Characterizing the impact of multiple potential enemies (predators and parasites) on the behaviour of ranid tadpoles

In order to fully understand an organism's behaviours the interactions between multiple enemies or selective pressures need to be considered, as these interactions are usually far more complex than the simple addition of their effects in isolation. In this thesis, I consider the impact of multiple enemies (fish predators and parasites) on the behaviour of three larval anurans (Lithobates sylvaticus, L. clamitans and L. catesbeianus). I also determine whether species that differ in life-histories and habitat preferences possess different antipredator mechanisms and how this affects species responses to multiple enemies. I show that the three Ranid larvae respond differently to the trade-off imposed by the presence of both fish predators and trematode parasites within the environment. The two more permanent pond breeders (L. clamitans and L. catesbeianus) increased activity when in the combined presence of predators and parasites. In contrast, the temporary pond breeder (L. sylvaticus) decreased activity in the combined presence of predator and parasites, in the same manner as they responded to fish alone. Further, the presence of fish along with parasites increased the susceptibility of both L. sylvaticus and L. clamitans to trematode infection, whereas parasite infection in L. catesbeianus was unaffected by the presence of fish. A second experiment to assess palatability of the three anuran species to fish, revealed a range of palatabilities, with L. catesbeianus being least palatable, L. clamitans being somewhat unpalatable, and L. sylvaticus being highly palatable. This result helps to explain the species differences in tthe observed behaviour to the combined presence of fish and parasites. In conclusion, the results from this study highlight the importance of considering multiple selective pressures faced by organisms and how this shapes their behaviour.

Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms

Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.