Camera Traps
- NOTOC**
Camera Traps in Wildlife Conservation and Biodiversity Monitoring
Camera traps have become one of the most important technologies for observing wildlife without requiring researchers to remain continuously in the field. Motion- or heat-triggered cameras can operate for weeks or months in forests, grasslands, mountains, deserts, wetlands, agricultural landscapes, protected areas, and cities. They allow researchers to document animals that are rare, nocturnal, secretive, widely dispersed, or otherwise difficult to observe directly.
The scientific role of camera trapping has expanded considerably. Early camera-trap research often concentrated on documenting the presence of particular species or estimating populations of individually recognizable animals such as tigers and leopards. Modern camera networks can monitor entire wildlife communities, estimate population abundance and density, study animal behavior, evaluate habitat use, identify wildlife corridors, measure responses to human disturbance, detect disease, and provide long-term evidence of ecological change.
At the same time, improvements in digital cameras, communications systems, data standards, machine learning, and artificial intelligence are changing how the enormous volume of photographs produced by camera surveys can be processed. Large coordinated projects now combine millions of photographs from hundreds or thousands of camera locations, making camera trapping an increasingly important component of regional, national, continental, and global biodiversity monitoring.
Camera-Trap Technology and Artificial Intelligence
Modern camera traps generally use infrared or motion sensors to detect an animal entering the camera's field of view. Once triggered, the device records photographs or video that can later be examined by researchers. Because cameras can operate continuously and remotely, they can collect information at times and places where direct human observation would be difficult or prohibitively expensive.
One of the major challenges created by large camera networks is the sheer quantity of images they generate. A substantial proportion may contain vegetation, shadows, people, vehicles, or no identifiable animals. Even useful photographs can require researchers to identify species manually, creating an enormous data-processing burden.
Artificial intelligence and computer vision are increasingly being used to automate this process. Machine-learning systems can identify animals within photographs and classify them by species. Newer systems are being trained using very large wildlife-image datasets and can sometimes be adapted to local ecosystems with comparatively modest amounts of additional training data.
Research is also examining ways to perform artificial-intelligence processing directly on camera devices. Smart cameras capable of identifying wildlife in the field could reduce the need to transmit or manually examine huge collections of images. Connected camera systems may eventually allow important detections to reach conservation managers soon after an animal is photographed.
Specialized camera systems are broadening the range of species that can be monitored. Conventional camera traps work particularly well for medium and large terrestrial mammals, but researchers have developed close-focus cameras, tunnel-based systems, arboreal cameras, and other specialized designs for small mammals, reptiles, amphibians, arthropods, and animals living in forest canopies.
Artificial intelligence is also being investigated for more complex measurements. Experimental systems can estimate the distance between an animal and a camera, determine aspects of animal size or height, and combine visual information with environmental information around individual camera locations.
Large-Scale Biodiversity Monitoring
Camera traps increasingly operate as components of large coordinated monitoring networks rather than isolated research projects.
Projects such as Snapshot Serengeti demonstrated that enormous collections of wildlife photographs could be gathered systematically and classified with assistance from citizen scientists. Similar collaborative approaches have subsequently expanded across other regions.
The SNAPSHOT USA network coordinates cameras across the United States, creating standardized datasets that allow scientists to investigate mammal distributions and ecological patterns across a continental scale. CamTrapAsia similarly combines information from numerous camera-trapping studies across tropical Asia.
Other initiatives have assembled data from tropical forests, the Amazon, Madagascar, Australia, Europe, Africa, and additional regions. Standardized camera networks can reveal patterns that individual projects cannot easily identify, including geographic differences in species diversity, responses to land-use change, and changes in wildlife communities over time.
Wildlife Insights represents an important step toward integrating camera trapping with global biodiversity information systems. It provides infrastructure for storing, identifying, analyzing, and sharing camera-trap observations while using automated image-recognition tools to reduce the enormous workload associated with photograph classification.
Long-term monitoring is particularly valuable. Repeated surveys at the same locations can reveal whether particular animals, populations, or ecological communities are increasing, declining, relocating, or changing their behavior. Decades of wildlife monitoring on Panama's Barro Colorado Island demonstrate how persistent observation can produce ecological information that would be impossible to obtain from short-term surveys alone.
Open standards are increasingly important as camera-trap datasets become larger and more widely shared. Initiatives such as Camtrap DP and earlier metadata standards seek to ensure that information about camera deployments, observations, species identifications, locations, and survey conditions can be exchanged and reused consistently among researchers.
Wildlife Population Density and Abundance
Camera traps are widely used to estimate how many animals inhabit a landscape.
For species such as tigers, leopards, jaguars, and some other individually recognizable animals, distinctive coat patterns can allow researchers to identify individual animals. Capture-recapture and spatial capture-recapture models can then estimate population size and density.
Many wildlife species cannot be individually identified from photographs. This has encouraged the development of statistical methods capable of estimating populations without recognizing individual animals.
One influential technique is the Random Encounter Model. Rather than identifying particular animals, it uses the frequency at which wildlife encounters cameras together with information about animal movement and camera detection characteristics to estimate population density.
Other approaches include camera-trap distance sampling, N-mixture models, Space-to-Event models, spatial count models, partial-identity models, and spatial mark-resight techniques.
Camera-trap distance sampling attempts to incorporate information about how far animals are from a camera when detected. Artificial-intelligence and photogrammetric systems are increasingly being investigated to automate these distance measurements.
Researchers continue to compare these approaches because population estimates can be strongly affected by assumptions about animal movement, detection probability, group behavior, camera placement, and sampling duration. Statistical models that appear to fit observed data well can occasionally produce biologically unrealistic estimates, making independent validation especially important.
Survey Design, Detection Bias, and Limitations
Camera traps provide powerful ecological information, but they do not observe wildlife perfectly.
An animal may walk through a survey location without triggering the camera. Small animals can be particularly difficult to detect. Vegetation, weather, temperature, camera angle, movement speed, distance, body size, and sensor characteristics can all influence whether an animal is photographed.
Camera placement can also affect conclusions. Cameras positioned along trails may record animals differently from randomly positioned cameras. Species that regularly travel along roads or trails can become overrepresented, while species avoiding those features may appear less common than they actually are.
Small habitat features such as fallen logs, paths, water sources, and vegetation openings can substantially influence detection rates.
Researchers therefore need to match camera placement and statistical methods carefully to the ecological question being investigated. A survey designed to estimate species richness may require a different arrangement from one intended to measure population density, activity patterns, or habitat preference.
Survey duration also matters. Some animals can be detected reliably after relatively short monitoring periods, while rare or elusive species may require substantially longer surveys and greater numbers of cameras.
Attractants such as scent lures can increase detections but may also modify animal behavior. If predators and prey respond differently to lures, the resulting photographs may not accurately represent their normal use of the landscape.
