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

Applying a Random Encounter Model to Estimate the Asiatic Black Bear (Ursus thibetanus) Density from Camera Traps in the Hindu Raj Mountains, Pakistan

Estimating the population density of vulnerable species, such as the elusive and nocturnal Asiatic black bear (Ursus thibetanus), is essential for wildlife conservation and management. We used camera traps and a Random Encounter Model (REM) to estimate the population density of U. thibetanus during the autumn and winter seasons in the Hindu Raj Mountains. We installed 23 camera traps from October to December 2020 and acquired 66 independent pictures of Asiatic black bears over 428 trap nights. Our results showed that the bears preferred lowland areas with the presence of Quercus spp. We estimated, using the REM, a population density of U. thibetanus of 1.875 (standard error = 0.185) per square kilometer, which is significantly higher than that in other habitats. Our results showed that during autumn and winter, the bear population density tends to concentrate at lower elevations. Forest cover showed a positive correlation with the rates of bear encounters unlike the Euclidean distance to human settlements, altitude, and aspect variables. The approaches used here are cost-effective for estimating the population density of rare and vulnerable species such as U. thibetanus, and can be used to estimate their population density in Pakistan. Population density estimation can identify areas where the bears live and human-bear conflicts occurred and use this information in future wildlife management plans.

Automated Detection and Counting of Wild Boar in Camera Trap Images

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

Using global remote camera data of a solitary species complex to evaluate the drivers of group formation

The social system of animals involves a complex interplay between physiology, natural history, and the environment. Long relied upon discrete categorizations of "social" and "solitary" inhibit our capacity to understand species and their interactions with the world around them. Here, we use a globally distributed camera trapping dataset to test the drivers of aggregating into groups in a species complex (martens and relatives, family Mustelidae, Order Carnivora) assumed to be obligately solitary. We use a simple quantification, the probability of being detected in a group, that was applied across our globally derived camera trap dataset. Using a series of binomial generalized mixed-effects models applied to a dataset of 16,483 independent detections across 17 countries on four continents we test explicit hypotheses about potential drivers of group formation. We observe a wide range of probabilities of being detected in groups within the solitary model system, with the probability of aggregating in groups varying by more than an order of magnitude. We demonstrate that a species' context-dependent proclivity toward aggregating in groups is underpinned by a range of resource-related factors, primarily the distribution of resources, with increasing patchiness of resources facilitating group formation, as well as interactions between environmental conditions (resource constancy/winter severity) and physiology (energy storage capabilities). The wide variation in propensities to aggregate with conspecifics observed here highlights how continued failure to recognize complexities in the social behaviors of apparently solitary species limits our understanding not only of the individual species but also the causes and consequences of group formation.

Vertebrate damage to Azorean vineyards: the role of the endemic Azores Woodpigeon Columba palumbus azorica

In the Azores, complaints about grape loss to birds have become recurrent. Although winegrowers frequently blame the endemic Azores Woodpigeon Columba palumbus azorica, data about the magnitude of grape damage and the species responsible for them are lacking. This study provides detailed information about grape damage caused by vertebrates on Pico Island, home to the main wine-growing area of the region. Vineyards were monitored during the ripening period in 2017 and 2018. Damage was assessed by determining the number of plucked, pecked and bitten grapes for a total of 850 bunches. Camera traps were placed in 113 enclosures providing detection and consumption rates of vertebrate species. GLM analysis was performed to test the effect of grape variety, distance to the woods, year and time before harvest on grape damage and consumption events. Damage was estimated at 10.3% (± 0.9) in 2017 and 8.7% (± 0.9) in 2018 and mostly consisted of plucked grapes. Ten vertebrate species were detected consuming grapes. Overall, 524 consumption events were registered. The Madeira lizard Teira dugesii, the Azores Blackbird Turdus merula azorensis and rodents (rats Rattus sp. and the house mouse Mus musculus) were responsible for most of those events. The Azores Woodpigeon accounted for three consumption events. GLM analysis showed that damage was greater in vines adjacent to woods and lower in traditional white varieties. This study constitutes an important baseline for the implementation of efficient measures to mitigate grape predation and sheds light on the minor role of the Azores Woodpigeon in grape damage.

Time for a paradigm shift? Small carnivores' sensitivity highlights the importance of monitoring mid-rank predators in future global change studies

Research Highlight: Jachowski, D. S., Marneweck, C. J., Olfenbuttel, C., & Harris, S. N. (2024). Support for the size-mediated sensitivity hypothesis within a diverse carnivore community. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.13916. A current paradigm in ecological research suggests that top predators are suitable sentinel species to identify ecosystem dysfunctions and monitor the effects of climate change. However, the adequacy of top predators to systematically take this function may be mistakenly inferred or unintentionally conflated from the fact that these species are regarded as biodiversity indicators or keystone, umbrella and flagship species in most ecosystems. Regarding terrestrial mammalian carnivores (order Carnivora), some researchers recently suggested that the smaller species likely possess a higher sensitivity to environmental changes than large carnivores because of their biological attributes and their intermediate position in food webs. To test this hypothesis, Jachowski et al. (2024) used camera trapping followed by occupancy and structural equation modelling to explore the dynamics of a diverse carnivore community and the factors that influence them. Their results confirmed that small carnivores are more sensitive to habitat changes and are interconnected by a greater number of significant pathways compared with larger carnivores. This support for the size-mediated sensitivity hypothesis strengthens the proposition that small carnivores (and other mid-rank predators) are ideal sentinel species for monitoring the effects of the wide range of contemporary and future environmental changes. Time will tell whether this new 'middle-out ecology' paradigm will be considered in future global change studies.

How abundant is a species at the limit of its distribution range? Crested porcupine Hystrix cristata and its northern population

The crested porcupine (Hystrix cristata) is a rodent present in Africa and southern Europe (Italy exclusively). The Italian population is expanding from the centre to the north and south, but little is known about the species' abundance. Reliable population density estimates are important for monitoring trends in wildlife populations and for developing effective conservation and management strategies. In this context, we aimed to first report crested porcupine population density on the northern limit of its current distribution range using a non-invasive approach. Specifically, we randomly placed 38 camera traps in an area of 242 km2 in north Italy (Lombardy region), and we applied camera trap distance sampling. We estimated a porcupine density of 0.49 ind·km-2 (±0.33, standard error). The results presented here are the first crested porcupine density estimate accounting for imperfect detection (i.e. species present but not detected). The abundance estimate reported here is fundamental for a better understanding of the species status in Europe and for implementing conservation and management plans.

