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USGS2023

Modeling, mapping, and measuring the risk of freshwater invasive species across Alaska

Freshwater ecosystems of the Alaskan Arctic and Subarctic provide resources that are culturally, ecologically, and economically invaluable. Presently, these regions are relatively free of the impacts from invasive species compared to southern latitudes. To date, there have been relatively few verified introductions of aquatic invasive species (AIS) to freshwater ecosystems in Alaska. The expanding list and distribution of AIS has led to significant negative ecological and economic impacts (e.g., waterweed Elodea nuttalli ; E. canadensis and northern pike Esox Lucius introduced outside its native range in Alaska). Escalating human activity across Alaskan lands and waters, coupled with rapidly shifting environmental conditions, increases the potential for new species introductions and subsequent establishment. Creating a proactive framework for well-informed decision-making and action can improve the effectiveness of prevention efforts and bolster decision support tools that help resource managers direct limited resources. Prioritizing AIS that may be introduced and become established, as well as the locations at highest risk of invasion, is foundational to building a proactive invasive species management framework in Alaska. This project sought to identify and prioritize AIS known to be invasive in the contiguous United States, evaluate current and future habitat suitability for AIS in Alaska, and assess potential for AIS to be transported to habitats across Alaska, utilizing similar assessment methods as implemented for Bering Sea marine invasive species and non-native plants in Alaska. To accomplish this goal, the objectives of the project were to: 1) develop a formal ranked list of potential AIS to freshwater systems of Alaska; 2) assess the level of establishment risk for potential AIS by developing habitat suitability models for waterbodies across Alaska; and 3), identify potential pathways and specific vectors for high-risk AIS to invade Alaska and develop a framework for how vector analysis will be completed to understand transport risk. Overall, our goal is horizon scanning which is defined by Roy et al. (2019) as “a systematic examination of potential threats and opportunities, within a given context, and likely future developments, which are at the margin of current thinking and planning.” The scans include pathway analyses and risk screening of species present at pathway origin points, with a focus on identifying species at high risk of being introduced, becoming established, spreading, and causing harm. We refined a list of 28 AIS from a list of hundreds based on characterizations of species’ invasiveness and species’ proximity to Alaska (USGS 2020; GBIF 2022). Next, we evaluated the relative invasiveness of individual species to create an initial AIS ranking. We sought to characterize habitat suitability of AIS by selecting variables that were continental in scale, covering North America to include Alaska as well as the lower 48 states comparing natural discharge, sub-basin average terrain slope (degrees), average silt fraction, average organic carbon, lithological class, and human footprint in sub-basin in 2009. We estimated AIS habitat suitability across the entire state of Alaska using the physiological tolerances of the AIS (Appendix 2). We also evaluated pathways and vectors for the introduction of AIS (Appendix 2). Many pathways and vectors considered did not meet the criteria for Alaska or freshwater systems. Of the 28 ranked species that we categorized as very high, high, and moderate levels of invasiveness; all three risk groups included fish and mollusks (Appendix 2). One commonality of the very high-invasiveness-ranked species was the availability of Ecological Risk Screening Summary documents (USFWS, 2022) produced by U.S. Fish and Wildlife Service (USFWS), except for the goldfish ( Carassius auratus ) and the New Zealand mudsnail ( Potamopyrgus antipodarum ). The Ecological Risk Screening Summary is now available for New Zealand mudsnails. In general, fish species often ranked very high or high in invasiveness and included sportfish and aquarium fish, suggesting the importance of pathways such as aquarium trade, fishing industry, intentional (but illegal) introductions of sportfishes and aquarium fishes for establishment. The technique we used for habitat suitability models necessitated aquatic environmental datasets that were continental in scale, which was often interpolated from very coarse resolution source data layers, particularly in Alaska. Better spatial data representing aquatic environments would likely improve this approach. While the lack of introductions in Alaska and nearby provinces and states is encouraging, the lack of occurrence data for the focal species also created complications for habitat suitability modeling. Despite the challenges, the habitat suitability models indicated limited suitability for warmwater species while some species, such as Brook trout (S alvelinus fontinalis ), have high habitat suitability across Alaska no matter what threshold approach is taken. Some environmental predictors were more important than others. Specifically, the most important predictor variable, ‘frost free days,’ was critical for 15 out of 28 species as expected due to harsh winter conditions in Arctic and Subarctic regions. The second most important predictor was ‘subbasin land surface runoff’, a variable that indicates the amount of discharge and runoff, while the third most important predictor was ‘snow cover’ another indication of winter conditions. Overall, the ability to understand the effect of future climate scenarios on the establishment of AIS was challenging. A detailed dataset of freshwater temperatures and water chemistry (e.g., pH, calcium) would greatly improve the ability to predict invasiveness of freshwater species to Alaska’s ecosystems on a regional basis. Future studies may benefit from a more focused geographic scope examining a group of subbasins or a regional basin rather than the entire state. These drainages could be selected based upon the mostly likely locations of introduction pathways. The two most prevalent pathway risks for AIS are in-state transfer and stowaways/contaminants. Although there are examples of introductions from other pathways, the risk is somewhat mitigated by Alaska’s climate and regulations. However, variable application of protocols for inspection and cleaning of fishing gear, watercraft, and other similar items while traveling into Alaska as well as transferring from waterbody to waterbody within the state creates a substantial risk in introducing invasive species. We plot cumulative invasive vulnerability for all subbasins and for the top 10% of subbasins (Appendix 3).

