红外相机技术在我国野生动物监测中的应用: 问题与限制

红外相机(camera traps)作为对野生动物进行“非损伤”性采样的技术, 已成为研究动物多样性、种群生态学及行为学的常用手段之一。其发展和普及为中国野生动物多样性和物种保育研究带来了诸多机会。如今, 国内大多数自然保护区都在运用红外相机技术开展物种监测工作。本文结合20年来已发表的相关研究, 从内容、实验设计以及发展趋势方面, 总结了目前红外相机技术在应用过程中出现的共性问题; 并就相机对动物的干扰性、影像识别、研究的适用范围及安全保障四个方面, 对该项技术在实践中存在的限制进行了探讨。最后结合红外相机技术未来的发展方向, 提出了建立技术规范、数据集成和共享、影像数据版权维护、提高监测效率等问题。     

Changing use of camera traps in mammalian field research: habitats,taxa and study types

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Motion vectors and deep neural networks for video camera traps

Commercial camera traps are usually triggered by a Passive Infra-Red (PIR) motion sensor necessitating a delay between triggering and the image being captured. This often seriously limits the ability to record images of small and fast moving animals. It also results in many “empty” images, e.g., owing to moving foliage against a background of different temperature. In this paper we detail a new triggering mechanism based solely on the camera sensor. This is intended for use by citizen scientists and for deployment on an affordable, compact, low-power Raspberry Pi computer (RPi). Our system introduces a video frame filtering pipeline consisting of movement and image-based processing. This makes use of Machine Learning (ML) feasible on a live camera stream on an RPi. We describe our free and open-source software implementation of the system; introduce a suitable ecology efficiency measure that mediates between specificity and recall; provide ground-truth for a video clip collection from camera traps; and evaluate the effectiveness of our system thoroughly. Overall, our video camera trap turns out to be robust and effective.     

A novel nest‐monitoring camera system using a Raspberry Pi micro‐computer

The utility, availability, cost‐effectiveness, and reliability of prefabricated video systems designed to monitor wildlife have lagged behind the unique and varied needs of many researchers. Many systems are limited by inflexible video settings, lack of adequate data storage, and cannot be programmed by the user. More sophisticated systems can be cost prohibitive, and the literature describing remote wildlife video monitoring has, for the most part, not incorporated advances in camera and computer technology. Here, we present details of a pilot study to design and construct a lower cost (US $340) nest camera system to record the behavior of Acorn Woodpeckers (Melanerpes formicivorus) in artificial tree cavity nests. This system incorporates a Raspberry Pi micro‐computer, Pi NoIR infrared camera, a wireless adapter to transmit video over the Internet, and Deka rechargeable gel batteries for power. We programmed the system to motion‐sense, to record exclusively during daylight hours, and to automatically upload videos to the cloud over wireless Internet. The Raspberry Pi micro‐computer does not require advanced programming or electrical engineering skills to build and configure and, because it is programmable, provides unprecedented flexibility for field researchers who wish to configure the system to the specific needs of their study.     

Advancing Primate Research and Conservation Through the Use of Camera Traps: Introduction to the Special Issue

Effective conservation and management of primates depend on our ability to accurately assess and monitor populations through research. Camera traps are proving to be useful tools for studying a variety of primate species, in diverse and often difficult habitats. Here, we discuss the use of camera traps in primatology to survey rare species, assess populations, and record behavior. We also discuss methodological considerations for primate studies, including camera trap research design, inherent biases, and some limitations of camera traps. We encourage other primatologists to use transparent and standardized methods, and when appropriate to consider using occupancy framework to account for imperfect detection, and complementary techniques, e.g., transect counts, interviews, behavioral observation, to ensure accuracy of data interpretation. In addition, we address the conservation implications of camera trapping, such as using data to inform industry, garner public support, and contributing photos to large-scale habitat monitoring projects. Camera trap studies such as these are sure to advance research and conservation of primate species. Finally, we provide commentary on the ethical considerations, e.g., photographs of humans and illegal activity, of using camera traps in primate research. We believe ethical considerations will be particularly important in future primate studies, although this topic has not previously been addressed for camera trap use in primatology or any wildlife species.     

