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At least 343 records · Page 19Linked to original sources
USGS1999

Death valley regional ground-water flow model calibration using optimal parameter estimation methods and geoscientific information systems

A regional-scale, steady-state, saturated-zone ground-water flow model was constructed to evaluate potential regional ground-water flow in the vicinity of Yucca Mountain, Nevada. The model was limited to three layers in an effort to evaluate the characteristics governing large-scale subsurface flow. Geoscientific information systems (GSIS) were used to characterize the complex surface and subsurface hydrogeologic conditions of the area, and this characterization was used to construct likely conceptual models of the flow system. Subsurface properties in this system vary dramatically, producing high contrasts and abrupt contacts. This characteristic, combined with the large scale of the model, make zonation the logical choice for representing the hydraulic-conductivity distribution. Different conceptual models were evaluated using sensitivity analysis and were tested by using nonlinear regression to determine parameter values that are optimal, in that they provide the best match between the measured and simulated heads and flows. The different conceptual models were judged based both on the fit achieved to measured heads and spring flows, and the plausibility of the optimal parameter values. One of the conceptual models considered appears to represent the system most realistically. Any apparent model error is probably caused by the coarse vertical and horizontal discretization.A regional-scale, steady-state, saturated-zone ground-water flow model was constructed to evaluate potential regional ground-water flow in the vicinity of Yucca Mountain, Nevada. The model was limited to three layers in an effort to evaluate the characteristics governing large-scale subsurface flow. Geoscientific information systems (GSIS) were used to characterize the complex surface and subsurface hydrogeologic conditions of the area, and this characterization was used to construct likely conceptual models of the flow system. Subsurface properties in this system vary dramatically, producing high contrasts and abrupt contacts. This characteristic, combined with the large scale of the model, make zonation the logical choice for representing the hydraulic-conductivity distribution. Different conceptual models were evaluated using sensitivity analysis and were tested by using nonlinear regression to determine parameter values that are optimal, in that they provide the best match between the measured and simulated heads and flows. The different conceptual models were judged based both on the fit achieved to measured heads and spring flows, and the plausibility of the optimal parameter values. One of the conceptual models considered appears to represent the system most realistically. Any apparent model error is probably caused by the coarse vertical and horizontal discretization.

Advances in Water Resources↗
USGS2017

Essential information: Uncertainty and optimal control of Ebola outbreaks

Early resolution of uncertainty during an epidemic outbreak can lead to rapid and efficient decision making, provided that the uncertainty affects prioritization of actions. The wide range in caseload projections for the 2014 Ebola outbreak caused great concern and debate about the utility of models. By coding and running 37 published Ebola models with five candidate interventions, we found that, despite this large variation in caseload projection, the ranking of management options was relatively consistent. Reducing funeral transmission and reducing community transmission were generally ranked as the two best options. Value of information (VoI) analyses show that caseloads could be reduced by 11% by resolving all model-specific uncertainties, with information about model structure accounting for 82% of this reduction and uncertainty about caseload only accounting for 12%. Our study shows that the uncertainty that is of most interest epidemiologically may not be the same as the uncertainty that is most relevant for management. If the goal is to improve management outcomes, then the focus of study should be to identify and resolve those uncertainties that most hinder the choice of an optimal intervention. Our study further shows that simplifying multiple alternative models into a smaller number of relevant groups (here, with shared structure) could streamline the decision-making process and may allow for a better integration of epidemiological modeling and decision making for policy.

Proceedings of the National Academy of Sciences of↗
USGS2012

Using the internet to understand angler behavior in the information age

Declining participation in recreational angling is of great concern to fishery managers because fishing license sales are an important revenue source for protection of aquatic resources. This decline is frequently attributed, in part, to increased societal reliance on electronics. Internet use by anglers is increasing and fishery managers may use the Internet as a unique means to increase angler participation. We examined Internet search behavior using Google Insights for Search, a free online tool that summarizes Google searches from 2004 to 2011 to determine (1) trends in Internet search volume for general fishing related terms and (2) the relative usefulness of terms related to angler recruitment programs across the United States. Though search volume declined for general fishing terms (e.g., fishing, fishing guide), search volume increased for social media and recruitment terms (e.g., fishing forum, family fishing) over the 7-year period. We encourage coordinators of recruitment programs to capitalize on anglers’ Internet usage by considering Internet search patterns when creating web-based information. Careful selection of terms used in web-based information to match those currently searched by potential anglers may help to direct traffic to state agency websites that support recruitment efforts.

