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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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... However, with the advent of camera trap technology studying natural behaviors is feasible. Currently, camera traps are widely used in ecology and conservation (Gilbert et al. 2021;Chen et al. 2022), and they are especially helpful to investigate animal behavior with minimal sound and visual disturbances (Wearn and Glover-Kapfer 2017). Camera traps have been used extensively to study terrestrial biodiversity across considerable variability in body size (Burton et al. 2015). ...
... There are some limitations to the use of camera traps to fully describe the wildlife biodiversity around large wood in streams. First, infrared detection is less reliable for detecting small sized birds and mammals, ectothermic species (Wearn and Glover-Kapfer 2017), and semi-aquatic mammals (Lerone et al. 2015). Despite these limitations, we document up to 40 species in our study sites. ...
Article
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Until the 1980s, large wood removal from streams was widely promoted across North America because in-stream logs were considered undesirable. At present, millions of dollars are invested annually to place large wood back in streams owing to its importance for the geomorphology of channels, stream discharge, sediment deposits, and habitat for fish. Yet, little is known about the role of large wood in streams for wildlife. Here, we used 12 months of camera trap videos (effort of 4703 camera days) to document wildlife biodiversity and animal activities at several log complexes located in Rock Creek, Wil-lamette River basin, Oregon. Our dataset (1921 independent videos) documented up to 40 species including small mammals, aquatic and terrestrial birds, meso-carnivores, large carnivores, and semi-aquatic mammals. We found a strong seasonality in detections and species richness with the highest values occurring in summer and spring, and the lowest values in winter. There were idiosyncratic responses for species richness and assemblages at each large wood complex. Most common animal activities included movement (68%), rest (18%), and food handling/eating (9%) suggesting that large wood structures in streams act as lateral corridors connecting terrestrial habitats year-round for wildlife. Collectively, we reveal multiple functions that large wood plays to support wildlife biodiversity across the aquatic-terrestrial interface demonstrating the value of restoration projects that involve wood placement into streams.
... Examining the ways these relationships change in response to anthropogenic pressures underlies numerous related questions in conservation biology (Gaynor et al., 2018;Suraci et al., 2019;Wilson et al., 2020). Yet, rigorously studying animal behaviour and species interactions in freeranging wildlife populations can be prohibitively difficult, particularly if the environment is complex and hard for observers to navigate or if the focal species are small, rare, nocturnal, cryptic or sensitive to human presence (Brown et al., 2013;Hughey et al., 2018;Wearn & Glover-Kapfer, 2017). Camera traps (CTs; also called trail cameras or remote cameras) are valuable tools in ecology and conservation, providing a noninvasive automated means of monitoring wildlife populations (Burton et al., 2015;O'Connell et al., 2010). ...
... Camera traps (CTs; also called trail cameras or remote cameras) are valuable tools in ecology and conservation, providing a noninvasive automated means of monitoring wildlife populations (Burton et al., 2015;O'Connell et al., 2010). These devices collect images or videos when triggered by the heat and/or motion of passing animals (Wearn & Glover-Kapfer, 2017) and operate continuously to gather data on diverse wildlife communities while minimizing potentially disruptive effects of direct human observation (Burton et al., 2015;Caravaggi et al., 2020). To date, CTs have been used overwhelmingly in a strictly observational context, providing valuable correlational inferences but not experimentally testing mechanisms that underlie species interactions and behavioural decisions (Caravaggi et al., 2020;Smith et al., 2020). ...
Article
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Camera traps (CTs) are a valuable tool in ecological research, amassing large quantities of information on the behaviour of diverse wildlife communities. CTs are predominantly used as passive data loggers to gather observational data for correlational analyses. Integrating CTs into experimental studies, however, can enable rigorous testing of key hypotheses in animal behaviour and conservation biology that are otherwise difficult or impossible to evaluate. We developed the 'BoomBox', an open‐source Arduino‐compatible board that attaches to commercially available CTs to form an Automated Behavioural Response (ABR) system. The modular unit connects directly to the CT’s passive infrared (PIR) motion sensor, playing audio files over external speakers when the sensor is triggered. This creates a remote playback system that captures animal responses to specific cues, combining the benefits of camera trapping (e.g. continuous monitoring in remote locations, lack of human observers, large data volume) with the power of experimental manipulations (e.g. controlled perturbations for strong mechanistic inference). Our system builds on previous ABR designs to provide a cheap (~100USD) and customizable field tool. We provide a practical guide detailing how to build and operate the BoomBox ABR system with suggestions for potential experimental designs that address a variety of questions in wildlife ecology. As proof‐of‐concept, we successfully field tested the BoomBox in two distinct field settings to study species interactions (predator–prey and predator–predator) and wildlife responses to conservation interventions. This new tool allows researchers to conduct a unique suite of manipulative experiments on free‐living species in complex environments, enhancing the ability to identify mechanistic drivers of species' behaviours and interactions in natural systems.
