi-scale habitat relationships, and estimating population state variables for rare species of conservation concern. © 2011 The Wildlife Society.     

Class incremental learning for wildlife biodiversity monitoring in camera trap images

Camera traps have been widely used for wildlife biodiversity monitoring, providing abundant ecological data. Manually classifying such abundant images is time-consuming and labor-intensive. Existing deep learning methods solve this problem for a fixed set of predefined wildlife species. The model trained on such sets cannot be applied to new wildlife species. Retraining models on new wildlife species can lead to catastrophic forgetting. Thus, in this work, we propose a class incremental learning method to identify new wildlife species. Our method employs a novel adaptive exemplar assignment (AEA) strategy with dynamic exemplar amounts to adapt to new species while alleviating the forgetting of old ones. Due to memory constraints, the data imbalance between limited exemplars and new species data can lead to class bias. We mitigate it by performing center vector retrieval (CVR) to classify samples in feature space and bypass the biased linear classifier. In addition, we propose two variants of CVR that incorporate the advantage of the linear classifier to further improve the performance. By using only 4% of old species data, our method achieves 77.09% accuracy at a low computational resource for recognition. Through extensive experiments and ablations, we demonstrate the superiority of our proposed approach over state-of-the-art methods. This method facilitates wildlife monitoring, biodiversity conservation, and ecological assessment.     

自动感应照相系统在大熊猫以及同域分布的野生动物研究中的应用

鉴于野生大熊猫种群的濒危现状,已经不允许对其生境进行破坏性或干扰其行为活动较多的调查活动。例如,野生大熊猫个体数量稀少、其栖息地地形复杂或植被茂密,野外直接观察和调查野生大熊猫极为困难。自动感应照相系统是一种非损伤性野生动物调查工具,在很大程度上弥补了传统调查方法的不足,为野生动物的调查和研究提供了新的有效途径。本研究利用自行研究和开发的自动感应照相系统,获得了野生大熊猫及与其同域分布的其它物种的重要生态信息,显示了自动感应照相系统在物种鉴定、区系调查、个体识别、种群监测、性别确定和行为生态学研究等多方面的应用价值[动物学报51(3)：495—500,2005]。     

A comparison of camera trap and permanent recording video camera efficiency in wildlife underpasses

In the current context of biodiversity loss through habitat fragmentation, the effectiveness of wildlife crossings, installed at great expense as compensatory measures, is of vital importance for ecological and socio‐economic actors. The evaluation of these structures is directly impacted by the efficiency of monitoring tools (camera traps…), which are used to assess the effectiveness of these crossings by observing the animals that use them. The aim of this study was to quantify the efficiency of camera traps in a wildlife crossing evaluation. Six permanent recording video systems sharing the same field of view as six Reconyx HC600 camera traps installed in three wildlife underpasses were used to assess the exact proportion of missed events (event being the presence of an animal within the field of view), and the error rate concerning underpass crossing behavior (defined as either Entry or Refusal). A sequence of photographs was triggered by either animals (true trigger) or artefacts (false trigger). We quantified the number of false triggers that had actually been caused by animals that were not visible on the images (“false” false triggers). Camera traps failed to record 43.6% of small mammal events (voles, mice, shrews, etc.) and 17% of medium‐sized mammal events. The type of crossing behavior (Entry or Refusal) was incorrectly assessed in 40.1% of events, with a higher error rate for entries than for refusals. Among the 3.8% of false triggers, 85% of them were “false” false triggers. This study indicates a global underestimation of the effectiveness of wildlife crossings for small mammals. Means to improve the efficiency are discussed.     

Simplified point and interval estimation for removal trapping

A regression technique, based on the limiting normal distribution of the multinomial, is given for point and interval estimation of the parameters in the removal trapping method of determining animal and insect populations. Comparisons are made with maximum likelihood estimates. Two examples of estimating spider populations are given.     

A novel camera trapping method for individually identifying pumas by facial features