Even apparently non-invasive cameras can potentially affect wildlife. Researchers have increasingly examined whether camera flashes, infrared illumination, sounds, scents, or the physical presence of equipment influence animal behavior.
Regional and Species Conservation Applications
Camera traps are now widely used across Africa, Asia, Europe, North America, South America, and Australia.
In Africa, camera surveys have documented wildlife communities in Kenya, Tanzania, Malawi, Botswana, Cameroon, the Democratic Republic of the Congo, and other regions. Large surveys can reveal differences between intact forests, hunted landscapes, agricultural areas, community-managed land, and protected areas.
Camera trapping has been especially important for studying African carnivores such as lions and leopards and for documenting elusive animals that researchers rarely encounter directly.
Asian camera-trap research includes extensive work on tigers, leopards, snow leopards, pangolins, dholes, and numerous forest mammals. Surveys in India, Nepal, Bhutan, China, Thailand, Cambodia, Vietnam, Indonesia, and other countries demonstrate how camera traps can identify threatened populations and document previously unknown distributions.
Camera surveys can also reveal severe conservation problems. Photographs may document animals injured or killed by wire snares, evidence of hunting, declining populations, or landscapes from which much of the original large-animal community has disappeared.
For highly cryptic species such as pangolins, incidental camera photographs collected during surveys designed for other animals can become valuable evidence about distribution and habitat use.
Long-term camera surveys are also capable of documenting recolonization. Photographs can provide convincing evidence when predators return to landscapes after being absent for many decades.
Rare and Threatened Wildlife
Camera trapping is particularly valuable for animals that are difficult to observe using conventional field methods.
Snow leopards provide an important example. Cameras can operate continuously in remote mountain habitats and photograph animals that researchers may rarely see in person. However, studies have also demonstrated that incorrectly identifying individual snow leopards can inflate population estimates, showing the need for careful image interpretation.
Camera trapping has similarly been used to study jaguars, leopards, ocelots, tapirs, tigers, pangolins, lynx, dholes, wolverines, Amur leopards, and many other threatened species.
Some projects gather hundreds of thousands of camera-trap nights across multiple countries. By combining observations from many separate studies, scientists may be able to examine the range-wide status of species whose individual populations would otherwise remain poorly understood.
Camera traps can also produce unexpected discoveries. Long-term remote observation sometimes records behaviors that have rarely or never been directly witnessed by researchers.
Animal Behavior and Ecological Interactions
The timestamps attached to camera photographs allow scientists to reconstruct daily and seasonal patterns of animal activity.
Researchers can determine whether animals are primarily nocturnal, diurnal, or active around dawn and dusk. Changes in activity through seasons can also be measured.
When several species are monitored simultaneously, cameras can reveal temporal relationships among predators, prey, and competitors. Researchers can examine whether species avoid one another in time or space and whether those relationships change when human disturbance increases.
Camera studies have examined interactions among large carnivores, relationships between predators and prey, group size, age structure, reproductive activity, and seasonal movements.
Cameras placed in forest canopies have opened another dimension of wildlife research. Traditional ground-level surveys may fail to record animals that spend most of their lives in trees. Arboreal cameras can document primates, arboreal mammals, and other canopy wildlife and reveal how they use resources and interact with one another.
Human Disturbance, Recreation, and Urban Wildlife
Because camera traps can record both animals and people, they are useful for investigating how wildlife responds to human activity.
Studies have shown that some mammals become increasingly nocturnal where human disturbance is greater. Animals may alter their activity schedules to avoid recreationists, roads, settlements, livestock, or other forms of human activity.
Camera networks in protected areas can compare wildlife behavior before, during, and after changes in human activity. Research conducted around the COVID-19 period, for example, provided opportunities to examine how altered recreation and movement patterns affected wildlife.
Urban camera networks have documented foxes, badgers, deer, raccoons, coyotes, rodents, and numerous other animals living alongside people. Large multi-city studies show that urban wildlife communities are shaped by development, vegetation, climate, human density, and species-specific adaptations.
Cities can alter wildlife behavior as well as distribution. Some mammals become more nocturnal in urban environments, apparently reducing the amount of direct contact they have with people.
Wildlife Corridors and Roads
Camera traps are frequently used to determine whether wildlife successfully moves through fragmented landscapes.
Cameras positioned near underpasses, culverts, bridges, viaducts, and wildlife overpasses can document which species use crossing structures and how frequently they do so.
Such information is important because roads can divide habitats, disrupt migration, and cause wildlife mortality through vehicle collisions.
Camera data can be combined with roadkill observations, GPS tracking, citizen science, and landscape-connectivity models to identify locations where new crossings could provide the greatest conservation benefit.
Restored wildlife corridors can also be evaluated with cameras. Photographs of large mammals moving through connecting landscapes provide direct evidence that habitat corridors are being used.
Protected Areas and Habitat Conservation
Camera-trap datasets have become increasingly useful for comparing protected and unprotected landscapes.
Large-scale analyses indicate that protected areas can support greater mammal diversity than otherwise comparable unprotected landscapes, although effectiveness varies substantially among regions.
Camera surveys can determine whether wildlife persists in logged forests, agricultural landscapes, community-managed forests, secondary forests, shade-coffee systems, timber concessions, and other human-modified environments.
Some modified landscapes retain surprisingly diverse wildlife communities, particularly when forest structure and connectivity remain intact. Others show strong evidence of defaunation despite apparently suitable vegetation.
These findings demonstrate that satellite imagery showing remaining forest cover does not necessarily reveal whether the original animal community remains present. Camera traps provide direct biological observations capable of detecting this difference.
Wildlife Disease and Conservation Enforcement
Camera traps have applications beyond conventional biodiversity surveys.
Visible symptoms of diseases can sometimes be detected in photographs, allowing researchers to follow changes in disease prevalence among wildlife populations. Camera surveys have been used to investigate conditions such as sarcoptic mange and to examine disease transmission risks involving wild animals and livestock.
Long-term monitoring of individually recognizable animals may allow researchers to compare health conditions across years.
Camera traps can also contribute to conservation enforcement. Remote cameras may record hunters, snares, illegal entry, or other activities occurring in protected areas.
This creates important ethical and privacy questions because cameras intended for wildlife can inadvertently photograph people.
Ethics and Privacy
As camera trapping expands, researchers increasingly recognize that ecological monitoring also has social consequences.
Cameras placed in landscapes used by local communities may unintentionally record identifiable people, private activities, or evidence of illegal behavior.
Ethical guidelines therefore emphasize transparency, community participation, responsible storage of photographs, protection of personal information, and clear rules concerning who may access human images.
Wildlife welfare also deserves consideration. Although camera trapping is usually described as non-invasive, cameras may still alter animal behavior in some circumstances. Researchers are increasingly encouraged to consider these effects when designing and reporting studies.
