Threats to Nature’s contributions to people provided by terrestrial vertebrates across Europe
Bibliographic record
Abstract
ABSTRACT Aim Species and ecosystem processes provide essential benefits to people, known as Nature's Contributions to People (NCP). However, we still lack a comprehensive understanding of NCP provided by terrestrial vertebrates on a macroecological scale, and of the threats they face. To address this, we built a comprehensive dataset that documents the NCP provided by terrestrial vertebrate species in Europe, and analysed the conservation status and threats to NCP provider species. Location Europe. Methods We synthesised existing literature on NCP associated with European terrestrial vertebrates, and leveraged ecological traits and trophic interactions from existing datasets. We identified 15 NCP (10 regulating NCP and 5 non‐material NCP), with 860 species providing at least one NCP (out of 1168 vertebrate species considered in total). Then, we harnessed spatial data on species distributions and European land systems to map the potential capacity of vertebrates to provide NCP across Europe at a 1 km 2 resolution, including societal demand for each NCP. Results We found that (i) for each NCP, at least 25% of NCP provider species are assessed as threatened with extinction; (ii) potential NCP multifunctionality was lowest in high‐intensity land systems; and (iii) direct exploitation and agricultural intensification are major threats to NCP, impacting provider species for both non‐material and regulating NCP. Main Conclusions NCP are supported by diverse vertebrate communities across Europe, but many provider species are threatened by anthropogenic pressures. Our results suggest that protecting threatened NCP provider species, reducing direct exploitation, de‐intensifying agricultural practices, and maintaining heterogeneous mosaic landscapes, may support NCP multifunctionality. By improving our understanding of the NCP provided by terrestrial vertebrates in Europe, their biogeography, and the threats, our work can help prioritise species and areas for conservation and restoration that jointly benefits both biodiversity and NCP.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.003 | 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".