Dr. Allan Hicks^1
1NWFSC
October 27, 2015 9:00 (PST): FSH 203

A management strategy evaluation for Pacific Hake: the past and future

Management strategy evaluation (MSE) is a tool that can be used to investigate data collection, assessment assumptions, and harvest rules for a fishery. A condition of the Marine Stewardship Council (MSC) certification for the Pacific Hake (Merluccius productus) fishery was to use MSE to investigate the harvest rule defined by an International Agreement between the U.S. and Canada. This started a more expansive MSE in 2012 for the Pacific Hake fishery that attempted to define fishery and management objectives as well as investigate data collection, the assessment model, and the harvest rule. This process has been a learning experience with incremental changes made along the way. I will describe this learning process along with an explanation of how the closed-loop simulation, a part of the larger MSE process, was implemented. Some results of our analysis and how they have influenced the assessment and management of Pacific Hake will be presented, followed by a vision of how this MSE may continue to evolve in the future.

Presentation

Dr. André Punt¹
1US SAFS
October 13, 2015 9:00 (PST): FSH 203

Some Insights into Data Weighting in Integrated Stock Assessments

The results of fishery stock assessments based on the integrated analysis paradigm can be sensitive to the values for the factors used to weight each of the data types included in the objective function minimized to obtain the estimates of parameters. These assessments generally include index data, length-composition information and conditional age-at-length data, and algorithms have been developed to select weighting factors for each of these data types. This paper introduces methods for weighting conditional age-at-length data that extend an approach developed by Francis (2011) to weight age- and length-composition data. Simulation based on single-zone and two-zone operating models are used to compare five tuning algorithms that are constructed as combinations of methods to weight each of these data types. The single-zone operating models allow evaluation of the algorithms in terms of the ability to provide unbiased estimates of management-related quantities and the correct data weights in the absence of model mis-specification while the two-zone operating models allow the impacts of model mis-specification on the performance of tuning algorithms to be explored.

Presentation

Dr. Elizabeth Councill^1,2
1NWFSC
²US SAFS
September 29, 2015 9:00 (PST): FSH 203

Autocorrelated recrutiment deviations

Abstract: To be posted …

Dr. Kotaro Ono^1
1UW SAFS

June 02, 2015 9:00 (PST): FSH 203

Harvest strategies for multispecies fisheries under catch constraints

Current groundfish species in the Bering Sea, Alaska, are managed under strict annual quotas. However, there are strong bycatch regulations in place that compromise the ability of the fleet to catch the entire quota. As a consequence, single species population forecasting might not be very accurate if the multispecies nature of the fishery is ignored. In addition, uncertainty in environmental fluctuation and fishermen behavior can affect the accuracy of population forecasting and bias the effect of management actions. Management strategy evaluation (MSE) is a computer simulation method that consists in modelling both the fish population and the management cycle (stock assessment and decision making) of a fishery to evaluate the performance of the management actions. This procedure has been shown to be very important as it can caution the user about the potential undesired effect of their proposed management actions (e.g. unwanted reduction in quotas). In this study, a MSE was developed to test 1) how the inclusion of a multispecies catch tradeoff affect the short- to long-term performance of the model (e.g. catches, discounted revenue) compared to traditional single species model, and 2) how these models perform in the presence of environmental and fishermen behavioral uncertainty.

Ian Stewart^1
1International Pacific Halibut Commission

May 05, 2015 9:00 (PST): FSH 203

It is standard practice to conduct fisheries stock assessments generating a point estimate of stock size, which is then translated into one or more future fishery catch targets and limits. The general approach often relies on the output of a single stock assessment model, and therefore does not thoroughly account for alternative hypotheses or major sources of uncertainty such as fixed parameter values, model approach (e.g., statistical catch-at-age, VPA), model structure (e.g., treatment of spatial dynamics, delineation of fishing fleets, time-series length) and data weighting. In 2012, the International Pacific Halibut Commission made the transition from point estimates to risk-assessment, based on a decision table produced for the annual management process. The decision table represents a composite of probability-weighted results from alternative models, allowing a comparison of potential benefits (fishery yields) with the probabilities of various risk metrics, including stock and fishery trends and status. In 2013, the approach was extended to include the use of an ‘ensemble’ of models, following methods used in climate and hurricane forecasting. These changes have led to increased transparency about perceived risk, the availability of more information for the decision makers, and a clear delineation between scientific and policy considerations. The ensemble approach to stock assessment also provides a conceptual link between single model analyses and fully developed Management Strategy Evaluation of harvest policy and management procedures. Potential extensions and improvements to the approach, as well as a brief summary of decisions based on this information will also be discussed.

Dave Fournier¹, James Ianelli^1,2 and Steve Martell^1,3
1ADMB Foundation; ²Alaska Fisheries Science Center; ³International Pacific Halibut Commission

April 21, 2015 9:00 (PST): FSH 203

A proposal for rehabilitating the multinomial likelihood for compositional data

The multinomial likelihood is widely used in many stock assessment models for evaluating compositional data. However, this simple model does not account for over-dispersion and does not allow for correlation among adjacent categories. In response to recent publications proposing alternatives for the multinomial, we propose to extend and modify the ideas put forward by Hranfnkelsson and Stefánsson (2004) to better accommodate over-dispersion and correlation in compositional data. Specifically, to develop a self-scaling multinomial type estimation procedure and optionally incorporate positively autocorrelated errors. Models that incorporate composition data need to take into account over-dispersion and correlation if uncertainty in model parameters, and the risks associated with management actions, is desired.

