Current course offerings

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Course description

Optimisation problems are some of the most common problems in biology. They occur in the form of maximum likelihood methods in many statistical analyses but also in behaviour (optimal foraging theory, game theory), life history theory (optimal resource allocation) and phylogenetics. Methods for solving optimisation problems include gradient methods such as the Newton-Raphson algorithm, stochastic methods such as simulated annealing, and combinatorial optimisation using the branch-and-bound algorithm. Some methods take their inspiration from biology, such as neural networks and genetic algorithms. Students will learn how to implement these methods in R, use them to solve realistic biological problems, and understand their unique limitations. In cases where optimisation methods fail, such as in NP hard problems, heuristic methods will also be studied. In the second part of this course we will apply these methods to various problems in ecology and conservation. We will study the use of eigenanalysis and multidimensional scaling methods both to reduce the dimensionality of ecological data and to find ordinations that parsimoniously explain data in interpretable ways. We will also explore methods to optimally allocate resources for the conservation of threatened species and the effective preservation and restoration of biodiversity.

Archived offerings