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

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)

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)

HypothesisOrganisms with related life history and habitat

characteristics will show similar exposure to environmental variables and exhibit common patterns of recruitment/abundance

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

(Cooper, 2006)

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

DataSalmon

Ruggerone et al., 2010Wild pink, chum, and sockeye salmon12 regions in North America and AsiaCatch + EscapementLagged to year of ocean entry

Map of regional salmon stock groups (Ruggerone et al., 2010)

Western AK

Russia Mainland & Islands

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

Stock-recruitment relationshipAccount for influences of

spawning stock biomass on recruitmentMay be impacted by

fishingBeverton Holt

Ricker

Constant

Residuals from best modelGOA arrowtooth flounder recruitment

and spawning stock biomass time series and

Beverton-Holt model fit

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

PC 1: 12.5% of variance

PC 2: 9.6% of variance

NW Pacific salmon

GOA marine fish

EBS flatfish

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)

Winter SST

Summer SST

Winter SLP

Summer SLP

PC 1 Correlation with SST and SLP

Winter SST

Summer SST

Winter SLP

Summer SLP

PC 2 Correlation with SST and SLP

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

Extra slides

Gulf of Alaska

California Current

Eastern Bering Sea and Aleutian Islands