Resources related to covered topics

Other Resources

R package websites and tutorials

• aniMotum package website

• momentuHMM package tutorial

  • momentuHMM GitHub repo

• bayesmove package website
  
  
  

• amt package tutorials

  • amt GitHub repo

• move package website

Additional methods

Accounting for Argos location error

• crawl and crawlUtils
  • publication
  • crawl GitHub repo
  • crawlUtils GitHub repo
  • guide to using crawl
• Bayesian state-space models for animal movement (bsam)
  • GitHub repo

Behavioral state estimation

• seclust2d
  • publication
  • package website
• hmmTMB
  • GitHub repo w/ tutorials
• move persistence mixed effects model (mpmm)
  • publication
  • GitHub repo
• Bayesian state-space models for animal movement (bsam)
  • GitHub repo

Space-use estimation

• autocorrelated kernel density estimation (aKDE)
  • publication
  • ctmm package website
  • ctmmweb Shiny app
• local convex hull (LoCoH)
  • publication
  • T-LoCoH package website
• movement-based kernel density estimation (MKDE) aka biased random bridges (BRB)
  • publication
  • adehabitatHR guide
• space-use as function of covariates
  • geostatistical mixed effects models (aka log Gaussian Cox process)
    • Bakka et al. 2019 “Non-stationary Gaussian models with physical barriers”
    • Winton et al. 2018 “Estimating the distribution and relative density of satellite-tagged loggerhead sea turtles using geostatistical mixed effects models”
    • Bachl et al. 2019 “inlabru: an R package for Bayesian spatial modelling from ecological survey data”
    • R-INLA package website
    • inlabru package website
    • geospatial INLA examples in R
  • continuous-time discrete space model
    • publication
    • CRAN package website
  • step-selection functions (SSFs)
    • publication
    • brief guide from pub
    • tutorial on fitting SSF w/ amt
  • Brownian bridge covariates model
    • publication
    • moveBrownianModel package website

Further Readings

Behavioral state estimation

State-space models

• Jonsen et al. 2003 Meta‐analysis of animal movement using state‐space models
• Jonsen et al. 2005 Robust state–space modeling of animal movement data
• Jonsen et al. 2007 Identifying leatherback turtle foraging behaviour from satellite telemetry using a switching state-space model
• Johnson et al. 2008 Continuous‐time correlated random walk model for animal telemetry data
• Patterson et al. 2008 State–space models of individual animal movement
• Schick et al. 2008 Understanding movement data and movement processes: current and emerging directions
• Breed et al. 2009 Sex‐specific, seasonal foraging tactics of adult grey seals (Halichoerus grypus) revealed by state–space analysis
• Breed et al. 2012 State-space methods for more completely capturing behavioral dynamics from animal tracks
• Bestley et al. 2013 Integrative modelling of animal movement: incorporating in situ habitat and behavioural information for a migratory marine predator
• Jonsen et al. 2013 State-space models for bio-loggers: A methodological road map
• McClintock et al. 2013 Combining individual animal movement and ancillary biotelemetry data to investigate population‐level activity budgets
• Schick et al. 2013 Estimating resource acquisition and at‐sea body condition of a marine predator
• Martins et al. 2014 Behavioral attributes of turbine entrainment risk for adult resident fish revealed by acoustic telemetry and state-space modeling
• Alos et al. 2016 Bayesian state-space modelling of conventional acoustic tracking provides accurate descriptors of home range behavior in a small-bodied coastal fish species
• Jonsen et al. 2016 Joint estimation over multiple individuals improves behavioural state inference from animal movement data
• Auger-Methe et al. 2017 Spatiotemporal modelling of marine movement data using Template Model Builder (TMB)
• Patterson et al. 2017 Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges
• Dorazio and Price 2018 State-space models to infer movements and behavior of fish detected in a spatial array of acoustic receivers
• Jonsen et al. 2019 Movement responses to environment: fast inference of variation among southern elephant seals with a mixed effects model
• Jonsen et al. 2020 A continuous-time state-space model for rapid quality control of argos locations from animal-borne tags
• Auger-Methe et al. 2021 A guide to state–space modeling of ecological time series
• Newman et al. 2022 State‐space models for ecological time‐series data: Practical model‐fitting
• Jonsen et al. 2023 aniMotum, an R package for animal movement data: Rapid quality control, behavioural estimation and simulation