Dr. Melissa Haltuch¹, Z. Teresa A’mar², Nicholas A. Bond³, and Juan L. Valero^4
1Northwest Fisheries Science Center, ²Alaska Fisheries Science Center, ³University of Washington, and ⁴Center for the Advancement of Population Assessment Methodology

March 10, 2015 9:00 (PST): FSH 213

Assessing the effects of climate change on U.S. west coast sablefish productivity and on the performance of alternative management strategies.

The U.S. west coast sablefish fishery is a valuable commercially targeted species, making assessing and understanding the interaction between climate change and fishing a priority for (1) forecasting future stock productivity and (2) for testing the robustness management strategies to climate variability and change. The horizontal-advection bottom-up forcing paradigm describes large-scale climate forcing that drives regional changes in alongshore and cross-shelf ocean transport, directly impacting the transport of nutrients, mass, and organisms. This concept provides a mechanistic framework through which climate variability and change alter sea surface height (SSH), zooplankton community structure, and sablefish recruitment, all of which are regionally correlated. This study assesses future trends in sablefish productivity as well as the robustness of harvest control rules to climate driven changes in recruitment by conducting a management strategy evaluation of the currently implemented harvest control rule as well as an alternative. We use 50 year ensemble forecasts of sablefish productivity under a suite of future climate variability and change scenarios. Future recruitments are generated under two scenarios (1) the fit of a Beverton-Holt stock-recruitment curve based on historical data and (2) recruitments driven by a SSH-recruitment relationship that is treated as an age-0 survey of abundance with associated uncertainty. Multi-decadal forecasts of sablefish productivity could provide long term strategic advice to allow fishers and managers to plan for and respond to shifts in productivity.

Hiromichi Ueno, Ph.D.^1
1Faculty of Fisheries Science, Hokkaido University
March 04, 2015 11:00 (PST)

Temporal and spatial variation in growth factors of Pacific salmon.

Dr. Ueno is an Associate Professor at Hokkaido University in Hokkaido, Japan. He has a PhD (2003) and Masters (2000) in Oceanography from the University of Tokyo, where he also later performed postdoctoral research. His research focuses on four main topics: 1) mesoscale eddies, physical characteristics and its impact on marine ecosystem; 2) pacific salmon and the physical environment affecting their growth; 3) temperature inversions and haloclines in the subarctic North Pacific; and 4) the physical environment and its impact on marine ecosystems in the Chukchi Sea.

This talk will start with the introduction of international intensive course for sustainable fishery resource use at Hokkaido University toward building a joint program in the North Pacific on board T/V Oshoro-maru. I will also talk about temporal and spatial variation in growth factors of Pacific salmon from the view point of bioenergetics. The prey-density function for consumption is used as the growth factor. For the estimation of the prey density, we used zooplankton density obtained from an ecosystem model, NEMURO embedded in a 3-D physical model. In this study we focus on the three species of Pacific salmon (chum, pink and sockeye), zooplankton feeders whose biomass exceeds 90% of Pacific salmon. Empirical Orthogonal Function (EOF) analysis is conducted for the growth factor in the subarctic North Pacific during 1948–2007. The growth factor of Pacific salmon varies in a decadal timescale. The variation of the growth factor in the Bering Sea, the western subarctic gyre and the eastern subarctic North Pacific are consistent with the variation of total catch of chum, pink and sockeye salmon.

Ole Shelton^1
1Northwest Fisheries Science Center
February 24, 2015 09:00 (PST)

Inferring marine communities from environmental DNA: merging traditional and new molecular techniques in the sampling of the oceans.

Environmental DNA (eDNA)—genetic material recovered from an environmental medium such as soil, water, or feces—reflects the membership of the ecological community present in the sampled environment. As such, eDNA is a potentially vast source of data for basic ecology, conservation, and management, because it offers the prospect of quantitatively sampling ecological communities from easily-obtained and non-lethal samples. However, like all sampling methods, eDNA sequencing is subject to methodological limitations that bias the resulting estimates of abundance and community membership. I will describe a framework for thinking about current methodological challenges for eDNA, discuss how these challenges are largely analogous to biases in more traditional survey methods, and provide detailed discussion of methods for an important step in the eDNA process – estimating relative abundance of DNA from marine community.

André Punt¹, Ian Taylor², and Owen Hamel^2
1School of Aquatic and Fishery Science
²Northwest Fisheries Science Center
February 11, 2015 14:00 (PST)

Data reweighting (and the Francis Method)

Weighting of data is fundamental to modern stock assessment science based on ‘integrated’ methods of assessment. There is considerable experience with weighting index data, and the methods for doing this have largely stabilized. This is not however the case for composition data even though it is clear that inappropriate weighting of composition data can lead to markedly biased estimates. This Think Tank focuses on two methods for calculating the effective sample size for composition data (McAllister-Ianelli and Francis) as well as how estimates of effective sample size should be combined over years. A key focus for the Think Tank and future work is how to apply the “Francis method” to conditional age-at-length data. With luck, approaches for addressing this question will arise during the Think Tank.