Limitations of recreational camera traps for wildlife management and conservation research: a practitioner's perspective

Automated Detection and Counting of Wild Boar in Camera Trap Images

Camera traps are becoming widely used for wildlife monitoring and management. However, manual analysis of the resulting image sets is labor-intensive, time-consuming and costly. This study shows that automated computer vision techniques can be extremely helpful in this regard, as they can rapidly and automatically extract valuable information from the images. Specific training with a set of 1600 images obtained from a study where wild animals approaching wild boar carcasses were monitored enabled the model to detect five different classes of animals automatically in their natural environment with a mean average precision of 98.11%, namely 'wild boar', 'fox', 'raccoon dog', 'deer' and 'bird'. In addition, sequences of images were automatically analyzed and the number of wild boar visits and respective group sizes were determined. This study may help to improve and speed up the monitoring of the potential spread of African swine fever virus in areas where wild boar are affected.

Characteristics of wild moose (Alces alces) vocalizations

Moose are a popular species with recreationists but understudied acoustically. We used publicly available videos to characterize and quantify the vocalizations of moose in New Hampshire separated by age/sex class. We found significant differences in peak frequency, center frequency, bandwidth, and duration across the groups. Our results provide quantification of wild moose vocalizations across age/sex classes, which is a key step for passive acoustic detection of this species and highlights public videos as a potential resource for bioacoustics research of hard-to-capture and understudied species.

Barriers to Using UAVs in Conservation and Environmental Management: A Systematic Review

The ability to adopt novel tools continues to become more important for governments and environmental managers tasked with balancing economic development, social needs and environmental protection. An example of an emerging technology that can enable flexible, cost-effective data collection for conservation and environmental management is Unmanned Aerial Vehicles (UAVs). It is clear that UAVs are beginning to be adopted for a diversity of purposes, identification of barriers to their use is the first step in increasing their uptake amongst the environmental management community. Identifying the barriers to UAV usage will enable research and management communities to confidently utilise these powerful pieces of technology. However, the implementation of this technology for environmental research has received little overall assessment attention. This systematic literature review has identified 9 barrier categories (namely Technological, Analytical and Processing, Regulatory, Cost, Safety, Social, Wildlife impact, work suitability and others) inhibiting the uptake of UAV technologies. Technological barriers were referenced in the literature most often, with the inability of UAVs to perform in poor weather (such as rain or windy conditions) commonly mentioned. Analytical and Processing and Regulatory barriers were also consistently reported. It is likely that some barriers identified will lessen with time (e.g. technological and analytical barriers) as this technology continues to evolve.

SOCRATES: Introducing Depth in Visual Wildlife Monitoring Using Stereo Vision

The development and application of modern technology are an essential basis for the efficient monitoring of species in natural habitats to assess the change of ecosystems, species communities and populations, and in order to understand important drivers of change. For estimating wildlife abundance, camera trapping in combination with three-dimensional (3D) measurements of habitats is highly valuable. Additionally, 3D information improves the accuracy of wildlife detection using camera trapping. This study presents a novel approach to 3D camera trapping featuring highly optimized hardware and software. This approach employs stereo vision to infer the 3D information of natural habitats and is designated as StereO CameRA Trap for monitoring of biodivErSity (SOCRATES). A comprehensive evaluation of SOCRATES shows not only a 3.23% improvement in animal detection (bounding box mAP75), but also its superior applicability for estimating animal abundance using camera trap distance sampling. The software and documentation of SOCRATES is openly provided.

Animal Detection and Classification from Camera Trap Images Using Different Mainstream Object Detection Architectures

Simple Summary

The imagery captured by cameras provides important information for wildlife research and conservation. Deep learning technology can assist ecologists in automatically identifying and processing imagery captured from camera traps, improving research capabilities and efficiency. Currently, many general deep learning architectures have been proposed but few have evaluated their applicability for use in real camera trap scenarios. Our study constructed the Northeast Tiger and Leopard National Park wildlife dataset (NTLNP dataset) for the first time and compared the real-world application performance of three currently mainstream object detection models. We hope this study provides a reference on the applicability of the AI technique in wild real-life scenarios and truly help ecologists to conduct wildlife conservation, management, and research more effectively.

