Navigating through the R packages for movement.

The advent of miniaturized biologging devices has provided ecologists with unprecedented opportunities to record animal movement across scales, and led to the collection of ever-increasing quantities of tracking data. In parallel, sophisticated tools have been developed to process, visualize and analyse tracking data, however many of these tools have proliferated in isolation, making it challenging for users to select the most appropriate method for the question in hand. Indeed, within the R software alone, we listed 58 packages created to deal with tracking data or 'tracking packages'. Here we reviewed and described each tracking package based on a workflow centered around tracking data (i.e. spatio-temporal locations (x,y,t)), broken down into three stages: pre-processing, post-processing and analysis, the latter consisting of data visualization, track description, path reconstruction behavioral pattern identification, space use characterization, trajectory simulation and others. Supporting documentation is key to render a package accessible for users. Based on a user survey, we reviewed the quality of packages' documentation, and identified 11 packages with good or excellent documentation. Links between packages were assessed through a network graph analysis. Although a large group of packages showed some degree of connectivity (either depending on functions or suggesting the use of another tracking package), one third of the packages worked in isolation, reflecting a fragmentation in the R movement-ecology programming community. Finally, we provide recommendations for users when choosing packages, and for developers to maximize the usefulness of their contribution and strengthen the links within the programming community.

[1]  O. Ovaskainen,et al.  State-space models of individual animal movement. , 2008, Trends in ecology & evolution.

[2]  Laurent Gueguen,et al.  Segmentation by Maximal Predictive Partitioning According to Composition Biases , 2000, JOBIM.

[3]  H. Dingle.  Migration: The Biology of Life on the Move , 1996 .

[4]  D. McLean,et al.  trajr: An R package for characterisation of animal trajectories , 2018 .

[5]  Bart Kranstauber,et al.  Wind estimation based on thermal soaring of birds , 2016, Ecology and evolution.

[6]  Edzer J. Pebesma,et al.  Applied Spatial Data Analysis with R - Second Edition , 2008, Use R!.

[7]  Markus Neteler,et al.  Wildlife tracking data management: a new vision , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[8]  Michael A. Fedak,et al.  A simple new algorithm to filter marine mammal Argos locations , 2008 .

[9]  E. Pebesma,et al.  Classes and Methods for Spatial Data , 2015 .

[10]  W. V. Winkle.  COMPARISON OF SEVERAL PROBABILISTIC HOME-RANGE MODELS' , 1975 .

[11]  C. O. Mohr,et al.  Table of equivalent populations of North American small mammals , 1947 .

[12]  James V. Zidek,et al.  Bayesian data fusion approaches to predicting spatial tracks: Application to marine mammals , 2016 .

[13]  Jun Yan,et al.  On Estimation for Brownian Motion Governed by Telegraph Process with Multiple Off States , 2018, Methodology and Computing in Applied Probability.

[14]  Stanley M Tomkiewicz,et al.  Global positioning system and associated technologies in animal behaviour and ecological research , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[15]  Nicholas J. Aebischer,et al.  Compositional Analysis of Habitat Use From Animal Radio-Tracking Data , 1993 .

[16]  Michael D. Sumner,et al.  Metropolis Sampler and Supporting Functions for EstimatingAnimal Movement from Archival Tags and Satellite Fixes , 2015 .

[17]  Guillaume Péron,et al.  Uncovering periodic patterns of space use in animal tracking data with periodograms, including a new algorithm for the Lomb-Scargle periodogram and improved randomization tests , 2016, Movement ecology.

[18]  Christopher C Wilmers,et al.  The golden age of bio-logging: how animal-borne sensors are advancing the frontiers of ecology. , 2015, Ecology.

[19]  J. Greeff,et al.  Dispersal , 2019, The African Wild Dog.

[20]  M. Lavielle.  Detection of multiple changes in a sequence of dependent variables , 1999 .

[21]  Wayne M. Getz,et al.  LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions , 2007, PloS one.

[22]  Daniel P. Costa,et al.  Accuracy of ARGOS Locations of Pinnipeds at-Sea Estimated Using Fastloc GPS , 2010, PloS one.

[23]  Brett T. McClintock,et al.  Incorporating Telemetry Error into Hidden Markov Models of Animal Movement Using Multiple Imputation , 2017 .

[24]  M. W. Jones,et al.  Step by step: reconstruction of terrestrial animal movement paths by dead-reckoning , 2015, Movement Ecology.

[25]  Kamran Safi,et al.  Linking animal movement and remote sensing – mapping resource suitability from a remote sensing perspective , 2018 .

[26]  E. Revilla,et al.  A movement ecology paradigm for unifying organismal movement research , 2008, Proceedings of the National Academy of Sciences.

