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Camera-trapping for conservation: a guide to best-practices

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100 YEARS OF HISTORY Over the last decade, millions of people around the world have become aware of the camera trap. The candid images and videos that camera traps produce have been featured in countless documentaries, are widely shared on social media, and have been the focus of hugely popular citizen science projects. Less well known is the fact that the camera trap has a long history that extends back more than 100 years. Over this time, they have gone from being an experimental technology used by just a handful of people to a commercialised technology being used by many thousands of photographers, hobbyists, hunters and biologists. THE MODERN CAMERA TRAP The modern camera trap is simply a digital camera connected to an infrared sensor which can “see” warm objects that are moving, like animals. When an animal moves past the sensor it causes the camera to fire, recording an image or video to the memory card for later retrieval. Camera traps can be left in the field to continuously watch an area of habitat for weeks or even months, recording the rarest events which occur in nature. This can include everything from a big cat patrolling its territory, to the raiding of a bird´s nest by a predator. Camera traps are also “wildlife friendly”, in that they cause little or no disturbance to wildlife. At the same time, they produce permanent and verifiable records of animals, akin to traditional museum voucher specimens. HIGHLY EFFECTIVE TOOLS Camera traps provide data on exactly where species are, what they are doing, and how large their populations are. They can be used to build up a picture of whole communities of species, including how they are structured and how species are interacting over space and time. Camera traps are also being deployed to understand how humans and livestock interact with wildlife. The development of networked camera traps, capable of sending images over phone or satellite networks in near real-time, has provided a new tool in the fight against poaching. New software tools and statistical models are also making it much easier and faster to obtain high quality information from the thousands of images that camera traps can quickly generate. This is improving our understanding of human impacts on wildlife, and helping land managers make better decisions at both small and large scales. CHALLENGES Despite the great potential of camera traps, there are a number of significant challenges involved in working with them. This can be frustrating for first-time users of the technology and can lead to wasted time and resources. Here we provide all the information needed to get up and running with camera traps as quickly as possible. Our aim is to maximise the effectiveness of camera traps for conservation and ecological research. We introduce the technology, help you decide if camera traps are right for your needs, provide the information you need when shopping for camera traps, and then give detailed recommendations on how exactly to deploy camera traps in the field.
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... Currently, the majority of camera trap models are equipped with passive infrared (PIR) sensors that are triggered by moving objects with a different surface temperature than the background environment (Welbourne et al., 2016;Wearn & Glover-Kapfer, 2017). This detection method is specific in that it will miss part of the endothermic species that produce too little heat (micromammals or small vertebrates) and most of the ectothermic species that do not produce heat (reptiles, amphibians, and invertebrates). ...
... However, it may momentarily disturb some individuals (see question on p. 73: "Does the presence of a camera trap impact the behaviour of animals using the structure?") who find themselves facing the cameras emitting "black" flashes and electronic noise (WEARN & GLOVER-KAPFER, 2017). This positioning is sometimes not (2017), however, showed that for a Reconyx HC600 infrared detection system, this parameter (vertical or horizontal movement) had no effect on detection efficiency. ...
... According to Wearn & Glover-Kapfer (2017), the manufacturers of camera traps often recommend camera heights for large fauna (1.5 metres for example in the Reconyx Hyperfire manual). However, in order to detect a broader range, notably small and medium-sized fauna, it is important to place the cameras lower. ...
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This report (printed also in French) follows on from the first feedback on experience published in June 2016, Restauration des continuités écologiques sur autoroutes – Retour d’expérience des aménagements et des suivis faunistiques sur le réseau VINCI Autoroutes Restoration of ecological continuities on motorways – Feedback on experience concerning adaptations for and monitoring of wildlife on the VINCI Autoroutes network). It notably provides further information on the factors that condition the utilisation of wildlife crossings and attempts, by statistical analysis, to answer the much more complex question of the effectiveness of these adapted structures. The first report nevertheless remains the document reference for any reader seeking technical information on the construction of such structures (cf. Method files)
... The relative abundance of each species was estimated from the number of independent events in the 1 km 2 square grid divided by the total number of days that camera traps were operating in each square. Independent events were defined as those obtained for the same species and camera-trap station, but taken at least 24 h apart, or when more than one individual was recorded in a photograph (e.g., Wearn & Glover-Kapfer, 2017). ...
