Overlap in habitat use and activity patterns between sika deer (<i>Cervus nippon</i>) and Japanese serows (<i>Capricornis crispus</i>) in subalpine habitats: exploitative competition rather than direct interference?
Bibliographic record
Abstract
Examining multiple niche relationships, such as habitat use and activity patterns, between sympatric species contributes to an understanding of the mechanisms of coexistence and competition. Sika deer ( Cervus nippon Temminck, 1838) and Japanese serows ( Capricornis crispus (Temminck, 1836)) often show different habitat use, and diversity in vegetation and topography seems to facilitate their coexistence. Conversely, their habitat use may overlap in areas with low-diversity habitats; however, this remains unstudied. Moreover, whether they spatiotemporally avoid each other to reduce direct interference is unclear. We examined the fine-scale patterns of habitat use and activity of deer and serows in the subalpine forests of Mount Fuji, which have low-diversity habitats, using camera trapping data over 3 years. Deer and serow habitat niches largely overlapped, especially in summer (92%–94%) and autumn (89%–91%), suggesting that low-diversity habitats facilitate overlapping habitat use. Both species selected areas frequently used by the other during spring to autumn and their temporal niches largely overlapped, especially in summer (88%) and autumn (83%), suggesting that they do not spatiotemporally avoid each other (i.e., there is no direct interference). Rapid range expansion of deer into these subalpine habitats may exclude native serows through resource exploitative competition rather than interference competition.
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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 | 0.001 |
| 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".