Chapter 1 Resource managers frequently are tasked with mitigating or reversing adverse effects of invasive species through management policies and actions. In Lake Superior, of the Laurentian Great Lakes, invasive sea lamprey populations are suppressed to protect valuable fish stocks. However, the relationship between choice of long-term control strategy and the future chance of achieving the suppression target is unclear. Using a 60+ year time-series of suppression effort and monitoring data from 50 assessment sites located on Lake Superior tributaries, we developed a Bayesian state-space model to forecast the probability of suppressing lamprey below the suppression target. With annual application of lampricide (i.e., lamprey-specific pesticide) at historical mean levels, we forecasted a 15% chance of achieving the Lake Superior sea lamprey suppression target in 2040. Increasing lampricide effort and/or supplementing lampricide control with age-1 recruitment reduction increased suppression chance. Annual application of the maximum historical lampricide effort resulted in a 50% predicted chance of achieving the target, annual application of the mean historic lampricide effort plus a 40% reduction in recruitment resulted in a 54% chance, and the maximum amount of effort considered (maximum historic lampricide and 60% reduction in recruitment) resulted in a 94% chance. We developed a simulation model from a robust, long-term monitoring dataset that improves understanding of why long-term sea lamprey suppression objectives have been difficult to achieve in Lake Superior. Furthermore, the model provides a means to gauge efficacy of sea lamprey control policy and action scenarios based on forecasted chance of achieving the suppression target. Creating processes for iteratively refining our forecasting model with stakeholder and technical-expert input and integration with a decision analysis framework could strengthen the link between ecological knowledge obtained from long-term monitoring and invasive sea lamprey management.Chapter 2 Quantifying fish spatial recruitment dynamics at the sibling group offers a powerful methodology for understanding density-dependent and environmental drivers of recruitment. We propose a continuous-time multistate modeling framework that combines sibship and abundance estimation datasets to estimate mean sibling group size, sibling group size process error, environmental and density-dependent effects on sibling group size, dispersal, and mortality rate. Geographic states in the model consist of discrete habitat patches connected through dispersal. Simulations were used to investigate the influence of sampling processes and mean sibling group size on parameter estimation accuracy and precision for our proposed modeling framework. Mean sibling-group size, environmental effects on recruitment, and dispersal rate among habitat patches could be estimated with high accuracy under a wide range of sampling conditions, including imprecise out-of-model estimates of capture probability, subsampling within habitat patches (extrapolating density estimates to habitat abundance using area expansion), and subsampling among habitat patches. Density-dependent effects on recruitment and process error tended to be estimated with lower accuracy than other model parameters, though accuracy improved as sibling group size increased and sampling intensity increased. The main contribution of this work is a flexible quantitative modeling framework for conducting power analyses and parameterizing mechanistic models of recruitment dynamics in spatially structured fish populations with empirical sibship data. Chapter 3 A major aim of invasive species management is to enact Integrated Pest Management (IPM) principles. However, operationalizing IPM can be challenging due to ecological and values-driven uncertainties. We applied decision analysis to develop a collaborative adaptive management framework that enables effective consideration of the societal and environmental consequences of control tactic selection and use decisions for invasive sea lamprey (Petromyzon marinus) in North America’s Laurentian Great Lakes. We developed a multi-level objective hierarchy that included both localized management and multi-stream coordination fundamental objectives, conducted a feasibility analysis that constrained alternatives to those with high probability of social acceptance and technical success, and quantified expected outcomes of alternatives in terms of multi-stream coordination objectives (minimize costs and maximize learning about efficacy of novel sea lamprey control strategies). Optimal deployment configurations for scenarios that favored maximize learning over minimize costs consisted of a more diverse portfolio of control tactics compared to scenarios that favored cost effectiveness, which demonstrates the sensitivity of sea lamprey control tactic selection and use decisions to values-driven uncertainty. Additionally, sensitivity analyses revealed that optimal deployment recommendations depended upon assumptions about social and technical feasibility. Iterative application of our collaborative adaptive management framework could support social learning and cross-scale linkages if ideas about multi-stream coordination and internal validity of invasive sea lamprey management practices can be exchanged in a trusting environment. Collaborative adaptive management frameworks capable of enabling such social learning may be broadly useful for operationalizing IPM in heterogeneous social-ecological landscapes.
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