Foraging strategy of a non-omniscient predator in a changing environment (I) model with a data window and absolute criterion

1. Almost all the models so far presented assume that predators are omniscient in the sense that they always have complete information about the spatial distribution of prey abundance and its change over time. But this type of model cannot cover the situation where the prey abundance in each patch changes over time due to factors other than predation. The model with a data window and absolute criterion (SAC) here enables us to treat such situations.
2. The strategy of non-omniscient predators can be generally devided into four procedures; collection of information, its memorization, decision of tactics and its execution. SAC involves only two tactics; to stay another time period in the patch the predator is staying presently or to move to another patch chosen at random. The choice of either one of the two tactics is made by comparing the profitability of the current patch estimated by the data window with a pre-determined absolute criterion.
3. Three changing patterns of prey abundance are considered. In the most general pattern good patches have a higher mean profitability than poor patches, but the profitability changes cyclically in each of patches.
4. There are only two possibilities for an optimal strategy; the “patch choice strategy” in which once the predator has taken a good patch, it tries to stay there even when the state becomes poor, and the ‘state choice strategy” in which the predator seeks for only good states in good patches. The condition for which either of the two foraging strategies is superior to the other is specified analytically.