Date: 11 June 2019
Venue: Forbes room, Irvine Building
From static to dynamic: Aggregating the conceptualisation of movement data better captures real world and simulated animal-environment relationships.
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. This study explores how systematically altering the conceptualisation of movement, environmental space, and temporal resolution affects the results of habitat selection analyses using real-world case studies and a virtual ecologist approach. Model performance and coefficient estimates were explored between conceptualisations of movement, with substantial differences found for the more aggregated representations (e.g., segment and area). Key findings from the virtual ecologist approach identified that altering the temporal resolution identified inversions in the movement-environment relationship for vectors and moves, while systematically increasing resistance to linear features (e.g., roads) was not identified for individual aggregations. These 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.
Bio: Paul Holloway is a lecturer in Geographic Information Science and Systems in the Department of Geography and a Principal Investigator in the Environmental Research Institute at University College Cork. His research and teaching interests include using GIScience and spatial analysis to address a suite of ecological, environmental, and geographic issues. His research addresses the long-standing issue of how to incorporate movement at different spatial and temporal extents into species distribution models, how the use of volunteered geographic information and machine learning can improve spatial predictions, and how movement data and geographic context are used to understand movement processes.