Alaska↗

USGS2010

Southeast Regional Assessment Project for the National Climate Change and Wildlife Science Center, U.S. Geological Survey

The Southeastern United States spans a broad range of physiographic settings and maintains exceptionally high levels of faunal diversity. Unfortunately, many of these ecosystems are increasingly under threat due to rapid human development, and management agencies are increasingly aware of the potential effects that climate change will have on these ecosystems. Natural resource managers and conservation planners can be effective at preserving ecosystems in the face of these stressors only if they can adapt current conservation efforts to increase the overall resilience of the system. Climate change, in particular, challenges many of the basic assumptions used by conservation planners and managers. Previous conservation planning efforts identified and prioritized areas for conservation based on the current environmental conditions, such as habitat quality, and assumed that conditions in conservation lands would be largely controlled by management actions (including no action). Climate change, however, will likely alter important system drivers (temperature, precipitation, and sea-level rise) and make it difficult, if not impossible, to maintain recent historic conditions in conservation lands into the future. Climate change will also influence the future conservation potential of non-conservation lands, further complicating conservation planning. Therefore, there is a need to develop and adapt effective conservation strategies to cope with the effects of climate and landscape change on future environmental conditions. Congress recognized this important issue and authorized the U.S. Geological Survey (USGS) National Climate Change and Wildlife Science Center (NCCWSC; http://nccw.usgs.gov/) in the Fiscal Year 2008. The NCCWSC will produce science that will help resource management agencies anticipate and adapt to climate change impacts to fish, wildlife, and their habitats. With the release of Secretarial Order 3289 on September 14, 2009, the mandate of the NCCWSC was expanded to address climate change-related impacts on all Department of the Interior (DOI) resources. The NCCWSC will establish a network of eight DOI Regional Climate Science Centers (RCSCs) that will work with a variety of partners to provide natural resource managers with tools and information that will help them anticipate and adapt conservation planning and design for projected climate change. The forecasting products produced by the RCSCs will aid fish, wildlife, and land managers in designing suitable adaptive management approaches for their programs. The DOI also is developing Landscape Conservation Cooperatives (LCCs) as science and conservation action partnerships at subregional scales. The USGS is working with the Southeast Region of the U.S. Fish and Wildlife Service (FWS) to develop science collaboration between the future Southeast RCSC and future LCCs. The NCCWSC Southeast Regional Assessment Project (SERAP) will begin to develop regional downscaled climate models, land cover change models, regional ecological models, regional watershed models, and other science tools. Models and data produced by SERAP will be used in a collaborative process between the USGS, the FWS (LCCs), State and federal partners, nongovernmental organizations, and academia to produce science at appropriate scales to answer resource management questions. The SERAP will produce an assessment of climate change, and impacts on land cover, ecosystems, and priority species in the region. The predictive tools developed by the SERAP project team will allow end users to better understand potential impacts of climate change and sea level rise on terrestrial and aquatic populations in the Southeastern United States. The SERAP capitalizes on the integration of five existing projects: (1) the Multi-State Conservation Grants Program project "Designing Sustainable Landscapes," (2) the USGS multidisciplinary Science Thrust project "Water Availability for Ecological Needs," (3) the USGS Southeast Pilot Project "Climate Change in the Southeastern U.S. and its Impacts on Bird Distributions and Habitats," (4) a sea-level rise impacts study envisioned jointly with the National Oceanic and Atmospheric Administration (NOAA), and (5) two USGS sea-level rise impact assessment projects that address inundation hazards and provide probabilistic forecasts of coastal geomorphic change. The SERAP will expand on these existing projects and include the following tasks, which were initiated in summer 2009: * Regionally downscaled probabilistic climate-change projections * Integrated coastal assessment * Integrated terrestrial assessment * Multi-resolution assessment of potential climate change effects on biological resources: aquatic and hydrologic dynamics * Optimal conservation strategies to cope with climate change The SERAP seeks to formally integrate these tasks to aid conservation planning and design so that ecosystem management decisions can be optimized for providing desirable outcomes across a range of species and environments. The following chapters detail SERAP's efforts in providing a suite of regional climate, watershed, and landscape-change analyses and develop the interdisciplinary framework required for the biological planning phases of adaptive management and strategic conservation. The planning phase will include the identification of conservation alternatives, development of predictive models and decision support tools, and development of a template to address similar challenges and goals in other regions. The project teams will explore and develop ways to link the various ecological models arising from each component. The SERAP project team also will work closely with members of the LCCs and other partnerships throughout the life of the project to ensure that the objectives of the project meet resources mangers needs in the Southeast.

