Habitat and predator heterogeneity influence density of a declining mammal
Bibliographic record
Abstract
Accurate density estimates are crucial for effective conservation management. However, in highly dynamic landscapes where variation in habitat composition and predator–prey interactions in both space and time is likely, integrating spatiotemporal covariate effects in density estimation is challenging and often large datasets are needed. Here, we used an 11‐year spatial capture–recapture (SCR) dataset from a typical mixed agroecosystem in England to estimate landscape‐scale densities of western European hedgehogs Erinaceus europaeus . We simultaneously integrated spatially varied habitat covariates, and the spatiotemporal variation in predator (Eurasian badger Meles meles ) den site into one SCR framework. Density was spatially structured (range 0.39–13.54 on a 1 km 2 grid), and was lower in arable fields and highest in amenity grasslands next to buildings. Density was also positively associated with soil permeability, density of edge habitats, proximity to the nearest building, and distance from the nearest badger sett. A new badger sett appeared halfway through the study period, resulting in a hedgehog density‐weighted population centre over the study area shift away from the badger sett and a decrease in annual hedgehog density estimates, supporting the landscape of fear for hedgehogs in response to their main predator the badger. Density estimates were also 43% lower after incorporating spatiotemporal covariate heterogeneity into the modelling process, highlighting the need to integrate dynamic habitat and predator influences into density modelling to provide more accurate estimations. Finally, our findings demonstrate the importance of long‐term monitoring for understanding population responses to changes in predator presence and provide clear empirical evidence for a prey species altering space use in relation to the increased predator, supporting the landscape of fear hypothesis.
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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) | 0.000 | 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".