Wild animals suppress the spread of socially transmitted misinformation.

Understanding the mechanisms by which information and misinformation spread through groups of individual actors is essential to the prediction of phenomena ranging from coordinated group behaviors to misinformation epidemics. Transmission of information through groups depends on the rules that individuals use to transform the perceived actions of others into their own behaviors. Because it is often not possible to directly infer decision-making strategies in situ, most studies of behavioral spread assume that individuals make decisions by pooling or averaging the actions or behavioral states of neighbors. However, whether individuals may instead adopt more sophisticated strategies that exploit socially transmitted information, while remaining robust to misinformation, is unknown. Here, we study the relationship between individual decision-making and misinformation spread in groups of wild coral reef fish, where misinformation occurs in the form of false alarms that can spread contagiously through groups. Using automated visual field reconstruction of wild animals, we infer the precise sequences of socially transmitted visual stimuli perceived by individuals during decision-making. Our analysis reveals a feature of decision-making essential for controlling misinformation spread: dynamic adjustments in sensitivity to socially transmitted cues. This form of dynamic gain control can be achieved by a simple and biologically widespread decision-making circuit, and it renders individual behavior robust to natural fluctuations in misinformation exposure.

Fish couple forecasting with feedback control to chase and capture moving prey.

Predator-prey interactions are fundamental to ecological and evolutionary dynamics. Yet, predicting the outcome of such interactions-whether predators intercept prey or fail to do so-remains a challenge. An emerging hypothesis holds that interception trajectories of diverse predator species can be described by simple feedback control laws that map sensory inputs to motor outputs. This form of feedback control is widely used in engineered systems but suffers from degraded performance in the presence of processing delays such as those found in biological brains. We tested whether delay-uncompensated feedback control could explain predator pursuit manoeuvres using a novel experimental system to present hunting fish with virtual targets that manoeuvred in ways that push the limits of this type of control. We found that predator behaviour cannot be explained by delay-uncompensated feedback control, but is instead consistent with a pursuit algorithm that combines short-term forecasting of self-motion and prey motion with feedback control. This model predicts both predator interception trajectories and whether predators capture or fail to capture prey on a trial-by-trial basis. Our results demonstrate how animals can combine short-term forecasting with feedback control to generate robust flexible behaviours in the face of significant processing delays.

Fast behavioral feedbacks make ecosystems sensitive to pace and not just magnitude of anthropogenic environmental change.

Anthropogenic environmental change is altering the behavior of animals in ecosystems around the world. Although behavior typically occurs on much faster timescales than demography, it can nevertheless influence demographic processes. Here, we use detailed data on behavior and empirical estimates of demography from a coral reef ecosystem to develop a coupled behavioral-demographic ecosystem model. Analysis of the model reveals that behavior and demography feed back on one another to determine how the ecosystem responds to anthropogenic forcing. In particular, an empirically observed feedback between the density and foraging behavior of herbivorous fish leads to alternative stable ecosystem states of coral population persistence or collapse (and complete algal dominance). This feedback makes the ecosystem more prone to coral collapse under fishing pressure but also more prone to recovery as fishing is reduced. Moreover, because of the behavioral feedback, the response of the ecosystem to changes in fishing pressure depends not only on the magnitude of changes in fishing but also on the pace at which changes are imposed. For example, quickly increasing fishing to a given level can collapse an ecosystem that would persist under more gradual change. Our results reveal conditions under which the pace and not just the magnitude of external forcing can dictate the response of ecosystems to environmental change. More generally, our multiscale behavioral-demographic framework demonstrates how high-resolution behavioral data can be incorporated into ecological models to better understand how ecosystems will respond to perturbations.

Bacteria push the limits of chemotactic precision to navigate dynamic chemical gradients.

