Sensing and decision-making in random search

Metabolic plasticity drives mismatches in physiological traits between prey and predator

Metabolic rate, the rate of energy use, underpins key ecological traits of organisms, from development and locomotion to interaction rates between individuals. In a warming world, the temperature-dependence of metabolic rate is anticipated to shift predator-prey dynamics. Yet, there is little real-world evidence on the effects of warming on trophic interactions. We measured the respiration rates of aquatic larvae of three insect species from populations experiencing a natural temperature gradient in a large-scale mesocosm experiment. Using a mechanistic model we predicted the effects of warming on these taxa's predator-prey interaction rates. We found that species-specific differences in metabolic plasticity lead to mismatches in the temperature-dependence of their relative velocities, resulting in altered predator-prey interaction rates. This study underscores the role of metabolic plasticity at the species level in modifying trophic interactions and proposes a mechanistic modelling approach that allows an efficient, high-throughput estimation of climate change threats across species pairs.

Individual exploration and selective social learning: balancing exploration-exploitation trade-offs in collective foraging

Search requires balancing exploring for more options and exploiting the ones previously found. Individuals foraging in a group face another trade-off: whether to engage in social learning to exploit the solutions found by others or to solitarily search for unexplored solutions. Social learning can better exploit learned information and decrease the costs of finding new resources, but excessive social learning can lead to over-exploitation and too little exploration for new solutions. We study how these two trade-offs interact to influence search efficiency in a model of collective foraging under conditions of varying resource abundance, resource density and group size. We modelled individual search strategies as Lévy walks, where a power-law exponent (μ) controlled the trade-off between exploitative and explorative movements in individual search. We modulated the trade-off between individual search and social learning using a selectivity parameter that determined how agents responded to social cues in terms of distance and likely opportunity costs. Our results show that social learning is favoured in rich and clustered environments, but also that the benefits of exploiting social information are maximized by engaging in high levels of individual exploration. We show that selective use of social information can modulate the disadvantages of excessive social learning, especially in larger groups and when individual exploration is limited. Finally, we found that the optimal combination of individual exploration and social learning gave rise to trajectories with μ ≈ 2 and provide support for the general optimality of such patterns in search. Our work sheds light on the interplay between individual search and social learning, and has broader implications for collective search and problem-solving.

Exploring the Evolution of Perception: An Agent-Based Approach

Perception is central to the survival of an individual for many reasons, especially as it affects the ability to gather resources. Consequently, costs associated with perception are partially shaped by resource availability. Understanding the interplay of environmental factors (such as the density and distribution of resources) with species-specific factors (such as growth rate, mutation, and metabolic costs) allows the exploration of possible trajectories by which perception may evolve. Here, we used an agent-based foraging model with a context-dependent movement strategy in which each agent switches between undirected and directed movement based on its perception of resources. This switching behavior is central to our goal of exploring how environmental and species-specific factors determine the evolution and maintenance of perception in an ecological system. We observed a non-linear response in the evolved perceptual ranges as a function of parameters in our model. Overall, we identified two groups of parameters, one of which promotes evolution of perception and another group that restricts it. We found that resource density, basal energy cost, perceptual cost and mutation rate were the best predictors of the resultant perceptual range distribution, but detailed exploration indicated that individual parameters affect different parts of the distribution in different ways.

Habitat Quality and Social Behavioral Association Network in a Wintering Waterbirds Community

Migratory waterbirds concentrated in freshwater ecosystems in mosaic environments rely on quality habitats for overwintering. At West Dongting Lake National Nature Reserve (WDLNNR), China, land-use change and hydrology alternation are compounding factors that have affected important wintering areas for migratory waterbirds. Presently, changes in the hydrology and landscape have reshaped natural wintering habitats and their availability, though the impact of hydrological management on habitat selection of wintering waterbirds is largely unknown. In this study, we classified differentially managed habitats and calculated their area using the normalized difference vegetation index (NDVI) to evaluate suitable habitat availability over the study period (2016–2017 and 2017–2018 wintering periods). We then used social behavioral association network (SBAN) model to compare habitat quality through species-species social interactions and species-habitat associations in lakes with different hydrological management. The results indicated that social interactions between and within species structured wintering waterbirds communities, which could be dominated by one or more species, while dominant species control the activities of other co-existing species. Analysis of variance (ANOVA) tests indicated significant differences in SBAN metrics between lakes (p = 0.0237) and habitat (p < 0.0001) levels. Specifically, lakes with managed hydrology were preferred by more species. The managed lakes had better habitat quality in terms of significantly higher habitat areas (p < 0.0001) and lower habitat transitions (p = 0.0113). Collectively, our findings suggest that proper hydrological management can provide continuous availability of quality habitats, especially mudflats and shallow waters, for a stable SBAN to ensure a wintering waterbirds community with more sympatric species in a dynamic environment.

