The glass half-full: Overestimating the quality of a novel environment is advantageous

Background/Question/Methods

We expect animals that lack knowledge about their environment to make the gaining of that knowledge a high priority. One of the most common means of obtaining information is by exploring the environment. Animals facing novel environments due to dispersal or translocation represent an extreme case in which the need to learn the environment is the highest. We propose that the expectation of the animal regarding the quality of a novel environment should be intimately linked with the animal’s propensity to explore.  Consequently, in a novel environment, it is better to overestimate the quality of the environment (to be an “optimist”) than underestimate it, since optimistic animals will learn more quickly of the true value of the environment due to higher exploration rate. Furthermore, we propose that when the animal has the capacity to remember the location and quality of resource patches, having a positively biased estimate of the environment will lead to higher fitness gains than having an unbiased estimate, due to the benefits of exploration. To validate our premise, we present here the results of a simple model simulating foraging in a novel heterogeneous landscape of renewable resource patches. We investigated the fitness gains dynamics (in terms of average long-term intake rate) in several model variants, corresponding to different learning capabilities by the foraging animal.

Results/Conclusions

We found out that having a positively biased estimate of the environment can indeed be the best strategy for foraging animals in a novel environment, and that the optimality of this strategy is a factor of both the costs of exploration and the life-span of the forager.