Foraging success under uncertainty: search tradeoffs and optimal space use

Consistent long-distance foraging flights across years and seasons at colony level in a neotropical bat

All foraging animals face a trade-off: how much time should they invest in exploitation of known resources versus exploration to discover new resources? For group-living central place foragers, this balance is challenging. Due to the nature of their movement patterns, exploration and exploitation are often mutually exclusive, while the availability of social information may discourage individuals from exploring. To examine these trade-offs, we GPS-tracked groups of greater spear-nosed bats (Phyllostomus hastatus) from three colonies on Isla Colón, Panamá. During the dry season, when these omnivores forage on the nectar of unpredictable balsa flowers, bats consistently travelled long distances to remote, colony-specific foraging areas, bypassing flowering trees closer to their roosts. They continued using these areas in the wet season, when feeding on a diverse, presumably ubiquitous diet, but also visited other, similarly distant foraging areas. Foraging areas were shared within but not always between colonies. Our longitudinal dataset suggests that bats from each colony invest in long-distance commutes to socially learned shared foraging areas, bypassing other available food patches. Rather than exploring nearby resources, these bats exploit colony-specific foraging locations that appear to be culturally transmitted. These results give insight into how social animals might diverge from optimal foraging.

Wasted Efforts Impair Random Search Efficiency and Reduce Choosiness in Mate-Pairing Termites

AbstractRandom search theories predict that animals employ movement patterns that optimize encounter rates with target resources. However, animals are not always able to achieve the best search strategy. Energy depletion, for example, limits searchers' movement activities, forcing them to adjust their behaviors before and after encounters. Here, we investigate the cost of mate search in a termite, Reticulitermes speratus, and reveal that the costs associated with mate finding reduce the selectivity of mating partners. After a dispersal flight, termites search for a mating partner with limited reserved energy. We found that their movement activity and diffusiveness progressively declined over extended mate search. Our data-based simulations qualitatively confirmed that the reduced movement diffusiveness decreased the searching efficiency. Also, prolonged search periods reduced survival rate and the number of offspring. Thus, mate search has two different negative effects on termites. Finally, we found that termites with an extended mate search reduced the selectivity of mating partners, where males immediately paired with any encountering females. Thus, termites dramatically changed their mate search behavior depending on their internal states. Our finding highlights that accounting for the searchers' internal states is essential to fill the gap between random search theories and empirical behavioral observations.

Visual social information use in collective foraging

Collective dynamics emerge from individual-level decisions, yet we still poorly understand the link between individual-level decision-making processes and collective outcomes in realistic physical systems. Using collective foraging to study the key trade-off between personal and social information use, we present a mechanistic, spatially-explicit agent-based model that combines individual-level evidence accumulation of personal and (visual) social cues with particle-based movement. Under idealized conditions without physical constraints, our mechanistic framework reproduces findings from established probabilistic models, but explains how individual-level decision processes generate collective outcomes in a bottom-up way. In clustered environments, groups performed best if agents reacted strongly to social information, while in uniform environments, individualistic search was most beneficial. Incorporating different real-world physical and perceptual constraints profoundly shaped collective performance, and could even buffer maladaptive herding by facilitating self-organized exploration. Our study uncovers the mechanisms linking individual cognition to collective outcomes in human and animal foraging and paves the way for decentralized robotic applications.

Collective incentives reduce over-exploitation of social information in unconstrained human groups

Collective dynamics emerge from countless individual decisions. Yet, we poorly understand the processes governing dynamically-interacting individuals in human collectives under realistic conditions. We present a naturalistic immersive-reality experiment where groups of participants searched for rewards in different environments, studying how individuals weigh personal and social information and how this shapes individual and collective outcomes. Capturing high-resolution visual-spatial data, behavioral analyses revealed individual-level gains-but group-level losses-of high social information use and spatial proximity in environments with concentrated (vs. distributed) resources. Incentivizing participants at the group (vs. individual) level facilitated adaptation to concentrated environments, buffering apparently excessive scrounging. To infer discrete choices from unconstrained interactions and uncover the underlying decision mechanisms, we developed an unsupervised Social Hidden Markov Decision model. Computational results showed that participants were more sensitive to social information in concentrated environments frequently switching to a social relocation state where they approach successful group members. Group-level incentives reduced participants' overall responsiveness to social information and promoted higher selectivity over time. Finally, mapping group-level spatio-temporal dynamics through time-lagged regressions revealed a collective exploration-exploitation trade-off across different timescales. Our study unravels the processes linking individual-level strategies to emerging collective dynamics, and provides tools to investigate decision-making in freely-interacting collectives.

Uncertainty about predation risk: a conceptual review

Uncertainty has long been of interest to economists and psychologists and has more recently gained attention among ecologists. In the ecological world, animals must regularly make decisions related to finding resources and avoiding threats. Here, we describe uncertainty as a perceptual phenomenon of decision-makers, and we focus specifically on the functional ecology of such uncertainty regarding predation risk. Like all uncertainty, uncertainty about predation risk reflects informational limitations. When cues are available, they may be novel (i.e. unknown information), incomplete, unreliable, overly abundant and complex, or conflicting. We review recent studies that have used these informational limitations to induce uncertainty of predation risk. These studies have typically used either over-responses to novelty (i.e. neophobia) or memory attenuation as proxies for measuring uncertainty. Because changes in the environment, particularly unpredictable changes, drive informational limitations, we describe studies assessing unpredictable variance in spatio-temporal predation risk, intensity of predation risk, predator encounter rate, and predator diversity. We also highlight anthropogenic changes within habitats that are likely to have dramatic impacts on information availability and thus uncertainty in antipredator decisions in the modern world.

