scholarly journals From single steps to mass migration: the problem of scale in the movement ecology of the Serengeti wildebeest

2018 ◽  
Vol 373 (1746) ◽  
pp. 20170012 ◽  
Author(s):  
Colin J. Torney ◽  
J. Grant C. Hopcraft ◽  
Thomas A. Morrison ◽  
Iain D. Couzin ◽  
Simon A. Levin
Keyword(s):  
Community Dynamics ◽  
Animal Movement ◽  
Multiple Scales ◽  
Movement Ecology ◽  
Population Fluctuations ◽  
Ecological Processes ◽  
Spatial And Temporal Scales ◽  
Collective Movement ◽  
Movement Data ◽  
Mass Migration

A central question in ecology is how to link processes that occur over different scales. The daily interactions of individual organisms ultimately determine community dynamics, population fluctuations and the functioning of entire ecosystems. Observations of these multiscale ecological processes are constrained by various technological, biological or logistical issues, and there are often vast discrepancies between the scale at which observation is possible and the scale of the question of interest. Animal movement is characterized by processes that act over multiple spatial and temporal scales. Second-by-second decisions accumulate to produce annual movement patterns. Individuals influence, and are influenced by, collective movement decisions, which then govern the spatial distribution of populations and the connectivity of meta-populations. While the field of movement ecology is experiencing unprecedented growth in the availability of movement data, there remain challenges in integrating observations with questions of ecological interest. In this article, we present the major challenges of addressing these issues within the context of the Serengeti wildebeest migration, a keystone ecological phenomena that crosses multiple scales of space, time and biological complexity. This article is part of the theme issue 'Collective movement ecology'.

scholarly journals Collective movement in ecology: from emerging technologies to conservation and management

2018 ◽  
Vol 373 (1746) ◽  
pp. 20170004 ◽  
Author(s):  
Peter A. H. Westley ◽  
Andrew M. Berdahl ◽  
Colin J. Torney ◽  
Dora Biro
Keyword(s):  
Quantitative Methods ◽  
Animal Movement ◽  
Movement Ecology ◽  
Theoretical Models ◽  
Collective Behaviour ◽  
Current Status ◽  
Theme Issue ◽  
Ecological Processes ◽  
Collective Movement ◽  
Sensing Technology

Recent advances in technology and quantitative methods have led to the emergence of a new field of study that stands to link insights of researchers from two closely related, but often disconnected disciplines: movement ecology and collective animal behaviour. To date, the field of movement ecology has focused on elucidating the internal and external drivers of animal movement and the influence of movement on broader ecological processes. Typically, tracking and/or remote sensing technology is employed to study individual animals in natural conditions. By contrast, the field of collective behaviour has quantified the significant role social interactions play in the decision-making of animals within groups and, to date, has predominantly relied on controlled laboratory-based studies and theoretical models owing to the constraints of studying interacting animals in the field. This themed issue is intended to formalize the burgeoning field of collective movement ecology which integrates research from both movement ecology and collective behaviour. In this introductory paper, we set the stage for the issue by briefly examining the approaches and current status of research in these areas. Next, we outline the structure of the theme issue and describe the obstacles collective movement researchers face, from data acquisition in the field to analysis and problems of scale, and highlight the key contributions of the assembled papers. We finish by presenting research that links individual and broad-scale ecological and evolutionary processes to collective movement, and finally relate these concepts to emerging challenges for the management and conservation of animals on the move in a world that is increasingly impacted by human activity. This article is part of the theme issue ‘Collective movement ecology’.

scholarly journals Behavioural valuation of landscapes using movement data

2019 ◽  
Vol 374 (1781) ◽  
pp. 20180046 ◽  
Author(s):  
George Wittemyer ◽  
Joseph M. Northrup ◽  
Guillaume Bastille-Rousseau
Keyword(s):  
Animal Movement ◽  
Temporal Dynamics ◽  
Economic Valuation ◽  
Movement Ecology ◽  
Optimization Theory ◽  
Population Level ◽  
Animal Behaviour ◽  
Tracking Data ◽  
Movement Data ◽  
Spatio Temporal

