Dr. Noble Hendrix^1
1QEDA Consulting
May 09, 2017 9:00 (PST): FSH 203

A court mandated life-cycle model to evaluate California Central Valley water operation effects on Chinook salmon

In 2009, NOAA Fisheries Central Valley California Office submitted a Biological Opinion on the operations of multiple water control structures (dams, routing gates, and export facilities), that was litigated by multiple water agencies. The Biological Opinion was remanded in 2011 by the judge, who also provided a strongly worded rebuking (fueled to some degree by the testimony of SAFS own Ray Hilborn) that stated, “this is the last time NMFS will be permitted to avoid studying, analyzing, and applying a life cycle model. NMFS’s chronic failure to do so now approaches bad faith in view of the undeniable importance of the information to resolve the perennial dispute over population dynamics. ”

NOAA Fisheries Southwest Fisheries Science Center shortly thereafter initated a life cycle modeling project to predict how various salmon populations would respond to suites of management actions, including changes to flow and export regimes, modification of water extraction facilities, and large-scale habitat restoration. I was asked to help with the first model in the project, which is a life cycle model for winter-run Chinook salmon (WRLCM). The WRLCM couples water planning models and physical models (e.g., HEC-RAS, DSM2, DSM2-PTM) and fishery models to evaluate management actions on winter-run population dynamics. I will describe the general model structure, the transition equations that define the movement and survival throughout the life cycle and linkages to physical models for integrating dynamics over finer spatial and temporal scales. Because the WRLCM was developed to evaluate management actions, the model sptial and temporal structure focuses on addressing questions of hydromanagment and habitat restoration. Unfortunately, this structure is not particularly well-suited for statistical estimation and presents numerous difficulties for estimating model parameters. We implemented a state-space structure and estimated the model parameters using a variant of the Expectation-Maximization algorithm to obtain MLE estimates and are actively pursuing MCMC approaches for Bayesian estimation. The fitted model is currently being used to evaluate proposed actions as part of a 2017 NOAA Fisheries Biological Opinion, meeting the judge’s 2011 request. More importantly, the calibrated WRLCM is providing a central framework that can be used to assess restoration projects, construct adaptive management frameworks, evaluate reintroduction success, simulate new monitoring designs, and prioritize recovery actions.

Kelli F. Johnson,¹ Dr. Andre E. Punt² and Dr. James T. Thorson^3
1SAFS
²SAFS
³NWFSC

April 25, 2017 9:00 (PST): FSH 203

Moving beyond Q-Q plots for spatio-temporal models

The assessment of models, which are always wrong, largely consists of two components, assessing the relative performance of candidate models and characterizing differences between observed data and model predictions. Residuals and relative error are traditionally used to identify the presence of outliers, quantify the relevance of modelled and excluded factors, and assess overall model fit. With linear models, the Q-Q plot is a standard a metric of model fit, e.g., the fat-pen test. Unfortunately, Q-Q plots can be misleading when used to assess models that include spatially-explicit parameters, where spatial patterns in residuals can indicate model misspecification. Here, we present the results of a simulation experiment to quantify the performance of the structural similarity index as a metric of model fit. Simulations were conducted using a vector-autoregressive spatio-temporal model that estimated spatio-temporal variation in density from simulated survey data for a hypothetical species off the US West coast. The structural similarity index provided spatially-explicit information regarding lack of model fit not uncovered by traditional model assessment methods. Future work will focus on how well the structural similarity index can identify misspecification, rather than lack of fit in a properly specified model, as was done here.

