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 Changing motivations during migration: linking movement speed to reproductive status in a migratory large mammal

2014 ◽  
Vol 10 (6) ◽  
pp. 20140379 ◽  
Author(s):  
Navinder J. Singh ◽  
Göran Ericsson
Keyword(s):  
Animal Ecology ◽  
Animal Movement ◽  
Movement Patterns ◽  
Reproductive Status ◽  
Movement Speed ◽  
Field Observations ◽  
Large Mammal ◽  
Movement Data ◽  
Calf Survival ◽  
Reproductive Females

A challenge in animal ecology is to link animal movement to demography. In general, reproducing and non-reproducing animals may show different movement patterns. Dramatic changes in reproductive status, such as the loss of an offspring during the course of migration, might also affect movement. Studies linking movement speed to reproductive status require individual monitoring of life-history events and hence are rare. Here, we link movement data from 98 GPS-collared female moose ( Alces alces ) to field observations of reproductive status and calf survival. We show that reproductive females move more quickly during migration than non-reproductive females. Further, the loss of a calf over the course of migration triggered a decrease in speed of the female. This is in contrast to what might be expected for females no longer constrained by an accompanying offspring. The observed patterns demonstrate that females of different reproductive status may have distinct movement patterns, and that the underlying motivation to move may be altered by a change in reproductive status during migration.

scholarly journals Inferring an animal's environment through biologging: quantifying the environmental influence on animal movement

2020 ◽  
Author(s):  
Jasper A.J. Eikelboom ◽  
Henrik J. de Knegt ◽  
Maayke Klaver ◽  
Frank van Langevelde ◽  
Tamme van der Wal ◽  
...  
Keyword(s):  
Environmental Variables ◽  
Animal Movement ◽  
Environmental Influence ◽  
Activity Patterns ◽  
Environmental Variation ◽  
Body Part ◽  
Environmental Variable ◽  
Machine Learning Algorithms ◽  
Movement Trajectories ◽  
Movement Data

Abstract Background: Animals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental influence on animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental influence on movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis.Methods: We propose a data-driven analytic framework, based on existing methods, to quantify the environmental influence on animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the influence of grass availability and time since milking on cow movements using machine learning algorithms.Results: We show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking influenced cow movements. Grass availability mostly influenced the cows’ neck movement during grazing, while time since milking mostly influenced the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the influence on animal movement.Conclusions: Not only is our proposed framework well-suited to study the environmental influence on animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment.

scholarly journals Inferring an animal’s environment through biologging: quantifying the environmental influence on animal movement

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
J. A. J. Eikelboom ◽  
H. J. de Knegt ◽  
M. Klaver ◽  
F. van Langevelde ◽  
T. van der Wal ◽  
...  
Keyword(s):  
Environmental Variables ◽  
Animal Movement ◽  
Environmental Influence ◽  
Activity Patterns ◽  
Environmental Variation ◽  
Body Part ◽  
Environmental Variable ◽  
Machine Learning Algorithms ◽  
Movement Trajectories ◽  
Movement Data

Abstract Background Animals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental influence on animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental influence on movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis. Methods We propose a data-driven analytic framework, based on existing methods, to quantify the environmental influence on animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the influence of grass availability and time since milking on cow movements using machine learning algorithms. Results We show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking influenced cow movements. Grass availability mostly influenced the cows’ neck movement during grazing, while time since milking mostly influenced the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the influence on animal movement. Conclusions Not only is our proposed framework well-suited to study the environmental influence on animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment.

