Does Differential Habitat Selection Facilitate Coexistence Between Badgers and Hedgehogs?
Bibliographic record
Abstract
ABSTRACT Predicting the spatial and temporal responses of species exhibiting intraguild predation (IGP) relationships is difficult due to variation in potential interactions and environmental context. Eurasian badgers (Meles meles) are intraguild predators of European hedgehogs (Erinaceus europaeus) and are implicated in their population decline via both direct predation and competition for shared food resources. Previous studies have shown spatial separation between these species and attributed this to hedgehogs experiencing a ‘landscape of fear’, but little is known about the potential role of differential habitat use. We estimated the density and occupancy of both species at 22 rural study sites in England and Wales, to explore whether food availability, habitat or the presence of badgers, explained hedgehog distributions. Hedgehog density varied significantly across major rural land uses, whereas badger density did not. Although both species coexisted at a regional (1 km2) scale, occupancy modelling showed spatial segregation at a finer (individual camera trap) scale, associated with differential habitat use. In contrast to badgers, hedgehogs were recorded near buildings, and in areas supporting lower invertebrate biomass. This is in agreement with IGP theory, whereby IG‐prey may occupy suboptimal habitat to avoid predation; however, hedgehog habitat use did not vary relative to the presence of badgers. Badger and hedgehog temporal activity showed no evidence of separation. Although these findings are consistent with hedgehogs avoiding badgers via a landscape of fear, they are also indicative of differential habitat use, highlighting the need for more holistic studies considering variation in habitat selection and food availability when investigating intraguild relationships. Future studies exploring alternative hypotheses for urban habitat selection by hedgehogs are needed to better understand how possible spatial niche partitioning may support their coexistence with badgers in some areas.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".