Grantee Research Project Results
2004 Progress Report: Application of Individual-based Fish Models to Regional Decision-making
EPA Grant Number: R830886Title: Application of Individual-based Fish Models to Regional Decision-making
Investigators: Lamberson, Roland H. , Railsback, Steven F.
Institution: Humboldt State University
EPA Project Officer: Packard, Benjamin H
Project Period: May 1, 2003 through April 30, 2006 (Extended to November 30, 2006)
Project Period Covered by this Report: May 1, 2004 through April 30, 2005
Project Amount: $418,710
RFA: Developing Regional-Scale Stressor-Response Models for Use in Environmental Decision-making (2002) RFA Text | Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Aquatic Ecosystems
Objective:
The objective of this research project is to develop methods for applying an individual-based model (IBM) of stream trout to typical management decisions for multiple stressors at regional scales. Our IBM, called inSTREAM, was originally developed for site-specific assessment of river management on trout population. inSTREAM, however, is naturally suited for multistressor decision-support because it represents effects of stressors on a population as emerging from how stressors affect individual fish and how they feed, grow, compete, survive, and reproduce. Population-level effects of stressors can, therefore, be predicted by adding to the IBM a simple representation of how the stressor affects individuals.
Progress Summary:
Task 1: Develop Methods for Regional Analysis
This task addresses the question of how to use inSTREAM, which simulates the trout populations of one or more linked stream reaches, to address issues at regional scales. The questions we are most focused on are: (1) how can input for inSTREAM be developed from the kinds of regional data sets that are typically available, and (2) how sensitive regional assessment results are to the number and location of modeled reaches. We are using the nearby South Fork Eel River and Bull Creek Watershed as a case study region.
Two graduate students, Garth Butcher and Mark Parrish, are now working on Task 1. The first step was identifying data sets that can represent stream habitat at the habitat unit (individual pools, riffles, etc.) to study site scales (several hundred meters of stream length) that also are widely available. We are utilizing extensively California Department of Fish and Game habitat surveys, available for streams throughout our study region. Recently, scientists interested in adopting our model and assessment approach have contributed two additional data sets, one from the Montana Fish, Wildlife, and Parks, and the other one from the U.S. Forest Service’s Boise Aquatic Sciences Laboratory.
A major emphasis this year was developing ways to use the statistical distribution of habitat characteristics (e.g., percent of stream in pool, riffle, and run habitat; distribution of pool depths, etc.), and relations between these characteristics and larger-scale variables (i.e., stream order, drainage size) to synthesize habitat input to inSTREAM. We developed software that generates sequences of habitat units that have statistical distributions of unit length and type that match those of field data sets. We then developed statistical models of typical channel cross-sections for each habitat type, treating bed elevation at a series of points across the channel as a Markov process. Parameters for the Markov process were obtained from a number of measurements of real cross-sections at sites in our study region.
We are currently working on how to model the depth and velocity at cells on each cross-section as a function of daily stream flow rate. This step will use existing stream hydraulic simulation techniques and models, but the techniques need adaptation to our approach, which lacks some of the site-specific data typically used for calibration.
Once we complete methods for statistically synthesizing habitat input to inSTREAM, we plan to conduct simulation experiments to explore: (1) how sensitive decisions based on inSTREAM are to variation among different study sites that represent the same stream size and elevation, and (2) how predicted trout population response to stressors varies throughout a watershed.
Task 2: Conduct Demonstration Assessment
The goal of this task is to apply inSTREAM and our regional analysis methods to a demonstration assessment of the cumulative and interacting effects of instream flow, turbidity, and an exotic predator species on trout populations. Key steps in this effort are producing a new version of inSTREAM, developing input for sites in the Bull Creek study watershed, and conducting the assessment simulations. In addition, we continued research to develop a better understanding of how some stressors affect individual trout, to improve how the stressors are represented in the model.
