PICES Scientific Report No. 34 2008
Forecasting Climate Impacts on Future Production of Commercially Exploited Fish and Shellfish
Edited by
Anne B. Hollowed, Richard J. Beamish, Thomas A. Okey and Michael J. Schirripa
August 2008 Secretariat / Publisher
North Pacific Marine Science Organization (PICES) P.O. Box 6000, Sidney, B.C., Canada. V8L 4B2
E-mail: [email protected] Home Page: http://www.pices.int
iii
Contents
Foreword...............................................................................................................................................................v Acknowledgements..............................................................................................................................................vi PHASE 1 Summary of the PICES/NPRB Workshop on Forecasting Climate Impacts on Future Production of Commercially Exploited Fish and Shellfish (July 19–20, 2007, Seattle, U.S.A.)
Background ..................................................................................................................................................3 Links to Other Programs ..............................................................................................................................4 Workshop Format.........................................................................................................................................4
Session I. Status of climate change scenarios in the PICES region .................................................4 Session II. What are the expected impacts of climate change on regional oceanography and
what are some scenarios for these drivers for the next 10 years?....................................5 Session III. Recruitment forecasting ..................................................................................................5 Session IV. What models are out there? How is climate linked to the model? .................................8 Session V. Assumptions regarding future fishing scenarios and enhancement activities .................9 Session VI. Where do we go from here? ..........................................................................................11
References ..................................................................................................................................................12 Appendix 1.1 List of Participants..............................................................................................................13
PHASE 2 Summary of the PICES/NPRB Workshop on Forecasting Climate Impacts on Future Production of Commercially Exploited Fish and Shellfish (October 30, 2007, Victoria, Canada)
Background ................................................................................................................................................19 Workshop Agenda......................................................................................................................................19 Forecast Feasibility ....................................................................................................................................19 Format of Information................................................................................................................................19 Modeling Approaches ................................................................................................................................20
Coupled bio-physical models.............................................................................................................20 Stock assessment projection models..................................................................................................20 Comparative approaches....................................................................................................................20
Similarities in Data Requests .....................................................................................................................20 Opportunities for Coordination with Other PICES Groups and International Efforts.........................................21
BACKGROUND REPORTS PREPARED FOR THE PHASE 2 WORKSHOP
Northern California Current (U.S.) groundfish production by Melissa Haltuch..............................................................................................................................33
Changes in sablefish (Anoplopoma fimbria) recruitment in relation to oceanographic conditions by Michael J. Schirripa......................................................................................................................35
Northern California Current (British Columbia) Pacific cod (Gadus macrocephalus) production by Caihong Fu and Richard Beamish ..................................................................................................37
Northern California Current (British Columbia) sablefish (Anoplopoma fimbria) production by Richard Beamish ...........................................................................................................................41
Northern California Current (British Columbia) pink (Oncorhynchus gorbuscha) and chum (O. keta) salmon production by Richard Beamish ............................................................................................45
Northern California Current (British Columbia) ocean shrimp (Pandalus jordani) production by Caihong Fu ...................................................................................................................................51
Alaska salmon production by Anne Hollowed ...............................................................................................................................51
iv
U.S. walleye pollock (Theragra chalcogramma) production in the eastern Bering Sea and Gulf of Alaska by Kevin Bailey and Anne Hollowed ......................................................................................................53
U.S. groundfish production in the eastern Bering Sea by Tom Wilderbuer..............................................................................................................................57
U.S. crab production in the eastern Bering Sea by Gordon H. Kruse ............................................................................................................................59
Forecasting Japanese commercially exploited species by Shin-ichi Ito, Kazuaki Tadokoro and Yasuhiro Yamanka .............................................................63
Russian fish production in the Japan/East Sea by Yury Zuenko, Vladimir Nuzhdin and Natalia Dolganova .............................................................65
Chum salmon (Oncorhynchus keta) production in Korea by Sukyung Kang, Suam Kim and Hyunju Seo......................................................................................69
Jack mackerel (Trachurus japonicus) production in Korea by Jae Bong Lee and Chang-Ik Zhang .................................................................................................73
Chub mackerel (Scomber japonicus) production in Korea by Jae Bong Lee, Sukyung Kang, Suam Kim, Chang-Ik Zhang and Jin Yeong Kim ...............................77
References ..................................................................................................................................................81 Appendix 2.1 List of Participants..............................................................................................................87
PHASE 3 Summary of the PICES Workshop on Linking Global Climate Model Output to (a) Trends in Commercial Species Productivity and (b) Changes in Broader Biological Communities in the World’s Oceans (May 18, 2008, Gijón, Spain) .............................................................................................................................93
Appendix 3.1 List of Participants..............................................................................................................97 Appendix 3.2 Workshop Agenda ............................................................................................................101
v
Foreword
The recent publication of the Intergovernmental Panel on Climate Change (IPCC) 4th Assessment Report (AR4) concludes that there is a growing body of evidence that indicates climatic conditions are likely to change due to natural and anthropogenic forcing. In response to these findings the PICES scientific community initiated a series of meetings to:
a ) discuss frameworks and methodologies for forecasting the impacts of climate change on the growth, distribution and abundance of marine life, with particular emphasis on commercial fish and shellfish;
b ) review the results of designated case studies to test methods; c ) plan for an inter-sessional meeting in early 2010 where scientists can present, discuss and publish
forecasts of climate change impacts on the world’s commercial fish and shellfish resources.
The research done within PICES on climate change and fisheries has been diverse and has included: • guidance on methods for selection of IPCC scenarios for use in projections, • guidance on techniques for downscaling IPCC scenarios to local regions, • development of coupled ecosystem models for use in evaluating climate-induced shifts in
environmental conditions, • numerous publications documenting relationships between climate forcing and marine fish and
shellfish distribution and production, and • stock assessment techniques for evaluating management strategies to mitigate the impacts of change.
A challenge facing PICES is the need to integrate this research to provide stakeholders with quantitative estimates of the potential impact of climate change on marine life in the North Pacific. This challenge calls for the establishment of interdisciplinary research teams composed of experts from around the Pacific Rim who will focus attention on the development of common and standardized frameworks for forecasting climate change impacts on marine life, with particular emphasis on commercially important fish and shellfish. The Science Plan for the new PICES scientific program, FUTURE (Forecasting and Understanding Trends, Uncertainty and Responses of North Pacific Marine Ecosystems), also highlights the need for integrated interdisciplinary research on the potential implications of climate change on marine ecosystems. PICES should act now to ensure that our research communities develop the capability to provide quantitative contributions to the next IPCC reports and guidance for management under climate change scenarios. PICES should strive to present and discuss results of the forecasting teams at an inter-sessional meeting, and papers should be published in a peer reviewed journal by 2011. The timing for the publication is critical because the future IPCC AR5 report is slated for release in 2013, and only published papers can be referenced in that report.
vi
Acknowledgements The following scientists provided written summaries for this report: Kevin Bailey, Richard Beamish, Natalia Dolganova, Caihong Fu, Melissa Halutch, Anne Hollowed, Shin-ichi Ito, Sukyung Kang, Jin Yeong Kim, Suam Kim, Gordon Kruse, Jae Bong Lee, Nathan Mantua, Vladimir Nuzhdin, Thomas Okey, James Overland, Michael Schirripa, Hyunju Seo, Kazuaki Tadokoro, Thomas Wilderbuer, Yasuhiro Yamanaka, Chang-Ik Zhang, and Yury Zuenko. The editors would like to thank all of these individuals for their efforts in preparing this report. The editors thank PICES and the North Pacific Research Board for providing the funding for the first two workshops, held in Seattle, U.S.A. and Victoria, Canada.
Phase 1
Summary of the PICES/NPRB Workshop on Forecasting Climate Impacts
on Future Production of Commercially Exploited Fish and Shellfish
July 19–20, 2007 Seattle, Washington, U.S.A.
3
Background The North Pacific Marine Science Organization (PICES) and the North Pacific Research Board (NPRB) co-sponsored a workshop on Forecasting Climate Impacts on Future Production of Commercially Exploited Fish and Shellfish on July 19–20, 2007, in Seattle, U.S.A. The workshop provided a forum for 38 participants from six countries (Appendix 1.1), including a significant number with expertise on the effects of climate on North Pacific fisheries and representatives from PICES’ Biological Oceanography, Fishery Science, and Physical Oceanography and Climate Committees (BIO, FIS, and POC) and Climate Forcing and Marine Ecosystem Response (CFAME) Task Team of the Climate Change and Carrying Capacity Program (CCCC). This workshop was a follow-up to an earlier workshop on Linking Climate to Trends in Productivity of Key Commercial Species in the Sub-arctic Pacific that was held October 13–14, 2006, at the PICES Fifteenth Annual Meeting in Yokohama, Japan. The goal of the endeavour was to develop a coordinated international effort to provide quantitative estimates of the impacts of climate change on major fish populations. Workshop
participants representing each of the PICES member countries agreed that they would be interested in participating in this effort. One outcome of the workshop was a concept for a PICES Panel on Fisheries and Climate Change (PPFCC) to continue this work. Participants discussed how these forecasting activities might differ from other national or international research programs that are focused on climate change impacts. In summary, they will complement several ongoing research activities within the PICES region and the sub-arctic as a whole, and several features of the concept make it unique. First, it will provide quantitative estimates of the impacts of climate change on fisheries in the North Pacific. Second, it will produce a coordinated interdisciplinary and multi-national effort involving the application of similar methods and forecasting approaches to compare responses of fish and shellfish species across their ranges. Third, it will focus on species of significant commercial interest and not on entire ecosystem responses. Forecasting tools will be developed to allow the inclusion of environmental impacts on fish and shellfish production, distribution and growth.
Fig. 1.1 Schematic showing linkages between U.S. national (dark blue) and international (red) research programs in the PICES region (see text for description of acronyms). National programs are expected to continue to fund research within the boxes.
4
Results of the Intergovernmental Panel on Climate Change (IPCC) 4th Assessment Report (IPCC AR4) will be used to develop scenarios of regional oceanographic changes that will, in turn, be linked to changes in ocean forcing of upper trophic level species (see discussion below). This builds on a history of research on mechanisms underlying fisheries production, setting the stage for the development of quantitative climate change impacts on fisheries. Participants at the workshop recognized the importance of ocean and climate effects on recruitment and accepted the concept of decadal-scale variation – a very important advancement and key result of the workshop. Links to Other Programs FIS-sponsored activities like the proposed PPFCC could provide a critical link to other national and international research programs that are expected to be active within the region during the next decade (Fig. 1.1). These would build on the work of the IPCC that has provided scenarios for modelling climate change impacts on fisheries. Examples of bio-physical models that are being developed by national programs include the U.S. National Science Foundation’s Bering Sea Ecosystem Study (BEST), the U.S. Global Ocean Ecosystem Dynamics Program (GLOBEC) Northeast Pacific Program (NEP), and the U.S. North Pacific Climate Regimes and Ecosystem Productivity (NPCREP) program as well as international programs such as GLOBEC International. PICES scientists can coordinate their activities with on-going research on upper trophic level responses to environmental forcing that are taking place in most national research institutions. In particular, the results will be first-order forecasts for use in developing more complex, process-oriented studies that seek to predict the responses of whole ecosystems such as is being attempted in the NPRB-funded Bering Sea Integrated Ecosystem Research Program (BSIERP) and the U.S. National Oceanic and Atmospheric Administration’s (NOAA’s) Loss of Sea Ice (LOSI) program. The regional forecasting approach can be used to guide comparative research across the northern hemisphere, some of which is sponsored by GLOBEC’s Ecosystem Study of Sub-Arctic Seas (ESSAS), and by the U.S. Comparative Analysis of Marine Ecosystem Organization (CAMEO) program. The goals of this initiative are consistent with the climate forecasting element of the proposed PICES
science program, FUTURE (Forecasting and Understanding Trends, Uncertainty and Responses of North Pacific Ecosystems), and the U.S. Fisheries and the Environment (FATE) program. The results of the coordinated research effort can be used by a wide range of individuals who are normally outside of the research community. Stakeholders who rely on fish and shellfish resources are expected to use the results to anticipate changes that may affect their businesses and communities. Fisheries managers will utilize the forecasts to evaluate whether actions are needed to sustain fisheries in their regions. Conservation groups will be interested in order to better understand the regional and species-specific risks and challenges that climate change poses for species of interest. Forecasts developed through this effort will help to identify research gaps that could be the focus of interdisciplinary research programs involving field work. Just as the recognition of regime shifts was used to promote the development of large national and international interdisciplinary research programs, participants at the workshop thought that the international research effort to investigate the impacts of climate change on marine fish populations may promote the expansion of national and international research programs on climate change and marine ecosystems. Workshop Format The workshop provided a forum for discussion of four components needed to complete the forecasts in a timely and coordinated fashion, including: IPCC scenarios, predictions of oceanographic impacts, modeling approaches, and scenarios for natural resource use and enhancement. The key outcomes of these discussions are included in this report. Session I. Status of climate change scenarios in the PICES region Drs. James Overland (U.S.A.) and Vladimir Kattsov (Russia) reported on the IPCC climate change scenarios arising from the 4th Assessment Report. A major conclusion from Session I was that most model projections involve large natural variability (including decadal variability) as well as persistent trends from anthropogenic climate change. These effects are expected to include persistent trends, shifts in the timing of seasonal events, such as the
5
spring transition, and an increased frequency of extreme events, such as the number of warm or cold days. From a spatial perspective, models differ markedly in the intensity of change across the North Pacific and its adjacent seas. Session leaders noted that the major challenge facing climatologists is to assess whether models give a reasonable representation of the large-scale response of the Pacific to anthropogenic greenhouse gas and aerosol emissions, relative to natural variability. Some progress on this issue was anticipated by the POC/CFAME workshop that was held during the PICES 2007 Annual Meeting, where climatologists were encouraged to exchange ideas on techniques to link the IPCC climate change scenarios with oceanographic change scenarios of the North Pacific.
