Editorial: Cognitive movement ecology

Papers, dissertations and books devoted to the analysis of animal movement often invite interest in the subject with the incontrovertible claim that all animals move. It is no less true and no less obvious that all animals perceive, remember, and think (though cognitive scientists seem less obligated to remind everyone of the fact). Perception, memory, orientation, and navigation are all cognitive components that have been identified, in a zeitgeisty collection of simultaneous independent studies, as central to animal movement (Nathan et al. 2008, Mueller and Fagan 2008, Schick et al. 2008). And yet, the cognitive causes and consequences of animal movement remain nearly as understudied now (Joo et al. 2022) as then (Holyoak et al. 2008).There are several reasons behind the apparent chasm dividing these fields. Many developments in movement ecology often "chase" both the data and the rapidly developing telemetry technology, the development of which is often driven in support of concrete needs to monitor animal populations for management or conservation. Although biologists are generally aware, and often in awe, of the cognitive ability of their study species, the very thought of trying to measure or quantify something as unobservable as cognition is daunting and of limited apparent practical utility.In contrast, the history and pedigree of ethological studies on animals is much longer. One might argue that, as an applied exercise, it includes all human groups that have ever engaged in the domestication of wild animals. In the Western scientific tradition, notably contributors include Darwin, Pavlov, and Lorenz. However, as a scientific endeavor, ethology has focused on animals that are easy to observe and therefore amenable to controlled experimentation, in almost all cases captive or domesticated (Wynne and Udell 2020). Much as the wildlife manager may wonder what practical information can be obtained from considering cognition in a wild deer, an ethologist may wonder what can possibly be inferred about the cognition of an animal that can only be indirectly observed through blips of satellite locations and upon whom experimental manipulation is impractical. With the exception of a handful of neurological phenomena, cognitive processes are latent, and there are good reasons to shy away from studying what we cannot observe.And yet, in the past decade there has been growing theoretical and empirical evidence that perception (Fagan et al. 2017), spatial memory (Fagan et al. 2013, Merkle et al. 2014, Avgar et al. 2015, Schlagel et al. 2017), and social and experiential learning (Mueller et al. 2013, Berdahl et al. 2018, Jesmer et al. 2018, Abrahms et al. 2021) are all fundamental to the way that freeranging animals use space. It therefore felt timely and important to collect original research under the novel rubric of "Cognitive Movement Ecology" into a single collection. We invited a wide array of conceptual, theoretical, and empirical papers, which we believe reflect the diversity of perspectives characterizing this relatively new sub-field, and which we hope will aid in create dialog among those of us who already study cognitive movement ecology and attract others to join the party.The resulting Special Topics collection includes 15 contributions which strike, we feel, an admirable balance between concepts, theory, methods and applications. Specifically, our collection is comprised of: 2 high-level reviews, 4 explicitly theoretical contributions leaning on numerical analysis and simulation, 2 articles that propose novel heuristic approaches to inferring cognition from movement data, and, finally, 7 articles that bravely seek to make direct inference and even predictions about cognitive processes of free ranging animals based primarily on movement data. We provided no explicit guidelines to definitions or themes and were struck by the ways in which important themes emerged and similar goals were set in papers with markedly different approaches. In this editorial, we summarize the four sections of this collection, making an effort to link the common themes across sections, and conclude with our view of the future of this young branch of ecology.The collection opens with a comprehensive review of the cognitive ecology of animal movement (Kashetsky et al. 2021), setting the stage with a clear definition: that cognition is one of several processes that deal with the acquisition, retention, and use of information. The authors further explore several critical mechanisms by which such acquisition occurs, with an emphasis on the important role of social learning. The authors consider several observable spatial phenomena -all direct consequences of movement -that are exhibited by animals, in particular migration, homing, home ranging, trail following, and spatial learning. There is emphasis on the perceptual mechanisms and ranges (e.g. viewsheds, soundscapes, and smellscapes), including a consideration of the complexity and "cognitive costs" of different kinds of learning. These themes are laid out with several compelling published examples, and are all returned to explicitly and specifically (though largely independently) in almost every subsequent paper in the collection. It bears noting, however, that the examples and synthesis provided are based primarily on experimental studies such as pigeon (Colomba livia domestica) releases and manipulated spatial feeding configurations for domestic sheep (Ovis aries).The second major conceptual contribution (Lewis et al. 2021) narrows the focus on learning (i.e.the acquisition and use of information), while broadening the disciplinary scope by pulling in vernacular, metaphors, and approaches from such fields as machine learning and robotics, as well as in psychology and behavior (their Box 1 provides a comprehensive glossary). Again -a clear definition rooted in the psychology literature is provided: that learning is a process of information acquisition that occurs via experience and leads to consistent and predictable neurophysiological or behavioral change. In the context of this collection, the relevant observable behavioral change is specifically movement data. Much effort goes into covering the various mechanisms of learning (individual, social, positively reinforced, negatively reinforced, etc.). A set of rigorous criteria are proposed to identify whether actual learning is observed in a given study. Important distinctions are made between the kind of "fundamental learning" that occurs in a novel, or significantly perturbed, environment, compared to the kind of "maintenance learning" that is continuously ongoing in a dynamic but stochastically stationary environment. The former is more dramatic and categorical and can occasionally be inferred from "uncontrolled experiments" like translocations, introductions, or major environmental perturbations like habitat fragmentation or destruction. The second kind of learning is more subtle and reflects the ability of animals to continuously update information and make decisions. These two papers provide crucial conceptual context for later contributions in the collection, all of which slot neatly into themes anticipated by these two overviews.Theoretical studies lean on numerical studies and simulations and have the freedom to essentially create universes from scratch. In so doing, researchers can explore processes