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 A multilayer perspective for inferring spatial and social functioning in animal movement networks

2019 ◽  
Author(s):  
Johann Mourier ◽  
Elodie J. I. Lédée ◽  
David M. P. Jacoby
Keyword(s):  
Animal Movement ◽  
Single Layer ◽  
Social Contact ◽  
Movement Patterns ◽  
Population Level ◽  
Individual Variability ◽  
List Type ◽  
Multilayer Network ◽  
Network Analyses ◽  
Movement Data

ABSTRACTAnimal movement patterns are increasingly analysed as spatial networks. Currently, structures of complex movements are typically represented as a single-layer (or monoplex) network. However, aggregating individual movements, to generate population-level inferences, considerably reduces information on how individual or species variability influences spatial connectivity and thus identifying the mechanisms driving network structure remains difficult.Here, we propose incorporating the recent conceptual advances in multilayer network analyses with the existing movement network approach to improve our understanding of the complex interaction between spatial and/or social drivers of animal movement patterns.Specifically, we explore the application and interpretation of this framework using an empirical example of shark movement data gathered using passive remote sensors in a coral reef ecosystem. We first show how aggregating individual movement networks can lead to the loss of information, potentially misleading our interpretation of movement patterns. We then apply multilayer network analyses linking individual movement networks (i.e. layers) to the probabilities of social contact between individuals (i.e. interlayer edges) in order to explore the functional significance of different locations to an animal’s ecology.This approach provides a novel and holistic framework incorporating individual variability in behaviour and inter-individual interactions. We discuss how this approach can be used in applied ecology and conservation to better assess the ecological significance of variable space use by mobile animals within a population. Further, we argue that the uptake of multilayer networks will significantly broaden our understanding of long-term ecological and evolutionary processes, particularly in the context of information or disease transfer between individuals.

scholarly journals Inferring An Animal's Environment Through Biologging: Quantifying the Environmental Contribution to Animal Movement

2020 ◽  
Author(s):  
Jasper A.J. Eikelboom ◽  
Henrik J. de Knegt ◽  
Maayke Klaver ◽  
Frank van Langevelde ◽  
Tamme van der Wal ◽  
...  
Keyword(s):  
Environmental Variables ◽  
Animal Movement ◽  
Activity Patterns ◽  
Environmental Variation ◽  
Body Part ◽  
Environmental Variable ◽  
Machine Learning Algorithms ◽  
Movement Trajectories ◽  
Movement Data ◽  
Environmental Contribution

Abstract Background: Animals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental contribution to animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental contribution to movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis. Methods: We propose a data-driven analytic framework to quantify the environmental contribution to animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting the response of a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data, i.e., the reverse of the route of causal inference. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the contribution of grass availability and time since milking to cow movements using machine learning algorithms. Results: We show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking contributed to cow movements. Grass availability contributed mostly to the cows’ neck movement during grazing, while time since milking contributed mostly to the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the contribution to animal movement. Conclusions: Not only is our proposed framework well-suited to study the environmental contribution to animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment.

scholarly journals Inferring an animal's environment through biologging: quantifying the environmental contribution to animal movement

2020 ◽  
Author(s):  
Jasper A.J. Eikelboom ◽  
Henrik J. de Knegt ◽  
Maayke Klaver ◽  
Frank van Langevelde ◽  
Tamme van der Wal ◽  
...  
Keyword(s):  
Environmental Variables ◽  
Animal Movement ◽  
Activity Patterns ◽  
Environmental Variation ◽  
Body Part ◽  
Environmental Variable ◽  
Machine Learning Algorithms ◽  
Movement Trajectories ◽  
Movement Data ◽  
Environmental Contribution

Abstract Background: Animals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental contribution to animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental contribution to movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis. Methods: We propose a data-driven analytic framework to quantify the environmental contribution to animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting the response of a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data, i.e., the reverse of the route of causal inference. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the contribution of grass availability and time since milking to cow movements using machine learning algorithms. Results: We show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking contributed to cow movements. Grass availability contributed mostly to the cows’ neck movement during grazing, while time since milking contributed mostly to the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the contribution to animal movement. Conclusions: Not only is our proposed framework well-suited to study the environmental contribution to animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment.

scholarly journals Inferring An Animal's Environment Through Biologging: Quantifying the Environmental Contribution to Animal Movement