Comparing the Use of Camera Traps and Farmer Reports to Study Crop Feeding Behavior of Moor Macaques (<Emphasis Type="Italic">Macaca maura</Emphasis>)

Investigating crop feeding patterns by primates is an increasingly important objective for primatologists and conservation practitioners alike. Although camera trap technology is used to study primates and other wildlife in numerous ways, i.e., activity patterns, social structure, species richness, abundance, density, diet, and demography, it is comparatively underused in the study of human–primate interactions. We compare photographic (N?=?210) and video (N?=?141) data of crop feeding moor macaques (Macaca maura) from remote sensor cameras, functioning for 231 trap days, with ethnographic data generated from semistructured interviews with local farmers. Our results indicate that camera traps can provide data on the following aspects of crop feeding behavior: species, crop type and phase targeted, harvesting technique used, and daily and seasonal patterns of crop feeding activity. We found camera traps less useful, however, in providing information on the individual identification and age/sex class of crop feeders, exact group size, and amount of crops consumed by the moor macaques. While farmer reports match camera trap data regarding crop feeding species and how wildlife access the gardens, they differ when addressing crop feeding event frequency and timing. Understanding the mismatches between camera trap data and farmer reports is valuable to conservation efforts that aim to mitigate the conflict between crop feeding wildlife and human livelihoods. For example, such information can influence changes in the way certain methods are used to deter crop feeding animals from damaging crops. Ultimately, we recommend using remote-sensing camera technology in conjunction with other methods to study crop feeding behavior.     

Optimising Camera Traps for Monitoring Small Mammals

Practical techniques are required to monitor invasive animals, which are often cryptic and occur at low density. Camera traps have potential for this purpose, but may have problems detecting and identifying small species. A further challenge is how to standardise the size of each camera’s field of view so capture rates are comparable between different places and times. We investigated the optimal specifications for a low-cost camera trap for small mammals. The factors tested were 1) trigger speed, 2) passive infrared vs. microwave sensor, 3) white vs. infrared flash, and 4) still photographs vs. video. We also tested a new approach to standardise each camera’s field of view. We compared the success rates of four camera trap designs in detecting and taking recognisable photographs of captive stoats ( Mustela erminea ), feral cats (Felis catus) and hedgehogs ( Erinaceus europaeus ). Trigger speeds of 0.2–2.1 s captured photographs of all three target species unless the animal was running at high speed. The camera with a microwave sensor was prone to false triggers, and often failed to trigger when an animal moved in front of it. A white flash produced photographs that were more readily identified to species than those obtained under infrared light. However, a white flash may be more likely to frighten target animals, potentially affecting detection probabilities. Video footage achieved similar success rates to still cameras but required more processing time and computer memory. Placing two camera traps side by side achieved a higher success rate than using a single camera. Camera traps show considerable promise for monitoring invasive mammal control operations. Further research should address how best to standardise the size of each camera’s field of view, maximise the probability that an animal encountering a camera trap will be detected, and eliminate visible or audible cues emitted by camera traps.     

A Novel Use of Camera Traps to Study Demography and Life History in Wild Animals: A Case Study of Spider Monkeys (Ateles belzebuth)

Demographic and life history data from wild populations of long-lived primate species are difficult to acquire but are critical for evaluating population viability and the success of conservation efforts. Camera trapping provides an opportunity for researchers to monitor wild animal populations indirectly and could help provide demographic and life history data in a way that demands fewer person-hours in the field, is less disruptive to the study population because it requires less direct contact, and may be cost effective. Using data on group composition collected concurrently though both direct observation and camera trap monitoring, we evaluate whether camera traps can provide reliable information on population dynamics (births, disappearances, interbirth intervals, and other demographic variables) for a wild population of white-bellied spider monkeys (Ateles belzebuth), an Endangered species. We placed camera traps focused on the sole access point used by the monkeys to visit a geophagy site located roughly in the center of one group’s home range, and we reviewed all of the photos collected at that site over a roughly 3-yr period to identify the individual monkeys recorded in the pictures. Group composition based on 2947 photos containing 3977 individual monkey images matched perfectly data collected concurrently through direct observation. The camera traps also provided estimates of the dates when individuals disappeared from the study group, and of infant births during the study. We conclude that long-term camera trap monitoring of wild populations of white-bellied spider monkeys—and other animals that are individually recognizable and that regularly visit predictable resources—can be a useful tool for monitoring their population dynamics indirectly.     

