Technological advancements: a global review of the use of camera technology in wildlife research
Bibliographic record
Abstract
Cameras have become widely used tools in wildlife research, providing new insights into the behavior, population dynamics, and habitat preference of species across a wide range of taxa. In this study, we conducted a systematic literature review to explore the use of camera technology, both still and video, in wildlife research over time. We analyzed 2472 peer-reviewed articles published between 2010 and 2023 from around the world that incorporated cameras into wildlife studies. Our review reveals a sharp increase in the number of English-language publications using cameras after 2018, which may be attributed in part to the increasing availability of drones and to the development of machine-learning algorithms for processing large datasets. Mammals (75%) and birds (19%) were the most studied organisms, and camera traps were the most used camera device type. Research topics were equally divided between behavioral studies, population dynamics, and species presence/absence monitoring. Despite the global spread of studies using camera technologies, geographic gaps remain, particularly in central Asia, northern Africa, and Greenland. Our findings highlight the increasing role of camera technology in studying wildlife. However, despite these technological advancements, we suggest that it is essential not to lose the direct connection with nature and the species being studied. We emphasize that time in the field remains important for ecologists to gain a deeper understanding of ecological processes and to foster a meaningful connection to the research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".