Category Archives: Uncategorized

Think Tank: Murdoch McAllister

Developing management options to deal with spasmodic recruitment in Atlantic redfish
Murdoch McAllister, UBC Institute for the Oceans and Fisheries
Tuesday, February 3rd, 2025, 9:30AM

Virtual

Webinar Link: https://attendee.gotowebinar.com/register/8831598901906187862

Both short-lived and long-lived fishes are known to exhibit so-called spasmodic recruitment, a population dynamics pattern in fisheries characterized by long, consecutive periods of low recruitment interrupted by infrequent, irregular, and intense pulses of high recruitment. For up to several decades such fish stocks may remain at low abundance. Very large recruitment events typically arrive unanticipated and come as a surprise to both industry and managers. Spasmodic behaviors have been observed recently on both coasts of North America, for example, in Pacific Ocean Bocaccio Rockfish, California Sardine and Anchovy, and Canadian Atlantic redfish and capelin. Given their highly unpredictable behaviors, spasmodic fish stocks present substantial challenges and also new opportunities for the design of fishery management plans for fisheries for such stocks when large recruitment events are confirmed. I summarize findings from my research group’s study of Canadian Atlantic redfish on characterizing stock trends and evaluating candidate management procedures.

Think Tank: James Thorson

Tutorial for adding nonstationarity, nonlinearity, and statistical interactions to dynamic structural equation models
James Thorson, NOAA Northwest Fisheries Science Center
Tuesday, January 13th, 2025, 9:30AM

FISH 203

Webinar Link: https://attendee.gotowebinar.com/register/8831598901906187862

Dynamic structural equation models (DSEM) provide a fast and general approach to incorporate ecological knowledge in multivariate time-series analysis, e.g., when linking covariates to stock-assessment models. However, DSEM has previously been restricted to stationary, linear, and additive relationships, whereas many ecological systems are nonstationary, nonlinear, and include statistical interactions.
In this workshop, we introduce “moderated SEM” and quickly summarize how moderated DSEM can incorporate nonstationarity, nonlinearity, and statistical interactions. We will then walk through three R-package vignettes in package dsem, showing (1) random slopes linking the Pacific Decadal Oscillation to local temperature, (2) nonlinear (Lotka-Volterra) predator-prey interactions for Wolf-Moose or Paramecium-Didinium systems, and (3) quadratic temperature-
dependence in zooplankton dynamics for Lake Washington. We encourage participants to have a laptop with R and Rtools installed, and/or pre-installing the dsem@dev branch using `remotes::install_github(“james-thorson-NOAA/dsem@dev”)`.

Think Tank: Steve Cadrin

Title TBD
Steve Cadrin, UMass Amherst
Tuesday, May 6th, 2025, 9:30AM

FISH 113

Webinar Link: https://attendee.gotowebinar.com/register/4413110263879871319

ABSTRACT TBD

Think Tank: Noel Cadigan

Title TBD
Noel Cadigan, Memorial University of Newfoundland
Tuesday, February 18th, 09:30 AM PST

FISH 203

Webinar Link: https://attendee.gotowebinar.com/register/5487004370240294486

ABSTRACT TBD

Think Tank: Moses Lurbur

Title TBD
Moses Lurbur, University of Washington
Tuesday, February 4th, 09:30 AM PST

FISH 203

Webinar Link: https://attendee.gotowebinar.com/register/6020211434618619488

ABSTRACT TBD

Think Tank: Derek Chamberlin and Jason Connor

Title TBD
Derek Chamberlin, NOAA Alaska Fishery Science Center
Tuesday, January 21st, 09:30 AM PST

FISH 113

Optimizing traditional and model-predicted fish ageing methods: a study of best practices
for quantifying ageing error and mitigating its various sources

Derek Chamberlin 1 and Jason Conner 1

1 National Marine Fisheries Service, Alaska Fisheries Science Center, 7600 Sand Point Way N.E.,
Building 4, Seattle, WA 98115

Age data are fundamental to population ecology and critical to age structured stock assessments.
The misspecification of ageing error can result in overly optimistic estimates of stock status
leading to inadvertent overfishing. The implementation of model-predicted age estimation
derived from Fourier transform near-infrared spectroscopy (FT-NIR) observations coupled with
multimodal convolution neural network (MMCNN) models introduces new sources of error in
the age estimation process. Given the impact of ageing error on stock assessment and new
sources of uncertainty in model predicted ages, the accurate quantification of ageing error is
critical to accurate stock assessment and sustainable fisheries management. We examined both
the quantification of ageing error and its effects on MMCNN predictions via two projects 1) a
simulation test of the optimal number of readers and proportion of a collection that is read by
those readers to reliably quantify ageing error and 2) a bootstrap resampling of age data based on
estimated ageing error to propagate ageing error through machine learning models. Initial results
suggest the proportion of collections that are tested by multiple readers can be reduced from the
current 20% but that test samples should be selected using a stratified random design.
Additionally, estimated ageing error resulted in a minimal decrease in FT-NIR model
performance (R 2 = 0.87 with ageing error and R 2 = 0.92 without), suggesting the FT-NIR
MMCNN is robust to noise in calibration age data. Finally, we explored application of the Punt
et al. 2008 ageing error matrix model to estimate total uncertainty in model predicted ages.

Webinar Link: https://attendee.gotowebinar.com/register/2275558504413507935

Two PuntLab Students receive NMFS PopDyn Fellowships

In a sweep, two PuntLab PhD students have been selected for the NMFS-Sea Grant Population Dynamics Fellowship this year. Students John Best and Maia Sosa Kapur each received three-year awards, which include tuition waiver and travel felowships. John’s research is entitled Improving spatial indices of abundance with fishery-dependent and -independent data, and he is mentored by Jim Thorson (another Puntlab graduate and previous PopDyn Fellow) at the AFSC. Maia is coadvised by Melissa Haltuch at the NWFSC (Puntlab graduate, and previous PopDyn Fellow) and Dana Hanselman at the AFSC, with a project entitled Effects of Spatial Mis-specification in Management Strategy Evaluation for Northeast Pacific Sablefish. Read the full announcement here.

Characterizing sources of uncertainty in future projections of the Eastern Bering Sea food web using a multi species-size spectrum model

Jonathan Reum1
1NWFSC
May 08, 2018 9:00 (PST): FSH 105

Characterizing sources of uncertainty in future projections of the Eastern Bering Sea food web using a multi species-size spectrum model

In this talk I’ll give an overview of my ongoing efforts to (1) calibrate and validate a multi-species size spectrum model of the Eastern Bering Sea and (2) generate future projections of the EBS food web using 11 different downscaled global climate model (GCM) projections. The size spectrum model can represent different hypothesized pathways through which climate may influence system dynamics. Specifically, temperature can influence predator feeding rates and natural morality and the productivity of low trophic level groups can also be modified in accordance with down-scaled estimates for the EBS using each GCM. While there are several potential sources of structural and parameter uncertainty that may influence the projection envelope, I focus on how projection uncertainty is apportioned according to GCM, climate impact hypothesis, and fishing mortality scenario over time. In a preliminary simulation experiment, near-term projection uncertainty (2020 – 2050) of biomass for some species (e.g., forage fish, snow crab, pollock) was dominated by uncertainty related to fishing mortality scenario. However, uncertainty stemming from GCMs and the specific climate hypothesis was more important for long-term (2075-2100) projection uncertainty. The reverse pattern was observed for other species (e.g., Pacific cod, Pacific halibut). I’ll discuss how this information can be useful for prioritizing future research and and developing ensemble projections. This modeling work is part of the Alaska Climate Integrated Modeling Project (ACLIM).