Dynamics, statistics, and task allocation of foraging ants

Ant foraging is one of the most fascinating examples of cooperative behavior observed in nature. It is well studied from an entomology viewpoint, but there is currently a lack of mathematical synthesis of this phenomenon. We address this by constructing an ant foraging model that incorporates simple behavioral rules within three task groups of the ant colony during foraging (foragers, transporters, and followers), pheromone trails, and memory effects. The motion of an ant is modeled as a discrete correlated random walk, with a characteristic zigzag path that is congruent with experimental data. We simulate the foraging cycle, which consists of ants searching for food, transporting food, and depositing chemical trails to recruit and orient more ants (en masse) to the food source. This allows us to gain insights into the basic mechanism of the cooperative interactions between ants and the dynamical division of labor within an ant colony during foraging to achieve optimal efficiency. We observe a disorder-order phase transition from the start to the end of a foraging process, signaling collective motion at the population level. Finally, we present a set of time delay ODEs that corroborates with numerical simulations.

Ants combine systematic meandering and correlated random walks when searching for unknown resources

Animal search movements are typically assumed to be mostly random walks, although non-random elements may be widespread. We tracked ants (Temnothorax rugatulus) in a large empty arena, resulting in almost 5 km of trajectories. We tested for meandering by comparing the turn autocorrelations for empirical ant tracks and simulated, realistic Correlated Random Walks. We found that 78% of ants show significant negative autocorrelation around 10 mm (3 body lengths). This means that turns in one direction are likely followed by turns in the opposite direction after this distance. This meandering likely makes the search more efficient, as it allows ants to avoid crossing their own paths while staying close to the nest, avoiding return-travel time. Combining systematic search with stochastic elements may make the strategy less vulnerable to directional inaccuracies. This study is the first to find evidence for efficient search by regular meandering in a freely searching animal.

Foraging behaviour and patch size distribution jointly determine population dynamics in fragmented landscapes

Increased fragmentation caused by habitat loss represents a major threat to the persistence of animal populations. How fragmentation affects populations depends on the rate at which individuals move between spatially separated patches. Whereas negative effects of habitat loss on biodiversity are well known, the effects of fragmentation per se on population dynamics and ecosystem stability remain less well understood. Here, we use a spatially explicit predator-prey model to investigate how the interplay between fragmentation and optimal foraging behaviour affects predator-prey interactions and, subsequently, ecosystem stability. We study systems wherein prey occupies isolated patches and are consumed by predators that disperse following Lévy random walks. Our results show that the Lévy exponent and the degree of fragmentation jointly determine coexistence probabilities. In highly fragmented landscapes, Brownian and ballistic predators go extinct and only scale-free predators can coexist with prey. Furthermore, our results confirm that predation causes irreversible habitat loss in fragmented landscapes owing to overexploitation of smaller patches of prey. Moreover, we show that predator dispersal can reduce, but not prevent or minimize, the amount of lost habitat. Our results suggest that integrating optimal foraging theory into population and landscape ecology is crucial to assessing the impact of fragmentation on biodiversity and ecosystem stability.

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.

Biased Learning as a Simple Adaptive Foraging Mechanism

Adaptive cognitive biases, such as “optimism,” may have evolved as heuristic rules for computationally efficient decision-making, or as error-management tools when error payoff is asymmetrical. Ecologists typically use the term “optimism” to describe unrealistically positive expectations from the future that are driven by positively biased initial belief. Cognitive psychologists on the other hand, focus on valence-dependent optimism bias, an asymmetric learning process where information about undesirable outcomes is discounted (sometimes also termed “positivity biased learning”). These two perspectives are not mutually exclusive, and both may lead to similar emerging space-use patterns, such as increased exploration. The distinction between these two biases may becomes important, however, when considering the adaptive value of balancing the exploitation of known resources with the exploration of an ever-changing environment. Deepening our theoretical understanding of the adaptive value of valence-dependent learning, as well as its emerging space-use and foraging patterns, may be crucial for understanding whether, when and where might species withstand rapid environmental change. We present the results of an optimal-foraging model implemented as an individual-based simulation in continuous time and discrete space. Our forager, equipped with partial knowledge of average patch quality and inter-patch travel time, iteratively decides whether to stay in the current patch, return to previously exploited patches, or explore new ones. Every time the forager explores a new patch, it updates its prior belief using a simple single-parameter model of valence-dependent learning. We find that valence-dependent optimism results in the maintenance of positively biased expectations (prior-based optimism), which, depending on the spatiotemporal variability of the environment, often leads to greater fitness gains. These results provide insights into the potential ecological and evolutionary significance of valence-dependent optimism and its interplay with prior-based optimism.

