Comparison of Methods of Monitoring Wildlife Crossing-Structures on Highways

ABSTRACT Wildlife crossing-structures (e.g., underpasses and overpasses) are used to mitigate deleterious effects of highways on wildlife populations. Evaluating performance of mitigation measures depends on monitoring structures for wildlife use. We analyzed efficacy of 2 noninvasive methods commonly used to monitor crossing-structure use by large mammals: tracking and motion-activated cameras. We monitored 15 crossing-structures every other day between 29 June and 24 October 2007 along the Trans-Canada Highway in Alberta, Canada. Our objectives were to determine how species-specific detection rates are biased by the detection method used, to determine factors contributing to crossing-event detection, and to evaluate the most cost-effective approach to monitoring. We detected 3,405 crossing events by tracks and 4,430 crossings events by camera for mammals coyote-sized and larger. Coyotes (Canis latrans) and grizzly bears (Ursus arctos) were significantly more likely to be detected by track-pads, whereas elk (Cervus elaphus) and deer (Odocoileus sp.) were more likely to be detected by cameras. Crossing-event detection was affected by species, track-pad length, and number of animals using the crossing structure. At the levels of animal activity observed in our study our economic analysis indicates that cameras are more cost-effective than track-pads for study durations >1 year. Understanding the benefits and limitations of camera and track-pad methods for monitoring large mammal movement at wildlife crossing-structures will help improve the efficiency of studies designed to evaluate the effectiveness of highway mitigation measures.     

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.     

Comparing motion capture cameras versus human observer monitoring of mammal movement through fence gaps: a case study from Kenya

Monitoring the movement and distribution of wildlife is a critical tool of an adaptive management framework for wildlife conservation. We installed motion‐triggered cameras to capture the movement of mammals through two purpose‐built migration gaps in an otherwise fenced conservancy in northern Kenya. We compared the results to data gathered over the same time period (1 Jan 2011–31 Dec 2012) by the human observers monitoring mammal tracks left at the same fence gaps in a sandy loam detection strip. The camera traps detected more crossing events, more species and more individuals of each species per crossing event than did the human track observers. We tested for volume detection differences between methods for the five most common species crossing each gap and found that all detection rates were heavily weighted towards the camera‐trap method. We review some of the discrepancies between the methods and conclude that although the camera traps record more data, the management of that data can be time‐consuming and ill‐suited to some time‐sensitive decision‐making. We also discuss the importance of daily track monitoring for adaptive management conservation and community security.     

探讨我国森林野生动物红外相机监测规范

野生动物多样性是生物多样性监测与保护管理评价的关键指标, 因此对野生动物进行长期监测是中国森林生物多样性监测网络(CForBio)等大尺度生物多样性监测研究计划的一个重要组成部分。2011年以来, CForBio网络陆续在多个森林动态监测样地开展以红外相机来监测野生动物多样性。随着我国野生动物红外相机监测网络的初步形成, 亟待建立和执行基于红外相机技术的统一监测规范。基于3年来在我国森林动态监测样地红外相机监测的进展情况, 以及热带生态评价与监测网络针对陆生脊椎动物(兽类和鸟类)所提出的红外相机监测规范, 本文从监测规范和监测注意事项等方面探讨了我国森林野生动物红外相机监测的现状和未来。     

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.     

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.     

红外相机安放于地面和林冠层对野生动物监测结果的影响

红外相机技术在野生动物研究中日趋普及, 逐渐成为重要的生物多样性监测手段。过去的监测常局限于地面, 而针对林冠层的监测较少, 这对野生动物的多样性评估影响尚未可知。为此, 本研究在生物多样性丰富的碧罗雪山南段, 将20台红外相机分别拍摄地面层(0.5-1.5 m)和林冠层(5-10 m)配对比较, 累计拍摄2,319个有效相机日, 平均每对相机同步进行112.5 d的监测。监测期间共拍摄到44种野生动物(不包括鼠形啮齿类), 其中兽类20种, 鸟类24种; 冠层和地面红外相机监测的物种相似度为29.54%; 15种动物仅拍摄于林冠层, 16种动物仅拍摄于地面, 13种动物拍摄于两个林层。研究结果表明不同林层监测的物种组成存在显著差异, 林冠层与地面层监测都具有不可替代性; 不同林层红外相机的监测手段也能用于研究野生动物的空间选择和生态位分化。红外相机监测中根据目标物种的习性在相应的林层设置相机能提高物种发现率; 为全面掌握区域森林生态系统野生动物的多样性, 红外相机监测需要兼顾不同林层这一点需要在监测规范中明细。     

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.     