A sex pheromone‐baited trapping system for management of sweetpotato weevil,Cylas formicarius (Coleoptera: Brentidae)

A potent male attractant of sweetpotato weevil helps in monitoring and control of sweetpotato weevil in many production areas around the world. At present, it has not been used in Malaysia. Cost of the components of a trapping system is a major constraint in the adoption of male lure‐baited trapping by growers in Malaysia. Seven field trapping experiments were conducted from February 2013 to November 2015 as part of an effort to develop a simple, easy to construct, cost‐effective and efficient sex pheromone‐baited trap acceptable for use by farmers in Malaysia for monitoring and control of sweetpotato weevil (Cylas formicarius Fabricius). Overall, sweetpotato weevil trap catch was significantly affected by the number of windows in the trap, the killing agent used in the trap and the position of the trap relative to sweetpotato foliage, while trap size and trap colour did not significantly affect trap catch. Trap catch was best in plastic pole traps made from polyethylene terephthalate, with four window opening to facilitate weevil entry, with detergent solution as a killing agent and with the trap positioned from 0 to 40 cm above the crop canopy level. In a comparison study with commercial trap designs, sex pheromone‐baited plastic pole traps caught 60%–78% more weevils than were caught in sex pheromone‐baited delta traps, wing traps or unitraps. Optimization of trap characteristics is important for improving the performance of pheromone‐baited traps for use in population monitoring or mass‐trapping efforts to minimize crop damage by sweetpotato weevil infestation.     

利用红外相机视频数据进行库姆塔格沙漠地区野骆驼集群行为研究的可行性

野骆驼(Camelus ferus)生性机警, 且栖息于远离人迹、自然条件极端恶劣的荒漠、半荒漠地区, 其种群动态和行为生态学研究一直较为缺乏。本研究通过在库姆塔格沙漠地区进行不同季节的野外观测和连续水源地红外相机监测, 对野骆驼的集群行为进行了研究。2011-2013年, 在库姆塔格沙漠地区进行了8次野外调查, 共记录野骆驼64群, 个体430峰。非繁殖季节野骆驼集群大小平均为2.94±0.67峰; 而繁殖季节野骆驼集群大小平均为10.74±3.08峰。野外观测数据证明了野骆驼集群行为存在季节性差异, 倾向于冬季繁殖季节的集群。并于2012年10月至2013年9月期间, 在11个水源地设置11台红外相机, 共记录野骆驼281群745峰。与野外调查结果相比, 红外相机数据表明繁殖期间和非繁殖期间野骆驼集群大小没有显著差异(t = 0.322, P = 0.748)。水源地的地形因素、红外相机监测视角和监测时间的限制可能是造成这一差异的原因。但是两种方法的结果均表明野骆驼在阿尔金山北麓比西湖地区容易形成较大的集群; 同时, 繁殖季节野骆驼最大集群的规模要大于非繁殖季节。尽管利用红外相机进行动物集群行为研究存在一定的局限性, 但与传统基于野外调查的方法相比, 无论是经济上还是实用性方面, 利用红外相机都为我们开展动物行为学研究提供了新的手段。     

Camera Traps Can Be Heard and Seen by Animals

Camera traps are electrical instruments that emit sounds and light. In recent decades they have become a tool of choice in wildlife research and monitoring. The variability between camera trap models and the methods used are considerable, and little is known about how animals respond to camera trap emissions. It has been reported that some animals show a response to camera traps, and in research this is often undesirable so it is important to understand why the animals are disturbed. We conducted laboratory based investigations to test the audio and infrared optical outputs of 12 camera trap models. Camera traps were measured for audio outputs in an anechoic chamber; we also measured ultrasonic (n = 5) and infrared illumination outputs (n = 7) of a subset of the camera trap models. We then compared the perceptive hearing range (n = 21) and assessed the vision ranges (n = 3) of mammals species (where data existed) to determine if animals can see and hear camera traps. We report that camera traps produce sounds that are well within the perceptive range of most mammals’ hearing and produce illumination that can be seen by many species.     

