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Aggregating the conceptualisation of movement data better captures real world and simulated animal-environment relationships
Taylor & Francis
Habitat selection analysis is a widely applied statistical framework used in spatial ecology. Many of the methods used to generate movement and couple it with the environment are strongly integrated within GIScience. The choice of movement conceptualisation and environmental space can potentially have long-lasting implications on the spatial statistics used to infer movement–environment relationships. The aim of this study was to explore how systematically altering the conceptualisation of movement, environmental space and temporal resolution affects the results of habitat selection analyses using both real-world case studies and a virtual ecologist approach. Model performance and coefficient estimates did not differ between the finest conceptualisations of movement (e.g. vector and move), while substantial differences were found for the more aggregated representations (e.g. segment and area). Only segments modelled the expected movement–environment relationship with increasing linear feature resistance in the virtual ecologist approach and altering the temporal resolution identified inversions in the movement–environment relationship for vectors and moves. The results suggest that spatial statistics employed to investigate movement–environment relationships should advance beyond conceptualising movement as the (relatively) static conceptualisation of vectors and moves and replace these with (more) dynamic aggregations of longer-lasting movement processes such as segments and areal representations.
Habitat selection , Movement , Segments , Trajectories , Virtual ecology
Holloway, P. (2019) 'Aggregating the conceptualization of movement data better captures real world and simulated animal–environment relationships', International Journal of Geographical Information Science. doi: 10.1080/13658816.2019.1618464
© 2019 Informa UK Limited, trading as Taylor & Francis Group. This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Geographical Information Science on 29 May 2019, available online: http://www.tandfonline.com/10.1080/13658816.2019.1618464