A novel camera-based approach to understanding the foraging behaviour of mycophagous mammals
Bibliographic record
Abstract
Mammal mycophagy (consumption of fungi by mammals) is an important process in forested ecosystems around the world. Of great interest to ecologists are those mammals that excavate and consume the below-ground truffle-forming fungi that are symbiotic with forest trees. By dispersing ingested spores a vital ecosystem function is performed by these mammals. Despite this importance, virtually nothing is known about how quickly a truffle patch is discovered and depleted by mammals, how different mammal species share a common food resource, or how truffles are excavated and handled by mycophagous mammals. Using passive infrared (PIR) video camera traps, we studied truffle excavation by mammals in two widely separated temperate ecosystems: (1) Conifer Forest in New Brunswick, Canada; and (2) Eucalyptus Woodland in Tasmania, Australia. Our results show that mammals discover and deplete localised truffle resources rapidly, and that very different mammals in both ecosystems (squirrels and voles in Canada; potoroos in Australia) respond similarly to the presence of truffles in terms of foraging rates and activity patterns. The technique yielded a novel dataset on truffle excavation by mammals and the first quantitative data on visitation rates to truffle patches by a range of mammal species, throwing light on how mammals exploit this food resource.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".