Session II. What are the expected impacts of climate change on regional oceanography and what are some scenarios for these drivers for the next 10 years? Dr. Nathan Mantua (U.S.A.), who led the discussion for this session, noted that the IPCC scenarios exhibit a wide range of possible outcomes (Fig. 1.2) associated with different assumptions regarding emissions build-up and climate sensitivity to a given change in emissions (geophysics). The oceanographic response to climate change is also uncertain and can give potentially conflicting results (Snyder et al., 2003). It was also recognized that trends in ocean conditions at the regional scale may not be easy to detect in the next few decades because shorter-term variations can mask them. The group discussed the possibility that different techniques may be needed to forecast ocean responses over the near-term period of 0–10 years and the longer-term period of 10–30 years. Participants were reminded that the PPFCC effort is challenging because operational climate forecasts at lead times greater than one year are simply not available at this time. In the U.S., only a few major centers routinely offer climate forecasts, and these centers (NCEP/CPC, IRI, ECMWF, etc.) only project climate conditions from one to four seasons into the future. There have been a handful of research studies that highlight the potential for making skillful multi-year forecasts for aspects of Pacific climate, most notably predictions for sea surface temperature (SST) variations in the Kuroshio Extension region (Schneider and Miller, 2001; Seager et al., 2001). Dr. Nicholas Bond (U.S.A.) provided an example of how an IPCC scenario can be tied to a quantitative
scenario for rock sole production in the Bering Sea. He used IPCC scenarios to estimate cross-shelf transport in the Bering Sea, and described how this index could be incorporated into a spawner-recruit relationship for rock sole to predict future run strength of this species in the Bering Sea. The major recommendations from this session were as follows: • Oceanographers and climatologists need to be
encouraged to exchange ideas on techniques for evaluating oceanographic responses to climate change.
• Oceanographers need to be made aware of detailed information on physical or bio-physical drivers that can be used to make projections of future fish distribution or production, particularly if there are critical environmental thresholds that govern competition for prey, predation or advection to suitable habitats.
• Fisheries biologists were encouraged to provide detailed information on the physical or bio-physical drivers or environmental thresholds that are needed to make a projection by the 2007 PICES Annual Meeting (e.g., Table 1.2).
Session III. Recruitment forecasting Dr. Richard Beamish (Canada) led the discussion during this session. Several participants provided examples of the influence of climate on local oceanography and fish production. Dr. Xianshi Jin (China) presented evidence that decadal changes in climate conditions may have influenced the fecundity of northern anchovy. He also demonstrated important regional differences in the production of small yellow croaker. Time trends in the annual catch of small yellow croaker and largehead hairtail in the Yellow Sea show a marked increase in abundance since 2000. Dr. Kazuaki Tadokoro (Japan) provided evidence that decadal shifts in the location of the transition between the Oyashio Current and the Kuroshio Current may influence the salinity and mixed layer depth which is strongly correlated to the production of the copepod Neocalanus plumchrus. Dr. Vladimir Radchenko (Russia) presented some recent work by Dr. Gennady Kantakov (Sakhalin Research Institute of Fisheries and Oceanography) which showed how climate linkages influence circulation patterns in the Sea of Okhotsk. The resulting changes may alter the distribution and survival of juvenile salmon in the region. Ms. Teresa A’mar (U.S.A.) described a
6
technique for forecasting Gulf of Alaska walleye pollock. Her projection incorporated environmental forcing on recruitment by modifying the mean age–1 abundance with environmental factors that had been previously linked to recruitment. There was insufficient time for participants to deal with recruitment mechanisms for all species, but from the limited discussion it was clear that understanding of the mechanisms that affect recruitment ranges from poor to good. Dr. Suam Kim (Korea) noted that retrospective studies have shown that environmental forcing accounts for a significant fraction of the variance in recruitment, and some improvement could be made by directed research on mechanisms. Participants were encouraged to examine the species
listed in Table 1.1 to assess the current state of knowledge regarding mechanisms linking climate forcing and fish production, and the uncertainty associated with these mechanisms (see Table 1.2 for an example; however, requests for output by latitude, longitude, and month were preferred). A few participants noted that some fraction of the recruitment of managed species may be random so there may be a threshold to predictability. Different views were also expressed on what percentage of explained variability constitutes a “good” forecast. One useful exercise would be for scientists across the Pacific Rim to report on the amount of variance explained in existing studies in order to determine if there is a common level of random variance associated with models of environmental links to recruitment.
Fig. 1.2 Solid lines are multi-model global averages of surface warming (relative to 1980–1999) for scenarios A2, A1B and B1, shown as continuations of the 20th century simulations. Shading denotes ±1 standard deviation range of individual model annual averages. The orange line is for the experiment where concentrations were held constant at year 2000 values. The grey bars at right indicate the best estimate (solid line within each bar) and the likely range assessed for the six SRES marker scenarios. The assessment of the best estimate and likely ranges in the grey bars includes the Atmosphere–Ocean Global Climate Models (AOGCMs) in the left part of the figure, as well as results from a hierarchy of independent models and observational constraints. (Reprinted from http://www.ipcc.ch/graphics/ gr-ar4-wg1.htm).1 1 Note that the IPCC did not make anthropogenic carbon emission forecasts. Instead, they developed a suite of future greenhouse gas and sulfate aerosol emissions scenarios, each of which is based on a story-line that includes scenarios for economic development, international cooperation, and technological change. These are termed “scenarios” because it is accepted that the political, socio-economic, and technological factors required for making true “greenhouse gas emissions forecasts” are essentially unknowable.
Tab
le 1
.1
Prel
imin
ary
list o
f spe
cies
acr
oss r
egio
ns sh
owin
g op
portu
nitie
s for
com
para
tive
stud
ies.
Tax
on
Reg
ion
Com
mon
nam
e Sc
ient
ific
nam
e U
.S.A
. W
C
Can
ada
U.S
.A.
GO
A
U.S
.A.
BSA
I Ja
pan
K-O
Ja
pan/
Eas
t Se
a R
ussi
a
Yel
low
Sea
/ E
ast C
hina
Se
a T
WC
Wal
leye
pol
lock
Th
erag
ra c
halc
ogra
mm
a –
X
X
X
X
X
X
– Pa
cific
cod
G
adus
mac
roce
phal
us
– X
X
X
–
– X
–
Paci
fic h
ake
Mer
lucc
ius p
rodu
ctus
X
X
–
– –
– –
– Pa
cific
hal
ibut
H
ippo
glos
sus s
teno
lepi
s X
X
X
X
–
– X
–
Arr
owto
oth
floun
der
Athe
rest
hes s
tom
ias
X
– X
X
–
– X
–
Nor
ther
n ro
ck so
le
Lepi
dops
etta
pol
yxys
tra
– –
– X
–
– –
– Sa
blef
ish
Anop
lopo
ma
fimbr
ia
X
X
X
X
– –
– –
Roc
kfis
hes (
POP)
Se
bast
es sp
p.
X
X
X
X
– –
– –
Paci
fic h
errin
g C
lupe
a pa
llasi
–
X
X
X
X
X
X
– Sa
rdin
e Sa
rdin
ops s
agax
, S. m
elan
ostic
tus
X
– –
– X
X
X
–
Anc
hovy
En
grau
lis ja
poni
cus,
E. m
orda
x X
–
– –
X
X
– X
C
hub
mac
kere
l Sc
ombe
r jap
nicu
s X
–
– –
X
X
– X
Ja
ck m
acke
rel
Trac
huru
s jap
onic
us
X
– –
– X
X
–
X
Paci
fic sa
ury
Col
olab
is sa
ira
– –
– –
X
X
– –
Yel
low
cro
aker
Ps
eudo
scia
ena
poly
actis
–
– –
– –
– –
X
Larg
ehea
d ha
irtai
l Tr
ichi
urus
lept
urus
–
– –
– –
– –
X
Saff
ron
cod
Eleg
inus
gra
cilis
–
– –
– –
X
– –
Pink
salm
on
Onc
orhy
nchu
s gor
busc
ha
– X
X
X
–
X
X
– C
hum
salm
on
O. k
eta
– X
X
X
X
X
X
–
Sock
eye
salm
on
O. n
erka
–
X
X
X
– –
X
– C
hino
ok sa
lmon
O
. tsh
awyt
scha
X
X
X
X
–
– –
– C
oho
salm
on
O. k
isut
ch
X
X
X
– –
– –
– C
omm
on sq
uid
Toda
rode
s pac
ificu
s –
– –
– X
X
–
X
Mar
ket s
quid
Lo
ligo
opal
esce
ns
X
– –
– –
– –
– O
cean
shrim
p Pa
ndal
us jo
rdan
i X
X
X
–
– –
– –
Red
kin
g cr
ab
Para
litho
des c
amts
chat
icus
–
– –
X
– –
– –
Tann
er c
rab
Chi
onoe
cete
s bai
rdi
– –
– X
–
– –
– Sn
ow c
rab
Chi
onoe
cete
s opi
lio
– –
– X
–
– –
–
WC
– W
est C
oast
, G
OA
– G
ulf o
f Ala
ska,
BSA
I – B
erin
g Se
a/A
leut
ian
Isla
nds,
K-O
– K
uros
hio–
Oya
shio
, TW
C –
Tsu
shim
a W
arm
Cur
rent
, PO
P –
Paci
fic o
cean
per
ch
7
8
Table 1.2 Example of a mechanism/climate/production table for Bering Sea salmon stocks.
Index Mechanism Season Citation
Spring temperature, out-migration timing, timing of spring bloom
Ice breakup affects timing of out-migration
Spring Rogers (1988); Burgner (1991); Shotwell et al. (2006)
Timing of spring bloom, apparent growth effect
Match/mismatch; Critical period
Spring Cushing (1972); Beamish and Mahnken (2001); Mackas et al. (2001)
Temperature effect on predation, diet composition and spatial distribution of predators, including young-of-the-year pollock recruitment.
Alternative prey for predators and daily ration for predators
Spring – Summer
Pearcy (1992); Farley et al. (2007)
Timing of spring transition Match/mismatch; Critical period
Spring–Summer
Logerwell et al. (2003); Peterson and Schwing (2003)
Prey availability for post-smolts, zooplankton abundance and composition
Growth, foraging success Summer Cushing (1972); Willette et al. (1997); Cooney (1993); Beamish and Mahnken (2001); Peterson and Schwing (2003)
Oceanic habitat volume–mixed layer depth and fronts
Competition for prey – partitioning predators and prey
Summer Coachman (1986); McRoy et al. (1986)
Euphausiid abundance Reduced predation risk when alternative prey abundant – high prey availability leads to accelerated growth; critical size
Summer Cooney (1993); Willette et al. (1997); Beamish and Mahnken (2001)
Diet composition of predators, abundance of predators
Predation Summer – Fall
Pearcy (1992)
Winter survival Critical size/critical period; winter survival of larvae and juveniles
Winter Beamish and Mahnken (2001)
Session IV. What models are out there? How is climate linked to the model? Dr. Michael Schirripa (U.S.A.) led this discussion and identified eight types of models:
1. PICES North Pacific Ecosystem Model for Understanding Regional Oceanography (NEMURO): light intensity + SST;
2. PICES NEMURO.FISH: Input from NEMURO; 3. NOAA Fisheries SS2: Generalized framework; 4. Single Species Management Strategy Evaluations; 5. ATLANTIS: ROMS output, flow, SST, salinity; 6. Ecopath-Ecosim; 7. Multi-species forecasting models; 8. Modularized models as building blocks.
