Remembering the good and the bad: memory-based mediation of the food–safety trade-off in dynamic landscapes

The role of memory-based movements in the formation of animal home ranges

Most animals live in spatially-constrained home ranges. The prevalence of this space-use pattern in nature suggests that general biological mechanisms are likely to be responsible for their occurrence. Individual-based models of animal movement in both theoretical and empirical settings have demonstrated that the revisitation of familiar areas through memory can lead to the formation of stable home ranges. Here, we formulate a deterministic, mechanistic home range model that includes the interplay between a bi-component memory and resource preference, and evaluate resulting patterns of space-use. We show that a bi-component memory process can lead to the formation of stable home ranges and control its size, with greater spatial memory capabilities being associated with larger home range size. The interplay between memory and resource preferences gives rise to a continuum of space-use patterns-from spatially-restricted movements into a home range that is influenced by local resource heterogeneity, to diffusive-like movements dependent on larger-scale resource distributions, such as in nomadism. Future work could take advantage of this model formulation to evaluate the role of memory in shaping individual performance in response to varying spatio-temporal resource patterns.

Spatial Memory Drives Foraging Strategies of Wolves, but in Highly Individual Ways

The ability of wild animals to navigate and survive in complex and dynamic environments depends on their ability to store relevant information and place it in a spatial context. Despite the centrality of spatial memory, and given our increasing ability to observe animal movements in the wild, it is perhaps surprising how difficult it is to demonstrate spatial memory empirically. We present a cognitive analysis of movements of several wolves (Canis lupus) in Finland during a summer period of intensive hunting and den-centered pup-rearing. We tracked several wolves in the field by visiting nearly all GPS locations outside the den, allowing us to identify the species, location and timing of nearly all prey killed. We then developed a model that assigns a spatially explicit value based on memory of predation success and territorial marking. The framework allows for estimation of multiple cognitive parameters, including temporal and spatial scales of memory. For most wolves, fitted memory-based models outperformed null models by 20 to 50% at predicting locations where wolves chose to forage. However, there was a high amount of individual variability among wolves in strength and even direction of responses to experiences. Some wolves tended to return to locations with recent predation success—following a strategy of foraging site fidelity—while others appeared to prefer a site switching strategy. These differences are possibly explained by variability in pack sizes, numbers of pups, and features of the territories. Our analysis points toward concrete strategies for incorporating spatial memory in the study of animal movements while providing nuanced insights into the behavioral strategies of individual predators.

Prey encounters and spatial memory influence use of foraging patches in a marine central place forager

Given the patchiness and long-term predictability of marine resources, memory of high-quality foraging grounds is expected to provide fitness advantages for central place foragers. However, it remains challenging to characterize how marine predators integrate memory with recent prey encounters to adjust fine-scale movement and use of foraging patches. Here, we used two months of movement data from harbour seals (Phoca vitulina) to quantify the repeatability in foraging patches as a proxy for memory. We then integrated these data into analyses of fine-scale movement and underwater behaviour to test how both spatial memory and prey encounter rates influenced the seals' area-restricted search (ARS) behaviour. Specifically, we used one month's GPS data from 29 individuals to build spatial memory maps of searched areas and archived accelerometery data from a subset of five individuals to detect prey catch attempts, a proxy for prey encounters. Individuals were highly consistent in the areas they visited over two consecutive months. Hidden Markov models showed that both spatial memory and prey encounters increased the probability of seals initiating ARS. These results provide evidence that predators use memory to adjust their fine-scale movement, and this ability should be accounted for in movement models.

Prey Foraging Behavior After Predator Introduction Is Driven by Resource Knowledge and Exploratory Tendency

Predator reintroductions are often used as a means of restoring the ecosystem services that these species can provide. The ecosystem consequences of predator reintroduction depend on how prey species respond. Yet, to date, we lack a general framework for predicting these responses. To address this knowledge gap, we modeled the impacts of predator reintroduction on foragers as a function of predator characteristics (habitat domain; i.e., area threatened) and prey characteristics (knowledge of alternative habitat and exploratory tendency). Foraging prey had the capacity to both remember and return to good habitat and to remember and avoid predators. In general, we found that forager search time increased and consumption decreased after predator introduction. However, predator habitat domain played a key role in determining how much prey habitat use changed following reintroduction, and the forager's knowledge of alternative habitats and exploratory inclinations affected what types of habitat shifts occurred. Namely, habitat shifts and consumption sacrifices by prey were extreme in some cases, particularly when they were pushed far from their starting locations by broad-domain predators, whereas informed foragers spent less time searching and displayed smaller reductions to consumption than their naïve counterparts following predator exposure. More exploratory foragers exhibited larger habitat shifts, thereby sacrificing consumption but reducing encounters by relocating to refugia, whereas less exploratory foragers managed risk in place and consequently suffered increased encounters while consuming more resources. By implication, reintroductions of predators with broad habitat domains are especially likely to impose foraging and movements costs on prey, but forager spatial memory state can mitigate these effects, as informed foragers can better access alternate habitat and avoid predators with smaller reductions in consumption.