Fisheries↗
USGS2020

Capturing spatiotemporal patterns in presence-absence data to inform monitoring and sampling designs for the threatened Dakota skipper (Lepidoptera: Hesperiidae) in the Great Plains of the United States

Declines among species of insect pollinators, especially butterflies, has garnered attention from scientists and managers. Often these declines have spurred governments to declare some species as threatened or endangered. We used existing presence–absence data from surveys for the threatened Dakota skipper Hesperia dacotae (Skinner) to build statistical maps of species presence that could be used to inform future monitoring designs. We developed a hierarchical Bayesian modeling approach to estimate the spatial distribution and temporal trend in Dakota skipper probability of presence. Our model included a spatial random effect and fixed effects for the proportion of two grassland habitat types: those on well-drained soils and those on poorly drained soils; as well as the topographic slope. The results from this model were then used to assess sampling strategies with two different monitoring objectives: locating new Dakota skipper colonies or monitoring the proportion of historically (pre-2000) extant colonies. Our modeling results suggested that the distribution of Dakota skippers followed the distribution of remnant grasslands and that probabilities of presence tended to be higher in topographically diverse grasslands with well-drained soils. Our analysis also showed that the probability of presence declined throughout the northern Great Plains range. Our simulations of the different sampling designs suggested that new detections were expected when sampling where Dakota skippers likely occurred historically, but this may lead to a tradeoff with monitoring existing sites. Prior information about the extant sites may help to ameliorate this tradeoff.

Iowa, Minnesota, North Dakota, South Dakota↗
USGS2026

Indicators of mercury concentration in Lake Trout: Can fish location and appearance provide information to anglers to reduce their exposure?

Objective People are exposed to mercury (Hg) through the consumption of fish. State and federal governments provide broad, often-generalized food safety guidance to reduce exposure; however, numerous rural fishing areas lack testing and location- or species-specific guidance. The aim of this study was to provide tangible, visible, or easily measured characteristics of Lake Trout Salvelinus namaycush that could convey information on Hg exposure to people harvesting and consuming fish where no location-specific guidance exists. Methods We investigated potential indicators of Lake Trout total Hg (THg) concentrations in muscle across 10 lakes in Alaska's national parks. Potential indicators, including lake, lake zone (i.e., littoral, pelagic, profundal), fish length, head size, body condition, and general appearance, were evaluated by competing linear mixed-effects models. Results Lake Trout THg concentrations ranged widely from 22 to 1,306 ng/g wet weight. Much of the variation (48%) in THg concentrations was attributed to differences among individual lakes, but the interaction of the fish's lake zone, body length, and head size accounted for an additional 21%. Predicted THg concentrations increased with Lake Trout length and head : body proportion, but the rate of THg concentration increase with length varied by head : body proportion and lake zone. Conclusions Given the overwhelming evidence of high lake-to-lake variability in Lake Trout THg concentrations, we find support for use of lake-specific guidance when data are available. When lake-specific THg concentrations are not available, the best potential way to reduce exposure is to harvest and consume Lake Trout with mean predicted THg concentrations that are within state and federal safe consumption guidelines. This included Lake Trout from surface waters (i.e., pelagic or littoral zone) that are ≤70 cm in length; if harvesting fish from deep waters (i.e., profundal zone), lower THg concentrations were found in Lake Trout with heads ≤25% of their body length. The indicators—lake zone, length, and head size—of Lake Trout THg concentrations can provide harvesters with additional information in the absence of data for specific lakes.

Alaska↗
USGS2022

Deconstructing the microbial necromass continuum to inform soil carbon sequestration

Microbial necromass is a large, dynamic and persistent component of soil organic carbon, the dominant terrestrial carbon pool. Quantification of necromass carbon stocks and its susceptibility to global change is becoming standard practice in soil carbon research. However, the typical proxies used for necromass carbon do not reveal the dynamic nature of necromass carbon flows and transformations within soil that ultimately determine necromass persistence. In this review, we define and deconstruct four stages of the necromass continuum: production, recycling, stabilization and destabilization. Current understanding of necromass dynamics is described for each continuum stage. We highlight recent advances, methodological limitations and knowledge gaps which need to be addressed to determine necromass pool sizes and transformations. We discuss the dominant controls on necromass process rates and aspects of soil microscale structure including biofilms and food web interactions. The relative importance of each stage of the continuum is then compared in contrasting ecosystems and for climate change drivers. From the perspective of the continuum, we draw three conclusions to inform future research. First, controls on necromass persistence are more clearly defined when viewed through the lens of the continuum; second, destabilization is the least understood stage of the continuum with recycling also poorly evidenced outside of a few ecosystems; and third, the response of necromass process rates to climate change is unresolved for most continuum stages and ecosystems. Future mechanistic research focused on the role of biotic and abiotic soil microscale structure in determining necromass process rates and the relative importance of organo–mineral and organo–organo interactions can inform necromass persistence in different climate change scenarios. Our review demonstrates that deconstructing the necromass continuum is key to predicting the vulnerability and persistence of necromass carbon in a changing world.

