Predation services: quantifying societal effects of predators and their prey
Bibliographic record
Abstract
Conservation of predators – especially large carnivores and those that potentially pose threats to humans – can be controversial among stakeholders who must coexist with them. What is often overlooked, however, are the direct and indirect ecosystem services and disservices predators provide as a result of consumption of herbivores (“predation services”). We used a theoretical predator–prey–economic model to examine when predators are likely to provide a net service to society, by comparing services/disservices to a predator‐free counterfactual scenario. We found that net predator services were strongly dependent on how per‐capita services and disservices of predators and prey changed with abundance (ie assumed marginal value [MV] functions of service/disservice). We suggest that further empirical research is needed into MVs of services/disservices of wildlife, because transferring net services among locations – a common practice – is problematic unless MV functions are known. Rigorously quantifying services/disservices of predators could improve conservation and management outcomes by increasing effective communication to diverse stakeholders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".