Henriette I. Jager

A theoretical study of river fragmentation by dams and its effects on white sturgeon populations

Most of the worlds large rivers are fragmented by dams. Fragmentation of the river ecosystem alters migration patterns among fish populations and converts free-flowing river to reservoir habitat. In this study, we used an individual-based genetic metapopulation model to study the effects of fragmentation on the population viability and genetic diversity of a large-river fish, the white sturgeon, Acipenser transmontanus. In the first of two simulation experiments, we fragmented a 200 km river reach by building 1 to 20 virtual dams. Increased fragmentation produced an exponential decline in the likelihood of persistence, but no extinction threshold to suggest a minimum viable length of river. Compounding isolation with the loss of free-flowing habitat did not further reduce viability until free-flowing habitat was nearly eliminated, at which point extinction was certain. Genetic diversity within (among) populations decreased (increased) as we ‘built’ the first several dams. Adding more dams caused the number of persisting populations to decline and eroded genetic diversity within and among populations. Our second simulation experiment evaluated the effects of different levels of upstream and downstream migration between river segments. The results of these migration experiments highlighted the importance of balanced migration rates. We found that extinction risk was high for populations linked by high downstream, and low upstream, migration rates, as is often the case in impounded rivers. Our results support the view that migration patterns will play a significant role in determining the viability of riverine fishes, such as the white sturgeon, in river ecosystems fragmented by dams.

Research in Thermal Biology: Burning Questions for Coldwater Stream Fishes

With the increasing appreciation of global warming impacts on ecological systems, in addition to the myriad of land management effects on water quality, the number of literature citations dealing with the effects of water temperature on freshwater fish has escalated in the past decade. Given the many biological scales at which water temperature effects have been studied, and the growing need to integrate knowledge from multiple disciplines of thermal biology to fully protect beneficial uses, we held that a survey of the most promising recent developments and an expression of some of the remaining unanswered questions with significant management implications would best be approached collectively by a diverse research community. We have identified five specific topic areas of renewed research where new techniques and critical thought could benefit coldwater stream fishes (particularly salmonids): molecular, organism, population/species, community and ecosystem, and policy issues in water quality. Our hope is that information gained through examination of recent research fronts linking knowledge at various scales will prove useful in managing water quality at a basin level to protect fish populations and whole ecosystems. Standards of the past were based largely on incipient lethal and optimum growth rate temperatures for fish species, while future standards should consider all integrated thermal impacts to the organism and ecosystem.

Individual-based model of sympatric populations of brown and rainbow trout for instream flow assessment: model description and calibration

Abstract This paper describes an individual-based model of sympatric populations of brown and rainbow trout in a stream habitat. The model provides a tool for projecting flow and temperature effects on trout populations by linking the hydraulic component of the instream flow incremental methodology/physical habitat simulation system (IFIM/PHABSIM) to an individual-based population model. PHABSIM simulates the spatial distribution of depth and velocity at different flows, and indirectly, the availability of spawning habitat, cover and feeding station. The individual-based model simulates reproduction, growth and mortality of individual trout as a function of flow and temperature. Population dynamics arise from the survival and reproduction of individual trout. The spatially explicit nature of the model permits evaluation of behavioral responses used by fish to changes in physical habitat. The model has been calibrated to a stream segment in the North Fork Middle Fork Tule River, California. Selected parameters were adjusted to calibrate the model for length and abundance (including production of a new year class) at the end of 1-year simulations for each of 9 years. Predicted and observed lengths were in good agreement, although neither varied appreciably among years. Predicted and observed abundances were not in as good agreement, and differed considerably for some years. These differences reflect a combination of uncertainties in the field data and uncertainties in the model structure and parameter values. Fifty-year simulations indicated that model projections of length and abundance were stationary, although abundance values fluctuated considerably. Seven advantages for using simulation models of this type are emphasized. How to most effectively interpret results from such simulation models as part of instream flow environmental assessments remains a challenge. Variability and uncertainty in both field data and replicate model simulations are realities that have implications for scientists, resource managers, and regulators in projecting growth and abundance responses of fish populations to alternative flow or temperature regimes.

Empirical geographic modeling of switchgrass yields in the United States

Switchgrass (Panicum virgatum L.) is a perennial grass native to the United States that has been studied as a sustainable source of biomass fuel. Although many field‐scale studies have examined the potential of this grass as a bioenergy crop, these studies have not been integrated. In this study, we present an empirical model for switchgrass yield and use this model to predict yield for the conterminous United States. We added environmental covariates to assembled yield data from field trials based on geographic location. We developed empirical models based on these data. The resulting empirical models, which account for spatial autocorrelation in the field data, provide the ability to estimate yield from factors associated with climate, soils, and management for both lowland and upland varieties of switchgrass. Yields of both ecotypes showed quadratic responses to temperature, increased with precipitation and minimum winter temperature, and decreased with stand age. Only the upland ecotype showed a positive response to our index of soil wetness and only the lowland ecotype showed a positive response to fertilizer. We view this empirical modeling effort, not as an alternative to mechanistic plant‐growth modeling, but rather as a first step in the process of functional validation that will compare patterns produced by the models with those found in data. For the upland variety, the correlation between measured yields and yields predicted by empirical models was 0.62 for the training subset and 0.58 for the test subset. For the lowland variety, the correlation was 0.46 for the training subset and 0.19 for the test subset. Because considerable variation in yield remains unexplained, it will be important in the future to characterize spatial and local sources of uncertainty associated with empirical yield estimates.