Open Data and the Future of Camera Trapping
The future of camera trapping is likely to involve increasingly integrated monitoring systems combining inexpensive sensors, artificial intelligence, communications networks, open databases, and standardized ecological methods.
Artificial intelligence can reduce the burden of manually examining millions of photographs. Connected cameras may allow rapid transmission of important observations. Improved statistical models can extract population estimates from species that cannot be individually recognized.
Open datasets can allow the same observations to support many different scientific questions. A camera deployment originally designed to study a particular predator may later contribute information about prey species, biodiversity, human activity, disease, climate responses, or ecosystem change.
Combining camera-trap information with other monitoring methods may be especially powerful. Environmental DNA, GPS telemetry, acoustic monitoring, satellite imagery, field surveys, citizen science, and local ecological knowledge can provide complementary information.
No single monitoring technology captures every component of biodiversity. Camera traps are strongest for animals that regularly pass through their detection zones, particularly terrestrial vertebrates. Combining multiple technologies can provide a much more complete picture of ecological communities.
Conclusion
Camera traps have evolved from relatively simple remote photographic devices into a major scientific infrastructure for biodiversity research and wildlife conservation.
They allow researchers to observe elusive animals, estimate populations, study behavior, evaluate protected areas, monitor wildlife corridors, detect disease, investigate human-wildlife interactions, and document ecological change across enormous areas.
Large collaborative networks increasingly make it possible to compare wildlife communities across countries and continents. At the same time, artificial intelligence and automated image processing are helping researchers manage the immense quantities of information generated by modern camera surveys.
The technology nevertheless has important limitations. Detection probability, camera placement, survey duration, statistical assumptions, animal behavior, equipment characteristics, privacy, and wildlife welfare can all affect the reliability or ethics of a project.
Used carefully, camera traps provide something especially valuable to conservation science: repeated and direct observations of animals in places where human observers cannot continuously be present. As standardized networks, artificial intelligence, open data, and complementary monitoring technologies continue to develop, camera trapping is likely to become an increasingly important part of understanding and protecting biodiversity worldwide.
- TOC**
Camera-Trap Technology, AI, and Data Processing
| Various authors | Science of the Total Environment | 2026-08-01
AI and computer vision for wildlife identification in camera-trap images examines how fine-tuning the global SpeciesNet model can achieve highly accurate local species classification while reducing the amount of training data required.
| Various authors | Ecological Informatics | 2026-06
This study explores AI-based monocular depth estimation as a way to automate animal-to-camera distance measurements for Camera Trap Distance Sampling and wildlife population monitoring.
| Various authors | Ecological Informatics | 2026-05
Distance-based population density estimates from camera traps examines how technical decisions in automated Camera Trap Distance Sampling pipelines can produce major differences in estimated wildlife density.
| Nature India | Nature India | 2026-02-11
Turning camera traps into conservation tools describes HBID24K, a large annotated camera-trap dataset designed to train algorithms for monitoring vulnerable houbara bustards and potential nest threats.
| Clark et al. | Ecology and Evolution | 2025-12
The SiMPL wildlife magnet combines bait with camera traps to improve passive monitoring of small- and medium-sized mammals that conventional camera configurations may overlook.
| Various authors | Environmental Reviews | 2025-10-09
This global review analyzes more than 2,400 wildlife-camera studies and documents the rapid growth of camera traps, machine learning, drones, and other camera technologies in ecological research.
| Various authors | Ecological Informatics | 2024-11
Researchers developed inexpensive smart camera traps capable of performing AI wildlife recognition and continual learning directly in the field rather than sending all images elsewhere for processing.
| Bubnicki et al. | Remote Sensing in Ecology and Conservation | 2023-12-09
Camtrap DP proposes an open FAIR data standard intended to make large camera-trap datasets easier to exchange, archive, combine, and reuse across projects.
| Delisle et al. | Frontiers in Ecology and Evolution | 2021-02-26
A systematic review of 2,167 publications traces the historical growth of camera trapping and identifies technological and scientific directions likely to shape next-generation wildlife monitoring.
| Forrester et al. | Biodiversity Data Journal | 2016
The Camera Trap Metadata Standard was an early effort to standardize project, deployment, image-sequence, and species-detection information so datasets could be shared among researchers.
Large-Scale Camera-Trap Networks and Biodiversity Monitoring
| World Wildlife Fund | WWF | Current
Wildlife Insights combines a major global camera-trap database with automated image recognition so conservation organizations can store, classify, analyze, and share wildlife observations.
| Rosannette Quesada-Hidalgo | Smithsonian Tropical Research Institute | 2026-05-13
Long-term camera monitoring on Panama's Barro Colorado Island illustrates how repeated observations over decades can reveal changes in individual animals, populations, and tropical mammal communities.
| Various authors | Biological Reviews | 2025
This continental synthesis evaluates large-scale and long-term Australian camera-trap research and discusses how standardized networks could strengthen terrestrial vertebrate monitoring.
| Shamon et al. | Ecology | 2024-05-01
SNAPSHOT USA 2021 assembled observations from 1,711 camera sites and more than 71,000 camera-trap nights, creating a standardized continental-scale resource for studying American mammals.
| Mendes et al. | Ecology | 2024-04-22
CamTrapAsia brings together data from 239 camera-trapping studies to improve access to information on tropical Asian vertebrate communities threatened by hunting and habitat change.
| Cove et al. | Ecology | 2021-04-01
SNAPSHOT USA 2019 coordinated camera trapping across all 50 U.S. states, demonstrating how distributed research networks can produce standardized national wildlife inventories.
| Swanson et al. | Scientific Data | 2015-06-09
Snapshot Serengeti describes a massive camera-trap dataset containing more than a million photographic sequences of African mammals classified with help from citizen scientists.
| Rovero et al. | PLOS ONE | 2014
A Tanzanian TEAM Network study uses camera traps to estimate tropical mammal species richness, habitat preferences, and occupancy while establishing a standardized biodiversity baseline.
| Ahumada et al. | PLOS ONE | 2013
Camera-trap data from Costa Rica demonstrate the Wildlife Picture Index, an approach for tracking changes in tropical terrestrial vertebrate communities while accounting for imperfect detection.
| Ahumada et al. | Philosophical Transactions of the Royal Society B | 2011
Researchers used standardized camera-trap arrays across tropical forests in Africa, Asia, and Latin America to compare mammal diversity and community structure globally.