Abstract

Camera traps are widely used in wildlife surveys and biodiversity monitoring. Depending on its triggering mechanism, a large number of images or videos are sometimes accumulated. Some literature has proposed the application of deep learning techniques to automatically identify wildlife in camera trap imagery, which can significantly reduce manual work and speed up analysis processes. However, there are few studies validating and comparing the applicability of different models for object detection in real field monitoring scenarios. In this study, we firstly constructed a wildlife image dataset of the Northeast Tiger and Leopard National Park (NTLNP dataset). Furthermore, we evaluated the recognition performance of three currently mainstream object detection architectures and compared the performance of training models on day and night data separately versus together. In this experiment, we selected YOLOv5 series models (anchor-based one-stage), Cascade R-CNN under feature extractor HRNet32 (anchor-based two-stage), and FCOS under feature extractors ResNet50 and ResNet101 (anchor-free one-stage). The experimental results showed that performance of the object detection models of the day-night joint training is satisfying. Specifically, the average result of our models was 0.98 mAP (mean average precision) in the animal image detection and 88% accuracy in the animal video classification. One-stage YOLOv5m achieved the best recognition accuracy. With the help of AI technology, ecologists can extract information from masses of imagery potentially quickly and efficiently, saving much time.

A questionnaire-based investigation to explore the social and legal implications derived from the use of camera traps for wildlife monitoring and conservation

Camera traps are non-invasive monitoring tools largely used to detect species presence or population dynamics. The use of camera traps for wildlife conservation purposes raises questions about privacy invasion when images of people are taken. Throughout the use of an online questionnaire survey, we assessed the degree of knowledge about social and legal implications derived from the deployment of camera traps. Our results revealed a consistent gap in term of knowledge about legal implications derived by the use of camera traps among respondents. Most of those who were aware of such legislation did not take specific actions to prevent legal consequences, probably to reduce the risk of theft or vandalism. Most respondents declared that images of people were unintentionally collected. Some of them stated that images which may violate privacy issues or showed nefarious activities were stored for internal processing or reported to local authorities. Our research thus confirmed that privacy invasion is a widely poorly treated issue in the wildlife conservation dimension. Furthermore, despite camera traps being used to improve conservation efforts, the detection of individuals engaged in private or illegal activities poses further complications in terms of pursuance of legal actions when an individual is identified by these images. So, appropriate guidelines for images analysis need to be designed, and subsequently followed. Lastly, adopting effective methods to protect cameras from the risk of theft and/or vandalism is of primary concern.

A global community-sourced assessment of the state of conservation technology

Conservation technology holds the potential to vastly increase conservationists' ability to understand and address critical environmental challenges, but systemic constraints appear to hamper its development and adoption. Understanding of these constraints and opportunities for advancement remains limited. We conducted a global online survey of 248 conservation technology users and developers to identify perceptions of existing tools' current performance and potential impact, user and developer constraints, and key opportunities for growth. We also conducted focus groups with 45 leading experts to triangulate findings. The technologies with the highest perceived potential were machine learning and computer vision, eDNA and genomics, and networked sensors. A total of 95%, 94%, and 92% respondents, respectively, rated them as very helpful or game changers. The most pressing challenges affecting the field as a whole were competition for limited funding, duplication of efforts, and inadequate capacity building. A total of 76%, 67%, and 55% respondents, respectively, identified these as primary concerns. The key opportunities for growth identified in focus groups were increasing collaboration and information sharing, improving the interoperability of tools, and enhancing capacity for data analyses at scale. Some constraints appeared to disproportionately affect marginalized groups. Respondents in countries with developing economies were more likely to report being constrained by upfront costs, maintenance costs, and development funding (p = 0.048, odds ratio [OR] = 2.78; p = 0.005, OR = 4.23; p = 0.024, OR = 4.26), and female respondents were more likely to report being constrained by development funding and perceived technical skills (p = 0.027, OR = 3.98; p = 0.048, OR = 2.33). To our knowledge, this is the first attempt to formally capture the perspectives and needs of the global conservation technology community, providing foundational data that can serve as a benchmark to measure progress. We see tremendous potential for this community to further the vision they define, in which collaboration trumps competition; solutions are open, accessible, and interoperable; and user-friendly processing tools empower the rapid translation of data into conservation action. Article impact statement: Addressing financing, coordination, and capacity-building constraints is critical to the development and adoption of conservation technology.

