Resources & Literature

Software Tutorials, Package Repositories, and Core Movement Literature

This page provides links to official package documentation and software repositories, alongside a curated bibliography of foundational and recent literature across movement ecology modeling topics.

📦 R Packages & Tutorials

Behavioural State Estimation

• aniMotum: Package Website | GitHub Repository
• momentuHMM: Package Vignette & Manual | GitHub Repository
• bayesmove: Package Website | GitHub Repository

Space-Use & Telemetry Processing

• amt (Animal Movement Tools): Package Documentation | GitHub Repository
• ctmm (Continuous-Time Movement Modeling): Package Vignettes | GitHub Repository
• move / move2: Package Website | Move2 Documentation

Geospatial Data Processing

• rsi (Remote Sensing Integration): Package Documentation
• rstac (STAC API Client): Package Documentation
• elevatr: Package Vignette
• terra: Package Documentation | GitHub Repository
• sf: Package Documentation | GitHub Repository
• tidyterra: Package Documentation | GitHub Repository
• Tutorial for working with Google Earth Engine (GEE)

🌍 Geospatial Data Sources

General Data Sources

• STAC Index: Platform for searching through numerous STAC catalogs of available data in the cloud.
• Google Earth Engine: A planetary-scale platform for Earth science data and analysis. Provides ability to process remote sensing products in the cloud without need for having powerful computer.
• Source Cooperative: Source is a nonprofit data publishing utility built on commodity cloud object storage. Researchers publish data at any scale without running servers, building portals, or writing APIs. Data are available in multiple formats.
• NASA Earthdata: Provides large suite of data products from related NASA satellite missions and tools to view and download these data. AppEEARS offers a simple and efficient way to perform data access and transformation processes.
• Microsoft Planetary Computer: Provides large catalog of multi-petabyte Earth observation data from a wide variety of products. Provides nice data viewer and data are readily available from their STAC catalog.
• Digital Earth Africa: Processes openly accessible and freely available data to produce decision-ready products to address key challenges across the continent. Also provides a learning platform for accessing, exploring, and analyzing their data products in Python.

Political & Administrative Boundaries

• Natural Earth Data: Global country and state/province boundary vectors. Accessed via rnaturalearth.
• geoBoundaries Data: High-resolution administrative boundary layers (ADM0–ADM5). Accessed via rgeoboundaries.

Water Extent & Recurrence

• JRC Global Surface Water: Global surface water products from the European Commission Joint Research Centre, based on Landsat 5, 7, and 8 imagery. Layers in this collection describe the occurrence, change, and seasonality of surface water from 1984-2021. Data available via STAC catalog and Google Earth Engine.
• Water Observations from Space (WOfS): African surface water products from the Digital Earth Africa, based on Landsat imagery (30 m). Layers in this collection describe the date-specific, annual summary, and all-time summary of surface water extent from 1984-present. Data available via AWS S3 bucket, OGC Web Services, and ODC API.

Elevation & Topography

• AWS Terrain Tiles / USGS DEM: Global digital elevation models accessed at varying zoom levels via elevatr. Derived metrics (slope, aspect, terrain ruggedness index [TRI]) calculated using terra::terrain().

OpenStreetMap (Vector Hydrology & Infrastructure)

• OpenStreetMap (OSM): Open-access global vector features (water bodies, rivers, roads, buildings). Queried directly via osmdata.

Human Pressure & Population Grids (Cloud COGs)

• Global Terrestrial Human Footprint (HFP-100 v1.2): 100 m resolution cumulative human pressure map (2021). Streamed as a Cloud-Optimized GeoTIFF (COG) from Source Cooperative.
• GHS-POP (Global Human Settlement Layer): 100 m modeled residential population estimates (2020). Streamed as a COG from Source Cooperative (cboettig/population).

Land Use / Land Cover (LULC)

• ESA WorldCover (10 m): High-resolution global land cover product with 11 classes (2020/2021). Queried via SpatioTemporal Asset Catalog (STAC) through Microsoft Planetary Computer, AWS, or WorldCover website.
• Blog comparing different LULC products based on this recent paper.