The first use of a photogrammetry drone to estimate population abundance and predict age structure of threatened Sumatran elephants

Wildlife monitoring in tropical rainforests poses additional challenges due to species often being elusive, cryptic, faintly colored, and preferring concealable, or difficult to access habitats. Unmanned aerial vehicles (UAVs) prove promising for wildlife surveys in different ecosystems in tropical forests and can be crucial in conserving inaccessible biodiverse areas and their associated species. Traditional surveys that involve infiltrating animal habitats could adversely affect the habits and behavior of elusive and cryptic species in response to human presence. Moreover, collecting data through traditional surveys to simultaneously estimate the abundance and demographic rates of communities of species is often prohibitively time-intensive and expensive. This study assesses the scope of drones to non-invasively access the Bukit Tigapuluh Landscape (BTL) in Riau-Jambi, Indonesia, and detect individual elephants of interest. A rotary-wing quadcopter with a vision-based sensor was tested to estimate the elephant population size and age structure. We developed hierarchical modeling and deep learning CNN to estimate elephant abundance and age structure. Drones successfully observed 96 distinct individuals at 8 locations out of 11 sampling areas. We obtained an estimate of the elephant population of 151 individuals (95% CI [124, 179]) within the study area and predicted more adult animals than subadults and juvenile individuals in the population. Our calculations may serve as a vital spark for innovation for future UAV survey designs in large areas with complex topographies while reducing operational effort.

Co-circulation of Hepatitis E Virus (HEV) Genotype 3 and Moose-HEV-Like Strains in Free-Ranging-Spotted Deer (Axis axis) in Uruguay

Hepatitis E caused by hepatitis E virus (HEV) is considered an emerging foodborne zoonosis in industrialized, non-endemic countries. Domestic pigs and wild boars are considered the main reservoir of HEV. However, HEV can also infect an ever-expanding host range of animals, but they exact role in transmitting the virus to other species or humans is mostly unknown. In this work, we investigated the spread of HEV in free-living and captive spotted deer (Axis axis) from Uruguay in a 2-year period (2020-2022) and examined the role of this invasive species as a new potential reservoir of the virus. In addition, with the aim to gain new insights into viral ecology in the context of One Health, by using camera trapping, we identified and quantified temporal and spatial coexistence of spotted deer, wild boars, and cattle. In free-living animals, we detected an anti-HEV seropositivity of 11.1% (6/54). HEV infection and viral excretion in feces were assessed by RT-PCR. Thirteen of 19 samples (68.4%) had HEV RNA. Six samples were amplified using a broadly reactive RT-PCR and sequenced. No captive animal showed evidence of HEV infection. Additionally, HEV RNA was detected in a freshwater pond shared by these species. Phylogenetic and p-distance analysis revealed that zoonotic HEV genotype 3 strains circulate together with unclassified variants related to moose HEV whose potential risk of transmission to humans and other domestic and wild animals is unknown. The data presented here suggest that spotted deer (A. axis) may be a novel host for zoonotic HEV strains.

Assessing the impacts of recreation on the spatial and temporal activity of mammals in an isolated alpine protected area

The management objectives of many protected areas must meet the dual mandates of protecting biodiversity while providing recreational opportunities. It is difficult to balance these mandates because it takes considerable effort to monitor both the status of biodiversity and impacts of recreation. Using detections from 45 camera traps deployed between July 2019 and September 2021, we assessed the potential impacts of recreation on spatial and temporal activity for 8 medium- and large-bodied terrestrial mammals in an isolated alpine protected area: Cathedral Provincial Park, British Columbia, Canada. We hypothesised that some wildlife perceive a level of threat from people, such that they avoid 'risky times' or 'risky places' associated with human activity. Other species may benefit from associating with people, be it through access to anthropogenic resource subsidies or filtering of competitors/predators that are more human-averse (i.e., human shield hypothesis). Specifically, we predicted that large carnivores would show the greatest segregation from people while mesocarnivores and ungulates would associate spatially with people. We found spatial co-occurrence between ungulates and recreation, consistent with the human shield hypothesis, but did not see the predicted negative relationship between larger carnivores and humans, except for coyotes (Canis latrans). Temporally, all species other than cougars (Puma concolor) had diel activity patterns significantly different from that of recreationists, suggesting potential displacement in the temporal niche. Wolves (Canis lupus) and mountain goats (Oreamnos americanus) showed shifts in temporal activity away from people on recreation trails relative to off-trail areas, providing further evidence of potential displacement. Our results highlight the importance of monitoring spatial and temporal interactions between recreation activities and wildlife communities, in order to ensure the effectiveness of protected areas in an era of increasing human impacts.

Occupancy of the Ethiopian endemic Moorland Francolin in pristine and degraded Afroalpine biome using a camera trap approach

Occupancy modeling is an essential tool for understanding species-habitat associations, thereby helping to plan the conservation of rare and threatened wildlife species. The conservation status and ecology of several avian species, particularly ground-dwelling birds, are poorly known in Ethiopia. We used camera trap-based occupancy modeling to investigate habitat covariate influence on occupancy (Ψ) and detection probability (ρ) estimates of Moorland Francolins Scleroptila psilolaema from spatially replicated surveys across both relatively pristine and disturbed landscapes in the Afroalpine biome of Ethiopia. Model-averaged estimate of ψ ^ across all sites was 0.76 (SD = 0.28) and ρ ^ was 0.77 (SD = 0.13) in the pristine landscape. The ψ ^ of the species in the disturbed landscape was 0.56 (SD = 0.19) and ρ ^ was 0.48 (SD = 0.06). As hypothesized, based on our model-averaged beta coefficient estimates (βmean ± SE), predators significantly negatively influenced the occupancy of Moorland Francolins in pristine habitat. We also found a significant positive association of occupancy with herb species richness. Contrary to our prediction, distance to road significantly negatively influence the occupancy of the species, suggesting that occupancy probability was highest in proximity to roadsides and trails in the pristine habitat. There was no significant influence of habitat covariates on the occupancy of the species in the disturbed habitat. The most important covariates that significantly influence the detectability of the species in pristine habitat included sampling occasion and precipitation. The greater occupancy and detectability of this endemic species in the pristine habitat could be linked with the particular conservation status and management of this biodiversity hotspot in the central highlands of Ethiopia. Our results suggest that strict legal enforcement is required to sustainably preserve Moorland Francolins and the ecological integrity of the entire Afroalpine biome. We recommend using camera traps in order to develop realistic and effective conservation and management strategies for rare, sensitive, cryptic, and ground-dwelling animals in the region.

