In managing fish populations, especially at-risk species, realistic mathematical models are needed to help predict population response to potential management actions in the context of environmental conditions and changing climate while effectively incorporating the stochastic nature of real world conditions. We provide a key component of such a model for the endangered pallid sturgeon (Scaphirhynchus albus) in the form of an individual-based bioenergetics model influenced not only by temperature but also by flow. This component is based on modification of a known individual-based bioenergetics model through incorporation of: the observed ontogenetic shift in pallid sturgeon diet from marcroinvertebrates to fish; the energetic costs of swimming under flowing-water conditions; and stochasticity. We provide an assessment of how differences in environmental conditions could potentially alter pallid sturgeon growth estimates, using observed temperature and velocity from channelized portions of the Lower Missouri River mainstem. We do this using separate relationships between the proportion of maximum consumption and fork length and swimming cost standard error estimates for fish captured above and below the Kansas River in the Lower Missouri River. Critical to our matching observed growth in the field with predicted growth based on observed environmental conditions was a two-step shift in diet from macroinvertebrates to fish.Bioenergetics models can be used in fish life-history models to help partition energy intake based on the laws of thermodynamics, where the energy consumed by a fish must balance the energy required by physiological processes and growth (Enders & Scruton 2006). A bioenergetics model provides an approach of estimating the consumed energy that is partitioned into three basic components: metabolism, waste loss and growth. This modelling framework accounts for the energy cost experienced by the fish and is used to solve for the level of consumption consistent with the observed growth, integrating the array of environmental conditions experienced by the fish (Moss 2001). Essentially, bioenergetics models are used to understand the relationship between growth and feeding rates under different environmental conditions. Over the last few decades, bioenergetics models have been used widely as a tool in fisheries management and the agricultural industry to address issues related to management of sport fish populations (Chipps & Wahl 2008).One well-known bioenergetics modelling approach used in fisheries is known as the Wisconsin model (Kitchell et al. 1977); its application has been reviewed by Hanson et al. (1997). The Wisconsin model refers to an approach that incorporates maximum feeding rates (C max ) and P values (% of C max ) as a way to explore consumption patterns. The P values are meant to put consumption estimates into context (i.e. a fish feeding at 40% or 95% of C max ). As part of the Wisconsin approach, consumption is equated to the sum of metabolic cost, waste loss and net gain in weight, wi...

We present a hierarchical series of spatially decreasing and temporally increasing models to evaluate the uncertainty in the atmosphere-ocean global climate model (AOGCM) and the regional climate model (RCM) relative to the uncertainty in the somatic growth of the endangered pallid sturgeon (Scaphirhynchus albus). For effects on fish populations of riverine ecosystems, climate output simulated by coarse-resolution AOGCMs and RCMs must be downscaled to basins to river hydrology to population response. One needs to transfer the information from these climate simulations down to the individual scale in a way that minimizes extrapolation and can account for spatio-temporal variability in the intervening stages. The goal is a framework to determine whether, given uncertainties in the climate models and the biological response, meaningful inference can still be made. The non-linear downscaling of climate information to the river scale requires that one realistically account for spatial and temporal variability across scale. Our downscaling procedure includes the use of fixed/calibrated hydrological flow and temperature models coupled with a stochastically parameterized sturgeon bioenergetics model. We show that, although there is a large amount of uncertainty associated with both the climate model output and the fish growth process, one can establish significant differences in fish growth distributions between models, and between future and current climates for a given model.

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The Neosho madtom, Noturus placidus, is a small (less than 75 millimeters in total length) ictalurid that is native to the main stems of the Neosho and Cottonwood Rivers in Kansas and Oklahoma and the Spring River in Kansas and Missouri. The Neosho madtom was federally listed as threatened by the U.S. Fish and Wildlife Service in May 1990. The U.S. Fish and Wildlife Service has been monitoring Neosho madtoms since 1991, and questioned whether or not Neosho madtom densities were affected by other catfish species, reservoirs, and hydrologic characteristics. Using the first 8 years of U.S. Fish and Wildlife Service monitoring data, Wildhaber and others (2000) analyzed whether or not Neosho madtom densities were related to these environmental characteristics. The goal of this report is to update these results with data from 1999 to 2008. The trends of Neosho madtom densities in respect to John Redmond Reservoir and other catfish species remains consistent with the previous report. In both the Neosho and Spring Rivers, Neosho madtoms had a significant positive association with all catfish species. Of those species tested, only in the population of Neosho madtoms were significantly different in density above verses below the John Redmond Reservoir after accounting for the yearly variation. The average density of Neosho madtoms at the streamgage immediately below the reservoir had the second lowest density compared to the other streamgages. The positive associations with Neosho madtoms that remained consistent from the previous report included the 1-, 3-, and 7-day minima discharges and the annual minimum discharge from the previous water year (water year prior to when the fish were sampled) and the 1-, 3-, 7-, and 30-day minima discharges from the current water year (same water year fish were sampled).

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