Camera Trap Science: How Remote Cameras Are Revolutionising Wildlife Research

The camera trap — a remotely triggered camera that photographs or videos any animal that passes in front of its motion or heat sensor — has transformed wildlife biology over the past three decades from a discipline constrained by the brief, daylight-hours observations of field researchers to one with continuous, round-the-clock surveillance of wildlife communities across entire landscapes. Camera traps reveal the secret lives of rare, cryptic, and nocturnal animals that were previously nearly impossible to study: jaguars hunting at night in Amazon rainforest, clouded leopards patrolling forest ridges in Borneo, saiga antelope moving across Central Asian steppes, and pangolins — among the world's most trafficked animals — moving through forest understoreys in patterns that guide conservation planning.

Individual Identification by Natural Markings

One of the most powerful capabilities enabled by camera traps is the individual identification of animals by their unique natural markings — stripe patterns in tigers and zebras, spot patterns in leopards and jaguars, ear notches in elephants, and whisker pad patterns in lions. Statistical software (such as APHIS or Wild-ID) can compare the spot or stripe patterns of different camera trap photographs and calculate the probability that two images show the same individual — enabling mark-recapture population estimates without the need to physically handle or tag animals. This capability has transformed population estimates for several flagship species: tigers, jaguars, and snow leopards can now be surveyed across large landscapes with population estimates that would have required years of intensive physical capture work using earlier methods.

AI and Machine Learning in Camera Trap Analysis

The explosion in camera trap deployments has generated an equivalent explosion in camera trap images — a data volume that has outpaced the capacity of human researchers to review and classify. A camera trap network of 1,000 stations operating for one year might generate 10-50 million images, the manual review of which would require thousands of researcher-hours. Artificial intelligence — specifically convolutional neural networks trained on large labelled datasets of camera trap images — can now classify species in camera trap photographs with accuracy exceeding 95% for common species, reducing the image review time for large surveys from months to days. Platforms like Wildlife Insights (Google AI for Social Good) and MegaDetector (Microsoft AI for Earth) provide free AI classification tools accessible to conservation organisations worldwide.

Camera Trap Networks — Global Biodiversity Monitoring

The eMammal and Wildlife Insights platforms — which aggregate camera trap data from thousands of projects globally into standardised, searchable databases — represent a new paradigm for biodiversity monitoring at continental and global scales. Where previously each research team maintained its own separate database of camera trap images (often in incompatible formats, with inconsistent species identification, and inaccessible to other researchers), these platforms provide shared infrastructure for image storage, species identification (using AI-assisted automated classification), and data sharing that enables meta-analyses of wildlife population trends across entire continents. A 2019 analysis of 2.9 million camera trap images from 1,509 sites across North America provided the first continental-scale assessment of mammal population trends — finding that mammals in more developed areas (defined by the "human footprint" index) showed dramatically lower diversity and abundance than those in less-modified areas, and that even small increases in human footprint index were associated with measurable reductions in mammal community integrity. This type of analysis, impossible with individual project data, illustrates the transformative potential of standardised, aggregated camera trap networks for understanding biodiversity change at scales relevant to policy.

Camera Trap Networks — Citizen Science at Scale

The combination of camera trap technology with citizen science platforms has transformed wildlife monitoring from a specialised scientific activity to a globally distributed, continuously operating observation network. Snapshot Safari — a network of camera trap arrays in southern African reserves, coordinated by the North Carolina Museum of Natural Sciences and processed by citizen scientists on the Zooniverse platform — has processed over 5 million images from 14 reserves, producing one of the most comprehensive datasets of African mammal community composition and temporal activity patterns ever assembled. Camera CATalogue — a network of 128 cameras in the Serengeti, processed by over 28,000 citizen scientists — generated data sufficient to produce the first ecosystem-wide estimate of Serengeti cheetah population size and to characterise the fine-scale habitat preferences of all large mammal species simultaneously. These citizen science camera trap projects are not merely processing assistance for professional researchers — they are generating novel scientific insights (the non-random spatial distribution of wildlife activity, the temporal overlap between species sharing the same camera sites, the response of wildlife to moon phase and weather) that would require decades of professional research effort to accumulate through conventional methods.