Response to "Specimen collection is essential for modern science"

Natural history museums are vital repositories of specimens, samples and data that inform about the natural world; this Reply responds to a Formal Comment that queried whether it will ever be possible to completely do away with whole animal specimen collection, inviting open conversations about the ethical implications of specimen collection.

Comparing wildlife habitat suitability models based on expert opinion with camera trap detections

Expert knowledge is used in the development of wildlife habitat suitability models (HSMs) for management and conservation decisions. However, the consistency of such models has been questioned. Focusing on 1 method for elicitation, the analytic hierarchy process, we generated expert-based HSMs for 4 felid species: 2 forest specialists (ocelot [Leopardus pardalis] and margay [Leopardus wiedii]) and 2 habitat generalist species (Pampas cat [Leopardus colocola] and puma [Puma concolor]). Using these HSMs, species detections from camera-trap surveys, and generalized linear models, we assessed the effect of study species and expert attributes on the correspondence between expert models and camera-trap detections. We also examined whether aggregation of participant responses and iterative feedback improved model performance. We ran 160 HSMs and found that models for specialist species showed higher correspondence with camera-trap detections (AUC [area under the receiver operating characteristic curve] >0.7) than those for generalists (AUC < 0.7). Model correspondence increased as participant years of experience in the study area increased, but only for the understudied generalist species, Pampas cat (β = 0.024 [SE 0.007]). No other participant attribute was associated with model correspondence. Feedback and revision of models improved model correspondence, and aggregating judgments across multiple participants improved correspondence only for specialist species. The average correspondence of aggregated judgments increased as group size increased but leveled off after 5 experts for all species. Our results suggest that correspondence between expert models and empirical surveys increases as habitat specialization increases. We encourage inclusion of participants knowledgeable of the study area and model validation for expert-based modeling of understudied and generalist species.

Optimizing the automated recognition of individual animals to support population monitoring

Reliable estimates of population size and demographic rates are central to assessing the status of threatened species. However, obtaining individual-based demographic rates requires long-term data, which is often costly and difficult to collect. Photographic data offer an inexpensive, noninvasive method for individual-based monitoring of species with unique markings, and could therefore increase available demographic data for many species. However, selecting suitable images and identifying individuals from photographic catalogs is prohibitively time-consuming. Automated identification software can significantly speed up this process. Nevertheless, automated methods for selecting suitable images are lacking, as are studies comparing the performance of the most prominent identification software packages. In this study, we develop a framework that automatically selects images suitable for individual identification, and compare the performance of three commonly used identification software packages; Hotspotter, I3S-Pattern, and WildID. As a case study, we consider the African wild dog, Lycaon pictus, a species whose conservation is limited by a lack of cost-effective large-scale monitoring. To evaluate intraspecific variation in the performance of software packages, we compare identification accuracy between two populations (in Kenya and Zimbabwe) that have markedly different coat coloration patterns. The process of selecting suitable images was automated using convolutional neural networks that crop individuals from images, filter out unsuitable images, separate left and right flanks, and remove image backgrounds. Hotspotter had the highest image-matching accuracy for both populations. However, the accuracy was significantly lower for the Kenyan population (62%), compared to the Zimbabwean population (88%). Our automated image preprocessing has immediate application for expanding monitoring based on image matching. However, the difference in accuracy between populations highlights that population-specific detection rates are likely and may influence certainty in derived statistics. For species such as the African wild dog, where monitoring is both challenging and expensive, automated individual recognition could greatly expand and expedite conservation efforts.

Ecoinformatic Analysis of the Gut Ecological Diversity of Wild and Captive Long-Tailed Gorals Using Improved ITS2 Region Primers to Support Their Conservation

Ex situ conservation is used to protect endangered wildlife. As captive and wild long-tailed gorals are known to be similar, individuals under ex situ conservation can be reintroduced into nature. However, there is no appropriate indicator to evaluate them. Here, we amplified the internal transcribed spacer 2 (ITS2) region and compared the gut ecological information (eco-information) of captive and wild long-tailed gorals. We validated the existing ITS86F and ITS4 universal primers using reference sequences of the National Center for Biotechnology Information (NCBI) and improved their matching rates. We compared the gut eco-information of captive and wild long-tailed gorals obtained through experiments using the improved primer pair and found that the gut ecological diversity of captive gorals was low. Based on this, we suggested that the gut eco-information can be used as an evaluation index before reintroducing captive long-tailed gorals. Furthermore, we identified four plant types from the gut eco-information of wild long-tailed gorals, which can be the additional food sources to enhance the reduced intestinal ecological diversity of the captive animals.

High Frequency of Apodemus Mice Boosts Inverse Activity Pattern of Bank Voles, Clethrionomys glareolus, through Non-Aggressive Intraguild Competition

Sympatric animals with similar requirements can separate their ecological niches along the microhabitat, food and time axes. There may be alternative reasons for an interspecific different activity pattern, such as intraspecific social constraints, predator avoidance or physical conditions such as temperature, precipitation and illumination. We investigated the importance of intraguild competition in a 2-year study in an inner-alpine mixed forest, using small forest rodents as our model species. Apodemus mice were the physically superior, and bank voles, Clethrionomys glareolus, the inferior competitor. We predicted that bank voles would exhibit increased diurnal activity when frequencies of the almost exclusively nocturnal Apodemus mice were high during a seed mast year. To investigate this, we recorded 19,138 1 min videos. Controlling for confounding variables, bank vole diurnal activity was significantly related to the frequency of Apodemus mice. We assume that at high densities of Apodemus mice, a purely nocturnal separation in the niche dimensions of time, habitat and microhabitat is no longer sufficient, and therefore an inverse activity pattern by the bank voles is reinforced. Our videos showed, however, that this does not require persistent aggressive meetings and we explain this by the long co-evolution of the taxa under study.

