Global wildlife roadkill research: a bibliometric synthesis of historical trends, thematic gaps, and future directions
Bibliographic record
Abstract
Abstract The global expansion of road networks has intensified ecological pressures on wildlife through roadkill, driving increased scholarly interest in recent decades. This study conducts a bibliometric and content analysis of 1,453 peer-reviewed publications—including journal articles, book chapters, conference papers, and reviews—published between 1955 and 2023, to explore historical trends, thematic developments, and geographic patterns in wildlife roadkill research. Publication output has grown rapidly since 2000, with over 75% of studies published after 2010. Research is concentrated in a few countries, with the United States, Brazil, Canada, and Australia accounting for 49% of total output. Taxonomic biases are evident, as mammals (44%) and herpetofauna (27%) are the most studied groups, while birds and invertebrates are underrepresented. Geographic imbalances also persist, with limited research focused on biodiversity-rich regions such as Southeast Asia and Africa. Keyword co-occurrence analysis identifies three dominant thematic clusters: core road ecology and applied conservation, human–wildlife interaction and theoretical perspectives, and taxon-specific and biodiversity-oriented studies. Despite the growing availability of scalable tools—such as citizen science, remote sensing, and machine learning—their application in roadkill research remains limited. Additionally, most studies focus on species classified as “Least Concern,” while those facing higher extinction risks receive little attention. These patterns reveal critical gaps in the taxonomic and conservation coverage of current literature. This review highlights the need for more longitudinal studies, inclusive taxonomic and geographic representation, and interdisciplinary approaches to better inform sustainable infrastructure planning and reduce biodiversity loss from wildlife–vehicle collisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.028 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".