The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Integrated step selection analysis: bridging the gap between resource selection and animal movement
Open accessAbstract
Summary: A resource selection function is a model of the likelihood that an available spatial unit will be used by an animal, given its resource value. But how do we appropriately define availability? Step selection analysis deals with this problem at the scale of the observed positional data, by matching each ‘used step’ (connecting two consecutive observed positions of the animal) with a set of ‘available steps’ randomly sampled from a distribution of observed steps or their characteristics. Here we present a simple extension to this approach, termed integrated step selection analysis (iSSA), which relaxes the implicit assumption that observed movement attributes (i.e. velocities and their temporal autocorrelations) are independent of resource selection. Instead, iSSA relies on simultaneously estimating movement and resource selection parameters, thus allowing simple likelihood‐based inference of resource selection within a mechanistic movement model. We provide theoretical underpinning of iSSA, as well as practical guidelines to its implementation. Using computer simulations, we evaluate the inferential and predictive capacity of iSSA compared to currently used methods. Our work demonstrates the utility of iSSA as a general, flexible and user‐friendly approach for both evaluating a variety of ecological hypotheses, and predicting future ecological patterns.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A movement ecology paradigm for unifying organismal movement research
Nathan, R., Getz, W. M., Revilla, E., Holyoak, M., Kadmon, R., Saltz, D., & Smouse, P. E. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0800375105 · Cited by 2,968
(2004) · doi:10.1007/0-306-48151-0 · Cited by 2,547
Resource Selection by Animals: Statistical Design and Analysis for Field Studies
Davis, A. J., Manly, B. F. J., McDonald, L. L., & Thomas, D. L. (1994) · The Journal of Animal Ecology · doi:10.2307/5247 · Cited by 1,805
Codling, E. A., Plank, M. J., & Benhamou, S. (2008) · Journal of The Royal Society Interface · doi:10.1098/rsif.2008.0014 · Cited by 1,482 · cited 1×
“tatistical (phenomenological) RSAs and mathematical (mechanistic) RW models (Potts et al. 2014b), models that form the backbone of much of the existing body of theory in the field of animal movement (Codling et al. 2008; Fagan & Calabrese 2014; Benhamou 2014) . In this paper we outline a -based approach for simult…”
MethodWolves influence elk movements: Behavior shapes a trophic cascade in Yellowstone National Park
Fortin, D., Beyer, H. L., Boyce, M. S., Smith, D. W., Duchesne, T., & Mao, J. S. (2005) · Ecology · doi:10.1890/04-0953 · Cited by 1,351 · cited 2×
“4 analysis (SSA), where each ‘used step’ (connecting two consecutive observed positions of the animal) is coupled with a set of ‘available steps’ randomly sampled from the empirical distribution of observed steps or their characteristics (e.g., their length and direction; Fortin et al. 2005; Duchesne et al. 2010; Thur…”
MethodSelf-citationTowards a behavioral ecology of ecological landscapes
Lima, S. L., & Zollner, P. A. (1996) · Trends in Ecology & Evolution · doi:10.1016/0169-5347(96)81094-9 · Cited by 950
State–space models of individual animal movement
PATTERSON, T., THOMAS, L., WILCOX, C., OVASKAINEN, O., & MATTHIOPOULOS, J. (2008) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2007.10.009 · Cited by 921
Relating populations to habitats using resource selection functions
Boyce, M. S., & McDonald, L. L. (1999) · Trends in Ecology & Evolution · doi:10.1016/s0169-5347(99)01593-1 · Cited by 904
Self-citationAnalyzing insect movement as a correlated random walk
Kareiva, P. M., & Shigesada, N. (1983) · Oecologia · doi:10.1007/bf00379695 · Cited by 681
Applications of step-selection functions in ecology and conservation
Thurfjell, H., Ciuti, S., & Boyce, M. S. (2014) · Movement Ecology · doi:10.1186/2051-3933-2-4 · Cited by 620
Self-citation
Cited by
Optimizing the use of biologgers for movement ecology research
Williams, H. J., Taylor, L. A., Benhamou, S., Bijleveld, A. I., Clay, T. A., de Grissac, S., Demšar, U., English, H. M., Franconi, N., Gómez‐Laich, A., et al. (2020) · Journal of Animal Ecology · doi:10.1111/1365-2656.13094 · Cited by 333
Self-citationNorthrup, J. M., Vander Wal, E., Bonar, M., Fieberg, J., Laforge, M. P., Leclerc, M., Prokopenko, C. M., & Gerber, B. D. (2022) · Ecological Applications · doi:10.1002/eap.2470 · Cited by 227
“Importantly, the iSSA approach produces an empirically parameterized mechanistic movement model capable of translating fine-scale movements and habitat-selection behaviors to coarser scale distributions (Potts et al. 2014b, Avgar et al. 2016, Signer et al. 2017).”
BackgroundMethodInfluentialTsalyuk, M., Kilian, W., Reineking, B., & Getz, W. M. (2019) · Ecological Monographs · doi:10.1002/ecm.1348 · Cited by 98
Predicting animal movement with deepSSF : A deep learning step selection framework
Forrest, S. W., Pagendam, D., Hassan, C., Potts, J. R., Drovandi, C., Bode, M., & Hoskins, A. J. (2026) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.70136 · Cited by 3
Self-citationSidder, S. A., D'Antonio, A., Hall, T. E., Dinkins, J. B., Bredeweg, E. M., & Monz, C. A. (2023) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2023.104796 · Cited by 3
Metadata
- Authors
- Avgar, T., Potts, J. R., Lewis, M. A., & Boyce, M. S.
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2016-01-23
- Volume
- 7
- Issue
- 5
- Pages
- 619-630
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W2281654679
- Cited by
- 594
- References
- 47
- FWCI
- 14.86
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Integrated step selection analysis: bridging the gap between resource selection and animal movement. https://app.cochat.ai/papers/10.1111/2041-210x.12528
- The paper
- Avgar, T., Potts, J. R., Lewis, M. A., & Boyce, M. S. (2016). Integrated step selection analysis: bridging the gap between resource selection and animal movement. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.12528
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