Organization of observations near underground shelters of burrowing carnivorans: a comparison of different methods

Observations near the burrows gives rich material on the biology, intraspecific and interspecific interactions, and individual behaviour of animals. In our work, we considered four methods of observation (visual observations, visual observations with photo-fixation, video surveillance, and camera trapping) of burrowing carnivorans near their underground shelters. The research was conducted in spring and summer in different years in the period from 2004 to 2021 in open and forest habitats near burrows of badgers (Meles meles Linnaeus, 1758) and foxes (Vulpes vulpes Linnaeus, 1758).Visual observations are always associated with the presence of humans near the underground shelter of burrowing carnivorans for a long period of time. The researcher can be present only at one burrow at a time. The advantages of this method are that it is simple, cheap and allows for observing not only the burrow, but also the surrounding area. Complementing the data of visual observations with photographs largely increases their scientific value and informativeness. The use of camera traps minimizes human impact on animal behaviour, covers more underground shelters (depending on the number of devices) and collects more concentrated material than other methods. Camera trapping and video surveillance is also more convenient for the researcher, especially during the round-the-clock collection of data. This method however requires significant material costs and time to review and sort materials before data analysis. Data collection is limited to the working area of devices that do not always have time to capture animals when they pass very quickly. It is important under different environmental conditions to choose the optimal method of observation in order to study the animals effectively. In open biotopes during the organization of observations, there are difficulties with the installation of photo- and video equipment and its camouflage. In our opinion, the method of visual observations with photo-fixation remains relevant in conducting research near underground shelters of burrowing carnivorans under such conditions. The method of camera trapping is optimal for forest biotopes.

Automated audio recording as a means of surveying tinamous (Tinamidae) in the Peruvian Amazon

The use of machine learning technologies to process large quantities of remotely collected audio data is a powerful emerging research tool in ecology and conservation.We applied these methods to a field study of tinamou (Tinamidae) biology in Madre de Dios, Peru, a region expected to have high levels of interspecies competition and niche partitioning as a result of high tinamou alpha diversity. We used autonomous recording units to gather environmental audio over a period of several months at lowland rainforest sites in the Los Amigos Conservation Concession and developed a Convolutional Neural Network-based data processing pipeline to detect tinamou vocalizations in the dataset.The classified acoustic event data are comparable to similar metrics derived from an ongoing camera trapping survey at the same site, and it should be possible to combine the two datasets for future explorations of the target species' niche space parameters.Here, we provide an overview of the methodology used in the data collection and processing pipeline, offer general suggestions for processing large amounts of environmental audio data, and demonstrate how data collected in this manner can be used to answer questions about bird biology.

An approach to rapid processing of camera trap images with minimal human input

Camera traps have become an extensively utilized tool in ecological research, but the manual processing of images created by a network of camera traps rapidly becomes an overwhelming task, even for small camera trap studies.We used transfer learning to create convolutional neural network (CNN) models for identification and classification. By utilizing a small dataset with an average of 275 labeled images per species class, the model was able to distinguish between species and remove false triggers.We trained the model to detect 17 object classes with individual species identification, reaching an accuracy up to 92% and an average F1 score of 85%. Previous studies have suggested the need for thousands of images of each object class to reach results comparable to those achieved by human observers; however, we show that such accuracy can be achieved with fewer images.With transfer learning and an ongoing camera trap study, a deep learning model can be successfully created by a small camera trap study. A generalizable model produced from an unbalanced class set can be utilized to extract trap events that can later be confirmed by human processors.

