Niche variation in burying beetles (<i>Nicrophorus</i>spp.) associated with marine and terrestrial carrion
Bibliographic record
Abstract
Ecological opportunity can influence niche variation within and among species. Forensic reconstruction of diet has been made possible with the now widespread use of stable isotope analysis, although it has not to date been applied to communities based on carrion resources. Within a salmon-bearing watershed in coastal British Columbia, we reconstructed the dietary niches of two burying beetle species (Silphidae: Nicrophorus investigator Zetterstedt, 1824 and Nicrophorus defodiens Mannerheim, 1846) using stable isotopes (δ15N, δ13C). We separated available carrion sources for beetles of chum salmon ( Oncorhynchus keta (Walbaum, 1792)) and pink salmon ( Oncorhynchus gorbuscha (Walbaum, 1792)), the shrew Sorex monticolus Merriam, 1890, songbirds ( Troglodytes troglodytes (L., 1758), Catharus ustulatus (Nuttall, 1840), Catharus guttatus (Pallas, 1811)), and black-tailed deer ( Odocoileus hemionus hemionus (Rafinesque, 1817)) into three isotopically homogeneous subsets: (1) salmon, (2) shrews and songbirds, and (3) deer. The majority (86.5%) of N. investigator individuals were raised on a diet of salmon carrion, while 100% of N. defodiens individuals had a larval diet consistent with carrion from shrews and songbirds. Larger isotopic variance predicts wider dietary niches, which may be useful for testing functional ecological variation within and among species.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".