Impact of food distribution on lifetime of a forager with or without sense of smell

Modeling foraging via basic models is a problem that has been recently investigated from several points of view. However, understanding the effect of the spatial distribution of food on the lifetime of a forager has not been achieved yet. We explore here how the distribution of food in space affects the forager's lifetime in several different scenarios. We analyze a random forager and a smelling forager in both one and two dimensions. We first consider a general food distribution, and then analyze in detail specific distributions including constant distance between food, certain probability of existence of food at each site, and power-law distribution of distances between food. For a forager in one dimension without smell we find analytically the lifetime, and for a forager with sense of smell we find the condition for immortality. In two dimensions we find based on analytical considerations that the lifetime (T) scales with the starving time (S) and food density (f) as T∼S^{4}f^{3/2}.

The power of odour cues in shaping fine-scale search patterns of foraging mammalian herbivores

Foraging by mammalian herbivores has profound impacts on natural and modified landscapes, yet we know little about how they find food, limiting our ability to predict and manage their influence. Mathematical models show that foragers exploiting odour cues outperform a random walk strategy. However, discovering how free-ranging foragers exploit odours in real, complex landscapes has proven elusive because of technological constraints. We took a novel approach, using a sophisticated purpose-built thermal camera system to record fine-scale foraging by a generalist mammalian herbivore, the swamp wallaby (Wallabia bicolor). We tested the hypothesis that odour cues shape forager movement and behaviour in vegetation patches. To do this, we compared wallaby foraging in two odour landscapes: Control (natural vegetation with food and non-food plants interspersed) and +Apple (the same natural vegetation plus a single, highly palatable food source with novel odour (apple)). The +Apple treatment led to strongly directed foraging by wallabies: earlier visits to vegetation patches, straighter movement paths, more hopping and fewer stops than in the Control treatment. Our results provide clear empirical evidence that odour cues are harnessed for efficient, directed search even at this fine scale. We conclude that random walk models miss a key feature shaping foraging within patches.

Adaptive switch to sexually dimorphic movements by partner-seeking termites

How should females and males move to search for partners whose exact location is unknown? Theory predicts that the answer depends on what they know about where targets can be found, raising the question of how actual animals update their mate search patterns to increase encounter probability when conditions change. Here, we show that termites adaptively alternate between sexually monomorphic and dimorphic movements during mate search. When the location of potential mates was completely unpredictable, both sexes moved in straight lines to explore widely. In contrast, when the stray partner was at least nearby, males moved while females paused. Data-based simulations confirmed that these movements increase the rate of successful encounters. The context-dependent switch of search modes is a key to enhance random encounters.

Towards a multi-trophic extension of metacommunity ecology

Metacommunity theory provides an understanding of how spatial processes determine the structure and function of communities at local and regional scales. Although metacommunity theory has considered trophic dynamics in the past, it has been performed idiosyncratically with a wide selection of possible dynamics. Trophic metacommunity theory needs a synthesis of a few influential axis to simplify future predictions and tests. We propose an extension of metacommunity ecology that addresses these shortcomings by incorporating variability among trophic levels in 'spatial use properties'. We define 'spatial use properties' as a set of traits (dispersal, migration, foraging and spatial information processing) that set the spatial and temporal scales of organismal movement, and thus scales of interspecific interactions. Progress towards a synthetic predictive framework can be made by (1) documenting patterns of spatial use properties in natural food webs and (2) using theory and experiments to test how trophic structure in spatial use properties affects metacommunity dynamics.