Dr. Yasuhiro Yamanaka (Japan) provided an overview of a collaborative research effort to couple a 3-D circulation model to NEMURO and a multi-species model that includes interactions between anchovy, saury and sardine. The sardine model includes migration estimated by an artificial network approach and a bio-energetic model to incorporate changes in growth of fish associated with local environmental conditions. Dr. Bernard Megrey (U.S.A.) discussed the international research effort to develop common software to couple fish bio-energetics to NEMURO (NEMURO.FISH). Dr. Richard Methot (U.S.A.) provided examples where single species stock assessment models could be adapted to incorporate
9
environmental forcing on recruitment, growth, or distribution of fish. This type of single species forecasting tool could be readily applied, and it was suggested that current forecasting tools could be shared among nations. Participants discussed the feasibility of utilizing NEMURO.FISH type models to develop forecasts. Japanese scientists reported that they have been working to embed bio-energetic models in regional climate–ocean models. U.S. participants noted that a NEMURO model had been linked to a salmon bio-energetics model to assess climate impacts on pink salmon in the sub-arctic Pacific. They pointed out that BEST recently provided funding to develop similar models for the Bering Sea. They also reported linking a Nutrient-Phytoplankton-Zooplankton (NPZ) model to a climate–ocean model for the California Current system (CCS) which could be used to assess climate impacts on small pelagic fishes in the CCS and in the Asian region. It was noted that the base model for the CCS is a 10-km grid model that may not adequately resolve coastal oceanography. U.S. scientists also remarked that applications of the ATLANTIS model have been attempted in the CCS. ATLANTIS provides a spatially explicit consideration of the amount of production as a function of temperature or other physical variables. In preparation for the October 2007 workshop, scientists were asked to be prepared to report on the types of forecasting models that are available, by species, to allow for an analysis of the opportunities for comparisons across regions. It was recognized that for several species, proposed mechanisms underlying recruitment variability included measures of prey availability and the volume of suitable habitat. For volumetric estimates of habitat suitability, NEMURO.FISH type models with imbedded climate–ocean models would be preferred; however, environmental proxies could be used to estimate prey volume. Participants discussed several techniques for using environmental proxies in forecasting models. Fisheries biologists were asked to create tables with detailed information on the required physical or biological variables (by latitude, longitude, and month) used to forecast processes underlying recruitment growth and distribution of fish stocks. This task was to be completed prior to the 2007 PICES Annual Meeting in Victoria. It was agreed
that the tables would be distributed to oceano-graphers as soon as they were finished. Session V. Assumptions regarding future fishing scenarios and enhancement activities Dr. Anne Hollowed (U.S.A.) led this discussion. She acknowledged that developing scenarios to forecast future fishing mortality rates and impacts of marine enhancement activities will be important. The Management Strategy Evaluation modeling approach explicitly calls for this type of effort. To stimulate discussion, members from each nation were asked their opinion on the: • future demand for fish and shellfish, • expected trends in management of marine resources, • future of fisheries enhancement activities, • implications of increased fuel prices on the choice
of target species by fisheries. There was insufficient time for a thorough discussion of this issue. However, the need for a serious treatment of these factors in models was highlighted. The following is a brief summary of the responses. Canada • A modest increase in demand is expected, driven
mostly by world markets. Eco-labelling is a new trend and poses a threat to the demand of some fisheries.
• There will be a trend toward science that supports ecosystem-based management, objectives-based fishery management plans (more precautionary), new “ocean to plate” (economics, eco-labelling) initiative.
• Substantial changes will take place for salmon management, including reduced commercial harvests by non-indigenous fishermen in most areas. The remaining fisheries will shift to quota management. Fisheries are curtailed by the Species at Risk Act (SARA).
• Hatcheries will not be expanding. However, there will be some expansion of ocean ranching, but not in large areas. Fish farming in British Columbia will continue to expand, but probably slowly in the first 10 years.
• No big changes in groundfish management are expected. There will be some interest in groundfish fish farming and enhancement. SARA constrains some groundfish fisheries.
10
• Active shellfish mariculture and enhancement programs (e.g., geoduck, abalone) are taking place.
• There is a need for more conservative catch quotas for Pacific hake in the U.S. (shared stock with Canada).
China • The demand for seafood is expected to increase. • Aquaculture production has been higher than
ocean capture fisheries, and it is likely that aquaculture programs will expand.
• Fleet reduction (buy-back) programs will reduce the fishery effort and will provide fishermen training for alternative employment.
• Enhancement programs have been used for more than 20 years to rebuild fish, shrimp, shellfish, and jellyfish populations. These programs will continue or expand.
• A ban on fishing during summer months, used to manage fisheries since 1995, will continue.
Japan • There will be a decrease in demand (consumption
per person) for seafood as diets shift to other meats, except that the demand for high-grade species, like tuna, is increasing.
• No big changes in management are expected for the next 10 years.
• Stock rebuilding for salmon is taking place, and hatcheries are releasing larvae of coastal fishes like flatfish, shellfish and others.
• Fish farming is important, particularly in the northern part of Japan.
Korea • Fisheries demand is increasing. • Rebuilding plans have been implemented for blue
crab and some other species. • Salmon enhancement programs will be developed. • Managers will adopt a more precautionary approach
to resources, including bycatch reduction. • The current open access to the fishery system may
be replaced by a license limitation and quota system over the long term. A new fishery management act may be passed by the government this year.
Russia • Demand is growing. The Russian Far East exports
to Japan and is exploring markets in Korea, China and other Asian countries. A large increase in domestic demand could be realized if transportation systems are improved.
• New fishery rules are established for all areas. There are some new restrictions on the crab fishery and some revision to the quota system. There is a plan to establish total allowable catches for all fished stocks. Changes are likely, perhaps with a shift in focus to the most commercially important fisheries.
• Enhancement programs for salmon are likely to continue or expand, with a focus on the Sakhalin–Kuril Islands region where 24 new hatcheries will be built. Enhancement is also done in some areas for clams and sea cucumbers.
• Fuel is an issue for Russian fisheries. It constrains current fisheries to the Exclusive Economic Zone.
• In the Russian Far East, oil and gas extraction on the shelf is being developed in the Sakhalin region. Further development of oil and gas in the Kamtchatka region may cause conflicts in this important fishery region.
U.S. Alaskan coast • Market demand for Alaskan fish and shellfish is
likely to continue to increase in Alaska. New markets for Arctic fish species may develop as access to new fishing grounds is improved by loss of sea ice.
• Fisheries management strategies will continue to employ the precautionary approach and multi-species management through target species and limit reference points. New constraints associated with the adoption of these measures are designed to sustain non-target species.
• Fish hatcheries are used for salmon in the Gulf of Alaska. It is unlikely that they will expand in Alaska in the near term. However, in the long term this approach may be considered. It is likely that efforts to rebuild Alaskan crab stocks will intensify in the next decade due to the combined interest of the fishing industry and the conservation community.
• Increases in fuel prices will have a great impact on Alaskan fisheries where many fishing grounds are located in remote regions.
• Several regions of the Bering Sea, Aleutian Islands and Gulf of Alaska have already been set aside as marine protected areas or marine reserves. The most recent action by the North Pacific Fishery Management Council to close the northern regions
11
of the Bering Sea to commercial trawling is likely to curtail (but not prohibit) expansion to the north.
U.S. West Coast • International markets are increasing, so demand
will be driven by markets in the Far East. The demand for some products are increasing in China, but decreasing in Japan. Local markets are expected to increase.
• Management changes are similar to Alaska because they are driven at the national level.
• Fishing opportunities are constrained for rockfish owing to strict rebuilding plans. Some overfished stocks may take 50 years to recover. Bycatch will constrain future fisheries.
• Enhancement will not be important, except for salmon.
• Fuel and other considerations are similar to Alaska. • Incorporation of ecosystem-based management is
increasing. • There is a national initiative to develop offshore
aquaculture, but there is some resistance in certain areas, particularly Alaska. It is uncertain if this will develop.
Workshop participants agreed that temporal trends in anthropogenic activities should be included in the forecast. At a minimum, this information should include scenarios regarding expected levels of fishing mortality. Fisheries enhancement efforts are likely to expand, which may mask the impact of climate on survival during the early life history period. The impact of fishery enhancement could be modeled by changing the expected mean recruitment, by changing the carrying capacity of the system, or both. A useful suggestion was that each nation
should prepare a document describing the future of fisheries management in 10–20 years. Session VI. Where do we go from here? It was decided to continue with a multi-national interdisciplinary research team approach that includes representatives from each PICES member country, as well as a broad spectrum of experts in climatology, oceanography, fisheries biology and modeling. For some nations, experts have to be identified to join the research effort. Participants recognized the need for discussions within disciplines to resolve technical issues, and it was decided that some of these issues could be resolved through e-mail prior to the 2007 PICES Annual Meeting. It was also noted that Asian scientists could discuss some of these issues at the 3rd Japan/China/Korea GLOBEC symposium scheduled for December 2007, in Hokkaido, Japan. The list of target species was reviewed and several were dropped from the list developed in October 2006. After careful deliberation, 28 species were selected for further consideration (Table 1.1). Participants also reviewed the regional partitions proposed in October 2006, but no change was recommended. Participants agreed to project implications of climate change at 10- and 30-year time horizons. To ensure that the forthcoming PICES Scientific Report adequately represents the opinions of participants, each scientist was requested to write a short statement describing the feasibility of implementing a program like PPFCC and the feasibility of completing the forecasts within the next 2–3 years.
12
References Beamish, R.J. and Mahnken, C. 2001. A critical size and
period hypothesis to explain natural regulation of salmon abundance and the linkage to climate and climate change. Prog. Oceanogr. 49: 423–438.
Burgner, R.L. 1991. Life history of sockeye salmon (Onchorhynchus nerka). pp. 3–117, in Pacific Salmon Life Histories, edited by C. Groot, and L. Margolis, UBC Press, Vancouver.
Coachman, L.K. 1986. Circulation, water masses, and fluxes on the southeastern Bering Sea shelf. Cont. Shelf Res. 5: 23–108.
Cooney, R.T. 1993. A theoretical evaluation of the carrying capacity of Prince William Sound, Alaska, for juvenile Pacific salmon. Fish. Res. 18: 77–87.
Cushing, D.H. 1972. The production cycle and the numbers of marine fish. Symp. Zoo. Soc. Lond. 29: 213–232.
Farley, E., Murphy, J., Adkison, M., Eisner, L. 2007. Juvenile sockeye salmon distribution, size, condition and diet during years with warm and cool spring sea temperatures along the eastern Bering Sea shelf. J. Fish Biol. 71: 1145–1158.
Logerwell, E.A, Mantua, N., Lawson, P.W., Francis, R.C. and Agostini, V.N. 2003. Tracking environmental processes in the coastal zone for understanding and predicting Oregon coho (Oncorhynchus kisutch) marine survival. Fish. Oceanogr. 12: 554–568.
Mackas, D., Thomson, R., Galbraith, M. 2001. Changes in the zooplankton community of the British Columbia continental margin, 1984–1999, and their covariation with oceanographic conditions. Can. J. Fish. Aquat. Sci. 58: 685–702.
McRoy, C.P., Hood, D.W., Coachman, L.K., Walsh, J.J. and Goering, J.J. 1986. Processes and resources of the Bering Sea shelf (PROBES): The development and accomplishments of the project. Cont. Shelf Res. 5: 5–22.
Pearcy, W.G. 1992. Ocean Ecology of North Pacific
Salmonids. Books in Recruitment Fishery Oceano-graphy. Washington Sea Grant Program, University of Washington, Seattle, 176 pp.
Peterson, W.T. and Schwing, F.B. 2003. A new climate regime in northeast Pacific ecosystems. Geophys. Res. Lett. 30: 1896, doi:10.1029/2003GL017528
Rogers, D.E. 1988. Bristol Bay smolt migrations: timing and size composition and the effects on distribution and survival at sea. pp. 87–101 in Salmon Production, Management, and Allocation, edited by W.J. McNeil, Oregon State University Press, Corvallis, OR.
Schneider, N. and Miller, A.J. 2001. Predicting western North Pacific Ocean climate. J. Climate 14: 3997–4002.
Seager, R., Kushnir, Y., Naik, N.H., Cane M.A. and Miller, J. 2001. Wind-driven shifts in the latitude of the Kuroshio-Oyashio Extension and generation of SST anomalies on decadal time scales. J. Climate 14: 4249–4265.
Shotwell, S.K., Adkison, M.D. and Hanselman, D.H. 2006. Accounting for climate variability in forecasting Pacific salmon in data – limited situations. pp. 871–900, in Fisheries Assessment and Management in Data Limited Situations, edited by G.H. Kruse, V.F. Gallucci, D.E. Hay, R.I. Perry, R.M. Peterman, T.C. Shirley, P.D. Spencer, B. Wilson, and D. Woodby, Alaska Sea Grant College Program, AK-SG-05-02, 958 pp.
Snyder, M.A., Sloan, L.C., Diffenbaugh, N.S. and Bell, J.L. 2003. Future climate change and upwelling in the California Current. Geophys. Res. Lett. 30: 1823.
Willette, M., Sturdevant, M. and Jewett, S. 1997. Prey resource partitioning among several species of forage fishes in Prince William Sound, Alaska. pp. 11–29, in Forage Fishes in Marine Ecosystems. Proceedings of the International Symposium on the Role of Forage Fishes in Marine Ecosystems, Alaska Sea Grant College Program Report No. 97-01, University of Alaska Fairbanks, AK.
13
Appendix 1.1 List of Participants
Seattle, Washington, U.S.A.