IDH knockdown alters foraging behavior in the termite Odontotermes formosanus in different social contexts

Foraging, as an energy-consuming behavior, is very important for colony survival in termites. How energy metabolism related to glucose decomposition and adenosine triphosphate (ATP) production influences foraging behavior in termites is still unclear. Here, we analyzed the change in energy metabolism in the whole organism and brain after silencing the key metabolic gene isocitrate dehydrogenase (IDH) and then investigated its impact on foraging behavior in the subterranean termite Odontotermes formosanus in different social contexts. The IDH gene exhibited higher expression in the abdomen and head of O. formosanus. The knockdown of IDH resulted in metabolic disorders in the whole organism. The dsIDH-injected workers showed significantly reduced walking activity but increased foraging success. Interestingly, IDH knockdown altered brain energy metabolism, resulting in a decline in ATP levels and an increase in IDH activity. Additionally, the social context affected brain energy metabolism and, thus, altered foraging behavior in O. formosanus. We found that the presence of predator ants increased the negative influence on the foraging behavior of dsIDH-injected workers, including a decrease in foraging success. However, an increase in the number of nestmate soldiers could provide social buffering to relieve the adverse effect of predator ants on worker foraging behavior. Our orthogonal experiments further verified that the role of the IDH gene as an inherent factor was dominant in manipulating termite foraging behavior compared with external social contexts, suggesting that energy metabolism, especially brain energy metabolism, plays a crucial role in regulating termite foraging behavior.

Memory and Conformity, but Not Competition, Explain Spatial Partitioning Between Two Neighboring Fruit Bat Colonies

Spatial partitioning between neighboring colonies is considered a widespread phenomenon in colonial species, reported mainly in marine birds. Partitioning is suspected to emerge due to various processes, such as competition, diet specialization, memory, information transfer, or even “foraging cultures.” Yet, empirical evidence from other taxa, and studies that tease apart the relative contribution of the processes underlying partitioning, remain scarce, mostly due to insufficiently detailed movement data. Here, we used high-resolution movement tracks (at 0.125 Hz) of 107 individuals belonging to two neighboring colonies of the Egyptian fruit bat (Rousettus aegyptiacus), a highly gregarious central-place forager, using the ATLAS reverse-GPS system in the Hula Valley, Israel. Based on comparisons between agent-based mechanistic models and observed spatial partitioning patterns, we found high levels of partitioning of both area and tree resources (<11% overlap) that were stable across different fruiting seasons. Importantly, partitioning could not have emerged if the bats’ movement was only limited by food availability and travel distances, as most commonly hypothesized. Rather than density-dependent or between-colony competition, memory, and, to a lesser extent, conformity in tree-use explain how partitioning develops. Elucidating the mechanisms that shape spatial partitioning among neighboring colonies in the wild under variable resource conditions is important for understanding the ecology and evolution of inter-group coexistence, space use patterns and sociality.

Movement, Encounter Rate, and Collective Behavior in Ant Colonies

Spatial patterns of movement regulate many aspects of social insect behavior, because how workers move around, and how many are there, determines how often they meet and interact. Interactions are usually olfactory; for example, in ants, by means of antennal contact in which one worker assesses the cuticular hydrocarbons of another. Encounter rates may be a simple outcome of local density: a worker experiences more encounters, the more other workers there are around it. This means that encounter rate can be used as a cue for overall density even though no individual can assess global density. Encounter rate as a cue for local density regulates many aspects of social insect behavior, including collective search, task allocation, nest choice, and traffic flow. As colonies grow older and larger, encounter rates change, which leads to changes in task allocation. Nest size affects local density and movement patterns, which influences encounter rate, so that nest size and connectivity influence colony behavior. However, encounter rate is not a simple function of local density when individuals change their movement in response to encounters, thus influencing further encounter rates. Natural selection on the regulation of collective behavior can draw on variation within and among colonies in the relation of movement patterns, encounter rate, and response to encounters.

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.

Optimal escape-and-feeding dynamics of random walkers: Rethinking the convenience of ballistic strategies

Excited random walks represent a convenient model to study food intake in a media which is progressively depleted by the walker. Trajectories in the model alternate between (i) feeding and (ii) escape (when food is missed and so it must be found again) periods, each governed by different movement rules. Here, we explore the case where the escape dynamics is adaptive, so at short times an area-restricted search is carried out, and a switch to extensive or ballistic motion occurs later if necessary. We derive for this case explicit analytical expressions of the mean escape time and the asymptotic growth of the depleted region in one dimension. These, together with numerical results in two dimensions, provide surprising evidence that ballistic searches are detrimental in such scenarios, a result which could explain why ballistic movement is barely observed in animal searches at microscopic and millimetric scales, therefore providing significant implications for biological foraging.

Spatial cognitive ability is associated with transitory movement speed but not straightness during the early stages of exploration

Memories about the spatial environment, such as the locations of foraging patches, are expected to affect how individuals move around the landscape. However, individuals differ in the ability to remember spatial locations (spatial cognitive ability) and evidence is growing that these inter-individual differences influence a range of fitness proxies. Yet empirical evaluations directly linking inter-individual variation in spatial cognitive ability and the development and structure of movement paths are lacking. We assessed the performance of young pheasants (Phasianus colchicus) on a spatial cognition task before releasing them into a novel, rural landscape and tracking their movements. We quantified changes in the straightness and speed of their transitory paths over one month. Birds with better performances on the task initially made slower transitory paths than poor performers but by the end of the month, there was no difference in speed. In general, birds increased the straightness of their path over time, indicating improved efficiency independent of speed, but this was not related to performance on the cognitive task. We suggest that initial slow movements may facilitate more detailed information gathering by better performers and indicates a potential link between an individual's spatial cognitive ability and their movement behaviour.

