Animal Movement Research Articles

Depicting Humans, Animals, and Objects in Motion: The Effect of Implied Motion on Engagement and Persuasion in Advertising

Over the past several decades, visual imagery has become the dominant element in modern advertising. A common content strategy involves depicting humans, animals, or objects in the midst of motion. Whereas previous research indicates that implied motion images enhance persuasion, it is unclear whether this effect is unique to depictions of moving humans or if it also applies to depictions of moving animals (e.g., a dolphin jumping out of the water) and moving objects (e.g., a car driving on a street, a burger being tossed in the air). Across a set of seven experimental studies, we provide robust evidence that images depicting animate and inanimate motion increase the persuasiveness of an advertisement and that this effect occurs through enhanced engagement. Our findings further indicate that the level of engagement is influenced by the complexity of the depicted motion, with more complex, nonlinear movements eliciting greater engagement than simpler, linear movements. Overall, this research contributes to the advertising literature by providing an empirically grounded account of implied motion imagery and by helping marketers create more effective advertising.

Movement decisions driving metapopulation connectivity respond to social resources in a long-lived ungulate, bighorn sheep (Ovis canadensis).

The spatial availability of social resources is speculated to structure animal movement decisions, but the effects of social resources on animal movements are difficult to identify because social resources are rarely measured. Here, we assessed whether varying availability of a key social resource-access to receptive mates-produces predictable changes in movement decisions among bighorn sheep in Nevada, the United States. We compared the probability that males made long-distance 'foray' movements, a critical driver of connectivity, across three ecoregions with varying temporal duration of a socially mediated factor, breeding season. We used a hidden Markov model to identify foray events and then quantified the effects of social covariates on the probability of foray using a discrete choice model. We found that males engaged in forays at higher rates when the breeding season was short, suggesting that males were most responsive to the social resource when its existence was short lived. During the breeding season, males altered their response to social covariates, relative to the non-breeding season, though patterns varied, and age was associated with increased foray probability. Our results suggest that animals respond to the temporal availability of social resources when making the long-distance movements that drive connectivity. This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

Anthropogenic impacts at the interface of animal spatial and social behaviour.

Human disturbance is contributing to widespread, global changes in the distributions and densities of wild animals. These anthropogenic impacts on wildlife arise from multiple bottom-up and top-down pathways, including habitat loss, resource provisioning, climate change, pollution, infrastructure development, hunting and our direct presence. Animal behaviour is an important mechanism linking these disturbances to population outcomes, although these behavioural pathways are often complex and can remain obscured when different aspects of behaviour are studied in isolation from one another. The spatial-social interface provides a lens for understanding how an animal's spatial and social environments interact to determine its spatial and social phenotype (i.e. measurable characteristics of an individual), and how these phenotypes interact and feed back to reshape environments. Here, we review studies of animal behaviour at the spatial-social interface to understand and predict how human disturbance affects animal movement, distribution and intraspecific interactions, with consequences for the conservation of populations and ecosystems. By understanding the spatial-social mechanisms linking human disturbance to conservation outcomes, we can better design management interventions to mitigate undesired consequences of disturbance.This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

A wrap-around movement path randomization method to distinguish social and spatial drivers of animal interactions.

Studying the spatial-social interface requires tools that distinguish between social and spatial drivers of interactions. Testing hypotheses about the factors determining animal interactions often involves comparing observed interactions with reference or 'null' models. One approach to accounting for spatial drivers of social interactions in reference models is randomizing animal movement paths to decouple spatial and social phenotypes while maintaining environmental effects on movements. Here, we update a reference model that detects social attraction above the effect of spatial constraints. We explore the use of our 'wrap-around' method and compare its performance to the previous approach using agent-based simulations. The wrap-around method provides reference models that are more similar to the original tracking data, while still distinguishing between social and spatial drivers. Furthermore, the wrap-around approach results in fewer false-positives than its predecessor, especially when animals do not return to one place each night but change movement foci, either locally or directionally. Finally, we show that interactions among GPS-tracked griffon vultures (Gyps fulvus) emerge from social attraction rather than from spatial constraints on their movements. We conclude by highlighting the biological situations in which the updated method might be most suitable for testing hypotheses about the underlying causes of social interactions. This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

Phylogenetic analyses and antigenic characterization of foot-and-mouth disease virus PanAsia lineage circulating in China between 1999 and 2023