Camera Trapping in Africa
| Various authors | Journal Article | 2026
Camera traps in Guinea's Upper Niger National Park documented 30 mammal taxa and revealed the conservation importance of the park's Mafou Fully Protected Area.
| Various authors | Biological Conservation | 2025
Camera trapping in the Congo Basin found differences in mammal community structure between long-term research areas and hunted forest, suggesting research presence can indirectly reduce hunting pressure.
| Various authors | Global Ecology and Conservation | 2024-10
A large camera-trap grid in Kenya's Mau Forest Complex recorded 52 mammal species and measured how primary forest, secondary forest, and agricultural conversion influence mammalian diversity.
| Van Vliet et al. | Oryx | 2023
The first comprehensive camera-trap survey of the Yangambi landscape in the Democratic Republic of the Congo evaluates mammal richness, occupancy, and conservation status in a heavily used forest landscape.
| Various authors | Global Ecology and Conservation | 2022-12
A systematic review identified more than 400 African camera-trap studies and documented geographic, taxonomic, habitat, and methodological biases in how the technology has been deployed.
| Various authors | Mammalian Biology | 2022-11-28
Researchers used incidental camera-trap detections to examine elephant habitat use and responses to human activity in Malawi's Kasungu National Park.
| Will Burrard-Lucas | WWF | 2021
A photographic account from Kenya demonstrates how carefully positioned remote cameras can document exceptionally elusive melanistic leopards while contributing observations of their behavior and habitat.
| Various authors | PLOS ONE | 2019-06-12
Researchers deployed 164 camera stations in Tanzania's Udzungwa Mountains to determine how protected-area size, habitat, and human disturbance affect leopard density.
| Nature | Nature | 2016-05-04
Nature reports how camera trapping in northern Botswana revealed particularly diverse mammal communities in grassland and floodplain habitats and demonstrated the technique's conservation potential.
| Cusack et al. | Journal of Wildlife Management | 2015
Camera traps in Serengeti National Park were used with a Random Encounter Model to estimate lion density without having to recognize individual animals.
Camera Trapping in Asia
| Various authors | Journal of Threatened Taxa | 2026
A rapid camera-trap assessment across protected areas in Tripura, India, produced new mammal records and demonstrated the biological importance of fragmented northeastern forests.
| Various authors | Journal Article | 2026
A year-long camera-trap survey of Hainan Tropical Rainforest National Park provides new information on the diversity, distribution, and conservation status of the island's mammals.
| Various authors | Biodiversity Data Journal | 2026
Infrared cameras in China's Huangshan Mountain region documented mammal diversity while revealing daily and seasonal activity patterns of threatened species.
| Gogoi et al. | Ecology and Evolution | 2025-12-05
Camera-trap distance sampling in protected areas of Mizoram, India, evaluates mammal diversity, ungulate density, predator abundance, and possible effects of hunting.
| Various authors | Biodiversity | 2025-08-12
Camera traps documented 25 mammal species in Odisha's Atei Reserve Forest, supporting its importance as a wildlife corridor between major tiger reserves.
| Various authors | Journal for Nature Conservation | 2024-07
Camera-trap occupancy models examine how twelve mammal species use the human-influenced buffer surrounding India's Melghat Tiger Reserve.
| Various authors | Biological Conservation | 2023-10
Camera traps and field surveys in southern India document animals caught in wire snares and demonstrate the continuing wildlife threat posed by indiscriminate snaring outside protected areas.
| Various authors | Wildlife Research | 2014
Camera traps and spatially explicit capture-recapture methods provided a baseline tiger-density estimate for tropical lowland forest in India's Pakke Tiger Reserve.
| Various authors | Biological Conservation | 2009-03
Camera trapping combined with capture-recapture analysis produced early reliable estimates of tiger and leopard populations in the high-altitude mountains of Bhutan.
| Wegge et al. | Animal Conservation | 2004-08-19
Intensive camera trapping in Nepal's Bardia National Park examined how survey effort and animals' reactions to cameras can influence estimates of tiger abundance.
Rare and Threatened Species
| Various authors | PLOS ONE | 2023
A large camera survey examines how ecological conditions, agriculture, protected land, and other human factors influence leopard density in a mixed-use South African landscape.
| Various authors | Global Ecology and Conservation | 2022-04
A Sri Lankan rainforest survey demonstrates the potential of camera traps to estimate habitat occupancy, abundance, activity, and population density of the cryptic Indian pangolin.
| Various authors | PLOS ONE | 2021
Camera trapping across South Africa's Western Cape was used to estimate leopard occupancy, population size, density, and seasonal ranging behavior.
| Various authors | Journal of Arid Environments | 2020-10
A systematic survey covering roughly 3,100 square kilometers evaluates leopard density and habitat preferences in South Africa's semi-arid Little Karoo.
| Johansson et al. | Scientific Reports | 2020-04-14
An experiment with known snow leopards shows that mistakes in identifying individuals from camera-trap photographs can systematically inflate population estimates.
| Various authors | Global Ecology and Conservation | 2019-10
Researchers combined more than half a million camera-trap nights from 22 countries to evaluate whether existing camera surveys can be used for range-wide pangolin monitoring.
| Various authors | Biological Conservation | 2016-05
Nineteen months of camera trapping in central China revealed substantial short-term variation in snow leopard detections and density estimates, illustrating the value of longer monitoring periods.
| Various authors | Biological Conservation | 2013-03
A review of jaguar camera-trap studies identifies weaknesses in traditional survey designs and recommends spatially explicit approaches for more reliable density estimation.
| Trolle and Kéry | Mammalia / USGS | 2005
One of the early South American camera-trap population studies estimates ocelot density in Brazil's Pantanal and shows how roads and trails strongly affect detection rates.
| Various authors | Tapir Conservation | 2003-06-01
This pioneering Bolivian study compares camera trapping and radio telemetry for estimating lowland tapir density, movement, and activity in dry forest.
Estimating Wildlife Abundance and Density
| Harris et al. | Scientific Reports | 2024-12-28
Researchers validate camera-trap imagery combined with N-mixture models as an affordable method for estimating the abundance of otherwise difficult-to-count ungulates.
| Koetke et al. | Methods in Ecology and Evolution | 2024-04-11
A comparison with aerial moose surveys shows that camera-based N-mixture estimates can be useful but also demonstrates that apparently preferred statistical models may produce unrealistic results.
| Lyet et al. | Remote Sensing in Ecology and Conservation | 2023-07-24
A Space-to-Event model combined with bootstrap resampling provides another way to estimate densities of unmarked mammals from motion-triggered camera data.
| McMurry et al. | Wildlife Society Bulletin | 2023
Camera traps in northeastern Washington were used with Space-to-Event modeling to estimate densities of moose, bears, mountain lions, wolves, and deer simultaneously.
| Various authors | Ecological Modelling | 2022-10
This methodological study evaluates several camera-based population estimators when detections involve groups or other non-independent animal encounters.
| Nakashima et al. | Scientific Reports | 2022-02-07
A double-observer camera method is proposed to correct for animals that pass within the detection area but are missed by the camera.
| Green et al. | Frontiers in Ecology and Evolution | 2020-12-18
This review examines the rapid adoption of spatially explicit capture-recapture analysis for estimating wildlife density from camera-trap surveys.
| Luo et al. | Wildlife Society Bulletin | 2020-02-09
Researchers develop an approach for estimating unmarked animal populations from camera traps while explicitly considering variation in animals' use of space.
| Murphy et al. | Scientific Reports | 2019-03-14
Researchers combine clustered camera traps, telemetry, and spatial mark-resight models to improve population-density estimates for elusive pumas.
| Rowcliffe et al. | Journal of Applied Ecology | 2008
The influential Random Encounter Model introduced a method for estimating population density from camera traps without requiring individual animals to have recognizable markings.
