Distance and size matters: A comparison of six wildlife camera traps and their usefulness for wild birds

Camera traps are increasingly used in ecological research. However, tests of their performance are scarce. It is already known from previous work that camera traps frequently fail to capture visits by animals. This can lead to a misinterpretation of ecological results such as density estimates or predation events. While previous work is mainly based on mammals, for birds, no data about if and how camera traps can be successfully used to estimate species diversity or density are available. Hence, the goal of our study was an empirical validation of six different camera traps in the field. We observed a total number of N = 4567 events (independent visits of a bird) in 100 different sessions from March 2017 until January 2018 while camera traps were deployed. In addition, N = 641 events are based on a comparison of the two close‐up camera traps especially designed for birds. These events were all directly observed by the authors. Thus, the cameras can be compared against the human observer. To give an overall assessment and a more generalizable result, we combined the data from the six camera traps and showed that bird size category (effect size = 0.207) and distance (effect size = 0.132) are the most important predictors for a successful trigger. Also, temperature had a small effect, and flock size had an impact with larger flocks being captured more often. The approach of the bird, whether it approached the camera frontally or laterally had no influence. In Table 8 , we give some recommendations, based on our results, at which distances camera traps should be placed to get a 25%, 50%, and 75% capture rate for a given bird size.     

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.     

不同相机布放模式在古田山兽类资源监测中的比较

2010年6月-2011年8月,利用一字型、十字型、随机型和海拔垂直型4种相机布放模式在古田山国家级自然保护区拍摄兽类独立照片(independent photograph,IP)共2551张,能识别物种的兽类IP有1240张,占整体IP的48.6%,不能识别物种的兽类(松鼠科以外的啮齿动物)IP1167张,占45.8%;无效IP144张,占5.6%。所拍能识别物种的兽类共有14种,隶属4目9科,其中黑麂(Muntiacus crinifrons)为国家Ⅰ级保护动物,鬛羚(Naemo-rhedus sumatraensis)和黑熊(Selenarctos thibetanus)为国家Ⅱ级保护动物。在种-监测日曲线当中,4种监测模式随监测日的不断延长,兽类种类大体呈现半年左右趋于稳定的规律,建议监测时间以半年为一个周期较为合适。研究发现,兽类IP和单相机拍摄率在一字型、十字型和随机型之间不存在显著差异(P>0.05),但与海拔垂直型存在极显著差异(P<0.01)。海拔垂直型由于相机分布均匀,在拍摄种类、拍摄率等各项监测指标上都显著高于其他3种模式。     

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。     

Automated location invariant animal detection in camera trap images using publicly available data sources

1. A time‐consuming challenge faced by camera trap practitioners is the extraction of meaningful data from images to inform ecological management. An increasingly popular solution is automated image classification software. However, most solutions are not sufficiently robust to be deployed on a large scale due to lack of location invariance when transferring models between sites. This prevents optimal use of ecological data resulting in significant expenditure of time and resources to annotate and retrain deep learning models.
2. We present a method ecologists can use to develop optimized location invariant camera trap object detectors by (a) evaluating publicly available image datasets characterized by high intradataset variability in training deep learning models for camera trap object detection and (b) using small subsets of camera trap images to optimize models for high accuracy domain‐specific applications.
3. We collected and annotated three datasets of images of striped hyena, rhinoceros, and pigs, from the image‐sharing websites FlickR and iNaturalist (FiN), to train three object detection models. We compared the performance of these models to that of three models trained on the Wildlife Conservation Society and Camera CATalogue datasets, when tested on out‐of‐sample Snapshot Serengeti datasets. We then increased FiN model robustness by infusing small subsets of camera trap images into training.
4. In all experiments, the mean Average Precision (mAP) of the FiN trained models was significantly higher (82.33%–88.59%) than that achieved by the models trained only on camera trap datasets (38.5%–66.74%). Infusion further improved mAP by 1.78%–32.08%.
5. Ecologists can use FiN images for training deep learning object detection solutions for camera trap image processing to develop location invariant, robust, out‐of‐the‐box software. Models can be further optimized by infusion of 5%–10% camera trap images into training data. This would allow AI technologies to be deployed on a large scale in ecological applications. Datasets and code related to this study are open source and available on this repository: https://doi.org/10.5061/dryad.1c59zw3tx.

     

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.     

