[RG #105] Movement Ecology: Establishing a Novel Interdisciplinary Field of Research to Explore the Causes, Patterns, Mechanisms and Consequences of Organism Movements

September 1, 2006 - August 31, 2007

Organizer:

Ran Nathan (The Hebrew University of Jerusalem)

We begin our research with the premise that movement is virtually a condition of life, as all living organisms move at some stage of their lives. There have been at least 25,000 papers published in the last decade on various aspects of movement, both in the ecological and allied biological literatures, but this field of study -- while extremely active, indeed growing -- still lacks a coherent focus. Previous attempts to provide this focus have moved the field along incrementally, but it can still be said that the literature consists of a voluminous collection of loosely related work, and the field is still defined more by what large numbers of people are doing individually rather than by any sense of a coherent field.

We aim to develop a coherent representation that captures the essential features of movement in terms of casual components, goals, information requirements and capacities, around which future studies could be organized and from which predictable consequences could be established for all sorts of organisms. This would be a launching pad for mathematical modeling, hypothesis generation, measurement and data analysis -- a coherent basis reaching from first principles to consequences, and allowing prediction and testing in real world situations. The four elements of the framework are the internal state of the organism, its movement and navigation mechanisms, and the external factors affecting the system, all resulting in the final movement behaviour and trajectory.

Once the framework has been developed, we can develop qualitative mathematical machinery that will allow us to simulate movement patterns under various explicit assumptions abot the four basic components of our conceptual model. If we can simulate under different scenarios, we can predict. If we can predict, we can compare prediction with observation, and we can test hypotheses about the model itself and our construction of it as being representative of reality.