Ephemeral aggregations of bacteria are ubiquitous in the environment, where they serve as hotbeds of metabolic activity, nutrient cycling, and horizontal gene transfer. In many cases, these regions of high bacterial concentration are thought to form when motile cells use chemotaxis to navigate to chemical hotspots. However, what governs the dynamics of bacterial aggregations is unclear. Here, we use an experimental platform to create realistic submillimeter-scale nutrient pulses with controlled nutrient concentrations. By combining experiments, mathematical theory, and agent-based simulations, we show that individual Vibrio ordalii bacteria begin chemotaxis toward hotspots of dissolved organic matter (DOM) when the magnitude of the chemical gradient rises sufficiently far above the sensory noise that is generated by stochastic encounters with chemoattractant molecules. Each DOM hotspot is surrounded by a dynamic ring of chemotaxing cells, which congregate in regions of high DOM concentration before dispersing as DOM diffuses and gradients become too noisy for cells to respond to. We demonstrate that V. ordalii operates close to the theoretical limits on chemotactic precision. Numerical simulations of chemotactic bacteria, in which molecule counting noise is explicitly taken into account, point at a tradeoff between nutrient acquisition and the cost of chemotactic precision. More generally, our results illustrate how limits on sensory precision can be used to understand the location, spatial extent, and lifespan of bacterial behavioral responses in ecologically relevant environments.

Disease and fire interact to influence transitions between savanna-forest ecosystems over a multi-decadal experiment.

Global change is shifting disturbance regimes that may rapidly change ecosystems, sometimes causing ecosystems to shift between states. Interactions between disturbances such as fire and disease could have especially severe effects, but experimental tests of multi-decadal changes in disturbance regimes are rare. Here, we surveyed vegetation for 35 years in a 54-year fire frequency experiment in a temperate oak savanna-forest ecotone that experienced a recent outbreak of oak wilt. Different fire regimes determined whether plots were savanna or forest by regulating tree abundance (r2  = 0.70), but disease rapidly reversed the effect of fire exclusion, increasing mortality by 765% in unburned forests, but causing relatively minor changes in frequently burned savannas. Model simulations demonstrated that disease caused unburned forests to transition towards a unique woodland that was prone to transition to savanna if fire was reintroduced. Consequently, disease-fire interactions could shift ecosystem resilience and biome boundaries as pathogen distributions change.

Conserved behavioral circuits govern high-speed decision-making in wild fish shoals.

To evade their predators, animals must quickly detect potential threats, gauge risk, and mount a response. Putative neural circuits responsible for these tasks have been isolated in laboratory studies. However, it is unclear whether and how these circuits combine to generate the flexible, dynamic sequences of evasion behavior exhibited by wild, freely moving animals. Here, we report that evasion behavior of wild fish on a coral reef is generated through a sequence of well-defined decision rules that convert visual sensory input into behavioral actions. Using an automated system to present visual threat stimuli to fish in situ, we show that individuals initiate escape maneuvers in response to the perceived size and expansion rate of an oncoming threat using a decision rule that matches dynamics of known loom-sensitive neural circuits. After initiating an evasion maneuver, fish adjust their trajectories using a control rule based on visual feedback to steer away from the threat and toward shelter. These decision rules accurately describe evasion behavior of fish from phylogenetically distant families, illustrating the conserved nature of escape decision-making. Our results reveal how the flexible behavioral responses required for survival can emerge from relatively simple, conserved decision-making mechanisms.

Social interactions among grazing reef fish drive material flux in a coral reef ecosystem.

In human financial and social systems, exchanges of information among individuals cause speculative bubbles, behavioral cascades, and other correlated actions that profoundly influence system-level function. Exchanges of information are also widespread in ecological systems, but their effects on ecosystem-level processes are largely unknown. Herbivory is a critical ecological process in coral reefs, where diverse assemblages of fish maintain reef health by controlling the abundance of algae. Here, we show that social interactions have a major effect on fish grazing rates in a reef ecosystem. We combined a system for observing and manipulating large foraging areas in a coral reef with a class of dynamical decision-making models to reveal that reef fish use information about the density and actions of nearby fish to decide when to feed on algae and when to flee foraging areas. This "behavioral coupling" causes bursts of feeding activity that account for up to 68% of the fish community's consumption of algae. Moreover, correlations in fish behavior induce a feedback, whereby each fish spends less time feeding when fewer fish are present, suggesting that reducing fish stocks may not only reduce total algal consumption but could decrease the amount of algae each remaining fish consumes. Our results demonstrate that social interactions among consumers can have a dominant effect on the flux of energy and materials through ecosystems, and our methodology paves the way for rigorous in situ measurements of the behavioral rules that underlie ecological rates in other natural systems.

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.

Reverse-engineering ecological theory from data.