July 19–20, 2007
CANADA
Richard J. Beamish Fisheries and Oceans Canada Pacific Biological Station 3190 Hammond Bay Road Nanaimo, BC, V9T 6N7 Canada [email protected] Stock assessment Donald Noakes Thompson Rivers University Main Campus Box 3010 900 McGill Road Kamloops, BC, V2C 5N3 Canada [email protected] Statistics
Brian Riddell Fisheries and Oceans Canada Pacific Biological Station 3190 Hammond Bay Road Nanaimo, BC, V9T 6N7 Canada [email protected] Stock assessment Richard E. Thomson Fisheries and Oceans Canada Institute of Ocean Sciences P.O. Box 6000 Sidney, BC, V8l 4B2 Canada [email protected] Oceanography
JAPAN
Kazuaki Tadokoro Tohoku National Fisheries Research Institute Fisheries Research Agency 3-27-5 Shinhama-cho Shiogama, Miyagi 985-0001 Japan [email protected] Lower trophic level response
Yasuhiro Yamanaka Hokkaido University N10W5, Kita-ku Sapporo, Hokkaido 060-0810 Japan [email protected] Coupled models
PEOPLE’S REPUBLIC OF CHINA
Xianshi Jin Yellow Sea Fisheries Research Institute, CAFS 106 Nanjing Road Qingdao, Shandong 266071 People’s Republic of China [email protected] Fisheries science
Hao Wei Ocean University of China 5 Yushan Road Qingdao, Shandong 266003 [email protected] People’s Republic of China Climate modeling/dynamics
14
REPUBLIC OF KOREA
In-Sook Heo Republic of Korea currently at: Daniel J. Evans School of Public Affairs University of Washington Seattle, WA 98195 U.S.A. [email protected] Policy Sukyung Kang National Fisheries Research and Development Institute 408-1 Sirang-ri, Gijang-eup, Gijang-gun Busan 215-821 Republic of Korea [email protected] Fisheries oceanography/Salmon ecology Suam Kim Pukyong National University 559-1 Daeyeon-3-dong, Nam-gu Busan 608-737 Republic of Korea [email protected] Oceanography
Sung-Tae Kim National Fisheries Research and Development Institute 408-1 Shirang-ri, Gijang-eup, Gijang-gun Busan 619-902 Republic of Korea [email protected] Stock assessment Jae Bong Lee National Fisheries Research and Development Institute 408-1 Sirang-ri, Gijang-eup, Gijang-gun Busan 619-905 Republic of Korea [email protected] Stock assessment Chang-Ik Zhang Pukyong National University 559-1 Daeyeon-3-dong, Nam-gu Busan 608-737 Republic of Korea [email protected] Ecosystem models
RUSSIA
Vladimir M. Kattsov Voeikov Main Geophysical Observatory 7 Karbyshev Street St. Petersburg 194021 Russia [email protected] Climatology
Vladimir I. Radchenko Sakhalin Research Institute of Fisheries and
Oceanography (SakhNIRO) 196 Komsomolskaya Street Yuzhno-Sakhalinsk 693023 Russia [email protected] Stock assessment
U.S.A.
Z. Teresa A’mar University of Washington Box 355020 SAFS Seattle, WA 98195-5020 U.S.A. [email protected] GOA AK, Stock assessment models
Kerim Y. Aydin Alaska Fisheries Science Center NMFS, NOAA 7600 Sand Point Way NE Seattle, WA 98115-0070 U.S.A. [email protected] BSAI AK, Ecosystem models
15
Nicholas A. Bond NOAA/PMEL, JISAO 7600 Sand Point Way, NE Seattle, WA 98115-6349 U.S.A. [email protected] AK, Climatology/Oceanography Martin Dorn Alaska Fisheries Science Center NMFS, NOAA 7600 Sand Point Way NE Seattle WA 98115 U.S.A. [email protected] GOA AK, Stock assessment models Carrie Eischens North Pacific Research Board 1007 West Third Avenue, Suite 100 Anchorage, AK 99501 U.S.A. [email protected] Ecology Melissa Haltuch Northwest Fisheries Science Center NMFS, NOAA 2725 Montlake Boulevard E Seattle, WA 98112 U.S.A. [email protected] Stock assessment Christopher J. Harvey Northwest Fisheries Science Center NMFS, NOAA 2725 Montlake Boulevard E Seattle, WA 98112 U.S.A. [email protected] North CC, Ecosystem models Alan Haynie Alaska Fisheries Science Center NOAA 7600 Sand Point Way NE Seattle, WA 98115 U.S.A. [email protected] Economics
Albert J. Hermann Joint Institute for the Study of the Atmosphere and
Ocean, PMEL 7600 Sand Point Way NE Seattle, WA 98115 U.S.A. [email protected] CC and AK, Circulation models Anne B. Hollowed Alaska Fisheries Science Center NMFS, NOAA 7600 Sand Point Way NE Seattle, WA 98115-6349 U.S.A. [email protected] AK, Stock assessment models Gordon H. Kruse University of Alaska Fairbanks 11120 Glacier Highway Juneau, AK 99801-8677 U.S.A. [email protected] BSAI AK, Stock assessment models Nathan J. Mantua University of Washington Box 55020 Seattle, WA 98195-5020 U.S.A. [email protected] North CC, Climatology/Oceanography Bernard A. Megrey Alaska Fisheries Science Center NOAA 7600 Sand Point Way NE Seattle, WA 98115-6349 U.S.A. [email protected] GOA AK, Stock assessment models Richard Methot Northwest Fisheries Science Center NMFS, NOAA 2725 Montlake Boulevard E Seattle, WA 98112 U.S.A. [email protected] Stock assessment models
16
Sam McClatchie Southwest Fisheries Science Center NOAA 8604 La Jolla Shores Drive La Jolla, CA 92037-1508 U.S.A. [email protected] South CC, Fisheries oceanography James E. Overland Alaska Fisheries Science Center NMFS, NOAA 7600 Sand Point Way NE Seattle, WA 98115-6349 U.S.A. [email protected] AK, Climatology Clarence Pautzke North Pacific Research Board 1107 W 3rd Avenue, Suite 100 Anchorage, AK 99501 U.S.A. [email protected] AK, Fisheries management Michael J. Schirripa Northwest Fisheries Science Center NMFS, NOAA Hatfield Marine Science Center 2032 SE OSU Drive Newport, OR 97365 U.S.A. [email protected] North CC, Stock assessment models
Frank Schwing NOAA Fisheries Environmental Laboratory 1352 Lighthouse Avenue Pacific Grove, CA 93950 U.S.A. [email protected] South CC, Oceanography Phyllis J. Stabeno NOAA/Pacific Marine Environmental Laboratory 7600 Sand Point Way NE Seattle, WA 98115-0070 U.S.A. [email protected] AK, Oceanography William Stockhausen Alaska Fisheries Science Center NMFS, NOAA 7600 Sand Point Way NE Seattle, WA 98115-0070 U.S.A. William [email protected] BSAI AK, Individual based models Tom K. Wilderbuer Alaska Fisheries Science Center NOAA 7600 Sand Point Way NE Seattle, WA 98115 [email protected] BSAI AK, Stock assessment models
Phase 2
Summary of the PICES/NPRB Workshop on Forecasting Climate Impacts
on Future Production of Commercially Exploited Fish and Shellfish
October, 30, 2007 Victoria, British Columbia, Canada
19
Background This report is a summary of the activities and results of a workshop (Phase 2) on Forecasting Climate Impacts on Future Production of Commercially Exploited Fish and Shellfish held on October 30, 2007, in conjunction with the PICES Sixteenth Annual Meeting, in Victoria, Canada. Twenty-two scientists attended the workshop (Appendix 2.1), including members of Ecosystem Studies of the Sub-Arctic Seas (ESSAS), PICES’ Biological Oceanography, Fishery Science, and Physical Oceanography and Climate Committees (BIO, FIS, and POC), Working Group on Evaluations of Climate Change Projections (WG 20) and Climate Forcing and Marine Ecosystem Response (CFAME) Task Team of the Climate Change and Carrying Capacity Program (CCCC). Participants from all PICES member countries except China were present. The workshop provided an interdisciplinary forum for communication of the data needed to forecast climate change impacts on commercial fish species, discussion of candidate modeling approaches that could be applied to develop such forecasts, and the potential constraints associated with fulfilling these data needs and modeling efforts. Workshop Agenda 1. Progress report 2. PICES workshop reports 3. Review handouts and requests 4. Discuss national data requests
a. China b. Japan c. Korea d. Russia e. Canada f. U.S. Bering Sea g. U.S. West Coast
5. Discuss timeline 6. ICES/PICES/IOC Climate Change symposium 7. State of North Pacific Ecosystem Status Report 8. Proposal for a PICES Working Group Prior to the meeting, the following requests were made to the workshop participants: 1. Oceanographers and climatologists should identify
what techniques they recommend for forecasting oceanographic responses to climate change.
2. Fisheries scientists should provide reports on the current state of knowledge regarding mechanisms
linking climate forcing and fish production, and the uncertainty associated with these mechanisms.
3. Fisheries scientists should identify what physical or bio-physical drivers or environmental thresholds would be needed to forecast future fish or shellfish production. These requests should specify the location (latitude and longitude) and time period for the requested physical variable.
Responses were discussed at the workshop and written summaries of these are included in this report. The following is a summary of the key outcomes of discussion. Forecast Feasibility The presented reports revealed that hypotheses linking climate and fish production (or distribution) exist for the majority of commercially fished species that were identified previously as potential candidates for quantitative forecasts (Phase 1 workshop report, this report). Several participants commented that there is considerable uncertainty surrounding the proposed linkages between climate forcing and fish production or distribution. It was pointed out that a PICES interdisciplinary forecasting effort should include a decision analysis tool that could be used to communicate the uncertainty associated with our forecasts. Uncertainty in future climatic conditions could be provided using ensembles based on several IPCC scenarios. Uncertainty associated with links to the population dynamics of selected species could be conveyed by developing forecasting tools that track true and perceived stock status where perceived stock status would incorporate measurement error and process errors associated with the assessment. Management Strategy Evaluations could be used to evaluate the performance of different harvest policies under changing environmental conditions. Format of Information The workshop provided an opportunity for information exchange between members of WG 20 and fisheries biologists. Participants concluded that requests for data/information/model output should be compiled in spreadsheet format and accompanied by written descriptions of the rationale for the requested information. This combination of tabular and written formats is expected to improve communication
20
between the two groups and would allow the climatologists and oceanographers to assess the workload associated with downscaling IPCC models to regional scales of interest. Modeling Approaches Participants discussed the modeling approach that they planned to use to develop the forecast. Three general approaches were identified: coupled bio-physical models, stock assessment projection models, and comparative approaches. Coupled bio-physical models Japanese scientists are planning to downscale IPCC model output to force regional circulation models with ensembles of future climate scenarios. They are exploring the possibility of modifying NEMURO.FISH type models for use in forecasting the response of small pelagic species to climate change. They are also planning to evaluate model performance by making a reconstruction of past oceanographic events and comparison of observed and predicted estimates of the distribution and abundance of target species. Scientists in the California Current region are coupling population dynamic models to ATLANTIS. Dr. Clarence Pautzke announced that the North Pacific Research Board (NPRB) had funded a major research effort as part of NPRB’s Bering Sea Integrated Ecosystem Research Program (BSIERP; www.nprb.org). The Program will support the development of several models for use in forecasting climate impacts on walleye pollock in the eastern Bering Sea. Some elements of the BSIERP modeling approach expand on the NEMURO.FISH type modeling by including fish behavior and complex feedback behavior between all trophic levels. Stock assessment projection models Scientists from several PICES nations plan to use ocean conditions from regional ocean models forced with IPCC climate change scenarios. Time trends in ocean conditions will be incorporated into population dynamics models for selected commercial species. Forecast models that track observed and perceived stock status will be used to assess the impacts of climate and fishing on the status of commercial species.
Comparative approaches PICES and ICES have previously sponsored symposia on ecosystem comparisons. These comparisons have identified differences in the structure, organization and energy flow of marine ecosystems. Knowledge of similarities and differences between systems may be helpful in predicting responses of marine fish to climate change. Participants at the workshop agreed that the comparative approach could also be applied to the study of climate change impacts on commercially exploited marine species. Although coupled bio-physical models and stock assessment projection models differ in terms of spatial and temporal complexity, the coupled bio-physical modeling approach has the distinct advantage of tracing complex interactions within the system. The stock assessment forecasting approach tracks the results of climate change using time trends in system forcing at population scales, based on proposed mechanisms linking population dynamics to ocean conditions. While less complex in terms of feedbacks, this approach provides statistical performance metrics that track sources of uncertainty associated with forecasting population dynamics. Similarities in Data Requests Common among the requests for bio-physical information were: sea surface temperature (SST), bottom temperatures (BT), seasonal advection (direction and intensity), timing of production (including spring blooms), stratification and upwelling events, zooplankton community structure, and time trends in the spatial overlap of habitats of predators and prey (Table 2.1). The marked similarity is encouraging because it implies that a common suite of core drivers influences production of commercial species. While regional features of North Pacific shelf regions may act to influence production of marine fish and shellfish, the commonality of drivers suggests that application of the comparative approach may provide insight as to how species will respond to climate change. This confirms the importance of moving forward with a coordinated international effort to develop a forecast of climate forcing on commercial species. Given the similarity in the drivers identified for each region, workshop participants discussed the
21
possibility that physical oceanographers and climatologists might coordinate their efforts to provide regional nodes where fisheries biologists could submit requests. Drs. James Overland and Muyin Wang offered to serve as the regional node for the eastern Pacific. A regional node for the western Pacific should be identified.