Camera traps (CTs), used in conjunction with capture–mark–recapture analyses (CMR; photo‐CMR), are a valuable tool for estimating abundances of rare and elusive wildlife. However, a critical requirement of photo‐CMR is that individuals are identifiable in CT images (photo‐ID). Thus, photo‐CMR is generally limited to species with conspicuous pelage patterns (e.g., stripes or spots) using lateral‐view images from CTs stationed along travel paths. Pumas (Puma concolor) are an elusive species for which CTs are highly effective at collecting image data, but their suitability to photo‐ID is controversial due to their lack of pelage markings. For a wide range of taxa, facial features are useful for photo‐ID, but this method has generally been limited to images collected with traditional handheld cameras. Here, we evaluate the feasibility of using puma facial features for photo‐ID in a CT framework. We consider two issues: (1) the ability to capture puma facial images using CTs, and (2) whether facial images improve human ability to photo‐ID pumas. We tested a novel CT accessory that used light and sound to attract the attention of pumas, thereby collecting face images for use in photo‐ID. Face captures rates increased at CTs that included the accessory (n = 208, χ ² = 43.23, p ≤ .001). To evaluate if puma faces improve photo‐ID, we measured the inter‐rater agreement of 5 independent assessments of photo‐ID for 16 of our puma face capture events. Agreement was moderate to good (Fleiss’ kappa = 0.54, 95% CI = 0.48–0.60), and was 92.90% greater than a previously published kappa using conventional CT methods. This study is the first time that such a technique has been used for photo‐ID, and we believe a promising demonstration of how photo‐ID may be feasible for an elusive but unmarked species.     

Revealing secondary seed removers: results from camera trapping

This paper reports the results of the first study on secondary seed removal of seeds dispersed by Sykes’ monkeys (Cercopithecus albogularis) using camera traps in Africa. Patterns of primary seed dispersal are often superimposed by secondary conveyance, emphasising the need to study these secondary processes carefully. As the agents and mechanisms of seed dispersal are often concealed, being carried out by cryptic or nocturnal animals in dense vegetation, camera trapping was deemed a viable means to investigate secondary removal of seeds disseminated by C. albogularis in the Western Soutpansberg, South Africa. Camera traps were established at preferred feeding trees of the focal Sykes’ monkey group to identify animal species that remove seeds and fruits spat and dropped to the forest floor and seed removal observations were carried out. This method proved to be effective in identifying seed removers and also allowed to get indications about the quantities of seeds removed. Ten animal species were recorded visiting the trees, of which eight were observed removing seeds and fruits. Overall seed and fruit removal rates were high, indicating that the foraging behaviour of C. albogularis attracts many terrestrial frugivores.     

Time‐lapse camera trapping as an alternative to pitfall trapping for estimating activity of leaf litter arthropods

Pitfall trapping is the standard technique to estimate activity and relative abundance of leaf litter arthropods. Pitfall trapping is not ideal for long‐term sampling because it is lethal, labor‐intensive, and may have taxonomic sampling biases. We test an alternative sampling method that can be left in place for several months at a time: verticallyplaced time‐lapse camera traps that have a short focal distance, enabling identification of small arthropods. We tested the effectiveness of these time‐lapse cameras, and quantified escape and avoidance behavior of arthropod orders encountering pitfall traps by placing cameras programed with a range of sampling intervals above pitfalls, to assess numerical, taxonomic, and body size differences in samples collected by the two methods. Cameras programed with 1‐ or 15‐min intervals recorded around twice as many arthropod taxa per day and a third more individuals per day than pitfall traps. Hymenoptera (ants), Embioptera (webspinners), and Blattodea (cockroaches) frequently escaped from pitfalls so were particularly under‐sampled by them. The time‐lapse camera method effectively samples litter arthropods to collect long‐term data. It is standardized, non‐lethal, and does not alter the substrate or require frequent visits.     

Human vs. machine: Detecting wildlife in camera trap images

As the capacity to collect and store large amounts of data expands, identifying and evaluating strategies to efficiently convert raw data into meaningful information is increasingly necessary. Across disciplines, this data processing task has become a significant challenge, delaying progress and actionable insights. In ecology, the growing use of camera traps (i.e., remotely triggered cameras) to collect information on wildlife has led to an enormous volume of raw data (i.e., images) in need of review and annotation. To expedite camera trap image processing, many have turned to the field of artificial intelligence (AI) and use machine learning models to automate tasks such as detecting and classifying wildlife in images. To contribute understanding of the utility of AI tools for processing wildlife camera trap images, we evaluated the performance of a state-of-the-art computer vision model developed by Microsoft AI for Earth named MegaDetector using data from an ongoing camera trap study in Arctic Alaska, USA. Compared to image labels determined by manual human review, we found MegaDetector reliably determined the presence or absence of wildlife in images generated by motion detection camera settings (≥94.6% accuracy), however, performance was substantially poorer for images collected with time-lapse camera settings (≤61.6% accuracy). By examining time-lapse images where MegaDetector failed to detect wildlife, we gained practical insights into animal size and distance detection limits and discuss how those may impact the performance of MegaDetector in other systems. We anticipate our findings will stimulate critical thinking about the tradeoffs of using automated AI tools or manual human review to process camera trap images and help to inform effective implementation of study designs.     

cameratrapR: An R package for estimating animal density using camera trapping data