秦岭中段野生动物多样性的红外相机监测数据库平台介绍

秦岭地处我国中西部, 生物地理位置重要, 拥有丰富的生物多样性, 有大熊猫(Ailuropoda melanoleuca)、秦岭羚牛(Budorcas bedfordi)、金丝猴(Rhinopithecus roxellana)和朱鹮(Nipponia nippon)等4个秦岭森林旗舰物种, 被称为“秦岭四宝”。利用红外相机技术开展秦岭野生动物的非损伤性监测不仅可以为秦岭山系提供物种名录信息, 还可以为了解秦岭野生动物的行为和活动格局提供科学数据。清华大学环境学院生态团队自2009-2020年在秦岭中段南坡先后实施了7个项目, 对秦岭南坡的4个保护区进行了野生动物监测, 面积达1,113 km ²(26.5 km × 42 km), 红外相机位点数267个, 相机日数152,160天, 共获取红外相机照片855,260张。共鉴定出27种野生兽类和63种野生鸟类, 并应用这些照片数据开展了信息挖掘工作, 对野生动物行为、稀有物种、与生境的关系, 以及人为活动对野生动物的影响等领域进行了研究, 已取得部分成果。在此基础上建立了“秦岭中段野生动物多样性的红外相机监测数据库平台”, 供团队内部及合作者使用。通过10年的监测, 我们提出未来研究建议: (1)对于非常偶见的物种, 还需要更长的时间并在更多样化的生境布设相机, 以获取更多影像数据评估其现状; (2)数据库需要在更大程度和深度上进行信息挖掘, 尤其在种间关系、物种-生境关系、种群动态等方面; (3)对典型大种群数量的物种(如秦岭羚牛和野猪Sus scrofa)及食物链顶端大型捕食动物(如金钱豹Panthera pardus)进行种群动态研究, 为整个秦岭生态系统的健康持续提供科学支撑; (4)利用数据库的数据及今后红外相机监测数据进行野生动物疾病的发生发展监测研究。     

龙溪—虹口国家级自然保护区兽类和鸟类多样性红外相机调查结果初报

掌握野生动植物本底资源是各级自然保护区生物多样性监测研究和保护管理的重要环节。为了建立龙溪-虹口国家级自然保护区内兽类和鸟类多样性资源的长期监测机制,于2013年9月至2014年11月,我们采用红外相机技术在龙溪沟和虹口峡谷等区域按公里网格布设了57个监测点,调查地面活动的兽类和鸟类。红外相机累计工作达11,847个工作日,共记录到兽类和鸟类物种61种,其中兽类5目12科21种,鸟类3目10科40种,包括猎隼（Falco cherrug）、光背地鸫（Zoothera mollissima）、长尾地鸫（Zoothera dixoni）、灰翅鸫（Turdus boulboul）、锈脸钩嘴鹛（Pomatorhinus erythrogenys）、红嘴鸦雀（Conostoma aemodium）和褐鸦雀（Paradoxornis unicolor）7种鸟类为保护区新记录种。调查到的兽类被列为国家I级和II级重点保护野生动物的分别为4种和5种,被IUCN红色名录评估为“濒危EN”和“易危VU” 的物种各3种,被评为 “近危NT”级别的物种有4种;鸟类被列为国家II级重点保护野生动物的有5种,被IUCN红色名录评估为“濒危EN”的物种有1种。本次调查补充更新了龙溪-虹口自然保护区地栖息鸟类名录,初步了解了保护区内地面活动大中型兽类和鸟类的物种组成和分布,为保护区建立野生动物红外相机常规监测和保护管理提供了基础数据。     

Factors associated with use of wildlife underpasses and importance of long-term monitoring

We evaluated use of 6 wildlife underpasses (UP) using video camera surveillance along State Route 260 in Arizona, USA. We documented wildlife use and compared successful UP crossings by various species and among UP. From 2002 to 2008, we recorded visits by 15,134 animals of 21 species (16 wildlife, 5 domestic) resulting in 72.4% crossing through UP. Elk (Cervus elaphus) accounted for 68% of recorded animals, white-tailed deer (Odocoileus virginianus) and mule deer (O. hemionus) accounted for 13% and 6%, respectively. As elk and white-tailed deer were the only species adequately represented across all UP, we used logistic regression to further evaluate factors associated with successful use of UP. To evaluate habituation over time we limited this analysis to 5 UP monitored for ≥4 yr. For elk, structural attributes and placement, season, time of day, and months monitored were associated with successful elk UP crossing in year 1, however, by year 4 only structural attributes and placement were significant, suggesting that UP structure and placement likely were of primary importance for successful elk passage. By year 4, probabilities of crossing at 4 of 5 UP converged on >0.70, indicating that given sufficient time to allow habituation, most UP we evaluated appeared to be effective for elk, regardless of structural attributes or placement. For deer, only structural attribute and placement were significant, and aside from one structure did not increase in probability of a successful crossing over time. The overall number of animals and species that crossed SR 260 via UP underscores efficacy of UP in promoting multi-species permeability. Long-term monitoring allows wildlife and highway managers to evaluate adaptation to wildlife crossing structures by different species. Results from this study add to our knowledge of mitigating the impact of highways on wildlife. © 2011 The Wildlife Society.     