Some Memories Never Fade: Inferring Multi-Scale Memory Effects on Habitat Selection of a Migratory Ungulate Using Step-Selection Functions

Understanding how animals use information about their environment to make movement decisions underpins our ability to explain drivers of and predict animal movement. Memory is the cognitive process that allows species to store information about experienced landscapes, however, remains an understudied topic in movement ecology. By studying how species select for familiar locations, visited recently and in the past, we can gain insight to how they store and use local information in multiple memory types. In this study, we analyzed the movements of a migratory mule deer (Odocoileus hemionus) population in the Piceance Basin of Colorado, United States to investigate the influence of spatial experience over different time scales on seasonal range habitat selection. We inferred the influence of short and long-term memory from the contribution to habitat selection of previous space use within the same season and during the prior year, respectively. We fit step-selection functions to GPS collar data from 32 female deer and tested the predictive ability of covariates representing current environmental conditions and both metrics of previous space use on habitat selection, inferring the latter as the influence of memory within and between seasons (summer vs. winter). Across individuals, models incorporating covariates representing both recent and past experience and environmental covariates performed best. In the top model, locations that had been previously visited within the same season and locations from previous seasons were more strongly selected relative to environmental covariates, which we interpret as evidence for the strong influence of both short- and long-term memory in driving seasonal range habitat selection. Further, the influence of previous space uses was stronger in the summer relative to winter, which is when deer in this population demonstrated strongest philopatry to their range. Our results suggest that mule deer update their seasonal range cognitive map in real time and retain long-term information about seasonal ranges, which supports the existing theory that memory is a mechanism leading to emergent space-use patterns such as site fidelity. Lastly, these findings provide novel insight into how species store and use information over different time scales.

Learning and Animal Movement

Integrating diverse concepts from animal behavior, movement ecology, and machine learning, we develop an overview of the ecology of learning and animal movement. Learning-based movement is clearly relevant to ecological problems, but the subject is rooted firmly in psychology, including a distinct terminology. We contrast this psychological origin of learning with the task-oriented perspective on learning that has emerged from the field of machine learning. We review conceptual frameworks that characterize the role of learning in movement, discuss emerging trends, and summarize recent developments in the analysis of movement data. We also discuss the relative advantages of different modeling approaches for exploring the learning-movement interface. We explore in depth how individual and social modalities of learning can matter to the ecology of animal movement, and highlight how diverse kinds of field studies, ranging from translocation efforts to manipulative experiments, can provide critical insight into the learning process in animal movement.

Regional variation in habitat matrix determines movement metrics in Baudin’s cockatoos in southwest Western Australia

Abstract ContextThe Baudin’s cockatoo is one of three black cockatoo species endemic to Western Australia and is listed as Endangered by state and federal governments. Although there is a Recovery Plan in place for this species, conservation efforts are hindered by gaps in knowledge regarding the species movement ecology. AimsTo identify key foraging and roosting habitat for Baudin’s cockatoos and to determine differences in flock movements, including the spatial extent of movement, in Urban, Peri-urban and Forest regions using telemetry data. MethodsWild Baudin’s cockatoos that had been injured and undergone rehabilitation were equipped with satellite PTT (platform transmitter terminal) and GPS tags and released back into wild flocks. The study birds, and the flocks into which they integrated, were tracked in the field to collect telemetry and observational data. Satellite data were used to define the types of movement behaviour (resident, ranging, migratory), and GPS data were analysed to determine key foraging and roosting habitat, and to calculate home range area estimates. Key resultsThere was a significant difference in flock movement between the Urban/Peri-urban regions and the Forest region in terms of daily distances moved and distances between roosts, with these parameters being far greater for the Forest region. Additionally, flock sizes were larger in the Forest region compared with the Urban and Peri-urban regions. In Urban and Peri-urban regions, key habitat comprised remnant vegetation in urban green space (nature reserves, parks and private property), and roadside and riparian vegetation, which served as movement corridors in the landscape. ConclusionsThe research shows that it is important to maintain vegetation connectivity in the landscape. This enables Baudin’s cockatoos to utilise key patches of remnant vegetation in their non-breeding wintering grounds in Urban and Peri-urban regions of the Perth Peel Coastal Plain. Further research on the movement ecology of Baudin’s cockatoos should focus on habitat suitability modelling, which, in combination with the identified key habitat sites, will benefit the decision-making process in relation to conservation management of this endangered black cockatoo species. ImplicationsThis research has benefited the conservation management of Baudin’s cockatoos by providing information on key habitat through satellite tracking and outlining the importance of the connective features of remnant vegetation. We advocate for further telemetry studies combined with habitat suitability modelling to preserve the necessary habitat for the persistence of this species in the Western Australian landscape.