Functional Ecology↗
USGS2014

Using a network modularity analysis to inform management of a rare endemic plant in the northern Great Plains, USA

1. Analyses of flower-visitor interaction networks allow application of community-level information to conservation problems, but management recommendations that ensue from such analyses are not well characterized. Results of modularity analyses, which detect groups of species (modules) that interact more with each other than with species outside their module, may be particularly applicable to management concerns. 2. We conducted modularity analyses of networks surrounding a rare endemic annual plant, Eriogonum visheri , at Badlands National Park, USA, in 2010 and 2011. Plant species visited were determined by pollen on insect bodies and by flower species upon which insects were captured. Roles within modules (network hub, module hub, connector and peripheral, in decreasing order of network structural importance) were determined for each species. 3. Relationships demonstrated by the modularity analysis, in concert with knowledge of pollen species carried by insects, allowed us to infer effects of two invasive species on E. visheri . Sharing a module increased risk of interspecific pollen transfer to E. visheri . Control of invasive Salsola tragus , which shared a module with E. visheri , is therefore a prudent management objective, but lack of control of invasive Melilotus officinalis , which occupied a different module, is unlikely to negatively affect pollination of E. visheri . Eriogonum pauciflorum may occupy a key position in this network, supporting insects from the E. visheri module when E. visheri is less abundant. 4. Year-to-year variation in species' roles suggests management decisions must be based on observations over several years. Information on pollen deposition on stigmas would greatly strengthen inferences made from the modularity analysis. 5. Synthesis and applications: Assessing the consequences of pollination, whether at the community or individual level, is inherently time-consuming. A trade-off exists: rather than an estimate of fitness effects, the network approach provides a broad understanding of the relationships among insect visitors and other plant species that may affect the focal rare plant. Knowledge of such relationships allows managers to detect, target and prioritize control of only the important subset of invasive species present and identify other species that may augment a rare species' population stability, such as E. pauciflorum in our study.

South Dakota↗
USGS2021

Weighing the unknowns: Value of information for biological and operational uncertainty in invasion management

The management of biological invasions is a worldwide conservation priority. Unfortunately, decision-making on optimal invasion management can be impeded by lack of information about the biological processes that determine invader success (i.e. biological uncertainty) or by uncertainty about the effectiveness of candidate interventions (i.e. operational uncertainty). Concurrent assessment of both sources of uncertainty within the same framework can help to optimize control decisions. Here, we present a Value of Information (VoI) framework to simultaneously analyze the effects of biological and operational uncertainties on management outcomes. We demonstrate this approach with a case study: minimizing the long-term population growth of musk thistle ( Carduus nutans ), a widespread invasive plant, using several insects as biological control agents, including Trichosirocalus horridus , Rhinocyllus conicus , and Urophora solstitialis . The ranking of biocontrol agents was sensitive to differences in the target weed’s demography and also to differences in the effectiveness of the different biocontrol agents. This finding suggests that accounting for both biological and operational uncertainties is valuable when making management recommendations for invasion control. Furthermore, our VoI analyses show that reduction of all uncertainties across all combinations of demographic model and biocontrol effectiveness explored in the current study would lead, on average, to a 15.6% reduction in musk thistle population growth rate. The specific growth reduction that would be observed in any instance would depend on how the uncertainties actually resolve. Resolving biological uncertainty (across demographic model combinations) or operational uncertainty (across biocontrol effectiveness combinations) alone would reduce expected population growth rate by 8.5% and 10.5%, respectively. Synthesis and applications . Our study demonstrates that intervention rank is determined both by biological processes in the targeted invasive populations and by intervention effectiveness. Ignoring either biological uncertainty or operational uncertainty may result in a sub-optimal recommendation. Therefore, it is important to simultaneously acknowledge both sources of uncertainty during the decision-making process in invasion management. The framework presented here can accommodate diverse data sources and modelling approaches, and has wide applicability to guide invasive species management and conservation efforts.

