Applying Bayesian model selection to determine ecological covariates for recruitment and natural mortality in stock assessment

Incorporating ecological covariates into fishery stock assessments may improve estimates, but most covariates are estimated with error. Model selection criteria are often used to identify support for covariates, have some limitations and rely on assumptions that are often violated. For a more rigorous evaluation of ecological covariates, we used four popular selection criteria to identify covariates influencing natural mortality or recruitment in a Bayesian stock assessment of Pacific herring (Clupea pallasii) in Prince William Sound, Alaska. Within this framework, covariates were incorporated either as fixed effects or as latent variables (i.e. covariates have associated error). We found most support for pink salmon increasing natural mortality, which was selected by three of four criteria. There was ambiguous support for other fixed effects on natural mortality (walleye pollock and the North Pacific Gyre Oscillation) and recruitment (hatchery-released juvenile pink salmon and a 1989 regime shift). Generally, similar criteria values among covariates suggest no clear evidence for a consistent effect of any covariate. Models with covariates as latent variables were sensitive to prior specification and may provide potentially very different results. We recommend using multiple criteria and exploring different statistical assumptions about covariates for their use in stock assessment.publishedVersio

Advice on fishing opportunities for Barents Sea capelin in 2024 — ICES subareas 1 and 2 excluding Division 2.a west of 5°W

publishedVersio

Advice on fishing opportunities for Barents Sea capelin in 2024 — ICES subareas 1 and 2 excluding Division 2.a west of 5°W

Source at https://www.hi.no/hi/nettrapporter

Modeling Population Collapse and Recovery in Herring

Thesis (Ph.D.)--University of Washington, 2021Population collapse in forage fish occurs both naturally and due to overfishing, and is a challenge to sustainable fisheries management. Sustained low abundance can result in prolonged fishery closures and impact the abundance of other species via predation or competition. The time taken to recover from collapse is determined by uncertain factors that control population dynamics and can widely vary between populations. Herring (Clupea spp.) are a major group of forage fishes with numerous populations throughout the Northern Hemisphere, that have sustained commercial fisheries for centuries and indigenous fishers for far longer, and support ecologically and economically valuable species including various pinnipeds, whales, seabirds, and predatory fishes. Herring populations across the world have shown varying durations of population collapse since the start of industrial fishing while hypotheses of the underlying factors have been underdetermined across and within individual populations. The Pacific herring (Clupea pallasii) population in Prince William Sound, Alaska is a modern enduring example of prolonged population collapse whose population dynamics remain largely uncertain and unpredictable despite intensive monitoring and modeling. In this dissertation, I explore and evaluate factors that potentially influence population collapse and recovery within herring. My overarching goal is to better inform the population dynamics of herring and more specifically improve the Bayesian stock assessment model of Prince William Sound herring. In Chapter 1, I conducted a meta-analysis on time series collected for 64 populations worldwide to statistically characterize population collapse and recovery in herring and model predictors of recovery times in adult biomass and recruitment. After collapse, herring populations recovered in 11 years on average, with a few populations remaining collapsed for multiple decades. Amongst populations, recovery time duration did not coincide with fishery closures, which occurred at low abundance in most Pacific herring populations but no Atlantic herring populations. Faster recovery in biomass was best associated with higher average recruitment and higher oceanographic variability in both sea surface height anomalies and sea surface temperatures. In Chapter 2, I modeled ecological factors impacting natural mortality and recruitment in Prince William Sound herring using a custom-built Bayesian age-structured stock assessment model. Support for individual factors was evaluated using multiple Bayesian model selection criteria and alternative modeling assumptions about the ecological data representing these factors. There was strongest evidence for effects on herring natural mortality from pink salmon abundance in Prince William Sound had the most broad and consistent support. Statistical support differed by the type of selection criteria, model assumptions regarding covariates, and time period modeled, resulting in generally weak evidence for most individual effects and the suggestion that results are sensitive to model flexibility. In Chapter 3, I developed a novel modeling framework and conducted a simulation study to test the usefulness of age-specific antibody, or seroprevalence, data in assessing the impact of disease-associated mortality on herring, for use in stock assessment models. Viral hemorrhagic septicemia virus (VHSV) in Prince William Sound herring is used as a case study due to its association with fish kills and well-established ecological principles for its epizootiology from extensive monitoring of VHSV in herring populations. I found that incorporating seroprevalence data within stock assessment can accurately inform infection history and disease mortality and improve population estimates. The first real application of age-specific VHSV seroprevalence is demonstrated with the Prince William Sound herring stock assessment. While motivated from VHSV in herring, these models can be easily adapted to different host populations and pathogens and I present advice for future applications of disease data within stock assessment

Applying Bayesian model selection to determine ecological covariates for recruitment and natural mortality in stock assessment

Incorporating ecological covariates into fishery stock assessments may improve estimates, but most covariates are estimated with error. Model selection criteria are often used to identify support for covariates, have some limitations and rely on assumptions that are often violated. For a more rigorous evaluation of ecological covariates, we used four popular selection criteria to identify covariates influencing natural mortality or recruitment in a Bayesian stock assessment of Pacific herring (Clupea pallasii) in Prince William Sound, Alaska. Within this framework, covariates were incorporated either as fixed effects or as latent variables (i.e. covariates have associated error). We found most support for pink salmon increasing natural mortality, which was selected by three of four criteria. There was ambiguous support for other fixed effects on natural mortality (walleye pollock and the North Pacific Gyre Oscillation) and recruitment (hatchery-released juvenile pink salmon and a 1989 regime shift). Generally, similar criteria values among covariates suggest no clear evidence for a consistent effect of any covariate. Models with covariates as latent variables were sensitive to prior specification and may provide potentially very different results. We recommend using multiple criteria and exploring different statistical assumptions about covariates for their use in stock assessment