Would hydrologic climate changes in Sierra Nevada streams influence trout persistence

Abstract We predicted the consequences of climate change for sympatric populations of brown trout Salmo trutta and rainbow trout Oncorhynchus mykiss in an upstream and a downstream reach of a Sierra Nevada stream with the help of an individual-based trout population model. The model evaluated the ecological effects of two anticipated responses to climate change: (1) a shift in peak flows from spring to winter and (2) an increase in stream temperature. Changes in temperature and flow regime both influenced simulated persistence of the two trout species. We hypothesized a decrease in the fall-spawning brown trout population as a result of winter floods that scour brown trout redds. Although scouring mortality showed the expected pattern, effects of seasonal shifts in flow on simulated dewatering of redds was equally important and tended to compensate for scouring. Because trout are coldwater fishes, we hypothesized that a rise in mean stream temperature would be harmful to both species, particularly in down...

Modelling the linkages between flow management and salmon recruitment in rivers

Abstract We developed a simulation model to predict instream flow effects on smolt production for fall chinook salmon ( Oncorhynchus tshawytscha ) in regulated rivers. The principal purpose of this model is to serve as a management tool to evaluate effects on salmon of instream releases from upstream reservoirs. The dramatic decline in chinook salmon in California rivers suggests a need for such a tool. We developed an individual-based and spatially explicit model to simulate the influences of riverine habitat on each lifestage leading to successful outmigration of chinook salmon. Model predictions of development, growth and survival showed good agreement with four years of field data collected on the Tuolumne River, California. Our analysis of parameter sensitivities identified flow-related redd mortality and temperature-related juvenile mortality as limitations on smolt production.

Designing Optimal Flow Patterns for Fall Chinook Salmon in a Central Valley, California, River

Abstract Widespread declines in stocks of Pacific salmon in the genus Oncorhynchus highlight the need for research to find new and effective management strategies for recovery. Two recovery objectives are (1) to ensure that recruitment is adequate to rebuild self-sustaining populations and (2) to maintain phenotypic diversity. This study seeks to understand how seasonal flow patterns in a flow-regulated California river might be managed to attain each of these recovery objectives, specifically for the fall and late-fall runs of chinook salmon O. tshawytscha. We ask two questions: (1) Does the optimal pattern of seasonal flows change as the amount of water available is constrained by droughts or diversions of flows? and (2) How do optimal flow regimes designed for the two conservation objectives differ? We coupled simulated annealing with a recruitment model to find flow regimes that maximize either the number of smolt out-migrant “recruits” (MR) or the variation in spawning times among recruits (MV). Opti...

Individual variation in life history characteristics can influence extinction risk

The white sturgeon (Acipenser transmontanus) shows great individual variation in the age at maturation. This study examines the consequences of model assumptions about individual variation in the age at maturation on predicted population viability. I considered: (1) the effects of variation in age at maturation alone; (2) the effects of heritability; and (3) the influence of a stable and an altered selective regime. Two selective regimes represented conditions before and after the impoundment of a river, blocking access of anadromous white sturgeon populations to the ocean. In contrast to previous simulation studies, I found that increased individual variation in the age at maturity did not necessarily lead to a higher likelihood of persistence. Individual variation increased the simulated likelihood of persistence when the variation was heritable and the selective regime had changed such that the mean age at maturity was no longer optimal.

Progress toward Evaluating the Sustainability of Switchgrass as a Bioenergy Crop using the SWAT Model

Adding bioenergy to the U.S. energy portfolio requires long-term profitability for bioenergy producers and long-term protection of affected ecosystems. In this study, we present steps along the path toward evaluating both sides of the sustainability equation (production and environmental) for switchgrass (Panicum virgatum) using the Soil and Water Assessment Tool (SWAT). We modeled production of switchgrass and river flow using SWAT for current landscapes at a regional scale. To quantify feedstock production, we compared lowland switchgrass yields simulated by SWAT with estimates from a model based on empirical data for the eastern U.S. The two produced similar geographic patterns. Average yields reported in field trials tended to be higher than average SWAT-predicted yields, which may nevertheless be more representative of production-scale yields. As a preliminary step toward quantifying bioenergy-related changes in water quality, we evaluated flow predictions by the SWAT model for the Arkansas-White-Red river basin. We compared monthly SWAT flow predictions to USGS measurements from 86 subbasins across the region. Although agreement was good, we conducted an analysis of residuals (functional validation) seeking patterns to guide future model improvements. The analysis indicated that differences between SWAT flow predictions and field data increased in downstream subbasins and in subbasins with higher percentage of water. Together, these analyses have moved us closer to our ultimate goal of identifying areas with high economic and environmental potential for sustainable feedstock production.

Spatial Uncertainty and Ecological Models

Applied ecological models that are used to understand and manage natural systems often rely on spatial data as input. Spatial uncertainty in these data can propagate into model predictions. Uncertainty analysis, sensitivity analysis, error analysis, error budget analysis, spatial decision analysis, and hypothesis testing using neutral models are all techniques designed to explore the relationship between variation in model inputs and variation in model predictions. Although similar methods can be used to answer them, these approaches address different questions. These approaches differ in (a) whether the focus is forward or backward (forward to evaluate the magnitude of variation in model predictions propagated or backward to rank input parameters by their influence); (b) whether the question involves model robustness to large variations in spatial pattern or to small deviations from a reference map; and (c) whether processes that generate input uncertainty (for example, cartographic error) are of interest. In this commentary, we propose a taxonomy of approaches, all of which clarify the relationship between spatial uncertainty and the predictions of ecological models. We describe existing techniques and indicate a few areas where research is needed.