2020 ◽  
Author(s):  
Jasper A.J. Eikelboom ◽  
Henrik J. de Knegt ◽  
Maayke Klaver ◽  
Frank van Langevelde ◽  
Tamme van der Wal ◽  
...  
Keyword(s):  
Environmental Variables ◽  
Animal Movement ◽  
Activity Patterns ◽  
Environmental Variation ◽  
Body Part ◽  
Environmental Variable ◽  
Machine Learning Algorithms ◽  
Movement Trajectories ◽  
Movement Data ◽  
Environmental Contribution

Abstract Background: Animals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental contribution to animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental contribution to movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis. Methods: We propose a data-driven analytic framework to quantify the environmental contribution to animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting the response of a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data, i.e., the reverse of the route of causal inference. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the contribution of grass availability and time since milking to cow movements using machine learning algorithms. Results: We show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking contributed to cow movements. Grass availability contributed mostly to the cows’ neck movement during grazing, while time since milking contributed mostly to the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the contribution to animal movement. Conclusions: Not only is our proposed framework well-suited to study the environmental contribution to animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment.

scholarly journals Inferring an animal's environment through biologging: quantifying the environmental contribution to animal movement

2020 ◽  
Author(s):  
Jasper A.J. Eikelboom ◽  
Henrik J. de Knegt ◽  
Maayke Klaver ◽  
Frank van Langevelde ◽  
Tamme van der Wal ◽  
...  
Keyword(s):  
Environmental Variables ◽  
Animal Movement ◽  
Activity Patterns ◽  
Environmental Variation ◽  
Body Part ◽  
Environmental Variable ◽  
Machine Learning Algorithms ◽  
Movement Trajectories ◽  
Movement Data ◽  
Environmental Contribution

Abstract Background: Animals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental contribution to animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental contribution to movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis. Methods: We propose a data-driven analytic framework to quantify the environmental contribution to animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data, i.e., the reverse of the route of causal inference. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the contribution of grass availability and time since milking to cow movements using machine learning algorithms. Results: We show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking contributed to cow movements. Grass availability contributed mostly to the cows’ neck movement during grazing, while time since milking contributed mostly to the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the contribution to animal movement. Conclusions: Not only is our proposed framework well-suited to study the environmental contribution to animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment.

scholarly journals Fencing affects African wild dog movement patterns and population dynamics

Oryx ◽  
2021 ◽  
pp. 1-9
Author(s):  
Helen M. K. O'Neill ◽  
Sarah M. Durant ◽  
Stefanie Strebel ◽  
Rosie Woodroffe
Keyword(s):  
Population Dynamics ◽  
Large Scale ◽  
Animal Movement ◽  
Landscape Connectivity ◽  
Ecological Impact ◽  
Movement Patterns ◽  
Wrong Side ◽  
African Wild Dog ◽  
Wild Dog ◽  
Wild Dogs

Abstract Wildlife fences are often considered an important tool in conservation. Fences are used in attempts to prevent human–wildlife conflict and reduce poaching, despite known negative impacts on landscape connectivity and animal movement patterns. Such impacts are likely to be particularly important for wide-ranging species, such as the African wild dog Lycaon pictus, which requires large areas of continuous habitat to fulfil its resource requirements. Laikipia County in northern Kenya is an important area for wild dogs but new wildlife fences are increasingly being built in this ecosystem. Using a long-term dataset from the area's free-ranging wild dog population, we evaluated the effect of wildlife fence structure on the ability of wild dogs to cross them. The extent to which fences impeded wild dog movement differed between fence designs, although individuals crossed fences of all types. Purpose-built fence gaps increased passage through relatively impermeable fences. Nevertheless, low fence permeability can lead to packs, or parts of packs, becoming trapped on the wrong side of a fence, with consequences for population dynamics. Careful evaluation should be given to the necessity of erecting fences; ecological impact assessments should incorporate evaluation of impacts on animal movement patterns and should be undertaken for all large-scale fencing interventions. Where fencing is unavoidable, projects should use the most permeable fencing structures possible, both in the design of the fence and including as many purpose-built gaps as possible, to minimize impacts on wide-ranging wildlife.

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