Foot-and-mouth disease (FMD) is one of the most important transboundary animal diseases caused by foot-and-mouth disease virus (FMDV), leading to significant economic losses worldwide. The first report of PanAsia lineage of FMDV in China was in 1999. Since 2011, 18 outbreaks attributed to PanAsia lineage viruses have been reported across 7 provinces or municipality in China. Phylogenetic analysis indicated that these PanAsia strains were clustered into three distinct clades (clade 1, clade 2, and clade 3), with nucleotide homology ranging from 91.4% to 100%. The outbreaks of FMD caused by clade 1 strains occurred around 1999 when this lineage was prevalent globally. Clade 2 strains dominated from 2011 to 2013, while clade 3 strains were prevalent during 2018–2019, sharing only 93% homology with clade 2 strains and 91% with clade 1 strains. Tracing analysis showed that these outbreaks represented 3 distinct introductions of PanAsia viruses into China. Virus neutralization tests (VNT) have demonstrated that current commercial vaccines are effective to protect susceptible animals against these strains (r1 ​> ​0.3). However, the growing demand for livestock has promoted animal movement and encouraged the exchange of products, services, and materials between countries, thereby heightening the risk of exotic strain incursions. Therefore, it is imperative to reinforce border controls and limit animal movements among various Asian countries continually to reduce the risk of new transboundary diseases, such as FMD incursion. Additionally, PanAsia-2 strains need to be taken seriously to prevent its incursions, and the relevant vaccines against PanAsia-2 strains need to be stockpiled in preparation for any possible incursion.

Estimating ungulate migration corridors from sparse movement data

AbstractMany ungulates migrate between distinct summer and winter ranges, and identifying, mapping, and conserving these migration corridors have become a focus of local, regional, and global conservation efforts. Brownian bridge movement models (BBMMs) are commonly used to empirically identify these seasonal migration corridors; however, they require location data sampled at relatively frequent intervals to obtain a robust estimate of an animal's movement path. Fitting BBMMs to sparse location data violates the assumption of conditional random movement between successive locations, overestimating the area (and width) of a migration corridor when creating individual and population‐level occurrence distributions and precluding the use of low‐frequency, or sparse, data in mapping migration corridors. In an effort to expand the utility of BBMMs to include sparse GPS data, we propose an alternative approach to model migration corridors from sparse GPS data. We demonstrate this method using GPS data collected every 2 h from four mule deer (Odocoileus hemionus) and four elk (Cervus canadensis) herds within Wyoming and Idaho. First, we used BBMMs to estimate a baseline corridor for the 2‐h data. We then subsampled the 2‐h data to one location every 12 h (a proxy for sparse data) and fitted BBMMs to the 12‐h data using a fixed motion variance (FMV) value, instead of estimating the Brownian motion variance empirically. A range of FMV values was tested to identify the value that best approximated the baseline migration corridor. FMV values within a species‐specific range (mule deer: 400–1200 m2; elk: 600–1600 m2) successfully delineated migration corridors similar to the 2‐h baseline corridors; overall, lower values delineated narrower corridors and higher values delineated wider corridors. Optimal FMV values of 800 m2 (mule deer) and 1000 m2 (elk) decreased the inflation of the 12‐h corridors relative to the 2‐h corridors from traditional BBMMs. This FMV approach thus enables using sparse movement data to approximate realistic migration corridor dimensions, providing an important alternative when movement data are collected infrequently. This approach greatly expands the number of datasets that can be used for migration corridor mapping—a useful tool for management and conservation across the globe.

Unveiling the roles of temporal periodicity, the spatial environment and behavioural modes in terrestrial animal movement