Survey Design, Detection Bias, Ethics, and Limitations
| Various authors | Animal Welfare | 2025
A systematic review asks whether camera traps are truly behaviorally non-invasive and finds that relatively few ecological studies consider possible welfare effects on photographed wildlife.
| Yang et al. | Integrative Zoology | 2024-12-06
Researchers find that elevation and season can substantially alter the camera numbers and monitoring duration needed to reliably detect medium- and large-sized mammals.
| Tanwar et al. | Scientific Reports | 2021-11-29
Comparisons between trail-based and randomly positioned cameras show that camera placement can alter estimates of abundance and activity even when estimated species richness is similar.
| Various authors | Biological Conservation | 2021-04
A broad review explains appropriate camera-trap methods for species richness, abundance, density, occupancy, and activity studies while highlighting their usefulness during restricted field access.
| Sharma et al. | Ecological Solutions and Evidence | 2020
Researchers propose an ethical code for camera trapping addressing human privacy, illegal activities, community participation, transparency, and the handling of inadvertently photographed people.
| Miller et al. | Journal of Outdoor Recreation and Tourism | 2017-03
Camera traps can simultaneously monitor recreational visitors and wildlife in protected areas, giving managers information about both human use and ecological impacts.
| Various authors | Ecology and Evolution | 2017
Comparison with continuously recording video showed that motion-triggered cameras can miss many small-animal events and can underestimate wildlife use of crossing structures.
| Various authors | Ambio | 2016
Conservation practitioners discuss practical failures and limitations of inexpensive recreational camera traps and caution that low purchase price does not necessarily mean low research cost.
| Niedballa et al. | Scientific Reports | 2015-11-24
Camera-trap occupancy data from Malaysian Borneo show why the spatial scale used to describe surrounding habitat can strongly affect species-habitat conclusions.
| Burton et al. | Journal of Applied Ecology | 2015-03-21
This major review evaluates hundreds of camera-trap studies and stresses the importance of matching sampling design and statistical analysis to the ecological processes being investigated.
Wildlife Corridors, Roads, Recreation, and Urban Wildlife
| Various authors | Journal of Mammalogy | 2026-06-01
Camera traps and grizzly-bear GPS data reveal how large mammals respond spatially and temporally to nonmotorized recreation in the Canadian Rockies.
| Mori et al. | Ecological Solutions and Evidence | 2025
A year-round camera network in Florence, Italy, shows that urban mammals frequently become more nocturnal and adjust their behavior in response to humans and domestic animals.
| Danielle Brigida | WWF | 2024-04-16
A network of cameras in British Columbia's South Chilcotin Mountains shows how bears, wolves, mountain goats, recreationists, and other species share trails through the same landscape.
| Various authors | Journal of Environmental Management | 2024
Researchers combine 78 camera traps, roadkill observations, citizen science, and connectivity modeling to identify priority wildlife-crossing locations in Costa Rica.
| Various authors | Global Ecology and Conservation | 2023-10
An experimental comparison uses camera traps to determine whether wildlife prefer drainage culverts, dedicated fauna underpasses, or a viaduct when crossing a major road.
| WWF | World Wildlife Fund | 2022-08-26
Images of tigers, rhinos, leopards, and other wildlife demonstrate the ecological value of Nepal's restored Khata Corridor connecting protected areas across an international border.
| Huerta-Rodríguez et al. | Ecological Processes | 2022-08-03
Camera traps identify natural routes used by mammals through a fragmented ecological corridor in Mexico's Sierra Madre Oriental.
| Various authors | Ecology and Evolution | 2022
Intensive camera trapping across southwest London shows how urban habitat and human disturbance affect the movements and activity schedules of foxes and badgers.
| Various authors | Ecological Indicators | 2020-03
Camera traps reveal striking differences in daily activity between urban and rural populations of striped field mice, illustrating how cities can reshape wildlife behavior.
| Various authors | Global Ecology and Conservation | 2019-04
Camera traps and visitor surveys in Japan were combined to identify priority locations for managing human-wild boar conflict in an urbanized landscape.
Animal Behavior, Activity, and Ecological Interactions
| Sahas Mehra | Smithsonian Magazine | 2026-04-23
Camera traps documented Iberian lynxes repeatedly placing rabbit prey in water, revealing a previously undocumented carnivore behavior and demonstrating the observational power of long-term remote cameras.
| Various authors | International Journal of Primatology | 2026-03-27
Arboreal camera traps reveal activity, resource use, demographics, and interactions involving endangered black lion tamarins and other tree-dwelling Atlantic Forest mammals.
| Various authors | Ecological Indicators | 2024-03
Researchers compare camera trapping with GPS telemetry for studying animal interactions and show that the suitability of each technique depends strongly on the interaction being measured.
| Various authors | Animals | 2019
Multi-year camera monitoring in Inner Mongolia examines Eurasian lynx population size, activity rhythms, prey availability, and temporal relationships between predators and potential prey.
| Various authors | Scientific Reports | 2018
Data from ten camera-trap surveys across Northern Ireland examine seasonal circadian patterns and temporal relationships among multiple predator-prey pairs.
| Various authors | Global Ecology and Conservation | 2017-07
Four years of camera data from southwestern China reveal wild-boar age structure, seasonal population patterns, group size, and differences in habitat use among age classes.
| Various authors | BMC Zoology | 2017
Camera trapping combined with telemetry and long-term individual observations was used to reconstruct the demography, survival, density, and recruitment of a small tiger population in western India.
| Ikeda et al. | PLOS ONE | 2016
More than 20,000 camera-trap days reveal seasonal and daily activity patterns of eight sympatric mammal species in northern Japan.
| Smithsonian Conservation Biology Institute | Smithsonian | 2014-04-01
Cameras placed high in Peruvian rainforest canopies demonstrate the potential of camera trapping for studying arboreal mammals rarely sampled by conventional ground-based arrays.
