Using video and automated tracking to understand species interactions

Title: Using video and automated tracking to understand species interactions.

Type: Laboratory experiments and video tracking and analysis

Degree: Bachelor or Master thesis

Description: Understanding the movement and behavior of individuals is integral to understanding how ecological systems operate (Patterson et al. 2008; Clutton-Brock & Sheldon 2010; Simpson et al. 2010; Pawar et al. 2012), but these properties are notoriously difficult to quantify in anything but the simplest of ecological settings. Technologies are now emerging that allow biologists to document the movement and behavior of organisms at high spatial and temporal resolutions not possible with traditional approaches. When combined with new software that can automatically quantify the movement and behavior of individuals (Buhl et al. 2006; Branson et al. 2009; Straw et al. 2010; Guttal et al. 2012; Ioannou et al. 2012), these technologies present exciting new opportunities for how ecologists can explore the role of individuals in structuring ecological systems. We have recently built a
video tracking system that allows us to explore in detail mechanisms shaping ecological interactions. Beginning 2013 we are looking for suitable Bachelor or Master students to use this equipment to address some fundamental questions in ecology. Specific questions that would make ideal student projects are:
1. Effect of habitat complexity on trophic interactions. How does habitat complexity alter patterns of consumer-resource interactions (e.g., search rate, time to attack, escape ability). This project links to work on how habitat heterogeneity can increase stability of predator-prey populations.
2. Patterns of animal movement during trophic interactions. As animals change speeds they typically transition from one gait to another. This is thought to minimize energy expenditure required to maintain the new speed, although energy conservation is not the only reason one gait might be preferred over another (Bender et al. 2011). One example is in escape responses, when an animal’s choice of gait may be dictated by speed rather than efficiency. For example a predator stalking prey is not trying to conserve energy. This project will explore how gait changes during predator-prey interactions.
3. Testing the thermal version of the life dinner principal. The life-dinner principle hypothesizes stronger selection pressure on prey to escape capture and death that for predators to catch an individual prey item (Dawkins & Krebs 1979; Brodie III & Brodie Jr 1999; Scales et al. 2009). Previous work of ours (Dell et al. 2011) has shown that prey might increased trait performance (e.g., body velocity) at lower temperatures relative to predators, which suggests a thermal version of the life-dinner principle. To date this idea has not been tested experimentally.
4. How does arena size affect predator-prey interactions? Predators must detect prey before they can attack, and arena size should alter the time taken for predators to find. Understanding this relationship is key to understanding how trophic interaction strength varies with prey density.
5. What are the mechanisms that determine optimum body size ratios in predator-prey interactions? Ecologists understand that body size is important in determining who eats whom (Woodward et al. 2005), but they have little idea about the precise mechanisms at play which determine why predators cannot optimally capture small or large prey.
6. How does the effectiveness of a visual predator change from day to night? Addressing this question may help ecologists understand differences in the organization between diurnal and nocturnal community organization.
7. How does the complexity of visual background affect the dynamics of predator-prey interactions?

Requirements: Students should be interested in ecological interactions, and the mechanisms that shape them. Each of these projects requires running laboratory experiments (predator-prey trials), and analysis of video data. Therefore, students should be comfortable working with invertebrate predators (e.g., crickets, spiders, scorpions). A significant amount of time will be spent analyzing the video data, so students should be computer literate (experience with Matlab and/or R is desirable, or a willingness to learn).

Supervisors: Anthony I. Dell, Björn C. Rall

Literature:
Angilletta M.J. (2009). Thermal adaptation: a theoretical and empirical synthesis. Oxford University Press, Oxford ; New York.
Bender J.A., Simpson E.M., Tietz B.R., Daltorio K.A., Quinn R.D. & Ritzmann R.E. (2011). Kinematic and behavioral evidence for a distinction between trotting and ambling gaits in the cockroach Blaberus discoidalis. The Journal of Experimental Biology, 214, 2057-2064.
Brady D.J., Gehm M.E., Stack R.A., Marks D.L., Kittle D.S., Golish D.R., Vera E.M. & Feller S.D. (2012). Multiscale gigapixel photography. Nature, 486, 386-389.
Branson K., Robie A., Bender J., Perona P. & Dickinson M. (2009). High-throughput ethomics in large groups of Drosophila. Nature methods, 6, 451-458.
Brodie III E.D. & Brodie Jr E.D. (1999). Predator-prey arms races. Bioscience, 49, 557-568.
Buhl J., Sumpter D.J.T., Couzin I.D., Hale J.J., Despland E., Miller E.R. & Simpson S.J. (2006). From Disorder to Order in Marching Locusts. Science, 312, 1402-1406.
Chiu C., Reddy P.V., Xian W., Krishnaprasad P.S. & Moss C.F. (2010). Effects of competitive prey capture on flight behavior and sonar beam pattern in paired big brown bats, Eptesicus fuscus. The Journal of Experimental Biology, 213, 3348-3356.
Clutton-Brock T. & Sheldon B.C. (2010). Individuals and populations: the role of long-term, individual-based studies of animals in ecology and evolutionary biology. Trends in ecology & evolution (Personal edition), 25, 562-573.
Dawkins R. & Krebs J.R. (1979). Arms races between and within species. Proceedings of the Royal Society of London Series B-Biological Sciences, 205, 489-511.
Dell A.I., Pawar S. & Savage V.M. (2011). Systematic variation in the temperature dependence of physiological and ecological traits. Proceedings of the National Academy of Sciences of the United States of America, 108, 10591-10596.
Guttal V., Romanczuk P., Simpson S.J., Sword G.A. & Couzin I.D. (2012). Cannibalism can drive the evolution of behavioural phase polyphenism in locusts. Ecology Letters, 15, 1158-1166.
Ioannou C.C., Guttal V. & Couzin I.D. (2012). Predatory Fish Select for Coordinated Collective Motion in Virtual Prey. Science, 337, 1212-1215.
Patterson T.A., Thomas L., Wilcox C., Ovaskainen O. & Matthiopoulos J. (2008). State space models of individual animal movement. Trends in ecology & evolution (Personal edition), 23, 87-94.
Pawar S., Dell A.I. & Van M.S. (2012). Dimensionality of consumer search space drives trophic interaction strengths. Nature, advance online publication.
Scales J.A., King A.A. & Butler M.A. (2009). Running for your life or running for your dinner: what drives fiber-type evolution in lizard locomotor muscles? American Naturalist, 173, 543-553.
Simpson S.J., Raubenheimer D., Charleston M.A. & Clissold F.J. (2010). Modelling nutritional interactions: from individuals to communities. Trends in ecology & evolution (Personal edition), 25, 53-60.
Straw A.D., Branson K., Neumann T.R. & Dickinson M.H. (2010). Multi-camera real-time three-dimensional tracking of multiple flying animals. Journal of The Royal Society Interface.
Woodward G., Ebenman B., Emmerson M., Montoya J.M., Olesen J.M., Valido A. & Warren P.H. (2005). Body size in ecological networks. Trends in Ecology & Evolution, 20, 402-409.