Article
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The distribution range and population abundance of species provide fundamental information on the species–habitat relationship required for management and conservation. Abundance inherently provides more information about the ecology of species than do occurrence data. However, information on abundance is scarce for most species, mainly at large spatial scales. The objective of this work was, therefore, to provide information regarding the population status of six wild felids inhabiting territories in Mexico that are inaccessible or politically unstable. This was done using species distribution models derived from occurrence data. We used distribution data at a continental scale for the wild felids inhabiting Mexico: jaguar ( Panthera onca ), bobcat ( Lynx rufus ), ocelot ( Leopardus pardalis ), cougar ( Puma concolor ), margay ( Leopardus wiedii ), and jaguarundi ( Herpailurus yagouaroundi ) to predict environmental suitability (estimated by both Maxent and the distance to niche centroid, DNC). Suitability was then examined by relating to a capture rate‐based index, in a well‐monitored area in central western Mexico in order to assess their performance as proxies of relative abundance. Our results indicate that the environmental suitability patterns predicted by both algorithms were comparable. However, the strength of the relationship between the suitability and relative abundance of local populations differed across species and between algorithms, with the bobcat and DNC, respectively, having the best fit, although the relationship was not consistent in all the models. This paper presents the potential of implementing species distribution models in order to predict the relative abundance of wild felids in Mexico and offers guidance for the proper interpretation of the relationship between suitability and population abundance. The results obtained provide a robust information base on which to outline specific conservation actions and on which to examine the potential status of endangered species inhabiting remote or politically unstable territories in which on‐field monitoring programs are not feasible.
... Null CNN_success ~1 + (1 | camera_number) Table 3) and thus produce a heat signature similar to a deer and are readily picked up by the PIR sensor (Wearn & Glover-Kapfer, 2017 Finally, on a more general note, camera traps have become an increasingly important tool for the monitoring of vertebrates, both because they are cost effective and relatively easy to deploy, but especially because many animals are extremely difficult to monitor at landscape scales using any other method. This tool is useful for monitoring shy and rare species that are in hard-to-reach locations either because of geography or because of military conflict, as is currently the case in Ukraine. ...
Article
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Camera traps have become in situ sensors for collecting information on animal abundance and occupancy estimates. When deployed over a large landscape, camera traps have become ideal for measuring the health of ecosystems, particularly in unstable habitats where it can be dangerous or even impossible to observe using conventional methods. However, manual processing of imagery is extremely time and labor intensive. Because of the associated expense, many studies have started to employ machine‐learning tools, such as convolutional neural networks (CNNs). One drawback for the majority of networks is that a large number of images (millions) are necessary to devise an effective identification or classification model. This study examines specific factors pertinent to camera trap placement in the field that may influence the accuracy metrics of a deep‐learning model that has been trained with a small set of images. False negatives and false positives may occur due to a variety of environmental factors that make it difficult for even a human observer to classify, including local weather patterns and daylight. We transfer‐trained a CNN to detect 16 different object classes (14 animal species, humans, and fires) across 9576 images taken from camera traps placed in the Chernobyl Exclusion Zone. After analyzing wind speed, cloud cover, temperature, image contrast, and precipitation, there was not a significant correlation between CNN success and ambient conditions. However, a possible positive relationship between temperature and CNN success was noted. Furthermore, we found that the model was more successful when images were taken during the day as well as when precipitation was not present. This study suggests that while qualitative site‐specific factors may confuse quantitative classification algorithms such as CNNs, training with a dynamic training set can account for ambient conditions so that they do not have a significant impact on CNN success.
... Camtrap DP can also stimulate the development of standardized camera trap data processing pipelines, including those focused on the application of Artificial Intelligence/Machine Learning methods for automatic image recognition (Tabak et al., 2018;Kellenberger et al., 2020) and the automation of camera trap data analysis using already well-established statistical frameworks for modeling, e.g., species distribution, species richness, activity patterns, occupancy and abundance (Rovero & Zimmermann, 2016;Wearn & Glover-Kapfer, 2017). Apart from one valuable initiative, https://lila.science, ...
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Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap-derived Big Data are becoming increasingly solvable with the help of scalable cyber-infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media, and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media-based and event-based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in-depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping, and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively, and with version control from the start and we encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard.