Visit, consume and quit: Patch quality affects the three stages of foraging

Foraging is a three-stage process during which animals visit patches, consume food and quit. Foraging theory exploring relative patch quality has mostly focused on patch use and quitting decisions, ignoring the first crucial step for any forager: finding food. Yet, the decision to visit a patch is just as important as the decision to quit, as quitting theories can only be used if animals visit patches in the first place. Therefore, to better understand the foraging process and predict its outcomes, it is necessary to explore its three stages together. We used the common brushtail possum (Trichosurus vulpecula) as a model to investigate foraging decisions in response to food varying in quality. In particular, we tested whether patch nutritional quality affected the following: (1) patch visits; (2) behaviours at the patch during a foraging visit; and (3) patch quitting decisions (quantified using giving up density-GUD). Free-ranging possums were presented with diets varying in nitrogen content and concomitantly volatile organic compound (VOC) composition at feeding stations in the wild. We found that possums were able to distinguish between different quality foods from afar, despite the location of the diets changed daily. Possums used VOC (i.e. odour cues) emitted by the diets to find and select patches from a distance. High-quality diets with higher protein and lower fibre were visited more often and for longer. Possums spent more time foraging on diets high in nutritional content, resulting in lower GUDs. Our study provides important quantitative evidence that foraging efficiency plays out during all the three stages of the foraging process (i.e. visit, consume and quit), and demonstrates the significance of considering all these stages together in future studies and foraging models. Sensory cues such as food odours play a critical role in helping foragers, including mammalian herbivores, find high-quality food. This allows foragers to make quick, accurate and important decisions about food patches well before patch quitting decisions come into play.

Decision making improves sperm chemotaxis in the presence of noise

To navigate their surroundings, cells rely on sensory input that is corrupted by noise. In cells performing chemotaxis, such noise arises from the stochastic binding of signalling molecules at low chemoattractant concentrations. We reveal a fundamental relationship between the speed of chemotactic steering and the strength of directional fluctuations that result from the amplification of noise in a chemical input signal. This relation implies a trade-off between steering that is slow and reliable, and steering that is fast but less reliable. We show that dynamic switching between these two modes of steering can substantially increase the probability to find a target, such as an egg to be found by sperm cells. This decision making confers no advantage in the absence of noise, but is beneficial when chemical signals are detectable, yet characterized by low signal-to-noise ratios. The latter applies at intermediate distances from a target, where signalling molecules are diluted, thus defining a ‘noise zone’ that cells have to cross. Our results explain decision making observed in recent experiments on sea urchin sperm chemotaxis. More generally, our theory demonstrates how decision making enables chemotactic agents to cope with high levels of noise in gradient sensing by dynamically adjusting the persistence length of a biased random walk.

Many cells can navigate upwards a concentration gradient of signalling molecules, a process called chemotaxis. Chemotaxis is used e.g. by sperm cells to find the egg. To measure and compare concentrations, cells count stochastic binding events of signalling molecules that diffuse to cellular receptors. Efficient chemotaxis strategies must be adapted to this molecular shot noise of concentration measurements. We reveal a fundamental relationship between the speed of chemotactic steering and the strength of directional fluctuations that result from the amplification of noise. This implies a trade-off between steering fast and steering reliable. Inspired by recent experiments on chemotaxis of sperm cells of marine invertebrates, we develop a theory that allows to efficiently compute optimal chemotaxis strategies. We show that dynamic switching between either fast or reliable steering can substantially increase the probability for a sperm cell to find the egg. Furthermore, the optimal strategy requires only minimal computational capacities of the chemotactic agent, a key constraint for biological cells. More generally, our work demonstrates a benefit of decision making for chemotaxis in the presence of noise, which could inspire optimal control designs for artificial microswimmers.