Ecologists have long sought to understand the dynamics of populations and communities by deriving mathematical theory from first principles. Theoretical models often take the form of dynamical equations that comprise the ecological processes (e.g. competition, predation) believed to govern system dynamics. The inverse of this approach-inferring which processes and ecological interactions drive observed dynamics-remains an open problem in ecology. Here, we propose a way to attack this problem using a machine learning method known as symbolic regression, which seeks to discover relationships in time-series data and to express those relationships using dynamical equations. We found that this method could rapidly discover models that explained most of the variance in three classic demographic time series. More importantly, it reverse-engineered the models previously proposed by theoretical ecologists to describe these time series, capturing the core ecological processes these models describe and their functional forms. Our findings suggest a potentially powerful new way to merge theory development and data analysis.

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.

Sensing and decision-making in random search.

Many organisms locate resources in environments in which sensory signals are rare, noisy, and lack directional information. Recent studies of search in such environments model search behavior using random walks (e.g., Lévy walks) that match empirical movement distributions. We extend this modeling approach to include searcher responses to noisy sensory data. We explore the consequences of incorporating such sensory measurements into search behavior using simulations of a visual-olfactory predator in search of prey. Our results show that including even a simple response to noisy sensory data can dominate other features of random search, resulting in lower mean search times and decreased risk of long intervals between target encounters. In particular, we show that a lack of signal is not a lack of information. Searchers that receive no signal can quickly abandon target-poor regions. On the other hand, receiving a strong signal leads a searcher to concentrate search effort near targets. These responses cause simulated searchers to exhibit an emergent area-restricted search behavior similar to that observed of many organisms in nature.

The dynamics of assembling food webs.

Community assembly is central to ecology, yet ecologists have amassed little quantitative information about how food webs assemble. Theory holds that colonisation rate is a primary driver of community assembly. We present new data from a mesocosm experiment to test the hypothesis that colonisation rate also determines the assembly dynamics of food webs. By manipulating colonisation rate and measuring webs through time, we show how colonisation rate governs structural changes during assembly. Webs experiencing different colonisation rates had stable topologies despite significant species turnover, suggesting that some features of network architecture emerge early and change little through assembly. But webs experiencing low colonisation rates showed less variation in the magnitudes of trophic fluxes, and were less likely to develop coupled fast and slow resource channels--a common feature of published webs. Our results reveal that food web structure develops according to repeatable trajectories that are strongly influenced by colonisation rate.

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.

Energetic and biomechanical constraints on animal migration distance.

Animal migration is one of the great wonders of nature, but the factors that determine how far migrants travel remain poorly understood. We present a new quantitative model of animal migration and use it to describe the maximum migration distance of walking, swimming and flying migrants. The model combines biomechanics and metabolic scaling to show how maximum migration distance is constrained by body size for each mode of travel. The model also indicates that the number of body lengths travelled by walking and swimming migrants should be approximately invariant of body size. Data from over 200 species of migratory birds, mammals, fish, and invertebrates support the central conclusion of the model - that body size drives variation in maximum migration distance among species through its effects on metabolism and the cost of locomotion. The model provides a new tool to enhance general understanding of the ecology and evolution of migration.

The rising cost of warming waters: effects of temperature on the cost of swimming in fishes.

Understanding the effects of water temperature on the swimming performance of fishes is central in understanding how fish species will respond to global climate change. Metabolic cost of transport (COT)-a measure of the energy required to swim a given distance-is a key performance parameter linked to many aspects of fish life history. We develop a quantitative model to predict the effect of water temperature on COT. The model facilitates comparisons among species that differ in body size by incorporating the body mass-dependence of COT. Data from 22 fish species support the temperature and mass dependencies of COT predicted by our model, and demonstrate that modest differences in water temperature can result in substantial differences in the energetic cost of swimming.

Ecological decision-making: From circuit elements to emerging principles.

The interactions an animal has with its prey, predators, neighbors, and competitors are known as ecological interactions. Making effective decisions during ecological interactions poses fundamental challenges for the nervous system. Among these are the need to filter relevant information out of complex and ever-changing sensory scenes, to balance competing objectives, and to generate robust behavior amid the strong mutual feedbacks that occur during interactions with other animals. Here, I review recent advancements in the study of ecological decision-making. Using research with fishes, I illustrate how knowledge of ethology and brain circuitry are converging to yield a more holistic understanding of how the brain solves these problems to produce robust sequences of natural behavior.

Predators, prey, and transient states in the assembly of spatially structured communities.