John Trochta^1
1SAFS
April 11, 2017 9:00 (PST): FSH 105

An investigation of popular hypotheses on the survival of Pacific herring in Prince William Sound, Alaska using Bayesian model selection

We investigate how changing environmental conditions may impact Pacific herring (Clupea pallasii) population dynamics in Prince William Sound, Alaska. The Prince William Sound herring collapsed in the early 1990’s and have yet to recover from low abundance that has remained below the lower regulatory threshold (i.e. for closing fishing) of 20,000 metric tons. This event continues to motivate herring research, especially in improving the predictability of herring biomass dynamics. There is large uncertainty in these dynamics, where we seek to improve biomass estimates by including covariates from physical and ecological data in a Bayesian age-structured assessment model of herring biomass. Indicators of potential environmental effects (i.e. sea surface temperature, air temperature, freshwater discharge, and sea level) and species interactions (i.e. pink salmon, humpback whales, and pollock) are modeled as covariates impacting either adult or juvenile survival in the assessment model. This effort will build upon previous hypothesis testing and reviews that examined the causes of both herring collapse and recovery failure by synthesizing both old and new hypotheses with updated data, as well as new data sets. Our results help to determine which effects may be most significant, while further improving the utility of herring stock assessment models in being able to simulate biomass dynamics under different environmental conditions.

Caitlin Allen Akselrud^1
1SAFS
February 21, 2017 3:30 (PST): FSH 203

A discussion-based Think Tank covering possible modeling approaches for a difficult-to-assess species, California market squid (Doryteuthis opalescens)

This will be a fun, discussion-based Think Tank covering possible modeling approaches for a difficult-to-assess species, California market squid (Doryteuthis opalescens). Please bring your model ideas, and a phone or laptop for active participation!

Market squid are short lived (6-9 months), semelparous, aggregate spawners. A recent dissertation (Cheng, 2015) suggests multiple temporally-separated sub-population, indicating multiple spawning groups throughout the year. This potentially presents a form of “portfolio effect” for this species. [You may notice some life history similarities to salmon.] Fluctuation in market squid abundance have been correlated to ENSO cycles, and squid have specific preferences for spawning ground conditions (Zeidberg et al., 2012). Currently, there is no fishery-independent survey of market squid. Fishery catch and effort data are available [reliably] from 2000-present.

Depletion methods are often used on squid populations as fishers fish down spawning aggregations. Several problems to this approach exist for the California market squid, including: a) multiple cohorts caught simultaneously (not fishing a single cohort), b) spawning aggregations immigrate into spawning grounds over several weeks (not a closed population), and c) fishing can be driven by external economic drivers, causing problems with CPUE estimates, as effort does not necessarily reflect abundance.

Bring your thinking caps and brainstorm some possible methods with me!

Cole Monnahan^1
1SAFS
February 07, 2017 3:30pm (PST): FSH 203

Introducing the no-U-turn MCMC sampler in ADMB and TMB: faster run times for large, complex fisheries models.

Bayesian analysis is a powerful tool for statistical inference in fisheries science. However, some analyses remain impracticable due to extremely long runtimes, despite increases in processor power. This is the case for data-rich age structured assessments written in AD Model Builder (e.g., stock synthesis) and large, complex spatio-temporal models for species distributions, typically built with Template Model Builder (TMB). Recently, the software package Stan, powered by the no-U-turn (NUTS) MCMC sampler, has emerged as state of the art for Bayesian analyses and it promises faster runtimes. However, for many fisheries scientists, it is not immediately feasible (nor desirable) to rewrite ADMB/TMB models in Stan. In these cases, it makes more sense to bring the algorithms to ADMB and TMB, rather than bring the models to Stan. In this workshop I will demonstrate Stan and how to interpret the diagnostic output from NUTS. I will then present and discuss my work on integrating NUTS into both TMB and ADMB. The workshop will contain a hands-on element in TMB, so bring your laptop with it installed, and will be very informal. Interested ADMB users can test their own models but need to compile an ADMB fork (contact me about this). I will expect that attendees are familiar with NUTS (read Monnahan et al. 2016) and the brave amongst you should try Betancourt 2017.

Monnahan, C. C., Thorson, J. T. and Branch, T. A. (2016), Faster estimation of Bayesian models in ecology using Hamiltonian Monte Carlo. Methods Ecol Evol. doi:10.1111/2041-210X.12681

Betancourt, Michael. A Conceptual Introduction to Hamiltonian Monte Carlo. arXiv preprint arXiv:1701.02434 (2017).

