Quantifying trophic niche spaces of small mammals using stable isotopes (δ<sup>15</sup>N and δ<sup>13</sup>C) at two scales across Alaska
Bibliographic record
Abstract
Changing climate conditions are causing global distribution shifts, resulting in altered food webs and novel species assemblages in terrestrial systems. How diets of sympatric small mammals overlap and whether this may translate into competitive exclusion among new species interactions remains largely unknown. Monitoring niche overlap in changing arctic and boreal communities can assist in forecasting interspecific competition and species turnover. We quantified the isotopic niche spaces of small mammals, which may reflect dietary niche spaces, at study sites along two megatransects spanning Alaska. Field sampling resulted in the capture of 724 small mammals belonging to 12 species of rodent (10 Arvicolinae and 2 Sciuridae) and 6 species of shrew (genus Sorex L., 1758). We created dietary mixing models based on hair samples for four rodent species using stable isotope (δ15N and δ13C) analyses in R. We also modeled isotopic niche ellipses and quantified niche overlap among species at small and large scales. A varied combination of fungi and herbaceous plants composed the diets of most species. Fundamental niche spaces overlapped considerably between sympatric species statewide, but realized niche spaces were largely segregated at individual sites. We conclude that some degree of dietary plasticity served to partition niche spaces and minimize interspecific competition, allowing sympatric species to co-exist.
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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) | 0.000 | 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".