Thermal performance of the electron transport system Complex III in seven Alabama fishes.

Management of fish populations for conservation in thermally variable systems requires an understanding of the fish's underlying physiology and responses to thermal stress. Physiological research at the organismal level provides information on the overall effects of stressors such as extreme temperature fluctuations. While experiments with whole organisms provide information as to the overall effects of temperature fluctuations, biochemical assays of thermal stress provide direct results of exposure that are both sensitive and specific. Electron transport system (ETS; Complex III) assays quantify a rate-limiting step of respiratory enzymes. Parameters that can be estimated via this approach include optimum thermal temperature (Topt ) and optimal breadth of thermal performance (Tbreadth ), which can both be related to organismal-level temperature thresholds. We exposed enzymes of seven fish species (native fish chosen to represent a typical community in Alabama streams) to temperatures in the range 11-44°C. The resultant enzymatic thermal performance curves showed that Topt , the lower temperature for enzyme optimal thermal performance (Tlow ), the upper temperature for enzyme optimal thermal performance (Tup ), and Tbreadth differed among species. Relationships between enzymatic activity and temperature for all fish followed a pattern of steadily increasing enzyme activity to Topt before gradually decreasing with increasing temperature. A comparison of our enzyme optimum and upper-temperature limit results versus published critical thermal maxima values supports that ETS Complex III assays may be useful for assessing organismal-level thermal tolerance.

Identifying monitoring information needs that support the management of fish in large rivers.

Management actions intended to benefit fish in large rivers can directly or indirectly affect multiple ecosystem components. Without consideration of the effects of management on non-target ecosystem components, unintended consequences may limit management efficacy. Monitoring can help clarify the effects of management actions, including on non-target ecosystem components, but only if data are collected to characterize key ecosystem processes that could affect the outcome. Scientists from across the U.S. convened to develop a conceptual model that would help identify monitoring information needed to better understand how natural and anthropogenic factors affect large river fishes. We applied the conceptual model to case studies in four large U.S. rivers. The application of the conceptual model indicates the model is flexible and relevant to large rivers in different geographic settings and with different management challenges. By visualizing how natural and anthropogenic drivers directly or indirectly affect cascading ecosystem tiers, our model identified critical information gaps and uncertainties that, if resolved, could inform how to best meet management objectives. Despite large differences in the physical and ecological contexts of the river systems, the case studies also demonstrated substantial commonalities in the data needed to better understand how stressors affect fish in these systems. For example, in most systems information on river discharge and water temperature were needed and available. Conversely, information regarding trophic relationships and the habitat requirements of larval fishes were generally lacking. This result suggests that there is a need to better understand a set of common factors across large-river systems. We provide a stepwise procedure to facilitate the application of our conceptual model to other river systems and management goals.

Can data from disparate long-term fish monitoring programs be used to increase our understanding of regional and continental trends in large river assemblages?

Understanding trends in the diverse resources provided by large rivers will help balance tradeoffs among stakeholders and inform strategies to mitigate the effects of landscape scale stressors such as climate change and invasive species. Absent a cohesive coordinated effort to assess trends in important large river resources, a logical starting point is to assess our ability to draw inferences from existing efforts. In this paper, we use a common analytical framework to analyze data from five disparate fish monitoring programs to better understand the nature of spatial and temporal trends in large river fish assemblages. We evaluated data from programs that monitor fishes in the Colorado, Columbia, Illinois, Mississippi, and Tallapoosa rivers using non-metric dimensional scaling ordinations and associated tests to evaluate trends in fish assemblage structure and native fish biodiversity. Our results indicate that fish assemblages exhibited significant spatial and temporal trends in all five of the rivers. We also document native species diversity trends that were variable within and between rivers and generally more evident in rivers with higher species richness and programs of longer duration. We discuss shared and basin-specific landscape level stressors. Having a basic understanding of the nature and extent of trends in fish assemblages is a necessary first step towards understanding factors affecting biodiversity and fisheries in large rivers.

Modeling the relations between flow regime components, species traits, and spawning success of fishes in warmwater streams.

Modifications to stream hydrologic regimes can have a profound influence on the dynamics of their fish populations. Using hierarchical linear models, we examined the relations between flow regime and young-of-year fish density using fish sampling and discharge data from three different warmwater streams in Illinois, Alabama, and Georgia. We used an information theoretic approach to evaluate the relative support for models describing hypothesized influences of five flow regime components representing: short-term high and low flows; short-term flow stability; and long-term mean flows and flow stability on fish reproductive success during fish spawning and rearing periods. We also evaluated the influence of ten fish species traits on fish reproductive success. Species traits included spawning duration, reproductive strategy, egg incubation rate, swimming locomotion morphology, general habitat preference, and food habits. Model selection results indicated that young-of-year fish density was positively related to short-term high flows during the spawning period and negatively related to flow variability during the rearing period. However, the effect of the flow regime components varied substantially among species, but was related to species traits. The effect of short-term high flows on the reproductive success was lower for species that broadcast their eggs during spawning. Species with cruiser swimming locomotion morphologies (e.g., Micropterus) also were more vulnerable to variable flows during the rearing period. Our models provide insight into the conditions and timing of flows that influence the reproductive success of warmwater stream fishes and may guide decisions related to stream regulation and management.

Tuning stochastic matrix models with hydrologic data to predict the population dynamics of a riverine fish.

We developed stochastic matrix models to evaluate the effects of hydrologic alteration and variable mortality on the population dynamics of a lotic fish in a regulated river system. Models were applied to a representative lotic fish species, the flathead catfish (Pylodictis olivaris), for which two populations were examined: a native population from a regulated reach of the Coosa River (Alabama, USA) and an introduced population from an unregulated section of the Ocmulgee River (Georgia, USA). Size-classified matrix models were constructed for both populations, and residuals from catch-curve regressions were used as indices of year class strength (i.e., recruitment). A multiple regression model indicated that recruitment of flathead catfish in the Coosa River was positively related to the frequency of spring pulses between 283 and 566 m3/s. For the Ocmulgee River population, multiple regression models indicated that year class strength was negatively related to mean March discharge and positively related to June low flow. When the Coosa population was modeled to experience five consecutive years of favorable hydrologic conditions during a 50-year projection period, it exhibited a substantial spike in size and increased at an overall 0.2% annual rate. When modeled to experience five years of unfavorable hydrologic conditions, the Coosa population initially exhibited a decrease in size but later stabilized and increased at a 0.4% annual rate following the decline. When the Ocmulgee River population was modeled to experience five years of favorable conditions, it exhibited a substantial spike in size and increased at an overall 0.4% annual rate. After the Ocmulgee population experienced five years of unfavorable conditions, a sharp decline in population size was predicted. However, the population quickly recovered, with population size increasing at a 0.3% annual rate following the decline. In general, stochastic population growth in the Ocmulgee River was more erratic and variable than population growth in the Coosa River. We encourage ecologists to develop similar models for other lotic species, particularly in regulated river systems. Successful management of fish populations in regulated systems requires that we are able to predict how hydrology affects recruitment and will ultimately influence the population dynamics of fishes.