Optimizing otoliths sampling design in fishery-independent surveys for stock assessments

Dr. Gwladys Lambert1
1AFSC
May 31, 2016 9:00 (PST): FSH 105

Optimizing otoliths sampling design in fishery-independent surveys for stock assessments

The sampling design of composition data can have a major impact on our perception of the stock status and the estimated reference points for management. Composition data as sampled from bottom trawl surveys are famously clustered which creates major challenges when it comes to estimating length and age composition of the population of interest and in particular with respect to effective sample size/data weighting in stock assessment models. Here I present the approach I have been exploring in order to prepare a simulation framework to compare otoliths sampling strategies and their impact on stock assessment outputs. First I will talk about the questionnaires I have designed as part of the Otolith Sampling Size Working Group that aimed at informing the development of an operating model. Then I will discuss the implications of removing age data in data rich stock assessments based on a couple of examples and I will show some early simulation work using Stock Synthesis and developments from the R package ss3sim. Ss3sim developments include the addition of an option to test for the effect of ageing error on stock assessment outputs. The main focus will be on another option I have been experimenting with which aimed at simulating the sampling of clustered length data (that resemble real survey data) from random or length-stratified sampling in order to investigate the effect of different sampling strategies on the effective sample size estimated in stock assessments. I will also briefly talk about the Stock Synthesis model for GOA Pacific Ocean Perch I have been working on in order to use in the simulations described above.

Coupled biophysical/individual-based models: can they inform recruitment, stock assessment and/or fishery management? DisMELS lessons from the GOAIERP

Dr. William T. Stockhausen1
1AFSC
May 24, 2016 9:00 (PST): FSH 105

Coupled biophysical/individual-based models: can they inform recruitment, stock assessment and/or fishery management? DisMELS lessons from the GOAIERP

One of the key hypotheses underlying NPRB’s recent Gulf of Alaska (GOA) Integrated Ecosystem Research Project (IERP) was that year-class strength for the five commercially- and ecologically-important groundfish stocks at the focus of the project was determined by success or failure in running the early life, pelagic “gauntlet” from adult spawning areas to young-of-the-year nursery areas. As part of the multi-institution, multidisciplinary effort to address this hypothesis for pollock, Pacific cod, arrowtooth flounder, sablefish, and Pacific ocean perch, a group of modelers (including the author) constructed early life, coupled biophysical/individual-based models (CBP-IBMs) for each stock. For marine species with pelagic early life stages, CBP-IBMs provide one of the best approaches to synthesizing disparate information on individual life stages (e.g., eggs, larvae) and environmental variability into an integrated picture of early life processes. Among our goals for these models was to be able to improve current stock assessments and enhance fishery management by identifying the critical drivers of recruitment variability and hindcasting (or potentially forecasting) recent trends in recruitment for the five focal species. In regard to identifying critical drivers of recruitment variability, I think we can say that variability in physical transport, i.e. where young-of-the-year end up, is less important to recruitment variability than what happens along the way. However, in regard to predicting recruitment variability itself, we were certainly not terribly successful—except perhaps for pollock, the most data-informed of all the IBMs. As an extended introduction, I’ll briefly 1) introduce the concepts behind CBP-IBMs; 2) discuss DisMELS (the Dispersal Model for Early Life Stages), a framework for creating and running CBP-IBMs; and 3) review the five species-specific IBMs created for the GOAIERP. Then I’ll discuss results from the models and what they tell us about early life processes in the GOA. Finally, I’ll present a couple of practical suggestions for some “best practices” using these types of models.

Simulation Testing Hierarchical Multi-Stock Assessment Models for a BC Flatfish Complex

Samuel Johnson1
Simon Fraser University
Novermbet 22, 2016 9:00 (PST): FSH 203

Simulation Testing Hierarchical Multi-Stock Assessment Models for a BC Flatfish Complex

I am interested in finding the conditions that favour joint stock assessments for multiple flatfish species using hierarchical models. Hierarchical multi-level modeling approaches are being increasingly applied in stock asssessment to share information across stocks and species. These so-called “Robin Hood” approaches borrow information from data-rich to potentially improve assessments for data poor species; however, little research exists to demonstrate the boundary conditions where hierarchical approaches outperform single species models. In this talk, I present a hierarchical surplus production modeling approach for a multi-species flatfish complex in British Columbia, Canada. If applicable, the hierarchical modeling approach could help provide more up-to-date assessments for species last assessed in the 1990s!