A Demi-Decade of Mammal Research: A Rapid Assessment within the Heart of Borneo in Sabah

Sabah contributes 4.2 million hectares to the total Heart of Borneo (HoB) areas. Some of the forest reserves in the HoB are newly gazetted as Totally Protected Forest. Hence, their mammal diversity has to be comprehensively documented. This study aims to record the presence of terrestrial mammal species, and assess the prevalence of poaching in selected forest reserves within the Sabah HoB area. A total of 15 forest reserves were surveyed within a 5-year timeframe which recorded 60 terrestrial mammal species, including 21 Bornean endemics. The variation in total enumerated mammal species in the study sites may be derived from unequal sampling efforts, geographical factors and anthropogenic influences. The intensity of poaching within the study sites is high. Though this study is a rapid assessment, it created baseline information for mammal diversity in some of the least studied forest reserves in Sabah, important for conservation of its terrestrial mammals.

Not Just Pictures: Utility of Camera Trapping in the Context of African Swine Fever and Wild Boar Management

African swine fever (ASF) is a highly contagious disease affecting all suids and wild boar (Sus scrofa). Since 2007, ASF has spread to more than 30 countries in Europe and Asian regions, and the most recent outbreak has been in mainland Italy (reported on January 2022). When the genotype II of the ASF virus infects a population, a mortality rate close to 90% is usually reported. This drop in wild boar abundance produces a cascade effect in the entire ecosystem. In this context, effective monitoring tools for deriving management parameters are a priority aspect, and the utility of camera trapping could have been overlooked. Here, sampling the infected area in north Italy, we showed the utility of camera traps in the context of ASF infection. Specifically, we used 43 camera traps randomly distributed to (i) estimate movement parameters and population density of wild boar, roe deer (Capreolus capreolus), and wolf (Canis lupus); (ii) quantify wild boar recruitment; and (iii) assess whether the human restriction rules are being met. On the first spring after the outbreak detection, our results for wild boar indicated a density of 0.27 ind·km−2 ± 0.11 (standard error, SE), a daily activity level of 0.49 ± 0.07 (i.e., 11.76 h·day−1), a daily distance travelled of 9.07 ± 1.80 km·day−1, a litter size of 1.72 piglets·group−1, and a 72% of pregnant females. Despite human outdoor activities being restricted in the infected zone, we recorded human presence in 19 camera traps. The wide range of parameters estimated from the camera trap data, together with some intrinsic and practical advantages of this tool, allows us to conclude that camera traps are well positioned to be a reference approach to monitor populations affected by ASF. The population-specific parameters are of prime importance for optimizing ASF control efforts.

Activity Patterns, Sex Ratio, and Social Organization of the Bare-Faced Curassow (Crax fasciolata) in the Northern Pantanal, Brazil

Among Neotropical cracids (Galliformes), many taxa are declining rapidly in population size and facing local extinction. However, in the Brazilian Pantanal, several species occur sympatrically and in abundant numbers to allow for long-term studies. Therefore, the study was intended to collect data and statistically evaluate the life history patterns of Bare-faced Curassow (Crax fasciolata), a high-conservation-priority species. Additionally, the effect of applying commonly used independence filters on camera trap data was evaluated. The study was conducted in the SESC Pantanal, Baía das Pedras, Mato Grosso, Brazil, a private protected area of approximately 4200 ha. Between July 2015 and December 2017 (4768 sampling days), 37 sampling locations were monitored with camera traps placed in a regular grid with a spacing of 1 km. Crax fasciolata was detected at 26 (70.27%) of them, with 357 independent captures (554 individuals). Capture success differed among the four seasonal periods, being highest during the receding and lowest during the high-water period. The seasonal difference was more pronounced in the savanna, with significantly lower activity during the rising period and higher activity during the receding period, while it was more uniform in forest-dominated areas. Groups with offspring were more active during the period of receding water, indicating the peak of reproductive activity in the months before. The daily activity of the species followed a bimodal pattern, with peaks between 06:00 and 07:00 and 16:00 and 17:00. Daily activity rhythms were similar when compared between seasonal periods, sexes, and adults with or without offspring and differed between two habitats (more homogeneous in the forest). The mean detected group size was 1.55 ± 0.81 SD, with four animals exhibiting the largest observed aggregation. Larger unisexual aggregations of adults were not observed. The offspring sex ratio was significantly female-skewed at 0.51:1.00, while the adult sex ratio was considered equal at 1.05:1.00 (male:female). The use of different independence filters did not alter the BFC general activity pattern estimates. Cracids can be considered important bioindicators of habitat quality. The results of this study outline the importance of the Pantanal as a stronghold for this species and the privately protected areas with low anthropogenic activity as highly beneficial to its populations.

Towards the fully automated monitoring of ecological communities

High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.

Snapshot of the Atlantic Forest canopy: surveying arboreal mammals in a biodiversity hotspot

The Atlantic Forest of South America supports a rich terrestrial biodiversity but has been reduced to only a small extent of its original forest cover. It hosts a large number of endemic mammalian species but our knowledge of arboreal mammal ecology and conservation has been limited because of the challenges of observing arboreal species from ground level. Camera trapping has proven to be an effective tool in terrestrial mammal monitoring but the technique has rarely been used for arboreal species. For the first time in the Atlantic Forest, we obtained data on the arboreal mammal community using arboreal camera trapping, focusing on Caparaó National Park, Brazil. We placed 24 infrared camera traps in the forest canopy in seven areas within the Park, operating them continuously during January 2017–June 2019. During this period the camera traps accumulated 4,736 camera-days of footage and generated a total of 2,256 photographs and 30-s videos of vertebrates. The arboreal camera traps were able to detect arboreal mammals of a range of body sizes. The mammal assemblage comprised 15 identifiable species, including the Critically Endangered northern muriqui Brachyteles hypoxanthus and buffy-headed marmoset Callithrix flaviceps as well as other rare, nocturnal and inconspicuous species. We confirmed for the first time the occurrence of the thin-spined porcupine Chaetomys subspinosus in the Park. Species richness varied across survey areas and forest types. Our findings demonstrate the potential of arboreal camera trapping to inform conservation strategies.