Vibration-guided mate searching in treehoppers: directional accuracy and sampling strategies in a complex sensory environment

Animal movement decisions involve an action-perception cycle in which sensory flow influences motor output. Key aspects of the action-perception cycle involved in movement decisions can be identified by integrating path information with measurement of environmental cues. We studied mate searching in insects for which the primary sensory cues are mechanical vibrations traveling through the tissues of living plants. We mapped search paths of male thornbug treehoppers locating stationary females through an exchange of vibrational signals. At each of the males' sampling locations, we used two-dimensional laser vibrometry to measure stem motion produced by female vibrational signals. We related properties of the vibrational signals to the males' movement direction, inter-sample distance and accuracy. Males experienced gradients in signal amplitude and in the whirling motion of the plant stem, and these gradients were influenced to varying degrees by source distance and local stem properties. Males changed their sampling behavior during the search, making longer inter-sample movements farther from the source, where uncertainty is higher. The primary directional cue used by searching males was the direction of wave propagation, and males made more accurate decisions when signal amplitude was higher, when time delays were longer between the front and back legs, and when female responses were short in duration. The whirling motion of plant stems, including both the eccentricity and the major axes of motion, is a fundamental feature of vibrational environments on living plants, and we show for the first time that it has important influences on the decisions of vibrationally homing insects.

Quantifying animal movement for caching foragers: the path identification index (PII) and cougars, Puma concolor

BACKGROUND

Many studies of animal movement have focused on directed versus area-restricted movement, which rely on correlations between step-length and turn-angles and on stationarity through time to define behavioral states. Although these approaches might apply well to grazing in patchy landscapes, species that either feed for short periods on large, concentrated food sources or cache food exhibit movements that are difficult to model using the traditional metrics of turn-angle and step-length alone.

RESULTS

We used GPS telemetry collected from a prey-caching predator, the cougar (Puma concolor, Linnaeus), to test whether combining metrics of site recursion, spatiotemporal clustering, speed, and turning into an index of movement using partial sums, improves the ability to identify caching behavior. The index was used to identify changes in movement characteristics over time and segment paths into behavioral classes. The identification of behaviors from the Path Identification Index (PII) was evaluated using field investigations of cougar activities at GPS locations. We tested for statistical stationarity across behaviors for use of topographic view-sheds. Changes in the frequency and duration of PII were useful for identifying seasonal activities such as migration, gestation, and denning. The comparison of field investigations of cougar activities to behavioral PII classes resulted in an overall classification accuracy of 81%.

CONCLUSIONS

Changes in behaviors were reflected in cougars' use of topographic view-sheds, resulting in statistical nonstationarity over time, and revealed important aspects of hunting behavior. Incorporating metrics of site recursion and spatiotemporal clustering revealed the temporal structure in movements of a caching forager. The movement index PII, shows promise for identifying behaviors in species that frequently return to specific locations such as food caches, watering holes, or dens, and highlights the potential role memory and cognitive abilities play in determining animal movements.

Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment

Finding the source of an accidental or deliberate release of a toxic substance into the atmosphere is of great importance for national security. The paper presents a search algorithm for turbulent environments which falls into the class of cognitive (infotaxi) algorithms. Bayesian estimation of the source parameter vector is carried out using the Rao-Blackwell dimension-reduction method, while the robots are controlled autonomously to move in a scalable formation. Estimation and control are carried out in a centralised replicated fusion architecture assuming all-to-all communication. The paper presents a comprehensive numerical analysis of the proposed algorithm, including the search-time and displacement statistics.

Perceptual Ranges, Information Gathering, and Foraging Success in Dynamic Landscapes

How organisms gather and utilize information about their landscapes is central to understanding land-use patterns and population distributions. When such information originates beyond an individual's immediate vicinity, movement decisions require integrating information out to some perceptual range. Such nonlocal information, whether obtained visually, acoustically, or via chemosensation, provides a field of stimuli that guides movement. Classically, however, models have assumed movement based on purely local information (e.g., chemotaxis, step-selection functions). Here we explore how foragers can exploit nonlocal information to improve their success in dynamic landscapes. Using a continuous time/continuous space model in which we vary both random (diffusive) movement and resource-following (advective) movement, we characterize the optimal perceptual ranges for foragers in dynamic landscapes. Nonlocal information can be highly beneficial, increasing the spatiotemporal concentration of foragers on their resources up to twofold compared with movement based on purely local information. However, nonlocal information is most useful when foragers possess both high advective movement (allowing them to react to transient resources) and low diffusive movement (preventing them from drifting away from resource peaks). Nonlocal information is particularly beneficial in landscapes with sharp (rather than gradual) patch edges and in landscapes with highly transient resources.