Wildlife tracking is one of the most frequently employed approaches to monitor and study wildlife populations. To date, the application of tracking data to applied objectives has focused largely on the intensity of use by an animal in a location or the type of habitat. While this has provided valuable insights and advanced spatial wildlife management, such interpretation of tracking data does not capture the complexity of spatio-temporal processes inherent to animal behaviour and represented in the movement path. Here, we discuss current and emerging approaches to estimate the behavioural value of spatial locations using movement data, focusing on the nexus of conservation behaviour and movement ecology that can amplify the application of animal tracking research to contemporary conservation challenges. We highlight the importance of applying behavioural ecological approaches to the analysis of tracking data and discuss the utility of comparative approaches, optimization theory and economic valuation to gain understanding of movement strategies and gauge population-level processes. First, we discuss innovations in the most fundamental movement-based valuation of landscapes, the intensity of use of a location, namely dissecting temporal dynamics in and means by which to weight the intensity of use. We then expand our discussion to three less common currencies for behavioural valuation of landscapes, namely the assessment of the functional (i.e. what an individual is doing at a location), structural (i.e. how a location relates to use of the broader landscape) and fitness (i.e. the return from using a location) value of a location. Strengthening the behavioural theoretical underpinnings of movement ecology research promises to provide a deeper, mechanistic understanding of animal movement that can lead to unprecedented insights into the interaction between landscapes and animal behaviour and advance the application of movement research to conservation challenges. This article is part of the theme issue ‘Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation’.

scholarly journals Animal movement analysis through residence in space and time

2016 ◽  
Author(s):  
Leigh G Torres ◽  
Rachael A. Orben ◽  
Irina Tolkova ◽  
David R Thompson
Keyword(s):  
Animal Movement ◽  
Search Time ◽  
Movement Ecology ◽  
Movement Analysis ◽  
Individual Variability ◽  
Behavior Patterns ◽  
Data Types ◽  
Space And Time ◽  
Movement Data ◽  
The Individual

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 distance-intensive (e.g., area restricted search), time-intensive (e.g., rest), 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 sub-sample albatross track data to illustrate RST’s response to less temporally 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.

scholarly journals A framework for understanding the architecture of collective movements using pairwise analyses of animal movement data

2010 ◽  
Vol 8 (56) ◽  
pp. 322-333 ◽  
Author(s):  
Leo Polansky ◽  
George Wittemyer
Keyword(s):  
Information Exchange ◽  
Animal Movement ◽  
Interspecific Interactions ◽  
Movement Patterns ◽  
African Elephants ◽  
Collective Movement ◽  
Movement Trajectories ◽  
Movement Data ◽  
Collective Movements ◽  
Free Ranging

The study of collective or group-level movement patterns can provide insight regarding the socio-ecological interface, the evolution of self-organization and mechanisms of inter-individual information exchange. The suite of drivers influencing coordinated movement trajectories occur across scales, resulting from regular annual, seasonal and circadian stimuli and irregular intra- or interspecific interactions and environmental encounters acting on individuals. Here, we promote a conceptual framework with an associated statistical machinery to quantify the type and degree of synchrony, spanning absence to complete, in pairwise movements. The application of this framework offers a foundation for detailed understanding of collective movement patterns and causes. We emphasize the use of Fourier and wavelet approaches of measuring pairwise movement properties and illustrate them with simulations that contain different types of complexity in individual movement, correlation in movement stochasticity, and transience in movement relatedness. Application of this framework to movements of free-ranging African elephants ( Loxodonta africana ) provides unique insight on the separate roles of sociality and ecology in the fission–fusion society of these animals, quantitatively characterizing the types of bonding that occur at different levels of social relatedness in a movement context. We conclude with a discussion about expanding this framework to the context of larger (greater than three) groups towards understanding broader population and interspecific collective movement patterns and their mechanisms.

scholarly journals Integrating animal movement with habitat suitability for estimating dynamic landscape connectivity