Efficient Lévy walks in virtual human foraging

Efficient foraging depends on decisions that account for the costs and benefits of various activities like movement, perception, and planning. We conducted a virtual foraging experiment set in the foothills of the Himalayas to examine how time and energy are expended to forage efficiently, and how foraging changes when constrained to a home range. Two hundred players foraged the human-scale landscape with simulated energy expenditure in search of naturally distributed resources. Results showed that efficient foragers produced periods of locomotion interleaved with perception and planning that approached theoretical expectations for Lévy walks, regardless of the home-range constraint. Despite this constancy, efficient home-range foraging trajectories were less diffusive by virtue of restricting locomotive search and spending more time instead scanning the environment to plan movement and detect far-away resources. Altogether, results demonstrate that humans can forage efficiently by arranging and adjusting Lévy-distributed search activities in response to environmental and task constraints.

Sediment availability provokes a shift from Brownian to Lévy-like clonal expansion in a dune building grass

In biogeomorphic landscapes, plant traits can steer landscape development through plant-mediated feedback interactions. Interspecific differences in clonal expansion strategy can therefore lead to the emergence of different landscape organisations. Yet, whether landscape-forming plants adopt different clonal expansion strategies depending on their physical environment remains to be tested. Here, we use a field survey and a complementary mesocosm approach to investigate whether sediment deposition affects the clonal expansion strategy employed by dune-building marram grass individuals. Our results reveal a consistent shift in expansion pattern from more clumped, Brownian-like, movement in sediment-poor conditions, to patchier, Lévy-like, movement under high sediment supply rates. Additional model simulations illustrate that the sediment-dependent shift in movement strategies induces a shift in optimisation of the cost-benefit relation between landscape engineering (i.e. dune formation) and expansion. Plasticity in expansion strategy may therefore allow landscape-forming plants to optimise their engineering ability depending on their physical landscape.

Seed dispersal as a search strategy: dynamic and fragmented landscapes select for multi-scale movement strategies in plants

BACKGROUND

Plant dispersal is a critical factor driving ecological responses to global changes. Knowledge on the mechanisms of dispersal is rapidly advancing, but selective pressures responsible for the evolution of dispersal strategies remain elusive. Recent advances in animal movement ecology identified general strategies that may optimize efficiency in animal searches for food or habitat. Here we explore the potential for evolution of similar general movement strategies for plants.

METHODS

We propose that seed dispersal in plants can be viewed as a strategic search for suitable habitat, where the probability of finding such locations has been optimized through evolution of appropriate dispersal kernels. Using model simulations, we demonstrate how dispersal strategies can optimize key dispersal trade-offs between finding habitat, avoiding kin competition, and colonizing new patches. These trade-offs depend strongly on the landscape, resulting in a tight link between optimal dispersal strategy and spatiotemporal habitat distribution.

RESULTS

Our findings reveal that multi-scale seed dispersal strategies that combine a broad range of dispersal scales, including Lévy-like dispersal, are optimal across a wide range of dynamic and patchy landscapes. At the extremes, static and patchy landscapes select for dispersal strategies dominated by short distances, while uniform and highly unpredictable landscapes both select for dispersal strategies dominated by long distances.

CONCLUSIONS

By viewing plant seed dispersal as a strategic search for suitable habitat, we provide a reference framework for the analysis of plant dispersal data. Consideration of the entire dispersal kernel, including distances across the full range of scales, is key. This reference framework helps identify plant species' dispersal strategies, the evolutionary forces determining these strategies and their ecological consequences, such as a potential mismatch between plant dispersal strategy and altered spatiotemporal habitat dynamics due to land use change. Our perspective opens up directions for future studies, including exploration of composite search behaviour and 'informed searches' in plant species with directed dispersal.

The Rules of Attraction: The Necessary Role of Animal Cognition in Explaining Conservation Failures and Successes

Integrating knowledge and principles of animal behavior into wildlife conservation and management has led to some concrete successes but has failed to improve conservation outcomes in other cases. Many conservation interventions involve attempts to either attract or repel animals, which we refer to as approach/avoidance issues. These attempts can be reframed as issues of manipulating the decisions animals make, which are driven by their perceptual abilities and attentional biases, as well as the value animals attribute to current stimuli and past learned experiences. These processes all fall under the umbrella of animal cognition. Here, we highlight rules that emerge when considering approach/avoidance conservation issues through the lens of cognitive-based management. For each rule, we review relevant conservation successes and failures to better predict the conditions in which behavior can be manipulated, and we suggest how to avoid future failures.

Termite males enhance mating encounters by changing speed according to density

Search theory predicts that animals evolve efficient movement patterns to enhance encounter rates with specific targets. The optimal movements vary with the surrounding environments, which may explain the observation that animals often switch their movement patterns depending on conditions. However, the effectiveness of behavioural change during search is rarely evaluated because it is difficult to examine the actual encounter dynamics. Here we studied how partner-seeking termites update their search strategies depending on the local densities of potential mates. After a dispersal flight, termites drop their wings and walk to search for a mate; when a female and a male meet, they form a female-led tandem pair and search for a favourable nesting site. If a pair is separated, they have two search options-reunite with their stray partner, or seek a new partner. We hypothesized that the density of individuals affects separation-reunion dynamics and thus the optimal search strategy. We observed the searching process across different densities and found that termite pairs were often separated but obtained a new partner quickly at high mate density. After separation, while females consistently slowed down, males increased their speed according to the density. Under high mate density, separated males obtained a partner earlier than females, who do not change movement with density. Our data-based simulations confirmed that the observed behavioural change by males contributes to enhancing encounters. Males at very low mate densities did best to move slowly and thereby reduce the risk of missing their stray partner, who is the only available mate. On the other hand, males that experienced high mate densities did better in mating encounters by moving fast because the risk of isolation is low, and they must compete with other males to find a partner. These results demonstrate that termite males adaptively update their search strategy depending on conditions. Understanding the encounter dynamics experienced by animals is key to connecting the empirical work to the idealized search processes of theoretical studies.