Hidden Markov models

• Morales et al. 2004 Extracting more out of relocation data: building movement models as mixtures of random walks
• Patterson et al. 2009 Classifying movement behaviour in relation to environmental conditions using hidden Markov models
• Langrock et al. 2012 Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions
• McClintock et al. 2012 A general discrete‐time modeling framework for animal movement using multistate random walks
• Michelot et al. 2016 moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models
• Leos-Barajas et al. 2017 Analysis of animal accelerometer data using hidden Markov models
• Leos-Barajas et al. 2017 Multi-scale modeling of animal movement and general behavior data using hidden Markov models with hierarchical structures
• Michelot et al. 2017 Estimation and simulation of foraging trips in land‐based marine predators
• Patterson et al. 2017 Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges
• Pohle et al. 2017 Selecting the number of states in hidden Markov models: pragmatic solutions illustrated using animal movement
• Grecian et al. 2018 Understanding the ontogeny of foraging behaviour: insights from combining marine predator bio-logging with satellite-derived oceanography in hidden Markov models
• Langrock et al. 2018 Spline‐based nonparametric inference in general state‐switching models
• Leos-Barajas and Michelot 2018 An introduction to animal movement modeling with hidden Markov Models using Stan for Bayesian inference
• McClintock and Michelot 2018 momentuHMM: R package for generalized hidden Markov models of animal movement
• Adam et al. 2019 Joint modelling of multi‐scale animal movement data using hierarchical hidden Markov models
• Michelot and Blackwell 2019 State‐switching continuous‐time correlated random walks
• Carter et al. 2020 From pup to predator: generalized hidden Markov models reveal rapid development of movement strategies in a naïve long‐lived vertebrate
• McClintock et al. 2020 Uncovering ecological state dynamics with hidden Markov models
• Glennie et al. 2022 Hidden Markov Models: Pitfalls and Opportunities in Ecology

Non-parametric Bayesian mixture and mixed-membership models

• Cullen et al. 2022 Identifying latent behavioural states in animal movement with M4, a nonparametric Bayesian method
• Valle et al. 2022 Automatic selection of the number of clusters using Bayesian clustering and sparsity-inducing priors
• Cullen et al. 2023 Biologging as an important tool to uncover behaviors of cryptic species: an analysis of giant armadillos (Priodontes maximus)
• Santos et al. 2023 Decoding the internesting movements of marine turtles using a finescale behavioral state approach

Space-use estimation

Minimum convex polygon

• Worton 1995 A convex hull-based estimator of home-range size
• Nilsen et al. 2008 Can minimum convex polygon home ranges be used to draw biologically meaningful conclusions?

Kernel density estimation

• Worton 1989 Kernel methods for estimating the utilization distribution in home‐range studies
• Seaman and Powell 1996 An evaluation of the accuracy of kernel density estimators for home range analysis
• Gitzen and Millspaugh 2003 Comparison of least-squares cross-validation bandwidth options for kernel home-range estimation
• Fieberg and Kochanny 2005 Quantifying home‐range overlap: The importance of the utilization distribution
• Fieberg 2007 Kernel density estimators of home range: Smoothing and the autocorrelation red herring
• Boyle et al. 2008 Home range estimates vary with sample size and methods
• Kie et al. 2010 The home-range concept: are traditional estimators still relevant with modern telemetry technology?
• Fieberg and Börger 2012 Could you please phrase “home range” as a question?
• Fleming et al. 2015 Rigorous home range estimation with movement data: a new autocorrelated kernel density estimator
• Fleming and Calabrese 2017 A new kernel density estimator for accurate home‐range and species‐range area estimation
• Noonan et al. 2019 A comprehensive analysis of autocorrelation and bias in home range estimation
• Signer and Fieberg 2021 A fresh look at an old concept: home-range estimation in a tidy world
• Fleming et al. 2022 Population-level inference for home-range areas
• Silva et al. 2022 Autocorrelation-informed home range estimation: A review and practical guide

Dynamic Brownian bridge movement model

• Horne et al. 2007 Analyzing animal movements using Brownian bridges
• Kranstauber et al. 2012 A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement
• Byrne et al. 2014 Using dynamic Brownian bridge movement modelling to measure temporal patterns of habitat selection
• Silva et al. 2020 Reptiles on the wrong track? Moving beyond traditional estimators with dynamic Brownian Bridge Movement Models