The genetic basis of pathogen resistance across outbred salmon populations: a comparative analysis using Random Forest

Dr. Kerry Naish1, Dr. Marine Brieuc1, Charles Waters1, Dr. Maureen Purcell2
1SAFS
2Western Fisheries Research Center, USGS
May 17, 2016 9:00 (PST): FSH 105

The genetic basis of pathogen resistance across outbred salmon populations: a comparative analysis using Random Forest

Key questions in both aquaculture and conservation of wild populations are whether genomic regions associated with specific traits are conserved across lines or populations, and to what extent trait-linked markers might be used to predict population responses to selection. In aquaculture, the ability to improve unrelated lines using common trait-linked markers provides the industry with tools necessary to maintain several diverse breeding populations. In conservation, predicting differential population responses to anthropogenic change provides a way to evaluate different remedial actions. Infectious hematopoietic necrosis virus (IHNV) is a rhabdovirus endemic to the northeast Pacific that can cause morbidity and mortality in the commercial rainbow trout and Atlantic salmon aquaculture industries, as well as in supplementation hatcheries. We have investigated the genetic basis of IHNV resistance across different populations of steelhead salmon Oncorhynchus mykiss, in a broader effort to characterize the role of differential host response in the epidemiology of the pathogen. Using the rainbow trout 50K SNP chip, we performed genome wide association analyses across five outbred populations challenged with the pathogen. Traits within these populations may be encoded by many segregating loci of small effect, whose interactions may be influenced by dominance and epistasis. Therefore, we used Random Forest because this non-parametric approach has the advantage of identifying suites of interacting loci that explain phenotypic variation collectively, but may not have large effect sizes on their own. Here, we report several genomic regions associated with resistance and evaluate their conservation across populations. We also review the key steps, analytical approaches, and lessons we have learned in the application of Random Forest to genome wide association studies in outbred salmon populations, with a view to improving genomic resources available to the community as a whole.

Incorporating climate in to a multispecies size spectrum model of the Eastern Bering Sea

Dr. Jon Reum1
1AFSC
May 03, 2016 9:00 (PST): FSH 105

Incorporating climate in to a multispecies size spectrum model of the Eastern Bering Sea

I’ve been working with Kirstin Holsman, Kerim Aydin, and Anne Hollowed at the AFSC on developing a multispecies size-based food web model of the Eastern Bering Sea. I’ll give an overview of the model and how I’m trying to parameterize it. The model is part of a larger effort to evaluate food web and fisheries responses (and associated uncertainty) to climate change by using a variety of ecological models that vary in complexity and structural assumptions.

Evaluating alternative harvest strategies for Alaska groundfish fisheries based on estimates of the probability of overfishing

Dr. Carey R. McGilliard1
1AKFSC
April 19, 2016 9:00 (PST): FSH 105

Evaluating alternative harvest strategies for Alaska groundfish fisheries based on estimates of the probability of overfishing

Increasingly, management systems require that catch limits account for scientific uncertainty by specifying a buffer whereby the catch limit is lower than that determined based on an assumption of perfect information. A “P* approach” is a method whereby catch limits are specified such that the probability that a future fishing mortality (F) exceeds the F corresponding to maximum sustainable yield (FMSY) remains at or below a pre-specified value (P*). Some sources of scientific uncertainty are typically ignored when calculating catch limits, leading to underestimates of uncertainty about population biomass. A management strategy evaluation was developed to examine the performance of a P* approach that included accounting for typically ignored uncertainties. The P* approach was considered because it seems mandated by U.S. law; however, our results demonstrate that the approach has concerning properties. First, the amount of uncertainty that is taken into account is arbitrary and influences resulting catch limits. Secondly, small changes in the acceptable probability of overfishing can result in large changes to resulting catch limits. Third, the estimated and true probability of overfishing diverge as additional uncertainty is taken into account. Simulated cases will be shown to demonstrate each of these properties. Alternative harvest policies will be discussed.

The Alaska arrowtooth flounder stock assessment: how can we make it better?

Dr. Ingrid Spies1
1AKFSC
February 16, 2016 9:00 (PST): FSH 203

The Alaska arrowtooth flounder stock assessment: how can we make it better?

Coauthors: Steve Barbeaux and Miriam Doyle

A single stock assessment model written in AD Model Builder is used for the Gulf of Alaska and Bering Sea/Aleutian Island stocks of arrowtooth flounder. The age-structured model begins in 1961 in the GOA and 1976 in the BSAI. The modeled population includes individuals from age 1 to age 21, with age 21 defined as a “plus” group (all individuals age 21 and older). The age composition of the species shows fewer males relative to females as fish increase in age, which suggests higher natural mortality (M) for males. To account for this process, natural mortality is fixed at 0.2 for females and 0.35 for males in the model. The model is fit to time series of catch, survey indices of abundance (3 surveys in the BSAI: Bering Sea slope, Bering Sea shelf, and Aleutian Islands), and estimates of age and length composition from fishery and survey. Model parameters are estimated by maximizing the log likelihood of the data, and there are likelihood components for: survey biomass, lengths from the fishery and the survey, survey ages, catch, recruitment, and female and male fishery selectivity.

