Optimal-Foraging Predator Favors Commensalistic Batesian Mimicry

BACKGROUND:Mimicry, in which one prey species (the Mimic) imitates the aposematic signals of another prey (the Model) to deceive their predators, has attracted the general interest of evolutionary biologists. Predator psychology, especially how the predator learns and forgets, has recently been recognized as an important factor in a predator-prey system. This idea is supported by both theoretical and experimental evidence, but is also the source of a good deal of controversy because of its novel prediction that in a Model/Mimic relationship even a moderately unpalatable Mimic increases the risk of the Model (quasi-Batesian mimicry). METHODOLOGY/PRINCIPAL FINDINGS:We developed a psychology-based Monte Carlo model simulation of mimicry that incorporates a "Pavlovian" predator that practices an optimal foraging strategy, and examined how various ecological and psychological factors affect the relationships between a Model prey species and its Mimic. The behavior of the predator in our model is consistent with that reported by experimental studies, but our simulation's predictions differed markedly from those of previous models of mimicry because a more abundant Mimic did not increase the predation risk of the Model when alternative prey were abundant. Moreover, a quasi-Batesian relationship emerges only when no or very few alternative prey items were available. Therefore, the availability of alternative prey rather than the precise method of predator learning critically determines the relationship between Model and Mimic. Moreover, the predation risk to the Model and Mimic is determined by the absolute density of the Model rather than by its density relative to that of the Mimic. CONCLUSIONS/SIGNIFICANCE:Although these predictions are counterintuitive, they can explain various kinds of data that have been offered in support of competitive theories. Our model results suggest that to understand mimicry in nature it is important to consider the likely presence of alternative prey and the possibility that predation pressure is not constant

A three-neuron model of information processing during Bayesian foraging

A foraging animal is often confronted with uncertainty of resource abundance. A Bayesian model provides the optimal forgaing policy when food occurrence is patchy. The solution of the Bayesian foraging policy requires elaborate calculations and it is unclear to what extent the policy could be implemented in a neural system. Here we suggest a network architecture of three neurones that approximately can perform an optimal Bayesian foraging policy. It remains to be shown how the network could be self-learned e.g. through Hebbian learning, and how close to to the optimal policy it can perform

Geographical variation in the timing of breeding and moult in dunlin Calidris alpina on the Palearctic tundra

Studies of how organisms are adapted to regional climatic conditions are valuable when predicting the effects of global climatic changes on biota. Here we report on the geographical variation in timing of breeding and moult of an Arctic breeding wader, the dunlin (Calidris alpina). The Palearctic study sites range latitudinally between 68 and 76 degreesN and longitudinally between 46 and 179 degreesE, and encompass a variety of local climates. The sites were visited in sequence from west to east within 1 year, and therefore the data are not affected by confounding interannual variations. The estimated breeding start ranged from 5 to 25 June across populations. Birds at more southern sites were found to breed earlier than those at more northern breeding sites. Within populations, the breeding start for first clutches spanned a period of 8 days and, when including replacement clutches, 3-4 weeks. No dunlin west of the Taimyr Peninsula were found moulting while incubating at the nest, whereas all dunlin on Taimyr Peninsula and eastwards were in active wing moult while incubating or rearing chicks. The onset of moult in these populations ranged from 23 to 27 June. The consequences of geographical variation of breeding conditions for variation in the annual cycle of this species are discussed

Does information sharing promote group foraging?

Individuals may join groups for several reasons, one of which is the possibility of sharing information about the quality of a foraging area. Sharing information in a patch-foraging scenario gives each group member an opportunity to make a more accurate estimate of the quality of the patch. In this paper we present a mathematical model in which we study the effect of group size on patch-leaving policy and per capita intake rate. In the model, group members share information equally in a random search for food. Food is distributed in patches according to a negative binomial distribution. A prediction from our model is that, the larger the group, the earlier each group member should leave the current patch. We also find that the benefit from enhanced exchange of information does not exceed the cost of sharing food with group members. The per capita intake rate decreases as the group size increases. Therefore, animals should only form groups when other factors outweigh the costs, which is easiest to achieve when the travelling time is short

Computational Population Biology: Linking the inner and outer worlds of organisms.

Computationally complex systems models are needed to advance research and implement policy in theoretical and applied population biology. Difference and differential equations used to build lumped dynamic models (LDMs) may have the advantage of clarity, but are limited in their inability to include fine-scale spatial information and individual-specific physical, physiological, immunological, neural and behavioral states. Current formulations of agent-based models (ABMs) are too idiosyncratic and freewheeling to provide a general, coherent framework for dynamically linking the inner and outer worlds of organisms. Here I propose principles for a general, modular, hierarchically scalable, framework for building computational population models (CPMs) designed to treat the inner world of individual agents as complex dynamical systems that take information from their spatially detailed outer worlds to drive the dynamic inner worlds of these agents, simulate their ecology and the evolutionary pathways of their progeny. All the modeling elements are in place, although improvements in software technology will be helpful; but most of all we need a cultural shift in the way population biologists communicate and share model components and the models themselves, fit, test, refute, and refine models, to make the progress needed to meet the ecosystems management challenges posed by global change biology