MULTI-SCALE MODELING OF RIVERINE ECOSYSTEMS AND RESPONSES OF FISH POPULATIONS IN THE CONTEXT OF GLOBAL CLIMATE CHANGE AND PREDICTIVE UNCERTAINTY: INTRODUCTION AND OVERVIEW

Climate change operates over a broad range of spatial and temporal scales. Understanding its effects on ecosystems requires multi-scale models. For understanding effects on fish populations of riverine ecosystems, climate predicted by course-resolution Global Climate Models (GCMs) must be downscaled to Regional Climate Models (RCMs) to watersheds to river hydrology to population response. An additional challenge is quantifying sources of uncertainty given the highly nonlinear nature of interactions between climate variables and community level processes. This special session presents a modeling approach for understanding and accomodating uncertainty by applying multi-scale climate models and hierarchical Bayesian modeling frameworks to Midwest fish population dynamics and by linking models for system components together by formal rules of probability. The proposed hierarchical modeling approach will account for sources of uncertainty in forecasts of community or population response. The goal is to evaluate the potential distributional changes in an ecological system, given distributional changes implied by a series of linked climate and system models under various emissions/use scenarios. This understanding will aid evaluation of management options for coping with global change.

Extant data and models will be used for each scale for the Missouri River. We will relate spatial and temporal patterns of Missouri River benthic fishes to physical and chemical factors (for example, Arab and others, 2008), sturgeon population models (Bajer and Wildhaber, 2007), and sturgeon life-history models (Wildhaber and others, 2007) to better understand factors affecting Lower Missouri River sturgeon spawning physiology, behavior, habitat choice, and success (for example, Delonay and others, 2007; Holan and others, 2009).

Presentations include: downscaling GCMs to RCMs, downscaling from watersheds to rivers, using downscaled results to model fish population responses, hierarchical Bayesian mark-recapture modeling for survival and population estimation, and using hierachical Bayesian approaches for across-scale integration of models and their uncertainty.

Arab, A., Wildhaber, M.L., Wikle, C.K., and Gentry, C.N., 2008, Zero-Inflated Modeling of Fish Catch per Unit Area Resulting from Multiple Gears: Application to Channel Catfish and Shovelnose Sturgeon in the Missouri River: North American Journal of Fisheries Management, v. 28, no. 4, p. 1044-1058.

Bajer, P.G., and Wildhaber, M.L., 2007, Population viability analysis of Lower Missouri River shovelnose sturgeon with initial application to the pallid sturgeon: Journal of Applied Ichthyology, v. 23, no. 4, p. 457-464.

DeLonay, A.J., Papoulias, D.M., Wildhaber, M.L., Annis, M.L., Bryan, J.L., Griffith, S.A., Holan, S.H., and Tillitt, D.E., 2007, Use of behavioral and physiological indicators to evaluate Scaphirhynchus sturgeon spawning success: Journal of Applied Ichthyology, v. 23, no. 4, p. 428-435.

Holan, S.H., Davis, G.M., Wildhaber, M.L., DeLonay, A.J., and Papoulias, D.M., 2009, Hierarchical Bayesian Markov switching models with application to predicting spawning success of shovelnose sturgeon: Journal of the Royal Statistical Society: Series C (Applied Statistics), v. 58, no. 1, p. 47-64.

Wildhaber, M.L., DeLonay, A.J., Papoulias, D.M., Galat, D.L., Jacobson, R., Simpkins, D.G., Braaten, P.J., Korschgen, C.E., and Mac, M.J., 2007, A Conceptual Life-History Model for Pallid and Shovelnose Sturgeon: US Geological Survey, p. 189.