... Besides, the data collected through different sensors by different research groups were scattered and not available in the public domain. With the rapid technological advancement, the camera trap technology emerged (Wearn and Kapfer 2017) and have matured to a point where they are readily available for the researchers for data collection, besides being easy to use and maintain. ...
Article
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The continuous monitoring of animals is crucial for the well-being of both humans and animals. A comprehensive animal monitoring system must incorporate animal detection, classification, and deterrence techniques. This review paper addresses 8 research questions related to animal monitoring by presenting a comprehensive literature review of animal deterrence, monitoring, classification, and detection techniques. Additionally, it covers various animal image acquisition techniques, different image modalities, photogrammetry types, and unmanned vehicles used for animal studies. The paper also highlights the problems faced by animals and humans in co-existence and lists the challenges faced while capturing animal images in different modalities, such as visible, thermal, and aerial images. The conclusion includes a comparative study based on benchmark datasets and highlights future scope and areas that require further research in animal monitoring systems.
... 5G will also increase by two-fold the total number of devices being run and connected at the same moment for transmission and reception. For example, in case of camera traps 5G can help to achieve faster data transmission, though battery life may be reduced [25]. In case of radio collar, 5G will improve the accuracy of reduce latency. ...
Article
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India is rich in flora and fauna and has thus developed a network of protected areas to conserve this socially, economically, and biologically valuable resource. However, India also faces many challenges, making its natural resources as well as those protecting it, vulnerable. These challenges have been exposed further by the pandemic reinforcing the fact that monitoring biodiversity in general and wildlife, in particular, would need constant up gradation in tools, processes, and methods in order to curb the growing menace of their loss. Currently, world over, the technologies being used for wildlife monitoring are camera traps, wireless sensor networks, drones etc. Many of these technologies are being used as stand-alone systems with no real time access to data. In order to enhance their effectiveness,4G network is being used but in a limited way. However, with increased use of technology in wildlife crime, it becomes pertinent to develop more effective systems that would provide multiple, reliable, remote and fast access to data so as to ensure timely action. This paper proposes a system that integrates different existing technologies used for wildlife monitoring. It proposes integration of 5G communication technology to the system for effective and real time transmission of data. The proposed integrated system would consist of camera traps, drones, LoRa (Long Range) based Wireless Sensor Nodes (WSN) that will be spread to cover the entire forest area to not only monitor movement of animals and poachers but would also sense forest fires.
... Given constraints related to the nature of the terrain and the presence of dense scrubwood, we made an effort to put cameras at an average height of c. 30-100 cm, to increase the detection probability of smaller animals such as mustelids and foxes [97]. Thus, 88.4% of 138 yearly deployments occurred at a height lower than 100 cm from the ground. ...
Article
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Background There is need of information on ecological interactions that keystone species such as apex predators establish in ecosystems recently recolonised. Interactions among carnivore species have the potential to influence community-level processes, with consequences for ecosystem dynamics. Although avoidance of apex predators by smaller carnivores has been reported, there is increasing evidence that the potential for competitive-to-facilitative interactions is context-dependent. In a protected area recently recolonised by the wolf Canis lupus and hosting abundant wild prey (3 ungulate species, 20–30 individuals/km2, together), we used 5-year food habit analyses and 3-year camera trapping to (i) investigate the role of mesocarnivores (4 species) in the wolf diet; (ii) test for temporal, spatial, and fine-scale spatiotemporal association between mesocarnivores and the wolf. Results Wolf diet was dominated by large herbivores (86% occurrences, N = 2201 scats), with mesocarnivores occurring in 2% scats. We collected 12,808 carnivore detections over > 19,000 camera trapping days. We found substantial (i.e., generally ≥ 0.75, 0–1 scale) temporal overlap between mesocarnivores—in particular red fox—and the wolf, with no support for negative temporal or spatial associations between mesocarnivore and wolf detection rates. All the species were nocturnal/crepuscular and results suggested a minor role of human activity in modifying interspecific spatiotemporal partitioning. Conclusions Results suggest that the local great availability of large prey to wolves limited negative interactions towards smaller carnivores, thus reducing the potential for spatiotemporal avoidance. Our study emphasises that avoidance patterns leading to substantial spatiotemporal partitioning are not ubiquitous in carnivore guilds.