BackgroundAnimal movement arises from complex interactions between animals and their heterogeneous environment. To better understand the movement process, it can be divided into behavioural, temporal and spatial components. Although methods exist to address those various components, it remains challenging to integrate them in a single movement analysis.MethodsWe present an analytic workflow that integrates the behavioural, temporal and spatial components of the movement process and their interactions, which also allows for the assessment of the relative importance of those components. We construct a daily cyclic covariate to represent temporally cyclic movement patterns, such as diel variation in activity, and combine the three components in a multi-modal Hidden Markov Model framework using existing methods and R functions. We compare the trends and statistical fits of models that include or exclude any of the behavioural, spatial and temporal components, and perform variance partitioning on the model predictions that included all components to assess their relative importance to the movement process, both in isolation and in interaction.ResultsWe apply our workflow to a case study on the movements of plains zebra, blue wildebeest and eland antelope in a South African reserve. Behavioural modes impacted movement the most, followed by diel rhythms and then the spatial environment (viz. tree cover and terrain slope). Interactions between the components often explained more of the movement variation than the marginal effect of the spatial environment did on its own. Omitting components from the analysis led either to the inability to detect relationships between input and response variables, resulting in overgeneralisations when drawing conclusions about the movement process, or to detections of questionable relationships that appeared to be spurious.ConclusionsOur analytic workflow can be used to integrate the behavioural, temporal and spatial components of the movement process and quantify their relative contributions, thereby preventing incomplete or overly generic ecological interpretations. We demonstrate that understanding the drivers of animal movement, and ultimately the ecological phenomena that emerge from it, critically depends on considering the various components of the movement process, and especially the interactions between them.

Influence of Fix Schedule on the Location Accuracy of a Low-Cost GPS Data Logger on Cattle

ABSTRACT Global positioning system (GPS) data loggers are commonly used to track the movements and distribution of both wild and domestic animals. However, the expense often poses a challenge for researchers. Recently, there has been a rise in the utilization of affordable and user-friendly GPS data loggers for tracking animal movements, albeit with compromised accuracy. We aimed to identify factors influencing the accuracy of a low-cost GPS data logger (I-gotU GT-600) and to enhance its location accuracy. Initial investigations revealed that recording intervals impacted the location error of the GPS data logger. To elucidate the relationship between recording intervals and location accuracy, we conducted stationary and motion tests. Our findings indicated that recording intervals of less than 15 sec substantially enhances the location accuracy of the low-cost GPS data logger. Our results highlight the relationship between the fix schedule and location accuracy for these GPS data loggers. Our study provides information that enhances the quality of data for researchers using low-cost GPS data loggers for short-term studies in various settings, such as zoos and livestock facilities.

Network theory and migration: Avoiding misapplications and misinterpretations

Network analysis is becoming a popular tool for the study of animal movement. Proliferation of software enables researchers to use network measures without reflecting on the underlying mathematics and biology. One common use characterizing movement networks, such as migration, with centrality measures. Predominantly developed to evaluate social systems, these measures now appear in applications across disciplines that vary greatly in system properties. Each network measure has a very specific mathematical definition, with implicit assumptions about system properties to which they are applied that often make them inappropriate for migration. We report mismatches between mathematical assumptions and applications of network measures to migration, primarily with bird examples. For example, of 11 trajectory-transmission network-flow processes observed in the real world, only two are applicable to bird migration, neither of which allows valid application of classical centrality measures. We also identify misinterpretations of network measures, such as nodes with the highest degree centrality as locations where individuals intermingle. Other misinterpretations include that a high betweenness score translates to a node that is frequently used or is an important site along a main migratory route. Finally, networks are used in different ways, ranging from descriptive visualization to quantitative prediction and we identify common cases, such as migratory connectivity, where this leads to miscommunication. Network-theoretic approaches to answer ecological research and conservation questions associated with migration are numerous, but new measures need to be developed for animal migration. We provide a guide for researchers of animal movement for measure selection, development, and documentation.

Rationalizing the use of common parameters and technological tools to follow up Mycoplasma hyopneumoniae infections in pigs

BackgroundMycoplasma (M.) hyopneumoniae is associated with respiratory disease in pigs and is the primary agent of enzootic pneumonia. Quantification of M. hyopneumoniae-related outcome parameters can be difficult, expensive, and time-consuming, in both research and field settings. In addition to well-established methods, technological tools are becoming available to monitor various aspects of relevant animal- and environment-related features, often in real-time. Therefore, this study aimed to assess whether certain parameters, such as animal movement and body temperature using microchips (IMT), correlate with established parameters and whether the currently used parameters can be rationalized.ResultsThe percentage of movement was significantly reduced by M. hyopneumoniae infection in pigs (p < 0.05), where the M. hyopneumoniae-infected group showed a lower percentage of movement (1.9%) when compared to the negative control group (6.9%). On the other hand, macroscopic (MLCL) and microscopic (MLL) lung lesions, respiratory disease score (RDS), M. hyopneumoniae-DNA load, and anti-M. hyopneumoniae antibody levels increased significantly in the M. hyopneumoniae-infected group 28 days post-inoculation (p < 0.05). Moderate (r > 0.30) to very strong correlations (> 0.80) were observed between the abovementioned parameters (p < 0.05), except for IMT. A significant and moderate correlation was reported between IMT and rectal temperature (r = 0.49; p < 0.05). Last, the average daily weight gain and the percentage of air in the lung were not affected by M. hyopneumoniae infection (p > 0.05).ConclusionsM. hyopneumoniae infection significantly reduced the movement of piglets and increased lung lesions, M. hyopneumoniae-DNA load, and anti-M. hyopneumoniae antibody levels; and, good correlations were observed between most parameters, indicating a direct relationship between them. Thus, we suggest that changes in movement might be a reliable indicator of M. hyopneumoniae infection in pigs, and that a selected group of parameters—specifically RDS, MLCL, MLL, M. hyopneumoniae-DNA load, anti-M. hyopneumoniae antibody levels, and movement—are optimal to assess M. hyopneumoniae infection under experimental conditions.