Measuring the Impact of Conservation: The Growing Importance of Monitoring Fauna, Flora and Funga

Many stakeholders, from governments to civil society to businesses, lack the data they need to make informed decisions on biodiversity, jeopardising efforts to conserve, restore and sustainably manage nature. Here we review the importance of enhancing biodiversity monitoring, assess the challenges involved and identify potential solutions. Capacity for biodiversity monitoring needs to be enhanced urgently, especially in poorer, high-biodiversity countries where data gaps are disproportionately high. Modern tools and technologies, including remote sensing, bioacoustics and environmental DNA, should be used at larger scales to fill taxonomic and geographic data gaps, especially in the tropics, in marine and freshwater biomes, and for plants, fungi and invertebrates. Stakeholders need to follow best monitoring practices, adopting appropriate indicators and using counterfactual approaches to measure and attribute outcomes and impacts. Data should be made openly and freely available. Companies need to invest in collecting the data required to enhance sustainability in their operations and supply chains. With governments soon to commit to the post-2020 global biodiversity framework, the time is right to make a concerted push on monitoring. However, action at scale is needed now if we are to enhance results-based management adequately to conserve the biodiversity and ecosystem services we all depend on.

Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning

Using machine learning (ML) to automate camera trap (CT) image processing is advantageous for time-sensitive applications. However, little is currently known about the factors influencing such processing. Here, we evaluate the influence of occlusion, distance, vegetation type, size class, height, subject orientation towards the CT, species, time-of-day, colour, and analyst performance on wildlife/human detection and classification in CT images from western Tanzania. Additionally, we compared the detection and classification performance of analyst and ML approaches. We obtained wildlife data through pre-existing CT images and human data using voluntary participants for CT experiments. We evaluated the analyst and ML approaches at the detection and classification level. Factors such as distance and occlusion, coupled with increased vegetation density, present the most significant effect on DP and CC. Overall, the results indicate a significantly higher detection probability (DP), 81.1%, and correct classification (CC) of 76.6% for the analyst approach when compared to ML which detected 41.1% and classified 47.5% of wildlife within CT images. However, both methods presented similar probabilities for daylight CT images, 69.4% (ML) and 71.8% (analysts), and dusk CT images, 17.6% (ML) and 16.2% (analysts), when detecting humans. Given that users carefully follow provided recommendations, we expect DP and CC to increase. In turn, the ML approach to CT image processing would be an excellent provision to support time-sensitive threat monitoring for biodiversity conservation.

Fine-scale habitat selection of a small mammalian urban adapter: the West European hedgehog (Erinaceus europaeus)

Understanding patterns of habitat selection and factors affecting space use is fundamental in animal conservation. In urban landscapes, such knowledge can be used to advise householders on how best to manage their gardens for wildlife. In this study, we tracked 28 West European hedgehogs (Erinaceus europaeus), a species of conservation concern in the UK, in an area of high-density housing using radio and GPS tags to quantify patterns of habitat use and identify factors associated with the proportion of time spent in individual gardens. Both males and females exhibited a preference for residential gardens, but there were subtle differences between the sexes in relation to house type and front versus back gardens. Hedgehogs spent significantly more time in gardens where artificial food was provided, where a compost heap was present, if foxes (Vulpes vulpes) were infrequent visitors, if it rained overnight and as daylength increased (i.e., shorter nights); garden use was not significantly associated with variables potentially likely to reflect invertebrate prey abundance. These data suggest that the primary positive action that householders can undertake for urban hedgehogs is providing supplementary food. However, householders often feed hedgehogs after they know they are already visiting their garden. Consequently, the presence of artificial food may make it difficult to identify other important influences affecting garden use. Finally, we report that a GPS fix acquisition rate < 60% likely had no major effect on the results of our analyses, but should be a consideration in future studies using this technique on this species and in this habitat.

Co-use of existing crossing structures along roads by wildlife and humans: Wishful thinking?

This study assesses existing human-purpose underpasses below an unfenced high-traffic 4-lane highway in the Appalachian region of Quebec, Canada, as potential crossing structures for native mammal species. Eight underpasses of three types (five water culverts with minimum height and width of 1.8 m, one low-use gravel road byway, and two railroad underpasses) were continuously monitored by motion-detection infrared camera traps for time periods spanning up to 778 days (September 2016 to November 2018). We asked how the ratios of successful crossings through the structures (termed full crossings) and aversions to the structures (termed aversions) differed between species and we explored the influence of human activity levels on the use of these structures by wildlife. All monitored crossing structures had low human observations (with averages of less than 35 human activities per day). Our results provide evidence that 21 species of mammals in the study area successfully crossed through at least one of the eight observed underpasses on a minimum of one occasion. Some species were observed crossing through some of the underpasses on a regular basis, namely raccoon, red fox, and white-tailed deer. We propose a classification of mammal species into five human co-use classes (no or low co-use to very high co-use) to explore the relationship between mammal use of the structures and human presence. We found that humans and mammals were observed sharing passages for the four mammal species identified as tolerant of human co-use (high and very high co-use classes), but co-use was observed to be limited or not occurring for most other species. The strengths of this study include the length of time during which monitoring took place, as well as the placement of four cameras at each structure (two facing inward and two facing outward) to determine whether individuals successfully crossed through the structures or displayed avoidance behaviour. The results suggest select species of mammals show some co-use with humans at existing underpasses. The activity patterns of mammals documented over the two-year study can assist with future estimates of highway permeability. Further, measurements of human and mammal co-use have species-specific implications for retrofitting existing structures and constructing wildlife fences and purpose-built wildlife passages.

Identifying Animals in Camera Trap Images via Neural Architecture Search

Wild animals are essential for ecosystem structuring and stability, and thus they are important for ecological research. Since most wild animals have high athletic or concealable abilities or both, it is used to be relatively difficult to acquire evidence of animal appearances before applications of camera traps in ecological researches. However, a single camera trap may produce thousands of animal images in a short period of time and inevitably ends up with millions of images requiring classification. Although there have been many methods developed for classifying camera trap images, almost all of them follow the pattern of a very deep convolutional neural network processing all camera trap images. Consequently, the corresponding surveillance area may need to be delicately controlled to match the network capability, and it may be difficult to expand the area in the future. In this study, we consider a scenario in which camera traps are grouped into independent clusters, and images produced by a cluster are processed by an edge device installed with a customized network. Accordingly, edge devices in this scenario may be highly heterogeneous due to cluster scales. Resultantly, networks popular in the classification of camera trap images may not be deployable for edge devices without modifications requiring the expertise which may be hard to obtain. This motivates us to automatize network design via neural architecture search for edge devices. However, the search may be costly due to the evaluations of candidate networks, and its results may be infeasible without considering the resource limits of edge devices. Accordingly, we propose a search method using regression trees to evaluate candidate networks to lower search costs, and candidate networks are built based on a meta-architecture automatically adjusted regarding to the resource limits. In experiments, the search consumes 6.5 hours to find a network applicable to the edge device Jetson X2. The found network is then trained on camera trap images through a workstation and tested on Jetson X2. The network achieves competitive accuracies compared with the automatically and the manually designed networks.