General Camera-Trap Conservation Resources and Applications
| World Wildlife Fund | WWF | Current
WWF explains the basic operation of infrared-triggered wildlife cameras and shows how conservation teams use them to study elusive species and remote habitats around the world.
| World Wildlife Fund | WWF | Current
Camera-trap photographs of critically endangered Amur leopards show how remote cameras can provide evidence of individual animals surviving in the forests of the Russian Far East.
| Nature Index | Nature | Current
This overview summarizes camera-trapping technology, occupancy and abundance modeling, long-term wildlife monitoring, and the growing role of machine learning in processing extremely large image collections.
| Rosannette Quesada Hidalgo | Smithsonian Magazine / STRI | 2026-05-14
Decades of camera monitoring on Barro Colorado Island have produced detailed records of ocelots and other tropical mammals while training successive generations of field ecologists.
| Conservation International | Conservation International | 2024-10-30
Cambodia's first major Central Cardamom Mountains camera survey detected 108 species, including numerous threatened mammals, birds, and reptiles, highlighting the landscape's global conservation significance.
| Jason T. Fisher | Ecology and Evolution | 2023-03-16
This editorial describes the transformation of camera trapping into a major ecological research field capable of gathering standardized wildlife observations over unprecedented spatial and temporal scales.
| Ruhi Manek | National Geographic | 2019-08-06
A photographic overview illustrates how automatic cameras have revolutionized scientists' ability to document rarely observed animals and natural behavior with minimal human presence.
| Allan F. O'Connell | U.S. Geological Survey | 2015-12-21
This overview traces the expanding use of camera traps for species inventories, population estimates, biodiversity monitoring, and conservation management.
| Nichols, Karanth and O'Connell | USGS | 2011-01-01
A foundational overview explains how camera-trap information should be connected to ecological questions and real conservation-management decisions rather than treated simply as collections of photographs.
Camera-Trap Technology, Artificial Intelligence, and Specialized Systems
| Various authors | Ecological Informatics | 2025-12
Researchers developed an automated pipeline for estimating both the distance and height of animals photographed by camera traps, potentially improving density estimation and wildlife morphometric measurements.
| Various authors | Ecological Informatics | 2025-12
Researchers combine specialized camera traps, citizen science, and AI object-detection algorithms to improve monitoring of reptiles, amphibians, arthropods, and other small animals often missed by conventional systems.
| Various authors | Ecological Informatics | 2025-11
A photogrammetric technique combining camera-trap photographs with three-dimensional modeling provides automated estimates of the distance between cameras and photographed wildlife.
| Various authors | Global Ecology and Conservation | 2025-10
Researchers compare cameras placed on and away from trails and show that the effects of camera placement vary according to habitat type and wildlife species.
| Various authors | Ecological Informatics | 2025-03
DeLoCo uses information about the environmental background surrounding camera stations to improve automated wildlife-species classification and explores how location context can make deep-learning systems more accurate.
| Dueser et al. | PLOS ONE | 2025
Experimental trials evaluate a low-cost small-mammal camera system and demonstrate how modified cameras can expand remote monitoring beyond traditional medium- and large-bodied wildlife.
| Various authors | Sensors | 2024-12-19
This study combines vision-language models and conventional image-recognition systems to generate richer ecological information automatically from large camera-trap datasets.
| Whyte and Garraway | Reptiles & Amphibians | 2024-09-10
Passive-activated cameras successfully detected Jamaican iguanas, illustrating the potential of camera trapping for monitoring threatened reptiles.
| Long et al. | Ecology and Evolution | 2024-05-02
Camera traps paired with automated scent dispensers were tested as a winter monitoring system for wolverines and other difficult-to-detect carnivores.
| Porter and Dueser | Bulletin of the Ecological Society of America | 2024-04-05
Researchers describe an inexpensive camera system designed specifically for detecting mice, shrews, and other small mammals that frequently fail to trigger conventional wildlife cameras.
| Various authors | Scientific Reports | 2024
Researchers compare traditional manual image processing with 4G-connected AI camera systems and examine potential savings in costs, staff time, and carbon emissions.
| Various authors | Data in Brief | 2024
Camera-trap records from three Dutch biodiversity-monitoring projects are published using open data standards so the images can support AI development and ecological research.
| DeWitt and Cocksedge | Environmental Monitoring and Assessment | 2023-10-27
A controlled experimental framework evaluates how camera sensors, animal characteristics, and environmental conditions interact to determine whether passing wildlife is successfully photographed.
| Mugambi et al. | Data in Brief | 2023-02
DSAIL-Porini provides an annotated wildlife-camera dataset from a Kenyan conservancy designed for developing and testing machine-learning species-recognition systems.
| Various authors | European Journal of Wildlife Research | 2023
The DeepFaune initiative brings together more than 50 wildlife organizations to develop automated recognition software capable of identifying common European animals in camera-trap photographs.
| Mos and Hofmeester | Mammal Research | 2020-06-08
The Mostela combines a camera with a small enclosed tunnel, greatly improving the ability of researchers to monitor elusive weasels, stoats, and other small mustelids.
| Schneider et al. | Ecology and Evolution | 2020-03-07
Researchers identify several major factors determining the success of automated camera-trap species recognition, including training-data quantity and differences between camera locations.
| Tabak et al. | Ecology and Evolution | 2020
MLWIC2 provides wildlife researchers with a more accessible machine-learning system capable of recognizing dozens of North American species in camera-trap photographs.
| Hobbs and Brehme | PLOS ONE | 2017
A modified close-focus camera system was developed to photograph amphibians, reptiles, small mammals, and large invertebrates that standard wildlife cameras frequently overlook.
| Nazir et al. | PLOS ONE | 2017
WiseEye is an open-source programmable camera-trap platform built around a Raspberry Pi that combines multiple sensors to reduce false triggers and expand research capabilities.
Population Density, Abundance, and Camera-Trap Survey Design
| Donini et al. | Landscape Ecology | 2025-02-05
Camera-trap estimates of red-deer habitat associations are compared directly with GPS-based habitat-selection data to test how reliably stationary cameras represent animal space use.
| Bollen et al. | Scientific Reports | 2023-09-27
Simulations test several hierarchical statistical models commonly used to estimate the abundance of unmarked wildlife from repeated camera detections.
| Various authors | Global Ecology and Conservation | 2022-10
Researchers compare spatial-count and partial-identity approaches for estimating populations of threatened animals that lack obvious individual markings.
| Various authors | Ecological Informatics | 2022-07
The cameratrapR software package estimates wildlife density by simulating animal movements through camera arrays using species-specific movement information.
| Palencia et al. | Remote Sensing in Ecology and Conservation | 2022-06-24
Field tests suggest that the Random Encounter Model can provide useful population-density estimates for multiple species when appropriate movement and camera parameters are available.
| Various authors | Basic and Applied Ecology | 2022-06
Researchers compare four methods for estimating wildlife population size from camera traps without requiring individual animals to be recognized.
| Connor et al. | Remote Sensing | 2022-02-23
Spatial capture-recapture models are extended to estimate wildlife density even when individual animals cannot be uniquely identified in photographs.
| Wearn et al. | Methods in Ecology and Evolution | 2022
A Bayesian extension of the Random Encounter Model estimates the densities of multiple wildlife species simultaneously using information obtained directly from camera traps.