The first public release of inSTREAM was made available on our Web site (http://www.humboldt.edu/~ecomodel) in May 2005. This release includes the model software, example input files, and approximately 250 pages of documentation. The documentation includes four parts that: (1) introduce inSTREAM and its objectives; (2) describe its formulation in detail; (3) recommend ways to parameterize, calibrate, and use the model; and (4) provide detailed guidance for installing, running, and modifying the software. We conducted our first training workshop on inSTREAM on July 18-19, 2005. Participants were from the Montana Fish, Wildlife, and Parks, the Forest Service’s Boise Aquatic Sciences Laboratory, Randolph Macon College ( Virginia), DePaul University, and a fisheries consulting firm.
Our Forest Service collaborators, led by Dr. Bret Harvey, continued development of four field sites in our demonstration region, the South Fork Eel River and the Bull Creek Watershed. Their goal is to assemble input to inSTREAM for sites representing two levels of stream size and condition in this watershed where preservation and restoration of salmonid habitat is a major management issue. The sites are:
- Elder Creek, watershed area approximate 10 km2, low sediment transport;
- Cuneo Creek, watershed area approximate 10 km2, high sediment transport;
- Upper S.F. Eel River , approximate 100 km2, low sediment transport; and
- Bull Creek, approximate 100 km2, high sediment transport.
Site data input to inSTREAM includes several habitat variables for each cell; daily records of flow, temperature, and turbidity; and measurements of depth and velocity at (ideally) three flow rates, plus water surface elevations at a flood flow. Flow data are available from permanent gages for three of the sites. To date, we have 1 year of temperature and turbidity data at all sites and 1 year of flow data for the site with no permanent gage; these data are adequate to estimate long-term records for all sites. Cell habitat variables have been measured, and at least two sets of depth and velocity measurements collected, at all sites.
Laboratory research by Dr. Harvey at the Forest Service’s Redwood Sciences Laboratory is improving our understanding of how turbidity affects trout feeding behavior and success. Turbidity resulting from suspended sediment is the leading cause of water quality impairment in Total Maximum Daily Load throughout the United States. A major effect is believed to be reducing the ability of fish to see and capture food. For trout, this effect may be stronger for the drift feeding (“sit and wait” for drifting prey) strategy that is normally more profitable than for the alternative strategy of actively searching for benthic food. Dr. Harvey tested the ability of salmonids to capture benthic versus drifting prey across a range of turbidity (0, 25, 50, 100, 200, 400 Nephelometric Turbidity Units [NTU]) in a laboratory stream. This experiment revealed strong differences in the effect of turbidity on the capture of drifting versus benthic prey (Figure 1). As other studies have shown, drift feeding success drops sharply with increasing turbidity, but benthic feeding appears only slightly affected by turbidity up to 100 NTU.
Figure 1 . A Contrast of the Effect of Turbidity on Benthic Verses Drifting Prey for Cutthroat Trout. Sample sizes range 14-20 fish for each data point (with repeated measures on individuals across turbidity levels); error bars indicate ±1 SE.
In a second study, we modified inSTREAM to represent a new stressor: loss of fish via entrainment in water diversions. This study was inspired by a recent review ( Moyle and Israel, 2005) showing that resource agents often must decide whether to build costly fish screens without a good understanding of how entrainment losses, which typically are dominated by small juveniles, affect population abundance and persistence. We represented entrainment in inSTREAM by treating it as an additional mortality source, with risk decreasing with increasing fish size and distance from the point of diversion, and increasing as the diversion flow increases. Simulation experiments then examined how diversion location and flow rate affect the number of fish entrained and long-term population status, in a relatively small stream. Diversion location was found to be very important; diversions near spawning habitat or in good juvenile rearing habitat had much stronger effects. Effects on adult populations, however, were undetectable to minor except when entrainment losses were extremely high.