An IoT System Using Deep Learning to Classify Camera Trap Images on the Edge

Camera traps deployed in remote locations provide an effective method for ecologists to monitor and study wildlife in a non-invasive way. However, current camera traps suffer from two problems. First, the images are manually classified and counted, which is expensive. Second, due to manual coding, the results are often stale by the time they get to the ecologists. Using the Internet of Things (IoT) combined with deep learning represents a good solution for both these problems, as the images can be classified automatically, and the results immediately made available to ecologists. This paper proposes an IoT architecture that uses deep learning on edge devices to convey animal classification results to a mobile app using the LoRaWAN low-power, wide-area network. The primary goal of the proposed approach is to reduce the cost of the wildlife monitoring process for ecologists, and to provide real-time animal sightings data from the camera traps in the field. Camera trap image data consisting of 66,400 images were used to train the InceptionV3, MobileNetV2, ResNet18, EfficientNetB1, DenseNet121, and Xception neural network models. While performance of the trained models was statistically different (Kruskal–Wallis: Accuracy H(5) = 22.34, p < 0.05; F1-score H(5) = 13.82, p = 0.0168), there was only a 3% difference in the F1-score between the worst (MobileNet V2) and the best model (Xception). Moreover, the models made similar errors (Adjusted Rand Index (ARI) > 0.88 and Adjusted Mutual Information (AMU) > 0.82). Subsequently, the best model, Xception (Accuracy = 96.1%; F1-score = 0.87; F1-Score = 0.97 with oversampling), was optimized and deployed on the Raspberry Pi, Google Coral, and Nvidia Jetson edge devices using both TenorFlow Lite and TensorRT frameworks. Optimizing the models to run on edge devices reduced the average macro F1-Score to 0.7, and adversely affected the minority classes, reducing their F1-score to as low as 0.18. Upon stress testing, by processing 1000 images consecutively, Jetson Nano, running a TensorRT model, outperformed others with a latency of 0.276 s/image (s.d. = 0.002) while consuming an average current of 1665.21 mA. Raspberry Pi consumed the least average current (838.99 mA) with a ten times worse latency of 2.83 s/image (s.d. = 0.036). Nano was the only reasonable option as an edge device because it could capture most animals whose maximum speeds were below 80 km/h, including goats, lions, ostriches, etc. While the proposed architecture is viable, unbalanced data remain a challenge and the results can potentially be improved by using object detection to reduce imbalances and by exploring semi-supervised learning.

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

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

Estimating the movements of terrestrial animal populations using broad-scale occurrence data

As human and automated sensor networks collect increasingly massive volumes of animal observations, new opportunities have arisen to use these data to infer or track species movements. Sources of broad scale occurrence datasets include crowdsourced databases, such as eBird and iNaturalist, weather surveillance radars, and passive automated sensors including acoustic monitoring units and camera trap networks. Such data resources represent static observations, typically at the species level, at a given location. Nonetheless, by combining multiple observations across many locations and times it is possible to infer spatially continuous population-level movements. Population-level movement characterizes the aggregated movement of individuals comprising a population, such as range contractions, expansions, climate tracking, or migration, that can result from physical, behavioral, or demographic processes. A desire to model population movements from such forms of occurrence data has led to an evolving field that has created new analytical and statistical approaches that can account for spatial and temporal sampling bias in the observations. The insights generated from the growth of population-level movement research can complement the insights from focal tracking studies, and elucidate mechanisms driving changes in population distributions at potentially larger spatial and temporal scales. This review will summarize current broad-scale occurrence datasets, discuss the latest approaches for utilizing them in population-level movement analyses, and highlight studies where such analyses have provided ecological insights. We outline the conceptual approaches and common methodological steps to infer movements from spatially distributed occurrence data that currently exist for terrestrial animals, though similar approaches may be applicable to plants, freshwater, or marine organisms.

Noninvasive Genetic Assessment Is an Effective Wildlife Research Tool When Compared with Other Approaches

Wildlife research has been indispensable for increasing our insight into ecosystem functioning as well as for designing effective conservation measures under the currently high rates of biodiversity loss. Genetic and genomic analyses might be able to yield the same information on, e.g., population size, health, or diet composition as other wildlife research methods, and even provide additional data that would not be possible to obtain by alternative means. Moreover, if DNA is collected non-invasively, this technique has only minimal or no impact on animal welfare. Nevertheless, the implementation rate of noninvasive genetic assessment in wildlife studies has been rather low. This might be caused by the perceived inefficiency of DNA material obtained non-invasively in comparison with DNA obtained from blood or tissues, or poorer performance in comparison with other approaches used in wildlife research. Therefore, the aim of this review was to evaluate the performance of noninvasive genetic assessment in comparison with other methods across different types of wildlife studies. Through a search of three scientific databases, 113 relevant studies were identified, published between the years 1997 and 2020. Overall, most of the studies (94%) reported equivalent or superior performance of noninvasive genetic assessment when compared with either invasive genetic sampling or another research method. It might be also cheaper and more time-efficient than other techniques. In conclusion, noninvasive genetic assessment is a highly effective research approach, whose efficacy and performance are likely to improve even further in the future with the development of optimized protocols.

Noninvasive Technologies for Primate Conservation in the 21st Century

Observing and quantifying primate behavior in the wild is challenging. Human presence affects primate behavior and habituation of new, especially terrestrial, individuals is a time-intensive process that carries with it ethical and health concerns, especially during the recent pandemic when primates are at even greater risk than usual. As a result, wildlife researchers, including primatologists, have increasingly turned to new technologies to answer questions and provide important data related to primate conservation. Tools and methods should be chosen carefully to maximize and improve the data that will be used to answer the research questions. We review here the role of four indirect methods—camera traps, acoustic monitoring, drones, and portable field labs—and improvements in machine learning that offer rapid, reliable means of combing through large datasets that these methods generate. We describe key applications and limitations of each tool in primate conservation, and where we anticipate primate conservation technology moving forward in the coming years.

