Resources

Where to go after the eight classes

Modified

September 30, 2026

Reading, session by session

The papers below are the ones worth reading in full.

Session 1 — Foundations

  • Nathan R, Getz WM, Revilla E, Holyoak M, Kadmon R, Saltz D, Smouse PE (2008). A movement ecology paradigm for unifying organismal movement research. PNAS 105:19052–19059. doi:10.1073/pnas.0800375105 — the spine of the course.
  • Kays R, Crofoot MC, Jetz W, Wikelski M (2015). Terrestrial animal tracking as an eye on life and planet. Science 348:aaa2478. doi:10.1126/science.aaa2478 — the opening bookend.
  • Hebblewhite M, Haydon DT (2010). Distinguishing technology from biology: a critical review of the use of GPS telemetry data in ecology. Phil Trans R Soc B 365:2303–2312. doi:10.1098/rstb.2010.0087
  • Laundré JW, Hernández L, Altendorf KB (2001). Wolves, elk, and bison: reestablishing the “landscape of fear” in Yellowstone National Park, U.S.A. Canadian Journal of Zoology 79:1401–1409.
  • Portugal SJ, White CR (2018). Miniaturization of biologgers is not alleviating the 5% rule. Methods Ecol Evol 9:1662–1666. doi:10.1111/2041-210X.13013
  • Laube P, Purves RS (2011). How fast is a cow? Cross-scale analysis of movement data. Transactions in GIS 15:401–418 — the sampling-interval problem, quantified.

Session 2 — Spatial data in R

Session 3 — From fixes to trajectories

  • Calenge C (2006). The package “adehabitat” for the R software: a tool for the analysis of space and habitat use by animals. Ecological Modelling 197:516–519.
  • Signer J, Fieberg J, Avgar T (2019). Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses. Ecol Evol 9:880–890. doi:10.1002/ece3.4823

Session 4 — Quantifying movement

  • Benhamou S (2004). How to reliably estimate the tortuosity of an animal’s path: straightness, sinuosity, or fractal dimension? J Theor Biol 229:209–220.
  • Turchin P (1996). Fractal analyses of animal movement: a critique. Ecology 77:2086–2090.
  • Fauchald P, Tveraa T (2003). Using first-passage time in the analysis of area-restricted search and habitat selection. Ecology 84:282–288.
  • Bunnefeld N, Börger L, van Moorter B, Rolandsen CM, Dettki H, Solberg EJ, Ericsson G (2011). A model-driven approach to quantify migration patterns: individual, regional and yearly differences. J Anim Ecol 80:466–476.
  • Gurarie E, Andrews RD, Laidre KL (2009). A novel method for identifying behavioural changes in animal movement data. Ecology Letters 12:395–408 — behavioural change-point analysis (bcpa).
  • Edelhoff H, Signer J, Balkenhol N (2016). Path segmentation for beginners: an overview of current methods for detecting changes in animal movement patterns. Movement Ecology 4:21.

Session 5 — Movement models and simulation

  • Codling EA, Plank MJ, Benhamou S (2008). Random walk models in biology. J R Soc Interface 5:813–834. doi:10.1098/rsif.2008.0014
  • Viswanathan GM, Afanasyev V, Buldyrev SV, Murphy EJ, Prince PA, Stanley HE (1996). Lévy flight search patterns of wandering albatrosses. Nature 381:413–415.
  • Edwards AM et al. (2007). Revisiting Lévy flight search patterns of wandering albatrosses, bumblebees and deer. Nature 449:1044–1048.
  • Benhamou S (2007). How many animals really do the Lévy walk? Ecology 88:1962–1969.
  • Clauset A, Shalizi CR, Newman MEJ (2009). Power-law distributions in empirical data. SIAM Review 51:661–703 — how to fit a power law properly.
  • Humphries NE et al. (2010). Environmental context explains Lévy and Brownian movement patterns of marine predators. Nature 465:1066–1069.

Session 6 — Home ranges

  • Powell RA, Mitchell MS (2012). What is a home range? J Mammal 93:948–958. doi:10.1644/11-MAMM-S-177.1
  • Worton BJ (1989). Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70:164–168.
  • Horne JS, Garton EO, Krone SM, Lewis JS (2007). Analyzing animal movements using Brownian bridges. Ecology 88:2354–2363.
  • Fleming CH, Fagan WF, Mueller T, Olson KA, Leimgruber P, Calabrese JM (2015). Rigorous home range estimation with movement data: a new autocorrelated kernel density estimator. Ecology 96:1182–1188. doi:10.1890/14-2010.1
  • Calabrese JM, Fleming CH, Gurarie E (2016). ctmm: an R package for analyzing animal relocation data as a continuous-time stochastic process. Methods Ecol Evol 7:1124–1132.
  • Börger L, Dalziel BD, Fryxell JM (2008). Are there general mechanisms of animal home range behaviour? A review and prospects for future research. Ecology Letters 11:637–650.