| Various authors | Wildlife Research | 2022
Spatial mark-resight models combine identifiable and unidentified deer photographs to generate population-density estimates from camera-trap surveys.
| Palencia et al. | Journal of Applied Ecology | 2021
Three methods—Random Encounter, Random Encounter and Staying Time, and camera-trap distance sampling—are compared using field populations with independently estimated densities.
| Harris et al. | Scientific Reports | 2020-10-20
Camera-trap distance sampling was tested for estimating populations of wild sheep and other Caprinae inhabiting rugged landscapes where conventional surveys are difficult.
| Holinda et al. | PLOS ONE | 2020-05-12
Experimental camera arrays demonstrate that scent lures can alter detection rates differently for predators and prey, creating potential biases in wildlife surveys.
| Findlay et al. | Mammal Research | 2020-02-17
Researchers break camera detection into sequential stages—animal passage, sensor triggering, image recording, and image quality—to determine how false negatives arise.
| Various authors | Global Ecology and Conservation | 2019-10
Tests in Malaysian Borneo show that adding a second camera can increase detection probability, with benefits differing according to animal body size, movement, and social behavior.
| Evans et al. | PLOS ONE | 2019-06-17
Researchers determine how the number of cameras and the duration of surveys affect the probability of detecting different North American mammal species.
| Lashley et al. | Scientific Reports | 2018-03-08
Camera traps and radiotelemetry are compared as methods for estimating wildlife activity curves, including the effects of sample size on resulting activity patterns.
| Kolowski and Forrester | PLOS ONE | 2017-10-18
Paired cameras demonstrate that trails, logs, and other small habitat features can substantially alter wildlife detection rates and create sampling bias.
| O'Connor et al. | PLOS ONE | 2017-04-19
Multi-camera arrays substantially improve the probability of detecting cryptic species and can provide an alternative to baiting cameras.
| Cusack et al. | PLOS ONE | 2015
Randomly placed cameras are compared with cameras positioned on game trails to determine how placement affects measurements of terrestrial mammal communities.
Human Disturbance, Roads, Recreation, Disease, and Wildlife Conflict
| Various authors | Wildlife Health Research | 2026
Six years of camera monitoring of individually identifiable Nubian ibex demonstrate how photographs can be used to estimate trends in wildlife diseases that produce visible symptoms.
| Various authors | Global Ecology and Conservation | 2025-04
A large American camera-trap dataset shows how mammal species alter daily activity patterns in response to land development and human presence.
| Bernard et al. | Journal of Applied Ecology | 2024-09-03
Camera traps and local ecological knowledge are combined to determine which African mammal traits allow species to persist in heavily modified landscapes.
| Hlatshwayo et al. | Road Ecology Systematic Review / Zenodo | 2024-03-18
A systematic review identifies 70 studies from six continents using cameras to evaluate wildlife use of bridges, culverts, tunnels, overpasses, and other road-crossing structures.
| Various authors | Global Ecology and Conservation | 2024
Camera data from multiple European landscapes show wolves becoming increasingly nocturnal where human disturbance is greater, while Eurasian lynx remain consistently nocturnal.
| Various authors | eLife | 2024
More than 43,000 camera-days in Himalayan forests reveal how human modification alters spatial and temporal associations among carnivores and ungulates.
| Lehnen et al. | Ecology and Evolution | 2024
Cameras document how wild felids changed their highway-crossing behavior before, during, and after construction of dedicated wildlife crossing structures.
| Ayars et al. | Fire Ecology | 2023-08-24
Camera traps reveal short-term changes in mammal activity associated with major wildfire-smoke events in eastern Washington State.
| Various authors | Global Ecology and Conservation | 2023
Camera-based occupancy models assess whether cattle or vegetation structure has the stronger influence on a community of carnivores in grazed forests.
| Barroso and Palencia | Research in Veterinary Science | 2023
Camera images reveal a high prevalence of visible sarcoptic mange among red foxes in northern Spain and demonstrate the value of remote cameras for wildlife-disease surveillance.
| Various authors | Journal for Nature Conservation | 2022-12
Camera traps in central Japan reveal how different levels of human activity influence mammal distribution and shifts toward nocturnal behavior.
| Chen et al. | Conservation Letters / Conservation International | 2022-01-26
Analysis of data from thousands of camera stations across multiple continents finds greater mammalian diversity in protected landscapes than in comparable unprotected areas.
| Various authors | Conservation Science and Practice | 2022
Cameras inside and outside a Canadian protected area document how recreation and other human activity influenced mammals before, during, and after COVID-19 lockdowns.
| Burton et al. | Ecology and Evolution | 2022
Behavioral information unintentionally recorded during camera surveys reveals how moose, caribou, and deer alter behavior in landscapes where humans change predator risk.
| Various authors | Veterinary Research Communications | 2022
Cameras around commercial pig farms in England document visits by free-ranging wild boar and help evaluate possible pathways for disease transmission.
| Various authors | Climate Change Ecology | 2021-12
Researchers examine whether long-term camera-trap datasets could reveal wildlife activity shifts caused by rising temperatures and climate change.
| Various authors | Frontiers in Veterinary Science | 2021-10-12
Camera-trap occupancy models are evaluated as a method for monitoring wild-boar population trends in areas affected by African swine fever.
| Various authors | Biological Conservation | 2021-04
More than 65,000 camera detections are used to measure how mammals interact with and cross the conservation fence surrounding Kenya's Lake Nakuru National Park.
| Various authors | Biological Conservation | 2021
Camera surveys show that traditional Ethiopian shade-coffee forests can retain mammal communities resembling those of natural forests, while more intensive plantations support fewer species.
| Various authors | Biological Conservation | 2017
Camera traps in Bangladesh's Sundarbans demonstrate their potential to detect poaching and other illegal activities in remote protected areas.