Eyes, ears, or nose? Comparison of three non-invasive methods to survey wolf recolonisation

The development and use of cost-effective and appropriate survey methods to assess species distribution and to monitor range expansion and contraction of wild populations is crucial due to the limited financial resources for conservation. Of particular importance, yet little studied, is the ability to collect information before a wild population is well established, i.e. at the early stages of recolonisation. During 2018 and 2019, we used camera traps, audio recorders, and scat detection dogs simultaneously to investigate composition, detection probability, and territorial extent of a pack of wolves in the Swiss Alps. We compared the efficacy of these survey methods by assessing sampling effort, data obtained, and costs. We show that, under the presented setup, camera traps and scat detection dogs substantially outperformed audio recorders in detecting wolves, representing the packs’ territorial extent, and revealing the number of adult wolves. The detection dogs did not detect pups but, unlike the other methods, allowed the identification of single individuals. The use of four camera traps during 13 weeks, a 24-km-long transect walked with the detection dog, or the use of one audio recorder during 148 weeks were necessary to obtain a comparable wolf detection probability. Our results show that no single method was able to return all information that we hoped to collect. Comprehensive and cost-effective information was best obtained by combining data from camera traps and detection dogs. We suggest both methods to be simultaneously used to successfully investigate wolf recolonisation into historical range.

Seasonal and diel activity patterns of the endangered taiga bean goose (Anser fabalis fabalis) during the breeding season, monitored with camera traps

Taiga bean goose (Anser fabalis fabalis) is an endangered subspecies that breeds sporadically in remote habitats in the arctic and boreal zones. Due to its elusive behaviour, there is a paucity of knowledge on the behaviour of taiga bean goose during the breeding season, and survey methods for monitoring numbers in the breeding areas are lacking. Camera traps are a useful tool for wildlife monitoring, particularly when there is a need for non-invasive methods due to the shy nature of the species. In this study, we tested the use of camera traps to investigate seasonal and diel activity patterns of taiga bean goose in Finland over two successive breeding seasons, 2018 and 2019. We did this by modelling counts of geese from images with generalized linear and additive mixed models. The camera type (cameras placed by experts specialized in bean goose ecology vs randomly placed cameras) did not influence the count of taiga bean goose (p = 0.386). However, the activity varied significantly by region, Julian day, time of day and temperature, with the study site (individual peatland) and year adding substantial random variation and uncertainty in the counts. Altogether, the best fitting model explained nearly 70% of the variation in taiga bean goose activity. The peak in activity occurred about a month later in the southernmost region compared to the more northern regions, which may indicate behaviours related to migration rather than breeding and moulting. Our results show that long-term monitoring with game camera traps provide a potential unobtrusive approach for studying the behavioural patterns of taiga bean goose and can increase our ecological knowledge of this little-known subspecies. The results can be applied to planning of the annual censuses and finding the optimal time frame for their execution.

Linking camera-trap data to taxonomy: Identifying photographs of morphologically similar chipmunks

Remote cameras are a common method for surveying wildlife and recently have been promoted for implementing large-scale regional biodiversity monitoring programs. The use of camera-trap data depends on the correct identification of animals captured in the photographs, yet misidentification rates can be high, especially when morphologically similar species co-occur, and this can lead to faulty inferences and hinder conservation efforts. Correct identification is dependent on diagnosable taxonomic characters, photograph quality, and the experience and training of the observer. However, keys rooted in taxonomy are rarely used for the identification of camera-trap images and error rates are rarely assessed, even when morphologically similar species are present in the study area. We tested a method for ensuring high identification accuracy using two sympatric and morphologically similar chipmunk (Neotamias) species as a case study. We hypothesized that the identification accuracy would improve with use of the identification key and with observer training, resulting in higher levels of observer confidence and higher levels of agreement among observers. We developed an identification key and tested identification accuracy based on photographs of verified museum specimens. Our results supported predictions for each of these hypotheses. In addition, we validated the method in the field by comparing remote-camera data with live-trapping data. We recommend use of these methods to evaluate error rates and to exclude ambiguous records in camera-trap datasets. We urge that ensuring correct and scientifically defensible species identifications is incumbent on researchers and should be incorporated into the camera-trap workflow.

Seasonal and daily activity patterns of mammals in the colony of great cormorants

Despite extreme changes of ecosystems made by breeding colonies of great cormorants (Phalacrocorax carbo), these territories are still used by mammals. We present results of the analysis of mammal seasonal and daily activity patterns, registered by camera traps in two colonies of the great cormorants in Lithuania. Red foxes and raccoon dogs mainly visited colonies in the cormorant breeding time, April to July. In the inland colony of great cormorants in Lukštas Lake, average red fox relative shooting frequency in cormorant breeding time considerably exceeded that in non-breeding time (on average, 41.8 and 5.9 photos/100 days respectively). In the peninsular colony of great cormorants in Juodkrantė average relative shooting frequency of red fox in the breeding time was 7.8 versus 3.4 photos/100 days in non-breeding time, relative shooting frequencies of raccoon dog were 50.0 versus 1.3 photos/100 days, respectively. Daily activity patterns of wild boar and red fox in both colonies, as well as activity patterns of raccoon dog in Lukštas were related to the activity of cormorants with various degree of significance.

Modelos de ocupación para datos de cámaras trampa

El uso de las cámaras trampa (CT) en la investigación de fauna silvestre puede generar conclusiones sesgadas cuando la detectabilidad imperfecta de especies no es considerada. Herramientas analíticas como los modelos de ocupación permiten estimar simultáneamente parámetros ecológicos corregidos por la probabilidad de detección. Sin embargo, es necesario implementar e interpretar de manera correcta los parámetros estimados por estos modelos para obtener inferencias con sentido biológico. Este trabajo presenta un marco conceptual base para diseñar de manera apropiada un análisis de ocupación por medio de datos de CT. Se discuten y se señalan recomendaciones generales para la definición de los elementos del modelo, el diseño del muestreo, así como estrategias de modelamiento estadísticos apropiadas dependiendo de los objetivos del estudio, las características de la especie y el tipo de datos obtenidos. Las decisiones tomadas por el investigador para definir cada uno de los componentes del modelo deben considerar la escala adecuada para que el fenómeno de estudio tenga sentido biológico. De esta manera, es posible generar inferencias y conclusiones robustas a partir de información de CT, lo que permite avanzar en el entendimiento de los mecanismos que subyacen a la ecología espacial de fauna silvestre y por lo tanto en su conservación.