Kristin Privitera-Johnson^1
1SAFS
January 24, 2017 3:30 (PST): FSH 203

Evaluating Uncertainties in Historical Groundfish Landing for Washington Coastal Commercial Fisheries

Fishery removal of fish population, catch, is important information used in conducting stock assessments.  However, groundfish, especially rockfish, landings were recorded in broad market categories historically.  Partitioning landings into single species category has relied on analysts, which caused the landing history changes from assessment to assessment depending on assumptions made by the analysts.  This has increased uncertainty among assessments.  In 2008, a major effort to reconstruct historical landings was initiated in response to the Pacific Fishery Management Council’s call to compile the best estimates of catch history early in the development of Pacific Coast groundfish fisheries. For Washington state, databases have been developed for raw landings, historical trawl logbooks and species composition data.  As part of the whole catch reconstruction project, this work explores a method for quantifying uncertainties in the catch estimates based on species composition data collected by department staff since the early 1960’s.

Kristin Privitera-Johnson
¹SAFS
January 24, 2017 3:300 (PST): FSH 203

Evaluating Uncertainties in Historical Groundfish Landing for Washington Coastal Commercial Fisheries

Fishery removal of fish population, catch, is important information used in conducting stock assessments.  However, groundfish, especially rockfish, landings were recorded in broad market categories historically.  Partitioning landings into single species category has relied on analysts, which caused the landing history changes from assessment to assessment depending on assumptions made by the analysts.  This has increased uncertainty among assessments.  In 2008, a major effort to reconstruct historical landings was initiated in response to the Pacific Fishery Management Council’s call to compile the best estimates of catch history early in the development of Pacific Coast groundfish fisheries. For Washington state, databases have been developed for raw landings, historical trawl logbooks and species composition data.  As part of the whole catch reconstruction project, this work explores a method for quantifying uncertainties in the catch estimates based on species composition data collected by department staff since the early 1960’s.

Merrill Rudd¹ and Dr. James Thorson^1
1SAFS
January 17, 2017 2:30 (PST): FSH 203

Development and application of a length-based, integrated mixed effects (LIME) assessment method for data-limited fisheries

Length measurements are often the easiest type of data to reliably collect. Existing stock assessment methods that primarily rely on length composition or mean length of the catch make the assumption that the population is in equilibrium (i.e. fishing mortality and recruitment are not changing over time) to make inferences on fishing pressure and stock status. There are many situations in which this equilibrium condition could be violated. We developed a length-based integrated mixed effects (LIME) method to account for variability in recruitment and fishing mortality using at least one year of length composition data and assumptions about biological parameters to make inferences on stock status. I will review the simulation testing of this method against several scenarios of life history, fishing mortality, and recruitment, as well as violations of model assumptions to identify strengths and weaknesses. I will also present progress and obstacles in the application of the LIME method for Kenyan coral reef fisheries.

Dr. James Thorson^1
1SAFS
November 29, 2016 9:00 (PST): FSH 203

A hands-on discussion and tutorial for using FishStats to estimate spatio-temporal abundance indices, distribution shifts, and range expansion in 2017 stock assessments

Collaborators and I have been developing, testing, and documenting spatio-temporal models and software for single- and multi-species analysis of fishery and/or survey data.  R packages for implementing these models are now publicly available and documented through www.FishStats.org, a spatio-temporal toolbox for fisheries science.

In this Think Tank, I will present a small number of slides (<15 total) to recap recent model developments and applications.  I will then lead a discussion of different applications for which FishStats could be useful in 2017 stock assessments, e.g., estimating survey indices, standardizing fishery catch rates, estimating distribution shifts or range expansion/contraction, or identifying multispecies associations.  With input from attendees, I will then provide a semi-structured, hands-on demonstration of how different modelling goals can be achieved.

I encourage attendees to bring a laptop and to install a recent version of R (e.g., 3.3.1) and appropriate Rtools (if using a windows OS).  Attendees will install other necessary software (including INLA and TMB) as we go through tutorials together.

Samuel Johnson^1  
1  Simon Fraser University

November 22, 2016 9:00 (PST): FSH 203

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!