The M.S. thesis work of Eric Stewart provides another demonstration of how inSTREAM can be applied to a diversity of stressors and management issues. The effects of hatchery fish on wild populations is one of the most controversial salmonid management issues. High densities of hatchery fish may compete with and suppress wild populations, yet hatchery-produced fish often have low survival because they lack behaviors appropriate for feeding and avoiding predators in streams. We are using inSTREAM to examine: (1) what specific traits of hatchery trout explain observed differences in behavior from wild trout, and (2) how the competitive effects of hatchery trout on wild trout would change if hatchery fish were raised to behave more naturally. Hatchery trout are represented as a separate species, and we model how their addition to a stream reach affects the survival, growth, and emigration of wild trout. We stepwise altered the traits of hatchery fish to determine which trait differences explain several patterns of interaction observed in real situations. Preliminary results indicate that observed interaction patterns can be explained by assuming hatchery trout lack awareness of predation risk, fail to produce feeding hierarchies, and do little exploration of their habitat. Negative effects of hatchery stocking on wild populations depend mostly on the number of fish stocked, but would increase if hatchery fish were more aware of predation risk or understood better how to compete for scarce food.
Task 3: Analyze Uncertainty and Sensitivity
The goal of this task is to develop an understanding of how decisions based on inSTREAM are affected by uncertainty in parameters. In our first year’s research, we developed a general strategy for sensitivity and uncertainty analysis of complex, stochastic, individual-based models such as inSTREAM. The first phase is a screening analysis that examines sensitivity of a single model output (mean biomass of adult trout) to individual parameters. This analysis was reported in our previous progress report; it succeeded in identifying a small number of parameters that the model is most sensitive to. The second phase, now underway, focuses on these few, most important parameters. We are designing analyses that vary these parameters in combination to look for interactions among them, using efficient value sampling techniques. The third phase will look at how sensitive the model’s ranking of alternative strategies is to parameter values. This work is the M.S. thesis work of Paul Cunningham.
Future Activities:
We will concentrate on finishing three main activities. The first is the sensitivity and uncertainty analysis of inSTREAM (Task 3). The second and third phases of this task (examining interactions among the most important parameters and sensitivity of management decisions to parameters) are scheduled for completion by the end of 2005. The second activity is developing methods for regional-scale assessments using individual-based fish models (Task 1). We will finish methods for stochastically generating model input for synthetic study sites throughout a watershed and examine how sensitive model results are to variation in input within and among these sites. The third activity is the demonstration assessment (Task 2) of multiple stressors on a watershed’s trout population. Data collection for new study sites will be completed by fall of 2005, and calibration of the sites will occur before the end of 2005. Realistic stressor scenarios and management alternatives then will be defined and analyzed.
References:
Moyle PB, Israel JA. Untested assumptions: effectiveness of screening diversions for conservation of fish populations. Fisheries 2005;30:20-28.
Journal Articles:
No journal articles submitted with this report: View all 27 publications for this projectSupplemental Keywords:
watersheds, risk assessment, cumulative effects, population, aquatic, ecosystem, decisionmaking, ecology, models, pacific northwest, individual-based model, habitat,, RFA, Economic, Social, & Behavioral Science Research Program, Scientific Discipline, Water, Ecosystem Protection/Environmental Exposure & Risk, Water & Watershed, Monitoring/Modeling, Regional/Scaling, decision-making, Ecology and Ecosystems, Biology, Watersheds, Economics & Decision Making, risk assessment, ecosystem modeling, aquatic ecosystem, watershed, ecosystem assessment, Bayesian approach, decision analysis, decision making, environmental decision making, ecological variation, TMDL, regional scale impacts, water quality, assessment endpoint mechanistic research, ecological indicators, ecology assessment models, ecosystem stress, watershed assessment, ecological models, fish models, individual based models, stressor response model, decision support tool, environmental risk assessment, water monitoring, adaptive implementation modelingRelevant Websites:
http://www.humboldt.edu/~ecomodel Exit
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.