Vulnerability of terrestrial vertebrate food webs to anthropogenic threats in Europe
Bibliographic record
Abstract
Vertebrate species worldwide are currently facing significant declines in many populations. Although we have gained substantial knowledge about the direct threats that affect individual species, these threats only represent a fraction of the broader vertebrate threat profile, which is also shaped by species interactions. For example, threats faced by prey species can jeopardize the survival of their predators due to food resource scarcity. Yet, indirect threats arising from species interactions have received limited investigation thus far. In this study, we investigate the indirect consequences of anthropogenic threats on biodiversity in the context of European vertebrate food webs. We integrated data on trophic interactions among over 800 terrestrial vertebrates, along with their associated human-induced threats. We quantified and mapped the vulnerability of various components of the food web, including species, interactions, and trophic groups to six major threats: pollution, agricultural intensification, climate change, direct exploitation, urbanization, and invasive alien species and diseases. Direct exploitation and agricultural intensification were two major threats for terrestrial vertebrate food webs: affecting 34% and 31% of species, respectively, they threaten 85% and 69% of interactions in Europe. By integrating network ecology with threat impact assessments, our study contributes to a better understanding of the magnitude of anthropogenic impacts on biodiversity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".