Opportunities for Coordination with Other PICES Groups and International Efforts Representatives of CFAME and WG 20 attended the workshop and reported that both CFAME and WG 20 plan to attempt forecasting climate change impacts on small pelagic species in the California Current System, Kuroshio/Oyashio Current System, and Yellow Sea/East China Sea region. After the meeting it was noted that scientists at the Scripps Institution of Oceanography, UCSD, and the Southwest Fisheries Science Center, NOAA, had
received funding for a project titled “Using ocean data assimilation to incorporate environmental variability into sardine and squid assessments.” It is anticipated that this project will provide important contributions to the PICES forecasting effort. The workshop organizers planned to attend the ICES/PICES/IOC Symposium on Climate Change in the World’s Oceans in Gijón, Spain, from May 19–23, 2008 and to hold a workshop during the symposium to promote communication with other programs. After the Phase 2 workshop, it was found that there were several international programs that have goals which are similar to the PICES/FIS forecasting effort. A schedule for production of a coordinated international forecasting effort was discussed by the workshop participants with the following proposed timelines:
March 2008 Physical oceanographers and climatologists to identify regional nodes for submission of
data requests April–May 2008 Oceanographers and climatologists from each regional node to review requests and
discuss the feasibility of extracting the requested data with fisheries biologists April 2008 PICES inter-sessional CFAME/WG 20 workshop in Honolulu, U.S.A. May 2008 ICES/PICES/IOC symposium in Gjión, Spain. Discussion of PICES/FIS forecasting
approach with QUEST – Fish and other representatives of international programs June–September 2008 Preliminary forecasts for selected species October 2008 Joint meeting with WG 20 to discuss preliminary forecasts October–June 2009 Development of written reports for PICES’ North Pacific Ecosystem Status Report
Tab
le 2
.1
Oce
an in
dice
s by
regi
on, s
peci
es a
nd se
ason
that
cou
ld b
e ex
tract
ed fr
om IP
CC
scen
ario
s dow
nsca
led
to P
ICES
regi
ons.
M
onth
Spec
ies
Inde
x M
echa
nism
1
2 3
4 5
6 7
8 9
10
11
12
U.S
. Cal
iforn
ia C
urre
nt S
yste
m
Roc
kfis
h W
ind
adve
ctio
n La
rval
drif
t to
nurs
ery
area
s
–
– 44
N,
126W
44N
, 12
6W
44
N,
126W
– –
– –
– –
–
Te
mpe
ratu
re a
t 40
m d
epth
La
rval
met
abol
ic ra
te (g
row
th o
ut
of p
reda
tor r
ange
)
–
– 44
N,
126W
44N
, 12
6W
44
N,
126W
– –
– –
– –
–
U
pwel
ling
(inte
nsity
) Su
mm
er p
rey
avai
labi
lity
(upw
ellin
g)
–
–
–
– 44
N,
126W
44N
, 12
6W
44
N,
126W
4
4N,
126
W
– –
– –
Sa
linity
Se
ttlem
ent h
abita
t
–
–
– –
44N
, 12
6W
44
N,
126W
44N
, 12
6W
44N
, 12
6W
–
– –
–
Te
mpe
ratu
re a
t 40
m d
epth
A
ge-0
gro
wth
rate
–
–
– –
44N
, 12
6W
44
N,
126W
44N
, 12
6W
44N
, 12
6W
–
– –
–
Tu
rbul
ence
+ so
lar h
eatin
g +
salin
ity +
upw
ellin
g =
MLD
Pr
ey a
vaila
bilit
y
–
–
–
– 44
N,
126W
44N
, 12
6W
44
N,
126W
44
N,
126W
– –
– –
Ti
me
sign
atur
e an
d di
rect
ion
of w
ind
forc
ing
(SST
/SSH
gr
adie
nts)
Dur
atio
n of
sum
mer
feed
ing
seas
on (t
ime
of sp
ring
trans
ition
)
–
–
– 44
N,
126W
44N
, 12
6W
44
N,
126W
44N
, 12
6W
44N
, 12
6W
–
– –
–
Tu
rbul
ence
+ so
lar h
eatin
g +
salin
ity +
upw
ellin
g =
MLD
t-1
Mat
erna
l con
ditio
n de
pend
ent o
n pr
ey a
vaila
bilit
y
–
–
–
– –
– –
44N
, 12
6W
44
N,
126W
44N
, 12
6W
44
N,
126W
44N
, 12
6W
Nor
ther
n C
alifo
rnia
Cur
rent
Sys
tem
, Can
ada
Pink
and
ch
um
salm
on
Tim
ing
of sp
ring
fres
het a
nd
dura
tion
Pink
and
chu
m sa
lmon
abu
ndan
ce
in th
e oc
ean
is re
gula
ted
by g
row
th
and
by p
reda
tion
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
– –
– –
– –
– –
W
ind
inte
nsity
and
dire
ctio
n G
row
th is
impo
rtant
bec
ause
rapi
d ea
rly m
arin
e gr
owth
redu
ces t
he
amou
nt o
f pre
datio
n an
d al
low
s fis
h to
bet
ter s
urvi
ve th
e m
arin
e w
inte
r
–
–
–
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
– –
– –
– –
In
tens
ity o
f Ale
utia
n Lo
w
Pres
sure
Inde
x G
row
th in
the
first
few
mon
ths i
n th
e ne
arsh
ore
area
s is c
ritic
al
49-5
4N,
122-
134W
–
– 49
-54N
, 12
2-13
4W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
– –
– –
49-5
4N,
122-
134W
49-5
4N,
122-
134W
SS
T A
n ea
rlier
and
stab
le m
ixin
g la
yer
favo
urs i
mpr
oved
surv
ival
49
-54N
, 12
2-13
4W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
– –
– –
– –
Pa
cific
Circ
ulat
ion
Inde
x H
atch
ery
prod
uctio
n m
ay o
verr
ide
natu
ral c
ontro
ls
49-5
4N,
122-
134W
–
– 49
-54N
, 12
2-13
4W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
– –
– –
49-5
4N,
122-
134W
49-5
4N,
122-
134W
22
Tab
le 2
.1
Con
tinue
d
M
onth
Spec
ies
Inde
x M
echa
nism
1
2 3
4 5
6 7
8 9
10
11
12
Nor
ther
n C
alifo
rnia
Cur
rent
Sys
tem
, Can
ada
Sock
eye
salm
on
Riv
er fl
ows a
nd
tem
pera
ture
s Si
ze a
t oce
an e
ntry
and
gro
wth
of j
uven
iles
in fr
esh
wat
er
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
– –
– –
– –
– –
O
cean
ent
ry ti
me
and
size
R
ate
of e
arly
mar
ine
grow
th (f
irst 6
wee
ks)
– –
–
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
– –
– –
– –
R
ate
of g
row
th in
firs
t 6
wee
ks
SST
in su
mm
er a
nd w
inte
r 49
-54N
, 12
2-13
4W
– –
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
– –
– –
49-5
4N,
122-
134W
49-5
4N,
122-
134W
A
leut
ian
Low
Pre
ssur
e In
dex
Spaw
ning
stoc
k si
ze a
nd fr
eshw
ater
re
arin
g co
nditi
ons
49-5
4N,
122-
134W
–
– 49
-54N
, 12
2-13
4W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
– –
– –
49-5
4N,
122-
134W
49-5
4N,
122-
134W
Spaw
ning
stoc
k si
ze a
nd fr
eshw
ater
re
arin
g co
nditi
ons
– –
– –
– –
– –
– –
– –
Pa
cific
Circ
ulat
ion
Inde
x Sp
awni
ng st
ock
size
and
fres
hwat
er
rear
ing
cond
ition
s 49
-54N
, 12
2-13
4W
– –
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
– –
– –
49-5
4N,
122-
134W
49-5
4N,
122-
134W
Chi
nook
an
d co
ho
salm
on
Tim
ing
of sp
ring
bloo
m
Tim
ing
of o
cean
ent
ry m
ust m
atch
with
ab
unda
nt p
lank
ton
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
– –
– –
– –
– –
W
ind
dire
ctio
n an
d in
tens
ity
Prey
mus
t be
plen
tiful
and
ava
ilabl
e w
ithin
th
e fir
st 6
wee
ks in
the
ocea
n; r
esul
ts in
gr
eate
r lip
id st
orag
e an
d im
prov
ed m
arin
e su
rviv
al
– –
– 49
-54N
, 12
2-13
4W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
– –
– –
– –
SS
T Fa
ster
ear
ly g
row
th fa
cilit
ates
a sw
itchi
ng
to la
rger
pre
y su
ch a
s Pac
ific
herr
ing
– –
– 49
-54N
, 12
2-13
4W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
– –
– –
– –
Hat
cher
y fis
h co
mpe
te w
ith w
ild fi
sh
– –
– –
– –
– –
– –
– –
Su
mm
er ri
ver t
empe
ratu
re
Stre
am c
ondi
tions
influ
ence
surv
ival
–
– –
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
– –
– –
– –
Sabl
efis
h Ti
min
g of
sprin
g tra
nsiti
on
Mat
chin
g of
cop
epod
abu
ndan
ce w
ith fi
rst
feed
ing
larv
al sa
blef
ish
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
– –
– –
– –
– –
A
leut
ian
Low
Pre
ssur
e In
dex
Mat
chin
g of
coa
stal
pla
nkto
n pr
oduc
tion
with
the
onsh
ore
mov
emen
ts o
f juv
enile
s –
– –
– –
– –
– –
– –
–
23
Tab
le 2
.1
Con
tinue
d
M
onth
Spec
ies
Inde
x M
echa
nism
1
2 3
4 5
6 7
8 9
10
11
12
Nor
ther
n C
alifo
rnia
Cur
rent
Sys
tem
, Can
ada
Sabl
efis
h W
ind
adve
ctio
n M
atch
ing
of c
oast
al p
lank
ton
prod
uctio
n w
ith th
e on
shor
e m
ovem
ents
of l
ate
larv
ae–e
arly
ju
veni
les
– –
– 49
-54N
, 12
2-13
4W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
49-5
4N,
122-
134W
– –
– –
–
SS
H
Mat
chin
g ov
eral
l pro
duct
ivity
of t
he
CC
S, n
amel
y no
rth–s
outh
mov
emen
t of
wat
er, w
hich
ties
into
firs
t fee
ding
– –
– 50
N,
120-
125W
50N
, 12
0-12
5W
50N
, 12
0-12
5W
– –
– –
– –
Wes
t coa
st o
f Van
couv
er Is
land
, BC
, Can
ada
Oce
an
shrim
p
SST
Tem
pera
ture
-rel
ated
larv
al su
rviv
al
rate
–
– 48
-50N
, 12
4-12
8W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
U
pwel
ling
Prey
ava
ilabi
lity
– –
48-5
0N,
124-
128W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
S
ea le
vel h
eigh
t Sp
ring
trans
ition
rela
ted
larv
al
adve
ctio
n –
– 48
-50N
, 12
4-12
8W
48-5
0N,
124-
128W
48-5
0N,
124-
128W
– –
– –
– –
–
Hec
ate
Stra
it, B
C, C
anad
a
Paci
fic c
od
Bar
omet
ric p
ress
ure-
adju
sted
sea
leve
l hei
ght
Egg
and
larv
al a
dvec
tion
52-5
5N,
130-
132W
52-5
5N,
130-
132W
52-5
5N,
130-
132W
– –
– –
– –
– –
–
SS
T, b
ecau
se w
ater
co
lum
n is
hig
hly
mix
ed
durin
g w
inte
r
Egg
and
larv
al g
row
th ra
te
52-5
5N,
130-
132W
52-5
5N,
130-
132W
52-5
5N,
130-
132W
– –
– –
– –
– –
–
M
LD
Her
ring
as fo
od so
urce
s for
mat
urin
g co
d –
– –
– –
– –
– 52
-55N
, 13
0-13
2W
52-5
5N,
130-
132W
52-5
5N,
130-
132W
52-5
5N,
130-
132W
Gul
f of A
lask
a
Wal
leye
po
llock
W
ind
adve
ctio
n Sp
awni
ng p
re-c
ondi
tioni
ng
(sub
surf
ace
flow
into
She
likof
St
rait)
– 57
N,
156W
57
N,
156W
–
– –
– –
– –
– –
W
ind
adve
ctio
n La
rval
tran
spor
t to
nurs
ery
area
–
– –
57N
, 15
6W
– –
– –
– –
– –
24
Tab
le 2
.1
Con
tinue
d
M
onth
Spec
ies
Inde
x M
echa
nism
1
2 3
4 5
6 7
8 9
10
11
12
Gul
f of A
lask
a
Wal
leye
pol
lock
Te
mpe
ratu
re a
t 40
m d
epth
H
atch
dat
e
– –
57N
, 15
6W
57N
, 15
6W
– –
– –
– –
– –
Pr
ecip
itatio
n La
rval
enc
ount
er w
ith p
rey
(edd
y fo
rmat
ion)
–
– 57
N,
156W
57
N,
156W
–
– –
– –
– –
–
W
ind
mix
ing
Larv
al e
ncou
nter
with
pre
y –
– 57
N,
156W
57
N,
156W
–
– –
– –
– –
–
Te
mpe
ratu
re a
t 40
m d
epth
La
rval
met
abol
ic ra
te (g
row
th o
ut o
f pr
edat
or ra
nge)
–
– –
57N
, 15
6W
57N
, 15
6W
– –
– –
– –
–
So
lar h
eat +
win
d m
ixin
g (ti
min
g of
stra
tific
atio
n)
Dur
atio
n of
sum
mer
feed
ing
seas
on
(tim
e of
stra
tific
atio
n)
– –
– 57
N,
156W
57
N,
156W
57
N,
156W
–
– –
– –
–
Te
mpe
ratu
re a
t 40
