My (circuitous) path to NOAA

What is NOAA?

Fisheries: what do we need to know?

How many fish can be caught sustainably?

Challenges addressed with math

Trawl surveys

How many fish are there now?

What does math have to do with management?

What kinds of models are there?

Components of assessment

Prerequisite: research

• Biology and ecology of the resource and its habitat
• Improving observations and sampling
  • Improving modeling and estimation methods

Mathematical modeling

• Population or ecosystem dynamics (numbers and or biomass)
• Observation models: fishery catch and survey sampling
  • Adding species interactions, climate and habitat effects

Parameter estimation

• Most often maximum likelihood, Bayesian increasingly common
  • Search algorithms needed for high dimensional space
  • Genetic algorithms, others in testing

Forecasting (1-3 years out)

• Predictions depend on modeled and unmodeled processes
• Characterize uncertainty

Math, models, and fisheries--examples

1. Prologue: What is a "forage fish"?

2. Pacific sardine: stock assessment performance testing
   "How does climate change affect our stock assessments?"

   • Overview of stock assessment process
   • Testing a real assessment model with fake (but known!) "data"

3. Atlantic herring: fishery management strategy evaluation
   "Which harvest control rules best consider herring's role as forage?"

   • Balancing fishing benefits and ecological services
   • Diverse stakeholder interests
   • Needed timely answers!

4. Epilogue: Integrated ecosystem assessment

• (Appendix) Alaska pollock: ecological research with a food web model
  "How does ecosystem structure affect dynamics?"
  • Distinguishing climate, fishing, and food web interactions
  • Dealing with uncertainty

1. Prologue: What is a forage fish?

2. How does climate change affect our stock assessments?

Virtual worlds: end-to-end ecosystem models

Atlantis modeling framework: Fulton et al. 2011, Fulton and Smith 2004

Building on global change projections: Hodgson et al. 2018, Olsen et al. 2018

Design: Ecosystem model scenario (climate and fishing)

What can we do so far?

A "sardine" assessment

Need: assessment model data inputs and life history parameters (model based on actual Sardine assessment in Stock Synthesis 3)

$N_{t+1,a} = \left\{\begin{array}{ll}R_{t+1} \\(N_{t,a}e^{-M/2} - C_{t,a})e^{-M/2} \\(N_{t,x-1}e^{-M/2} - C_{t,x-1})e^{-M/2} + (N_{t,x}e^{-M/2} - C_{t,x})e^{-M/2}\end{array}\right.$

A "sardine" assessment: setup

• California Current Atlantis run with and without climate signal
• Input data generated (e.g. sardine survey, below in green)
• Parameters derived; simpler recruitment distribution

A "sardine" assessment: fits to data

A "sardine" assessment: skill? (proof of concept)

Assessment performance under climate: in progress

3. Are any Atlantic herring harvest control rules good for both fisheries and predators?

What is Management Strategy Evaluation?

The Dream and The Reality

Design: link models matching stakeholder-identified objectives

Design: multiple (herring) operating models spanning uncertainty

Economics: more data, models, assessments!

• The dream: predator response links to ecosystem services, human well being
• Fishery complexity rivals or exceeds that of food webs!

Predators

Seabirds: data collected throughout Gulf of Maine

• Colony adult and fledgling count data used to develop population model
• Chick diet observations examined in relation to fledgling success

The overall population in numbers for each predator $P$ each year $N_{y}^P$ is modeled with a delay-difference function, where annual predator survival $S_{y}^P$ is based on annual natural mortality $v$ and exploitation $u$:

$N_{y+1}^P = N_{y}^PS_{y}^P + R_{y+1}^P$ ; $S_{y}^P = (1-v_{y})(1-u)$ ,

and annual recruitment $R_{y}^P$ (at recruitment age a) is a Beverton-Holt function.

Terns and herring--developing a modeled relationship

No clear significant relationships of common tern productivity and the proportion of herring in diets across all colonies, there were some correlations between herring total biomass and tern productivity. This relationship (right plot) was developed to relate herring biomass to common tern productivity (recruitment):

Testing the model--does it work?