that are impossible to observe over a range of conditions that stretch the credible, potentially leading to profound insights into fundamental principles that produce patterns that are -in fact -widely observed in the wild. Swain et al. (2021) -focusing on the evolution of perception -used millions of agent-based models to incorporate the relatively unexplored costs of perception to constrain the simulated emergence of optimal evolutionary scales of perception ranges. In identifying the conditions under which non-local perception is selected for, the authors found unintuitive interactions between, among others, resource density and energetic costs. Notably, low-resource environments led to the evolution of either zero perceptual range, or large perceptual rangespointing towards two divergent and apparently contradictory strategies in low-resource environments, consistent with observations (e.g., deep-water crustaceans either are entirely blind, or have exceptionally large eyes). The dramatic evolutionary trade-offs inherent in the evolution of perception (steep costs, high returns), leading to the wide range of evolutionary outcomes, is likely mirrored in the emergence of cognitive properties, like spatial memory and social learning, and the dizzying range of those adaptations. Indeed, it can be argued that memory itself is a kind of "temporal perceptual range", that uses information from the past to "perceive" the future. 2021), ask a complementary question: what possible non-genetic mechanisms can lead to the emergence, maintenance, and resilience of seasonal migrations, a very widespread and successful strategy that involves considerably uncertainty, risk, and energetic cost. Using a different computational approach from the other three theoretical studies (partial differential equations rather than agent-based simulations), the authors explore how collective memory, sociality, exploration, resource following, and learning all interact to exploit a highly seasonal and disconnected resource environment; i.e. one where the "patchiness" is extreme, but the predictability is high. For migration to emerge, all these ingredients are required, but mixed in just the right proportions: social cohesion to share information must be balanced against exploratory behavior to acquire new information, and a deep well of reference memory to lean on must be balanced against the ability to modify that reference in response to new information. Even in the highly synthetic conditions of the model, striking optimal balance is not easy; but, much as in the evolutionary model of Swain et al. (2021), the rewards can be considerable. And, though there is no selection in the model per se, it is clear that social learning as a mechanism can operate at time scales that are much more rapid than genetic selection.Cognition is, however, not only about what the animals know (perception and memory), it is also about what they do not know, and how they might learn and make movement decision in the face of uncertainty. In the absence of perfect information, animals must rely on approximations to update their knowledge of their environment, as well as the expected outcomes of their decisions. Using individual-based simulations in a dynamic depleting and regenerating resource landscape, Avgar and Berger-Tal (2022) examine the role of two types of optimism as adaptive strategy for partially informed optimal foragers. Using a simple agentbased model, they show that moderate discounting of information from undesirable outcomes ('positivity biased learning' or 'valence-dependent optimism') results in improved fitness in environments characterized by high resource variability.As if expressly to punish any irrationally optimistic foragers, Bracis and Wirsing (2021) introduce predators into a similar simulated dynamic resource environment to study the widely reported phenomenon of the "Landscape of Fear". The authors build on a versatile continuous-time, continuous-space framework developed for the exploration of the role of spatial memory in guiding mobile foragers navigating dynamic landscapes (Bracis et al. 2015(Bracis et al. , 2018)). Within this habituated prey/resource system, the authors then release predators in high resource areas.The prey are left to learn from near escapes, and eventually to associate high quality habitat with increased risk. Somewhat analogous to Gurarie et al. (2021) This method of learning relies on two memory streams -a long-term "reference memory" (e.g. of fundamentally suitable habitat) and a short-term "working memory" which pushes the forager from recently depleted patches. Interestingly, these apparently simplistic two streams of memory are capable of both fundamentally learning about the new predator element, and of continuous maintenance learning (sensu Lewis et al. 2021). The authors find that landscape of fear effects, in particular more time spent searching and less net consumption, do emerge with the presence of predators. However, the factors that lead to the most dramatic effects are primarily intrinsic, i.e. related to memory and personality, rather than external, i.e. related to the configuration of the environment. Specifically, the effects are greatest when animals are initially naïve to their environment and when they are highly conservative (akin to Avgar and Berger-Tal's pessimists).This result is important as a reminder that in real systems intrinsic states can easily be as important as the kinds of external, environmental factors that are most commonly used to model animal movements.While very different in purpose and technique, a clear theme emerges from this suite of theoretical explorations: that the value of perception, memory, and learning for fitness is a direct consequence of the spatial structure and temporal dynamics of the environment the animal moves through. Thus, a somewhat unexpected corollary emerges: cognitive abilities serve above all else to compensate for constraints and limitations in the ability to move across the landscape itself.While all the empirical studies rely to varying extents on methodological innovations, two contributions to this collection stand out for proposing purely trajectory-based approaches to analyzing movement data, pointing towards widely observed spatial patterns that -the authors claim -can only emerge from memory-driven movement process. Gautestad (2022) explores the topological properties of movement tracks that emerge from a model of self-reinforcing (i.e. memory-driven) returns to previously visited locations. This ultimately very simple model leads to patterns of space use that can be represented as a "scalefree network". In other words, it has rare "dominant nodes" and very many "rarely visited" nodes, distributed in such a way that the frequency of degree centrality scores has a predictable log-log relationship. Gautestad shows that -when decomposed to a node-to-node type -empirical data on black bear movements (Ursus americanus) consistently show precisely the scale-free properties expected by this memory-driven random walk. A fascinating analogy is made with the global internet network, which is also scale-free and therefore to on In similar Gautestad an applied that the movements and of a animal is to to of There is an corollary to this if a movement these scale-free properties, this may a in movement et al. 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