... In this study, two methods were used, i.e., live trapping and camera trapping that target the more typical terrestrial non-volant small mammals. Advancements in camera-trapping technology have led to the widespread use of this survey method in the study of terrestrial mammals (Wearn & Glover-Kapfer 2017;Jessica et al. 2021). The two methods were used for a more comprehensive inventory documentation of species such as using camera traps for the documentation of less trappable species (Tasker & Dickman 2001;De Bondi et al. 2010;Thomas et al. 2020). ...
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Deforestation in Cameron Highlands, Malaysia has increased significantly in the past few years to accommodate the growing population of Cameron Highlands. This led to a rapid urbanisation in Cameron Highlands which increased anthropogenic activities, causing degradation of the natural environment. Such environmental changes highlight the necessity of wildlife and resource inventories of available forested areas to improve existing conservation and management plans, especially for threatened taxa such as the non-volant small mammals. However, very few studies are known to focus on the effect of deforestation on non-volant small mammals, especially in the adjacent forest. This survey aimed to document non-volant small mammals from four habitat types (restoration areas, boundary, disturbed and undisturbed areas) of Terla A and Bertam, and undisturbed forest of Bukit Bujang Forest Reserve, Cameron Highlands, Malaysia. Samplings were conducted in two phases between August 2020 to January 2021. A total of 80 live traps were deployed along the transect lines in all three study sites, and 10 camera traps were set randomly in each forested area. Results demonstrated that species diversity (H') is higher at Terla A Forest Reserve compared to Bertam and Bukit Bujang Forest Reserve. In contrast, species diversity in the boundary area (S = 8, H' = 2.025) and disturbed forest area (S = 8, H' = 1.992) had similar number of species (S) compared to others study habitat; restoration area had the lowest species diversity (S = 3, H' = 0.950). Berylmys bowersi was the most captured species from trappings and Lariscus insignis was the most frequently recorded species from camera trappings for all study sites. The results of the survey provided new information on non-volant small mammals in Cameron Highlands for future research, conservation, and management.
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Abstract Indicator species are frequently used to monitor restoration areas. However, species of conservation concern are usually absent in highly fragmented landscapes, making the selection of indicator species a challenging task. Here, we select indicator species of birds and mammals to be used for the evaluation of restoration sites in a highly fragmented landscape, the Capivara-Taquaruçu Dams region located in north Paraná, Brazil. By using the Index of Biotic Integrity (IBI), we show that the Capivara-Taquaruçu Dams landscape has low IBI values and bird richness when compared with two other landscapes in the north of Paraná. Therefore, we used the Individual Indicate Value to identify birds and mammals associated with forest fragments in the Capivara-Taquaruçu Dams landscape. Six bird and four mammal species were selected as indicators of forest fragments, none of which were of conservation concern. However, monitoring of these species could help evaluate the recovery of restoration sites in the Capivara-Taquaruçu Dams region. Lastly, several species of birds and mammals were frequently recorded in the restoration sites, including vulnerable species such as the lowland tapir (Tapirus terrestris). This is indicative that restoration sites can be important habitats in highly fragmented landscapes despite the loss of biodiversity.
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Background: Zoonotic diseases represent a significant societal challenge in terms of their health and economic impacts. One Health approaches to managing zoonotic diseases are becoming more prevalent, but require novel thinking, tools and cross-disciplinary collaboration. Bovine tuberculosis (bTB) is one example of a costly One Health challenge with a complex epidemiology involving human, domestic animal, wildlife and environmental factors, which require sophisticated collaborative approaches. Objective: We undertook a scoping review of multi-host bTB epidemiology to identify recent trends in species publication focus, methodologies, scales and One Health approaches. We aimed to identify research gaps where novel research could provide insights to inform control policy, for bTB and other zoonoses. Results: The review included 167 articles. We found different levels of research attention across episystems, with a significant proportion of the literature focusing on the badger-cattle-TB episystem, with far less attention given to the multi-host episystems of southern Africa. We found a limited number of studies focusing on management solutions and their efficacy, with very few studies looking at modelling exit strategies. Surprisingly, only a small number of studies looked at the effect of human disturbances on the spread of bTB involving wildlife hosts. Most of the studies we reviewed focused on the effect of badger vaccination and culling on bTB dynamics with few looking at how roads, human perturbations and habitat change may affect wildlife movement and disease spread. Finally, we observed a lack of studies considering the effect of weather variables on bTB spread, which is particularly relevant when studying zoonoses under climate change scenarios. Conclusions: Significant technological and methodological advances have been applied to bTB episystems, providing explicit insights into its spread and maintenance across populations. We identified a prominent bias towards certain species and locations. Generating more high-quality empirical data on wildlife host distribution and abundance, high-resolution individual behaviours and greater use of mathematical models and simulations are key areas for future research. Integrating data sources across disciplines, and a "virtuous cycle" of well-designed empirical data collection linked with mathematical and simulation modelling could provide additional gains for policy-makers and managers, enabling optimised bTB management with broader insights for other zoonoses.
