TY  - JOUR
A1  - Schlägel, Ulrike E.
A1  - Lewis, Mark A.
T1  - A framework for analyzing the robustness of movement models to variable step discretization
JF  - Journal of mathematical biology
N2  - 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 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.
KW  - Animal movement
KW  - Random walk
KW  - Sampling rate
KW  - Discretization
KW  - GPS data
KW  - Parameter estimation
Y1  - 2016
U6  - https://doi.org/10.1007/s00285-016-0969-5
SN  - 0303-6812
SN  - 1432-1416
VL  - 73
SP  - 815
EP  - 845
PB  - Springer
CY  - Heidelberg
ER  - 
TY  - JOUR
A1  - Schlägel, Ulrike E.
A1  - Lewis, Mark A.
T1  - Robustness of movement models: can models bridge the gap between temporal scales of data sets and behavioural processes?
JF  - Journal of mathematical biology
KW  - Animal movement
KW  - Sampling rate
KW  - Resource selection
KW  - GPS data
KW  - Parameter estimation
KW  - Markov model
Y1  - 2016
U6  - https://doi.org/10.1007/s00285-016-1005-5
SN  - 0303-6812
SN  - 1432-1416
VL  - 73
SP  - 1691
EP  - 1726
PB  - Springer
CY  - Heidelberg
ER  -