Online games: a novel approach to explore how partial information influences human random searches

Many natural processes rely on optimizing the success ratio of a search process. We use an experimental setup consisting of a simple online game in which players have to find a target hidden on a board, to investigate how the rounds are influenced by the detection of cues. We focus on the search duration and the statistics of the trajectories traced on the board. The experimental data are explained by a family of random-walk-based models and probabilistic analytical approximations. If no initial information is given to the players, the search is optimized for cues that cover an intermediate spatial scale. In addition, initial information about the extension of the cues results, in general, in faster searches. Finally, strategies used by informed players turn into non-stationary processes in which the length of e ach displacement evolves to show a well-defined characteristic scale that is not found in non-informed searches.

Smelling Time: A Neural Basis for Olfactory Scene Analysis

Behavioral evidence from phylogenetically diverse animals and from humans suggests that, by extracting temporal information inherent in the olfactory signal, olfaction is more involved in interpreting space and time than heretofore imagined. If this is the case, the olfactory system must have neural mechanisms capable of encoding time at intervals relevant to the turbulent odor world in which many animals live. Here, we review evidence that animals can use populations of rhythmically active or 'bursting' olfactory receptor neurons (bORNs) to extract and encode temporal information inherent in natural olfactory signals. We postulate that bORNs represent an unsuspected neural mechanism through which time can be accurately measured, and that 'smelling time' completes the requirements for true olfactory scene analysis.

Foraging success under uncertainty: search tradeoffs and optimal space use

Understanding the structural complexity and the main drivers of animal search behaviour is pivotal to foraging ecology. Yet, the role of uncertainty as a generative mechanism of movement patterns is poorly understood. Novel insights from search theory suggest that organisms should collect and assess new information from the environment by producing complex exploratory strategies. Based on an extension of the first passage time theory, and using simple equations and simulations, we unveil the elementary heuristics behind search behaviour. In particular, we show that normal diffusion is not enough for determining optimal exploratory behaviour but anomalous diffusion is required. Searching organisms go through two critical sequential phases (approach and detection) and experience fundamental search tradeoffs that may limit their encounter rates. Using experimental data, we show that biological search includes elements not fully considered in contemporary physical search theory. In particular, the need to consider search movement as a non-stationary process that brings the organism from one informational state to another. For example, the transition from remaining in an area to departing from it may occur through an exploratory state where cognitive search is challenged. Therefore, a more comprehensive view of foraging ecology requires including current perspectives about movement under uncertainty.

Natural search algorithms as a bridge between organisms, evolution, and ecology

The ability to navigate is a hallmark of living systems, from single cells to higher animals. Searching for targets, such as food or mates in particular, is one of the fundamental navigational tasks many organisms must execute to survive and reproduce. Here, we argue that a recent surge of studies of the proximate mechanisms that underlie search behavior offers a new opportunity to integrate the biophysics and neuroscience of sensory systems with ecological and evolutionary processes, closing a feedback loop that promises exciting new avenues of scientific exploration at the frontier of systems biology.

Neurally Encoding Time for Olfactory Navigation

Accurately encoding time is one of the fundamental challenges faced by the nervous system in mediating behavior. We recently reported that some animals have a specialized population of rhythmically active neurons in their olfactory organs with the potential to peripherally encode temporal information about odor encounters. If these neurons do indeed encode the timing of odor arrivals, it should be possible to demonstrate that this capacity has some functional significance. Here we show how this sensory input can profoundly influence an animal’s ability to locate the source of odor cues in realistic turbulent environments—a common task faced by species that rely on olfactory cues for navigation. Using detailed data from a turbulent plume created in the laboratory, we reconstruct the spatiotemporal behavior of a real odor field. We use recurrence theory to show that information about position relative to the source of the odor plume is embedded in the timing between odor pulses. Then, using a parameterized computational model, we show how an animal can use populations of rhythmically active neurons to capture and encode this temporal information in real time, and use it to efficiently navigate to an odor source. Our results demonstrate that the capacity to accurately encode temporal information about sensory cues may be crucial for efficient olfactory navigation. More generally, our results suggest a mechanism for extracting and encoding temporal information from the sensory environment that could have broad utility for neural information processing.

