Efficient harvesting of renewing resources
Abstract
Many foraging animals return to feeding sites to harvest replenishing resources, but little is known about efficient tactics for doing this. Can animals with adequate cognitive abilities increase their efficiency by modifying their behavior according to memories of past experience at particular sites? We developed a simulation model of animals harvesting renewable resources from isolated patches in undefended, competitive situations. We compared four foraging tactics: (1) moving stochastically without using any information from past experiences (random searching); (2) moving stochastically, but going longer distances after encountering lower reward (area-restricted searching); (3) repeatedly moving along a fixed route (complete traplining); and (4) traplining, but sampling and shifting to neighboring rewarding patches after encountering low reward (sample-and-shift traplining). Following Possingham, we tracked both the resources actually harvested by a focal forager (i.e., rewards) and the standing crops of resources that accumulated at patches. Complete traplining always produces less variation in elapsed time between visits than random searching or area-restricted searching, which has three benefits: increasing the reward crop harvested, if resource renews nonlinearly; reducing resource standing crop in patches; and reducing variation in reward crop per patch. Moreover, the systematic revisitation schedule produced by complete traplining makes it more competitive, regardless of resource renewal schedule or competitor frequency. By responding to their past experiences, using sample-and-shift traplining, foragers benefit only when many patches are left unvisited in the habitat. Otherwise, the exploratory component of sample-and-shift traplining, which increases the movement distance and the variation in elapsed time between visits, makes it more costly than complete traplining. Thus, traplining will usually be beneficial, but foragers should switch between “impatient” (sample-and-shift traplining) and “tenacious” (complete traplining) traplining, according to temporal changes in surrounding situations.
Local Knowledge Graph (9 entities)
Related Works
Items connected by shared entities, co-authorship, citations, or semantic similarity.
Trapline foraging by bumblebees: I. Persistence of flight-path geometry
Optimal foraging: a case for random movement
Optimal foraging: movement patterns of bumblebees between inflorescences
Data from: Extreme site fidelity as an optimal strategy in an unpredictable and homogeneous environment
Data from: Foraging strategy predicts foraging economy in a facultative secondary nectar robber
Data supplementing Lichtenberg et al. (2020) Competition for nectar resources does not affect bee foraging tactic constancy. Ecological Entomology
Colorado Ranch Management School (Part 7)
Beaver Management in Grazed Riparian Ecosystems
Resource Planning: A Method for Allocating Land Uses in Natural Areas
Cited By (81 times, 2 in Knowledge Hub)
References (72)
7 in Knowledge Hub, 65 external
