Ichthyoplankton abundance and dversity as indicators of climate and ecosystem change in the Pacific

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Ichthyoplankton abundance and dversity as
indicators of climate and ecosystem change in the
Pacific Northwest

• Ric Brodeur, Bill Peterson, Bob Emmett
• FE Division, NOAA/NMFS/NWFSC,
• Newport, Oregon
• Heather Soulen, Toby Auth, Maria Parnel
• Oregon State University, HMSC
• Newport, Oregon

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Why study Ichthyoplankton?

• Survival of larvae may directly influence future
  abundances of adult fish stocks
• The distribution and abundance of fish eggs and
  larvae can provide clues to the spawning
  locations, success, and environmental
  requirements of important fish species
• Ichthyoplankton can represent a vital link in
  the food chain between smaller planktonic and
  larger piscivorous organisms

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Objectives

• Process larval fish collections from 1996 to
  2005 from collections off the Columbia River and
  the Newport Hydroline

• Compare larval communities with historical
  sampling in the 1960s - 1980s off Newport

• Examine long-term trends in ichthyoplankton
  abundance and relate these to basin-wid and
  regional environmental conditions

• Relate trends seen in ichthyoplankton to
  subsequent recruitment of adults

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FATE IchthyoplanktonColumbia River Plume Studies

• Sampling
• Two stations off Columbia River (11 and 16 miles
  from shore)
• Plankton samples collected approximately every 10
  days April-July
• 1-M net (round net with 1-M diameter)
• Oblique tow down to 60 m
• Daytime sampling
• CTD cast at each station

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FATE IchthyoplanktonColumbia River Plume Studies
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FATE IchthyoplanktonColumbia River Plume Studies
EGGS
LARVAE
Anchovy eggs present all years but larvae more
prevalent in warm years
(Parnel et al. Submitted)
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FATE IchthyoplanktonRelation to Adult Abundance
(Brodeur et al. 2006)
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FATE Ichthyoplankton
Northern Anchovy Egg Densities by Cruise
No Eggs
(Parnel et al. Submitted)
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EGGS
Warm Water/ Spring
Intermediate Water/ Mixed
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FATE IchthyoplanktonNewport Line Long-term
Sampling

• NH-Line Zooplankton
• Time Series

• Bi-weekly Sampling
• 1972 1973 (Miller, Peterson)
• 1977 1978 (Richardson, Laroche)
• 1983 (Miller, Brodeur)
• 1996 2005 (Peterson et al.)

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FATE IchthyoplanktonNewport Line Studies
N 10 14 22 32
28 22 31 29 19
20 21 13
Most larvae found during winter months
Means and standard errors
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FATE Ichthyoplankton
Copepod Biomass (Jan.-May) 7.5
12.6
11.0
299.9
120.3
97.9
(Brodeur et al. Submitted)
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FATE Ichthyoplankton
0.64
0.61
0.29
Year
(Brodeur et al. Submitted)
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FATE IchthyoplanktonNewport Line Studies
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Information Remaining ()
100
75
50
25
0
Cluster Analysis of Monthly Catches
June-December
January-May
84 Months by 24 Species

• Different larval fish communities found during
  winter/spring than the rest of the year

• Exceptions were June and July of 2004 and June
  of 2005

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Winter
Feb.- March
April - May
Summer
Fall
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FATE Ichthyoplankton
Dominant Taxa by Month All Years Combined
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FATE Ichthyoplankton
MRPP Test for Differences in Community Structure
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Dominant Ichthyoplankton
Dominant Taxa by Month
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Dominant IchthyoplanktonTop 10 Taxa (all years,
months combined)
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Dominant IchthyoplanktonTop 10 Taxa (all years,
months combined)
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Dominant IchthyoplanktonTop 10 Taxa (all years,
months combined)
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Dominant IchthyoplanktonTop 10 Taxa (all years,
months combined)
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FATE Ichthyoplankton
Analysis Methodology Application of Generalized
Additive Models
Associations between Larval Species Abundance
(Overall and Dominant Species) and Physical
Variables (Basin-scale and Regionalscale)
Non-parametric, additive regression technique
that models a response variable onto one or more
independent variables Assumes no a priori
specification of the functional form between the
response variable and independent variables
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FATE Ichthyoplankton
General Additive Models (GAM) Environmental
variables were taken from online sources
Correlations (Spearmans Rho) of environmental
variables were conducted to eliminate highly
correlated variables from inclusion in GAM
GAMs were run using both abundance (Gaussian) and
pres./abs. (binomial) for 5 most dominant species
and total fish density and diversity stepwise
model selection dropping variables to minimize
GCV score used various lag periods in the
models (0-3 months)
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FATE IchthyoplanktonColumbia River Plume Studies
Time Series of Environmental Conditions (adapted
from Hooff and Peterson, 2006)
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FATE Ichthyoplankton
GAM Results Abundance
Lag periods 1 month, 2 months, 3
months
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FATE Ichthyoplankton
P Value 0.39 0.12
0.03 Var. 22.3
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FATE Ichthyoplankton
P Value 0.03 , lt0.001 0.84,
0.44 0.07 Var. 37.3
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Conclusions

• Overall mean density is highest in January and
  February
• Overall mean diversity is highest in March and
  April
• Time series plots indicate that density and
  diversity is generally higher during winter
  (Nov-Apr) than in summer (May-Oct)
• Osmerids were the most abundance species/species
  groupings, particularly in the early and late
  1970s
• Dramatic changes in the top 10 species by regime,
  specifically in Osmerids, English sole (cold) and
  anchovy, rockfish (warm)
• Abundance of species, when present, seem to be
  affected when there are lags in large scale
  environmental indices (i.e. PDO, MEI, NOI)
• Abundance of species seems to be affected when
  there is at least two large scale and one small
  scale environmental indices

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Applications

• Shown that ichthyoplankton diversity and
• abundance are affected by climate change

• Relate changes in abundance of early life
  stages to adult spawners and subsequent year
  class strength

• Develop indices of coastal conditions related
• to the survival of early life stages

• Provide managers early indications of strong
• year classes that may recruit into fisheries

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Northern Shift in the Location of Spawning and
Recruitment of Pacific Hake (Merluccius
productus) in the Northern California Current
1   Oregon State University, Hatfield Marine
Science Center, Newport, OR 2   NOAA Fisheries,
Hatfield Marine Science Center, Newport, OR 3
NOAA Fisheries, SWFSC Santa Cruz, Santa Cruz,
CA 4 Pacific Whiting Conservation Cooperative,
Seattle, WA
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Acknowledgements
NOAA Northwest Fisheries Science Center NOAA FATE
Program US GLOBEC Program Oregon State
University Leah Feinberg, Tracy Shaw, Julie
Kiester, Paul Bentley, Greg Krutzikowsky for
sampling Stacy Remple for database
development Jim Colbert for assistance with
environmental data Lorenzo Ciannelli for
statistical advice
Fisheries And The Environment