Regional Biodiversity Surveys and Species Conservation
| SANParks | South African National Parks | 2025-11-12
A remote camera recorded a leopard in West Coast National Park, providing evidence that the species has naturally recolonized part of South Africa's coastal landscape.
| Various authors | Global Ecology and Conservation | 2025-09
Camera trapping in an unprotected central Vietnamese forest reveals both substantial defaunation and surviving populations of threatened and endemic species.
| Various authors | Frontiers in Conservation Science | 2025
Intensive camera trapping in Sumatra's Leuser Ecosystem produces one of the strongest recent datasets for estimating the density of the endangered Sumatran tiger.
| Various authors | Journal for Nature Conservation | 2024-06
Community-operated camera networks in Oaxaca, Mexico, document medium and large mammals across locally managed conservation areas.
| Joey Pitchford | NC State University | 2024-04-26
Modern camera surveys along Theodore Roosevelt's historic route on Mount Kenya allow researchers to compare present-day mammal communities with observations made more than a century earlier.
| Various authors | Zoological Research: Diversity and Conservation | 2024-01-04
Field surveys, specimens, literature, and camera traps document more than 200 mammal species in China's biodiverse Gaoligong Mountains.
| Silva-Rodríguez et al. | Gayana | 2024
Camera traps across protected areas of Chilean Patagonia provide baseline information about both native mammals and introduced species requiring management.
| Various authors | African Journal of Ecology | 2023-04-04
Camera traps show how canopy closure, leaf litter, grass cover, and other microhabitat characteristics influence mammals inhabiting South Africa's fragmented Mistbelt forests.
| Poulain et al. | African Journal of Ecology | 2023-01-30
Camera surveys in the community-use zone of Cameroon's Lobéké National Park recorded a surprisingly intact community of medium and large mammals.
| Various authors | Global Ecology and Conservation | 2023
Surveys in a Bornean timber concession demonstrate that retained secondary forests can support numerous threatened mammals even within commercial plantation landscapes.
| Various authors | Ethnobiology and Conservation | 2023
Camera trapping is compared with hunters' local ecological knowledge for measuring wildlife richness and relative abundance in heavily hunted forests of the Democratic Republic of the Congo.
| Various authors | Biodiversity Data Journal | 2023
Long-term camera monitoring in Portugal's Peneda-Gerês National Park provides data for studying rewilding, livestock interactions, and changing mammal communities.
| Various authors | Current Biology | 2023
Camera data across nine Colombian landscapes show that habitat modification can destabilize spatial relationships among jaguars and smaller carnivores.
| Various authors | Conservation International | 2022
AMAZONIA CAMTRAP combines more than 154,000 records of mammals, birds, and reptiles from eight Amazonian countries into a standardized biodiversity dataset.
| Various authors | Ecology | 2022
The Madagascar Terrestrial Camera Survey Database combines surveys from numerous protected forests into a standardized resource covering dozens of native and introduced species.
| Various authors | Global Ecology and Conservation | 2021-12
Camera surveys in Thailand examine spatial and temporal coexistence among tigers, leopards, dholes, and their principal prey.
| Haysom et al. | Frontiers in Forests and Global Change | 2021-07-09
Cameras installed in rainforest canopies demonstrate how conventional ground surveys can miss important components of Borneo's arboreal mammal community.
| Lyet et al. | Scientific Reports | 2021-06-15
Mammal detections from environmental DNA collected in streams are compared with simultaneous camera-trap detections to evaluate complementary biodiversity-monitoring approaches.
| Alempijevic et al. | American Journal of Primatology | 2021-05-06
Camera traps provide new information about the appearance, habitat, and natural history of the poorly known dryas monkey in the Democratic Republic of the Congo.
| Duľa et al. | Scientific Reports | 2021-04-29
Repeated systematic camera surveys reveal substantial year-to-year variation and individual turnover in Carpathian lynx populations near the western edge of their native range.
| Various authors | Global Ecology and Conservation | 2021-04
Literature, interviews, observations, and camera photographs are combined to improve knowledge of the critically endangered Chinese pangolin's remaining distribution in mainland China.
| Various authors | Global Ecology and Conservation | 2021
Camera traps and field surveys assess the condition of medium and large mammals in Peru's remote Ichigkat Muja–Cordillera del Cóndor National Park.
| Various authors | Journal of Threatened Taxa | 2021
Camera traps provide some of the first photographic records of Indian pangolins in Pakistan, including observations associated with reproduction.
| Various authors | Scientific Reports | 2021
A decade of camera monitoring provides demographic, survival, recruitment, and density estimates for Eurasian lynx living inside a strictly protected Central European landscape.
| Lamelas-López and Salgado | Oryx | 2020-10-19
Camera trapping is evaluated as a practical technique for early detection and long-term monitoring of invasive mammals on oceanic islands.
| Various authors | Journal of Mammalogy | 2020-10-03
Long-term camera surveys in Costa Rica reveal daily activity relationships and temporal niche partitioning among multiple predator and prey species.
| Various authors | Biological Conservation | 2020-06
Camera traps and citizen observations are combined to estimate survival rates of an endangered African vulture, demonstrating applications beyond terrestrial mammals.
| Li et al. | Oryx | 2020-04-14
Camera surveys in Medog, Tibet, record a highly diverse mammal and pheasant community, including threatened large carnivores and the first photographic evidence of a Bengal tiger in China.
| Various authors | Oryx | 2020
Cameras reveal an unusually diverse high-elevation mammal community in Peru's isolated Cerros del Sira mountains and document threats from human activity and climate change.
| Tilker et al. | Communications Biology | 2019
Landscape-scale camera surveys across Southeast Asia show that habitat degradation and indiscriminate hunting affect mammal and ground-bird communities in distinctly different ways.
Additional Camera-Trap Applications
| Various authors | Oryx | 2024
Camera surveys, literature records, and interviews suggest endangered dholes are recolonizing portions of Nepal from which they had disappeared during earlier decades.
| Li et al. | Ecology and Evolution | 2023-10-18
Camera-trap distance sampling is used along a rural-to-urban gradient in China to measure how native and introduced mammal population densities change with urbanization.
| Haight et al. | Nature Ecology & Evolution | 2023-09-04
A standardized camera network spanning 725 sites in 20 North American cities reveals how urbanization, climate, vegetation, and species traits interact to shape mammal communities.
| Mary Kate McCoy | Conservation International | 2023-06-07
A comparison of Wildlife Insights and broader global biodiversity databases shows how camera-trap observations can help fill major geographic and taxonomic gaps in existing wildlife records.
| Zainol et al. | Pertanika Journal of Tropical Agricultural Science | 2021-05-28
Cameras along Malaysia's Felda Aring–Tasik Kenyir Road reveal which mammal species use viaducts and bridges and which remain particularly vulnerable to vehicle collisions.
| Tshabalala et al. | Ecological Indicators | 2021-02
Camera records from South Africa show that sites containing leopards and multiple mesopredators tend to support greater overall mammalian species richness.
| Various authors | Scientific Reports | 2021
A sequence of camera-trap photographs documents apparent rescue behavior in wild boar after individuals became caught in a trap, illustrating the ability of remote cameras to reveal unexpected social behavior.
| Higashide et al. | Wildlife Biology | 2021
Camera-derived wild-boar density estimates are compared with digging marks, rubbing sites, feces, and other field signs to determine whether traditional indices reliably reflect abundance.
| Various authors | Frontiers in Conservation Science | 2021
Camera observations contribute to a nationwide assessment of dhole distribution, habitat protection, and landscape connectivity in Bhutan.
| Comer et al. | Scientific Reports | 2018-03-28
Camera traps and occupancy models evaluate whether a large-scale feral-cat control program in Western Australia successfully reduced the presence of invasive predators.