... Camera trap data can also provide information about animal community structure (composition and abundance), which are also used to assess negative anthropogenic (i.e. habitat fragmentation) impacts in an area (Ahumada et al., 2011;Wearn & Glover-Kapfer, 2017). Community structure is characterised by populations of several species that are associated with a particular habitat and can be broadly categorised by the functional traits of body size and trophic category (Ahumada et al., 2011;Paker et al., 2014). ...
... for imperfect and variable detection and have been criticised in this regard (Foster & Harmsen, 2012;Sollmann et al., 2013;Tobler et al., 2008). Despite the controversy that RAIs invoke, their use can still offer some meaningful insights into the abundance of wildlife populations (Wearn & Glover-Kapfer, 2017). For example, the reliability of RAIs from camera traps has been tested against robust density estimates, with correlations found to be positive and linear (Carbone et al., 2001;Rovero & Marshall, 2009), and in the Serengeti, RAIs from camera traps provided a good approximation of aerial census abundance data (Palmer et al., 2018). ...
Article
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en Biological monitoring in protected areas is essential for making management decisions, especially in small (<1000 km²), fenced reserves which require intensive intervention to maintain core habitat characteristics. Estimates of species richness and community structure provide important information for planning and evaluating conservation strategies. Majete Wildlife Reserve (MWR) is a small (691 km²), isolated reserve in southern Malawi in the Miombo Woodland Ecoregion. We investigated species richness and community structure of the terrestrial medium and large mammals at MWR through a standardised camera trap survey. During the 2018 dry season, 140 camera locations were sampled for 40 days each. Thirty-five mammal species were detected and Chao 2, ICE and Jackknife 1 and 2 richness estimators indicated between 36–41 species present which aligns closely with historic accounts. Non-detection of some species is attributed to species specialised habitat requirements not catered for in the systematic camera trap survey design. Mammal community structure, calculated from the camera detected species’ relative abundance indices (RAI), was atypical for Miombo woodland, with an underrepresentation of elephants. Camera trap-derived RAI was positively related with 2018 aerial census species encounter data. These results can assist management in refining survey techniques and act as a baseline to monitor conservation efforts. Résumé fr La surveillance biologique des aires protégées est essentielle aux prises de décision en matière de gestion, en particulier dans les petites réserves clôturées (< 1 000 km²) qui nécessitent une intervention intensive visant à maintenir les caractéristiques principales de l’habitat. Les estimations de la richesse en espèces et l’étude de la structure de la communauté fournissent des informations importantes aux fins de planification et l’évaluation des stratégies de conservation. La réserve faunique de Majete (MWR) est une petite réserve isolée (691 km²) située dans l’écorégion de la forêt de Miombo, dans le sud du Malawi. Nous avons étudié la richesse en espèces et la structure de la communauté des mammifères terrestres de taille moyenne et grande au sein de la MWR, en nous appuyant sur une enquête normalisée et réalisée par piège photographique. Au cours de la saison sèche de l’année 2018, 140 emplacements d’appareils photo ont été échantillonnés pendant 40 jours chacun. Trente-cinq espèces de mammifères ont été identifiées, et les estimateurs de richesse en espèces Chao 2, ICE et Jackknife 1 et 2 ont indiqué la présence de 36 à 41 espèces, ce qui correspond étroitement aux résultats des évaluations réalisées par le passé.. La non-détection de certaines espèces est attribuée aux exigences particulières liées à l’habitat de ces dernières, auquel la méthode d’échantillonnage par piège photographique ne peut être appliquée. La structure de la communauté de mammifères, calculée à partir des indices d’abondance relative (IAR) des espèces détectées par l’appareil photo, était atypique pour la forêt claire de Miombo et mettait en évidence une sous-représentation des éléphants. L’IAR dérivé des pièges photographiques était en corrélation positive avec les données de présence des espèces issues du recensement aérien réalisé en 2018. Ces résultats peuvent aider la Direction à affiner les techniques d’enquête et servir de référence aux fins de suivi des efforts de conservation.