Evaluation of commercial and field‐expedient baited traps for house flies,Musca domestica L. (Diptera: Muscidae)

A comparison of nine commercial baited fly traps on Florida dairy farms demonstrated that Terminator traps collected significantly more (13,323/trap) house flies (Musca domestica L.) than the others tested. Final Flight, Fly Magnet, and FliesBeGone traps collected intermediate numbers of flies (834‐2,166), and relatively few were caught with ISCA, Advantage, Fermone Big Boy, Squeeze & Snap, or OakStump traps (<300). Terminator traps collected about twice as many flies (799.8/trap) as FliesBeGone traps (343.8) when each trap was baited with its respective attractant, but when the attractants were switched between the two trap types, collections were significantly lower (77‐108) than was observed with traps baited with their respective attractant. Solutions of molasses were significantly more attractive to house flies than honey, maple syrup, or jaggery (date palm sugar). Field‐expedient traps constructed from discarded PET water bottles were much less effective than commercial traps, but painting the tops of such traps with black spray paint resulted in a six‐fold increase in trap capture.     

Accuracy of identifications of mammal species from camera trap images: A northern Australian case study

Camera traps are a powerful and increasingly popular tool for mammal research, but like all survey methods, they have limitations. Identifying animal species from images is a critical component of camera trap studies, yet while researchers recognize constraints with experimental design or camera technology, image misidentification is still not well understood. We evaluated the effects of a species’ attributes (body mass and distinctiveness) and individual observer variables (experience and confidence) on the accuracy of mammal identifications from camera trap images. We conducted an Internet‐based survey containing 20 questions about observer experience and 60 camera trap images to identify. Images were sourced from surveys in northern Australia and included 25 species, ranging in body mass from the delicate mouse (Pseudomys delicatulus, 10 g) to the agile wallaby (Macropus agilis, >10 kg). There was a weak relationship between the accuracy of mammal identifications and observer experience. However, accuracy was highest (100%) for distinctive species (e.g. Short‐beaked echidna [Tachyglossus aculeatus]) and lowest (36%) for superficially non‐distinctive mammals (e.g. rodents like the Pale field‐rat [Rattus tunneyi]). There was a positive relationship between the accuracy of identifications and body mass. Participant confidence was highest for large and distinctive mammals, but was not related to participant experience level. Identifications made with greater confidence were more likely to be accurate. Unreliability in identifications of mammal species is a significant limitation to camera trap studies, particularly where small mammals are the focus, or where similar‐looking species co‐occur. Integration of camera traps with conventional survey techniques (e.g. live‐trapping), use of a reference library or computer‐automated programs are likely to aid positive identifications, while employing a confidence rating system and/or multiple observers may lead to a collection of more robust data. Although our study focussed on Australian species, our findings apply to camera trap studies globally.     

Studying Large Mammals With Imperfect Detection: Status and Habitat Preferences of Wild Cattle and Large Carnivores in Eastern Cambodia

Studying large mammal species in tropical forests is a conservation challenge with species’ behavior and ecology often increasing the probability of non‐detection during surveys. Consequently, knowledge of the distribution, status, and natural history of many large mammal species in Southeast Asia is limited. I developed occupancy models from camera‐trapping data, thereby accounting for imperfect detection at sampling sites, to clarify the status and habitat requirements of four globally threatened or near threatened large mammals (banteng Bos javanicus, gaur Bos gaurus, dhole Cuon alpinus, and leopard Panthera pardus) in Mondulkiri Protected Forest, eastern Cambodia. Camera traps were operational for >3500 trap nights with 202 photographic encounters of the four study species. Model averaged occupancy estimates were between 5 percent (leopard) and 140 percent (gaur) higher than naive estimates (i.e., proportion of camera‐trap sites species recorded from) thus highlighting the importance of accounting for detectability during conservation surveys. I recommend the use of an occupancy framework when using camera‐trap data to study the status, ecology, and habitat preferences of poorly known and elusive species. The results highlight the importance of mixed deciduous and semi‐evergreen forest for wild cattle in eastern Cambodia and I emphasize that these habitats must be considered in conservation planning across the Lower Mekong Dry Forest Ecoregion.     