Many animals navigate turbulent environments using odor cues, a behavior known as olfactory search. We propose a neural mechanism for olfactory search based on evidence that a functional subset of olfactory receptor neurons (ORNs) called bursting ORNs or bORNs can encode the time intervals between successive encounters with odor. We show that these time intervals are estimators of the recurrence time, an information-rich statistic of the turbulent flow. Using a computational model parameterized with data from an actual turbulent plume, we demonstrate that a searcher can locate an odor source efficiently using only input from bORNs. These findings provide scientific evidence that the most important navigational information captured by the olfactory system may come in the form of measurements of time.

The evolution of distributed sensing and collective computation in animal populations

Many animal groups exhibit rapid, coordinated collective motion. Yet, the evolutionary forces that cause such collective responses to evolve are poorly understood. Here, we develop analytical methods and evolutionary simulations based on experimental data from schooling fish. We use these methods to investigate how populations evolve within unpredictable, time-varying resource environments. We show that populations evolve toward a distinctive regime in behavioral phenotype space, where small responses of individuals to local environmental cues cause spontaneous changes in the collective state of groups. These changes resemble phase transitions in physical systems. Through these transitions, individuals evolve the emergent capacity to sense and respond to resource gradients (i.e. individuals perceive gradients via social interactions, rather than sensing gradients directly), and to allocate themselves among distinct, distant resource patches. Our results yield new insight into how natural selection, acting on selfish individuals, results in the highly effective collective responses evident in nature.

DOI:http://dx.doi.org/10.7554/eLife.10955.001

In nature, we see many examples of highly coordinated movements of groups of individuals; think of a flock of birds turning swiftly in unison or a crowd of people filing through the exit of a building. A common feature of these behaviors is that they occur without any centralized control, and that they involve sudden and often dramatic changes in the 'collective state' of the group (i.e. speed, or the distances between individuals). In the past, researchers have likened these transitions in collective behavior to phase transitions in physical systems, for example, the transition between liquid water and water vapor. However, it is not clear how such collective responses could have evolved.

Natural selection is an evolutionary process whereby individuals with particularly 'fit' traits produce more offspring than others. Over many generations, these beneficial traits tend to become more common in the population. Hein, Rosenthal, Hagstrom et al. developed a mathematical model to investigate whether the capacity of a population to perform collective motions could evolve through natural selection.

The model shows that over many generations, populations consistently evolve a unique collective trait whereby small responses of individuals to an environmental cue can cause spontaneous changes in the collective state of the local population. These transitions in collective state greatly enhance the ability of individuals to locate and exploit resources. Hein, Rosenthal, Hagstrom et al.’s findings suggest that natural selection acting on the behavior of individuals can cause a population to evolve a distinctive, collective behavior.

The next challenge will be to identify a biological system in which the evolution of collective motion can be studied experimentally to test these predictions.

DOI:http://dx.doi.org/10.7554/eLife.10955.002

Cool Headed Individuals Are Better Survivors: Non-Consumptive and Consumptive Effects of a Generalist Predator on a Sap Feeding Insect

Non-consumptive effects (NCEs) of predators are part of the complex interactions among insect natural enemies and prey. NCEs have been shown to significantly affect prey foraging and feeding. Leafhopper's (Auchenorrhyncha) lengthy phloem feeding bouts may play a role in pathogen transmission in vector species and also exposes them to predation risk. However, NCEs on leafhoppers have been scarcely studied, and we lack basic information about how anti-predator behaviour influences foraging and feeding in these species. Here we report a study on non-consumptive and consumptive predator-prey interactions in a naturally co-occurring spider-leafhopper system. In mesocosm arenas we studied movement patterns during foraging and feeding of the leafhopper Psammotettix alienus in the presence of the spider predator Tibellus oblongus. Leafhoppers delayed feeding and fed much less often when the spider was present. Foraging movement pattern changed under predation risk: movements became more frequent and brief. There was considerable individual variation in foraging movement activity. Those individuals that increased movement activity in the presence of predators exposed themselves to higher predation risk. However, surviving individuals exhibited a 'cool headed' reaction to spider presence by moving less than leafhoppers in control trials. No leafhoppers were preyed upon while feeding. We consider delayed feeding as a "paradoxical" antipredator tactic, since it is not necessarily an optimal strategy against a sit-and-wait generalist predator.