Spnaf: An R package for analyzing and mapping the hotspots of flow datasets

This paper introduces {spnaf} (spatial network autocorrelation for flows), an R package designed for the hotspot analysis of flow (e.g., human mobility, transportation, and animal movement) datasets based on Berglund and Karlström’s G index. We demonstrate the utility of the {spnaf} package through two example analyses by data forms: 1) bike-sharing trip patterns in Columbus, Ohio, USA, using polygon data, and 2) U.S. airports’ passenger travel patterns, using point data. The {spnaf} is available for download from the Comprehensive R Archive Network (CRAN), which contains a vignette and sample data/code for immediate use. This package addresses limitations in existing spatial analysis packages and emphasizes its efficiency in detecting flow hotspots. It is highly applicable in various urban and geographic data science applications. {spnaf} is still in its early stages and we hope that interested readers can contribute to the development and enhancement of the package.

Migratory-derived resources induce elongated food chains through middle-up food web effects

BackgroundSeasonal movements of animals often result in the transfer of large amounts of energy and nutrients across ecosystem boundaries, which may have large consequences on local food webs through various pathways. While this is known for both terrestrial- and aquatic organisms, quantitative estimates on its effects on food web structure and identification of key pathways are scarce, due to the difficulty in obtaining replication on ecosystem level with negative control, i.e. comparable systems without migration.MethodsIn this study, we estimate the impact of Arctic charr (Salvelinus alpinus) migration on riverine ecosystem structure, by comparing multiple streams with strictly resident populations above natural migration barriers with streams below those barriers harboring partially migratory populations. We compared density estimates and size structure between above and below populations. Diet differences were examined through the analysis of stomach contents, changes in trophic position were examined by using stable isotopes. To infer growth rate of resident individuals, back-growth calculation was performed using otoliths.ResultsWe find higher densities of small juveniles in partially migratory populations, where juvenile Arctic charr show initially lower growth, likely due to higher intraspecific competition. After reaching a size, where they can start feeding on eggs and smaller juveniles, which are both more frequent in partially migratory populations, growth surpasses that of resident populations. Cannibalism induced by high juvenile densities occurred almost exclusively in populations with migration and represents an altered energy pathway to the food web. The presence of large cannibalistic charr feeding on smaller ones that have a similar trophic level as charr from strictly resident populations (based on stomach content) coupled with steeper δ15N-size regression slopes illustrate the general increase of food chain length in systems with migration.ConclusionsOur results thus suggest that the consumption of migration-derived resources may result in longer food chains through middle-up rather than bottom-up effects. Furthermore, by occupying the apex of the food chain and feeding on juvenile conspecifics, resident individuals experience reduced competition with their young counterparts, which potentially balances their fitness with migratory individuals.