Optimising camera trap height and model increases detection and individual identification rates for a small mammal, the numbat (Myrmecobius fasciatus)

Camera traps are widely used to collect data for wildlife management, but species-specific testing is crucial. We conducted three trials to optimise camera traps for detecting numbats (Myrmecobius fasciatus), a 500–700-g mammal. We compared detection rates from (1) Reconyx PC900 camera traps installed at heights ranging from 10–45cm, and (2) Reconyx PC900, Swift 3C standard and wide-angle camera traps with differing detection zone widths. Finally, we compared elevated, downward-angled time-lapse cameras installed at heights ranging from 1–2m to obtain dorsal images for individual numbat identification. Camera traps set at 25cm had the highest detection rates but missed 40% of known events. During model comparison, Swift 3C wide-angle camera traps recorded 89%, Swift 3C standard 51%, and Reconyx PC900 37% of known events. The number of suitable images from elevated, downward-angled cameras, depicting dorsal fur patterns, increased with increasing camera height. The use of well regarded camera trap brands and generic recommendations for set-up techniques cannot replace rigorous, species-specific testing. For numbat detection, we recommend the Swift 3C wide-angle model installed at 25-cm height. For individual numbat identification, elevated, downward-angled time-lapse cameras were useful; however, more research is needed to optimise this technique.

Development of a camera trap for perching dragonflies: a new tool for freshwater environmental assessment

Although dragonflies are excellent environmental indicators for monitoring terrestrial water ecosystems, automatic monitoring techniques using digital tools are limited. We designed a novel camera trapping system with an original dragonfly detector based on the hypothesis that perching dragonflies can be automatically detected using inexpensive and energy-saving photosensors built in a perch-like structure. A trial version of the camera trap was developed and evaluated in a case study targeting red dragonflies (Sympetrum spp.) in Japan. During an approximately 2-month period, the detector successfully detected Sympetrum dragonflies while using extremely low power consumption (less than 5 mW). Furthermore, a short-term field experiment using time-lapse cameras for validation at three locations indicated that the detection accuracy was sufficient for practical applications. The frequency of false positive detection ranged from 17 to 51 over an approximately 2-day period. The detection sensitivities were 0.67 and 1.0 at two locations, where a time-lapse camera confirmed that Sympetrum dragonflies perched on the trap more than once. However, the correspondence between the detection frequency by the camera trap and the abundance of Sympetrum dragonflies determined by field observations conducted in parallel was low when the dragonfly density was relatively high. Despite the potential for improvements in our camera trap and its application to the quantitative monitoring of dragonflies, the low cost and low power consumption of the detector make it a promising tool.

Acoustic vs. photographic monitoring of gray wolves (Canis lupus): a methodological comparison of two passive monitoring techniques

Remote camera traps are often used in large-mammal research and monitoring programs because they are cost-effective, allow for repeat surveys, and can be deployed for long time periods. Statistical advancements in calculating population densities from camera-trap data have increased the popularity of camera usage in mammal studies. However, drawbacks to camera traps include their limited sampling area and tendency for animals to notice the devices. In contrast, autonomous recording units (ARUs) record the sounds of animals with a much larger sampling area but are dependent on animals producing detectable vocalizations. In this study, we compared estimates of occupancy and detectability between ARUs and remote cameras for gray wolves (Canis lupus Linnaeus, 1758) in northern Alberta, Canada. We found ARUs to be comparable with cameras in their detectability and occupancy of wolves, despite only operating for 3% of the time that cameras were active. However, combining cameras and ARUs resulted in the highest detection probabilities for wolves. These advances in survey technology and statistical methods provide innovative avenues for large-mammal monitoring that, when combined, can be applied to a broad spectrum of conservation and management questions, provided assumptions for these methods are rigorously tested and met.

