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Fisheries and Oceans Canada. Pêches et Océans Canada. Canada. Ecosystem Variability in NAFO Waters Adjacent to the Newfoundland and Labrador Shelf during the Decade of 2000-2009. By E. Colbourne, P. Pepin and G. Maillet Fisheries and Oceans Canada. OUTLINE. Introduction

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slide1

Fisheries and Oceans

Canada

Pêches et Océans

Canada

Canada

Ecosystem Variability in NAFO Waters Adjacent to the Newfoundland and Labrador Shelf during the Decade of 2000-2009

By

E. Colbourne, P. Pepin and G. Maillet

Fisheries and Oceans Canada

slide2

OUTLINE

  • Introduction
  • Decadal Scale Variability in Meteorological and Oceanographic Climate (1950-2009)
  • Intra-Decadal Variability in the Physical, Biological and Chemical Environment (2000-2009)
  • Summary
slide3

The Study Area/ Observations

  • ICNAF 1949-1979
  • NAFO 1979-Present
  • Focused on Physical Observations
  • Canadian Atlantic Zone Monitoring Program (AZMP)
  • 1999-Present
slide4

Groundfish surveys - Selected stations

Atlantic Zone Monitoring Program (AZMP)- a joint initiative of 5 DFO labs- Physical, Biological and Chemical Observations

Sections – Spring Summer and fall

Fixed stations – Every two weeks

Remote Sensing -

Bi-weekly composites

Continuous Plankton Recorder (CPR)

slide5

OUTLINE

  • Introduction
  • Decadal Scale Variability in Meteorological and Oceanographic Climate (1950-2009)
  • Intra-Decadal Variability in the Physical, Biological and Chemical Environment (2000-2009)
  • Summary
slide6

NWA AIR TEMPERATURES

NAO

AIR

TEMP

  • Highest on record during 2000s
  • Highest variability during the 1970-1990s

SST

slide7

NL SHELF SEA ICE EXTENT

Sea-Ice

Lowest Since 1960s

  • Sea-Ice Lowest Since 1960s
  • Highly variable during 1970-1990s
slide8

NL SHELF TEMPERATURES

STATION 27

  • Record High Temp in 2000s
  • Salinity Record Low in 1990s

Record High in 2000s

slide9

STANDARD SECTIONS CIL

Low Variability in 2000s, 1950S

slide10

LABRADOR CURRENT GEOSTROPHIC TRANSPORT

  • Geostrophic Transport of Labrador Current above average in 2000s.
slide11

1970s

1980s

1990s

1960s

2000s

1950s

COMPOSITE CLIMATE INDEX

  • Standardized Anomalies of 27 Meteorological, Sea Ice, Temperature, Salinity Time Series

(Xi-Xmean)/σ (1971-2000)

slide13

RECAP DECADAL TRENDS

  • The 1970s, 1980s and 1990s experienced extreme intra-decadal variability with cold/warm, fresh/saline conditions.
  • The 1990s experienced the most extreme variability and was the coldest and freshest in the 6 decades of observations.
  • The decade of 2000s was the warmest decade in over 60 years of ocean measurements but very similar to 1960s.
  • The 2000s showed among the least variability reflecting a continuing warming trend throughout the region.
slide14

OUTLINE

  • Introduction
  • Decadal Scale Variability in Meteorological and Oceanographic Climate (1950-2009)
  • Intra-Decadal Variability in the Physical, Biological and Chemical Environment (2000-2009)
  • Summary
slide15

STATION 27 – Zooplankton Biomass

  • Biomass highly variable, record low in 2008 to near-record high in 2009.
  • High levels of abundance of large and small copepods in 2009Microcalanus sp., Oithona sp., Pseudocalanus sp. and Oncaea sp.
  • Warm water species Acartia sp., Centropages sp., and Temora longicornis were at low levels of abundance.
slide16

Standard Sections (NL) Zooplankton Abundance

  • Both small and large copepods demonstrated similar patterns of variation.
  • Lowest abundances at the start of the time-series, with a gradual increase to a peak between 2004-2006.
  • After 2006, copepods were about average for the AZMP period.
  • Other groups carnivores, omnivores and non-copepod secondary producers (meroplankton) exhibited similar patterns.
slide17

SALINITY

Environmental Forcing and Biogeochemical State

  • During the 12 years of AZMP observations, the physical environment was characterized by warm saline conditions.
  • Low intra-decadal variability, reflecting the warming trend throughout the region.
slide18

PHYTOPLANKTON

NITRATE (0-50 M)

Environmental Forcing and Biogeochemical State

Standard Sections

Station 27

  • Most correlations were weak and not consistent across the region.
  • Phytoplankton at S27 demonstrated consistent negative correlations with most climate indices.
  • In general, nutrient and phytoplankton had a significantly greater proportion of negative correlations than expected by chance (85/130).

Correlation coefficients of environmental variables with biochemical indices

slide19

Environmental Forcing and Biogeochemical State

27 Zooplankton Taxa (Standard Sections)

  • No single environmental variable demonstrated a consistent pattern of correlations with the 27 zooplankton taxa.
  • And no single zooplankton taxa demonstrated a consistent pattern of correlation with any environmental index.
  • 59% of the 3080 correlations were positive, which is a significant departure from random.

Box-whisker plots of the distribution of Pearson’s correlation coefficient of each index of physical environmental variables (y-axis) with all indices of zooplankton abundance (27 taxonomic groups) from each of the four oceanographic sections.

slide20

NUTRIENTS

PHYTOPLANKTON

ZOOPLANKTON

AZMP BIOGEOCHEMICAL COMPOSITE (2000-2009)

(Xi-Xmean)/σ (Referenced 1999-2010)

slide21

Environmental Forcing and Biogeochemical State

  • The decade of 2000s was characterized by warm climate conditions.
  • The biochemical state exhibited a high degree of inter-annual variability during this period.
slide22

BIOCHEMICAL SUMMARY 2000s

  • Nutrient were weakly correlated (-ive) with the physical environment.
  • Phytoplankton at Station 27 was significantly correlated (-ive) with the physical environment.
  • Some zooplankton taxa varied by 2-orders of magnitude with a general tendency towards +ive correlations with the physical environment.
  • No single environmental index provides strong predictive capacity of the biochemical state.
  • Environmental conditions have not varied sufficiently during AZMP to allow an accurate assessment of the full response of ecosystem productivity to changes in physical drivers of the system.
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