The competitive exclusion–tolerance rule explains habitat partitioning among co‐occurring species of burying beetles
Bibliographic record
Abstract
Habitat partitioning among co-occurring, ecologically similar species is widespread in nature and thought to be an important mechanism for coexistence. The factors that cause habitat partitioning, however, are unknown for most species. We experimentally tested among three alternative hypotheses to explain habitat partitioning among two species of co-occurring burying beetle (Nicrophorus) that occupy forest (Nicrophorus orbicollis) and wetland (Nicrophorus hebes) habitats. Captive experiments revealed that the larger N. orbicollis (forest) was consistently dominant to N. hebes (wetland) in competitive interactions for carcasses that they require for reproduction. Transplant enclosure experiments in nature revealed that N. hebes had poor reproductive success whenever the dominant N. orbicollis was present. In the absence of N. orbicollis, N. hebes performed as well, or better, in forest versus its typical wetland habitat. In contrast, N. orbicollis performed poorly in wetlands regardless of the presence of N. hebes. These results support the competitive exclusion-tolerance rule where the competitively dominant N. orbicollis excludes the subordinate N. hebes from otherwise suitable or preferable forest habitat, while the subordinate N. hebes is uniquely able to tolerate the challenges of breeding in wetlands. Transplant experiments further showed that carcass burial depth-an important trait thought to enhance the competitive ability of the dominant N. orbicollis-is costly in wetland habitats. In the presence of N. hebes, N. orbicollis buried carcasses deeper; deeper burial is thought to provide a competitive advantage in forests but further compromised the reproductive success of N. orbicollis in wetlands. Overall, results provide evidence that the competitive exclusion-tolerance rule underlies habitat partitioning among ecologically similar species and that the traits important for competitive dominance in relatively benign environments are costly in more challenging environments, consistent with a trade-off.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".