Multispectral Satellite Imagery

• Sentinel-2A Optical Imagery (10 m): Global multi-spectral imagery available at 5-day revisit rates (2017 - present). Available across multiple platforms, such as STAC Catalogs, Copernicus Data Browser, and Google Earth Engine.
• Landsat (30 m; multiple missions): Global multi-spectral imagery available at 16-day revisit rates (1972 - present). Available across multiple platforms, such as STAC Catalogs, USGS EarthExplorer, and Google Earth Engine
• Harmonized Landsat Sentinel-2 (HLS; 30 m): The combined measurement across both remote sensing products enables global observations of the land every 2-3 days (2015 - present) and is available from major data sources such as STAC and Google Earth Engine.

🧠 Behavioural State Estimation

Method and Time Step Comparison

• Cullen et al. (2025) Method selection and temporal scale greatly influence ecological inferences on estimated animal behavioral states

State-Space Models (SSMs)

• Jonsen et al. (2005) Robust state–space modeling of animal movement 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
• Auger-Méthé et al. (2017) Spatiotemporal modelling of marine movement data using Template Model Builder (TMB)
• Eisaguirre et al. (2019) Dynamic-Parameter Movement Models Reveal Drivers of Migratory Pace in a Soaring Bird
• Jonsen et al. (2020) A continuous-time state-space model for rapid quality control of argos locations from animal-borne tags
• Auger-Méthé et al. (2021) A guide to state–space modeling of ecological time series
• Jonsen et al. (2023) aniMotum, an R package for animal movement data: Rapid quality control, behavioural estimation and simulation

Hidden Markov Models (HMMs)

• Morales et al. (2004) Extracting more out of relocation data: building movement models as mixtures of random walks
• 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
• Pohle et al. (2017) Selecting the number of states in hidden Markov models: pragmatic solutions illustrated using animal movement
• Leos-Barajas et al. (2017) Multi-scale Modeling of Animal Movement and General Behavior Data Using Hidden Markov Models with Hierarchical Structures
• Leos-Barajas et al. (2017) Analysis of animal accelerometer data using hidden Markov models
• 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
• McClintock et al. (2020) Uncovering ecological state dynamics with hidden Markov models
• Glennie et al. (2022) Hidden Markov Models: Pitfalls and Opportunities in Ecology
• Klappstein et al. (2023) Flexible hidden Markov models for behaviour-dependent habitat selection
• Dupont et al. (2025) Improved order selection method for hidden Markov models: A case study with movement data
• Michelot (2025) hmmTMB: Hidden Markov Models with Flexible Covariate Effects in R
• Cisneros-Araujo et al. (2026) A hidden Markov framework for joint identification of animal activity modes and movement phases

Non-Parametric & 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)

🗺️ Space-Use & Home Range

Classical Methods (MCP & Conventional KDE)

• Worton (1989) Kernel methods for estimating the utilization distribution in home‐range studies
• 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
• Nilsen et al. (2008) Can minimum convex polygon home ranges be used to draw biologically meaningful conclusions?

Brownian Bridges & Autocorrelated KDE (dBBMM & AKDE)

• 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
• 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
• Winner et al (2018) Statistical inference for home range overlap
• 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
• Silva et al. (2022) Autocorrelation-informed home range estimation: A review and practical guide
• Fleming et al. (2022) Population-level inference for home-range areas
• Anand et al. (2025) Estimating population range distributions from animal tracking data
• Silva et al. (2026) Too Few, Too Many, or Just Right? Optimizing Sample Sizes for Population-Level Inferences in Animal Tracking Projects
• Alston et al. (2026) Clarifying space use concepts in ecology: Range vs. occurrence distributions

🌲 Resource & Step Selection (RSF / SSF / iSSF)

Comprehensive resources

• Matthiopoulos, Fieberg, and Aarts (2023) Species-Habitat Associations: Spatial data, predictive models, and ecological insights, 2nd Edition
• Gerber et al. (2025) A plain language review and guidance for modeling animal habitat-selection