Predator results summary

Three control rule types--Constant catch, conditional constant catch, and 15% restriction on change--were rejected at the second stakeholder meeting for poor fishery and predator performance.

What control rules give us 90% of everything we want?

• Tern productivity at 1.0 or above more than 90% of the time
• Herring biomass more than 90% of SSBmsy
• Fishery yield more than 90% of MSY  
   
• AND fishery closures (F=0) less than 1% of the time (second plot).

Food web modeling; supplemental results

Tradeoffs between forage groups apparent

What have we learned? Data-backed models allow us to test options

• Tern/Tuna/Groundfish/Mammal productivity is also affected by predators, weather, and other factors not modeled here
• Even relatively weak relationships still showed which herring control rules were poor
• Managers did select a harvest control rule considering a wide range of factors!

4. Epilogue: integrated ecosystem assessment

Indicator example: Marine heatwaves in the Mid-Atlantic

New Indicator 2020

Collaborators - THANK YOU!

The New England and Mid-Atlantic State of the Ecosystem reports made possible by (at least) 38 contributors from 8 intstitutions

Multiple objectives, multiple challenges

Fisheries stock assessment and ecosystem modeling continue to develop
Can we keep pace with climate?

Existing management systems are at least as complex as the ecosystems, with diverse interests and emerging industries

If you want more details

• 2: Atlantis Model Documentation

• 2: atlantisom R package in progress

• 2: Testing atlantisom in progress

• 3: New England herring MSE modeling paper

• 3: New England herring MSE stakeholder process paper

• 4: NOAA State of the Ecosystem summary webpage

• 4: State of the Ecosystem Technical Documentation

• 4: ecodata R package

• Appendix: Wasp Waist or Beer Belly?

• Slides available at https://noaa-edab.github.io/presentations

Questions? Thank you!

Appendix 1. How does ecosystem structure affect dynamics?

Alaska pollock: a tale of two ecosystems

Alaska pollock: a tale of two ecosystems

Alaska pollock: a tale of two ecosystems

Ecosystem models and uncertainty

Model ensemble https://xkcd.com/1885/

What is a food web model?

$$B_i\Big(\frac{P}{B}\Big)_i*EE_i+IM_i+BA_i=\sum_{j}\Big[ B_j\Big (\frac{Q}{B}\Big)_j*DC_{ij}\Big ]+EM_i+C_i$$

Ecosystem models and uncertainty: grading inputs

Ecosystem models and uncertainty: run a scenario

Ecosystem models and uncertainty: run a scenario

Ecosystem models and uncertainty: run a scenario

Ecosystem models and uncertainty: run a scenario in an ensemble

Ecosystem models and uncertainty: run a scenario in an ensemble

Ecosystem models and uncertainty: run a scenario in an ensemble

Ecosystem models and uncertainty: run a scenario in an ensemble

Defining ecosystem structure in the EBS and GOA: expectations

Ecosystem reaction to pollock if pollock is a "wasp waist":

Pollock reaction to other groups if control is bottom up or top down:

Perturbation results: ecosystem reaction to 10% pollock increase

Perturbation results: pollock reaction to 10% increase in others

Perturbation results: ecosystem reaction to 10% phytoplankton increase

Insights for fishery management

• Differences in food web structure between two adjacent ecosystems with similar biological communities and fishery management

  • Which species respond to the same perturbation
  • Level of uncertainty / predictability in response

• EBS: Influential group at mid trophic levels

  • Wasp waist transmits signal to other groups (neither AK system)
  • Self regulating dominant group (beer belly) absorbs signals
  • Beer belly systems are more predictable, stable as long as the beer belly maintains itself?

• GOA: Influential groups at high trophic levels

  • Magnifies bottom up signals and top down?
  • A less predictable system?
  • Subject to more radical change?

• Structure of a food web may determine how predictable a system is under perturbation, and how changes in primary production propagate through systems

Appendix 2: Fishery management in the US

Eight regional Fishery Management Councils establish plans for sustainable management of stocks within their jurisdictions. All are governed by the same law, but tailor management to their regional stakeholder needs.

More information: http://www.fisherycouncils.org/ https://www.fisheries.noaa.gov/topic/laws-policies#magnuson-stevens-act