Our lab often measures whole-organismal performance, such as swimming in fish, feeding in praying mantises, and gliding in mammals. This often requires identifying individual animals and ensuring they are healthy and able to perform well. We work on live animals and students involved in our research are required to complete IACUC training. Our fish room houses many fish individually in 5.5 and 10 gallon aquaria, along with larger tanks for larger fish and swordtail breeding stocks (30 – 300 gallon). We also house our praying mantids in the fish room, often individually in 2.5 gallon aquaria and 32 oz plastic cups when they are small. 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.
The lab has a sprint speed racetrack and we are currently modifying the design to measure agility in fish. We have several Brett style swim flumes, one with a mirror above for steady swimming and kinematic analyses (including critical swimming speed and constant acceleration). We have a 1-1.5 L Blazka and 5L swim respirometer from Loligo, with fiber optic oxygen probes to measure oxygen consumption of fish (including standard metabolic rate, cost of transport, and maximal metabolic rate). Lastly we have a Fastec IL3 and Edgertronic SC1 high-speed cameras to capture kinematics of fish, insects and gliders, including feeding performance (Oufiero et al. 2012, 2016).
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).