Integrating threat mapping and animal movement data to identify high‐risk areas for endangered mobile species

AbstractGiven the current biodiversity crisis, understanding how animals move across a landscape dotted with different anthropogenic threats and the consequences of those threats for animals is paramount to devising evidence‐based conservation interventions. Vultures roam across large areas and are highly exposed to poisoning, which represents a particularly damaging form of wildlife crime. In this study, we introduce a framework for quantifying the exposure to threats and illustrate an example of poisoning risk as a threat in an endangered African vulture species, the Lappet‐faced Vulture (Torgos tracheliotos). We combined GPS tracking data of 19 individuals collected between 2012 and 2022 with food availability and spatial threat maps of both intentional (poachers directly targeting vultures) and unintentional (farmers aiming to kill carnivores, with vultures being secondarily affected) poisoning across most of Southern Africa. We identified poisoning hotspots in northern Botswana and south‐eastern Namibia. These areas were also associated with a high number of vulture mortalities, providing additional support for poisoning risk. Northern Botswana and areas at the border between Botswana and South Africa were characterized by high food availability, potentially amplifying the mortality rate by attracting vultures from surrounding areas. Our results offer valuable insights for regional vulture conservation, together with a methodological framework for quantifying and mapping the spatial exposure to threats for mobile species of conservation concern, enabling improved targeting of conservation actions.

Special issue on adaptive motion of animals and machines

Special issue on adaptive motion of animals and machines

Navigating the Landscape of Tick Diversity: Integrating Molecular Approaches for Enhanced Control Measures.

The emergence and spread of infectious diseases, particularly zoonotic diseases originating from wildlife, pose significant threats to global health and economy. This review examines the pivotal role of ticks as vectors in transmitting pathogens to humans, livestock, and wildlife and the use of molecular techniques in their identification. Tick infestations result in economic losses through reduced animal productivity, anemia, and quality deterioration of hides. Furthermore, ticks serve as reservoirs for a wide range of pathogens including viruses, bacteria, fungi, protozoa, and nematodes, contributing to the transmission of diseases such as Crimean-Congo hemorrhagic fever, tick-borne encephalitis, and African swine fever among others. The interface between wildlife, livestock, and humans facilitates the transmission of zoonotic pathogens, exacerbated by nomadic and pastoralist lifestyles that promote interactions between wildlife and domestic animals. This movement of animals across landscapes enhances the dispersion of tick vectors, increasing the risk of pathogen exposure for diverse populations. Historically, tick identification in sub-Saharan Africa has relied on morphological characteristics despite limitations such as species overlap and variability. Molecular techniques offer a more precise means of species identification, providing critical data for effective tick and pathogen management strategies. Integrating molecular approaches into tick research enhances our understanding of tick diversity, distribution patterns, and pathogen dynamics. This knowledge is essential for developing targeted interventions to mitigate the impact of tick-borne diseases on public and veterinary health worldwide.

Ranavirus and Its Impact on the Amphibian Population

Mass mortality of amphibians has occurred globally since at least the early 1990s from viral pathogens that are members of the genus Ranavirus, family Iridoviridae. This disease has a wide host range and with the movement of ranavirus-infected animals in commerce, ranaviruses have become an increasing threat to captive and wild populations, especially amphibians. The clinical signs of amphibians include haemorrhaging, lethargy, swelling, and ulcerative skin lesions. The virus is highly contagious, spreading through direct contact with infected individuals or contaminated environments, such as water bodies. Environmental factors, including temperature and pollution, can influence the severity and spread of ranavirus infections. The ecological impact of ranavirus is profound, as it threatens biodiversity and disrupts ecosystems. Consequently, understanding the epidemiology, pathology, and control measures for ranavirus is critical for conserving amphibian populations. Current research efforts are focused on developing effective strategies for monitoring, managing, and mitigating the impacts of this pathogen on amphibian communities. This review presents distribution, hosts, and epidemiology to better understand this disease.

Should I stay or move? Quantifying landscape of fear to enhance environmental management of road networks in a highly transformed landscape