Open-File Report↗

USGS2021

Oyster model inventory: Identifying critical data and modeling approaches to support restoration of oyster reefs in coastal U.S. Gulf of Mexico waters

Executive Summary Along the coast of the U.S. Gulf of Mexico, the eastern oyster ( Crassostrea virginica ) plays important ecological and economic roles. Commercial landings from this region account for more than 50 percent of all U.S. landings; these oyster reefs also provide varied ecosystem services, including nursery habitat for many fish and macroinvertebrate species, shoreline protection, and water-quality maintenance. Declining trends in both total oyster production and functional reef area across this region have spurred investment in restoration of oyster resources, with specific calls for restoration projects to develop a network of reefs and identify broodstock and sanctuary reef restoration sites. Decision making related to restoration and establishment of a network of oyster reefs in the Gulf of Mexico requires information on both the environment and the effects of the environment on the oyster life cycle (including larval movement, survival, oyster recruitment, reproduction, growth, and mortality). Here, we examined the current state of data and model development in this region with the goal of providing an overview of oyster modeling approaches and an inventory of available data and existing oyster models. This report is meant to provide an overview to managers for understanding existing efforts and identify a path forward to most efficiently inform oyster resource management and restoration planning in moving from a single reef management approach to a reef network management approach. Numerous models related to some aspect of the oyster life cycle have been built, calibrated, and validated for various Gulf of Mexico estuaries over the last few decades (over 30 models identified). These models, which could inform site restoration, can be classified into four approaches: (1) oyster Habitat Suitability Index (HSI) models; (2) larval transport models; (3) on-reef oyster models that may include oyster growth, mortality and reproduction, and substrate persistence; and (4) coupled larval transport on-reef metapopulation models that simulate the entire oyster life cycle. The data requirements, model complexity and assumptions, and transferability vary by approach. Specifically, some approaches may offer greater accessibility, flexibility, and transferability spatially or temporally, with minimal data input, but only provide broad information to support site selection. In contrast, other approaches may require significant site-specific data for their construction and validation but may provide more accurate and location-specific data to support site selection for broodstock reefs. Regardless of modeling approach used, data on environmental drivers, such as salinity, water temperature, or water flow impacting oyster metabolism and movement, are required at appropriate spatial and temporal scales. While numerous data collection platforms, environmental models, and research products exist within Gulf of Mexico estuaries to provide important environmental data to use as drivers in the oyster models, significant variability in temporal and spatial coverage of the data, and variation in the availability of future condition models, exists across estuaries. This variation influences the spatial and temporal scales at which oyster models may be developed and impacts the calibration and validation of the oyster models within a given estuary, affecting its potential ability to address specific management or restoration questions. While multiple modeling approaches exist for informing site selection of broodstock or sanctuary oyster reefs, the development, calibration, and validation of a single modeling platform presents the most efficient, transferable, and useful tool for managers across the Gulf of Mexico. The development of a single modeling platform would involve using standardized input variables, governing equations, and assumptions for the modeled oyster processes and outputs, and for standardized calibration and validation procedures that could be applied within each estuary. The differences among estuary applications would require substituting only estuary-specific environmental data, and calibrating and validating the modeling approach with local oyster data. Two modeling approaches likely to be useful include (1) development of a general geospatial HSI modeling framework that could be applied consistently across estuaries and (2) a mechanistic coupled larval transport on-reef metapopulation model requiring only estuarine specific calibration and hydrodynamic models. Both approaches benefit from existing work across multiple Gulf of Mexico estuaries and could provide valuable support for oyster restoration, but may differ in their ability to address specific questions related to oyster restoration. HSI models specifically guide restoration practitioners in determining suitable habitat based on available data. The HSI approach, while currently more widely used and accessible, requires more development of larval suitability and larval input and output components in order to inform reef connectivity. A metapopulation approach considering the full oyster life cycle that simulates both on-reef oyster growth, mortality, reproduction, substrate persistence, and larval transport (ideally with larval growth and mortality) would provide the greatest detail and level of understanding but requires significant up-front investment. The larval oyster model and on-reef oyster model are usually developed independently for systems, although the two approaches can be coupled to represent the entire oyster life cycle in order to characterize and assess a reef metapopulation. This approach may be less accessible and much more data-intensive, however, and it requires some expertise to run and apply to inform oyster resource management. Ultimately, the development of single modeling platforms for each of these approaches would provide flexible tools applicable across all Gulf of Mexico oyster supporting estuaries. By using a single platform for model development, testing, calibrating and validating, and evaluation of modeled future scenarios, oyster restoration scientists and managers would not only be able to examine different scenario outcomes within a single estuary, but could also have comparable modeled results to evaluate potential outcomes, across estuaries and regions, that are not confounded by varying modeled data inputs, governing equations, assumptions, or user judgement.

