Step Bracis et al. 2015 Merkle et al. 2017 Avgar et al. 2016 Schlägel et al. 2017
Task
Maximize consumption Reduce predation
Forage efficiently
Forage efficiently and survive
Patrol
Experience Movement Movement among patches Movement Movement
Model prediction Consumption and predator encounter rate Patch selection Redistribution kernel Entire movement path
Null model
Context-dependent behavioural switching
Connectivity, size, and quality of patch
Forage quality, predation risk, competitors, and snow
Movement in response to prey density Distance to territory boundary
Information updated
Location and quality of forage and encounters
Location and quality of patches Memory of past patch quality
Location and quality of habitat
Time since last visit to territorial locations
Improvement via learning Learning forager outperforms null model Learning forager is more efficient Yes Yes
Plausible connections to fitness
Foraging efficiency Reducing encounters with predators
Past experience leads to foraging in higher quality patches
Past experience leads to better habitat use
Territorial maintenance and defense
Plausible learning mechanism Sampling and trial-and-error plus reinforcement Positive reinforcement Positive reinforcement Positive reinforcement