Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».