Stochastic optimal foraging: tuning intensive and extensive dynamics in random searches

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.

Decision accuracy in complex environments is often maximized by small group sizes

Individuals in groups, whether composed of humans or other animal species, often make important decisions collectively, including avoiding predators, selecting a direction in which to migrate and electing political leaders. Theoretical and empirical work suggests that collective decisions can be more accurate than individual decisions, a phenomenon known as the 'wisdom of crowds'. In these previous studies, it has been assumed that individuals make independent estimates based on a single environmental cue. In the real world, however, most cues exhibit some spatial and temporal correlation, and consequently, the sensory information that near neighbours detect will also be, to some degree, correlated. Furthermore, it may be rare for an environment to contain only a single informative cue, with multiple cues being the norm. We demonstrate, using two simple models, that taking this natural complexity into account considerably alters the relationship between group size and decision-making accuracy. In only a minority of environments do we observe the typical wisdom of crowds phenomenon (whereby collective accuracy increases monotonically with group size). When the wisdom of crowds is not observed, we find that a finite, and often small, group size maximizes decision accuracy. We reveal that, counterintuitively, it is the noise inherent in these small groups that enhances their accuracy, allowing individuals in such groups to avoid the detrimental effects of correlated information while exploiting the benefits of collective decision-making. Our results demonstrate that the conventional view of the wisdom of crowds may not be informative in complex and realistic environments, and that being in small groups can maximize decision accuracy across many contexts.

Optimal search in interacting populations: Gaussian jumps versus Lévy flights

We investigated the relationships between search efficiency, movement strategy, and nonlocal communication in the biological context of animal foraging. We considered situations where the members of a population of foragers perform either Gaussian jumps or Lévy flights, and show that the search time is minimized when communication among individuals occurs at intermediate ranges, independently of the type of movement. Additionally, while Brownian strategies are more strongly influenced by the communication mechanism, Lévy flights still result in shorter overall search durations.

Sensory information and encounter rates of interacting species

Most motile organisms use sensory cues when searching for resources, mates, or prey. The searcher measures sensory data and adjusts its search behavior based on those data. Yet, classical models of species encounter rates assume that searchers move independently of their targets. This assumption leads to the familiar mass action-like encounter rate kinetics typically used in modeling species interactions. Here we show that this common approach can mischaracterize encounter rate kinetics if searchers use sensory information to search actively for targets. We use the example of predator-prey interactions to illustrate that predators capable of long-distance directional sensing can encounter prey at a rate proportional to prey density to the [Formula: see text] power (where [Formula: see text] is the dimension of the environment) when prey density is low. Similar anomalous encounter rate functions emerge even when predators pursue prey using only noisy, directionless signals. Thus, in both the high-information extreme of long-distance directional sensing, and the low-information extreme of noisy non-directional sensing, encounter rate kinetics differ qualitatively from those derived by classic theory of species interactions. Using a standard model of predator-prey population dynamics, we show that the new encounter rate kinetics derived here can change the outcome of species interactions. Our results demonstrate how the use of sensory information can alter the rates and outcomes of physical interactions in biological systems.

Optimizing the search for resources by sharing information: Mongolian gazelles as a case study

We investigate the relationship between communication and search efficiency in a biological context by proposing a model of Brownian searchers with long-range pairwise interactions. After a general study of the properties of the model, we show an application to the particular case of acoustic communication among Mongolian gazelles, for which data are available, searching for good habitat areas. Using Monte Carlo simulations and density equations, our results point out that the search is optimal (i.e., the mean first hitting time among searchers is minimum) at intermediate scales of communication, showing that both an excess and a lack of information may worsen it.