[27]  Pedro M. Valero-Mora,et al.  ggplot2: Elegant Graphics for Data Analysis , 2010 .

[28]  Simon Wotherspoon,et al.  Twilight‐free geolocation from noisy light data , 2017 .

[29]  I.,et al.  COLD SPRING HARBOR SYMPOSIA ON QUANTITATIVE BIOLOGY , 2011 .

[30]  Richard W. Brill,et al.  Horizontal movements of bigeye tuna (Thunnus obesus) near Hawaii determined by Kalman filter analysis of archival tagging data , 2003 .

[31]  J. Speakman,et al.  Accelerometers can measure total and activity-specific energy expenditures in free-ranging marine mammals only if linked to time-activity budgets , 2017 .

[32]  A. Dufour,et al.  K-select analysis: a new method to analyse habitat selection in radio-tracking studies , 2005 .

[33]  L. Rivest,et al.  STATISTICAL METHODS FOR ESTIMATING CARIBOU ABUNDANCE USING POSTCALVING AGGREGATIONS DETECTED BY RADIO TELEMETRY , 1998 .

[34]  Bart Kranstauber,et al.  A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement. , 2012, The Journal of animal ecology.

[35]  Manuel Schabus,et al.  ‘nparACT’ package for R: A free software tool for the non-parametric analysis of actigraphy data , 2016, MethodsX.

[36]  Nirvana Meratnia,et al.  Spatiotemporal Compression Techniques for Moving Point Objects , 2004, EDBT.

[37]  Chloe Bracis,et al.  Revisitation analysis uncovers spatio‐temporal patterns in animal movement data , 2018 .

[38]  Simeon Lisovski,et al.  GeoLight – processing and analysing light‐based geolocator data in R , 2012 .

[39]  F. Cagnacci,et al.  Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[40]  Lewis G. Halsey,et al.  Relationships grow with time: a note of caution about energy expenditure-proxy correlations, focussing on accelerometry as an example , 2017 .

[41]  H. Weimerskirch,et al.  Exchange of the Wandering Albatross Diomedea Exulans Between the Prince Edward and Crozet Islands: Implications for Conservation , 2003 .

[42]  Greg DeCelles,et al.  Acoustic and Radio Telemetry , 2014 .

[43]  C. Limpus,et al.  Sea turtles return home after intentional displacement from coastal foraging areas , 2016 .

[44]  Christoffer Moesgaard Albertsen,et al.  argosTrack: Fit Movement Models to Argos Data for Marine Animals. R package version 0.1.1 , 2015 .

[45]  Nathaniel Bowditch,et al.  The new American practical navigator , 1802 .

[46]  Rory P. Wilson,et al.  Prying into the intimate details of animal lives: use of a daily diary on animals , 2008 .

[47]  Pascal Monestiez,et al.  Prey capture attempts can be detected in Steller sea lions and other marine predators using accelerometers , 2010, Polar Biology.

[48]  Brett T. McClintock,et al.  A general discrete‐time modeling framework for animal movement using multistate random walks , 2012 .

[49]  Yihui Xie,et al.  animation: An R Package for Creating Animations and Demonstrating Statistical Methods , 2013 .

[50]  Max Kuhn,et al.  caret: Classification and Regression Training , 2015 .

[51]  V. Afanasyev.  A miniature daylight level and activity data recorder for tracking animals over long periods , 2004 .

[52]  M. Boyce,et al.  WOLVES INFLUENCE ELK MOVEMENTS: BEHAVIOR SHAPES A TROPHIC CASCADE IN YELLOWSTONE NATIONAL PARK , 2005 .

[53]  Mark S Boyce,et al.  Applications of step-selection functions in ecology and conservation , 2014, Movement Ecology.

[54]  Ioannis Kosmidis,et al.  trackeR: Infrastructure for Running and Cycling Data from GPS-Enabled Tracking Devices in R , 2017 .

[55]  Jonathan R. Potts,et al.  Flexible characterization of animal movement pattern using net squared displacement and a latent state model , 2016, Movement ecology.

[56]  Wayne M Getz,et al.  Home range plus: a space-time characterization of movement over real landscapes , 2013, Movement ecology.

[57]  Peter Leimgruber,et al.  From Fine-Scale Foraging to Home Ranges: A Semivariance Approach to Identifying Movement Modes across Spatiotemporal Scales , 2014, The American Naturalist.

[58]  Roland Langrock,et al.  Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. , 2012, Ecology.

[59]  Matthew B. Jones,et al.  Challenges and Opportunities of Open Data in Ecology , 2011, Science.

[60]  Nehal Magdy,et al.  Review on trajectory similarity measures , 2015, 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS).