Jinks E, Buelow CA, Brown CJ, Wang D, Kusy B, M. Ditria E, Connolly RM. Automatic detection of fish and tracking of movement for ecology

Animal movement studies are conducted to monitor ecosystem health, understand ecological dynamics, and address management and conservation questions. In marine environments, traditional sampling and monitoring methods to measure animal movement are invasive, labor intensive, costly, and limited in the number of individuals that can be feasibly tracked. Automated detection and tracking of small-scale movements of many animals through cameras are possible but are largely untested in field conditions, hampering applications to ecological questions.Here, we aimed to test the ability of an automated object detection and object tracking pipeline to track small-scale movement of many individuals in videos. We applied the pipeline to track fish movement in the field and characterize movement behavior. We automated the detection of a common fisheries species (yellowfin bream, Acanthopagrus australis) along a known movement passageway from underwater videos. We then tracked fish movement with three types of tracking algorithms (MOSSE, Seq-NMS, and SiamMask) and evaluated their accuracy at characterizing movement.We successfully detected yellowfin bream in a multispecies assemblage (F1 score =91%). At least 120 of the 169 individual bream present in videos were correctly identified and tracked. The accuracies among the three tracking architectures varied, with MOSSE and SiamMask achieving an accuracy of 78% and Seq-NMS 84%.By employing this integrated object detection and tracking pipeline, we demonstrated a noninvasive and reliable approach to studying fish behavior by tracking their movement under field conditions. These cost-effective technologies provide a means for future studies to scale-up the analysis of movement across many visual monitoring systems.

Lemur paparazzi: Arboreal camera trapping and occupancy modeling as conservation tools for monitoring threatened lemur species

Primate species face growing risks of extinction throughout the world. To better protect their populations, effective monitoring techniques are needed. The goal of this study was to evaluate the use of arboreal camera traps and occupancy modeling as conservation tools for threatened lemur species. This project aimed to (1) estimate the occupancy and detection probabilities of lemur species, (2) investigate factors potentially affecting lemur habitat use, and (3) determine whether ground or arboreal cameras are better for surveying lemur assemblages. We conducted camera trapping research in five forest fragments (total trap nights = 1770; 900 arboreal trap nights (134 photo events); 870 ground trap nights (2 photo events)) and reforestation areas (total trap nights = 608; 1 photo event) in Kianjavato, Madagascar from May to September 2019. We used arboreal trap data from fragments to estimate occupancy for five species: the red-fronted brown lemur (Eulemur rufifrons; ψ = 0.54 ± SD 0.03), Jolly's mouse lemur (Microcebus jollyae; ψ = 0.14 ± 0.17), the greater dwarf lemur (Cheirogaleus major; ψ = 0.42 ± 0.30), the red-bellied lemur (Eulemur rubriventer; ψ = 0.24 ± 0.03), and the black-and-white ruffed lemur (Varecia variegata; ψ = 0.24 ± 0.08). Tree diameter, elevation, distance to village, and canopy connectivity were important predictors of occupancy, while camera height, canopy connectivity, fragment ID, and fragment size predicted detection. Arboreal cameras recorded significantly higher species richness compared with ground cameras. We suggest expanded application of arboreal camera traps in future research, but we recommend longer trapping periods to better sample rarer species. Overall, arboreal camera trapping combined with occupancy modeling can be a highly efficient and useful approach for monitoring and predicting the occurrence of elusive lemur species and has the potential to be effective for other arboreal primates and canopy taxa across the globe.

Review: COVID-19 highlights the importance of camera traps for wildlife conservation research and management

COVID-19 has altered many aspects of everyday life. For the scientific community, the pandemic has called upon investigators to continue work in novel ways, curtailing field and lab research. However, this unprecedented situation also offers an opportunity for researchers to optimize and further develop available field methods. Camera traps are one example of a tool used in science to answer questions about wildlife ecology, conservation, and management. Camera traps have long battery lives, lasting more than a year in certain cases, and photo storage capacity, with some models capable of wirelessly transmitting images from the field. This allows researchers to deploy cameras without having to check them for up to a year or more, making them an ideal field research tool during restrictions on in-person research activities such as COVID-19 lockdowns. As technological advances allow cameras to collect increasingly greater numbers of photos and videos, the analysis techniques for large amounts of data are evolving. Here, we describe the most common research questions suitable for camera trap studies and their importance for biodiversity conservation. As COVID-19 continues to affect how people interact with the natural environment, we discuss novel questions for which camera traps can provide insights on. We conclude by summarizing the results of a systematic review of camera trap studies, providing data on target taxa, geographic distribution, publication rate, and publication venues to help researchers planning to use camera traps in response to the current changes in human activity.

Assessing population structure and body condition to inform conservation strategies for a small isolated Asian elephant (Elephas maximus) population in southwest China

The Asian elephant (Elephas maximus) population in Nangunhe National Nature Reserve in China represents a unique evolutionary branch that has been isolated for more than twenty years from neighboring populations in Myanmar. The scarcity of information on population structure, sex ratio, and body condition makes it difficult to develop effective conservation measures for this elephant population. Twelve individuals were identified from 3,860 valid elephant images obtained from February to June 2018 (5,942 sampling effort nights) at 52 camera sites. Three adult females, three adult males, one subadult male, two juvenile females, two juvenile males and one male calf were identified. The ratio of adult females to adult males was 1:1, and the ratio of reproductive ability was 1:0.67, indicating the scarcity of reproductive females as an important limiting factor to population growth. A population density of 5.32 ± 1.56 elephants/100 km² was estimated using Spatially Explicit Capture Recapture (SECR) models. The health condition of this elephant population was assessed using an 11-point scale of Body Condition Scoring (BCS). The average BCS was 5.75 (n = 12, range 2–9), with adult females scoring lower than adult males. This isolated population is extremely small and has an inverted pyramid age structure and therefore is at a high risk of extinction. We propose three plans to improve the survival of this population: improving the quality and quantity of food resources, removing fencing and establishing corridors between the east and wet parts of Nangunhe reserve.