Alabama, Florida, Louisiana, Mississippi, Texas↗

USGS2016

Effects of water-supply reservoirs on streamflow in Massachusetts

State and local water-resource managers need modeling tools to help them manage and protect water-supply resources for both human consumption and ecological needs. The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, has developed a decision-support tool to estimate the effects of reservoirs on natural streamflow. The Massachusetts Reservoir Simulation Tool is a model that simulates the daily water balance of a reservoir. The reservoir simulation tool provides estimates of daily outflows from reservoirs and compares the frequency, duration, and magnitude of the volume of outflows from reservoirs with estimates of the unaltered streamflow that would occur if no dam were present. This tool will help environmental managers understand the complex interactions and tradeoffs between water withdrawals, reservoir operational practices, and reservoir outflows needed for aquatic habitats. A sensitivity analysis of the daily water balance equation was performed to identify physical and operational features of reservoirs that could have the greatest effect on reservoir outflows. For the purpose of this report, uncontrolled releases of water (spills or spillage) over the reservoir spillway were considered to be a proxy for reservoir outflows directly below the dam. The ratio of average withdrawals to the average inflows had the largest effect on spillage patterns, with the highest withdrawals leading to the lowest spillage. The size of the surface area relative to the drainage area of the reservoir also had an effect on spillage; reservoirs with large surface areas have high evaporation rates during the summer, which can contribute to frequent and long periods without spillage, even in the absence of water withdrawals. Other reservoir characteristics, such as variability of inflows, groundwater interactions, and seasonal demand patterns, had low to moderate effects on the frequency, duration, and magnitude of spillage. The reservoir simulation tool was used to simulate 35 single- and multiple-reservoir systems in Massachusetts over a 44-year period (water years 1961 to 2004) under two water-use scenarios. The no-pumping scenario assumes no water withdrawal pumping, and the pumping scenario incorporates average annual pumping rates from 2000 to 2004. By comparing the results of the two scenarios, the total streamflow alteration can be parsed into the portion of streamflow alteration caused by the presence of a reservoir and the additional streamflow alteration caused by the level of water use of the system. For each reservoir system, the following metrics were computed to characterize the frequency, duration, and magnitude of reservoir outflow volumes compared with unaltered streamflow conditions: (1) the median number of days per year in which the reservoir did not spill, (2) the median duration of the longest consecutive period of no-spill days per year, and (3) the lowest annual flow duration exceedance probability at which the outflows are significantly different from estimated unaltered streamflow at the 95-percent confidence level. Most reservoirs in the study do not spill during the summer months even under no-pumping conditions. The median number of days during which there was no spillage was less than 365 for all reservoirs in the study, indicating that, even under reported pumping conditions, the reservoirs refill to full volume and spill at least once during nondrought years, typically in the spring. Thirteen multiple-reservoir systems consisting of two or three hydrologically connected reservoirs were included in the study. Because operating rules used to manage multiple-reservoir systems are not available, these systems were simulated under two pumping scenarios, one in which water transfers between reservoirs are minimal and one in which reservoirs continually transferred water to intermediate or terminal reservoirs. These two scenarios provided upper and lower estimates of spillage under average pumping conditions from 2000 to 2004. For sites with insufficient data to simulate daily water balances, a proxy method to estimate the three spillage metrics was developed. A series of 4,000 Monte Carlo simulations of the reservoir water balance were run. In each simulation, streamflow, physical reservoir characteristics, and daily climate inputs were randomly varied. Tobit regression equations that quantify the relation between streamflow alteration and physical and operational characteristics of reservoirs were developed from the results of the Monte Carlo simulations and can be used to estimate each of the three spillage metrics using only the withdrawal ratio and the ratio of the surface area to the drainage area, which are available statewide for all reservoirs. A graphical user-interface for the Massachusetts Reservoir Simulation Tool was developed in a Microsoft Access environment. The simulation tool contains information for 70 reservoirs in Massachusetts and allows for simulation of additional scenarios than the ones considered in this report, including controlled releases, dam seepage and leakage, demand management plans, and alternative water withdrawal and transfer rules.