野生动物监测技术和方法应用进展与展望

野生动物是生态系统研究和保护管理的重要生物类群, 对调控生态系统结构和功能, 维持生态系统健康平衡具有显著作用。科学监测数据是野生动物研究、保护、管理决策的前提基础, 但由于传统监测技术的限制, 野生动物的多样性特征及其与环境、生态系统的平衡机制未得到充分关注和研究。随着自动化、智能化、信息化技术的发展及应用, 野生动物监测技术和方法出现较大突破和变革。该文论述了近年来野生动物监测研究领域的重要新技术, 包括红外相机技术、全球定位系统(GPS)追踪技术、DNA条形码技术和高通量测序技术等。通过综合介绍相关的基础概念、基本原理, 该文总结了新技术的应用优势和重大应用进展, 同时探讨了新技术应用中存在的问题, 并对未来野生动物监测技术的发展趋势进行了展望。     

Density‐dependent space use affects interpretation of camera trap detection rates

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.     

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.     

Bring your own camera to the trap: An inexpensive,versatile, and portable triggering system tested on wild hummingbirds

The study of animals in the wild offers opportunities to collect relevant information on their natural behavior and abilities to perform ecologically relevant tasks. However, it also poses challenges such as accounting for observer effects, human sensory limitations, and the time intensiveness of this type of research. To meet these challenges, field biologists have deployed camera traps to remotely record animal behavior in the wild. Despite their ubiquity in research, many commercial camera traps have limitations, and the species and behavior of interest may present unique challenges. For example, no camera traps support high‐speed video recording. We present a new and inexpensive camera trap system that increases versatility by separating the camera from the triggering mechanism. Our system design can pair with virtually any camera and allows for independent positioning of a variety of sensors, all while being low‐cost, lightweight, weatherproof, and energy efficient. By using our specialized trigger and customized sensor configurations, many limitations of commercial camera traps can be overcome. We use this system to study hummingbird feeding behavior using high‐speed video cameras to capture fast movements and multiple sensors placed away from the camera to detect small body sizes. While designed for hummingbirds, our application can be extended to any system where specialized camera or sensor features are required, or commercial camera traps are cost‐prohibitive, allowing camera trap use in more research avenues and by more researchers.     

红外相机技术在物种监测中的应用及数据挖掘

由于濒危物种数量稀少以及大多数野生动物对人类活动敏感, 增加了传统调查的难度。众所周知,红外相机在野生动物调查研究中具有天然优势; 然而随着红外相机技术的广泛推广应用及数据采集量的不断加大, 科研人员也面临了一系列关于红外相机监测及后续数据处理中出现的问题。本文详细阐述了红外相机数据管理和利用方面存在的3个关键问题: 数据管理缺乏规范化、数据网络缺乏一体化、数据获取缺乏标准化。同时以秦岭、卧龙等地的一些研究为主体, 列举分析了红外相机照片后续数据挖掘中8个方面的内容, 即兽类的个体识别、物种时间活动格局、物种空间活动格局、偶见物种信息利用、物种行为活动、繁殖信息、疾病情况、人为干扰。这些信息的有效利用可为野生动物及生物多样性的保护、管理提供一定科学支撑。     

Community structure and diversity of tropical forest mammals: data from a global camera trap network

Terrestrial mammals are a key component of tropical forest communities as indicators of ecosystem health and providers of important ecosystem services. However, there is little quantitative information about how they change with local, regional and global threats. In this paper, the first standardized pantropical forest terrestrial mammal community study, we examine several aspects of terrestrial mammal species and community diversity (species richness, species diversity, evenness, dominance, functional diversity and community structure) at seven sites around the globe using a single standardized camera trapping methodology approach. The sites-located in Uganda, Tanzania, Indonesia, Lao PDR, Suriname, Brazil and Costa Rica-are surrounded by different landscape configurations, from continuous forests to highly fragmented forests. We obtained more than 51 000 images and detected 105 species of mammals with a total sampling effort of 12 687 camera trap days. We find that mammal communities from highly fragmented sites have lower species richness, species diversity, functional diversity and higher dominance when compared with sites in partially fragmented and continuous forest. We emphasize the importance of standardized camera trapping approaches for obtaining baselines for monitoring forest mammal communities so as to adequately understand the effect of global, regional and local threats and appropriately inform conservation actions.     

