Snap happy: camera traps are an effective sampling tool when compared with alternative methods

Next-Generation Camera Trapping: Systematic Review of Historic Trends Suggests Keys to Expanded Research Applications in Ecology and Conservation

Camera trapping is an effective non-invasive method for collecting data on wildlife species to address questions of ecological and conservation interest. We reviewed 2,167 camera trap (CT) articles from 1994 to 2020. Through the lens of technological diffusion, we assessed trends in: (1) CT adoption measured by published research output, (2) topic, taxonomic, and geographic diversification and composition of CT applications, and (3) sampling effort, spatial extent, and temporal duration of CT studies. Annual publications of CT articles have grown 81-fold since 1994, increasing at a rate of 1.26 (SE = 0.068) per year since 2005, but with decelerating growth since 2017. Topic, taxonomic, and geographic richness of CT studies increased to encompass 100% of topics, 59.4% of ecoregions, and 6.4% of terrestrial vertebrates. However, declines in per article rates of accretion and plateaus in Shannon's H for topics and major taxa studied suggest upper limits to further diversification of CT research as currently practiced. Notable compositional changes of topics included a decrease in capture-recapture, recent decrease in spatial-capture-recapture, and increases in occupancy, interspecific interactions, and automated image classification. Mammals were the dominant taxon studied; within mammalian orders carnivores exhibited a unimodal peak whereas primates, rodents and lagomorphs steadily increased. Among biogeographic realms we observed decreases in Oceania and Nearctic, increases in Afrotropic and Palearctic, and unimodal peaks for Indomalayan and Neotropic. Camera days, temporal extent, and area sampled increased, with much greater rates for the 0.90 quantile of CT studies compared to the median. Next-generation CT studies are poised to expand knowledge valuable to wildlife ecology and conservation by posing previously infeasible questions at unprecedented spatiotemporal scales, on a greater array of species, and in a wider variety of environments. Converting potential into broad-based application will require transferable models of automated image classification, and data sharing among users across multiple platforms in a coordinated manner. Further taxonomic diversification likely will require technological modifications that permit more efficient sampling of smaller species and adoption of recent improvements in modeling of unmarked populations. Environmental diversification can benefit from engineering solutions that expand ease of CT sampling in traditionally challenging sites.

Abundance estimation of unmarked animals based on camera-trap data

The rapid improvement of camera traps in recent decades has revolutionized biodiversity monitoring. Despite clear applications in conservation science, camera traps have seldom been used to model the abundance of unmarked animal populations. We sought to summarize the challenges facing abundance estimation of unmarked animals, compile an overview of existing analytical frameworks, and provide guidance for practitioners seeking a suitable method. When a camera records multiple detections of an unmarked animal, one cannot determine whether the images represent multiple mobile individuals or a single individual repeatedly entering the camera viewshed. Furthermore, animal movement obfuscates a clear definition of the sampling area and, as a result, the area to which an abundance estimate corresponds. Recognizing these challenges, we identified 6 analytical approaches and reviewed 927 camera-trap studies published from 2014 to 2019 to assess the use and prevalence of each method. Only about 5% of the studies used any of the abundance-estimation methods we identified. Most of these studies estimated local abundance or covariate relationships rather than predicting abundance or density over broader areas. Next, for each analytical approach, we compiled the data requirements, assumptions, advantages, and disadvantages to help practitioners navigate the landscape of abundance estimation methods. When seeking an appropriate method, practitioners should evaluate the life history of the focal taxa, carefully define the area of the sampling frame, and consider what types of data collection are possible. The challenge of estimating abundance of unmarked animal populations persists; although multiple methods exist, no one method is optimal for camera-trap data under all circumstances. As analytical frameworks continue to evolve and abundance estimation of unmarked animals becomes increasingly common, camera traps will become even more important for informing conservation decision-making.

Sign surveys can be more efficient and cost effective than driven transects and camera trapping: a comparison of detection methods for a small elusive mammal, the numbat (Myrmecobius fasciatus)

Abstract ContextDetermining the most efficient detection method for a target species is key for successful wildlife monitoring and management. Driven transects and sign surveys are commonly used to monitor populations of the endangered numbat (Myrmecobius fasciatus). Camera trapping is being explored as a new method. These methods were unevaluated for efficacy and cost for numbat detection. AimsTo compare efficacy and costing of driven transects, sign surveys and camera trapping for detecting numbats in the Upper Warren region, Western Australia. MethodsSeven repeat sign surveys and driven transects, as well as 4 months of camera trapping, were conducted concurrently at 50 sites along three transects. Numbat detection rates and costing of the three techniques were compared, and detection probabilities were compared between sign surveys and camera trapping. Key resultsNumbat signs were detected during 88 surveys at 39 sites, exceeding camera trapping (26 detections at 13 sites) and driven transects (seven detections near five sites). The estimated probability for detecting a numbat or a sign thereof (at a site where numbats were present) ranged from 0.21 to 0.35 for a sign survey, and 0.02 to 0.06 for 7 days of camera trapping. Total survey costs were lowest for driven transects, followed by camera trapping and sign surveys. When expressed as cost per numbat detection, sign surveys were cheapest. ConclusionsComparative studies of survey methods are essential for optimal, cost-effective wildlife monitoring. Sign surveys were more successful and cost effective than camera trapping or driven transects for detecting numbats in the Upper Warren region. Together with occupancy modelling, sign surveys are appropriate to investigate changes in occupancy rates over time, which could serve as a metric for long-term numbat monitoring. ImplicationsThere is no ‘best’ method for wildlife surveys. Case-specific comparison of animal detection methods is recommended to ensure optimal methods. For the numbat population in the Upper Warren region, further studies are needed to improve numbat detection rates from camera trapping, and to test sign surveys in autumn (March to May), when surviving juvenile numbats have established their own territory and assumptions regarding population closure are less likely to be violated.

