Data from: Mammal communities of primeval forests as sentinels of global change
Bibliographic record
Abstract
Understanding the drivers and consequences of global environmental change is crucial to inform predictions of effects on ecosystems. We used the mammal community of Białowieża Forest, the last lowland near-primeval forest in temperate Europe, as a sentinel of global change. We analyzed changes in stable carbon (δ13C) and nitrogen (δ15N) isotope values of hair in 687 specimens from 50 mammal species across seven decades (1946–2011). We classified mammals into four taxonomic-dietary groups (herbivores, carnivores, insectivores, bats). We found a significant negative trend in hair δ15N for the mammal community, particularly strong for herbivores. This trend is consistent with temporal patterns in nitrogen deposition from (15N depleted) industrial fertilizers and fossil fuel emissions. It is also in line with global-scale declines in δ15N reported in forests and other unfertilized, non-urban terrestrial ecosystems and with local decreases in N foliar concentrations. The global depletion of 13C content in atmospheric CO2 due to fossil fuel burning (Suess effect) was detected in all groups. After correcting for this effect, the hair δ13C trend became non-significant for both community and groups, except for bats, which showed a strong decline in δ13C. This could be related to an increase in the relative abundance of freshwater insects taken by bats or increased use of methane-derived carbon in food webs used by bats. This work is the first broad-scale and long-term mammal isotope ecology study in a near-primeval forest in temperate Europe. Mammal communities from natural forests represent a unique benchmark in global change research; investigating their isotopic temporal variation can help identify patterns and early detections of ecosystem changes and provide more comprehensive and integrative assessments than single-species approaches.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".