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.     

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.     

The selfie trap: A novel camera trap design for accurate small mammal identification

Camera traps are a popular tool for monitoring wildlife though they can fail to capture enough morphological detail for accurate small mammal species identification. Camera trapping small mammals is often limited by the inability of camera models to: (i) record at close distances; and (ii) provide standardised photos. This study aims to provide a camera trapping method that captures standardised images of the faces of small mammals for accurate species identification, with further potential for individual identification. A novel camera trap design coined the ‘selfie trap’ was developed. The selfie trap is a camera contained within an enclosed PVC pipe with a modified lens that produces standardised close images of small mammal species encountered in this study, including: Brown Antechinus (Antechinus stuartii), Bush Rat (Rattus fuscipes) and Sugar Glider (Petaurus breviceps). Individual identification was tested on the common arboreal Sugar Glider. Five individual Sugar Gliders were identified based on unique head stripe pelage. The selfie trap is an accurate camera trapping method for capturing detailed and standardised images of small mammal species. The design described may be useful for wildlife management as a reliable method for surveying small mammal species. However, intraspecies individual identification using the selfie trap requires further testing.     

A comparison of trapping methods for Tabanidae (Diptera) in North Queensland, Australia

The ability to monitor the abundance and diversity of tabanid flies over wide areas requires effective and low-cost surveillance methods. Such monitoring activities help to quantify the risk of transmission of pathogens by tabanids. Here we examine the effectiveness and practicality of two types of trap (canopy traps and Nzi traps) and two types of attractant (octenol and carbon dioxide) for monitoring tabanid flies in tropical Australia. The Nzi trap consistently caught more tabanids and more species of tabanids than the canopy trap. It was also more robust and therefore required less maintenance in remote locations. The use of attractants substantially increased capture rates, both of individuals and species, and traps using both attractants were consistently the most effective. However, in remote locations, where it is not possible to check traps frequently, the use of attractants may not be feasible. When attractants were not used, the canopy trap caught very few tabanids, but the Nzi trap remained effective enough to be useful as a monitoring device. In addition, the number of tabanid species caught by the Nzi traps remained high, and included those that were most abundant. We therefore conclude that, in this region, Nzi traps are preferable for tabanid monitoring and that attractants greatly improve their effectiveness. However, for longterm monitoring, especially in remote locations, Nzi traps without attractants are a satisfactory option.     

Camera traps and guard observations as an alternative to researcher observation for studying anthropogenic foraging

Foraging by wildlife on anthropogenic foods can have negative impacts on both humans and wildlife. Addressing this issue requires reliable data on the patterns of anthropogenic foraging by wild animals, but while direct observation by researchers can be highly accurate, this method is also costly and labor‐intensive, making it impractical in the long‐term or over large spatial areas. Camera traps and observations by guards employed to deter animals from fields could be efficient alternative methods of data collection for understanding patterns of foraging by wildlife in crop fields. Here, we investigated how data on crop‐foraging by chacma baboons and vervet monkeys collected by camera traps and crop guards predicted data collected by researchers, on a commercial farm in South Africa. We found that data from camera traps and field guard observations predicted crop loss and the frequency of crop‐foraging events from researcher observations for crop‐foraging by baboons and to a lesser extent for vervets. The effectiveness of cameras at capturing crop‐foraging events was dependent on their position on the field edge. We believe that these alternatives to direct observation by researchers represent an efficient and low‐cost method for long‐term and large‐scale monitoring of foraging by wildlife on crops.     