Optimising deployment time of remote cameras to estimate abundance of female bighorn sheep

Abstract ContextWildlife biologists accumulate large quantities of images from remote cameras, which can be time- and cost-prohibitive to archive and analyse. Remote-camera projects would benefit from not setting cameras longer than needed and not analysing more images than needed; however, there is a lack of information about optimal deployment time required for remote-camera surveys to estimate ungulate abundance. AimsThe objective was to estimate abundance of adult females in a population of Rocky Mountain bighorn sheep (Ovis canadensis canadensis) in Utah, USA, from 2012 to 2014, and determine whether this type of study can be conducted more efficiently. Because females are the most important cohort for population growth, remote cameras were set at three water sources and mark–resight models in Program MARK were used. MethodsWe compared estimated abundance of collared and uncollared females by number of days cameras were set using 31 replicated abundance estimates from each year starting 1 July. Each replicated estimate used a different number of days and photographs from a 62-day sampling period (1 July to 31 August). Key resultsAbundance estimates ranged from 44 to 98 animals. Precise estimates of abundance, however, were obtained with only 12 days of sampling in each year. By analysing only 12 days of images rather than 62 days in all years, the estimated mean of 58 adult females would have changed by only 7 individuals (±4 individuals, range=3–10 animals), the s.e. would have increased by a mean of only 4 individuals (±1.6, range=2.0–5.2 individuals) and a mean of only 18% (±10.5%, range=8–29%) of images would have been analysed. Across the study, analysis of >23000 (>80%) images could have been avoided, saving time and money. ConclusionsThe results indicate that an asymptotic relationship exists between estimated abundance of female bighorn sheep and remote-camera deployment time. ImplicationsThe mark–resight methods used in the present study would work for other ungulates in which individuals are radio collared or marked using remote cameras set at water sources, trail crossings or mineral licks. These findings can help researchers reduce cost of setting, servicing, archiving and analysing photographs from remote cameras for ungulate population monitoring.

The secret social lives of African crested rats, Lophiomys imhausi

The crested rat, Lophiomys imhausi, is the only mammal known to sequester plant toxins. Found in eastern Africa, this large rodent is thought to defend against predation by coating specialized hairs along its sides with cardenolide toxins from the poison arrow tree, Acokanthera schimperi. To better understand the ecology of this unusual poisonous mammal, we used camera traps, livetrapping, and captive behavioral observations, to study L. imhausi in central Kenya. Although crested rats were rarely detected with camera traps, 25 individuals were caught in live traps, with estimated densities of up to 15 rats/km² at one of nine trapping sites. Trapping records and behavioral observations suggest that L. imhausi live in male-female pairs, with juveniles that might exhibit delayed dispersal. We observed chewing of A. schimperi and/or anointing in 10 of 22 individuals, confirming the previous poison sequestration observation. We monitored crested rat activity using cameras and found that chewing on A. schimperi and cardenolide exposure had no effect on feeding, movement, or total activity. One crested rat also fed on milkweed (Gomphocarpus physocarpus; Gentaniales: Apocynaceae), but did not anoint with this cardenolide containing plant. This observation, combined with L. imhausi's selective use of A. schimperi, suggests the potential for use of alternative poison sources. This research provides novel insight into the ecology of L. imhausi, while also suggesting that more field observations, feeding trials, and chemical analyses are needed to understand their behavior and physiology. Furthermore, their complex social interactions, slow life history, and fragmented populations suggest that L. imhausi could be at risk of decline.

Camera settings and biome influence the accuracy of citizen science approaches to camera trap image classification

Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best-practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open-grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data.

Applying camera traps to detect and monitor introduced mammals on oceanic islands

The introduction of mammal predators has been a major cause of species extinctions on oceanic islands. Eradication is only possible or cost-effective at early stages of invasion, before introduced species become abundant and widespread. Although prevention, early detection and rapid response are the best management strategies, most oceanic islands lack systems for detecting, responding to and monitoring introduced species. Wildlife managers require reliable information on introduced species to guide, assess and adjust management actions. Thus, a large-scale and long-term monitoring programme is needed to evaluate the management of introduced species and the protection of native wildlife. Here, we evaluate camera trapping as a survey technique for detecting and monitoring introduced small and medium-sized terrestrial mammals on an oceanic island, Terceira (Azores). Producing an inventory of introduced mammals on this island required a sampling effort of 465 camera-trap days and cost EUR 2,133. We estimated abundance and population trends by using photographic capture rates as a population index. We also used presence/absence data from camera-trap surveys to calculate detection probability, estimated occupancy rate and the sampling effort needed to determine species absence. Although camera trapping requires large initial funding, this is offset by the relatively low effort for fieldwork. Our findings demonstrate that camera trapping is an efficient survey technique for detecting and monitoring introduced species on oceanic islands. We conclude by proposing guidelines for designing monitoring programmes for introduced species.