Remote sensing of physical cycles in Lake Superior using a spatio-temporal analysis of optical water typologies

© 2015. An optical class-based approach was adapted and applied to satellite imagery of aquatic color radiometry over Lake Superior. Lake Superior exhibits an optically complex environment whose physical and biogeochemical variability is unknown for much of the year. We characterized optical classes or optical water types (OWTs) from remotely sensed data and determined the temporal and spatial distribution of the OWTs. OWT distributions were interpreted through their inherent optical properties (IOPs) and physical drivers. Five identified OWTs based on spectrally normalized remote sensing reflectance imagery from the MODIS-Aqua mission revealed a gradient between class-specific IOPs for colored dissolved organic matter absorption (aCDOM) and particulate backscattering (bbp). The clearest OWT displayed widespread prominence over the lake and within and across years, indicating strongly stable physical dynamics including stratification and permanent circulation patterns. The most optically complex OWT (highest aCDOM and bbp) followed more stochastic dynamics coinciding with localized runoff and mixing events. OWTs with intermediate optical complexity delineated river outflow plumes, upwelling, and annual lake wide turnover events while revealing annual and biannual harmonic patterns. OWTs verified both previously observed and unobserved dynamics in Lake Superior, demonstrating a valuable utility to characterize spatio-temporal, optical, and biogeochemical variability

Ecosystem-based fisheries management: Perception on definitions, implementations, and aspirations

<div><p>Ecosystem-based fisheries management (EBFM) was developed to move beyond single species management by incorporating ecosystem considerations for the sustainable utilization of marine resources. Due to the wide range of fishery characteristics, including different goals of fisheries management across regions and species, theoretical best practices for EBFM vary greatly. Here we highlight the lack of consensus in the interpretation of EBFM amongst professionals in marine science and its implementation. Fisheries policy-makers and managers, stock assessment scientists, conservationists, and ecologists had very different opinions on the degree to which certain management strategies would be considered EBFM. We then assess the variability of the implementation of EBFM, where we created a checklist of characteristics typifying EBFM and scored fisheries across different regions, species, ecosystems, and fishery size and capacity. Our assessments show fisheries are unlikely to meet all the criteria on the EBFM checklist. Consequentially, it is unnecessary for management to practice all the traits of EBFM, as some may be disparate from the ecosystem attributes or fishery goals. Instead, incorporating some ecosystem-based considerations to fisheries management that are context-specific is a more realistic and useful way for EBFM to occur in practice.</p></div

Ecosystem-based fisheries management: Perception on definitions, implementations, and aspirations - Fig 1

<p>A. Survey results for defining EBFM. The y-axis is the list of scenarios asked and the x-axis the score of the final response. We divided the scenarios by categories for different management actions (gray shading) and the respondents by profession or background (colored). Each tube represents the range of responses. <b>B</b>. Shows the average responses by survey for each respondent background.</p

EBFM scoring criteria.

<p>Criteria used to score the fisheries listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190467#pone.0190467.s002" target="_blank">S1 File</a> and justification for each scoring criteria.</p

Evaluating signals of oil spill impacts, climate, and species interactions in Pacific herring and Pacific salmon populations in Prince William Sound and Copper River, Alaska

<div><p>The <i>Exxon Valdez</i> oil spill occurred in March 1989 in Prince William Sound, Alaska, and was one of the worst environmental disasters on record in the United States. Despite long-term data collection over the nearly three decades since the spill, tremendous uncertainty remains as to how significantly the spill affected fishery resources. Pacific herring (<i>Clupea pallasii</i>) and some wild Pacific salmon populations (<i>Oncorhynchus spp</i>.) in Prince William Sound declined in the early 1990s, and have not returned to the population sizes observed in the 1980s. Discerning if, or how much of, this decline resulted from the oil spill has been difficult because a number of other physical and ecological drivers are confounded temporally with the spill; some of these drivers include environmental variability or changing climate regimes, increased production of hatchery salmon in the region, and increases in populations of potential predators. Using data pre- and post-spill, we applied time-series methods to evaluate support for whether and how herring and salmon productivity has been affected by each of five drivers: (1) density dependence, (2) the EVOS event, (3) changing environmental conditions, (4) interspecific competition on juvenile fish, and (5) predation and competition from adult fish or, in the case of herring, humpback whales. Our results showed support for intraspecific density-dependent effects in herring, sockeye, and Chinook salmon, with little overall support for an oil spill effect. Of the salmon species, the largest driver was the negative impact of adult pink salmon returns on sockeye salmon productivity. Herring productivity was most strongly affected by changing environmental conditions; specifically, freshwater discharge into the Gulf of Alaska was linked to a series of recruitment failures—before, during, and after EVOS. These results highlight the need to better understand long terms impacts of pink salmon on food webs, as well as the interactions between nearshore species and freshwater inputs, particularly as they relate to climate change and increasing water temperatures.</p></div