The research in our lab is aimed at understanding the evolution of functional diversity, using an integrative, cross-disciplinary approach. This includes measures of whole-organismal performance, such as swimming in fish; morphology, including size and shape; and statical approaches, including phylogenetic comparative methods, model selection and mixed-models. We work on live animals and students involved in our research are required to complete IACUC training. The lab is set up to house many fish individually in 5.5 and 10 gallon aquaria, along with larger tanks for larger fish and swordtail breeding stocks. Work in the lab requires care and maintenance of animals to ensure they are healthy and perform well. This entails getting your hands wet
to clean fish tanks. Working with live animals is not always easy as animals do not always perform, but with some patience they perform and often amaze. Below are some specifics on what we measure and how we analyze our data.
The lab at Towson is set up to measure several whole-organismal performance traits. In collaboration with Jay Nelson’s Lab, we are set up at Towson to measure any aspect of fish swimming, from kinematics of fins with high speed cameras, to burst speeds, sprint speeds, endurance and rates of oxygen consumption. We measure these traits in a variety of fish at the intra- and interspecific level (Oufiero and Garland 2009, Oufiero et al. 2011, Oufiero et al. 2012, Oufiero et al. 2014). We also have a Fastec IL3 and GoPro cameras for filming other functional traits such as feeding performance in fish and praying mantises (Oufiero et al. 2012, Oufiero et al. 2016). These high-speed videos are then analyzed in ImageJ and R to obtain kinematic variables.
We measure both linear morphological traits and landmark based morphology using geometric morphometrics on live and preserved specimens. Both are used throughout our research to understand how morphology evolves and relates to function.
Phylogenetic Comparative Methods
Most of the research in the lab is conducted in a comparative framework, utilizing modern phylogenetic tools. We do not build phylogenetic trees, but we use them to understand the evolution of structure/function relationships. This has included the evolution of morphology from museum specimens (see. Oufiero, Gartner et al. 2011, Oufiero and Gartner 2014, Price et al. 2014), physiological data from previously published research (see Angilletta et al. 2006, Vant Sant et al. 2012) as well as physiological and functional traits measured in the lab, such as swimming performance and its underlying predictors (see Oufiero et al. 2014, Evolution).
We use a variety of statistical methods to understand the evolution of functional diversity. Most of our small scaled performance data is analyzed using a mixed-model approach to account for individual variation and repeated measures of performance variables (see Oufiero et al. 2014, Functional Ecology). We also use model selection with the Akaike Information Criterion (AIC and AICc) is a method to compare statistical models, accounting for the number of parameters in the models. It therefore allows for the evaluation of the best fit model for the data, regardless of how many predictors are included (Angilletta et al. 2006, Oufiero and Angilletta 2010, Oufiero et al. 2012).Lastly, we often incorporate path analysis to understand the complex, integrative nature of performance data (Angilletta et al. 2006, Oufiero and Garland 2007).