Geography of global change and species richness in the North
Bibliographic record
Abstract
Different components of global change (e.g., climate change, land use, pollution, and introduced species) continue to alter biodiversity worldwide. As northern regions are still relatively undisturbed and will likely face clear increases in temperature in the near-future, we examined the signs of biodiversity change due to anthropogenic stressors using a systematic review of previous studies. Our aim was to map where, in which way, and owing to which stressor biodiversity in northern regions has changed. We made a systematic literature search covering the years between 2000 and 2015 to obtain a comprehensive selection of recent research. As species richness was clearly the most commonly used indicator of biodiversity, we only concentrated on this aspect of biodiversity. We compared different biological groups, regions, and ecosystems. In the majority of the cases, anthropogenic stressors had decreased species richness, or had no effects on it, while increasing or multiple effects of stressors on species richness were less common. Freshwater ecosystems were most sensitive to anthropogenic stressors, as species richness often decreased owing to these stressors. The effects of land use on richness were covered relatively widely in the selected set of articles, but the effects of other components of global change on species richness require further attention. Despite the fact that pollution was not as commonly studied stressor as land use, it was the most harmful stressor type affecting species richness. Geographically, most studies were located in boreal Canada or Fennoscandia, while no studies were executed in vast circumpolar areas where the temperature rise has been greatest and the projected climate change is likely to be fast. Overall, we could find an alarmingly small set of studies that described the effects of actual anthropogenic stressors in real-life circumstances in northern high latitudes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".