Inverse Square Lévy Walks are not Optimal Search Strategies for d≥2

The Lévy hypothesis states that inverse square Lévy walks are optimal search strategies because they maximize the encounter rate with sparse, randomly distributed, replenishable targets. It has served as a theoretical basis to interpret a wealth of experimental data at various scales, from molecular motors to animals looking for resources, putting forward the conclusion that many living organisms perform Lévy walks to explore space because of their optimal efficiency. Here we provide analytically the dependence on target density of the encounter rate of Lévy walks for any space dimension d; in particular, this scaling is shown to be independent of the Lévy exponent α for the biologically relevant case d≥2, which proves that the founding result of the Lévy hypothesis is incorrect. As a consequence, we show that optimizing the encounter rate with respect to α is irrelevant: it does not change the scaling with density and can lead virtually to any optimal value of α depending on system dependent modeling choices. The conclusion that observed inverse square Lévy patterns are the result of a common selection process based purely on the kinetics of the search behavior is therefore unfounded.

Information limitation and the dynamics of coupled ecological systems

The dynamics of large ecological systems result from vast numbers of interactions between individual organisms. Here, we develop mathematical theory to show that the rate of such interactions is inherently limited by the ability of organisms to gain information about one another. This phenomenon, which we call 'information limitation', is likely to be widespread in real ecological systems and can dictate both the rates of ecological interactions and long-run dynamics of interacting populations. We show how information limitation leads to sigmoid interaction rate functions that can stabilize antagonistic interactions and destabilize mutualistic ones; as a species or type becomes rare, information on its whereabouts also becomes rare, weakening coupling with consumers, pathogens and mutualists. This can facilitate persistence of consumer-resource systems, alter the course of pathogen infections within a host and enhance the rates of oceanic productivity and carbon export. Our findings may shed light on phenomena in many living systems where information drives interactions.

Bumblebees learn foraging routes through exploitation-exploration cycles

How animals explore and acquire knowledge from the environment is a key question in movement ecology. For pollinators that feed on multiple small replenishing nectar resources, the challenge is to learn efficient foraging routes while dynamically acquiring spatial information about new resource locations. Here, we use the behavioural mapping t-Stochastic Neighbouring Embedding algorithm and Shannon entropy to statistically analyse previously published sampling patterns of bumblebees feeding on artificial flowers in the field. We show that bumblebees modulate foraging excursions into distinctive behavioural strategies, characterizing the trade-off dynamics between (i) visiting and exploiting flowers close to the nest, (ii) searching for new routes and resources, and (iii) exploiting learned flower visitation sequences. Experienced bees combine these behavioural strategies even after they find an optimal route minimizing travel distances between flowers. This behavioural variability may help balancing energy costs-benefits and facilitate rapid adaptation to changing environments and the integration of more profitable resources in their routes.

Foraging as an evidence accumulation process

The patch-leaving problem is a canonical foraging task, in which a forager must decide to leave a current resource in search for another. Theoretical work has derived optimal strategies for when to leave a patch, and experiments have tested for conditions where animals do or do not follow an optimal strategy. Nevertheless, models of patch-leaving decisions do not consider the imperfect and noisy sampling process through which an animal gathers information, and how this process is constrained by neurobiological mechanisms. In this theoretical study, we formulate an evidence accumulation model of patch-leaving decisions where the animal averages over noisy measurements to estimate the state of the current patch and the overall environment. We solve the model for conditions where foraging decisions are optimal and equivalent to the marginal value theorem, and perform simulations to analyze deviations from optimal when these conditions are not met. By adjusting the drift rate and decision threshold, the model can represent different “strategies”, for example an incremental, decremental, or counting strategy. These strategies yield identical decisions in the limiting case but differ in how patch residence times adapt when the foraging environment is uncertain. To describe sub-optimal decisions, we introduce an energy-dependent marginal utility function that predicts longer than optimal patch residence times when food is plentiful. Our model provides a quantitative connection between ecological models of foraging behavior and evidence accumulation models of decision making. Moreover, it provides a theoretical framework for potential experiments which seek to identify neural circuits underlying patch-leaving decisions.

Foraging is a ubiquitous animal behavior, performed by organisms as different as worms, birds, rats, and humans. Although the behavior has been extensively studied, it is not known how the brain processes information obtained during foraging activity to make subsequent foraging decisions. We form an evidence accumulation model of foraging decisions that describes the process through which an animal gathers information and uses it to make foraging decisions. By building on studies of the neural decision mechanisms within systems neuroscience, this model connects the foraging decision process with ecological models of patch-leaving decisions, such as the marginal value theorem. The model suggests the existence of different foraging strategies, which optimize for different environmental conditions and their potential implementation by neural decision making circuits. The model also shows how state-dependence, such as satiation level, can affect evidence accumulation to lead to sub-optimal foraging decisions. Our model provides a framework for future experimental studies which seek to elucidate how neural decision making mechanisms have been shaped by evolutionary forces in an animal’s surrounding environment.

