Premium
Integrating evolutionary biology with digital arts to quantify ecological constraints on vision‐based behaviour
Author(s) -
Bian Xue,
Chandler Tom,
Laird Warwick,
Pinilla Angela,
Peters Richard
Publication year - 2018
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12912
Subject(s) - motion (physics) , computer science , adaptation (eye) , ecology , animation , visualization , human–computer interaction , sensory cue , artificial intelligence , data science , biology , computer graphics (images) , neuroscience
Motion vision is crucial in the life of animals, in controlling locomotion, in foraging, for predator evasion and in communication. However, information on the conditions for motion vision in natural environments is limited. Advancing knowledge of the ecological limitations that affect functional behaviour requires novel methodologies. To explore motion ecology in more detail we describe an innovative method that integrates evolutionary biology with digital arts. A visualization tool that simulates three spatial dimensions plus movement through time, 3D animation is an innovative approach to understand dynamic environments. Animal signalling systems have provided useful insights into ecological limitations on behaviour, and we demonstrate the utility of our approach by examining motion displays of lizards surrounded by plant motion noise. The effectiveness of signals in noise was considered under different circumstances, and in each case, we had complete control over the simulations. We used these scenarios to both validate our approach and to demonstrate its potential. The relevance to motion signalling of prevailing wind and resultant plant motion is now well established and we begin by replicating this effect and illustrate how we can explore this in quantitative detail. We further demonstrate its utility by providing novel insights into the benefits of signalling in the right place and at the right time, by manipulating immediate signalling backgrounds, variation in signaller–plant distances and light environments. Each of these simulations provide opportunities for investigation that would be impossible in nature. Systematic measurements of motion ecology in detail are now achievable. In addition to insights into the evolution of motion signals, 3D environmental reconstruction will provide a test bed for other topics in the field of motion ecology, and a resource to enhance public engagement with science.