Ecosystem Research Initiative (ERI) for the Gulf of Maine Area (GoMA)

Ecosystem Research Initiative (ERI) Ecosystem Research Initiative (ERI) for the Gulf of Maine Area (GoMA)for the Gulf of Maine Area (GoMA)

Activity Outputs Outcomes

Analyses and Interpretations of Observed Oceanographic and Ecosystem Variability

Syntheses of observations

Evaluation of climate change and variability impacts on GoMA ecosystems from plankton to fish.

Contributions to climate change scenarios and climate indicators

Population and NPZ models to investigate climate sensitivity

Ecosystem responses to climate variability and change (key commercial species: scallop)

Databases on recruitment and environmental data.Particle tracking simulationsEffect of environmental variability on recruitment

Downscaling climate change scenarios

Update of climate change reports

Climate change scenarios (with CCSI)

Climate change and ecosystem indicators

Theme I – Influence of climate change on ecosystems

1. Analyses and Interpretations of Observed Oceanographic and Ecosystem Variability

1. Physical oceanographic variability in the GoM-GB region and linkages with large-scale variability (Brickman, Petrie)

2. Nutrient inventories and supply to the GoMA (Harrison, Yeats, Greenan) 3. Zooplankton and lower-trophic–level variability in the GoMA (Johnson, Head) 4. Ecosystem-level evaluation and analysis of oceanographic and fish distributions in

extreme states (e.g. different NAO regimes) in the GoMA (Frank, Shackell, Petrie)5. Model simulations of oceanographic and lower-trophic-level variability in the

GoMA (Brickman)

2. Ecosystem Responses to Climate Variability and Change6. Key commercial species - scallops (Dibacco, Johnson)

3. Downscaling climate change scenarios (linkage with CCSI)7. Update of the Wright et al. and Frank et al. reports (Loder, Frank)8. Climate change indicators for the GoMA (all)9. Oceanographic scenarios of changes for NW Atlantic focussed on the GoM-GB

region (all)

Analyses and Interpretations of Observed Oceanographic and Ecosystem Variability

1. Physical oceanographic variability in the GoM-GB region and linkages with large-scale variability (Brickman, Petrie)

• Investigate long term temperature time-series from single stations in the GoMA (e.g. Prince-5, St. Andrews),

• Quantify inflow to eastern Gulf of Maine at Cape Sable,• Develop climatological indicators,• Establish relation of GoMA hydrographic variability to large scale forcing.

2. Nutrient inventories and supply to the GoMA (Harrison, Yeats, Greenan)

• Improve estimates of the advective components of the nutrient fluxes and their fate in the Gulf, particularly the relative proportions of Warm Slope and cold Labrador Slope source waters,

• Examine the stratification pattern in the GOM and its relationship to wind forcing, focusing particularly on winter nutrient inventories and mixing in the late summer/fall and relationship to phytoplankton.

NOTE: Link with IGS Hypoxia project.

Analyses and Interpretations of Observed Oceanographic and Ecosystem Variability (cont’d)

Central Scotian Shelf

y = -0.1569x + 321.7

R2 = 0.5312

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Analyses and Interpretations of Observed Oceanographic and Ecosystem Variability (cont’d)

3. Zooplankton and lower-trophic–level variability in the GoMA (Johnson, Head)

• Seasonal and spatial variability in zooplankton and phytoplankton communities and abundance of dominant species in GoMA and WSS - identify interannual variability patterns in the time series,

• Correlate physical and biological properties (e.g. salinity, stratification, abundance indices of primary producers, zooplankton predators) on seasonal and spatial scales,

• Relate changes in zooplankton community structure or abundance, and/or in primary producer and predator indices to environmental extreme states,

• Develop simple population models for the dominant zooplankton species, including differences in growth, development, and egg production rates as a function of temperature and food, as well as life history traits.

Calanus finmarchicus

Centropages typicus

Oithona spp.

Pseudocalanus spp.

Analyses and Interpretations of Observed Oceanographic and Ecosystem Variability (cont’d)4. Ecosystem-level evaluation and analysis of oceanographic and

fish distributions in extreme states (e.g. different NAO regimes) in the GoMA (Frank, Shackell, Petrie)

• Update and expand existing abiotic, biotic, human activities databases (SS and GoMA),

• Complete WSS and BoF State of the Ecosystem Overviews, including analysis interpreting the differences in trophic changes/cascades,

• Analyses expanded in GoMA, including spatial statistical analyses using GIS software and newly developed temporal algorithms for regime shift detection.