m d
epth
A
ge-0
met
abol
ic ra
te (g
row
th
over
win
terin
g su
cces
s)
– –
– –
– 56
N,
157W
56
N,
157W
56
N,
157W
–
– –
–
M
LD
Sum
mer
pre
y ha
bita
t con
cent
ratio
n –
– –
– –
56N
, 15
7W
56N
, 15
7W
56N
, 15
7W
– –
– –
W
ind
adve
ctio
n Su
mm
er p
rey
avai
labi
lity
(bas
in–s
helf
exch
ange
) –
– –
– –
57N
, 15
6W
57N
, 15
6W
57N
, 15
6W
– –
– –
C
ross
-she
lf tra
nspo
rt
(4 y
ears
ear
lier)
Pr
edat
ion
by a
rrow
toot
h flo
unde
r –
– 58
N,
156W
58
N,
156W
–
– –
– –
– –
–
Arr
owto
oth
floun
der
Cro
ss-s
helf
trans
port
(4
yea
rs e
arlie
r)
Larv
al d
rift t
o co
asta
l nur
sery
are
as
– –
57N
, 15
6W
57N
, 15
6W
– –
– –
– –
– –
SS
T Su
mm
er g
row
th ra
te (c
ritic
al si
ze)
– –
– –
– 57
N,
156W
57
N,
156W
57
N,
156W
–
– –
–
Pink
salm
on
SST
Tim
ing
of su
mm
er e
mig
ratio
n –
– –
– 58
N,
154W
–
– –
– –
– –
So
lar h
eat +
win
d m
ixin
g D
urat
ion
of su
mm
er fe
edin
g se
ason
(ti
me
of st
ratif
icat
ion)
–
– –
58N
, 15
4W
58N
, 15
4W
58N
, 15
4W
– –
– –
– –
W
ind
adve
ctio
n, in
tens
ity o
f A
lask
a co
asta
l cur
rent
Su
mm
er a
long
shor
e ad
vect
ion
– –
– –
58N
, 15
4W
58N
, 15
4W
58N
, 15
4W
– –
– –
–
SS
T Sm
olt g
row
th ra
te (m
etab
olic
rate
) –
– –
– 58
N,
154W
58
N,
154W
58
N,
154W
58
N,
154W
–
– –
–
Paci
fic c
od
Bot
tom
tem
pera
ture
H
atch
dat
e –
– 57
N,
156W
57
N,
156W
–
– –
– –
– –
–
SS
T La
rval
met
abol
ic ra
te (g
row
th o
ut o
f pr
edat
or ra
nge)
–
– –
57N
, 15
6W
57N
, 15
6W
– –
– –
– –
–
25
Tab
le 2
.1
Con
tinue
d
Mon
thSp
ecie
s In
dex
Mec
hani
sm
1 2
3 4
5 6
7 8
9 10
11
12
Gul
f of A
lask
a
Paci
fic c
od
Tem
pera
ture
40
m d
epth
A
ge-0
gro
wth
rate
–
– –
– 57
N,
156W
57
N,
156W
57
N,
156W
–
– –
– –
M
LD
Sum
mer
pre
y ha
bita
t con
cent
ratio
n –
– –
– –
56N
, 15
7W
56N
, 15
7W
56N
, 15
7W
– –
– –
W
ind
adve
ctio
n Su
mm
er p
rey
avai
labi
lity
(bas
in–s
helf
exch
ange
) –
– –
– –
57N
, 15
6W
57N
, 15
6W
57N
, 15
6W
– –
– –
Paci
fic
halib
ut
Cro
ss-s
helf
trans
port
(4
yea
rs e
arlie
r)
Larv
al d
rift t
o co
asta
l nur
sery
are
as
– –
58N
, 15
6W
58N
, 15
6W
– –
– –
– –
– –
M
LD
Sum
mer
pre
y ha
bita
t con
cent
ratio
n –
– –
– –
56N
, 15
7W
56N
, 15
7W
56N
, 15
7W
– –
– –
W
ind
adve
ctio
n Su
mm
er p
rey
avai
labi
lity
(bas
in–s
helf
exch
ange
) –
– –
– –
57N
, 15
6W
57N
, 15
6W
57N
, 15
6W
– –
– –
Paci
fic
herr
ing
SST
(Prin
ce W
illia
m
Soun
d)
Larv
al g
row
th
– –
– –
58N
, 15
4W
58N
, 15
4W
– –
– –
– –
W
ind
adve
ctio
n Su
mm
er p
rey
avai
labi
lity
(bas
in–s
helf
exch
ange
) –
– –
– –
58N
, 15
4W
58N
, 15
4W
58N
, 15
4W
– –
– –
SS
T Su
mm
er g
row
th ra
te (c
ritic
al si
ze)
– –
– –
– 58
N,
154W
58
N,
154W
58
N,
154W
–
– –
–
Beri
ng S
ea –
Ale
utia
n Is
land
s
Arr
owto
oth
floun
der
Cro
ss-s
helf
trans
port
(4
yea
rs e
arlie
r)
Larv
al d
rift t
o co
asta
l nur
sery
are
as
– –
56N
, 16
5W
56N
, 16
5W
– –
– –
– –
– –
SS
T Su
mm
er g
row
th ra
te (c
ritic
al si
ze)
– –
– –
– 56
N,
165W
56
N,
165W
56
N,
165W
–
– –
–
Tann
er c
rab
Min
imum
bot
tom
te
mpe
ratu
re
Col
d te
mpe
ratu
res i
nter
rupt
or d
elay
gam
etog
enes
is–
– –
– –
56-5
8N,
164-
166W
56-5
8N,
164-
166W
56-5
8N,
164-
166W
– –
– –
SS
T W
arm
tem
pera
ture
s pro
mot
e pr
oduc
tion
of c
opep
od
naup
lii
– –
– 55
N,
165W
55
N,
165W
55
N,
165W
55
N,
165W
55
N,
165W
55
N,
165W
55
N,
165W
–
–
W
inds
from
the
north
east
(6
0°)
Win
ds a
long
shor
e th
e no
rth si
de o
f the
Ala
ska
Peni
nsul
a pr
omot
e co
asta
l upw
ellin
g an
d pr
oduc
tion
of c
opep
od n
aupl
ii
– –
– 60
N,
165W
60N
, 16
5W60
N,
165W
60
N,
165W
60
N,
165W
60
N,
165W
60
N,
165W
–
–
W
inds
from
the
north
east
(6
0°)
Nor
thea
st w
inds
pro
mot
e re
tent
ion
of la
rvae
in
offs
hore
, dee
p-w
ater
, fin
e se
dim
ent n
urse
ry h
abita
ts–
– –
60N
, 16
5W
60N
, 16
5W
60N
, 16
5W
60N
, 16
5W
60N
, 16
5W
60N
, 16
5W
60N
, 16
5W
– –
26
Tab
le 2
.1
Con
tinue
d
M
onth
Spec
ies
Inde
x M
echa
nism
1
2 3
4 5
6 7
8 9
10
11
12
Beri
ng S
ea –
Ale
utia
n Is
land
s
Tann
er
crab
R
OM
S ou
tput
of T
anne
r cr
ab a
dvec
tion
rela
tive
to
cold
poo
l in
area
Larv
al se
ttlem
ent i
n co
ld p
ool
adve
rsel
y af
fect
s sur
viva
l –
– –
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
– –
R
OM
S ou
tput
of T
anne
r cr
ab a
dvec
tion
rela
tive
to
Paci
fic c
od g
eogr
aphi
c di
strib
utio
n in
are
a
Larv
al se
ttlem
ent i
n ar
eas o
ccup
ied
by
cod
adve
rsel
y af
fect
s sur
viva
l –
– –
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
– –
Red
kin
g cr
ab
Nor
th P
acifi
c In
dex
or
Ale
utia
n Lo
w P
ress
ure
Inde
x
Stro
nger
win
ds, a
ssoc
iate
d w
ith
deep
er A
leut
ian
Low
s, in
crea
se w
ater
co
lum
n m
ixin
g, re
duci
ng th
e pr
opor
tion
of T
hala
ssio
sira
dia
tom
s (p
rey
of c
rab
larv
ae)
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
– –
– –
– –
– –
55.5
-58
N,
165-
174W
W
inds
in B
risto
l Bay
Sa
me
mec
hani
sm a
s abo
ve, b
ut d
irect
m
easu
re o
f win
d –
– –
55-5
8N,
158-
164W
55-5
8N,
158-
164W
55-5
8N,
158-
164W
55-5
8N,
158-
164W
55-5
8N,
158-
164W
55-5
8N,
158-
164W
55-5
8N,
158-
164W
– –
B
otto
m te
mpe
ratu
re in
B
risto
l Bay
D
istri
butio
n of
bro
od st
ock
shift
s to
north
east
with
war
min
g –
– –
– –
55-5
8N,
158-
164W
55-5
8N,
158-
164W
55-5
8N,
158-
164W
– –
– –
R
OM
S dr
ift tr
acks
from
st
art l
ocat
ions
with
in
Surv
ival
dep
ends
on
succ
essf
ul
adve
ctio
n to
nea
rsho
re n
urse
ry a
reas
in
Bris
tol B
ay
– –
– 55
.5-
57.5
N,
161-
163W
55.5
-57
.5N
, 16
1-16
3W
55.5
-57
.5N
, 16
1-16
3W
55.5
-57
.5N
, 16
1-16
3W
55.5
-57
.5N
, 16
1-16
3W
55.5
-57
.5N
, 16
1-16
3W
55.5
-57
.5N
, 16
1-16
3W
– –
Ti
min
g of
sprin
g bl
oom
M
atch
/mis
mat
ch o
f cra
b la
rvae
and
di
atom
s
– –
– 55
-58N
, 15
8-16
4W
55-5
8N,
158-
164W
55-5
8N,
158-
164W
55-5
8N,
158-
164W
55
-58N
, 15
8-16
4W
55-5
8N,
158-
164W
55
-58N
, 15
8-16
4W
– –
B
iom
ass o
f Pac
ific
cod,
ye
llow
fin so
le
Pred
atio
n of
age
s 0–3
yea
rs
55
.5-
58N
, 16
5-17
4W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
55.5
-58
N,
165-
174W
Snow
cr
ab
Bot
tom
tem
pera
ture
in
east
ern
Ber
ing
Sea
shel
f D
istri
butio
n of
bro
od st
ock
shift
s to
north
wes
t with
war
min
g
56.5
-62
N,
168-
175W
56.5
-62
N,
168-
175W
56.5
-62
N,
168-
175W
56.5
-62
N,
168-
175W
56.5
-62
N,
168-
175W
56.5
-62
N,
168-
175W
56.5
-62
N,
168-
175W
56.5
-62
N,
168-
175W
56.5
-62
N,
168-
175W
56.5
-62
N,
168-
175W
56.5
-62
N,
168-
175W
56.5
-62
N,
168-
175W
27
Tab
le 2
.1
Con
tinue
d
M
onth
Spec
ies
Inde
x M
echa
nism
1
2 3
4 5
6 7
8 9
10
11
12
Beri
ng S
ea –
Ale
utia
n Is
land
s
Snow
cr
ab
RO
MS
drift
trac
ks fr
om st
art
loca
tions
in a
rea
Surv
ival
dep
ends
on
succ
essf
ul a
dvec
tion
to th
e no
rthea
st, s
hallo
wer
wat
ers
– –
– –
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
– –
SS
T
Tem
pera
ture
in u
pper
wat
er
colu
mn
affe
cts r
ate
of
deve
lopm
ent a
nd se
ttlem
ent
time
– –
– –
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
– –
R
OM
S ou
tput
of s
now
cra
b ad
vect
ion
rela
tive
to P
acifi
c co
d ge
ogra
phic
dis
tribu
tion
in a
rea
Larv
al se
ttlem
ent i
n ar
eas
occu
pied
by
cod
adve
rsel
y af
fect
s sur
viva
l
– –
– –
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
175W
– –
Ti
min
g of
sprin
g bl
oom
M
atch
/mis
mat
ch o
f cra
b la
rvae
and
sprin
g bl
oom
– –
– 56
.5-
62N
, 16
8-
175W
56.5
-62
N,
168-
17
5W
– –
– –
– –
–
A
bund
ance
of i
mm
atur
e sn
ow c
rabs
Se
ttlin
g sn
ow c
rabs
are
ca
nnib
aliz
ed b
y ju
veni
les
alre
ady
occu
pyin
g th
e nu
rser
y ar
eas
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
56.5
-62
N,
168-
17
5W
Kor
ean
coas
tal w
ater
s
Chu
b m
acke
rel
Sprin
g cu
rren
ts a
nd sa
linity
from
ab
out 5
0 m
dep
th to
the
surf
ace
for
the
sam
e ar
eas a
s des
crib
ed a
bove
Tran
spor
t to
nurs
ery
area
s –
32-3
5N,
123-
13
1E
32-3
5N,
123-
131E
32-3
5N,
123-
13
1E
32-3
5N,
123-
131E
32-3
5N,
123-
131E
– –
– –
– –
Jack
m
acke
rel
Prob
abili
ty th
at a
surf
ace
parti
cle
wou
ld la
nd in
the
‘touc
h do
wn
zone
’ w
here
larv
al ja
ck m
acke
rel s
ettle
out
of
the
plan
kton
ic la
rval
stag
e
Rec
ruitm
ent i
s rel
ated
to
succ
ess o
f set
tlem
ent i
n nu
rser
y gr
ound
tran
spor
ted
by
the
Kur
oshi
o W
arm
Cur
rent
– –
– 32
-35N
, 12
5-
131E
32-3
5N,
125-
131E
32-3
5N,
125-
131E
– –
– –
– –
Te
mpe
ratu
re a
nd sa
linity
at t
he
surf
ace
and
50 m
laye
r dep
th
Gro
wth
is a
func
tion
of
met
abol
ic ra
te
– –
– 32
-35N
, 12
5-
131E
32-3
5N,
125-
131E
32-3
5N,
125-
131E
– –
– –
– –
Zo
opla
nkto
n ab
unda
nce
Apr
il–Ju
ne
Gro
wth
is a
func
tion
of p
rey
avai
labi
lity
– –
– 32
-35N
, 12
5-
131E
32-3
5N,
125-
131E
32-3
5N,
125-
131E
– –
– –
– –
28
Tab
le 2
.1
Con
tinue
d
M
onth
Spec
ies
Inde
x M
echa
nism
1
2 3
4 5
6 7
8 9
10
11
12
Japa
nese
coa
stal
wat
ers
Wal
leye
po
llock
W
ind
adve
ctio
n La
rval
tran
spor
t (re
tent
ion)
–
– H
idak
a B
ay
Hid
aka
Bay
–
–
– –
– –
–
Paci
fic
cod
SST
Sum
mer
gro
wth
rate
(crit
ical
size
) –
– –
– –
Toho
ku
Bay
–
– –
– –
–
Sa
linity
Su
mm
er g
row
th ra
te (c
ritic
al si
ze)
– –
– –
– –
– –
– –
– –
W
ind
adve
ctio
n C
ircul
atio
n
– –
– –
– –
– –
– –
– –
Sard
ine
SST
Sum
mer
gro
wth
rate
(crit
ical
size
) –
– –
– 30
-40N
, 13
0-14
0E
30-4
0N,
130-
140E
30-4
0N,
130-
140E
30-4
0N,
130-
140E
– –
– –
W
ind
adve
ctio
n La
rval
tran
spor
t (re
tent
ion)
–
– –
– –
– –
– –
– –
– C
hum
sa
lmon
SS
T Su
mm
er g
row
th ra
te (c
ritic
al si
ze)
– –
– –
30-4
0N,
130-
140E
30-4
0N,
130-
140E
30-4
0N,
130-
140E
30-4
0N,
130-
140E
– –
– –
Paci
fic
herr
ing
SST
Sum
mer
gro
wth
rate
(crit
ical
size
) –
– –
– 30
-40N
, 13
0-14
0E
30-4
0N,
130-
140E
30-4
0N,
130-
140E
30-4
0N,
130-
140E
– –
– –
Sa
linity
Su
mm
er g
row
th ra
te (c
ritic
al si
ze)
– –
– –
30-4
0N,
130-
140E
30-4
0N,
130-
140E
30-4
0N,
130-
140E
30-4
0N,
130-
140E
– –
– –
Se
a ic
e re
treat
G
row
ing
seas
on
– –
– –
– –
– –
– –
– –
Japa
n/Ea
st S
ea
Wal
leye
po
llock
Su
bsur
face
wat
er te
mpe
ratu
re
(the
war
mer
, the
bet
ter)
Pr
ey a
vaila
