A framework for analyzing the robustness of movement models to variable step discretization
- When sampling animal movement paths, the frequency at which location measurements are attempted is a critical feature for data analysis. Important quantities derived from raw data, e.g. travel distance or sinuosity, can differ largely based on the temporal resolution of the data. Likewise, when movement models are fitted to data, parameter estimates have been demonstrated to vary with sampling rate. Thus, biological statements derived from such analyses can only be made with respect to the resolution of the underlying data, limiting extrapolation of results and comparison between studies. To address this problem, we investigate whether there are models that are robust against changes in temporal resolution. First, we propose a mathematically rigorous framework, in which we formally define robustness as a model property. We then use the framework for a thorough assessment of a range of basic random walk models, in which we also show how robustness relates to other probabilistic concepts. While we found robustness to be a strongWhen sampling animal movement paths, the frequency at which location measurements are attempted is a critical feature for data analysis. Important quantities derived from raw data, e.g. travel distance or sinuosity, can differ largely based on the temporal resolution of the data. Likewise, when movement models are fitted to data, parameter estimates have been demonstrated to vary with sampling rate. Thus, biological statements derived from such analyses can only be made with respect to the resolution of the underlying data, limiting extrapolation of results and comparison between studies. To address this problem, we investigate whether there are models that are robust against changes in temporal resolution. First, we propose a mathematically rigorous framework, in which we formally define robustness as a model property. We then use the framework for a thorough assessment of a range of basic random walk models, in which we also show how robustness relates to other probabilistic concepts. While we found robustness to be a strong condition met by few models only, we suggest a new method to extend models so as to make them robust. Our framework provides a new systematic, mathematically founded approach to the question if, and how, sampling rate of movement paths affects statistical inference.…
Author details: | Ulrike SchlägelORCiDGND, Mark A. Lewis |
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DOI: | https://doi.org/10.1007/s00285-016-0969-5 |
ISSN: | 0303-6812 |
ISSN: | 1432-1416 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/26852021 |
Title of parent work (English): | Journal of mathematical biology |
Publisher: | Springer |
Place of publishing: | Heidelberg |
Publication type: | Article |
Language: | English |
Year of first publication: | 2016 |
Publication year: | 2016 |
Release date: | 2020/03/22 |
Tag: | Animal movement; Discretization; GPS data; Parameter estimation; Random walk; Sampling rate |
Volume: | 73 |
Number of pages: | 31 |
First page: | 815 |
Last Page: | 845 |
Funding institution: | iCORE of Alberta Innovates-Technology Futures; University of Alberta; Natural Sciences and Engineering Research Council; Killam Research Fellowship |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie |
Peer review: | Referiert |