The effect of camera-trap viewshed obstruction on wildlife detection: implications for inference

Abstract ContextCamera traps are one of the most popular tools used to study wildlife worldwide. Numerous recent studies have evaluated the efficiency and effectiveness of camera traps as a research tool. Nonetheless, important aspects of camera-trap methodology remain in need of critical investigation. One such issue relates to camera-trap viewshed visibility, which is often compromised in the field by physical obstructions (e.g. trees) or topography (e.g. steep slopes). The loss of visibility due to these obstructions could affect wildlife detection rates, with associated implications for study inference and management application. AimsWe aimed to determine the effect of camera-trap viewshed obstruction on wildlife detection rates for a suite of eight North American species that vary in terms of ecology, commonness and body size. MethodsWe deployed camera traps at 204 sites throughout an extensive semi-urban park system in Cleveland, Ohio, USA, from June to September 2016. At each site, we quantified camera-trap viewshed obstruction by using a cover-board design. We then modelled the effects of obstruction on wildlife detection rates for the eight focal species. Key resultsWe found that detection rates significantly decreased with an increasing viewshed obstruction for five of the eight species, including both larger and smaller mammal species (white-tailed deer, Odocoileus virginianus, and squirrels, Sciurus sp., respectively). The number of detections per week per camera decreased two- to three-fold as visibility at a camera site decreased from completely free of obstruction to mostly obstructed. ConclusionsThese results imply that wildlife detection rates are influenced by site-level viewshed obstruction for a variety of species, and sometimes considerably so. ImplicationsResearchers using camera traps should address the potential for this effect to ensure robust inference from wildlife image data. Accounting for viewshed obstruction is critical when interpreting detection rates as indices of abundance or habitat use because variation in detection rate could be an artefact of site-level viewshed obstruction rather than due to underlying ecological processes.

Assessing arrays of multiple trail cameras to detect North American mammals

Motion triggered camera traps are an increasingly popular tool for wildlife research and can be used to survey for multiple species simultaneously. As with all survey techniques, it is crucial to conduct camera trapping research following study designs that include adequate spatial and temporal replication, and sufficient probability of detecting species presence. The use and configuration of multiple camera traps within a single survey site are understudied considerations that could have a substantial impact on detection probability. Our objective was to test the role that camera number (one, two or three units), and spacing along a linear transect (100 m or 150 m), have on the probability of detecting a species given it is present. From January to March, 2017 we collected data on six mammal species in Maine, USA: coyote (Canis latrans), fisher (Pekania pennanti), American marten (Martes americana), short-tailed weasel (Mustela erminea), snowshoe hare (Lepus americanus), and American red squirrel (Tamiasciurus hudsonicus). We used multi-scale occupancy modelling to compare pooled detection histories of different configuration of five cameras deployed at the same survey site (n = 32), and how the configuration would influence the probability of detecting a species given it was available at the site. Across all six species, we found substantial increases in probability of detection as the number of cameras increased from one to two (22 to 400 percent increase), regardless of the spacing between cameras. For most species the magnitude of the increase was less substantial when adding a third camera (4 to 85 percent increase), with coyote and snowshoe hare showing a pronounced effect. The influence of survey station features also varied by species. We suggest that using pooled data from two or three cameras at a survey site is a cost effective approach to increase detection success over a single camera.

The influence of the delay-period setting on camera-trap data storage, wildlife detections and occupancy models