Orographic lift shapes flight routes of gulls in virtually flat landscapes

Interactions between landscape and atmosphere result in a dynamic flight habitat which birds may use opportunistically to save energy during flight. However, their ability to utilise these dynamic landscapes and its influence on shaping movement paths is not well understood. We investigate the degree to which gulls utilise fine scale orographic lift created by wind deflected upwards over landscape features in a virtually flat landscape. Using accelerometer measurements and GPS tracking, soaring flight is identified and analysed with respect to orographic lift, modelled using high-resolution digital elevation models and wind measurements. The relationship between orographic lift and flight routes suggests gulls have advanced knowledge of their aerial surroundings and the benefits to be gained from them, even regarding small features such as tree lines. We show that in a landscape constantly influenced by anthropogenic change, the structure of our landscape has an aerial impact on flight route connectivity and costs.

Recurrence time correlations in random walks with preferential relocation to visited places

Random walks with memory usually involve rules where a preference for either revisiting or avoiding those sites visited in the past are introduced somehow. Such effects have a direct consequence on the transport properties as well as on the statistics of first-passage and subsequent recurrence times through a site. A preference for revisiting sites is thus expected to result in a positive correlation between consecutive recurrence times. Here we derive a continuous-time generalization of the random walk model with preferential relocation to visited sites proposed in Phys. Rev. Lett. 112, 240601 (2014)PRLTAO0031-900710.1103/PhysRevLett.112.240601 to explore this effect, together with the main transport properties induced by the long-range memory. Despite the long-range memory effects governing the process, our analytical treatment allows us to (i) observe the existence of an asymptotic logarithmic (ultraslow) growth for the mean square displacement, in accordance to the results found for the original discrete-time model, and (ii) confirm the existence of positive correlations between first-passage and subsequent recurrence times. This analysis is completed with a comprehensive numerical study which reveals, among other results, that these correlations between first-passage and recurrence times also exhibit clear signatures of this ultraslow relaxation dynamics.

A Lévy expansion strategy optimizes early dune building by beach grasses

Lifeforms ranging from bacteria to humans employ specialized random movement patterns. Although effective as optimization strategies in many scientific fields, random walk application in biology has remained focused on search optimization by mobile organisms. Here, we report on the discovery that heavy-tailed random walks underlie the ability of clonally expanding plants to self-organize and dictate the formation of biogeomorphic landscapes. Using cross-Atlantic surveys, we show that congeneric beach grasses adopt distinct heavy-tailed clonal expansion strategies. Next, we demonstrate with a spatially explicit model and a field experiment that the Lévy-type strategy of the species building the highest dunes worldwide generates a clonal network with a patchy shoot organization that optimizes sand trapping efficiency. Our findings demonstrate Lévy-like movement in plants, and emphasize the role of species-specific expansion strategies in landscape formation. This mechanistic understanding paves the way for tailor-made planting designs to successfully construct and restore biogeomorphic landscapes and their services.

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.

Foraging behaviour of snowshoe hares (Lepus americanus) in conifer forests regenerating after fire

Wildfires in conifer forests create patchy, heterogeneous landscapes. For many animal species, this post-fire variability means having to navigate quite different habitat patches to locate adequate cover and food. For snowshoe hares (Lepus americanus Erxleben, 1777), post-fire landscapes could include risky open patches, as well as dense regenerating stands rich in food and cover. We analyzed snowshoe hare tortuosity, speed of movement, and amount of browse along winter foraging pathways in unburned mature forest and in dense regenerating stands or open areas with sparse regeneration 12–13 years after the Okanagan Mountain Park fire (>25 000 ha near Kelowna, British Columbia, Canada) to determine whether hares change foraging behaviour in relation to cover type. Hares moved the fastest and browsed the least in open habitats. Hares browsed most often in areas where sapling regeneration was dense; their main forage was lodgepole pine (Pinus contorta Douglas ex Loudon). No differences were found in pathway tortuosity in relation to cover type (open, regenerating, or mature patches). When hares moved slower along foraging pathways, they also moved slightly more tortuously and ate more. These results suggest that hares prefer post-fire areas with dense tree regeneration.

Enhanced transport of nutrients powered by microscale flows of the self-spinning dinoflagellate Symbiodinium sp

The metabolism of a living organism (e.g. bacteria, algae, zooplankton) requires a continuous uptake of nutrients from the surrounding environment. However, within local spatial scales, nutrients are quickly used up under dense concentrations of organisms. Here, we report that self-spinning dinoflagellates Symbiodinium sp. (clade E) generate a microscale flow that mitigates competition and enhances the uptake of nutrients from the surrounding environment. Our experimental and theoretical results reveal that this incessant active behavior enhances transport by approximately 80-fold when compared with Brownian motion in living fluids. We found that the tracer ensemble probability density function for displacement is time-dependent, but consists of a Gaussian core and robust exponential tails (so-called non-Gaussian diffusion). This can be explained by interactions of far-field Brownian motions and a near-field entrainment effect along with microscale flows. The contribution of exponential tails sharply increases with algal density, and saturates at a critical density, implying a trade-off between aggregated benefit and negative competition for the spatially self-organized cells. Our work thus shows that active motion and migration of aquatic algae play key roles in diffusive transport and should be included in theoretical and numerical models of physical and biogeochemical ecosystems.

