Bergmann Lab Resources
The Evolutionary Functional Morphology lab uses a variety of approaches and techniques. These include equipment and software for collecting data, code that we write in R to do analyses, and various statistical techniques. Below are a series of standard operating procedures (SOPs) that we use to standardize how we do things and ensure data consistency and quality (please obtain in-person training prior to doing these techniques), R code along with citations for analyses we have implemented, and Dr. Bergmann’s Advanced Biostatistics (Biol 206/306) labs, covering more standard statistical techniques.
Lab Standard Operating Procedures
- Recording High-Speed Video of Animal Behavior
- Collecting Locomotor Data from High-Speed Video
- Digitizing Points on a High-Speed Video
- Obtaining Instantaneous Velocity and Acceleration Data from a High-Speed Video
- Syncing a High-Speed Video Camera with Other Equipment
- Using a Kistler Force Plate
- Using a Kistler Force Transducer
- Fixing and Preserving Specimens
R code
Description |
Reference |
|
| Calculates a reduced major axis regression and its residuals in two ways, and calculates Kolmogorov-Smirnov and Shapiro-Wilks tests for normality on the residuals. | Bergman, P.J., Berk, C.P. 2012. The evolution of positive allometry of weaponry in horned lizards (Phrynosoma). Evolutionary Biology 39: 311-323. | |
| Does a Mantel test to compare two matrices in different ways: You can compare just the lower triangle, the lower triangle and diagonal, or the whole matrix. | Bergmann, P.J., McElroy, E.J. 2014. Many-to-many mapping of phenotype to performance: An extension of the F-matrix for studying functional complexity. Evolutionary Biology 41: 546-560. | |
| Uses multiple regression to construct an F-matrix and associated statistics and metrics, including the FFT and FTF matrices for a data set of multiple phenotypic traits and multiple performance measures. | Bergmann, P.J., McElroy, E.J. 2014. Many-to-many mapping of phenotype to performance: An extension of the F-matrix for studying functional complexity. Evolutionary Biology 41: 546-560. | |
| Creates an F-array and associated statistics for multiple phenotypic traits, multiple performance measures, and multiple species related by a phylogeny. Uses the Mantel and F-matrix functions. | Bergmann, P.J., McElroy, E.J. 2014. Many-to-many mapping of phenotype to performance: An extension of the F-matrix for studying functional complexity. Evolutionary Biology 41: 546-560. | |
| Does a randomization ANOVA of any design that can be handled by the aov function in R with a user-specified number of data randomizations. | Mitchell, A., Bergmann, P.J. 2016. Thermal and moisture habitat preferences do not maximize jumping performance in frogs. Functional Ecology 30: 733-742. |
Advanced Biostatistics Labs (Biol 206/306, Fall 2016)
- Lab 1 – Introduction to R
- Lab 2 – Experimental Design
- Lab 3 – ANOVA
- Lab 4 – OLS versus RMA Regression
- Lab 5 – Multiple Regression and ANCOVA
- Lab 6 – MANOVA
- Lab 7 – PCA
- Lab 8 – Randomization Tests
- Lab 9 – Model Selection Using AIC
- Lab 10 – Phylogenetic Correlation and Regression
- Lab 11 – Models of Trait Evolution
- Lab 12 – Introduction to Bayesian Inference
© 2005-2017 Philip J. Bergmann | Updated 1.26.2017