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Optimal foraging and the information theory of gambling.

ABSTRACT: At a macroscopic level, part of the ant colony life cycle is simple: a colony collects resources; these resources are converted into more ants, and these ants in turn collect more resources. Because more ants collect more resources, this is a multiplicative process, and the expected logarithm of the amount of resources determines how successful the colony will be in the long run. Over 60 years ago, Kelly showed, using information theoretic techniques, that the rate of growth of resources for such a situation is optimized by a strategy of betting in proportion to the probability of pay-off. Thus, in the case of ants, the fraction of the colony foraging at a given location should be proportional to the probability that resources will be found there, a result widely applied in the mathematics of gambling. This theoretical optimum leads to predictions as to which collective ant movement strategies might have evolved. Here, we show how colony-level optimal foraging behaviour can be achieved by mapping movement to Markov chain Monte Carlo (MCMC) methods, specifically Hamiltonian Monte Carlo (HMC). This can be done by the ants following a (noisy) local measurement of the (logarithm of) resource probability gradient (possibly supplemented with momentum, i.e. a propensity to move in the same direction). This maps the problem of foraging (via the information theory of gambling, stochastic dynamics and techniques employed within Bayesian statistics to efficiently sample from probability distributions) to simple models of ant foraging behaviour. This identification has broad applicability, facilitates the application of information theory approaches to understand movement ecology and unifies insights from existing biomechanical, cognitive, random and optimality movement paradigms. At the cost of requiring ants to obtain (noisy) resource gradient information, we show that this model is both efficient and matches a number of characteristics of real ant exploration.

SUBMITTER: Baddeley RJ

PROVIDER: S-EPMC6731492 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Optimal foraging and the information theory of gambling.

Baddeley Roland J RJ Franks Nigel R NR Hunt Edmund R ER

Journal of the Royal Society, Interface 20190807 157

At a macroscopic level, part of the ant colony life cycle is simple: a colony collects resources; these resources are converted into more ants, and these ants in turn collect more resources. Because more ants collect more resources, this is a multiplicative process, and the expected logarithm of the amount of resources determines how successful the colony will be in the long run. Over 60 years ago, Kelly showed, using information theoretic techniques, that the rate of growth of resources for suc ...[more]

PMID: 31387483

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Project description:BackgroundGeneral Practitioners (GPs) employ strategies to identify and retrieve medical evidence for clinical decision making which take workload and time constraints into account. Optimal Foraging Theory (OFT) initially developed to study animal foraging for food is used to explore the information searching behaviour of General Practitioners. This study is the first to apply foraging theory within this context.Study objectives were: 1. To identify the sequence and steps deployed in identifiying and retrieving evidence for clinical decision making. 2. To utilise Optimal Foraging Theory to assess the effectiveness and efficiency of General Practitioner information searching.MethodsGPs from the Wellington region of New Zealand were asked to document in a pre-formatted logbook the steps and outcomes of an information search linked to their clinical decision making, and fill in a questionnaire about their personal, practice and information-searching backgrounds.ResultsA total of 115/155 eligible GPs returned a background questionnaire, and 71 completed their information search logbook. GPs spent an average of 17.7 minutes addressing their search for clinical information. Their preferred information sources were discussions with colleagues (38% of sources) and books (22%). These were the two most profitable information foraging sources (15.9 min and 9.5 min search time per answer, compared to 34.3 minutes in databases). GPs nearly always accessed another source when unsuccessful (95% after 1st source), and frequently when successful (43% after 2nd source). Use of multiple sources accounted for 41% of searches, and increased search success from 70% to 89%.ConclusionsBy consulting in foraging terms the most 'profitable' sources of information (colleagues, books), rapidly switching sources when unsuccessful, and frequently double checking, GPs achieve an efficient trade-off between maximizing search success and information reliability, and minimizing searching time. As predicted by foraging theory, GPs trade time-consuming evidence-based (electronic) information sources for sources with a higher information reward per unit time searched. Evidence-based practice must accommodate these 'real world' foraging pressures, and Internet resources should evolve to deliver information as effectively as traditional methods of information gathering.

Project description:BackgroundIn highly seasonal environments, animals face critical decisions regarding time allocation, diet optimisation, and habitat use. In the Arctic, the short summers are crucial for replenishing body reserves, while low food availability and increased energetic demands characterise the long winters (9-10 months). Under such extreme seasonal variability, even small deviations from optimal time allocation can markedly impact individuals' condition, reproductive success and survival. We investigated which environmental conditions influenced daily, seasonal, and interannual variation in time allocation in high-arctic muskoxen (Ovibos moschatus) and evaluated whether results support qualitative predictions derived from upscaled optimal foraging theory.MethodsUsing hidden Markov models (HMMs), we inferred behavioural states (foraging, resting, relocating) from hourly positions of GPS-collared females tracked in northeast Greenland (28 muskox-years). To relate behavioural variation to environmental conditions, we considered a wide range of spatially and/or temporally explicit covariates in the HMMs.ResultsWhile we found little interannual variation, daily and seasonal time allocation varied markedly. Scheduling of daily activities was distinct throughout the year except for the period of continuous daylight. During summer, muskoxen spent about 69% of time foraging and 19% resting, without environmental constraints on foraging activity. During winter, time spent foraging decreased to 45%, whereas about 43% of time was spent resting, mediated by longer resting bouts than during summer.ConclusionsOur results clearly indicate that female muskoxen follow an energy intake maximisation strategy during the arctic summer. During winter, our results were not easily reconcilable with just one dominant foraging strategy. The overall reduction in activity likely reflects higher time requirements for rumination in response to the reduction of forage quality (supporting an energy intake maximisation strategy). However, deep snow and low temperatures were apparent constraints to winter foraging, hence also suggesting attempts to conserve energy (net energy maximisation strategy). Our approach provides new insights into the year-round behavioural strategies of the largest Arctic herbivore and outlines a practical example of how to approximate qualitative predictions of upscaled optimal foraging theory using multi-year GPS tracking data.

Project description:BackgroundMimicry, 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 findingsWe 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/significanceAlthough 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.

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Optimal foraging and the information theory of gambling.

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Optimal foraging and the information theory of gambling.

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