Simulating exploration versus exploitation in agent foraging under different environment uncertainties

For artificial agents trading off exploration (food seeking) versus (short-term) exploitation (or consumption), our experiments suggest that uncertainty (interpreted information, theoretically) magnifies food seeking. In more uncertain environments, with food distributed uniformly randomly, exploration appears to be beneficial. In contrast, in biassed (less uncertain) environments, with food concentrated in only one part, exploitation appears to be more advantageous. Agents also appear to do better in biassed environments.

Resource landscapes explain contrasting patterns of aggregation and site fidelity by red knots at two wintering sites

BACKGROUND

Space use strategies by foraging animals are often considered to be species-specific. However, similarity between conspecific strategies may also result from similar resource environments. Here, we revisit classic predictions of the relationships between the resource distribution and foragers' space use by tracking free-living foragers of a single species in two contrasting resource landscapes. At two main non-breeding areas along the East-Atlantic flyway (Wadden Sea, The Netherlands and Banc d'Arguin, Mauritania), we mapped prey distributions and derived resource landscapes in terms of the predicted intake rate of red knots (Calidris canutus), migratory molluscivore shorebirds. We tracked the foraging paths of 13 and 38 individual red knots at intervals of 1 s over two and five weeks in the Wadden Sea and at Banc d'Arguin, respectively. Mediated by competition for resources, we expected aggregation to be strong and site fidelity weak in an environment with large resource patches. The opposite was expected for small resource patches, but only if local resource abundances were high.

RESULTS

Compared with Banc d'Arguin, resource patches in the Wadden Sea were larger and the maximum local resource abundance was higher. However, because of constraints set by digestive capacity, the average potential intake rates by red knots were similar at the two study sites. Space-use patterns differed as predicted from these differences in resource landscapes. Whereas foraging red knots in the Wadden Sea roamed the mudflats in high aggregation without site fidelity (i.e. grouping nomads), at Banc d'Arguin they showed less aggregation but were strongly site-faithful (i.e. solitary residents).

CONCLUSION

The space use pattern of red knots in the two study areas showed diametrically opposite patterns. These differences could be explained from the distribution of resources in the two areas. Our findings imply that intraspecific similarities in space use patterns represent responses to similar resource environments rather than species-specificity. To predict how environmental change affects space use, we need to understand the degree to which space-use strategies result from developmental plasticity and behavioural flexibility. This requires not only tracking foragers throughout their development, but also tracking their environment in sufficient spatial and temporal detail.

Lévy flight movements prevent extinctions and maximize population abundances in fragile Lotka-Volterra systems

Multiple-scale mobility is ubiquitous in nature and has become instrumental for understanding and modeling animal foraging behavior. However, the impact of individual movements on the long-term stability of populations remains largely unexplored. We analyze deterministic and stochastic Lotka-Volterra systems, where mobile predators consume scarce resources (prey) confined in patches. In fragile systems (that is, those unfavorable to species coexistence), the predator species has a maximized abundance and is resilient to degraded prey conditions when individual mobility is multiple scaled. Within the Lévy flight model, highly superdiffusive foragers rarely encounter prey patches and go extinct, whereas normally diffusing foragers tend to proliferate within patches, causing extinctions by overexploitation. Lévy flights of intermediate index allow a sustainable balance between patch exploitation and regeneration over wide ranges of demographic rates. Our analytical and simulated results can explain field observations and suggest that scale-free random movements are an important mechanism by which entire populations adapt to scarcity in fragmented ecosystems.

The emergence of metabolic heterogeneity and diverse growth responses in isogenic bacterial cells

Microorganisms adapt to frequent environmental changes through population diversification. Previous studies demonstrated phenotypic diversity in a clonal population and its important effects on microbial ecology. However, the dynamic changes of phenotypic composition have rarely been characterized. Also, cellular variations and environmental factors responsible for phenotypic diversity remain poorly understood. Here, we studied phenotypic diversity driven by metabolic heterogeneity. We characterized metabolic activities and growth kinetics of starved Escherichia coli cells subject to nutrient upshift at single-cell resolution. We observed three subpopulations with distinct metabolic activities and growth phenotypes. One subpopulation was metabolically active and immediately grew upon nutrient upshift. One subpopulation was metabolically inactive and non-viable. The other subpopulation was metabolically partially active, and did not grow upon nutrient upshift. The ratio of these subpopulations changed dynamically during starvation. A long-term observation of cells with partial metabolic activities indicated that their metabolism was later spontaneously restored, leading to growth recovery. Further investigations showed that oxidative stress can induce the emergence of a subpopulation with partial metabolic activities. Our findings reveal the emergence of metabolic heterogeneity and associated dynamic changes in phenotypic composition. In addition, the results shed new light on microbial dormancy, which has important implications in microbial ecology and biomedicine.