Journal of Applied Ecology↗
USGS2024

Leveraging relationships between species abundances to improve predictions and inform conservation

Many management and conservation contexts can benefit from understanding relationships between species abundances, which can be used to improve predictions of species occurrence and abundance. We present conditional prediction as a tool to capture information about species abundances via residual covariance between species. From a fitted joint species distribution model, this framework produces a species coefficient matrix that contains relationships between species abundances. The species coefficients allow co-observed species to be treated as a second set of predictors supplementing covariates in the model to improve prediction. We use simulations to demonstrate the potential benefits and limitations of conditional prediction across data types and species covariance before applying conditional prediction to two management contexts with real data. Simulations demonstrate that conditional prediction provides the largest benefits to continuous data and when there is residual covariance between many species. In our first application, we show that conditioning on other species improves in-sample and out-of-sample predictions of fish and invertebrate species, including Atlantic cod. In our second application, we show that the species coefficient matrix can be used to identify bird species at risk of nest parasitism by Brown-headed Cowbirds. Synthesis and applications . We present guidelines for using conditional prediction, which can help understand relationships between species abundances, improve predictions and inform conservation in a variety of contexts.

Journal of Applied Ecology↗
USGS2016

An analysis of water data systems to inform the Open Water Data Initiative

Improving access to data and fostering open exchange of water information is foundational to solving water resources issues. In this vein, the Department of the Interior's Assistant Secretary for Water and Science put forward the charge to undertake an Open Water Data Initiative (OWDI) that would prioritize and accelerate work toward better water data infrastructure. The goal of the OWDI is to build out the Open Water Web (OWW). We therefore considered the OWW in terms of four conceptual functions: water data cataloging, water data as a service, enriching water data, and community for water data. To describe the current state of the OWW and identify areas needing improvement, we conducted an analysis of existing systems using a standard model for describing distributed systems and their business requirements. Our analysis considered three OWDI-focused use cases—flooding, drought, and contaminant transport—and then examined the landscape of other existing applications that support the Open Water Web. The analysis, which includes a discussion of observed successful practices of cataloging, serving, enriching, and building community around water resources data, demonstrates that we have made significant progress toward the needed infrastructure, although challenges remain. The further development of the OWW can be greatly informed by the interpretation and findings of our analysis.

Journal of the American Water Resources Associatio↗
USGS2021

Applying assessments of adaptive capacity to inform natural-resource management in a changing climate

Adaptive capacity (AC)—the ability of a species to cope with or accommodate climate change—is a critical determinant of species vulnerability. Using information on species’ AC in conservation planning is key to ensuring successful outcomes. We identified connections between a list of species’ attributes (e.g., traits, population metrics, and behaviors) that were recently proposed for assessing species’ AC and management actions that may enhance AC for species at risk of extinction. Management actions were identified based on evidence from the literature, a review of actions used in other climate adaptation guidance, and our collective experience in diverse fields of global-change ecology and climate adaptation. Selected management actions support the general AC pathways of persist in place or shift in space, in response to contemporary climate change. Some actions, such as genetic manipulations, can be used to directly alter the ability of species to cope with climate change, whereas other actions can indirectly enhance AC by addressing ecological or anthropogenic constraints on the expression of a species’ innate abilities to adapt. Ours is the first synthesis of potential management actions directly linked to AC. Focusing on AC attributes helps improve understanding of how and why aspects of climate are affecting organisms, as well as the mechanisms by which management interventions affect a species’ AC and climate change vulnerability. Adaptive-capacity-informed climate adaptation is needed to build connections among the causes of vulnerability, AC, and proposed management actions that can facilitate AC and reduce vulnerability in support of evolving conservation paradigms.

