Some like it burnt: species differences in small mammal assemblage in a Mediterranean basin nearly 3 years after a major fire
Bibliographic record
Abstract
Abstract The habitat accommodation model (HAM) is a theoretical framework that predicts wildlife community recovery based on their habitat requirements. While post-fire habitat-related research is well documented in the Mediterranean basin, studies specifically focusing on HAM are scarce. Here, we described the small mammal assemblage in a Mediterranean area ~3 years after a fire, specifically examining three functional small mammalian categories: ground-foraging insectivorous, ground-foraging herbivorous/granivorous, and arboreal-foraging species. The study was conducted in Monte Pisano (Italy), where fire burnt ~12 km 2 in September 2018. A stratified random sampling was adopted, basing on burnt status and forest type. In each of the 50 sites, during late spring-summer 2021, 12 hair-tubes were deployed, and collected hairs were taxa-attributed based on morphology. A presence/absence dataset was built, and db-RDA was used to explore assemblage composition, and single-species occupancy models to test specific hypotheses. The relative abundance of ground-foraging herbivorous/granivorous was higher in the burnt area, characterised by a dense undergrowth, which could be related to anti-predatory strategies and food opportunities. Insectivorous could be in a recolonisation phase, masking their earlier absence, which could explain why their abundance was not associated with any factor tested. Arboreal-foraging species were associated with forest type, indicating a primary role for tree cover and other factors such as rocky cover and likely in situ survival. The HAM was overall confirmed also in Mediterranean basin ecosystems. This may facilitate predictions about post-fire animal successions, which in turn may provide valuable insights into post-fire management practices and biodiversity conservation strategies.
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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 | 0.001 | 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) | 0.001 | 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".