2017 ◽  
Author(s):  
Mariëlle L. van Toor ◽  
Bart Kranstauber ◽  
Scott H. Newman ◽  
Diann J. Prosser ◽  
John Y. Takekawa ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Disease Ecology ◽  
Animal Movement ◽  
Landscape Connectivity ◽  
Population Level ◽  
Tracking Data ◽  
Migratory Connectivity ◽  
Ecological Processes ◽  
Movement Model ◽  
Movement Data

AbstractContextHigh-resolution animal movement data are becoming increasingly available, yet having a multitude of empirical trajectories alone does not allow us to easily predict animal movement. To answer ecological and evolutionary questions at a population level, quantitative estimates of a species’ potential to link patches or populations are of importance.ObjectivesWe introduce an approach that combines movement-informed simulated trajectories with an environment-informed estimate of the trajectories’ plausibility to derive connectivity. Using the example of bar-headed geese we estimated migratory connectivity at a landscape level throughout the annual cycle in their native range.MethodsWe used tracking data of bar-headed geese to develop a multi-state movement model and to estimate temporally explicit habitat suitability within the species’ range. We simulated migratory movements between range fragments, and calculated a measure we called route viability. The results are compared to expectations derived from published literature.ResultsSimulated migrations matched empirical trajectories in key characteristics such as stopover duration. The viability of the simulated trajectories was similar to that of the empirical trajectories. We found that, overall, the migratory connectivity was higher within the breeding than in wintering areas, corresponding to previous findings for this species.ConclusionsWe show how empirical tracking data and environmental information can be fused for meaningful predictions of animal movements throughout the year and even outside the spatial range of the available data. Beyond predicting connectivity, our framework will prove useful for modelling ecological processes facilitated by animal movement, such as seed dispersal or disease ecology.

scholarly journals Generative Adversarial Networks (GAN) for the simulation of central-place foraging trajectories

2021 ◽  
Author(s):  
Amédée Roy ◽  
Sophie Lanco Bertrand ◽  
Ronan Fablet
Keyword(s):  
Large Scale ◽  
Animal Movement ◽  
Multiple Scales ◽  
Electronic Device ◽  
Central Place ◽  
Generative Adversarial Networks ◽  
Central Place Foraging ◽  
Ecological Processes ◽  
Adversarial Networks ◽  
Wide Range

1. Miniature electronic device such as GPS have enabled ecologists to document relatively large amount of animal trajectories. Modeling such trajectories may attempt (1) to explain mechanisms underlying observed behaviors and (2) to elucidate ecological processes at the population scale by simulating multiple trajectories. Existing approaches to animal movement modeling mainly addressed the first objective and they are yet soon limited when used for simulation. Individual-based models based on ad-hoc formulation and empirical parametrization lack of generability, while state-space models and stochastic differential equations models, based on rigorous statistical inference, consist in 1st order Markovian models calibrated at the local scale which can lead to overly simplistic description of trajectories. 2. We introduce a 'state-of-the-art' tool from artificial intelligence - Generative Adversarial Networks (GAN) - for the simulation of animal trajectories. GAN consist in a pair of deep neural networks that aim at capturing the data distribution of some experimental dataset, and that enable the generation of new instances of data that share statistical similarity. In this study, we aim on one hand to identify relevant deep networks architecture for simulating central-place foraging trajectories and on the second hand to evaluate GAN benefits over classical methods such as state-switching Hidden Markov Models (HMM). 3. We demonstrate the outstanding ability of GAN to simulate 'realistic' seabirds foraging trajectories. In particular, we show that deep convolutional networks are more efficient than LSTM networks and that GAN-derived synthetic trajectories reproduce better the Fourier spectral density of observed trajectories than those simulated using HMM. Therefore, unlike HMM, GAN capture the variability of large-scale descriptive statistics such as foraging trips distance, duration and tortuosity. 4. GAN offer a relevant alternative to existing approaches to modeling animal movement since it is calibrated to reproduce multiple scales at the same time, thus freeing ecologists from the assumption of first-order markovianity. GAN also provide an ultra-flexible and robust framework that could further take environmental conditions, social interactions or even bio-energetics model into account and tackle a wide range of key challenges in movement ecology.

