Roads as ecological traps for giant anteaters
Bibliographic record
Abstract
Abstract Wildlife‐vehicle collisions (WVCs) represent a serious source of mortality for many species, threatening local populations’ persistence while also carrying high economic and human safety costs. Animals may adapt their behaviour to road‐associated threats, but roadside resources can also attract individuals to dangerous roadside habitats, ultimately acting as an ecological trap. Yet, the extent to which individuals modify their behaviour and space use to roads is largely unknown for most taxonomic groups. Using fine‐scale movement data from 38 giant anteaters Myrmecophaga tridactyla tracked in the Brazilian Cerrado, we aimed to identify facets of movement behaviour that might exhibit plasticity to roads and traffic volume. Specifically, the analysis of daily and instantaneous movement speeds, home‐range characteristics and crossing rates/times allowed us to test for an effect of road proximity, traffic volume and natural linear features on movement behaviour. We found no effect of road proximity or traffic volume on space use or movement behaviour. While individuals tended to reduce their movement speed when approaching roads and crossed roads ~3 times less than would have been expected by random chance, none of the three highways we monitored were impervious. The majority of tracked anteaters living near roads (<2 km) crossed them, with higher crossing rates for males than females. Habitat near roads may function as an ecological trap where healthy individuals occupy the territories nearby or bisected by roads but eventually are road‐killed given their regular crossings, leaving the territory vacant for subsequent occupation. Crucially, we found no evidence that anteaters actively searched for passage structures to cross the roads. This suggests that crossing structures alone are unlikely to mitigate WVC‐induced mortality in giant anteaters. Our research reinforces the need to implement fencing, leading to existing passages, and minimizing the amount of night‐time driving to reduce the number of WVCs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".