Context The use of camera traps in ecological research has grown exponentially over the past decade, but questions remain about the effect of camera-trap settings on ecological inference. The delay-period setting controls the amount of time that a camera trap is idle between motion-activated triggers. Longer delay periods may potentially extend battery life, reduce data-storage requirements, and shorten data-analysis time. However, they might result in lost data (i.e. missed wildlife detections), which could bias ecological inference and compromise research objectives. Aims We aimed to examine the effect of the delay period on (1) the number of camera-trap triggers, (2) detection and site-occupancy probabilities for eight mammalian species that varied in size, movement rate and commonness and (3) parameter estimates of habitat-based covariates from the occupancy models for these species. Methods We deployed 104 camera traps for 4 months throughout an extensive urban park system in Cleveland, Ohio, USA, using a spatially random design. Using the resultant data, we simulated delay periods ranging from 10s to 60min. For each of these delay periods and for each of our eight focal species, we calculated the number of camera-trap triggers and the parameter estimates of hierarchical Bayesian occupancy models. Key results A simulated increase in the delay period from 10s to 10min decreased the number of triggers by 79.6%, and decreased detection probability and occupancy probability across all species by 1.6% and 4.4% respectively. Further increases in the delay period (i.e. from 10 to 60min) resulted in modest additional reductions in the number of triggers and detection and occupancy probabilities. Variation in the delay period had negligible effects on the qualitative interpretations of habitat-based occupancy models for all eight species. Conclusions Our results suggest that delay-period settings ranging from 5 to 10min can drastically reduce data-storage needs and analysis time without compromising inference resulting from occupancy modelling for a diversity of mammalian species. Implications Broadly, we provide guidance on designing camera-trap studies that optimally trade-off research effort and potential bias, thereby increasing the utility of camera traps as ecological research tools.

Measuring agreement among experts in classifying camera images of similar species

Camera trapping and solicitation of wildlife images through citizen science have become common tools in ecological research. Such studies collect many wildlife images for which correct species classification is crucial; even low misclassification rates can result in erroneous estimation of the geographic range or habitat use of a species, potentially hindering conservation or management efforts. However, some species are difficult to tell apart, making species classification challenging-but the literature on classification agreement rates among experts remains sparse. Here, we measure agreement among experts in distinguishing between images of two similar congeneric species, bobcats (Lynx rufus) and Canada lynx (Lynx canadensis). We asked experts to classify the species in selected images to test whether the season, background habitat, time of day, and the visible features of each animal (e.g., face, legs, tail) affected agreement among experts about the species in each image. Overall, experts had moderate agreement (Fleiss' kappa = 0.64), but experts had varying levels of agreement depending on these image characteristics. Most images (71%) had ≥1 expert classification of "unknown," and many images (39%) had some experts classify the image as "bobcat" while others classified it as "lynx." Further, experts were inconsistent even with themselves, changing their classifications of numerous images when they were asked to reclassify the same images months later. These results suggest that classification of images by a single expert is unreliable for similar-looking species. Most of the images did obtain a clear majority classification from the experts, although we emphasize that even majority classifications may be incorrect. We recommend that researchers using wildlife images consult multiple species experts to increase confidence in their image classifications of similar sympatric species. Still, when the presence of a species with similar sympatrics must be conclusive, physical or genetic evidence should be required.

A Comparison between Video and Still Imagery as a Methodology to Determine Southern Hairy-Nosed Wombat (Lasiorhinus latifrons) Burrow Occupancy Rates

Simple Summary

While many people have great affection for southern hairy-nosed wombats, they are also considered by others to be an agricultural pest, because of the damage they can cause to farmland and agricultural infrastructure. Therefore, we need to have a good understanding of how many wombats there might be and how the population is changing, if we are to make properly informed decisions on how to best manage them. Unfortunately, because wombats are nocturnal and live underground, and because they use a number of different burrows throughout their home ranges, counting them can be difficult. We used motion-activated cameras to record how often wombats use each burrow in order to develop a reliable method of counting wombats that we can apply at the broad scale. We found that, on average, there are around 0.43 wombats for each active burrow. The use of video cameras to record this information provided a much simpler and less invasive means of researching wombat behaviour than methods such as trapping. However, video cameras do have limitations that need to be considered, and researchers need to fully understand their capabilities and limitations before employing them in the field.