Foundational Papers & Recent Advances

• Manly et al. (2002) Resource Selection by Animals: Statistical Design and Analysis for Field Studies (2nd ed.). Springer.
• Boyce et al. (2002) Evaluating resource selection functions
• Rhodes et al. (2005) Continuous-time movement models: an integrated framework for statistical inference on animal movement
• Fortin et al. (2005) Wolves influence elk movements: behavior shapes a trophic cascade in Yellowstone National Park
• Thurfjell et al. (2014) Applications of step-selection functions in ecology and conservation
• Avgar et al. (2016) Integrated step-selection analysis: bridging the gap between movement and habitat selection
• Wittemyer et al. (2019) Behavioural valuation of landscapes using movement data
• Muff et al. (2020) How to account for inter-individual variation when fitting resource selection functions
• Fieberg et al. (2021) A ‘How to’ guide for interpreting parameters in habitat-selection analyses
• Klappstein et al. (2022) Energy-based step selection analysis: Modelling the energetic drivers of animal movement and habitat use
• Northrup et al. (2022) Conceptual and methodological advances in habitat-selection modeling: guidelines for ecology and evolution
• Klappstein et al. (2024) Step selection functions with non-linear and random effects
• Chatterjee et al. (2024) Modelling individual variability in habitat selection and movement using integrated step-selection analysis
• Fieberg et al. (2024) Using lineups to evaluate goodness of fit of animal movement models
• Michelot et al. (2024) Understanding step selection analysis through numerical integration
• Signer et al. (2024) Simulating animal space use from fitted integrated Step-Selection Functions (iSSF)
• McClintock and Lander (2024) A multistate Langevin diffusion for inferring behavior-specific habitat selection and utilization distributions
• Forrest et al. (2025) Predicting fine-scale distributions and emergent spatiotemporal patterns from temporally dynamic step selection simulations
• Lambert et al. (2025) Bridging the Gap Between Lagrangian and Eulerian Species Distribution Models for Abundance Estimation—A Simulation Experiment
• Florko et al. (2025) An introduction to statistical models used to characterize species-habitat associations with animal movement data

🤖 Bayesian & Machine Learning Directions

Bayesian Spatial & Movement Inference

• Hooten et al. (2017) Animal Movement: Statistical Models for Telemetry Data. CRC Press.
• Bakka et al. (2018) Spatial modeling with R-INLA: A review
• Bachl et al. (2019) inlabru: an R package for Bayesian spatial modelling from ecological survey data
• Bürkner (2017) brms: An R Package for Bayesian Multilevel Models Using Stan
• Gabry et al. (2019) Visualization in Bayesian Workflow
• McShane et al. (2019) Abandon Statistical Significance
• Lemoine (2019) Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses
• McElreath (2020) Statistical rethinking: A Bayesian course with examples in R and STAN (2nd ed)
• Wesner and Pomeranz (2021) Choosing priors in Bayesian ecological models by simulating from the prior predictive distribution
• Gelman et al. (2026). Statistical workflow

Machine Learning in Movement Ecology

• Browninig et al. (2018) Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds
• Hirakawa et al. (2018) Can AI predict animal movements? Filling gaps in animal trajectories using inverse reinforcement learning
• Carlson (2020) embarcadero: Species distribution modelling with Bayesian additive regression trees in r
• Wijeyakulasuriya et al. (2020) Machine learning for modeling animal movement
• Tuia et al. (2022) Perspectives in machine learning for wildlife conservation
• Bergen et al. (2023) A review of supervised learning methods for classifying animal behavioural states from environmental features
• Pichler & Hartig (2023) Machine learning and deep learning—A review for ecologists
• Dunford et al. (2024) Identifying animal behaviours from accelerometers: Improving predictive accuracy of machine learning by refining the variables selected, data frequency, and sample duration
• Hoffman et al. (2024) A benchmark for computational analysis of animal behavior, using animal-borne tags
• Fronville et al. (2026) Analyzing animal movement using deep learning
• Forrest et al. (2026) Predicting animal movement with deepSSF: A deep learning step selection framework
• Agarwal et al. (2026) Leveraging machine learning and accelerometry to classify animal behaviours with uncertainty
• Methods in Ecology & Evolution (2026) Special Feature: Conservation, Ecology and Artificial Intelligence — Advances and Symbiotic Solutions