megan stachura and nathan mantua university of washington school of aquatic and fishery sciences...
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Patterns in biological productivity in the North Pacific Ocean
Megan Stachura and Nathan MantuaUniversity of Washington
School of Aquatic and Fishery Sciences
September 8, 2012
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Synchrony in the Northeast PacificSynchrony of
biological time series
Ecosystem regime shifts
Correspond with climatic shifts
Leading principal component scores from principal component analysis of 69 North Pacific biological time series (Hare
and Mantua, 2000)
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Common patterns of variabilityPositive correlation
within groundfish taxonomic group and region, negative between (Hollowed et al. 1987, Mueter et al. 2007)
Grouping based on life history (Spencer and Collie, 1997)
BSAI groundfish stock-recruit residuals and cluster dendogram
based on these (Mueter et al., 2007)
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HypothesisOrganisms with related life history and habitat
characteristics will show similar exposure to environmental variables and exhibit common patterns of recruitment/abundance
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DataRecruitment
Number of fish surviving to enter the fishery or some specified age
We have survey and fishing data of adult stages but don’t know a lot about what happens during early life
Age of recruitment varies by species GOA walleye pollock: age-2 GOA Pacific cod: age-0 GOA arrowtooth flounder: age-3
Lag time series to line up by age-0Estimated in stock assessment models
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(Cooper, 2006)
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DataMarine fish
recruitment13 Eastern Bering
Sea and Aleutian Islands (BSAI) stocks
15 Gulf of Alaska (GOA) stocks
31 California Current (CC) stocks
Russian saffron cod
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DataSalmon
Ruggerone et al., 2010Wild pink, chum, and sockeye salmon12 regions in North America and AsiaCatch + EscapementLagged to year of ocean entry
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Map of regional salmon stock groups (Ruggerone et al., 2010)
Western AK
Russia Mainland & Islands
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DataCrabs
Recruitment estimates from stock assessmentsLagged to age-06 Eastern Bering Sea stocks1 Gulf of Alaska stock
OtherEastern Bering Sea zooplankton biomassEastern Bering Sea jellyfish biomass
106 biological time series total
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Stock-recruitment relationshipAccount for influences of
spawning stock biomass on recruitmentMay be impacted by
fishingBeverton Holt
Ricker
Constant
Residuals from best model
GOA arrowtooth flounder recruitment and spawning stock biomass time series
andBeverton-Holt model fit
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Multivariate analysisReduce these 106 time series into a smaller
number of uncorrelated time seriesThree methods
Principal component analysisNon-metric multidimensional scalingDynamic factor analysis
Similar results for all- only presenting PCA results
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PC 1: 12.5% of variance
PC 2: 9.6% of variance
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NW Pacific salmon
GOA marine fish
EBS flatfish
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Correlation with physical variablesPC 1 positively correlated with:
Arctic Oscillation Index (r=0.40)Multivariate El Nino-Southern Oscillation
Summer Index (r=0.42)Pacific Decadal Oscillation Summer Index
(r=0.50) PC 2 positively correlated with
Pacific Decadal Oscillation Summer Index (r=0.63)
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Winter SST
Summer SST
Winter SLP
Summer SLP
PC 1 Correlation with SST and SLP
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Winter SST
Summer SST
Winter SLP
Summer SLP
PC 2 Correlation with SST and SLP
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ConclusionsCommon patterns are exhibited by marine species
throughout the North PacificSimilar to those identified by Hare and Mantua (2000)
Loadings on these patterns seem related to region and life history
Large scale physical forces coordinate environment/ecosystem changes important to many stocks using different habitats across large distances
We hypothesize this is a consequence of multiple mechanisms operating in different places at the same timeA mixed-bag of sometimes similar and sometimes different
mechanisms at play for different stocks within and between large marine ecosystems
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Extra slides
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Gulf of Alaska
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California Current
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Eastern Bering Sea and Aleutian Islands