Abstract

Broad-scale abundance estimates of the southern hairy-nosed wombat population use a proxy measure based on counting the number of active burrows, which is multiplied by an index of ‘wombats/active burrow’. However, the extant indices were calculated in the 1980s, prior to the use of calicivirus to control rabbits, and used invasive monitoring methods which may have affected the results. We hypothesise that the use of video might provide a logistically simple, non-invasive means of calculating updated indices. To this end, motion-activated, infra-red still and video cameras were placed at various distances outside active wombat burrows in the South Australian Murraylands and Eyre Peninsula regions. The captured imagery was inspected to determine how often the burrow was occupied by one or more wombats, and how effective the cameras were at detecting wombat activity. Video data was clearly superior to the still imagery, with more than twice as many burrow occupancies being positively identified (still: 43%). The indices of wombats/active burrow calculated based on video imagery were: Murraylands: 0.43, Eyre Peninsula: 0.42. 1948 false positive videos were recorded, of which 1674 (86%) occurred between noon and sunset.

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.

Camera trap arrays improve detection probability of wildlife: Investigating study design considerations using an empirical dataset

Camera trapping is a standard tool in ecological research and wildlife conservation. Study designs, particularly for small-bodied or cryptic wildlife species often attempt to boost low detection probabilities by using non-random camera placement or baited cameras, which may bias data, or incorrectly estimate detection and occupancy. We investigated the ability of non-baited, multi-camera arrays to increase detection probabilities of wildlife. Study design components were evaluated for their influence on wildlife detectability by iteratively parsing an empirical dataset (1) by different sizes of camera arrays deployed (1–10 cameras), and (2) by total season length (1–365 days). Four species from our dataset that represented a range of body sizes and differing degrees of presumed detectability based on life history traits were investigated: white-tailed deer (Odocoileus virginianus), bobcat (Lynx rufus), raccoon (Procyon lotor), and Virginia opossum (Didelphis virginiana). For all species, increasing from a single camera to a multi-camera array significantly improved detection probability across the range of season lengths and number of study sites evaluated. The use of a two camera array increased survey detection an average of 80% (range 40–128%) from the detection probability of a single camera across the four species. Species that were detected infrequently benefited most from a multiple-camera array, where the addition of up to eight cameras produced significant increases in detectability. However, for species detected at high frequencies, single cameras produced a season-long (i.e, the length of time over which cameras are deployed and actively monitored) detectability greater than 0.75. These results highlight the need for researchers to be critical about camera trap study designs based on their intended target species, as detectability for each focal species responded differently to array size and season length. We suggest that researchers a priori identify target species for which inference will be made, and then design camera trapping studies around the most difficult to detect of those species.

WiseEye: Next Generation Expandable and Programmable Camera Trap Platform for Wildlife Research

The widespread availability of relatively cheap, reliable and easy to use digital camera traps has led to their extensive use for wildlife research, monitoring and public outreach. Users of these units are, however, often frustrated by the limited options for controlling camera functions, the generation of large numbers of images, and the lack of flexibility to suit different research environments and questions. We describe the development of a user-customisable open source camera trap platform named ‘WiseEye’, designed to provide flexible camera trap technology for wildlife researchers. The novel platform is based on a Raspberry Pi single-board computer and compatible peripherals that allow the user to control its functions and performance. We introduce the concept of confirmatory sensing, in which the Passive Infrared triggering is confirmed through other modalities (i.e. radar, pixel change) to reduce the occurrence of false positives images. This concept, together with user-definable metadata, aided identification of spurious images and greatly reduced post-collection processing time. When tested against a commercial camera trap, WiseEye was found to reduce the incidence of false positive images and false negatives across a range of test conditions. WiseEye represents a step-change in camera trap functionality, greatly increasing the value of this technology for wildlife research and conservation management.

Digital technology and the conservation of nature

Digital technology is changing nature conservation in increasingly profound ways. We describe this impact and its significance through the concept of 'digital conservation', which we found to comprise five pivotal dimensions: data on nature, data on people, data integration and analysis, communication and experience, and participatory governance. Examining digital innovation in nature conservation and addressing how its development, implementation and diffusion may be steered, we warn against hypes, techno-fix thinking, good news narratives and unverified assumptions. We identify a need for rigorous evaluation, more comprehensive consideration of social exclusion, frameworks for regulation and increased multi-sector as well as multi-discipline awareness and cooperation. Along the way, digital technology may best be reconceptualised by conservationists from something that is either good or bad, to a dual-faced force in need of guidance.