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Record W3151869553 · doi:10.1002/fee.2336

Predation services: quantifying societal effects of predators and their prey

2021· review· en· W3151869553 on OpenAlexaff
Sophie L. Gilbert, Neil Carter, Robin Naidoo

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2021
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPredationEcosystem servicesPredatorWildlifeEcologyPer capitaApex predatorEnvironmental resource managementGeographyEcosystemBiologyPopulationEconomics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.758
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.223
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations15
Published2021
Admission routes1
Has abstractyes

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