MétaCan
Menu
← Back to cohort
Record W4244998835 · doi:10.7287/peerj.preprints.2353

Fear of predators as an ecosystem service

2016· preprint· en· W4244998835 on OpenAlexaff
Liana Zanette, Michael Clinchy

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsWestern University
Fundersnot available
KeywordsPredationWildlifeTrophic cascadeForagingTrophic levelEcologyPredatorNational parkAffect (linguistics)GeographyEcosystemEnvironmental resource managementPsychologyBiologyEconomics

Abstract

fetched live from OpenAlex

The fear that predators instill in prey induces short-term anti-predator behaviours across every animal taxa that are beneficial in avoiding immediate death, but carry costs; one of the most well-established being that scared prey eat less. These findings, that animals stop eating to avoid being eaten under perceived predation risk, are not controversial. What is controversial is whether such fear effects can be long-term and powerful enough to affect wildlife prey populations and generate trophic cascades. For example, some have suggested that the restoration of wolves to Yellowstone National Park also restored the fear of predators, reducing elk foraging and in turn the pregnancy rate, contributing to rapidly declining elk numbers. Other Yellowstone researchers have suggested that the restoration of fear has generated a trophic cascade whereby scared elk eat less, increasing the food that elk eat. The prospect that fear can help restore populations and ecosystems has critical management implications, but to implement a management plan fear effects must be quantified. Indeed, the enormous amount of often acrimonious debate centred on whether fear can affect populations and ecosystems lingers in both the scientific and public policy domains because studies have largely been based on natural experiments. Manipulations that establish whether fear effects actually do exist for wildlife can help resolve management debates, and are critical for conservation and management on a global scale because the status of large carnivores world-wide is quite dismal with 77 % of species in decline. Public policy would also benefit because if restoring large carnivores also restores fear such that degraded ecosystems can become healthy again, then this has real implications for human lives and livelihoods. We present research from our lab and others, in which perceived predation risk has been experimentally manipulated in free-living wildlife. The results to date definitively demonstrate that fear effects do exist. Fear alters prey foraging behaviour, and fearful prey in turn produce 50 % fewer offspring; fear permanently impairs the reproduction of surviving offspring; and restoring the fear of large carnivores generates cascading effects down at least four tiers in the food chain. Given the enormous effects that fear has in nature, we elaborate on how manipulating fear using sound can be a particularly useful management tool for diagnosing and treating environmental ills. We describe a new system we have designed (Automated Behavioural Response systems-ABRs) that allows any researcher working on any wildlife species to conduct manipulations that quantify fear effects. We conclude that fear has its uses. Fear is good for the environment and as such, management may sometimes need to inject fear artificially for short-term goals (e.g. crop protection) but in many cases, the best and cheapest long-term solution might be to restore native predators where lost.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.012
GPT teacher head0.230
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicWildlife Ecology and Conservation→French-language works237,207→