Conservation Biology↗
USGS2024

Modeling rare plant habitat together with public land managers using an iterative, coproduced process to inform decision-making on multiple-use public lands

Public lands across the United States are managed for multiple uses, resources, and values ranging from energy development to rare plant conservation. Intensified energy development and other land use changes across the Southwestern United States have increased the need for proactive management to mitigate impacts to rare plants. Habitat suitability models can inform decision-making and lead to more effective conservation of rare plants and their habitats, but high-quality models that are suited for use at local scales are lacking for many species. Our team of scientists and managers developed ensembles of habitat suitability models for five rare plant species in New Mexico using a coproduced, iterative framework complemented by comprehensive ground truthing and tailoring of products for use in public land decisions. Our process resulted in substantial differences from initial models through changes to environmental predictors, species occurrence and background data, and development of new species-specific predictors. Involving species experts and end users in model development can strengthen the process and resulting model and build understanding and trust in final products. Both factors can promote use of models to inform public land permitting and planning decisions that may affect rare plants, including by guiding development away from highly suitable habitats.

Colorado, New Mexico↗
USGS2017

Bioremediation in fractured rock: 1. Modeling to inform design, monitoring, and expectations

Field characterization of a trichloroethene (TCE) source area in fractured mudstones produced a detailed understanding of the geology, contaminant distribution in fractures and the rock matrix, and hydraulic and transport properties. Groundwater flow and chemical transport modeling that synthesized the field characterization information proved critical for designing bioremediation of the source area. The planned bioremediation involved injecting emulsified vegetable oil and bacteria to enhance the naturally occurring biodegradation of TCE. The flow and transport modeling showed that injection will spread amendments widely over a zone of lower‐permeability fractures, with long residence times expected because of small velocities after injection and sorption of emulsified vegetable oil onto solids. Amendments transported out of this zone will be diluted by groundwater flux from other areas, limiting bioremediation effectiveness downgradient. At nearby pumping wells, further dilution is expected to make bioremediation effects undetectable in the pumped water. The results emphasize that in fracture‐dominated flow regimes, the extent of injected amendments cannot be conceptualized using simple homogeneous models of groundwater flow commonly adopted to design injections in unconsolidated porous media (e.g., radial diverging or dipole flow regimes). Instead, it is important to synthesize site characterization information using a groundwater flow model that includes discrete features representing high‐ and low‐permeability fractures. This type of model accounts for the highly heterogeneous hydraulic conductivity and groundwater fluxes in fractured‐rock aquifers, and facilitates designing injection strategies that target specific volumes of the aquifer and maximize the distribution of amendments over these volumes.

Groundwater↗
USGS2011

Evolution of natural history information in the 21st century – developing an integrated framework for biological and geographical data

Threats to marine and estuarine species operate over many spatial scales, from nutrient enrichment at the watershed/estuarine scale to invasive species and climate change at regional and global scales. To help address research questions across these scales, we provide here a standardized framework for a biogeographical information system containing queriable biological data that allows extraction of information on multiple species, across a variety of spatial scales based on species distributions, natural history attributes and habitat requirements. As scientists shift from research on localized impacts on individual species to regional and global scale threats, macroecological approaches of studying multiple species over broad geographical areas are becoming increasingly important. The standardized framework described here for capturing and integrating biological and geographical data is a critical first step towards addressing these macroecological questions and we urge organizations capturing biogeoinformatics data to consider adopting this framework.

Journal of Biogeography↗
USGS2007

Integrating a geographic information system, a scientific visualization system, and a precipitation model

Investigating natural, potential, and human-induced impacts on hydrologic systems commonly requires complex modeling with overlapping data requirements, plus massive amounts of one- to four-dimensional data at multiple scales and formats. Given the complexity of most hydrologic studies, the requisite software infrastructure must incorporate many components including simulation modeling and spatial analysis with a flexible, intuitive display. Integrating geographic information systems (GIS) and scientific visualization systems (SVS) provides such an infrastructure. This paper describes an integrated system consisting of an orographic precipitation model, a GIS, and an SVS. The results of this study provide a basis for improving the understanding of hydro-climatic processes in mountainous regions. An additional benefit of the integrated system, the value of which is often underestimated, is the improved ability to communicate model results, leading to a broader understanding of the model assumptions, sensitivities, and conclusions at a management level.Investigating natural, potential, and human-induced impacts on hydrologic systems commonly requires complex modeling with overlapping data requirements, plus massive amounts of one- to four-dimensional data at multiple scales and formats. Given the complexity of most hydrologic studies, the requisite software infrastructure must incorporate many components including simulation modeling and spatial analysis with a flexible, intuitive display. Integrating geographic information systems (GIS) and scientific visualization systems (SVS) provides such an infrastructure. This paper describes an integrated system consisting of an orographic precipitation model, a GIS, and an SVS. The results of this study provide a basis for improving the understanding of hydro-climatic processes in mountainous regions. An additional benefit of the integrated system, the value of which is often underestimated, is the improved ability to communicate model results, leading to a broader understanding of the model assumptions, sensitivities, and conclusions at a management level.

