The efficacy of anti-predator behaviour in the wood fog tadpole (Rana sylvatica) /

Activity has been suggested as an important behaviour that is tightly linked with predator avoidance in tadpoles. In this thesis I examine predator-prey relationships using wood frog tadpoles {Rana sylvaticd) as prey and dragonfly larvae {AnaxJunius) and backswimmers {Notonecta undulatd) as predators. I explore the role of prey activity in predator attack rates, prey response to single and multiple predator introductions, and prey survivorship. The data suggest that Anax is the more successful predator, able to capture both active and inactive tadpoles. In contrast, Notonecta strike at inactive prey less frequently and are seldom successftil when they do. A mesocosm study revealed that the presence of any predator resulted in reduced activity level of tadpoles. Each predator species alone had similar effects on tadpole activity, as did the combined predator treatment. Tadpole survivorship, however, differed significantly among both predator treatments and prey populations. Tadpwles in the combined predator treatment had enhanced risk; survivorship was lower than that expected if the two predators had additive effects. Differences in survivorship among wood frog populations showed that tadpoles from a lake habitat had the lowest survivorship, those from a shallow pond habitat had an intermediate survivorship, and tadpoles from a marsh habitat had the highest survivorship. The frequency of interactions with predators in the native habitat may be driving the population differences observed. In conclusion, results from this study show that complex interactions exist between predators, prey, and the environment, with activity playing a key role in the survival of tadpoles.

Improving Short DNA Sequence Alignment with Parallel Computing

Variations in different types of genomes have been found to be responsible for a large degree of physical diversity such as appearance and susceptibility to disease. Identification of genomic variations is difficult and can be facilitated through computational analysis of DNA sequences. Newly available technologies are able to sequence billions of DNA base pairs relatively quickly. These sequences can be used to identify variations within their specific genome but must be mapped to a reference sequence first. In order to align these sequences to a reference sequence, we require mapping algorithms that make use of approximate string matching and string indexing methods. To date, few mapping algorithms have been tailored to handle the massive amounts of output generated by newly available sequencing technologies. In otrder to handle this large amount of data, we modified the popular mapping software BWA to run in parallel using OpenMPI. Parallel BWA matches the efficiency of multithreaded BWA functions while providing efficient parallelism for BWA functions that do not currently support multithreading. Parallel BWA shows significant wall time speedup in comparison to multithreaded BWA on high-performance computing clusters, and will thus facilitate the analysis of genome sequencing data.