... In recent decades, camera-trapping has become a popular technique for conducting inventories of mammals (Silveira et al., 2003) and are also used in ecological studies of species richness, diversity and distribution for common and rare species (O'Connell et al., 2011;Wearn and Glover-Kapfer, 2017). They can be used for long-term surveys in remote regions with less environmental disturbance and provide additional information for behavioural studies, such as activity patterns and habitat use (Caravaggi et al., 2017;Wearn and Glover-Kapfer, 2017). ...
... In recent decades, camera-trapping has become a popular technique for conducting inventories of mammals (Silveira et al., 2003) and are also used in ecological studies of species richness, diversity and distribution for common and rare species (O'Connell et al., 2011;Wearn and Glover-Kapfer, 2017). They can be used for long-term surveys in remote regions with less environmental disturbance and provide additional information for behavioural studies, such as activity patterns and habitat use (Caravaggi et al., 2017;Wearn and Glover-Kapfer, 2017). Camera traps are widely used around the world, and provide critically important ecological knowledge, not only for wildlife researchers and park managers but also for determining conservation priorities (Van Berkel, 2014). ...
Article
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Northern Myanmar lies at the intersection of three globally important biodiversity hotspots. However, little information on diversity and the distributions of large mammals (>1 kg) is available for conservation and management. To fill these data gaps, we established 174 camera stations in protected areas (PAs) and adjacent non-PA in northern Myanmar, with an elevational range from 470 m to 3150 m, to survey the large mammals from December 2015 to June 2019. We recorded 34 large mammal species (29 from PAs and 28 from non-PA), and plus one species Hoolock leuconedys which was documented from vocalisations. By inspecting wildlife body parts of animal found in local houses a further six species were recorded. In total, 41 species belonging to six orders and 18 families with five Endangered and ten Vulnerable species were recorded. Northern Myanmar accounts for 29% of Myanmar’s threatened mammal species (based on the IUCN Red List), including one Evolutionarily Distinct species (based on EDGE score), one keystone, two flagships and two range restricted species. Species richness and diversity showed a clear humped shaped pattern with elevation. However, higher species diversity was found in non-PA (H’=2.38) than PAs (H’=2.23), and similarity index was 0.81. We conclude that both PAs and non-PA are important to maintain the mammal diversity and enhance the conservation in northern Myanmar. However, shifting cultivation, agricultural expansion, and hunting frequently occurred in non-PA. Therefore, increasing the law enforcement and the establishment of proposed lowland southern extension of non-PA into PA is urgently needed to effectively conserve biodiversity in northern Myanmar.
... However, during the last five years different studies have estimated the locations of the animals captured by CT (e.g. Caravaggi et al., 2016); and machine learning procedures are being developed to reduce associated-efforts (Wearn & Glover-Kapfer, 2017). It is expected that the estimation of speed parameters from camera trapping will be notably optimized in the next years. ...
Thesis
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A better understanding of population density (i.e. the number of individuals per unit area) is essential for wildlife conservation and management. Despite the fact that a wide variety of methods with which to estimate population density have already been described and broadly used, there are still relevant gaps. In the last few decades, the use of remotely activated cameras (camera traps) has been established as an effective sampling tool when compared with alternative methods. Camera trapping could, therefore, be considered a reliable tool with which to monitor those situations in which classical methods have relevant limitations. It could, for example, be used with species whose behaviour is elusive and which have low detectability (as is the case of most mammals), or populations in which the animals can be identified individually by the spot patterns on their bodies. However, there is lack of information regarding those species for which it is not possible to identify individual animals (i.e. unmarked species). Some authors that have applied camera trapping originally considered relative abundance indexes in order to monitor unmarked populations. These indices were based on encounter rates (i.e. the number of animals detected per sampling unit) observed in camera trapping studies. Methods with which to estimate the population density of unmarked populations were later described, the first of which was the random encounter model (REM). The REM models the random encounters between moving animals and static cameras in order to estimate population density. The REM does this by employing three basic parameters: i) encounter rate, ii) detection zone (area in which the cameras effectively detect animals), and iii) day range (average daily distance travelled by each individual in the population). When this thesis was first started, it was broadly discussed that the application of the REM was limited by the difficulties involved in estimating the parameters required, especially the day range. In this context, the aim of this thesis was to develop and harmonise camera trapping methodologies so as to estimate the population density and movement parameters of unmarked populations, working principally in the REM framework. The first research carried out for this thesis comprised a review of published studies concerning REM, which found that i) wrong practices in the estimation of REM parameters were frequent, and ii) the REM has rarely been compared with reference densities in empirical studies. We, therefore, then went on to evaluate the main factors that affect the probability of detection and the trigger speed of camera traps, which are relevant for encounter rate and detection zone estimation. This is shown in Chapter 1. We subsequently evaluated and described new methodologies that use camera traps to estimate the movement parameters of unmarked