bilit
y fo
r lar
vae
and
juve
nile
s –
–
43N
, 13
2E
43N
, 13
2E
43N
, 13
2E
43N
, 13
2E
43N
, 13
2E
– –
– –
Paci
fic
herr
ing
Are
a of
bot
tom
are
as w
ith su
itabl
e su
bstra
te (r
ecen
tly d
estro
yed
by
pollu
tion
and
othe
r rea
sons
)
Spaw
ning
subs
trate
ava
ilabi
lity
and
qual
ity
(sea
gra
ss o
r arti
ficia
l sub
stra
tes a
re
nece
ssar
y)
– –
43N
, 13
2E
43N
, 13
2E
– –
– –
– –
– –
Saff
ron
cod
Sibe
rian
Hig
h In
dex
(the
high
er/c
olde
r, th
e be
tter)
Sp
awni
ng p
erio
d du
ratio
n (s
paw
ns u
nder
ice
in c
ondi
tions
of t
empe
ratu
re b
elow
zer
o)
43N
, 13
2E
43N
, 13
2E
– –
– –
– –
– –
– 43
N,
132E
Sard
ine
SST
ratio
in w
inte
r and
sprin
g (s
tabl
e an
omal
ies a
re p
refe
rabl
e)
Larv
al e
ncou
nter
with
pre
y (m
atch
the
times
of
hat
chin
g an
d bl
oom
ing)
13
5E,
35N
13
5E,
35N
13
5E,
35N
13
5E,
35N
35
N,
135E
–
– –
– –
– 35
N,
135E
29
Tab
le 2
.1
Con
tinue
d
M
onth
Spec
ies
Inde
x M
echa
nism
1
2 3
4 5
6 7
8 9
10
11
12
Japa
n/Ea
st S
ea
Sard
ine
Sprin
g SS
T, su
bsur
face
tem
pera
ture
(low
SS
T an
d hi
gh su
bsur
face
tem
pera
ture
are
pr
efer
able
)
Pare
nts f
eedi
ng su
cces
s –
– –
– 42
N,
132E
42
N,
132E
42
N,
132E
42
N,
132E
–
– –
–
Paci
fic sa
ury
SST,
subs
urfa
ce te
mpe
ratu
re (l
ow S
ST a
nd
high
subs
urfa
ce te
mpe
ratu
re a
re p
refe
rabl
e)
Prey
ava
ilabi
lity
for l
arva
e an
d ju
veni
les
– –
– –
135E
, 35
N
135E
, 35
N
135E
, 35
N
135E
, 35
N
– –
– –
Japa
nese
co
mm
on sq
uid
SST,
subs
urfa
ce te
mpe
ratu
re (l
ow S
ST a
nd
high
subs
urfa
ce te
mpe
ratu
re a
re p
refe
rabl
e)
Prey
ava
ilabi
lity
for p
aral
arva
e an
d ju
veni
les
135E
, 35
N
135E
, 35
N
135E
, 35
N
135E
, 35
N
135E
, 35
N
135E
, 35
N
135E
, 35
N
135E
, 35
N
135E
, 35
N
135E
, 35
N
135E
, 35
N
135E
, 35
N
W
ind
dire
ctio
n an
d ra
te (u
pwel
ling
is
favo
rabl
e, d
ownw
ellin
g is
dan
gero
us)
Para
larv
ae p
elag
ic h
abita
t (M
LD
mus
t be
shal
low
eno
ugh)
13
5E,
35N
13
5E,
35N
13
5E,
35N
13
5E,
35N
35
N,
135E
35
N,
135E
35
N,
135E
35
N,
135E
35
N,
135E
35
N,
135E
35
N,
135E
35
N,
135E
Yello
w S
ea
Paci
fic h
errin
g A
rea
of b
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30
Background Reports Prepared for the Phase 2 Workshop
33
Northern California Current (U.S.) groundfish production
Melissa Haltuch
Northwest Fisheries Science Center, NMFS, NOAA, 2725 Montlake Boulevard, East, Seattle, WA 98112, U.S.A. E-mail: [email protected] Relatively few mechanistic hypotheses regarding groundfish production (recruitment) have been proposed and rigorously tested for individual species on the U.S. west coast. The most compelling
hypotheses for environmental effects occur at early life stages, and as year-class strength is determined in the first year, processes acting on recruitment are of highest priority. This short paper attempts to outline
Table 2.2 Summary of proposed processes forcing groundfish recruitment.
Process Variable Time scale Spatial scale Advection of larvae away from or towards areas amenable for settlement
Variables indicative of upwelling habitat conditions, perhaps surface winds, salinity, and SST
Less than 2 weeks, monthly, seasonal (winter/spring)
Prey abundance for larvae influenced by currents, upwelling, turbulence and/or water mixing
Variables indicative of upwelling habitat conditions, mixed layer depth, lower trophic level model output including nutrient concentrations, the density and distribution of phytoplankton and zooplankton, SST and/or SST gradients, and frontal occurrence and complexity
Less than 2 weeks, monthly, seasonal (winter/spring)
Food availability for adult females before and during copulation
SST, lower trophic level model output including nutrient concentrations, the density and distribution of phytoplankton and zooplankton
Monthly, seasonal (autumn/winter)
Environmentally driven larval metabolic rates and resulting growth/ developmental rates
SST and SST gradients Less than two weeks, monthly, seasonal (winter/spring)
Distribution of predators in relation to larvae
SST, SST gradients, and current strength Less than 2 weeks, monthly, seasonal (winter/spring)
Timing of the spring transition in the California Current System when the predominantly northward winter currents reverse to predominantly southward currents which is thought to impact many of the above processes
This would likely be a composite index which might consist of a number of physical variables including timing and/or duration of upwelling periods, magnitude of upwelling, and SST/SSH gradients. Lower trophic level model output including nutrient concentrations, the density and distribution of phytoplankton and zooplankton
Less than 2 weeks, seasonal (spring)
Little is known about the spatial scale of the process important for groundfish recruitment. Small spatial scales on the order of 10s of kilometers are likely important for recruitment success; however, the spatial scale of SST covariation in the California Current ranges between 500–2000 km and is also potentially important
34
the processes which may influence groundfish recruitment, the physical variables which may be indicative of the processes and conditions in the California Current, and the time and spatial scales at which they occur. Important processes, variables, and their time and spatial scales will vary by species, but some similarities across groups of species may emerge. This paper is primarily a synthesis of workshops and discussions from a project on incorporating climate information into rebuilding analyses for overfished rockfish led by Drs. Carrie Holt (University of Washington), Andre Punt (University of Washington), and Nathan Mantua (University of Washington) with participation from Elizabeth Clarke (NOAA), Richard Methot (NOAA), Ed Armstrong (NASA), Ben Holt (NASA), Yi Chao (NASA), Nick Tolimieri (NOAA), Ian Stewart (NOAA), and Melissa Haltuch (NOAA). Table 2.2 proposes the process which may be forcing recruitment, variables which might be used to describe the process, and the time and spatial scales of likely importance. Results from Carrie Holt’s work on identifying dates of spring transition and the relationship between spring transition and west coast groundfish
recruitment can be summarized as follows: • Caution is advised when interpreting results of
studies that relate dates of spring transition to biological variables due to uncertainty in defining those dates. For example, three methods examined for identifying the timing of spring transition pertain to seasonal shifts in different features of ocean conditions. It is unclear which metric best reflects conditions important for groundfish recruitment.
• Identifying dates of spring transition that are appropriate for both northern and southern regions of the California Current has proven difficult.
• Modeling results to date have found statistically significant negative relationships between dates of spring transition and recruitment deviations for U.S. west coast groundfish for only a few northern species that spawn in winter offshore and rear nearshore.
This summary is meant to provide a basis for further discussion of mechanistic hypotheses and the physical data that are available from IPCC climate models which might be used to forecast fish production as part of the PICES project on “Forecasting climate impacts on future production of commercially exploited fish and shellfish”, should a hypothesis stand up to testing.
35
Changes in sablefish (Anoplopoma fimbria) recruitment in relation to oceanographic conditions
Michael J. Schirripa
Northwest Fisheries Science Center, NMFS, NOAA, Hatfield Marine Science Center, 2032 SE OSU Drive, Newport, OR 97365, U.S.A. E-mail: [email protected] Sablefish (Anoplopoma fimbria) range from the southern west coast of the United States, north to Alaska, the Bering Sea, and west to Japan. The species supports substantial fisheries in both the eastern and western Pacific Ocean. Juvenile recruitment along the west coast of the continental United States has been highly variable over the past three decades. Examining the estimates of spawning stock biomass from extensive surveys made over this same period point to the fact that factors external to the sablefish population dynamics have significant effects on population level recruitment. Using a General Additive Model (GAM), it was demonstrated that there are physical oceanographic variables that significantly interact with sablefish recruitment. Significant relations were found between juvenile recruitment and northward Ekman transport, eastward Ekman transport, and sea level during key times and at key locations within the habitat of this species (Schirripa and Colbert, 2006). The overall model explains nearly 70 percent of the variability in sablefish recruitment between the years 1974 and 2000. Bootstrapping techniques were applied to the parameter estimates, and the resulting distributions were found to support the modeling assumptions of normality. Given the above model, it is possible to draw preliminary conclusions concerning year-class strength of cohorts, not yet available to the survey gear, as well as historic year-class strengths. We used data from Neah Bay and Toke Point, Washington, and Astoria and Newport, Oregon and averaged the monthly sea surface height (SSH) over April, May, and June to arrive at a coastal SSH between 44° and 50°N latitude. Data on annual zooplankton (copepod) anomalies were those reported in Mackas et al. (2006). Total dry weights of northern and southern species of copepods from southern Vancouver Island were used as an index to deviations from the stock-recruitment curve (Fig. 2.1). These anomalies are used to characterize the zooplankton species composition of the larval/juvenile sablefish habitat.