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Contemporary methods for sampling wildlife populations include the use of remotely triggered wildlife cameras (i.e., camera traps). Such methods often result in the collection of hundreds of thousands of photos that must be identified, archived, and transformed into data formats required for statistical analyses. Cpw Photo Warehouse is a freely available software based in Microsoft Access ® that has been customized for this purpose using Visual Basic ® for Applications ( VBA ) code. Users navigate a series of point‐and‐click menu items that allow them to input information from camera deployments, automatically import photos (and image data stored within the photos) related to those deployments, and store data within a relational database. Images are seamlessly incorporated into the database windows, but are stored separately from the database. The database includes menu options that (i) facilitate identification of species within the images, (ii) allow users to view and filter any subset of the databased on study area, species, season, etc., and (iii) produce input files for common analyses such as occupancy, abundance, density and activity patterns using Programs mark , presence , density and the r packages ‘secr’ and ‘overlap’. Our database makes explicit use of multiple observers, which greatly enhances the efficiency and accuracy with which a large number of photos can be identified. Modular subsets of the data can be distributed to an unlimited number of observers on or off site for identification. Modules are then re‐incorporated into the database using a custom import function.
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Camera trapping surveys frequently capture individuals whose identity is only known from a single flank. The most widely used methods for incorporating these partial identity individuals into density analyses discard some of the partial identity capture histories, reducing precision, and while not previously recognized, introducing bias. Here, we present the spatial partial identity model (SPIM), which uses the spatial location where partial identity samples are captured to probabilistically resolve their complete identities, allowing all partial identity samples to be used in the analysis. We show that the SPIM out-performs other analytical alternatives. We then apply the SPIM to an ocelot data set collected on a trapping array with double-camera stations and a bobcat data set collected on a trapping array with single-camera stations. The SPIM improves inference in both cases and in the ocelot example, individual sex determined from photographs is used to further resolve partial identities, one of which is resolved to near certainty. The SPIM opens the door for the investigation of trapping designs that deviate from the standard 2 camera design, the combination of other data types between which identities cannot be deterministically linked, and can be extended to the problem of partial genotypes.
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Camera traps set to monitor target species generate large amounts of bycatch data of non-target species, which are secondary to the study’s objectives. Bycatch data pooled from multiple studies can answer additional questions that were not the objective of the primary studies. Variation in field and data management techniques creates logistical and statistical problems when pooling data from multiple sources. Successful multi-collaborator projects use standardized field and data management methods, and combine their data to answer valuable broad-scale research questions (e.g. monitoring threatened species). Long term, multi-collaborator projects, however, are rare and limited in geographical scope. Hundreds of small, fixed-term independent camera trap studies operate in otherwise un-represented regions, often using field and data management methods tailored to their own study’s objectives. Inconsistent data management practices may lead to loss of bycatch data, or an inability to easily share it. To maximize the benefit of camera trapping, small studies should anticipate that their bycatch data will be useful to others in research of non-target species. During a range-wide assessment of sun bears Helarctus malayanus in Southeast Asia we documented a range of common data management problems encountered when processing data from multiple research groups. From our experiences, and from a review of the published literature and online resources, we generated nine key recommendations on data management best practices. Following these practices can further the usefulness of camera trap by-catch data, by improving the ease of sharing, enabling collaborations, and expanding the scope of research.