The evolutionary origins of Lévy walk foraging

We study through a reaction-diffusion algorithm the influence of landscape diversity on the efficiency of search dynamics. Remarkably, the identical optimal search strategy arises in a wide variety of environments, provided the target density is sparse and the searcher’s information is restricted to its close vicinity. Our results strongly impact the current debate on the emergentist vs. evolutionary origins of animal foraging. The inherent character of the optimal solution (i.e., independent on the landscape for the broad scenarios assumed here) suggests an interpretation favoring the evolutionary view, as originally implied by the Lévy flight foraging hypothesis. The latter states that, under conditions of scarcity of information and sparse resources, some organisms must have evolved to exploit optimal strategies characterized by heavy-tailed truncated power-law distributions of move lengths. These results strongly suggest that Lévy strategies—and hence the selection pressure for the relevant adaptations—are robust with respect to large changes in habitat. In contrast, the usual emergentist explanation seems not able to explain how very similar Lévy walks can emerge from all the distinct non-Lévy foraging strategies that are needed for the observed large variety of specific environments. We also report that deviations from Lévy can take place in plentiful ecosystems, where locomotion truncation is very frequent due to high encounter rates. So, in this case normal diffusion strategies—performing as effectively as the optimal one—can naturally emerge from Lévy. Our results constitute the strongest theoretical evidence to date supporting the evolutionary origins of experimentally observed Lévy walks.

How organisms improve the search for food, mates, etc., is a key factor to their survival. Mathematically, the best strategy to look for randomly distributed re-visitable resources—under scarce information and sparse conditions—results from Lévy distributions of move lengths (the probability of taking a step ℓ is proportional to 1/ℓ²). Today it is well established that many animal species in different habitats do perform Lévy foraging. This fact has raised a heated debate, viz., the emergent versus evolutionary hypotheses. For the former, a Lévy foraging is an emergent property, a consequence of searcher-environment interactions: certain landscapes induce Lévy patterns, but others not. In this view, the optimal strategy depends on the particular habitat. The evolutionary explanation, in contrast, is that Lévy foraging strategies are adaptations that evolved via natural selection. In this article, through simulations we exhaustively analyze the influence of distinct environments on the foraging efficiency. We find that the optimal procedure is the same in all situations, provided density is low and landscape information is scarce. So, the best search strategy is remarkably independent of details. These results constitute the strongest theoretical evidence to date supporting the evolutionary origins of experimentally observed Lévy walks.

Nonstationary dynamics of encounters: Mean valuable territory covered by a random searcher

Inspired by recent experiments on the organism Caenorhabditis elegans we present a stochastic problem to capture the adaptive dynamics of search in living beings, which involves the exploration-exploitation dilemma between remaining in a previously preferred area and relocating to new places. We assess the question of search efficiency by introducing a new magnitude, the mean valuable territory covered by a Browinan searcher, for the case where each site in the domain becomes valuable only after a random time controlled by a nonhomogeneous rate which expands from the origin outwards. We explore analytically this magnitude for domains of dimensions 1, 2, and 3 and discuss the theoretical and applied (biological) interest of our approach. As the main results here, we (i) report the existence of some universal scaling properties for the mean valuable territory covered as a function of time and (ii) reveal the emergence of an optimal diffusivity which appears only for domains in two and higher dimensions.

Wild bee nutritional ecology: predicting pollinator population dynamics, movement, and services from floral resources

Pollination services are inherently shaped by floral resource availability, through the mediation of pollinator population dynamics and the influence on energetically costly processes, such as foraging. Here, we review recent insights that have improved our mechanistic understanding of how floral resources shape bee populations and pollination services. Our scope includes advances in our understanding of how individual bees and their populations are shaped by nutrient availability; investigations into how contemporary floral resource landscapes influence foraging; and new insights into how these relationships are indirectly impacted by biotic and abiotic factors across communities and landscapes. Throughout our review, we take a mechanistic, multi-scalar approach that highlights the complexity of interactions between floral resources and bees, across space and time.

Search in patchy media: Exploitation-exploration tradeoff

How to best exploit patchy resources? We introduce a minimal exploitation-migration model that incorporates the coupling between a searcher's trajectory, modeled by a random walk, and ensuing depletion of the environment by the searcher's consumption of resources. The searcher also migrates to a new patch when it takes S consecutive steps without finding resources. We compute the distribution of consumed resources F_{t} at time t for this non-Markovian searcher and show that consumption is maximized by exploring multiple patches. In one dimension, we derive the optimal strategy to maximize F_{t}. This strategy is robust with respect to the distribution of resources within patches and the criterion for leaving the current patch. We also show that F_{t} has an optimum in the ecologically relevant case of two-dimensional patchy environments.

Variability in individual activity bursts improves ant foraging success

Using experimental and computational methods, we study the role of behavioural variability in activity bursts (or temporal activity patterns) for individual and collective regulation of foraging in A. senilis ants. First, foraging experiments were carried out under special conditions (low densities of ants and food and absence of external cues or stimuli) where individual-based strategies are most prevalent. By using marked individuals and recording all foraging trajectories, we were then able to precisely quantify behavioural variability among individuals. Our main conclusions are that (i) variability of ant trajectories (turning angles, speed, etc.) is low compared with variability of temporal activity profiles, and (ii) this variability seems to be driven by plasticity of individual behaviour through time, rather than the presence of fixed behavioural stereotypes or specialists within the group. The statistical measures obtained from these experimental foraging patterns are then used to build a general agent-based model (ABM) which includes the most relevant properties of ant foraging under natural conditions, including recruitment through pheromone communication. Using the ABM, we are able to provide computational evidence that the characteristics of individual variability observed in our experiments can provide a functional advantage (in terms of foraging success) to the group; thus, we propose the biological basis underpinning our observations. Altogether, our study reveals the potential utility of experiments under simplified (laboratory) conditions for understanding information-gathering in biological systems.