• 1.Camera trapping plays an important role in wildlife surveys, and provides valuable information for estimation of population density. While mark-recapture techniques can estimate population density for species that can be individually recognized or marked, there are no robust methods to estimate density of species that cannot be individually identified.
• 2.We developed a new approach to estimate population density based on the simulation of individual movement within the camera grid. Simulated animals followed a correlated random walk with the movement parameters of segment length, angular deflection, movement distance and home-range size derived from empirical movement paths. Movement was simulated under a series of population densities. We used the Random Forest algorithm to determine the population density with the highest likelihood of matching the camera trap data. We developed an R package, cameratrapR, to conduct simulations and estimate population density.
• 3.Compared with line transect surveys and the random encounter model, cameratrapR provides more reliable estimates of wildlife density with narrower confidence intervals. Functions are provided to visualize movement paths, derive movement parameters, and plot camera trapping results.
• 4.The package allows researchers to estimate population sizes/densities of animals that cannot be individually identified and cameras are deployed in a grid pattern.

     

基于核密度估计的动物生境适宜度制图方法

生境适宜度制图能提供动物适宜生境的空间分布信息,对野生动物种群管理、保护地规划等非常重要。生境适宜度制图的关键是构建生境适宜度模型(habitat suitability model, HSM),只基于动物出现位置数据构建HSM的方法在实践中得到了非常广泛的应用。然而现有的只基于动物出现位置数据构建HSM的方法还不能很好地直接表达动物生境适宜度和环境因子之间具有生态学意义的数量关系,因此也就不能很好地体现环境因子对动物生境利用的生态学作用。 本文提出了一种基于核密度估计构建HSM的方法,在地理信息系统技术支持下,通过运用核密度估计从代表性的动物出现位置数据中估计出动物出现对各个环境因子的概率密度函数来直接表达生境适宜度与各个环境因子之间的数量关系,以体现环境因子对动物生境利用的生态学作用,在此基础上对生境适宜度与各个环境因子之间的数量关系进行综合构建了具有明确生态学意义的HSM用于动物生境适宜度制图。以美国Voyageures国家公园的白尾鹿(Odocoileus virginianus)生境适宜度制图为例,基于365个出现位置点位数据并结合积雪深度、地表覆被类型、森林边界长度和坡度等环境因子数据,开展了该方法的案例研究。通过交叉验证计算连续Boyce指数对制图结果进行评价,结果表明：基于核密度估计方法构建的HSM预测能力强,所得出的生境适宜度图经10次交叉验证,连续Boyce指数平均值为0.75,标准差为0.11,达到了较高精度。此外,由于基于核密度估计的方法能以“生境适宜度和环境因子之间具有生态学意义的数量关系”的形式来直接体现环境因子对动物生境利用的生态学作用,就模型的可解释性而言,该方法要优于现有的其他构建HSM的方法。     

Performance of camera trapping and track counts for surveying large mammals in rainforest remnants

Getting information on terrestrial large mammals is particularly difficult in tropical rainforests and in altered landscapes, since the traditionally used method (line-transect census) presents low efficiency in dense vegetation, and is difficult to standardize among heterogeneous, fragmented areas where the small size of patches restricts the length of transects. Aiming to generate information to guide the choice of field protocols for surveying terrestrial large mammals in heterogeneous rainforest remnants, we compared the performance and the correlation between the results of two alternative techniques (track counts and camera trapping), and of two types of bait, in 24 forest remnants in a fragmented Atlantic forest landscape. Techniques resulted in similar observed and estimated richness and species composition at the study landscape, including medium-sized and nocturnal species usually poorly represented in line-transect censuses. Although camera trapping resulted in a higher recording rate of the most common species (Didelphis aurita) and track counts in higher recording rates of some less common species (e.g. Dasypus novemcinctus), observed richness and recording rates of most species were correlated across the 24 sites between techniques. Conversely, the use of different baits strongly influenced results, indicating the importance of standardizing baits in comparative studies. Our results suggest that the two alternative techniques present similar performance and are suitable for studying the factors affecting the distribution of large mammals in altered rainforest landscapes. The choice of field protocols should then focus on the available resources and infrastructure, and on particularities of the study area.     

Bayesian estimation of genomic distance

We present a Bayesian approach to the problem of inferring the number of inversions and translocations separating two species. The main reason for developing this method is that it will allow us to test hypotheses about the underlying mechanisms, such as the distribution of inversion track lengths or rate constancy among lineages. Here, we apply these methods to comparative maps of eggplant and tomato, human and cat, and human and cattle with 170, 269, and 422 markers, respectively. In the first case the most likely number of events is larger than the parsimony value. In the last two cases the parsimony solutions have very small probability.