Dataset Information

Memory Effects on Movement Behavior in Animal Foraging.

ABSTRACT: An individual's choices are shaped by its experience, a fundamental property of behavior important to understanding complex processes. Learning and memory are observed across many taxa and can drive behaviors, including foraging behavior. To explore the conditions under which memory provides an advantage, we present a continuous-space, continuous-time model of animal movement that incorporates learning and memory. Using simulation models, we evaluate the benefit memory provides across several types of landscapes with variable-quality resources and compare the memory model within a nested hierarchy of simpler models (behavioral switching and random walk). We find that memory almost always leads to improved foraging success, but that this effect is most marked in landscapes containing sparse, contiguous patches of high-value resources that regenerate relatively fast and are located in an otherwise devoid landscape. In these cases, there is a large payoff for finding a resource patch, due to size, value, or locational difficulty. While memory-informed search is difficult to differentiate from other factors using solely movement data, our results suggest that disproportionate spatial use of higher value areas, higher consumption rates, and consumption variability all point to memory influencing the movement direction of animals in certain ecosystems.

SUBMITTER: Bracis C

PROVIDER: S-EPMC4542208 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Memory Effects on Movement Behavior in Animal Foraging.

Bracis Chloe C Gurarie Eliezer E Van Moorter Bram B Goodwin R Andrew RA

PloS one 20150819 8

An individual's choices are shaped by its experience, a fundamental property of behavior important to understanding complex processes. Learning and memory are observed across many taxa and can drive behaviors, including foraging behavior. To explore the conditions under which memory provides an advantage, we present a continuous-space, continuous-time model of animal movement that incorporates learning and memory. Using simulation models, we evaluate the benefit memory provides across several ty ...[more]

PMID: 26288228

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Project description:Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are time-intensive (e.g., rest), time & distance-intensive (e.g., area restricted search), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST's ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST's ability to discriminate between behavior states relative to other classical movement metrics. We then temporally sub-sample albatross track data to illustrate RST's response to less resolved data. Finally, we evaluate RST's performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology.

Project description:Background:Foraging performance is widely hypothesized to play a key role in shaping age-specific demographic rates in wild populations, yet the underlying behavioral changes are poorly understood. Seabirds are among the longest-lived vertebrates, and demonstrate extensive age-related variation in survival, breeding frequency and success. The breeding season is a particularly critical phase during the annual cycle, but it remains unclear whether differences in experience or physiological condition related to age interact with the changing degree of the central-place constraint in shaping foraging patterns in time and space. Methods:Here we analyze tracking data collected over two decades from congeneric black-browed (BBA) and grey-headed (GHA) albatrosses, Thalassarche melanophris and T. chrysostoma, breeding at South Georgia. We compare the foraging trip parameters, at-sea activity (flights and landings) and habitat preferences of individuals aged 10-45?years and contrast these patterns between the incubation and early chick-rearing stages. Results:Young breeders of both species showed improvements in foraging competency with age, reducing foraging trip duration until age 26. Thereafter, there were signs of foraging senescence; older adults took gradually longer trips, narrowed their habitat preference (foraging within a smaller range of sea surface temperatures) (GHA), made fewer landings and rested on the water for longer (BBA). Some age-specific effects were apparent for each species only in certain breeding stages, highlighting the complex interaction between intrinsic drivers in determining individual foraging strategies. Conclusions:Using cross-sectional data, this study highlighted clear age-related patterns in foraging behavior at the population-level for two species of albatrosses. These trends are likely to have important consequences for the population dynamics of these threatened seabirds, as young or old individuals may be more vulnerable to worsening environmental conditions.

Project description:Animal movement research relies on biotelemetry, and telemetry-based locations are increasingly augmented with ancillary information. This presents an underutilized opportunity to enhance movement process models. Given tags designed to record specific behaviors, efforts are increasing to update movement models beyond reliance solely upon horizontal movement information to improve inference of space use and activity budgets. We present two state-space models adapted to incorporate ancillary data to inform three discrete movement states: directed, resident, and an activity state. These were developed for two case studies: (1) a "haulout" model for Weddell seals, and (2) an "activity" model for Antarctic fur seals which intersperse periods of diving activity and inactivity. The methodology is easily implementable with any ancillary data that can be expressed as a proportion (or binary) indicator. A comparison of the models augmented with ancillary information and unaugmented models confirmed that many behavioral states appeared mischaracterized in the latter. Important changes in subsequent activity budgets occurred. Haulout accounted for 0.17 of the overall Weddell seal time budget, with the estimated proportion of time spent in a resident state reduced from a posterior median of 0.69 (0.65-0.73; 95% HPDI) to 0.54 (0.50-0.58 HPDI). The drop was more dramatic in the Antarctic fur seal case, from 0.57 (0.52-0.63 HPDI) to 0.22 (0.20-0.25 HPDI), with 0.35 (0.31-0.39 HPDI) of time spent in the inactive (nondiving) state. These findings reinforce previously raised contentions about the drawbacks of behavioral states inferred solely from horizontal movements. Our findings have implications for assessing habitat requirements; estimating energetics and consumption; and management efforts such as mitigating fisheries interactions. Combining multiple sources of information within integrated frameworks should improve inference of relationships between movement decisions and fitness, the interplay between resource and habitat dependencies, and their changes at the population and landscape level.

Dataset Information

Memory Effects on Movement Behavior in Animal Foraging.

Publications

Memory Effects on Movement Behavior in Animal Foraging.

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