基于红外相机技术对广东车八岭国家级自然保护区大中型兽类与雉类的编目清查与评估

近年来, 红外相机技术已被广泛应用于国内外自然保护地内地栖鸟兽的物种编目和动态评估。本文以广东车八岭国家级自然保护区为例, 探讨基于红外相机技术如何进行保护区全境大中型兽类和雉鸡类的物种编目清查与评估。通过对车八岭保护区全境为期1年的调查, 共记录兽类和雉鸡类18种, 其中兽类15种, 鸡形目鸟类3种。基于物种累计曲线, 采用全年数据所需的最小调查网格数、最少调查相机日均要少于雨季或旱季, 而旱季调查需要的最小调查网格数和最少调查相机日比雨季更少。通过红外相机图像数据获得了车八岭保护区的大中型兽类和地栖雉鸡类物种名录、物种丰富度、每个物种的相对多度、分布图和凭证标本等重要内容。     

Terrestrial wildlife responses to logging and fire in a Bolivian tropical humid forest

Logging and wildfire are significant anthropogenic disturbance agents in tropical forests. We compared the abundance and species richness of selected terrestrial wildlife taxa including small mammals, amphibians, reptiles, and terrestrial invertebrates in areas burned by wildfire and then logged and in adjacent undisturbed areas of a tropical humid forest in Bolivia. Disturbed areas had 24% less canopy cover than undisturbed areas but had 2.6 times the cover of large woody debris. Understory cover did not differ between disturbed and undisturbed areas. Small mammal abundance and species richness in disturbed areas were 43 and 70% higher, respectively, than in adjacent undisturbed areas. Herpetofaunal abundance did not differ significantly among disturbed and undisturbed areas, but trends for higher abundance were observed for both reptiles and amphibians in disturbed areas. Herpetofaunal species richness was significantly higher in disturbed compared to undisturbed areas. Total terrestrial invertebrate abundance, as estimated by pitfall traps, was significantly higher in undisturbed compared to disturbed areas mostly due to higher abundances of Formicidae and Blattidae. However, two invertebrate groups, Orthoptera and Lepidoptera (larvae) were more abundant in disturbed areas. Wildlife conservation strategies for areas where logging or wildfire occur should take into account species- or guild-specific responses to these disturbance agents.     

Assessing species occurrence and species‐specific use patterns of bais (forest clearings) in Central Africa with camera traps

The impacts of increasing resource extraction on biodiversity in the Central African rainforest are largely unknown, in part due to the lack of baseline data on species occurrence across the basin. Natural forest clearings (bais) in this region are key habitats for a variety of vertebrates and offer opportunities for monitoring species distribution. Information on species composition, however, is lacking from the majority of areas (except for long‐term study sites). Approaches and protocols for short‐term bai assessments can greatly advance such baseline knowledge. This study demonstrates that camera traps provide an effective method for species inventories (species occurrence and temporal activity patterns) and monitoring at bais across the broader region. In comparison with direct observational studies, they performed especially well regarding rare and nocturnal species. Camera traps during sampling sessions of 4 weeks or less recorded previously undocumented, and 65–94% of the mammals known to use each of seven Central African bais. Results indicate that many mammal species, in particular African forest elephants (Loxodonta africana cyclotis), visit bais preferentially at night. This underlines the urgent need for monitoring tools providing both diurnal and nocturnal data to provide baseline data that address conservation and management objectives.     

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.     

基于视频监控系统的欧亚水獭活动节律初报及红外相机监测效果评估

欧亚水獭(Lutra lutra)是淡水生态系统保护的旗舰物种, 目前三江源或为其在中国仅存的连片栖息地之一。然而, 该区域欧亚水獭基础生态学信息尚属空白, 已开展的工作也多依赖于红外相机而可靠性有待评估。本文基于2018-2020年间在青海玉树通过视频监控系统和红外相机收集到的数据, 在分析区内水獭活动节律的同时, 对红外相机监测的有效性和准确性进行了评估。结果表明, 在玉树, 欧亚水獭在每日17时至次日9时活动频率较高, 每年12月至次年6月间拍摄频率较高; 繁殖期或随每年10月个体交配而开始, 至次年6月幼崽独立而终止。红外相机约可捕获69.18%的水獭出现事件, 且拍摄率随事件持续时间的延长而提高; 通过红外相机可以准确地描述出水獭活动节律, 仅有约半数红外相机影像可以准确还原事件主题(56.28%)和多只个体参与的事件中的个体数量(49.35%)。因此, 在使用红外相机对欧亚水獭进行行为学研究时应对这一情况进行充分考虑。