[61]  H. Berg.  Cold Spring Harbor Symposia on Quantitative Biology.: Vol. LII. Evolution of Catalytic Functions. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 1987, ISBN 0-87969-054-2, xix + 955 pp., US $150.00. , 1989 .

[62]  Joss Langford,et al.  Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents , 2014, Journal of applied physiology.

[63]  Scott C. Williams,et al.  A moving–resting process with an embedded Brownian motion for animal movements , 2014, Population Ecology.

[64]  Trisalyn A Nelson,et al.  A critical examination of indices of dynamic interaction for wildlife telemetry studies. , 2014, The Journal of animal ecology.

[65]  Marc Lavielle,et al.  Using penalized contrasts for the change-point problem , 2005, Signal Process..

[66]  Anders Nielsen,et al.  Fast fitting of non-Gaussian state-space models to animal movement data via Template Model Builder. , 2015, Ecology.

[67]  Ross G. Dwyer,et al.  V-Track: software for analysing and visualising animal movement from acoustic telemetry detections , 2012 .

[68]  Christian Rutz,et al.  New frontiers in biologging science , 2009, Biology Letters.

[69]  Horst Bornemann,et al.  All at sea with animal tracks; methodological and analytical solutions for the resolution of movement , 2007 .

[70]  Rory P. Wilson,et al.  Moving towards acceleration for estimates of activity-specific metabolic rate in free-living animals: the case of the cormorant. , 2006, The Journal of animal ecology.

[71]  Benjamin Merkel,et al.  A probabilistic algorithm to process geolocation data , 2016, Movement ecology.

[72]  Simon Benhamou,et al.  Incorporating Movement Behavior and Barriers to Improve Kernel Home Range Space Use Estimates , 2010 .

[73]  Mark Hebblewhite,et al.  ‘MigrateR’: extending model‐driven methods for classifying and quantifying animal movement behavior , 2017 .

[74]  Devin S Johnson,et al.  Continuous-time correlated random walk model for animal telemetry data. , 2008, Ecology.

[75]  Mark W. Horner,et al.  A Characteristic‐Hull Based Method for Home Range Estimation , 2009, Trans. GIS.

[76]  Mevin B. Hooten,et al.  Continuous-time discrete-space models for animal movement , 2012, 1211.1992.

[77]  Jun Yan,et al.  On modeling animal movements using Brownian motion with measurement error. , 2014, Ecology.

[78]  Niko Balkenhol,et al.  Reproducible home ranges (rhr): A new, user‐friendly R package for analyses of wildlife telemetry data , 2015 .

[79]  George Wittemyer,et al.  Applying network theory to animal movements to identify properties of landscape space use. , 2018, Ecological applications : a publication of the Ecological Society of America.

[80]  J. Kocik,et al.  Aquatic animal telemetry: A panoramic window into the underwater world , 2015, Science.

[81]  Francesca Cagnacci,et al.  A framework for modelling range shifts and migrations: asking when, whither, whether and will it return , 2017, The Journal of animal ecology.

[82]  Brett T. McClintock,et al.  momentuHMM: R package for generalized hidden Markov models of animal movement , 2017, 1710.03786.

[83]  Stephen M. Krone,et al.  Analyzing animal movements using Brownian bridges. , 2007, Ecology.

[84]  Clément Calenge,et al.  The package “adehabitat” for the R software: A tool for the analysis of space and habitat use by animals , 2006 .

[85]  Guangqing Chi,et al.  Applied Spatial Data Analysis with R , 2015 .

[86]  Rory P. Wilson,et al.  The need for speed: testing acceleration for estimating animal travel rates in terrestrial dead-reckoning systems. , 2012, Zoology.

[87]  Ning Jiang,et al.  Our path to better science in less time using open data science tools , 2017, Nature Ecology &Evolution.

[88]  S. Dolédec,et al.  NICHE SEPARATION IN COMMUNITY ANALYSIS: A NEW METHOD , 2000 .

[89]  Rory P. Wilson,et al.  DETERMINATION OF MOVEMENTS OF AFRICAN PENGUINS SPHENISCUS DEMERSUS USING A COMPASS SYSTEM: DEAD RECKONING MAY BE AN ALTERNATIVE TO TELEMETRY , 1991 .

[90]  Michael D. Sumner,et al.  Bayesian Estimation of Animal Movement from Archival and Satellite Tags , 2009, PloS one.

[91]  Ian D. Jonsen,et al.  ROBUST STATE-SPACE MODELING OF ANIMAL MOVEMENT DATA , 2005 .

[92]  Michael Catt,et al.  A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer , 2015, PloS one.

[93]  Carol Dezateux,et al.  Technical report on accelerometry data processing in the Millennium Cohort Study , 2012 .