Water Resources Bulletin↗
USGS2024

Understanding key mineral supply chain dynamics using economics-informed material flow analysis and Bayesian optimization

The low-carbon energy transition requires significant increases in production for many mineral commodities. Understanding demand, technological requirements, and prices associated with this production increase requires understanding the supply chain dynamics of many minerals simultaneously, and via a consistent framework. A generalized economics-informed material flow method, global materials modeling using Bayesian optimization, captures the market dynamics of key mineral commodities. The method relies only on a limited set of widely available historical data as input, enabling quantification of economic relationships (elasticities) for supply chain components where data are sparse, and relationships cannot be obtained via traditional statistical approaches. Building upon established material flow analysis (MFA) and economic modeling techniques, Bayesian optimization was applied to fit an economics-informed MFA model to global historical demand, supply, and price for aluminum, copper, gold, lead, nickel, silver, iron, tin, and zinc. This approach enables estimates for the evolution of ore grades, mine costs, refining charges, sector-specific demand, and scrap collection for each commodity. Economic relationships were quantified and compared with a database compiled from the literature, including 1333 values from 213 analyses across 65 publications. Discrepancies in methods and limited coverage make use of these parameters in modeling efforts difficult. This work provides a single, homogeneous, probabilistic approach to identifying economic relationships across mineral supply chains, with uncertainty quantification, a literature database for comparison, and a modeling framework in which to use them. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges .

Journal of Industrial Ecology↗
USGS2016

U.S. Geological Survey collections—Preserving the past to inform the future: Tour three federal repositories—Core Research Center, Paleontological Collection, and the NSF National Ice Core Laboratory

This 2016 Geological Society of America (GSA) Annual Meeting trip will explore the Core Research Center, Paleontological Collection, and National Science Foundation National Ice Core Laboratory—three collections of major national signifi cance managed by the U.S. Geological Survey (USGS). Since its inception in 1879, USGS has collected, preserved, and managed physical collections for scientifi c investigations of Earth’s systems. The Core Research Center is the largest federal core repository in the United States, where over 74 million meters (242 million feet) of the subsurface are represented by the collection of rock cores and well cuttings, available for use by researchers investigating resource potential, tectonics, structures, aquifers, and more. The USGS has conducted paleontological research for more than 110 years to inform geological mapping, biostratigraphy, paleoecology, paleoclimate, and other research. Most of these paleontological samples are at the Smithsonian Institution National Museum of Natural History (NMNH) and USGS Denver facilities. The USGS Denver paleontological collection includes ~1.2 million samples. Ancillary materials consisting of handwritten ledgers, index cards, fi eld reports, maps, and other information produced by USGS investigators provide profound knowledge about the specimens and associated geological systems. The USGS is working with NMNH to systematically digitize the collection to preserve and expose samples and data to research. The National Science Foundation (NSF) National Ice Core Laboratory (NICL) is the nation’s repository for preserving, archiving, and sampling meteoric ice cores collected from the world’s ice sheets, ice caps, and glaciers, mostly from Antarctica and Greenland. NICL’s primary mission is to store and curate ice cores, primarily collected during NSF-sponsored projects, for present and future sample investigations.

Colorado↗
USGS2019

A Bayesian life-cycle model to estimate escapement at maximum sustained yield in salmon based on limited information

Life-cycle models combine several strengths for estimating population parameters and biological reference points of harvested species and are particularly useful for those exhibiting distinct habitat shifts and experiencing contrasting environments. Unfortunately, time series data are often limited to counts of adult abundance and harvest. By incorporating data from other populations and by dynamically linking the life-history stages, Bayesian life-cycle models can be used to estimate stage-specific productivities and capacities as well as abundance of breeders that produce maximum sustained yield (MSY). Using coho salmon ( Oncorhynchus kisutch ) as our case study, we show that incorporating information on marine survival variability from nearby populations can improve model estimates and affect management parameters such as escapement at MSY. We further show that the expected long-term average yield of a fishery managed for a spawner escapement target that produces MSY strongly depends on the average marine survival. Our results illustrate the usefulness of incorporating information from other sources and highlight the importance of accounting for variation in marine survival when making inferences about the management of Pacific salmon.

Washington↗