Massachusetts↗

USGS2015

In-reservoir behavior, dam passage, and downstream migration of juvenile Chinook salmon and juvenile steelhead from Detroit Reservoir and Dam to Portland, Oregon, February 2013-February 2014

In the second year of 2 years of study, the movements of juvenile spring Chinook salmon ( Oncorhynchus tshawytscha ) and juvenile summer steelhead ( Oncorhynchus mykiss ) through Detroit Reservoir, passing Detroit Dam, and migrating downstream to Portland, Oregon, were studied during a 1-year-long period beginning in February 2013. The primary purpose of the study was to provide empirical data to inform decisions about future alternatives for improving downstream passage of salmonids at Detroit Dam. A secondary purpose was to design and assess the performance of a system to detect juvenile salmonids implanted with acoustic transmitters migrating in the Willamette River. Inferences about fish migration were made from detections of juvenile fish of hatchery origin at least 95 millimeters in fork length surgically implanted with an acoustic transmitter and released during the spring (March–May) and fall (September–November) of 2013. Detection sites were placed throughout the reservoir, near the dam, and at two sites in the North Santiam River and at three sites in the Willamette River culminating at Portland, Oregon. We based most inferences on an analysis period up to the 90th percentile of tag life (68–78 days after release, depending on species and season), although a small number of fish passed after that period as late as April 8, 2014. Chinook salmon migrated from the tributaries of release to the reservoir in greater proportion than steelhead, particularly in the fall. The in-reservoir migration behaviors and dam passage of the two species were similar during the spring study, but during the fall study, few steelhead reached the reservoir and none passed the dam within the analysis period. Migrations in the reservoir were directed and non-random, except in the forebay. Depths of fish within 25 meters of the dam were deeper in the day than at night for Chinook salmon and similar in the day and night for steelhead; steelhead generally were at shallower depths than Chinook salmon. The primary factors affecting dam passage rates were seasonal dam operating conditions and diel period. Fish passage rates were much greater during the spring and summer than in the fall and winter, and the difference was attributed to the availability and use of the spillway near the top of the dam during the spring and summer. The flood-control purpose of the reservoir prevented spillway use during much of the fall and winter because of the low forebay elevation. Passage rates at night were greater than in the day during spring and summer (4.2 times) and during the fall and winter (14.9 times). Fish length, dam discharge, and forebay elevation also affected dam passage rates. Travel times from Detroit Dam passage to the downstream sites were shorter during the fall and winter than during the spring and summer, and were less than a median of 8.68 days to Portland. The estimated survival in the 11 kilometers (km) between Detroit Dam and the Minto Dam forebay was lower than in the remaining 241 km to the Portland site. Estimated survival per 100 km in the free-flowing reach from Minto Dam to Portland was 0.675–0.836, depending on species and season, and was similar to other free-flowing rivers in the Western United States. The high probability of fish in the reservoir reaching the dam, the chance for repeated presence near the dam, the fish depths, and the factors known to affect passage rates suggest that a properly designed surface passage route could be a viable downstream passage alternative for juvenile Chinook salmon and steelhead at Detroit Dam. As part of the evaluations conducted at Detroit Dam, we continued to refine and improve methods for monitoring fish movements in the Willamette River. The goal was to develop stable, cost-effective, long-term monitoring arrays suitable for detection of any Juvenile Salmon Acoustic Telemetry System (JSATS)-tagged fish in the Willamette River. These data then could be used to estimate timing, migration rates, and survival of JSATS-tagged fish from various studies in the Willamette River Basin. The challenge, however, is that acoustic telemetry generally performs poorly in shallow, turbulent water, like that found in the Willamette River. We successfully designed, deployed, and maintained a series of monitoring sites near the Oregon cities of Salem, Wilsonville, and Portland. In the spring, detection probabilities at these sites ranged from 0.900 to 1.000. In the fall, the detection probabilities decreased and ranged from 0.526 to 1.000. The lower detection probabilities, particularly at the Salem site (0.526), were owing to loss of data caused by abnormally high flows as well as the 2013 Federal government shutdown, which prevented us from servicing the equipment. The monitoring sites that we installed seem to be robust and enable the efficient use of acoustic-tagged fish for studies of migration or survival in the Willamette River and similar environments.