There are several aspects of arrowtooth distribution and life history that have not been considered in the stock assessment, and this presentation is designed to stimulate a discussion on how to improve the assessment. Models suggest that the population sizes have increased 8-fold in the BSAI and four-fold in the GOA over the past few decades. The model does not incorporate carrying capacity, but there are some signs that population growth is decreasing. In addition, the maximum age and sex ratio of arrowtooth flounder appears different by region, which may contradict the assumption of different natural mortality rates for males and females.

Investigating measures of fishing impact for use in informing management

Dr. Owen Hamel1
1NWFSC
February 02, 2016 9:00 (PST): FSH 213

Investigating measures of fishing impact for use in informing management

NOTE THE ROOM CHANGE == FSH 213

Coauthors: Christopher M. Legault, Grant G. Thompson and Richard D. Methot, Jr.

Fishery management is informed by measures of the impacts of fishing on fish stocks. However, while a number of these measures have been proposed and used, none is able to capture all the short- and long-term impacts of fishing on fish stocks. One set of such measures are fishing intensity metrics, which under one definition are invariant to changing population structure, but because of that very attribute may poorly reflect the impact of fishing on a stock in any one year. Other common fishing intensity metrics are either inconsistent measures given changes in fishery selectivity (e.g. apical fishing rate F or exploitation rate U) or reflect only long term equilibrium effects (e.g. Spawning Potential Ratio (SPR) or Equilibrium Stock Depletion (ESD)). Other measures, which we call “fishing impact metrics”, may better reflect the short-term impacts of fishing or the impacts of short-term fishing. This may be especially important in certain situations, such as when fisheries target strong cohorts, and therefore fishing intensity metrics that are independent of the beginning of year numbers-at-age may underestimate both short- and long-term impacts. We will discuss a number of alternative fishing intensity and impact metrics with the goal of finding metrics which reflect short-term impacts under a variety of fishing strategies, as well as to allow for comparison across stocks and across alternative stock assessment methodologies.

Exploring Bayesian state-space age-structured fisheries population dynamics models

Dr. Darcy Webber1,2
1QUANTIFISH
2SAFS, UW
January 19, 2016 9:00 (PST): FSH 213

Exploring Bayesian state-space age-structured fisheries population dynamics models

Fisheries stock assessment models use incomplete observational data and knowledge of the system we are modeling to provide estimates of key management parameters, and require that we assume that the resulting uncertainty is properly reflected in the outputs. Current models typically follow a ‘deterministic’ approach. They are deterministic in the sense that the population follows sets of equations that deterministically define the population state from a previous state, without stochastic error.
State-space models provide an alternative approach to implementing fisheries assessment models. They can incorporate both observation uncertainty and the uncertainty that arises from modelling a simplification of reality — usually referred to as process error. Specifically, state-space models relate observations to unobserved states (e.g. numbers of fish in this context) through stochastic observation equations. Stochastic transition equations define how the unobserved states are assumed to evolve over time. Because state-space models incorporate both observation and process error explicitly, they may be able to help us better quantify the uncertainty of parameters of interest for management.
Until recently, state-space models have not been more frequently applied because they have been technically difficult to implement. However as computational methods in general and Bayesian methods in particular, become more sophisticated, the approach is becoming increasingly tractable.

Maturing estimates of maturity – probabilistic maturation reaction norms for British Columbia sablefish (Anoplopoma fimbria)

Michelle Jones1
1Simon Fraser University
January 05, 2016 9:00 (PST): FSH 203

Maturing estimates of maturity – probabilistic maturation reaction norms for British Columbia sablefish (Anoplopoma fimbria)

Predicting the size and age at which fish will mature is a key factor in estimating stock productivity. In fisheries stock assessment, this relationship is usually defined by estimating maturity ogives, which describe the proportion of the stock mature at a given age or length. However, length is a function of growth. Thus, differences in growth rates among years may cause apparent changes in maturation rates. While consistent spatial variation is expected in maturity ogives, in the case of sablefish (Anoplopoma fimbria), significant temporal variation in maturity ogives is unexpected and is large enough to cause substantial concern to stock assessment modeling. Here, we examine Probabilistic Maturation Reaction Norms (PMRNs) as an alternative for estimating sablefish maturity schedules. PMRNs condition the probability an individual will mature over a set time interval given its age, size, and survival, which may reduce confounding among each of these parameters, providing a temporally-stable model. If PMRNs can provide a stable relationship to estimate sablefish maturity schedules, then uncertainty in stock assessment models may be significantly reduced.