Session 7 — Habitat selection

  • Fortin D, Beyer HL, Boyce MS, Smith DW, Duchesne T, Mao JS (2005). Wolves influence elk movements: behavior shapes a trophic cascade in Yellowstone National Park. Ecology 86:1320–1330 — the step selection function.
  • Lele SR, Keim JL (2006). Weighted distributions and estimation of resource selection probability functions. Ecology 87:3021–3028.
  • Avgar T, Potts JR, Lewis MA, Boyce MS (2016). Integrated step selection analysis: bridging the gap between resource selection and animal movement. Methods Ecol Evol 7:619–630. doi:10.1111/2041-210X.12528
  • Avgar T, Lele SR, Keim JL, Boyce MS (2017). Relative selection strength: quantifying effect size in habitat- and step-selection inference. Ecol Evol 7:5322–5330.
  • Fieberg J, Signer J, Smith B, Avgar T (2021). A “how-to” guide for interpreting parameters in habitat-selection analyses. J Anim Ecol 90:1027–1043. doi:10.1111/1365-2656.13441 — the single most useful paper in this course.
  • Muff S, Signer J, Fieberg J (2020). Accounting for individual-specific variation in habitat-selection studies. J Anim Ecol 89:80–92.
  • LaPoint S, Gallery P, Wikelski M, Kays R (2013). Animal behavior, cost-based corridor models, and real corridors. Landscape Ecology 28:1615–1630 — the fisher data.

Session 8 — Hidden Markov models and the frontier

  • Langrock R, King R, Matthiopoulos J, Thomas L, Fortin D, Morales JM (2012). Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. Ecology 93:2336–2342.
  • McClintock BT, Michelot T (2018). momentuHMM: R package for generalized hidden Markov models of animal movement. Methods Ecol Evol 9:1518–1530.
  • McClintock BT, Langrock R, Gimenez O, Cam E, Borchers DL, Glennie R, Patterson TA (2020). Uncovering ecological state dynamics with hidden Markov models. Ecology Letters 23:1878–1903 — the review to read.
  • Jonsen ID, Mills Flemming J, Myers RA (2005). Robust state–space modeling of animal movement data. Ecology 86:2874–2880.
  • Johnson DS, London JM, Lea M-A, Durban JW (2008). Continuous-time correlated random walk model for animal telemetry data. Ecology 89:1208–1215.
  • Wilson RP, Quintana F, Hobson VJ (2012). Construction of energy landscapes can clarify the movement and distribution of foraging animals. Proc R Soc B 279:975–980; and Shepard ELC et al. (2013). Energy landscapes shape animal movement ecology. Am Nat 182:298–312.
  • Couzin ID, Krause J, Franks NR, Levin SA (2005). Effective leadership and decision-making in animal groups on the move. Nature 433:513–516.
  • Nathan R, Spiegel O, Fortmann-Roe S, Harel R, Wikelski M, Getz WM (2012). Using tri-axial acceleration data to identify behavioral modes of free-ranging animals. J Exp Biol 215:986–996.
  • Wang G (2019). Machine learning for inferring animal behavior from location and movement data. Ecological Informatics 49:69–76.
  • Nathan R et al. (2022). Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 375:eabg1780 — the closing bookend.

Books

  • Hooten MB, Johnson DS, McClintock BT, Morales JM (2017). Animal Movement: Statistical Models for Telemetry Data. CRC Press. The rigorous reference. Heavy on maths, worth it.
  • Fieberg J (2024). Statistics for Ecologists: A Frequentist and Bayesian Treatment of Modern Regression Models — free online, with excellent habitat-selection chapters.
  • Cagnacci F, Boitani L, Powell RA, Boyce MS, eds. (2010). Special issue on GPS telemetry, Phil Trans R Soc B 365(1550) — still the best single collection of overviews.

Software and communities

amt Tracks, steps, resampling, RSF/SSF — used in most sessions
adehabitatLT Trajectories (ltraj), NSD, first-passage time
bcpa Behavioural change-point analysis
move2 Movebank access and the modern move successor
ctmm Continuous-time models, AKDE. Has a point-and-click app.
momentuHMM Hidden Markov models with covariates and multiple data streams
adehabitatHR Classic home range estimators
sf / terra The spatial foundation
AniMove Annual summer school — the best training available in this field
Movebank Data repository; also hosts Env-DATA annotation

Getting help

When code breaks, in this order:

  1. Read the error message. Genuinely. Most R errors name the object and the function that failed.
  2. ?function_name and the package vignettes (browseVignettes("amt")) — these are written by the people who wrote the code.
  3. r-spatial on Stack Overflow and the amt GitHub issues.
  4. Ask me — but bring a reproducible example: the smallest piece of code plus the smallest piece of data that reproduces the problem. Building that example solves the problem about half the time by itself.