The DOI matches its Crossref record.
Translating Foraging Movements in Heterogeneous Environments into the Spatial Distribution of Foragers
Abstract
The relationship between movement and population dynamics in heterogeneous environments has been approached from two directions: theoretical analyses using diffusion models and empirical studies that often employ computer simulations. In this paper I am proposing a unified framework for studying individual movements that brings both of these approaches together. The proposed framework is based on the d iffusion—approximation" procedure developed by Patlak (1953a, b), which translates a probabilistic description of the pattern of individual movements (such as provided by simulation models) into a partial differential model describing the population redistribution in a patchy habitat. My first goal is this paper was to make Patlak's work accessible to ecologists interested in realistic movement models. Secondly, making some simplifying assumptions I solved Patlick's model to obtain the equilibrium distribution of organisms among patches in a heterogeneous environment. This led me to a definition of "residence index." The residence index of a patch is estimated by observing and recording movement trajectories of organisms within the patch. The pattern of variation in the residence index among different kinds of patches specifies the predicted equilibrium densities of organisms in each patch. I assessed the utility of the above approach by using data on movement tracks of Euphydryas anicia females to calculate butterfly residence indices in host—plant patches vs. nonhost environment. The predicted distribution of butterflies among host and nonhost areas differed by only 16% from the actual distribution, as documented by an independent data set. I also applied the model to data from three studies on insect movement and spatial distribution. The accuracy of model predictions varied from adequate to excellent. Finally, I argue that Patlak's model and its residence—index extension provides a general, and at the same time realistic, framework for quantifying individual movements, and for relating movement patterns to spatial population dynamics. Although this approach, as developed here, is explicitly concerned only with population redistribution within an area, birth/death or immigration/emigration terms can be added in a straightforward manner. Thus, this framework can be very useful in studies of consumer—resource (e.g., predator—prey or herbivore—host) spatial dynamics, since movement behaviors of consumers searching for prey, and prey attempting to avoid consumers, are known to be important in affecting the outcome of such population interactions.
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
Cited by
Integrated step selection analysis: bridging the gap between resource selection and animal movement
Avgar, T., Potts, J. R., Lewis, M. A., & Boyce, M. S. (2016) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12528 · Cited by 594
Northrup, 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
“Animals move to acquire resources (Owen-Smith et al. 2010), precipitate or diminish interactions with other animals, and ultimately facilitate how they distribute themselves on the landscape (Turchin 1991, 1998).”
BackgroundSuite of simple metrics reveals common movement syndromes across vertebrate taxa
Abrahms, B., Seidel, D. P., Dougherty, E., Hazen, E. L., Bograd, S. J., Wilson, A. M., Weldon McNutt, J., Costa, D. P., Blake, S., Brashares, J. S., et al. (2017) · Movement Ecology · doi:10.1186/s40462-017-0104-2 · Cited by 109
Boettiger, A. N., Wittemyer, G., Starfield, R., Volrath, F., Douglas-Hamilton, I., & Getz, W. M. (2011) · Ecology · doi:10.1890/10-0106.1 · Cited by 38
Metadata
- Authors
- Turchin, P.
- Venue
- Ecology
- Publisher
- Wiley
- Published
- 1991-08
- Volume
- 72
- Issue
- 4
- Pages
- 1253-1266
- ISSN
- 0012-9658 (ISSN-L)
- 1939-9170
- OpenAlex
- W2054973894
- Cited by
- 402
- References
- 0
- FWCI
- 3.08
- Type
- journal article
- Language
- en
- Open access
- closed
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: Translating Foraging Movements in Heterogeneous Environments into the Spatial Distribution of Foragers. https://app.cochat.ai/papers/10.2307/1941099
- The paper
- Turchin, P. (1991). Translating Foraging Movements in Heterogeneous Environments into the Spatial Distribution of Foragers. Ecology. https://doi.org/10.2307/1941099
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.