Update: Management Strategy Evaluation (MSE) used in Herring ...
4.1
Deirdre Boelke, NEFMC Herring PDT Chair Scientific and Statistical Committee Meeting May 25, 2017 1
Outline of Presentation 1. Background on development of Acceptable Biological Catch (ABC) 2. 3. 4. 5.
control rules in the Herring Fishery Management Plan (FMP). Review MSE process used for Herring Amendment 8. Summary of MSE models developed and initial results. Summary of external peer review. Recent Council action and next steps.
2
1. ABC control rules in the Herring FMP Council has been discussing development of a long-term CR for the
Herring FMP for over five years. SSC has provided input several times over the years. Interim CR: ABC set for 3 years equal to the catch that is projected to produce a ≤50% probability of exceeding FMSY in year 3. 2015 - Council initiated Amendment 8 to develop and implement an ABC control rule that manages herring within an ecosystem context.
EBFM/Herring PDT preliminary work with SSC input.
Council decides to use MSE to develop the ABC control rule. 3
2. Management Strategy Evaluation (MSE) Definition: A decision-making process for managers and stakeholders to
collaboratively develop alternatives with more input and analysis upfront. Overall MSE Process (Document 4.2) Council hosted 2 public workshops to get input on objectives and specific metrics to measure ABC CR performance (May 2016), as well as tradeoffs of specific ABC CR alternatives (Dec 2016). In June – Council approved MSE approach with objectives and metrics identified by stakeholders. In Jan 2017– Council removed handful of CR approaches based on poor performance and input from second stakeholder workshop. 4
Council Action
Management Strategy Evaluation Process
Council Action
Council Action
Council Action
5
Stakeholder workshops A public approach was important, consistent with Council process and
improve buy-in potential. Developed with steering committee, Council oversight. Used external facilitators; lead facilitator well-versed in MSE, less familiar with fishery/stakeholders. Used multiple approaches to solicit input from attendees (large/small group discussions, worksheets, note cards). Drew 65-70 diverse, engaged stakeholders with varying technical expertise; 49% of 2nd workshop attendees had not gone to 1st workshop. Workshop summaries provided in SSC materials (Doc.#4.4 and 4.6). 6
3. MSE Technical Methods Overview – Doc.#4.3 Dr. Sarah Gaichas
Dr. Jonathan Deroba Herring N, B, Wt
Uncertainties from Workshop #1 Herring recruitment (high or low?) Herring natural mortality (high or low?) Herring growth (good or poor?) Herring assessment error/bias (yes or no?)
Dr. Min-Yang Lee
7
3. Herring Model Parameters based on assessment
results (recruitment, M, growth) Evaluated ABC control rules for each Operating Model (OM) Tested handful of CR types: biomass based (annual, 3yr, and 5yr), constant catch, etc. About 44,000 alternatives total!
Production Hi Lo x x x x
Growth Good Poor x x x x
x
x x
x x x
x x
Assessment bias On Off x x x x x x x x
8
3. Predator Models Stakeholders most interested in
predator modelling for: tuna, whales, groundfish, and sea birds. Data insufficient to create whale/marine mammal model. Modelled tuna, common tern and dogfish as proxy predators.
9
3. Predator models (cont.) Are
Are not
Focused on evaluating stock-wide
Spatial, do not address local scale or
herring ABC harvest control rules applied annually. Developed balancing Council/ stakeholder specifications and time constraints of MSE. Based on information from the Northeast US shelf and most recent stock assessments.
seasonal dynamics. New or full stock assessments. Accounting for any impacts on predators other than changes due to herring control rules. Intended to predict actual predator population dynamics.
10
Predator-prey relationships: Northeast US Herring vs. Antarctic krill
11
Predator relationships summary Predator and overlap
Modeled herring relationship
Western Atlantic bluefin tuna Forage throughout North Atlantic, seasonally in GOM Common terns Forage seasonally near island breeding colonies in GOM Spiny dogfish Forage through same range as herring most of the year Marine mammals
Herring population average weight
affects bluefin tuna growth
Herring total biomass affects common
tern reproductive success (productivity)
Herring total abundance affects dogfish
survival
Food web model simulations 12
Predator results summary TUNA MODEL
TERN MODEL
Growth of herring had the largest effect on tuna condition.
Some CR shapes reduced tern production.
