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Abstract The idea that organisms benefit by acquiring information through social connections is a cornerstone of our understanding of social evolution and collective behaviour. Yet, while learning about the world through social ties can confer many benefits, these connections can also serve as conduits for misinformation. Studies of misinformation in human social systems are rapidly proliferating, yet our understanding of the biological origins of misinformation remains surprisingly limited. In this review, we survey examples of socially transmitted misinformation across biological systems. Our central findings are (i) that the transmission and use of misinformation is widespread in biological systems spanning levels of organization, and (ii) that the production and transmission of misinformation is probably an inevitable property that inherits from fundamental constraints on biological communication systems, rather than a pathology that lies apart from the normal functioning of such systems. In this light, we argue that there is a need for a more integrated theoretical and empirical science of misinformation in biology. We end by highlighting four emerging questions about misinformation and its role in driving ecological and evolutionary dynamics that this new field of inquiry should address. 

Abstract Behavioral plasticity in animals influences direct species interactions, but its effects can also spread unpredictably through ecological networks, creating indirect interactions that are difficult to anticipate. We use coarse‐grained models to investigate how changes in species behavior shape indirect interactions and influence ecological network dynamics. As an illustrative example, we examine predators that feed on two types of prey, each of which temporarily reduces activity after evading an attack, thereby lowering vulnerability at the expense of growth. We demonstrate that this routine behavior shifts the indirect interaction between prey species from apparent competition to mutualism or parasitism. These shifts occur when predator capture efficiency drops below a critical threshold, causing frequent hunting failures. As a result, one prey species indirectly promotes the growth of the other by relaxing its density dependence through a cascade of network effects, paradoxically increasing predator biomass despite decreased hunting success. Empirical capture probabilities often fall within the range where such dynamics are predicted. We characterize such shifts in the qualitative nature of species interactions as changes ininteraction valence, highlighting how routine animal behaviors reshape community structure through cascading changes within ecological networks. 

ABSTRACT Disparate bodies of literature implicate risk avoidance and energy conservation as important drivers of animal movement decisions. Theory posits that these phenomena interact in ecologically consequential ways, but rigorous empirical tests of this hypothesis have been hampered by data limitations. We fuse fluid dynamics, telemetry, and attack data to reconstruct risk and energy landscapes traversed by migrating juvenile salmon and their predators. We find that migrants primarily use midriver microhabitats that facilitate migration at night. During daylight, predators become more aggressive in the midriver, and prey reduce midriver use in favour of nearshore microhabitats, resulting in increased energy expenditure and decreased migration efficiency. Predators attack most when migrants are not prioritising threat avoidance and during ephemeral periods of low lighting. Our findings suggest that predator–prey interactions result from an interplay between landscapes of fear and energy, which can determine the degree to which predators affect prey through mortality or fear. 

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. 

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. 

Image-based machine learning methods are becoming among the most widely-used forms of data analysis across science, technology, engineering, and industry. These methods are powerful because they can rapidly and automatically extract rich contextual and spatial information from images, a process that has historically required a large amount of human labor. A wide range of recent scientific applications have demonstrated the potential of these methods to change how researchers study the ocean. However, despite their promise, machine learning tools are still under-exploited in many domains including species and environmental monitoring, biodiversity surveys, fisheries abundance and size estimation, rare event and species detection, the study of animal behavior, and citizen science. Our objective in this article is to provide an approachable, end-to-end guide to help researchers apply image-based machine learning methods effectively to their own research problems. Using a case study, we describe how to prepare data, train and deploy models, and overcome common issues that can cause models to underperform. Importantly, we discuss how to diagnose problems that can cause poor model performance on new imagery to build robust tools that can vastly accelerate data acquisition in the marine realm. Code to perform analyses is provided at https://github.com/heinsense2/AIO_CaseStudy .