Notice bibliographique
Résumé
If you spend more energy catching your next meal than it provides, you run the risk of creating an energy deficit, which will in turn negatively impact your growth and potentially your survival. This suggests that predators may adaptively choose prey that are more economically profitable. Klemen Koselj and colleagues, from the University of Tubingen and the Max Planck Institute for Ornithology in Germany, set out to determine whether prey selection in horseshoe bats is based on optimizing energy profitability. The team wanted to see whether the bats could learn which prey offered the greatest amount of energy, whether they could estimate how often they would encounter their prey, and whether they could integrate the two pieces of information and make prey selection decisions that would help to maximize their energy intake.First, the team designed two different sized rotating propellers that simulated two different sized prey items and created echo patterns that the bats could distinguish using echolocation. The team then trained horseshoe bats to associate the large propeller with a large mealworm reward, and the small propeller with a small mealworm reward. Following training, the team ran each bat through a series of hunting trials where they were sequentially and repeatedly offered both large and small propellers. The order in which the propellers were presented was varied for each trial. The team also varied the frequency with which they were presented to mimic the effect of different prey densities and abundances. In theory, if the bats were making economical decisions about their meals, the more abundant the large prey the less often the bats should respond to smaller prey.In fact, as the frequency of the larger prey increased (i.e. the more often the large propeller was presented to the bats), the bats preyed more predominantly on the larger prey and more often ignored smaller prey when it was presented. Conversely, as the frequency of the larger prey was decreased, the bats began to feed on both large and small prey items equally. This suggests that not only can bats distinguish prey items based on their energy content but also they can estimate their abundance based on how frequently they encounter the prey. These two pieces of information then contribute to the bats' prey choice, helping them to make adaptive decisions. This suggests that the prey selection biologists see in the field may be a result of bats choosing the most profitable prey.Koselj and colleagues have shown that bats can and do make economical decisions when hunting. Having a surplus in your energy budget allows for growth and reproduction, and choosing prey that gives you the biggest bang for your buck would certainly help you achieve that. Overall, this suggests that predators may be making complicated decisions about prey selection and that foraging may be based on more than happenstance.
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,004 |
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 ».