The development and expansion of road networks pose considerable threats to natural habitats and wildlife, fostering a landscape of fear. In addition to direct mortality caused by road collisions, road construction and maintenance often result in habitat fragmentation and loss, impeding animal movement and gene flow between populations. Mountain ungulates are already confined to fragmented habitat patches and roads can cause substantial disturbances to their critical ecological processes, such as dispersal and migration. In this study, we employed two key mountain ungulates, the wild goat (Capra aegagrus) and mouflon (Ovis gmelini), as functional models to examine how road networks impact the quantity and connectivity of natural habitats in southwestern Iran, where extensive road construction has led to significant landscape changes. We used the MaxEnt method to predict species distribution, the circuit theory to evaluate habitat connectivity, and the Spatial Road Disturbance Index (SPROADI) to assess road impacts. During the modeling process, we selected eleven important variables and employed a model parametrization strategy to identify the optimal configuration for the MaxEnt model. For SPROADI index we used three sub-indices, including traffic intensity, vicinity impact, and fragmentation grade. We then integrated the results of these analyses to identify areas with the most significant environmental impacts of roads on the coherency of the natural habitats. The findings indicate that suitable habitats for wild goats are widely distributed across the study area, while suitable habitats for mouflon are primarily concentrated in the northeastern region. Conservation gap analysis revealed that only 8% of wild goat habitats and 7% of mouflon habitats are covered by protected areas (PAs). The SPROADI map highlighted that 23% of the study area is negatively influenced by road networks. Moreover, 30.4% of highest-probability corridors for mouflon, and 25.7% for wild goat, were highly vulnerable to the impacts of roads. Our combined approach enabled us to quantitatively assess species-specific vulnerability to the impacts of heavy road networks. This study emphasizes the urgent need to address the negative effects of road networks on wildlife habitats and connectivity corridors. Our approach effectively identifies sensitive areas, which can help inform mitigation strategies and support more effective conservation planning in significantly transformed landscapes.

Machine learning-based bee recognition and tracking for advancing insect behavior research

The study of insect behavior, particularly that of honey bees, has a broad scope and significance. Tracking bee flying patterns grants much helpful information about bee behavior. However, tracking a small yet fast-moving object, such as a bee, is difficult. Hence, we present artificial intelligence, machine-learning-based bee recognition, and tracking systems to assist the researcher in studying the bee’s behavior. To develop a machine learning system, a labeled database is required for model training. To address this, we implemented an automated system for analyzing and labeling bee videos. This labeled database served as the foundation for two distinct bee-tracking solutions. The first solution (planar bee tracking system) tracked individual bees in closed mazes using a neural network. The second solution (spatial bee tracking system) utilized a neural network and a tracking algorithm to identify and track flying bees in open environments. Both systems tackle the challenge of tracking small-bodied creatures with rapid and diverse movement patterns. Although we applied these systems to entomological cognition research in this paper, their relevance extends to general insect research and developing tracking solutions for small organisms with swift movements. We present the complete architecture and detailed methodologies to facilitate the utilization of these models in future research endeavors. Our approach is a simple and inexpensive method that contributes to the growing number of image-analysis tools used for tracking animal movement, with future potential applications under less sterile field conditions. The tools presented in this paper could assist the study of movement ecology, specifically in insects, by providing accurate movement specifications. Following the movement of pollinators or natural enemies, for example, greatly contributes to the study of pollination or biological control, respectively, in natural and agro-ecosystems.

Grid Cells in Cognition: Mechanisms and Function.

The activity patterns of grid cells form distinctively regular triangular lattices over the explored spatial environment and are largely invariant to visual stimuli, animal movement, and environment geometry. These neurons present numerous fascinating challenges to the curious (neuro)scientist: What are the circuit mechanisms responsible for creating spatially periodic activity patterns from the monotonic input-output responses of single neurons? How and why does the brain encode a local, nonperiodic variable-the allocentric position of the animal-with a periodic, nonlocal code? And, are grid cells truly specialized for spatial computations? Otherwise, what is their role in general cognition more broadly? We review efforts in uncovering the mechanisms and functional properties of grid cells, highlighting recent progress in the experimental validation of mechanistic grid cell models, and discuss the coding properties and functional advantages of the grid code as suggested by continuous attractor network models of grid cells.

A Markovian dynamics for Caenorhabditis elegans behavior across scales

How do we capture the breadth of behavior in animal movement, from rapid body twitches to aging? Using high-resolution videos of the nematode worm Caenorhabditis elegans, we show that a single dynamics connects posture-scale fluctuations with trajectory diffusion and longer-lived behavioral states. We take short posture sequences as an instantaneous behavioral measure, fixing the sequence length for maximal prediction. Within the space of posture sequences, we construct a fine-scale, maximum entropy partition so that transitions among microstates define a high-fidelity Markov model, which we also use as a means of principled coarse-graining. We translate these dynamics into movement using resistive force theory, capturing the statistical properties of foraging trajectories. Predictive across scales, we leverage the longest-lived eigenvectors of the inferred Markov chain to perform a top-down subdivision of the worm's foraging behavior, revealing both "runs-and-pirouettes" as well as previously uncharacterized finer-scale behaviors. We use our model to investigate the relevance of these fine-scale behaviors for foraging success, recovering a trade-off between local and global search strategies.