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 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 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 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 The importance of individual variation in the dynamics of animal collective movements

2018 ◽  
Vol 373 (1746) ◽  
pp. 20170008 ◽  
Author(s):  
Maria del Mar Delgado ◽  
Maria Miranda ◽  
Silvia J. Alvarez ◽  
Eliezer Gurarie ◽  
William F. Fagan ◽  
...  
Keyword(s):  
Individual Variation ◽  
Decision Rules ◽  
Movement Ecology ◽  
Individual Variability ◽  
Collective Movement ◽  
Individual Based Models ◽  
Theoretical Investigations ◽  
Group Heterogeneity ◽  
Collective Movements ◽  
The Individual

Animal collective movements are a key example of a system that links two clearly defined levels of organization: the individual and the group. Most models investigating collective movements have generated coherent collective behaviours without the inclusion of individual variability. However, new individual-based models, together with emerging empirical information, emphasize that within-group heterogeneity may strongly influence collective movement behaviour. Here we (i) review the empirical evidence for individual variation in animal collective movements, (ii) explore how theoretical investigations have represented individual heterogeneity when modelling collective movements and (iii) present a model to show how within-group heterogeneity influences the collective properties of a group. Our review underscores the need to consider variability at the level of the individual to improve our understanding of how individual decision rules lead to emergent movement patterns, and also to yield better quantitative predictions of collective behaviour. This article is part of the theme issue ‘Collective movement ecology’.

Partially strict non-recursive data types

1993 ◽  
Vol 3 (2) ◽  
pp. 191-215 ◽  
Author(s):  
Eric Nöcker ◽  
Sjaak Smetsers
Keyword(s):  
Data Type ◽  
Functional Languages ◽  
Data Types ◽  
Composite Object ◽  
Space And Time ◽  
Input And Output ◽  
The Individual ◽  
Recursive Data

AbstractValues belonging to lazy data types have the advantage that sub-components can be accessed without evaluating the values as a whole: unneeded components remain unevaluated. A disadvantage is that often a large amount of space and time is required to handle lazy data types properly. Many special constructor cells are needed to ‘glue’ the individual parts of a composite object together and to store it in the heap. We present a way of representing data in functional languages which makes these special constructor cells superfluous. In some cases, no heap at all is needed to store this data. To make this possible, we introduce a new kind of data type: (partially) strict non-recursive data types. The main advantage of these types is that an efficient call-by-value mechanism can be used to pass arguments. A restrictive subclass of (partially) strict non-recursive data types, partially strict tuples, is treated more comprehensively. We also give examples of important classes of applications. In particular, we show how partially strict tuples can be used to define very efficient input and output primitives. Measurements of applications written in Concurrent Clean which exploit partially strict tuples have shown that speedups of 2 to 3 times are reasonable. Moreover, much less heap space is required when partially strict tuples are used.

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 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 Association rules mining applied in the animal movement exploratory analysis

2019 ◽  
Author(s):  
S. G. Fontes ◽  
P. L. P. Côrrea ◽  
S. L. Stanzani ◽  
R. G. Morato
Keyword(s):  
Association Rules ◽  
Animal Behavior ◽  
Animal Movement ◽  
Movement Analysis ◽  
Cebus Capucinus ◽  
Exploratory Analysis ◽  
Spatiotemporal Data ◽  
Association Rules Mining ◽  
Movement Data ◽  
Over Time

The animal movement analysis determines the animal behavior, which is the basis for understanding the interaction between species and the environment and to guide actions of preservation and conservation. The challenge is how to explore this movement data, getting indications about how the animal behaves over time and space. In this sense, a framework to animal movement exploratory analysis is presented, that combines algorithms for spatiotemporal data analysis and association rules mining, as a first step to answer questions related to animal behavior. We performed the framework’s evaluation in the exploratory analysis of monitored monkeys (Cebus capucinus) in the Panamá.

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.

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