populations. We also evaluated the seasonal and spatial variation in these parameters. The information regarding this is provided in Chapter 2. Finally, we assessed the performance of the REM in a wide range of scenarios, and we compared it with other recently described camera trapping methods used to estimate the population density of unmarked species, as detailed in Chapter 3. The results reported in Chapter 1 show that camera trap performance as regards trigger speed and detection probability are highly influenced by different factors, such as the period of the day, the camera trap model, deployment height or sensitivity, among others. We monitored the community of birds and mammals in the study area, and we discovered that a relevant proportion of the animals that entered the theoretical detection zone were not usually recorded. These missed detections introduce bias into the encounter rate, and consequently into density. However, several camera trapping methods with which to estimate effective detection zone have been described, and they should be applied to all the populations monitored. With regard to the day range, we considered the wild boar as a model species and showed that assuming straight-line distances between consecutive locations obtained by telemetry devices underestimates this parameter, while movement behaviours should be accounted when using camera traps to estimate day range, as shown in Chapter 2.1. We then explored the use of camera traps to monitor movement parameters in greater depth, and showed that they are a reliable method. We described a new procedure with which to estimate the day range that accounts for movement behaviour, and for the ratio between fast and slow speeds. The new procedure performed well in the wide range of scenarios that we simulated, and was also tested with populations of mammals around the world. In this respect, we also described a machine learning protocol with which to identify movement behaviour obtained from camera trap records. All of this is described in Chapter 2.2. We subsequently showed that geographical (e.g. altitude), environmental (e.g. habitat fragmentation), biological (e.g. species) and management (e.g. hunting) factors affect the day range, and we reported variable day ranges in ungulates and carnivores across Europe, as shown in Chapter 2.3. We use the combination of a literature review and an empirical study to compare REM densities with those obtained using reference methods. The results showed a strong correspondence between the REM and reference densities, especially when REM parameters are estimated accurately for the target population. We also showed that the precision of the REM is lower than that of the reference methods, and provided further insights into the survey design in order to increase precision. This information is provided in Chapter 3.1. Finally, and as shown in Chapter 3.2, we used ungulates and carnivores as a target in order to compare the REM, random encounter and staying time (REST), and camera trap distance sampling (CT-DS). The REST and CTDS are two recently described methods with which to estimate the population density of unmarked species using camera traps. The results showed that the performance of the three methods is similar in terms of accuracy and precision. We recommend a survey design that will make it possible to apply all the methods, as the final selection of one of them will be mediated by the number of animals recorded and the camera trap performance. In conclusion, the results of this thesis show the usefulness of camera trapping to monitor the movement parameters and population density of wildlife and contributes with a methodological practical step forwards. In summary, the REM approach, which was tuned in this thesis, proved to be a reliable method in a wide range of environmental scenarios. The REM can be firmly established as a reference method to be implemented in multispecies monitoring programmes in the coming years, considering that it does not need to identify individual animals or spatial autocorrelation in captures. However, future developments of the REM in particular, and camera trapping unmarked methods in general, should be focused on optimising surveys designs in order to increase precision. Before this thesis was begun, the main limitations of applying the REM were the estimation of REM parameters, along with its reliability. This has, however, already been dealt with, and the main gap now concerns the low precisions obtained.
... IWILDCAM2020-WILDS (Appendix H.1). Animal populations have declined 68% on average since 1970 (Grooten et al., 2020). To better understand and monitor wildlife biodiversity loss, ecologists commonly deploy camera trapsheat or motion-activated static cameras placed in the wild (Wearn & Glover-Kapfer, 2017)-and then use ML models to process the data collected (Weinstein, 2018;Norouzzadeh et al., 2019;Tabak et al., 2019;Beery et al., 2019;Ahumada et al., 2020). Typically, these models would be trained on photos from existing camera traps and then used across new camera trap deployments. ...
Conference Paper
Distribution shifts—where the training distribution differs from the test distribution—can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild. Despite their ubiquity in the real-world deployments, these distribution shifts are under-represented in the datasets widely used in the ML community today. To address this gap, we present WILDS, a curated benchmark of 10 datasets reflecting a diverse range of distribution shifts that naturally arise in real-world applications, such as shifts across hospitals for tumor identification; across camera traps for wildlife monitoring; and across time and location in satellite imaging and poverty mapping. On each dataset, we show that standard training yields substantially lower out-of-distribution than in-distribution performance. This gap remains even with models trained by existing methods for tackling distribution shifts, underscoring the need for new methods for training models that are more robust to the types of distribution shifts that arise in practice. To facilitate method development, we provide an open-source package that automates dataset loading, contains default model architectures and hyperparameters, and standardizes evaluations. The full paper, code, and leaderboards are available at https://wilds.stanford.edu.
