Optimal Intermittence in Search Strategies under Speed-Selective Target Detection

Campos, Daniel; Méndez, Vicenç; Bartumeus, Frederic

doi:10.1103/physrevlett.108.028102

Cited by 16 publications

(22 citation statements)

References 23 publications

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“…These elementary results are in general concordance with simulation results exploring the effect of different encounter reactions and system dimensions on optimal search strategies [17], [35], [37]–[40]. They are also valid for intermittent search strategies where mixtures of short and fast trips exist but scanning is discontinuous [41]–[44].…”

Section: Discussionsupporting

confidence: 85%

Stochastic Optimal Foraging: Tuning Intensive and Extensive Dynamics in Random Searches

et al. 2014

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Recent theoretical developments had laid down the proper mathematical means to understand how the structural complexity of search patterns may improve foraging efficiency. Under information-deprived scenarios and specific landscape configurations, Lévy walks and flights are known to lead to high search efficiencies. Based on a one-dimensional comparative analysis we show a mechanism by which, at random, a searcher can optimize the encounter with close and distant targets. The mechanism consists of combining an optimal diffusivity (optimally enhanced diffusion) with a minimal diffusion constant. In such a way the search dynamics adequately balances the tension between finding close and distant targets, while, at the same time, shifts the optimal balance towards relatively larger close-to-distant target encounter ratios. We find that introducing a multiscale set of reorientations ensures both a thorough local space exploration without oversampling and a fast spreading dynamics at the large scale. Lévy reorientation patterns account for these properties but other reorientation strategies providing similar statistical signatures can mimic or achieve comparable efficiencies. Hence, the present work unveils general mechanisms underlying efficient random search, beyond the Lévy model. Our results suggest that animals could tune key statistical movement properties (e.g. enhanced diffusivity, minimal diffusion constant) to cope with the very general problem of balancing out intensive and extensive random searching. We believe that theoretical developments to mechanistically understand stochastic search strategies, such as the one here proposed, are crucial to develop an empirically verifiable and comprehensive animal foraging theory.

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Section: Discussionsupporting

confidence: 85%

Stochastic Optimal Foraging: Tuning Intensive and Extensive Dynamics in Random Searches

et al. 2014

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“…These kinds of constrained detection problems have been studied before [21,22] for specific situations (also within the context of imperfect or evanescent trap models [23][24][25]), but here we show that universal and intuitive conclusions for the mean detection time (MDT) can be reached under very general conditions. We consider (see Fig.…”

Section: Introductionmentioning

confidence: 99%

“…In order to adapt this formalism to a bounded domain and to introduce adequate boundary conditions one needs to use periodic summation techniques [22,35]. This leads to an extension of (11) and (12) which looks formally the same as that system of equations, but now j and p must be seen as periodic functions in space whose expression within the finite domain of volume L d coincides with the solution we are looking for.…”

Section: Examplesmentioning

confidence: 99%

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Search times with arbitrary detection constraints

2013

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Random encounters in space are central to describing diffusion-limited reactions, animal foraging, search processes, and many other situations in nature. These encounters, however, are often constrained by the capacity of the searcher to detect and/or recognize its target. This can be due to limited binding and perception abilities of the searcher or hiding and avoiding mechanisms used by the target. Hence detection failure upon passage over the target location turns the process into an n-passage problem, with n being random. Here we provide a general description of this detection problem for arbitrary dimensions and arbitrary detection constraints. The mean detection time (MDT) for a random searcher embedded in a sea of homogeneously distributed targets is obtained as a function of the target density ρ, the size domain L, and the effective detection distance a. While the scaling with ρ and L is found to be universal and equivalent to that found for the corresponding first-passage problem, the scaling of the MDT on a depends on the specific detection mechanism considered.

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“…Some quite interesting research in intermittent behavior has been developed in the field of intermittent search strategies, where kinetic models have been proposed in which intermittent behavior emerges as an optimal strategy for detecting prey [9,11]. As well, intermittent behavior in gradient-climbing organisms based on sporadic cues and partial information has been modeled by the so-called infotaxis model, in which the searcher adopts a strategy of movement alternating exploration and exploitation phases, in order to maximize the expected rate of information gain [27].…”

Section: Introductionmentioning

confidence: 99%

Intermittent Animal Behavior: The Adjustment-Deployment Dilemma

et al. 2014

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Intermittency is ubiquitous in animal behavior. We depict a coordination problem that is part of the more general structure of intermittent adaptation: the adjustment-deployment dilemma. It captures the intricate compromise between the time spent in adjusting a response and the time used to deploy it: The adjustment process improves fitness with time, but during deployment fitness of the solution decays as environmental conditions change. We provide a formal characterization of the dilemma, and solve it using computational methods. We find that the optimal solution always results in a high intermittency between adjustment and deployment around a non-maximal fitness value. Furthermore we show that this non-maximal fitness value is directly determined by the ratio between the exponential coefficient of the fitness increase during adjustment and that of its decay coefficient during deployment. We compare the model results with experimental data obtained from observation and measurement of intermittent behavior in animals. Among other phenomena, the model is able to predict the uneven distribution of average duration of search and motion phases found among various species such as fishes, birds, and lizards. Despite the complexity of the problem, it can be shown to be solved by relatively simple mechanisms. We find that a model of a single continuous-time recurrent neuron, with the same parametric configuration, is capable of solving the dilemma for a wide set of conditions. We finally hypothesize that many of the different patterns of intermittent behavior found in nature might respond to optimal solutions of complexified versions of the adjustment-deployment dilemma under different constraints.

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Optimal Intermittence in Search Strategies under Speed-Selective Target Detection

Cited by 16 publications

References 23 publications

Stochastic Optimal Foraging: Tuning Intensive and Extensive Dynamics in Random Searches

Stochastic Optimal Foraging: Tuning Intensive and Extensive Dynamics in Random Searches

Search times with arbitrary detection constraints

Intermittent Animal Behavior: The Adjustment-Deployment Dilemma

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