scholarly journals Exploratory movement analysis and report building with R package stmove

2019 ◽  
Author(s):  
Dana P. Seidel ◽  
Eric R. Dougherty ◽  
Wayne M. Getz
Keyword(s):  
Best Practice ◽  
Animal Movement ◽  
Movement Ecology ◽  
Movement Analysis ◽  
R Package ◽  
Movement Data ◽  
Starting Point ◽  
Terrestrial Movement ◽  
Using Data ◽  
Analytical Tools

AbstractBackgroundAs GPS tags and data loggers have become lighter, cheaper, and longer-lasting, there has been a growing influx of data on animal movement. Simultaneously, methods of analyses and software to apply such methods to movement data have expanded dramatically. Even so, for many interdisciplinary researchers and managers without familiarity with the field of movement ecology and the open-source tools that have been developed, the analysis of movement data has remained an overwhelming challenge.DescriptionHere we present stmove, an R package designed to take individual relocation data and generate a visually rich report containing a set of preliminary results that ecologists and managers can use to guide further exploration of their data. Not only does this package make report building and exploratory data analysis (EDA) simple for users who may not be familiar with the extent of available analytical tools, but it sets forth a framework of best practice analyses, which offers a common starting point for the interpretation of terrestrial movement data.ResultsUsing data from African elephants (Loxodonta africana) collected in southern Africa, we demonstrate stmove’s report building function through the main analyses included: path visualization, primary statistic calculation, summary in space and time, and space-use construction.ConclusionsThe stmove package provides consistency and increased accessibility to managers and researchers who are interested in movement analysis but who may be unfamiliar with the full scope of movement packages and analytical tools. If widely adopted, the package will promote comparability of results across movement ecology studies.

scholarly journals Advances in Terrestrial Mammal Movement Ecology: An Overview

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Wang Jingxuan ◽  
Guangshun Jiang
Keyword(s):  
Data Analysis ◽  
Animal Movement ◽  
Movement Ecology ◽  
Research Field ◽  
Terrestrial Mammal ◽  
The Past ◽  
Research Fields ◽  
Movement Data ◽  
Scientific Facts

As a research field which is blooming quickly in recent years, movement ecology has been a worldwide concern and interest. However, movement ecology is so comprehensive and complicated that many articles only focus on few aspects or species. As tracking technologies and methods of movement data analysis develop, the abundance of movement data becomes available for demonstrating more scientific facts about animal movement. This article is aimed to summarize the advances of terrestrial mammal movement ecology in the past years to show its critical and potential research fields, as well as trying to ascertain direction of these advances.

scholarly journals Learning and Animal Movement

2021 ◽  
Vol 9 ◽  
Author(s):  
Mark A. Lewis ◽  
William F. Fagan ◽  
Marie Auger-Méthé ◽  
Jacqueline Frair ◽  
John M. Fryxell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Animal Movement ◽  
Movement Ecology ◽  
Field Studies ◽  
Movement Data ◽  
Emerging Trends ◽  
Recent Developments ◽  
Ecology Of Learning ◽  
Psychological Origin ◽  
Task Oriented

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.

scholarly journals Animal movement analysis through residence in space and time

2016 ◽  
Author(s):  
Leigh G Torres ◽  
Rachael A. Orben ◽  
Irina Tolkova ◽  
David R Thompson
Keyword(s):  
Animal Movement ◽  
Search Time ◽  
Movement Ecology ◽  
Movement Analysis ◽  
Individual Variability ◽  
Behavior Patterns ◽  
Data Types ◽  
Space And Time ◽  
Movement Data ◽  
The Individual

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 distance-intensive (e.g., area restricted search), time-intensive (e.g., rest), 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 sub-sample albatross track data to illustrate RST’s response to less temporally 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.

Sign in / Sign up

Export Citation Format

Share Document