... IWILDCAM2020-WILDS (Appendix H.1). Animal populations have declined 68% on average since 1970 (Grooten et al., 2020). To better understand and monitor wildlife biodiversity loss, ecologists commonly deploy camera trapsheat or motion-activated static cameras placed in the wild (Wearn & Glover-Kapfer, 2017)-and then use ML models to process the data collected (Weinstein, 2018;Norouzzadeh et al., 2019;Tabak et al., 2019;Beery et al., 2019;Ahumada et al., 2020). Typically, these models would be trained on photos from existing camera traps and then used across new camera trap deployments. ...
... Every sampling station was geo-referenced; each station, corre- sponding SD cards, and locks were given a unique identity to enable correct notation of date, time, and location of each photographic capture (Fig. 1). We used Bushnell Trophy Cam 119576C HD Max Trail Cams (Bushnell ® , Kansas, USA) and installed one in each of the selected grids ( Fig. 1 Boitani and Powell (2012), and Wearn and Kapfer (2017) for the installation protocol. The average distance between neighboring cameras ranged between 0.65 km to 1.5 km (Fig. 1); cameras were placed about 20-30 cm high above the ground. ...
Article
Bangladesh holds 191 km2 semi-evergreen northeastern (NE) forests where systematic camera-trapping has never been carried out. An effort of 587 trap nights in Satchari National Park, a NE forest, revealed ten carnivores, two ungulates, two primates, two rodents, and one treeshrew (12 threatened in Bangladesh; of which three globally threatened; dhole and northern treeshrew were new discoveries). Pairwise circadian homogeneity, coefficient of temporal overlap (Δ̂ ), and spatial cooccurrence pattern were measured. High values (Δ̂ > 0.75) were noted in 36 pairwise comparisons, and positive spatial association (Pgt < 0.05) in five. Anthropogenic activities overlapped with diurnal species (0.65 ≤ Δ̂ 1 ≤ 0.88) but stood dissimilar (P < 0.05 in the Mardia-Watson-Wheeler test) except for yellow-throated marten–livestock movement (Δ̂ 1 = 0.70). Although species-specific dietary or temporal preference explains the observed associations, low detection of the jungle cat (2) compared to the leopard cat (56), absence of the fishing cat, homogenous activity (P > 0.05) in yellow-throated marten–crab-eating mongoose (Δ̂ 1 = 0.83) and rhesus macaque–pig-tailed macaque (Δ̂ 4 = 0.93) pairs need further research. These insights are remarkable as NE forests, the western cusp of the Indo-Burma biodiversity hotspot, are contrarily deemed ‘empty’, receiving least scientific investments.
Preprint
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The U Minh wetlands of southern Vietnam in Ca Mau and Kieng Giang provinces are a degraded, peat-swamp wetland mosaic known to retain several globally threatened species. We deployed intensive, targeted camera-traps across U Minh Thuong National Park and U Minh Ha National Park from December 2019 to May 2020, and from November 2020 to June 2021, respectively. Our aim was to detect threatened otters, wild cats, and pangolins in each protected area, to identify what potential threats they may face, and to inform conservation priorities for park managers. Our results showed that both protected areas harbour significant regionally important populations of globally threatened Sunda pangolins ( Manis javanica ), and Hairy-nosed otters ( Lutra sumatrana ). However, Fishing cats ( Prionailurus viverrinus ) and Large-spotted civet ( Viverra megaspila ) previously recorded from U Minh Thuong National Park, were not observed. Other than wide-ranging species insensitive to human disturbance (i.e., Common palm civets and Leopard cats), all small carnivores were most active in Melaleuca and swamp/ Melaleuca habitats in U Minh Thuong, and both the wetland plantations and disturbed forests of U Minh Ha according to their photographic rates. Human and domestic dogs’ activity periods in both protected areas overlapped strongly with Hairy-nosed otters, which could influence their dispersal abilities and access to resources. Furthermore, dogs in this part of southern Vietnam are often used for hunting, so there is a strong possibility the overlap could lead to deadly interactions as well. Long-term and short-term threats are discussed with relevance to U Minh ecosystem health and future recommendations.
Article
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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.
Article
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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.
Article
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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.
Article
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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.
Article
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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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A comprehensive manual for camera trapping wildlife populations for conservation. Includes technical details covering equipment, practical advice, survey types and data management and analysis.
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