DOGFISH MODEL No major variation in projected biomass (survival) of dogfish between CRs.
13
Economic model Stakeholders identified 3 economic metrics (maximize yield, maximize profit, catch
stability).Yield from herring model converted to gross and net revenues. Ideally the economic model would convert effects of CRs on 4 user groups to dollars (users of landed herring, herring harvesters, herring as forage, indirect users of herring as forage (bird and whale watching)). Low IAV, Stable
Low IAV, Streaky
14
Example Output Metrics from MSE workshop Probability that SSB < SSBMSY & 0.5 SSBMSY (Probability of overfished) Probability that F > FMSY (Probability of overfishing) Yield and yield relative to MSY Interannual yield variation Probability that Atlantic herring fishery closes Frequency that dogfish (groundfish) are not overfished Frequency that tern production >=1 (population replaces itself) Frequency that tuna weight > average Stability = the degree to which net revenue was “stable” or “streaky”
15
Initial MSE Results:Yield vs Stability
16
Initial MSE Results: Frequency tuna in good condition
17
Identifying a Reasonable Range of Alternatives Eliminate the CR types with poor performance. Restrict range based on preferred performance – what metrics do you
value most? Workshops started this process but not fully complete. To get the ball rolling – the PDT developed some examples based on metrics and tradeoffs discussed most at workshops. Can compare tradeoffs and uncertainty to inform alternatives.
18
Example CR Shapes Upper Biomass Parameter
Strawman A Max F parameter Strawman B
Lower Biomass Parameter “fishery cutoff”
19
Recent AP and Cmte work (April): AP and Cmte encouraged to identify preferred performance of
“primary” metrics and MSE analysis could be used to identify CR shapes that fit that criteria. PDT identified candidate “primary” metrics and candidate performance values. CR should be based on X% of MSY CR should be based on X% variation in yield CR should be based on X% frequency that ABC=0 (no fishery) CR should be based on X% probability that herring is overfished CR should be based on not allowing biomass to fall below % of unfished SSB
20
Recent AP and Cmte work (cont.): Recommended three specific CR shapes: (Strawman A, B, and one that identifies CR parameters upfront). Task PDT to identify CR alternatives that meet specific criteria: - 100% MSY, acceptable range >85%-100% -
Deirdre Boelke, NEFMC Herring PDT Chair Scientific and Statistical Committee Meeting May 25, 2017 1
Outline of Presentation 1. Background on development of Acceptable Biological Catch (ABC) 2. 3. 4. 5.
control rules in the Herring Fishery Management Plan (FMP). Review MSE process used for Herring Amendment 8. Summary of MSE models developed and initial results. Summary of external peer review. Recent Council action and next steps.
2
1. ABC control rules in the Herring FMP Council has been discussing development of a long-term CR for the
Herring FMP for over five years. SSC has provided input several times over the years. Interim CR: ABC set for 3 years equal to the catch that is projected to produce a ≤50% probability of exceeding FMSY in year 3. 2015 - Council initiated Amendment 8 to develop and implement an ABC control rule that manages herring within an ecosystem context.
EBFM/Herring PDT preliminary work with SSC input.
Council decides to use MSE to develop the ABC control rule. 3
2. Management Strategy Evaluation (MSE) Definition: A decision-making process for managers and stakeholders to
collaboratively develop alternatives with more input and analysis upfront. Overall MSE Process (Document 4.2) Council hosted 2 public workshops to get input on objectives and specific metrics to measure ABC CR performance (May 2016), as well as tradeoffs of specific ABC CR alternatives (Dec 2016). In June – Council approved MSE approach with objectives and metrics identified by stakeholders. In Jan 2017– Council removed handful of CR approaches based on poor performance and input from second stakeholder workshop. 4
Council Action
Management Strategy Evaluation Process
Council Action
Council Action
Council Action
5
Stakeholder workshops A public approach was important, consistent with Council process and
improve buy-in potential. Developed with steering committee, Council oversight. Used external facilitators; lead facilitator well-versed in MSE, less familiar with fishery/stakeholders. Used multiple approaches to solicit input from attendees (large/small group discussions, worksheets, note cards). Drew 65-70 diverse, engaged stakeholders with varying technical expertise; 49% of 2nd workshop attendees had not gone to 1st workshop. Workshop summaries provided in SSC materials (Doc.#4.4 and 4.6). 6
3. MSE Technical Methods Overview – Doc.#4.3 Dr. Sarah Gaichas
Dr. Jonathan Deroba Herring N, B, Wt
Uncertainties from Workshop #1 Herring recruitment (high or low?) Herring natural mortality (high or low?) Herring growth (good or poor?) Herring assessment error/bias (yes or no?)