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Vandalism and theft of camera traps is common, imposing financial and data losses on wildlife professionals. Like many ‘victims’, our response to a spate of thefts was to attempt to install camera traps at heights we suspected would reduce detection and interference by vandals. We sought to determine if placing camera traps above humans’ eye line, to reduce the likelihood of detection and theft by vandals, would compromise predator detection in road-based surveys. Our efforts to resolve this problem led us to discover the importance of placing camera traps at a height commensurate with the height of the animals being studied. Monitoring stations comprised of two camera traps, one at 0.9 m and another at 3 m above ground level, were established at regular intervals along trails during two survey periods. We also conducted a pilot trial to compare vertical (facing downwards) to horizontal (facing across) orientation of camera traps to detect medium-sized mammals. We compared images recorded by the pairs of camera to consider whether height made a significant difference to detections of predators. We found that cameras placed 3 m high and those facing downwards reduced the detection rate of all species compared to those at 0.9 m, so placing camera traps higher than normal significantly compromised our survey data. It is important to note that such data loss would not necessarily be apparent without a robust comparison between deployment strategies. Saving camera traps but concurrently sacrificing data quality is unlikely to be an acceptable outcome for many wildlife professionals. This study reports that placing camera traps too high will reduce the detection of animals and compromise the quality of the survey data. © 2016 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
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Motivation: Several spatial capture-recapture (SCR) models have been developed to estimate animal abundance by analyzing the detections of individuals in a spatial array of traps. Most of these models do not use the actual dates and times of detection, even though this information is readily available when using continuous-time recorders, such as microphones or motion-activated cameras. Instead most SCR models either partition the period of trap operation into a set of subjectively chosen discrete intervals and ignore multiple detections of the same individual within each interval, or they simply use the frequency of detections during the period of trap operation and ignore the observed times of detection. Both practices make inefficient use of potentially important information in the data. Model and data analysis: We developed a hierarchical SCR model to estimate the spatial distribution and abundance of animals detected with continuous-time recorders. Our model includes two kinds of point processes: a spatial process to specify the distribution of latent activity centers of individuals within the region of sampling and a temporal process to specify temporal patterns in the detections of individuals. We illustrated this SCR model by analyzing spatial and temporal patterns evident in the camera-trap detections of tigers living in and around the Nagarahole Tiger Reserve in India. We also conducted a simulation study to examine the performance of our model when analyzing data sets of greater complexity than the tiger data. Benefits: Our approach provides three important benefits: First, it exploits all of the information in SCR data obtained using continuous-time recorders. Second, it is sufficiently versatile to allow the effects of both space use and behavior of animals to be specified as functions of covariates that vary over space and time. Third, it allows both the spatial distribution and abundance of individuals to be estimated, effectively providing a species distribution model, even in cases where spatial covariates of abundance are unknown or unavailable. We illustrated these benefits in the analysis of our data, which allowed us to quantify differences between nocturnal and diurnal activities of tigers and to estimate their spatial distribution and abundance across the study area. Our continuous-time SCR model allows an analyst to specify many of the ecological processes thought to be involved in the distribution, movement, and behavior of animals detected in a spatial trapping array of continuous-time recorders. We plan to extend this model to estimate the population dynamics of animals detected during multiple years of SCR surveys.
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The challenges associated with monitoring low‐density carnivores across large landscapes have limited the ability to implement and evaluate conservation and management strategies for such species. Non‐invasive sampling techniques and advanced statistical approaches have alleviated some of these challenges and can even allow for spatially explicit estimates of density, one of the most valuable wildlife monitoring tools. For some species, individual identification comes at no cost when unique attributes (e.g. pelage patterns) can be discerned with remote cameras, while other species require viable genetic material and expensive laboratory processing for individual assignment. Prohibitive costs may still force monitoring efforts to use species distribution or occupancy as a surrogate for density, which may not be appropriate under many conditions. Here, we used a large‐scale monitoring study of fisher Pekania pennanti to evaluate the effectiveness of occupancy as an approximation to density, particularly for informing harvest management decisions. We combined remote cameras with baited hair snares during 2013–2015 to sample across a 70 096‐km ² region of western New York, USA . We fit occupancy and Royle–Nichols models to species detection–non‐detection data collected by cameras, and spatial capture–recapture (SCR) models to individual encounter data obtained by genotyped hair samples. Variation in the state variables within 15‐km ² grid cells was modelled as a function of landscape attributes known to influence fisher distribution. We found a close relationship between grid cell estimates of fisher state variables from the models using detection–non‐detection data and those from the SCR model, likely due to informative spatial covariates across a large landscape extent and a grid cell resolution that worked well with the movement ecology of the species. Fisher occupancy and density were both positively associated with the proportion of coniferous‐mixed forest and negatively associated with road density. As a result, spatially explicit management recommendations for fisher were similar across models, though relative variation was dampened for the detection–non‐detection data. Synthesis and applications . Our work provides empirical evidence that models using detection–non‐detection data can make similar inferences regarding relative spatial variation of the focal population to models using more expensive individual encounters when the selected spatial grain approximates or is marginally smaller than home range size. When occupancy alone is chosen as a cost‐effective state variable for monitoring, simulation and sensitivity analyses should be used to understand how inferences from detection–non‐detection data will be affected by aspects of study design and species ecology.