Oregon↗

USGS2025

Calibration of the Stream Salmonid Simulator (S3) model to estimate annual survival, movement, and food consumption by juvenile Chinook salmon (Oncorhynchus tshawytscha) in the restoration reach of the Trinity River, California, 2006–18

Executive Summary The Trinity River is managed in two sections: (1) from the upper 64-kilometer “restoration reach” downstream from Lewiston Dam to the confluence with the North Fork Trinity River, and (2) the 120-kilometer lower Trinity River downstream from the restoration reach. The Stream Salmonid Simulator (S3) has been previously applied to these reaches and the Klamath River. To estimate fish growth, past S3 calibration efforts in the Trinity and Klamath Rivers used maximum likelihood methods that considered only the abundance of juvenile Chinook salmon ( Oncorhynchus tshawytscha ) passing a fish trap to estimate survival and movement parameters, but not fish consumption. Previous calibrations did not estimate the average proportion of maximum consumption ( C y ) when estimating survival ( S y ) and movement ( M 0 y ) parameters across years ( y ) of data, but because no other information was available in the literature a fixed value of C y =0.66 was assumed. Therefore, the goal of this report is to present an alternative approach that calibrates the S3 model to multivariate data (that is, abundance and size), enabling the estimation of the average proportion of maximum consumption, in conjunction with survival and movement parameters for a particular migration year. We fit the S3 model to individual years of weekly trap abundance estimates and mean fish sizes (fork length) at the Pear Tree Gulch (hereafter referred to as Pear Tree) fish trap representing the restoration reach. We used the Earth Mover’s Distance (EMD) as the objective value to be minimized in parameter optimization. This approach estimated survival, movement, and consumption parameters for each migration year. Because we had information on the abundance of natural and hatchery produced juvenile salmon at the fish traps, we estimated survival and movement for natural and hatchery fish. S3 is a deterministic life-stage-structured population model that tracks daily growth, movement, and survival of juvenile Chinook Salmon. A key theme of the model is that river discharge affects habitat availability and capacity, which in turn drives density-dependent population dynamics. To explicitly link population dynamics to habitat quality and quantity, the river environment is constructed as a one-dimensional series of linked habitat units, each of which has an associated daily timeseries of discharge, water temperature, and useable habitat area or carrying capacity. In turn, the physical characteristics of each habitat unit and the number of fish occupying each unit drive survival and growth within each habitat unit and movement of fish among habitat units. The physical template of the restoration reach of the Trinity River was classified into 356 meso-habitat units comprised of runs, riffles, and pools. For each habitat unit, we developed a timeseries of daily discharge, water temperature, amount of available spawning habitat, and fry and parr carrying capacity. Capacity time series were constructed using state-of-the-art models of spatially explicit hydrodynamics and quantitative fish habitat relationships developed for the Trinity River. These variables were then used to drive population dynamics such as egg maturation and survival, and in turn, juvenile movement, growth, and survival. We estimated movement, survival, and consumption parameters by calibrating the model to 12 years of weekly juvenile abundance estimates and fish sizes at the Pear Tree fish trap near the downstream end of the restoration reach. We estimated parameters for 12 years that included a wide range of female spawner abundances (1,414–11,494) and water year types (critically dry–extremely wet). We contrast the estimated parameters to the corresponding number of female spawners and the total annual volume of water discharged for the Trinity River (Trinity River Restoration Program; https://www.trrp.net/restoration/flows/summary/ ). The calibration consisted of replicating historical conditions as