4:30-4:55 PM A Bayesian integrated population dynamics model to analyze data for the eastern Pacific Ocean spotted dolphin - Mark Maunder & Simon Hoyle Restrictions on fishing operations have been increasing in an effort to protect at-risk species taken as bycatch. With the increasing popularity of the precautionary approach, these restrictions are often conservative, because there is insufficient information about the effects of bycatch on many protected species. Informed decision-making requires quantitative analyses of both the effects of bycatch on these species and the effect of regulations on the fisheries. Uncertainty pervades management of protected species and, to be consistent with the precautionary approach, the uncertainty in analyses of these species must be described if the appropriate decisions are to be made. Bayesian analysis is an ideal framework for considering uncertainty during the decision-making process. Bayesian analysis also allows expert judgment or information from other populations or species to be included in the analysis if appropriate. Integrated analysis attempts to include all relevant data for a population into one analysis by combining analyses, sharing parameters, and simultaneously estimating all parameters, using a combined objective function. Integrated analysis ensures that model assumptions and parameter estimates are consistent throughout the analysis, that uncertainty is propagated through the analysis, and that the correlations among parameters are preserved. We combine Bayesian analysis and integrated analysis to develop a population dynamics model for the eastern Pacific Ocean (EPO) spotted dolphin. The model is developed to include the various types of data that are available for this population. Informative priors are included for several model parameters. Forward projections are used to investigate different management options. 4:55-5:20 PM Application of Bayesian decision making and MCMC to the conservation of a harvested species - Chris Fonnesbeck & Mike Conroy When endeavoring to make informed decisions, conservation biologists must frequently contend with disparate sources of data and competing hypotheses about the likely impacts of proposed decisions on the resource's status. Frequently, statistical analyses, modeling (e.g., for population projection) and optimization or simulation to investigate candidate alternative decisions, are conducted as separate exercises. For example, a population model might be constructed, whose parameters are then estimated from data (e.g., ringing studies, population surveys); finally, the parameterized model might then be used to investigate alternative candidate decisions, via simulation, optimization, or both. This approach, while effective, does not take full advantage of the integration of data and model components for prediction and updating; we propose a Bayesian context to provide this integration. In the case of American black ducks (Anas rubripes) managers are simultaneously faced with trying to extract a sustainable harvest from the species, while maintaining individual stocks above acceptable thresholds. The problem is complicated by spatial heterogeneity in the growth rates and carrying capacity of black ducks stocks, movement between stocks, regional differences in the intensity of harvest pressure, and heterogeneity in the degree of competition from a close congener, mallards (Anas platyrynchos) among stocks. We have constructed a population life cycle model that takes these components into account and simultaneously performs parameter estimation and population prediction in a Bayesian framework. Ringing data are used to develop posterior predictive distributions for harvest mortality rates, given as input decisions about harvest regulations. Population surveys of black ducks and mallards are used to obtain stock-specific estimates of population size for both species, for inputs into the population life-cycle model. These estimates are combined with the posterior distributions for harvest mortality, to obtain posterior predictive distributions of future population status for candidate sets of regional harvest regulations, under alternative biological hypotheses for black duck population dynamics. These distributions are then used both for the exploration of optimal harvest policies and for sequential updating of model posteriors, via comparison of predictive distributions to future survey estimates of stock-specific abundance. Our approach illustrates advantages of MCMC for integrating disparate data sources into a common predictive framework, for use in conservation decision making. 5:20-5:45 PM Decision models for the optimal management of biodiversity trust fund - Martin Drechler & Frank Wätzold The conservation of species generally requires long-lasting commitments over many years or decades. Even though technically, management plans can be designed for such long timeframes their practical implementation is constrained by the future available financial budget which may vary in time. A possibility to deal with the problem of variable future budgets is the employment of trust funds. At any point in time the decision maker can pay a certain proportion of the currently available money into the fund (with the remaining money being spent for conservation) or alternatively, draw a certain amount from the fund to add to the currently available budget. The optimal decision depends on various ecological and economic factors and state variables which may vary in time. We present two types of conservation problems: the management of an endangered population and the selection of nature reserves which are described by mathematical models in a general manner. Using a stochastic dynamic programming approach we derive analytical solutions for these two decision problems and deduce general guidelines for the efficient use of trust funds in the conservation of biodiversity. 5:45-6:10 PM Costs of population measurement uncertainty in index-based monitoring - Clint Moore & Bill Kendall Managers of wildlife populations commonly rely on indirect measures of the population in making decisions regarding conservation, harvest, or control. The main appeal in the use of such measures, or "indices," is their low material expense compared to methods that directly measure the population. Implicit in the use of indices is the assumption that they proportionately reflect population size. However, this assumption is rarely affirmed in practice, and decisions based on indices may or may not be the same as those that would be made if population status were known. Therefore, if the relationship between population size and its indirect measurement is unknown, then management based on indices incurs expected costs beyond the directly measurable costs of the monitoring program itself. Here, we analyze the making of optimal silvicultural decisions at the Piedmont National Wildlife Refuge (USA) for the joint benefit of two bird populations: the endangered red-cockaded woodpecker (Picoides borealis) and a shrub-nesting neotropical migrant, the wood thrush (Hylocichla mustelina). Response of the wood thrush population to management actions is largely unknown, therefore the degree to which management for the woodpecker conflicts with management for the wood thrush is uncertain. In our dynamic optimization model, we specifically address this form of structural uncertainty. We also address uncertainty in the relationship of wood thrush population status to an indirect measurement of abundance. In the model, state variables available to the manager at any decision opportunity are: (1) amounts of forest in each of three seral classes, (2) the indirect measure of wood thrush population size in each seral class, and (3) class-specific estimates of wood thrush population growth rate (8) obtained from values of the indirect population measures at successive decision opportunities. If the indirect measure of population size is a true index, then estimates of lambda should be unbiased, and decisions based on such indices would be exactly the same as those that would be made had the corresponding population abundances been known; that is, management based on such indices is optimal. Otherwise, management based on an incorrect belief in the strict proportionality of the index is suboptimal. Through analysis of the expected value of information, we calculate the expected cost of uncertainty in the relationship between the monitoring index and population size, and we do so in currency units of woodpecker habitat and wood thrush population growth. Thus, financial savings achieved by foregoing surveys that yield unbiased estimates of abundance may be balanced against expected resource costs incurred under simpler monitoring programs in which the relationship between the index and population size is not established.