Toward reliable population density estimates of partially marked populations using spatially explicit mark–resight methods

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Wildlife surveillance using deep learning methods

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Field evaluation of female attractants for monitoring Ceratitis capitata (Diptera: Tephritidae) under a range of climatic conditions and population levels in Western Australia

Field trials were conducted in Western Australia to compare captures of the Mediterranean fruit fly, Ceratitis capitata (Wiedemann), in a standard male-targeted trap (Lynfield trap baited with Capilure) with a synthetic, female-targeted attractant marketed as BioLure. BioLure was also compared with other fenale attractants (orange ammonia, liquid protein bait) and tested in plastic McPhail, Tephri, and Lynfield traps. The possibility of using one trap to monitor female and male C. capitata populations was also tested by combining BioLure in a trap with the male attractant, Capilure. The results of these experiments show that BioLure outperformed the female-targeted system currently used for monitoring female C. capitala (liquid protein in MePhail trap). More male C. capitata were caught in the standard male-targeted trap, but more females were caught in traps baited with BioLure irrespective of trap type, climate, host tree, or population level. Combined lure traps caught equivalent total numbers of C. capitata to the standard male-targeted trap, but fewer females were captured. Tephri traps caught more flies than McPhail traps, but McPhail traps caught equivalent proportions of females. We compared the performance in commercial orchards of the standard male-targeted trap with a female-targeted trap (McPhail with BioLure). We found that the male trap detected C. capitata more often, caught more flies, triggered the economic threshold more often (66% of the time) and was more cost effective. The male-targeted trap is recommended for use on commercial orchards if cost is limiting. However, using both male and female-targeted traps increases the chance of detecting flies and triggering the economic threshold level. The synthetic female attractant is recommended for replacement of protein hydrolysate lures and may be used in either Tephri or McPhail traps.     

Application of an image and environmental sensor network for automated greenhouse insect pest monitoring

This work presents an automated insect pest counting and environmental condition monitoring system using integrated camera modules and an embedded system as the sensor node in a wireless sensor network. The sensor node can be used to simultaneously acquire images of sticky paper traps and measure temperature, humidity, and light intensity levels in a greenhouse. An image processing algorithm was applied to automatically detect and count insect pests on an insect sticky trap with 93% average temporal detection accuracy compared with manual counting. The integrated monitoring system was implemented with multiple sensor nodes in a greenhouse and experiments were performed to test the system’s performance. Experimental results show that the automatic counting of the monitoring system is comparable with manual counting, and the insect pest count information can be continuously and effectively recorded. Information on insect pest concentrations were further analyzed temporally and spatially with environmental factors. Analyses of experimental data reveal that the normalized hourly increase in the insect pest count appears to be associated with the change in light intensity, temperature, and relative humidity. With the proposed system, laborious manual counting can be circumvented and timely assessment of insect pest and environmental information can be achieved. The system also offers an efficient tool for long-term insect pest behavior observations, as well as for practical applications in integrated pest management (IPM).     

Animal Scanner: Software for classifying humans,animals, and empty frames in camera trap images

Camera traps are a popular tool to sample animal populations because they are noninvasive, detect a variety of species, and can record many thousands of animal detections per deployment. Cameras are typically set to take bursts of multiple photographs for each detection and are deployed in arrays of dozens or hundreds of sites, often resulting in millions of photographs per study. The task of converting photographs to animal detection records from such large image collections is daunting, and made worse by situations that generate copious empty pictures from false triggers (e.g., camera malfunction or moving vegetation) or pictures of humans. We developed computer vision algorithms to detect and classify moving objects to aid the first step of camera trap image filtering—separating the animal detections from the empty frames and pictures of humans. Our new work couples foreground object segmentation through background subtraction with deep learning classification to provide a fast and accurate scheme for human–animal detection. We provide these programs as both Matlab GUI and command prompt developed with C++. The software reads folders of camera trap images and outputs images annotated with bounding boxes around moving objects and a text file summary of results. This software maintains high accuracy while reducing the execution time by 14 times. It takes about 6 seconds to process a sequence of ten frames (on a 2.6 GHZ CPU computer). For those cameras with excessive empty frames due to camera malfunction or blowing vegetation automatically removes 54% of the false‐triggers sequences without influencing the human/animal sequences. We achieve 99.58% on image‐level empty versus object classification of Serengeti dataset. We offer the first computer vision tool for processing camera trap images providing substantial time savings for processing large image datasets, thus improving our ability to monitor wildlife across large scales with camera traps.