closely as possible (for example, discharge; temperature; spawner abundance, spawning location and timing, and hatchery releases), and then running the model to predict weekly abundance passing the trap location from each brood year of adults and subsequent migration year of their juvenile progeny. Because density-dependent movement was favored in past evaluations, we estimated S3 parameters based on density-independent survival and density-dependent movement. Likewise, each year’s estimated survival parameter for natural ( S N y ) and hatchery ( S H y ) fish may be interpreted as the mean daily survival probability from emergence or hatchery release to the Pear Tree fish trap. Under density dependence, the estimated movement parameter for natural ( M 0N y ) and hatchery ( M 0H y ) fish represents the intercept of the Beverton-Holt model; the probability of remaining in a habitat at near-zero abundance. We estimated C y by using EMD and incorporating abundance and fish size into model calibration. Average daily proportions of maximum consumption, , across the years were generally high (=0.640; standard deviation (SD) SD=0.176), suggesting that fish were feeding at about two-thirds of expected maximum consumption rates. This average proportion of maximum consumption,is very similar to what has been assumed (=0.66) in previous Trinity and Klamath River S3 calibration and simulation efforts. In 2017, we estimated the lowest C y , suggesting lower average consumption for juvenile salmon in high-discharge water years. When this high discharge year was excluded, there was no apparent trend in C y with annual water volume. Estimates of survival showed little trend over the range in spawner abundances, but a trend towards higher natural and hatchery fish survival with higher annual volumes of water was apparent. Over the 12 years, the average survival of hatchery fish was =0.888 (SD=0.079) and the average survival natural fish was=0.969 (SD=0.01). With respect to fish movement, we estimated higher M 0N y and M 0H y with higher annual volumes of water in the Trinity River. Higher M N0 y or M H0 y suggest greater probability of remaining in a habitat at low fish densities, with potential for density-dependent processes in movement to occur. The highest M 0N y = 0.676 was estimated during brood year 2012, and the overall average for natural fish was =0.276 (SD=0.188) and for hatchery fish was=0.467 (SD=0.235). Under the Beverton-Holt model, as M 0N y or M 0H y approach zero, there is less capacity for change in fish movement as fish density increases. The S3 model was initialized with only the spatiotemporal distribution of spawners, so it performed well at capturing the essential outmigration features that are ultimately governed by rates of growth, movement, and mortality. We used a new optimization method that could accommodate multivariate data on abundance and fish size collected at the Pear Tree fish trap, enabling the calibration of S3 to estimate five parameters for 12 separate years of data. Incorporating weekly fish size data for each year in our parameter optimization process made the estimation of C y possible and represents a step forward in the fitting of the S3 model to fish trap data for the purposes of parameter calibration and the estimation of growth parameters with respect to annual conditions. We identified lack of fit and adding important effects into the S3 model may improve the S3 estimation and simulation of water scenarios. The Trinity River Restoration Program (TRRP) Science Advisory Board recommended that the TRRP focus on developing core elements of a decision support system (DSS; Buffington and others, 2014). Toward that end, the habitat and S3 models described in this report are both core elements of the DSS. The structure of S3 makes it a particularly useful fish production model for the DSS because population dynamics are sensitive to (1) water temperature, (2) daily discharge management, and (3) habitat quality and quantity. Each of these variables are key management parameters under consideration in the TRRP. As such, the S3 model may provide valuable insights into the potentially variable effects of different management decisions on the Trinity River.

California↗