Book
A comprehensive manual for camera trapping wildlife populations for conservation. Includes technical details covering equipment, practical advice, survey types and data management and analysis.
Book
Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition, provides a synthesis of model-based approaches for analyzing presence-absence data, allowing for imperfect detection. Beginning from the relatively simple case of estimating the proportion of area or sampling units occupied at the time of surveying, the authors describe a wide variety of extensions that have been developed since the early 2000s. This provides an improved insight about species and community ecology, including, detection heterogeneity; correlated detections; spatial autocorrelation; multiple states or classes of occupancy; changes in occupancy over time; species co-occurrence; community-level modeling, and more. Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition has been greatly expanded and detail is provided regarding the estimation methods and examples of their application are given. Important study design recommendations are also covered to give a well rounded view of modeling.
Article
Snow tracking is often used to inventory carnivore communities, but species identification using this method can produce ambiguous and misleading results. DNA can be extracted from hair and scat samples collected from tracks made in snow. Using DNA analysis could allow positive track identification across a broad range of snow conditions, thus increasing survey accuracy and efficiency. We investigated the efficacy of DNA identification using hairs and scats collected during the winter along putative Canada lynx (Lynx canadensis) snow tracks and compared our findings to those obtained using hair-snaring techniques during the summer. We were able to positively identify 81% and 98% of the hair and scat samples, respectively, that were collected in or near snow tracks. Samples containing amplifiable lynx DNA were collected at rates of 1.2–1.3 per km of lynx tracks followed. These amplification rates and encounter frequencies validate the collection and use of DNA samples from snow tracks as a feasible technique for identifying Canada lynx and possibly other rare carnivores. We recommend that biologists include the collection of hairs and scats for DNA analysis as part of snow-tracking surveys whenever species identification is a high priority.
Article
Recent work in the tropics has advanced our understanding of the local impacts of land-use change on species richness. However, we still have a limited ability to make predictions about species abundances, especially in heterogeneous landscapes. Species abundances directly affect the functioning of an ecosystem and its conservation value. We applied a hierarchical model to camera- and live-trapping data from a region in Borneo, and estimated the relative abundance (controlling for imperfect detection) of 57 terrestrial mammal species, as a function of either categorical or continuous metrics of land-use change. We found that mean relative abundance increased (by 28%) from old-growth to logged forest, but declined substantially (by 47%) in oil palm plantations compared to forest. Abundance responses to above-ground live tree biomass (a continuous measure of local logging intensity) were negative overall, whilst they were strongly positive for landscape forest cover. From old-growth to logged forest, small mammals increased in their relative abundance proportionately much more than large mammals (169% compared to 13%). Similarly, omnivores and insectivores increased more than other trophic guilds (carnivores, herbivores and frugivores). From forest to oil palm, species of high conservation concern fared especially poorly (declining by 84%). Invasive species relative abundance consistently increased along the gradient of land-use intensity. Changes in relative abundance across nine functional effects groups based on diet were minimal from old-growth to logged forest, but in oil palm only the vertebrate predation function was maintained. Our results show that, in the absence of hunting, even the most intensively logged forests can conserve the abundance and functional effects of mammals. Recent pledges made by companies to support the protection of High Carbon Stock logged forest could therefore yield substantial conservation benefits. Within oil palm, our results support the view that “wildlife-friendly” practices offer a low potential for reducing biodiversity impacts.