[94]  Roland Langrock,et al.  moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models , 2016 .

[95]  Eliezer Gurarie,et al.  A novel method for identifying behavioural changes in animal movement data. , 2009, Ecology letters.

[96]  Julia Karagicheva,et al.  FLightR: an r package for reconstructing animal paths from solar geolocation loggers , 2017 .

[97]  Robert E. Kenward,et al.  A manual for wildlife radio tagging , 2000 .

[98]  C H Fleming,et al.  Rigorous home range estimation with movement data: a new autocorrelated kernel density estimator. , 2015, Ecology.

[99]  Yang Liu,et al.  Bias correction and uncertainty characterization of Dead-Reckoned paths of marine mammals , 2015, Animal Biotelemetry.

[100]  Anders Nielsen,et al.  Incorporating sea-surface temperature to the light-based geolocation model TrackIt , 2010 .

[101]  Oliver Montenbruck,et al.  Astronomy on the personal computer , 1991 .

[102]  Ian Jonsen,et al.  Joint estimation over multiple individuals improves behavioural state inference from animal movement data , 2016, Scientific Reports.

[103]  Jun Yan,et al.  Discretely Observed Brownian Motion Governed by Telegraph Process: Estimation , 2019 .

[104]  Michael D. Sumner,et al.  Tools for the Analysis of Animal Track Data , 2015 .

[105]  Alan M. Wilson,et al.  Improving the accuracy of estimates of animal path and travel distance using GPS drift‐corrected dead reckoning , 2016, Ecology and evolution.

[106]  James K. Sheppard,et al.  Movement-Based Estimation and Visualization of Space Use in 3D for Wildlife Ecology and Conservation , 2014, PloS one.

[107]  Edzer Pebesma,et al.  Simple Features for R: Standardized Support for Spatial Vector Data , 2018, R J..

[108]  Simon Benhamou,et al.  Dynamic Approach to Space and Habitat Use Based on Biased Random Bridges , 2011, PloS one.

[109]  Christophe Guinet,et al.  How Elephant Seals (Mirounga leonina) Adjust Their Fine Scale Horizontal Movement and Diving Behaviour in Relation to Prey Encounter Rate , 2016, PloS one.

[110]  Anders Nielsen,et al.  Improving light-based geolocation by including sea surface temperature , 2006 .

[111]  J. Daudin,et al.  A Segmentation/Clustering Model for the Analysis of Array CGH Data , 2007, Biometrics.

[112]  Simon Benhamou,et al.  Mean squared displacement and sinuosity of three-dimensional random search movements , 2018, 1801.02435.

[113]  Rory P. Wilson,et al.  Identification of animal movement patterns using tri-axial magnetometry , 2017, Movement ecology.

[114]  Jonathan R. Potts,et al.  Integrated step selection analysis: bridging the gap between resource selection and animal movement , 2015, 1512.01614.

[115]  Frederic Bartumeus,et al.  Expectation-Maximization Binary Clustering for Behavioural Annotation , 2015, PloS one.

[116]  B. Manly,et al.  Resource selection by animals: statistical design and analysis for field studies. , 1994 .

[117]  Philip A. Ekstrom,et al.  An advance in geolocation by light , 2004 .

[118]  Dora Biro,et al.  Collective movement in ecology: from emerging technologies to conservation and management , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[119]  Andrew E. Myers,et al.  Derivation of body motion via appropriate smoothing of acceleration data , 2008 .

[120]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[121]  Eldar Rakhimberdiev,et al.  A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity , 2015, Movement Ecology.

[122]  Jorge Mateu,et al.  trajectories : Classes and Methods for Trajectory Data , 2018 .

[123]  Camrin D. Braun,et al.  HMMoce: An R package for improved geolocation of archival‐tagged fishes using a hidden Markov method , 2017 .

[124]  L. Halsey,et al.  Accelerometry to Estimate Energy Expenditure during Activity: Best Practice with Data Loggers , 2008, Physiological and Biochemical Zoology.

[125]  Jacob S. Ivan,et al.  A functional model for characterizing long‐distance movement behaviour , 2016 .

[126]  Carlos M. Duarte,et al.  Estimates for energy expenditure in free-living animals using acceleration proxies; a reappraisal. , 2019, The Journal of animal ecology.

[127]  Bart Kranstauber,et al.  move: Visualizing and analyzing animal track data. R package version 3.1.0 , 2018 .

[128]  R. Kays,et al.  Terrestrial animal tracking as an eye on life and planet , 2015, Science.

[129]  Mark T. Hamann,et al.  Improving data retention and home range estimates by data-driven screening , 2012 .

[130]  Martin Wæver Pedersen,et al.  State-space models for bio-loggers: A methodological road map , 2013 .