Western Scotian Shelf Body size, condition, Growth rate

Lower trophic biomass, pelagics, Med benthos

5. Model simulations of oceanographic and lower-trophic-level variability in the GoMA (Brickman)

• Nemo ocean model enhanced for shelf processes and evaluated for the Scotian Shelf and adjacent regions.

• Nemo coupled with plankton dynamics (N-P-Z) models to hindcast and interpret physical, nutrient and plankton variability in the Gulf of St. Lawrence and Scotian Shelf.

• Enhanced Mercator model, at a demonstration level, for similar applications in GoMA. • The model and associated plankton models will be used for preliminary simulations of

interannual hydrographic, circulation and related biological variability in the GoMA, drawing on and complementing the results of the observational analyses.

NOTE: Link with CCSI modelling project

Analyses and Interpretations of Observed Oceanographic and Ecosystem Variability (cont’d)

APDMAPDMadaptive biological modeladaptive biological model

(BIO, V(BIO, Vézina/Casault)ézina/Casault)

GSS4 model(GFC-BIO)

OPTIMALOPTIMALBIOLOGICAL MODELBIOLOGICAL MODEL (DFO, Zonal) (DFO, Zonal)

IML/ISMER Biological modelIML/ISMER Biological model

Ecosystem Responses to Climate Variability and Change

6. Key commercial species – scallops (DiBacco, Johnson)• Retrospective analysis of biological and environmental data and particle tracking

simulations to document how large-scale physical forcing affects spawning periodicity and larval dispersal of scallops in the GoMA.

• The goal is to gain a mechanistic understanding of how variability in egg production, recruitment and larval transport influence scallop recruitment.

Key Collaborators: Ian Jonsen, Steve Smith (PED); Wendy Gentleman (Dalhousie)

GSC

NEP

Connectivity between GSC and NEP, May

Downscaling Climate Change Scenarios

7. Revision of the Wright et al. and Frank et al. reports (Loder, Frank)

8. Climate change indicators for the GoMA (all)

Climate Change Indicators 1. Atmospheric

- Temperature - Winds - Storm tracks/intensity - Precipitation/evaporation - Cloud cover

2. Ocean-Physics - Sea-level - Temperature - Winter (convective) mixing - Labrador/slope-water transport - Mixed-layer - Stratification - Freshwater

3. Ocean-Chemistry - CO2 content (pH) - Oxygen content (deep waters) - Nutrient inventories (surface and deep

waters) 4. Ocean-Biology

- Trends in bloom dynamics and overall primary productivity (through mixing-to-nutrients/light-to-productivity linkages)

- Trends in zooplankton reproduction (linked to bloom dynamics)

- Geographic distribution (plankton to fish) – looking for boundary shifts

- Community composition (plankton to fish) – looking for warm/cold species shifts

Downscaling Climate Change Scenarios

9. Oceanographic/ecosystem scenarios of change for NW Atlantic focussed on the GoMA (all)

A climate change scenario is not a prediction of future climate!A climate change scenario is:

• a coherent, internally consistent and plausible description of a a coherent, internally consistent and plausible description of a possible future state of the world …possible future state of the world …

[Environment Canada, http://www.ccsn.ca/index-e.html].[Environment Canada, http://www.ccsn.ca/index-e.html].

ERI-GoMAClimate

ERI-GoMABenthic Patterns

ERI-GoMAEco-Models

Table 1. Attributes and strategies are generic and pertain to all managed activities. Tactics may be specific to an activity; those shown are applicable to harvest fisheries. ATTRIBUTES OBJECTIVES STRATEGIES with associated pressures MANAGED ACTIVITIES TACTICS

Groundfish Fishery

Herring Fishery

Salmon Aquaculture

etc.

Productivity

1. Keep fishing mortality moderate Promote positive biomass change when biomass is low - Manage discards for all harvested species 2. Allow sufficient escapement from exploitation of spawning

biomass

3. Limit disturbing activity in spawning areas/seasons 4. Control alteration of nutrient concentrations affecting primary

production at the base of the food chain by algae

Biodiversity

5. Control incidental mortality for all non-harvested species 6. Minimize unintended transmission of invasive species 7. Distribute population component mortality in relation to

component biomass

Habitat

8. Manage area disturbed of bottom habitat 9. Limit introduction of pollutants in habitat 10. Minimize deaths from structures/equipment/lost gear

yield biomass recruitment size/age structure spatial extent spatial occupancy population richness predator forage community assemblage size spectrum trophic structure ‘special species’ habitat type spectrum ‘special places’ breeding behavior

11. Control noise and light disturbance

catch control effort control gear specification, size-based release area/season closure ballast water control

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EBM FrameworkEBM Framework