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  - 
TY  - JOUR
A1  - Schlägel, Ulrike E.
A1  - Merrill, Evelyn H.
A1  - Lewis, Mark A.
T1  - Territory surveillance and prey management: Wolves keep track of space and time
JF  - Ecology and evolution
N2  - Identifying behavioral mechanisms that underlie observed movement patterns is difficult when animals employ sophisticated cognitive‐based strategies. Such strategies may arise when timing of return visits is important, for instance to allow for resource renewal or territorial patrolling. We fitted spatially explicit random‐walk models to GPS movement data of six wolves (Canis lupus; Linnaeus, 1758) from Alberta, Canada to investigate the importance of the following: (1) territorial surveillance likely related to renewal of scent marks along territorial edges, to reduce intraspecific risk among packs, and (2) delay in return to recently hunted areas, which may be related to anti‐predator responses of prey under varying prey densities. The movement models incorporated the spatiotemporal variable “time since last visit,” which acts as a wolf's memory index of its travel history and is integrated into the movement decision along with its position in relation to territory boundaries and information on local prey densities. We used a model selection framework to test hypotheses about the combined importance of these variables in wolf movement strategies. Time‐dependent movement for territory surveillance was supported by all wolf movement tracks. Wolves generally avoided territory edges, but this avoidance was reduced as time since last visit increased. Time‐dependent prey management was weak except in one wolf. This wolf selected locations with longer time since last visit and lower prey density, which led to a longer delay in revisiting high prey density sites. Our study shows that we can use spatially explicit random walks to identify behavioral strategies that merge environmental information and explicit spatiotemporal information on past movements (i.e., “when” and “where”) to make movement decisions. The approach allows us to better understand cognition‐based movement in relation to dynamic environments and resources.
KW  - animal movement
KW  - cognition
KW  - GPS data
KW  - landscape of fear
KW  - movement ecology
KW  - predator-prey
KW  - spatial memory
KW  - step selection
KW  - territoriality
KW  - time since last visit
Y1  - 2017
U6  - https://doi.org/10.1002/ece3.3176
SN  - 2045-7758
VL  - 7
SP  - 8388
EP  - 8405
PB  - Wiley
CY  - Hoboken
ER  -