Ecological theory suggests that both dispersal limitation and resource limitation can exert strong effects on community assembly. However, empirical studies of community assembly have focused almost exclusively on communities with a single trophic level. Thus, little is known about the combined effects of dispersal and resource limitation on assembly of communities with multiple trophic levels. We performed a landscape-scale experiment using spatially arranged mesocosms to study effects of dispersal and resource limitation on the assembly dynamics of aquatic invertebrate communities with two trophic levels. We found that interplay between dispersal and resource limitation regulated the assembly of predator and prey trophic levels in these pond communities. Early in assembly, predators and prey were strongly dispersal limited, and resource (i.e., prey) availability did not influence predator colonization. Later in assembly, after predators colonized, resource limitation was the strongest driver of predator abundance, and dispersal limitation played a negligible role. Thus, habitat isolation affected predators directly by reducing predator colonization rate, and indirectly through the effect of distance on prey availability. Dispersal and resource limitation of predators resulted in a transient period in which predators were absent or rare in isolated habitats. This period may be important for understanding population dynamics of vulnerable prey species. Our findings demonstrate that dispersal and resource limitation can jointly regulate assembly dynamics in multi-trophic systems. They also highlight the need to develop a temporal picture of the assembly process in multi-trophic communities because the availability and spatial distribution of limiting resources (i.e., prey) and the distribution of predators can shift radically over time.

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.

An Algorithmic Approach to Natural Behavior.

Uncovering the mechanisms and implications of natural behavior is a goal that unites many fields of biology. Yet, the diversity, flexibility, and multi-scale nature of these behaviors often make understanding elusive. Here, we review studies of animal pursuit and evasion - two special classes of behavior where theory-driven experiments and new modeling techniques are beginning to uncover the general control principles underlying natural behavior. A key finding of these studies is that intricate sequences of pursuit and evasion behavior can often be constructed through simple, repeatable rules that link sensory input to motor output: we refer to these rules as behavioral algorithms. Identifying and mathematically characterizing these algorithms has led to important insights, including the discovery of guidance rules that attacking predators use to intercept mobile prey, and coordinated neural and biomechanical mechanisms that animals use to avoid impending collisions. Here, we argue that algorithms provide a good starting point for studies of natural behavior more generally. Rather than beginning at the neural or ecological levels of organization, we advocate starting in the middle, where the algorithms that link sensory input to behavioral output can provide a solid foundation from which to explore both the implementation and the ecological outcomes of behavior. We review insights that have been gained through such an algorithmic approach to pursuit and evasion behaviors. From these, we synthesize theoretical principles and lay out key modeling tools needed to apply an algorithmic approach to the study of other complex natural behaviors.

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior.

We demonstrate a method for the generation of controlled, dynamic chemical pulses-where localized chemoattractant becomes suddenly available at the microscale-to create micro-environments for microbial chemotaxis experiments. To create chemical pulses, we developed a system to introduce amino acid sources near-instantaneously by photolysis of caged amino acids within a polydimethylsiloxane (PDMS) microfluidic chamber containing a bacterial suspension. We applied this method to the chemotactic bacterium, Vibrio ordalii, which can actively climb these dynamic chemical gradients while being tracked by video microscopy. Amino acids, rendered biologically inert ('caged') by chemical modification with a photoremovable protecting group, are uniformly present in the suspension but not available for consumption until their sudden release, which occurs at user-defined points in time and space by means of a near-UV-A focused LED beam. The number of molecules released in the pulse can be determined by a calibration relationship between exposure time and uncaging fraction, where the absorption spectrum after photolysis is characterized by using UV-Vis spectroscopy. A nanoporous polycarbonate (PCTE) membrane can be integrated into the microfluidic device to allow the continuous removal by flow of the uncaged compounds and the spent media. A strong, irreversible bond between the PCTE membrane and the PDMS microfluidic structure is achieved by coating the membrane with a solution of 3-aminopropyltriethoxysilane (APTES) followed by plasma activation of the surfaces to be bonded. A computer-controlled system can generate user-defined sequences of pulses at different locations and with different intensities, so as to create resource landscapes with prescribed spatial and temporal variability. In each chemical landscape, the dynamics of bacterial movement at the individual scale and their accumulation at the population level can be obtained, thereby allowing the quantification of chemotactic performance and its effects on bacterial aggregations in ecologically relevant environments.