A camera trap assessment of terrestrial vertebrates in Bwindi Impenetrable National Park,Uganda

We placed camera traps for a month at sixty locations in Bwindi Impenetrable National Park to determine the species composition and distribution of medium‐to‐large terrestrial vertebrates. A total of 15912 images were recorded from 1800 camera trap days. These provided a total of 625 and 338 camera events when filtered by hour and day, respectively. Twenty mammal species were recorded from 594 and 314 camera events by hour and day, respectively. Four bird species were recorded from 31 and 24 camera events by hour and day, respectively. The African golden cat Profelis aurata Temminck was recorded from 27 and nineteen camera events by hour and day, respectively. The black‐fronted duiker Cephalophus nigrifrons Gray was most frequently photographed with 179 and 65 camera events by hour and day, respectively. Analyses reveal two species possessed a significantly interior‐biased distribution. One species showed an edge‐biased pattern. Five species were detected to have significantly biased altitudinal distributions with higher elevations. Distance to park edge and elevation can significantly influence species distribution. The selective use of the park limits the area that each species utilizes, with implications for maximum population sizes and viability. Our observations provide a baseline for long‐term terrestrial vertebrate monitoring in Bwindi.     

Assessing bee (Hymenoptera: Apoidea) diversity of an Illinois restored tallgrass prairie: methodology and conservation considerations

Bee species diversity and the effectiveness of four sampling methods were investigated in a west-central Illinois restored tallgrass prairie. Bees were sampled using malaise traps, ground-level pan traps, elevated pan traps, and vane traps. A total of 4,622 bees representing 31 genera and 111 species were collected. Malaise traps collected the greatest number of bees and species, and ground-level pan traps the least. Among the pan traps and vane traps, blue-colored traps collected the greatest abundance and species richness, and yellow traps the least. Chao1 estimator and rarefaction analyses showed that substantial increases in sample sizes would be necessary to achieve asymptotic species richness levels, particularly if ground-level pan traps alone were used. Elevated pan traps and vane traps collected relatively similar species composition. Different colored pan traps at the same height collected more similar species composition than did those at different heights, but species composition of blue ground-level pan traps was relatively similar to elevated pan traps, regardless of color. Indicator species analysis revealed 22 species that were significantly associated with a specific trap type, and 11 species that were associated with a particular pan trap color/elevation. Results of this study show that elevated traps can increase the effectiveness of bee surveys in tallgrass prairie, and that a combination of trap types gives a more complete picture of the bee fauna than does a single survey method. These results should be considered along with cost, ease of use, and goals when planning and designing bee inventories.     

利用红外相机对四川白水河国家级自然保护区鸟兽资源的初步调查

2017年5月至2018年5月, 我们在四川白水河国家级自然保护区内设置红外相机对地面活动鸟兽进行了初步调查。布设在24个位点的24台相机累计工作3,832天, 共获得可识别物种的独立有效照片535张。经鉴定, 兽类有4目10科17种, 鸟类有2目4科10种。其中, 国家I级重点保护野生动物5种, 国家II级重点保护野生动物8种, 中国豪猪(Hystrix hodgsoni)、宝兴歌鸫(Turdus mupinensis)和黑顶噪鹛(Trochalopteron affine)为保护区新记录种, 而大熊猫(Ailuropoda melanoleuca)为汶川地震后首次拍到。兽类中, 花面狸(Paguma larvata)、黄喉貂(Martes flavigula)和中华斑羚(Naemorhedus griseus) 3种动物的独立有效照片总数占全部兽类独立有效照片数的50.2%。鸟类中, 血雉(Ithaginis cruentus)和红腹角雉(Tragopan temminckii)的独立有效照片总数占全部鸟类独立有效照片数的91.6%。本研究为白水河国家级自然保护区野生动物资源管理和保护提供了参考依据。     

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.     

Camera trapping mammals in the scrubland’s of the Cape Floristic Kingdom—the importance of effort,spacing and trap placement

As a non-invasive monitoring method camera traps are noted as being an effective, accurate and rapid means of compiling species richness estimates of medium to large terrestrial mammals. However, crucial elements of camera trap survey design are rarely empirically addressed, which has raised the need for both a standardised and optimised camera trapping protocol. Our study confirms that an appropriate camera placement buffer and targeting areas of animal activity, contributes to more complete species richness estimates as well as significantly reducing the rate of false trigger events. However, attaining the required survey effort in terms of camera days was the most important factor in providing accurate species richness estimates. Our results suggest that reliable estimates of species richness can be achieved in open scrubland when cameras are spaced 1 × 1 km apart and left in the targeted area until a survey effort of a 1000 camera days is realised.     

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

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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.