Dr. Min-Yang Lee
7
3. Herring Model Parameters based on assessment
results (recruitment, M, growth) Evaluated ABC control rules for each Operating Model (OM) Tested handful of CR types: biomass based (annual, 3yr, and 5yr), constant catch, etc. About 44,000 alternatives total!
Production Hi Lo x x x x
Growth Good Poor x x x x
x
x x
x x x
x x
Assessment bias On Off x x x x x x x x
8
3. Predator Models Stakeholders most interested in
predator modelling for: tuna, whales, groundfish, and sea birds. Data insufficient to create whale/marine mammal model. Modelled tuna, common tern and dogfish as proxy predators.
9
3. Predator models (cont.) Are
Are not
Focused on evaluating stock-wide
Spatial, do not address local scale or
herring ABC harvest control rules applied annually. Developed balancing Council/ stakeholder specifications and time constraints of MSE. Based on information from the Northeast US shelf and most recent stock assessments.
seasonal dynamics. New or full stock assessments. Accounting for any impacts on predators other than changes due to herring control rules. Intended to predict actual predator population dynamics.
10
Predator-prey relationships: Northeast US Herring vs. Antarctic krill
11
Predator relationships summary Predator and overlap
Modeled herring relationship
Western Atlantic bluefin tuna Forage throughout North Atlantic, seasonally in GOM Common terns Forage seasonally near island breeding colonies in GOM Spiny dogfish Forage through same range as herring most of the year Marine mammals
Herring population average weight
affects bluefin tuna growth
Herring total biomass affects common
tern reproductive success (productivity)
Herring total abundance affects dogfish
survival
Food web model simulations 12
Predator results summary TUNA MODEL
TERN MODEL
Growth of herring had the largest effect on tuna condition.
Some CR shapes reduced tern production.
DOGFISH MODEL No major variation in projected biomass (survival) of dogfish between CRs.
13
Economic model Stakeholders identified 3 economic metrics (maximize yield, maximize profit, catch
stability).Yield from herring model converted to gross and net revenues. Ideally the economic model would convert effects of CRs on 4 user groups to dollars (users of landed herring, herring harvesters, herring as forage, indirect users of herring as forage (bird and whale watching)). Low IAV, Stable
Low IAV, Streaky
14
Example Output Metrics from MSE workshop Probability that SSB < SSBMSY & 0.5 SSBMSY (Probability of overfished) Probability that F > FMSY (Probability of overfishing) Yield and yield relative to MSY Interannual yield variation Probability that Atlantic herring fishery closes Frequency that dogfish (groundfish) are not overfished Frequency that tern production >=1 (population replaces itself) Frequency that tuna weight > average Stability = the degree to which net revenue was “stable” or “streaky”
15
Initial MSE Results:Yield vs Stability
16
Initial MSE Results: Frequency tuna in good condition
17
Identifying a Reasonable Range of Alternatives Eliminate the CR types with poor performance. Restrict range based on preferred performance – what metrics do you
value most? Workshops started this process but not fully complete. To get the ball rolling – the PDT developed some examples based on metrics and tradeoffs discussed most at workshops. Can compare tradeoffs and uncertainty to inform alternatives.
18
Example CR Shapes Upper Biomass Parameter
Strawman A Max F parameter Strawman B
Lower Biomass Parameter “fishery cutoff”
19
Recent AP and Cmte work (April): AP and Cmte encouraged to identify preferred performance of
“primary” metrics and MSE analysis could be used to identify CR shapes that fit that criteria. PDT identified candidate “primary” metrics and candidate performance values. CR should be based on X% of MSY CR should be based on X% variation in yield CR should be based on X% frequency that ABC=0 (no fishery) CR should be based on X% probability that herring is overfished CR should be based on not allowing biomass to fall below % of unfished SSB
20
Recent AP and Cmte work (cont.): Recommended three specific CR shapes: (Strawman A, B, and one that identifies CR parameters upfront). Task PDT to identify CR alternatives that meet specific criteria: - 100% MSY, acceptable range >85%-100% -