Disease-Induced Mortality Outweighs Hunting in Causing Wild Boar Population Crash After African Swine Fever Outbreak

African swine fever (ASF) has been spreading in the Eurasian continent for more than 10 years now. Although the course of ASF in domestic pigs and its negative economic impact on the pork industry are well-known, we still lack a quantitative assessment of the impact of ASF on wild boar (Sus scrofa) populations under natural conditions. Wild boar is not only a reservoir for ASF; it is also one of the key wildlife species affecting structure and functioning of ecosystems. Therefore, knowledge on how ASF affects wild boar populations is crucial to better predict ecosystem response and for the design of scientific-based wild boar management to control ASF. We used a long-term camera trap survey (2012-2017) from the Białowieza Primeval Forest (BPF, Poland), where an ASF outbreak occurred in 2015, to investigate the impact of the disease on wild boar population dynamics under two contrasting management regimes (hunted vs. non-hunted). In the hunted part of BPF ("managed area"), hunting was drastically increased prior and after the first ASF case occurred (March 2015), whereas inside the National Park, hunting was not permitted ("unmanaged area," first detected case in June 2015). Using a random encounter model (REM), we showed that the density and abundance of wild boar dropped by 84 and 95% within 1 year following ASF outbreak in the unmanaged and managed area, respectively. In the managed area, we showed that 11-22% additional mortality could be attributed to hunting. Our study suggests that ASF-induced mortality, by far, outweighs hunting-induced mortality in causing wild boar population decline and shows that intensified hunting in newly ASF-infected areas does not achieve much greater reduction of population size than what is already caused by the ASF virus.

How to learn to stop worrying and love environmental DNA monitoring

Environmental DNA is one of the most promising new tools in the aquatic biodiversity monitoring toolkit, with particular appeal for applications requiring assessment of target taxa at very low population densities. And yet there persists considerable anxiety within the management community regarding the appropriateness of environmental DNA monitoring for certain tasks and the degree to which environmental DNA methods can deliver information relevant to management needs. This brief perspective piece is an attempt to address that anxiety by offering some advice on how end-users might best approach these new technologies. I do not here review recent developments in environmental DNA science, but rather I explore ways in which managers and decision-makers might become more comfortable adopting environmental DNA tools-or choosing not to adopt them, should circumstances so dictate. I attempt to contextualize the central challenges associated with acceptance of environmental DNA detection by contrasting them with traditional "catch-and-look" approaches to biodiversity monitoring. These considerations lead me to recommend the cultivation of four "virtues," attitudes that can be brought into engagement with environmental DNA surveillance technologies that I hope will increase the likelihood that those engagements will be positive and that the future development and application of environmental DNA tools will further the cause of wise management.

Innovations in Camera Trapping Technology and Approaches: The Integration of Citizen Science and Artificial Intelligence

Simple Summary

Camera traps, also known as “game cameras” or “trail cameras”, have increasingly been used in wildlife research over the last 20 years. Although early units were bulky and the set-up was complicated, modern camera traps are compact, integrated units able to collect vast digital datasets. Some of the challenges now facing researchers include the time required to view, classify, and sort all of the footage collected, as well as the logistics of establishing and maintaining camera trap sampling arrays across wide geographic areas. One solution to this problem is to enlist or recruit the public for help as ‘citizen scientists’ collecting and processing data. Artificial Intelligence (AI) is also being used to identify animals in digital photos and video; however, this process is relatively new, and machine-based classifications are not yet fully reliable. By combining citizen science with AI, it should be possible to improve efficiency and increase classification accuracy, while simultaneously maintaining and promoting the benefits associated with public engagement with, and awareness of, wildlife.

Abstract

Camera trapping has become an increasingly reliable and mainstream tool for surveying a diversity of wildlife species. Concurrent with this has been an increasing effort to involve the wider public in the research process, in an approach known as ‘citizen science’. To date, millions of people have contributed to research across a wide variety of disciplines as a result. Although their value for public engagement was recognised early on, camera traps were initially ill-suited for citizen science. As camera trap technology has evolved, cameras have become more user-friendly and the enormous quantities of data they now collect has led researchers to seek assistance in classifying footage. This has now made camera trap research a prime candidate for citizen science, as reflected by the large number of camera trap projects now integrating public participation. Researchers are also turning to Artificial Intelligence (AI) to assist with classification of footage. Although this rapidly-advancing field is already proving a useful tool, accuracy is variable and AI does not provide the social and engagement benefits associated with citizen science approaches. We propose, as a solution, more efforts to combine citizen science with AI to improve classification accuracy and efficiency while maintaining public involvement.