Predictive invasion ecology and decisions under uncertainty

This thesis focuses on the development of methods for forecasting and managing the spread of non-native species. By combining statistical modelling and computational simulations with both biological and sociological data, this research aims to provide decision support tools to resource managers and policy makers. With an emphasis on the quantification and propagation of uncertainty through the construction of both classical and Bayesian models, I analyse the implications of various human and biological factors on forecasting the spread of fresh water invasive species. These include: 1) dispersal network structure, 2) population dynamics, 3) environmental suitability, and 4) human behavioural feedbacks to policy interventions.The first section compares two current approaches to predicting the secondary spread of aquatic invasive species and introduces a novel methodology for the quantitative validation of such predictions. Chapter 2 compares alternative models of human-mediated dispersal and assesses the consequences of the resulting dispersal network structures for predictions of invasion risk at both the local and landscape level. A new approach to validating the predictions made by models of spreading species is developed in Chapter 3. The new approach accommodates both stochastic and epistemic prediction uncertainty and I demonstrate that it has both the appropriate expected error rates as well as increased power compared to existing methods. Application to a published forecast model of Bythotrephes longimanus in central Ontario confirms the predicted invasion pattern.The second section deals with the development and application of new forecasting and management models which are applicable in common situations of limited data availability and limited management resources. Chapter 4 solves problems posed by presence-only data by extending current approaches to species distribution modelling using an observation model of the detection process. Application of this approach to 10 aquatic invasive species in Ontario revealed that the number of sites at which species are detected is not alone predictive of their current and potential range. By quantifying between-species differences in prevalence and detectability, this approach can provide guidance for sampling efforts and management interventions. Finally, Chapter 5 addresses the predicted efficacy of specific management interventions by modelling the human behavioural responses to such interventions. By integrating behavioural responses into a gravity model formulation, the predicted consequences of various policy scenarios on the future spread of aquatic invasives in Ontario is compared. Together, this research provides novel insights into both ecological processes and environmental policy.

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