-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4
Northern Copepods
-2
-1
0
1
2
Rec
ruit
Dev
iatio
ns
y = -0.118 + x(1.191)
R2 = 0.312p-value = 0.0037
-0.2 0.0 0.2 0.4 0.6 0.8
Southern Copepods
-2
-1
0
1
2
Rec
ruit
Dev
iatio
ns
y = 0.052 + x(-1.378)
R2 = 0.236p-value = 0.0037
Fig. 2.1 Relation between sablefish recruitment deviations and northern (top) and southern (bottom) copepod species. Recruitment deviations were estimated either from 1971–2005, or from 1925–2006, depending on whether or not the long-term SSH data were used. The variance of the stock-recruit function (sigma-R) was estimated through iteration and matching the assumed variance to the resulting residual mean square error. The three environmental variables, SSH, northern zooplankton anomalies, and southern zooplankton anomalies were considered as covariates for
36
y = -0.321 + x(-0.566)
R2 = 0.403p-value < 0.0001
-3 -2 -1 0 1
SSH Index
-2
-1
0
1
2
Rec
ruit
Dev
iatio
nsy = -0.321 + x(-0.566)
R2 = 0.403p-value < 0.0001
-3 -2 -1 0 1
SSH Index
-2
-1
0
1
2
Rec
ruit
Dev
iatio
ns
Fig. 2.2 Relation between sablefish recruitment deviations and sea surface height (SSH). recruitment deviations from the fitted stock-recruit relation. The method employed in this assessment treats the natural log of the z-score of the environmental data in the same manner as all other survey data and is used as a tuning index for recruitment deviations from the stock-recruit function. The link between zooplankton and sablefish survival was first reported by McFarlane and Beamish (1992). To determine if these indices could be used to track changes in sablefish survival, each was regressed against the recruitment deviations from the model that included none of the indices (Fig. 2.2). While all three indices had highly significant (P < 0.05) relations to recruitment deviations, the most variation was explained by the SSH time series (P < 0.0001, R2 = 0.403). In late spring and early summer, young-of-year sablefish have developed from the larval stage, are free swimming and free feeding. At this stage, they search for zooplankton and other food while moving onshore to the nursery grounds. Low sea level and low values of the North Pacific Index suggest higher than expected recruitment. The tide gauge sea level data we use are not adjusted for barometric pressure, so they integrate both the atmospheric effects and the large-scale ocean conditions. That is, they integrate both the large-scale northeastern Pacific Ocean conditions with local upwelling and pressure. Sea level is also a good predictor of near-bottom ocean temperature along the shelf. Lower sea level is associated with colder than average water, more
upwelling, stronger southward currents and lower salinity. All these factors provide better habitat for young sablefish, as they occupy the shelf at this time of year. The timing of the spring transition may be as critical as the SSH level itself. That is, the contribution of the April SSH may have more of an influence on sablefish survival than the contributions from May and/or June. There is little doubt at this point that sablefish larval survival is modulated, at least in part, by climate and the manner in which climate affects the annual strength of the California Current System. This was evident in 2005, a remarkable year off the West Coast (Kosro et al., 2006), when in spring and early summer, the northern California Current System was anomalously warm because the spring transition to a wind-driven upwelling was delayed by 2–3 months. This delay worked its way up the food chain and resulted in the zooplankton community off the West Coast being dominated by small, southern species of copepods, which are of relatively poor nutritional value. It is this chain of events that presumably led to, among other things, poor sablefish recruitment in 2005. While one year does establish a theory such as this, the significant regressions on recruitment deviations and zooplankton anomalies are convincing in this regard; as goes the climate, so goes sablefish recruitment. Furthermore, SSH was well above average for the month of April, an early indication that 2005 survival would be low. In April 2006, SSH was similar to that of April 2005 but eventually decreased to below average levels in May and June, indicating that the spring transition was late in 2006. As a result, the 2006 year-class abundance may also be below average. This outcome should be evident in the 2007 shelf survey. While the significant relation between the SSH index and sablefish age-0 survival demonstrates that this should be a reliable (at least near term) index, the association with the zooplankton index may support the underlying biological mechanism for WHY this relationship exists. Investigations of the food habits of age-0 fish, especially during the spring months, could help provide this understanding. Also, further research should be conducted to evaluate alternative methods for incorporating ecosystem metrics into the assessment.
37
Northern California Current (British Columbia) Pacific cod (Gadus macrocephalus) production
Caihong Fu and Richard Beamish
Fisheries and Oceans Canada, Pacific Biological Station, 3190 Hammond Bay Road, Nanaimo, BC, V9T 6N7, Canada. E-mail: [email protected] Biology Pacific cod (Gadus macrocephalus) grow rapidly in Canada in the first year, reaching 30 cm by age 1, and are sexually mature by ages 2 to 3 years (Westrheim, 1987). Length at first maturity is approximately 40 cm, and length at 50% maturity is 55 cm. Pacific cod tend to disperse into deeper waters to feed, and congregate to spawn in shallower waters from February to March. They undergo a seasonal migration from shallow waters in the spring and summer to deeper waters in the fall and winter. Pacific cod in Canada are at the southern limit of their distribution and are therefore vulnerable to the expected climate and ocean changes. Four stocks of Pacific cod are defined for management in British Columbia: Strait of Georgia, west coast of Vancouver Island, Queen Charlotte Sound and Hecate Strait. In Hecate Strait, stock abundance remains at historic lows, recruitment of the last 9 year classes has been below historic levels, and the 1998 year class was the smallest ever. Abundance off the west coast of Vancouver Island also remains low. Pacific cod were common in the Strait of Georgia in the past. Presently, few remain, possibly because of the increase in water temperatures in recent years.
Fishery Small Pacific cod fisheries take place in Queen Charlotte Sound and off the lower west coast of Vancouver Island. The major fishing occurs in Hecate Strait. Spawning stock biomass and recruitment has been estimated for Hecate Strait using stock reconstruction based on ages estimated from lengths. The species is a significant component of the multi-species groundfish fishery in Hecate Strait. Annual yields have varied between a high of 8,870 t in 1987 to a low of approximately 200 t in 2001. Landings since the mid-1990s have been very low (Fig. 2.3). The trawl fishery has undergone a number of significant changes in recent years. Prior to 1992, the total catch of Pacific cod was unrestricted and the main management measures were area and season closures. Total allowable catches were introduced in the Hecate Strait area in 1992, in response to declining abundance. Trip limits were also introduced in the same year and these decreased steadily until 1995. For the 1996 season, trawl catches were limited to bycatch only because of stock concerns. Stock declines resulted from a decade of below average recruitment. The pattern of recruitment (age 2+) in Hecate Strait from 1960 to 1988 was similar but the recent regime (1989–1997) is characterized by 9 years of very poor recruitment.
I
0
2
4
6
8
10
12
14
1985 1987 1989 1991 1993 1995 1997 1999 2001
Land
ings
(100
0t)
0
1
2
3
4
5
6
7
8
9
10
Land
ed v
alue
C
A$m
illio
n
Fig. 2.3 Food and Agriculture Organization landings (grey bars), Fisheries and Oceans landings (white bars) and landed value (solid line) of Pacific cod in British Columbia fisheries.
38
Table 2.3 Summary of mechanisms underlying production of commercial species in Canada.
Region Species Index Mechanism
British Columbia
Pink and chum salmon
• Timing and duration of spring freshet,
• Strength of winds, • Intensity of Aleutian Low, • Sea surface temperature, • Pacific Circulation Index.
• Pink and chum salmon abundance in the ocean is regulated by growth and by predation,
• Growth is important because rapid early marine growth reduces the amount of predation and allows fish to better survive the marine winter,
• Thus growth in the first few months in the nearshore areas is critical,
• An earlier and stable mixing layer favours improved survival,
• Hatchery production may override natural controls.
Sockeye salmon
• River flows and temperatures, • Ocean entry time and size, • Rate of growth in first 6 weeks, • Aleutian Low, • Sea surface temperature in
summer and winter, • Pacific Circulation Index.
• Size at ocean entry and growth of juveniles in fresh water; • Rate of early marine growth (first 6 weeks), • Sea surface temperature in summer and winter, • Spawning stock size and freshwater rearing conditions.
Coho and chinook salmon
• Timing of spring river flows, • Wind direction and intensity, • Aleutian Low, • Sea surface temperature; • Timing of ocean entry, • Snow pack depth, • Summer river temperature.
• Timing of ocean entry must match with abundant plankton; • Prey must be plentiful and available within the first
6 weeks in the ocean, • Rapid growth by the end of June results in greater lipid
storage and improved marine survival over the winter, • Faster early growth facilitates a switching to larger prey
such as Pacific herring, • Hatchery fish compete with wild fish.
British Columbia
Sablefish • Timing of spring transition, • Aleutian Low, • Wind advection.
• Matching of copepod abundance with first feeding larval sablefish,
• Matching of coastal plankton production with the onshore movements of juveniles.
Pacific cod
• Bottom temperature, • Cross-shelf transportation, • Aleutian Low, • Wind direction and intensity.
• Cooler bottom temperature required for eggs to hatch; • Matching of prey and larval feeding, • Wind direction and intensity move juveniles into suitable
nursery areas.
Strait of Georgia, BC
Pacific hake and walleye pollock
• Fraser River flow, • Aleutian Low, • Timing of spring transition, • Behaviour of Neocalanus
plumchrus, • Bottom temperature, • Wind duration and intensity.
• Timing of spring bloom, • Size and abundance of adult fish, • Abundance of predators, • Productivity of the Strait of Georgia.
Climate and ocean effects The potential impacts of climate change on Pacific cod off British Columbia are summarized in Table 2.3. High sea levels in the Prince Rupert area are associated with high transport rates through Hecate
Strait, resulting in poor recruitment for Pacific cod. Sea levels were high in the Prince Rupert area up until 2003 when they began to decline. The relationship between sea level and recruitment can be
39
interpreted as an indication of the sensitivity of Pacific cod to ocean conditions. Temperatures in the range of 6.0 to 7.0°C appear to be optimal for Pacific cod recruitment. Temperatures higher than 7.0°C will likely decrease recruitment. Laboratory studies on the effect of temperature on Pacific cod egg survival indicate that the optimal temperature is between 3.5 to 4.0°C, with an acceptable range of 2.5 to 8.5°C (Alderdice and Forrester, 1971). Bottom temperatures in February that exceed 8.5°C would most probably reduce or eliminate recruitment. The southern limit of the commercial abundance of Pacific cod is northern Oregon and the southern limit of landings has been southern Oregon. Thus, there is little doubt that Pacific cod in British Columbia are at the southern limit of their distribution and are a
sensitive indicator of temperature increases. Projections of temperature changes are for both surface waters and ocean temperatures in general to change more slowly than land temperatures. However, in 2006 bottom temperatures in March at the Nanoose Lighthouse in the Strait of Georgia were approximately 9.6°C. Thus, it is apparent that several degrees of warming will change the southern limit of Pacific cod distribution, and will perhaps move it as far north as southern Alaska. Pacific cod recruitment is therefore a sensitive indicator of ocean changes affecting groundfish. It is predicted that Pacific cod will gradually disappear from the Strait of Georgia and off the west coast of Vancouver Island as bottom temperatures warm.
41
Northern California Current (British Columbia) sablefish (Anoplopoma fimbria) production
Richard Beamish
Fisheries and Oceans Canada, Pacific Biological Station, 3190 Hammond Bay Road, Nanaimo, BC, V9T 6N7, Canada. E-mail: [email protected] Biology In Canada, most juvenile sablefish (Anoplopoma fimbria) inhabit the shallow waters of Hecate Strait and the west coast of Vancouver Island, and move to slope waters off northern and southern British Columbia as they mature. It is believed that there are two sablefish populations of the west coast of North America, separated at approximately 50°N into an Alaskan population and a west coast population. There is debate about the degree of movement between these two populations. Recent information using stable isotopes found in otoliths indicates that a third population may exist at the southern limit of the distribution. Adult sablefish are abundant in coastal British Columbia waters at depths greater than 200 m, and are most abundant between 600 and 800 m. Spawning occurs from January to March along the entire Pacific coast, at depths of about 300 to 500 m, with no appreciable latitudinal spawning migration. Larval fish hatch at about 300 to 400 m, and then descend to 1000 m by 18 days after spawning. Within a few days of their descent, larval sablefish begin to ascend and feed on copepod larvae. Recruitment appears to be determined at the larval stage. Juveniles are found in more shallow (< 200 m) inshore waters and rear in nearshore and shelf habitats until age 2–5. As sablefish mature, they move back into the deeper water where spawning occurred. Growth of sablefish is rapid in the first few years, and then slows appreciably in this long-lived species. Length and age at 50% maturity are 58 cm, age 5 for females, and 53 cm, age 5 for males. Males tend to undergo a reduction in growth rate earlier than females. The majority of fish in the fishery are between the ages of 4 and 35, but the oldest sablefish aged to date is 113 years. In Canadian waters important prey items for sablefish include rockfish, Pacific herring and squid. Fishery The sablefish fishery is one of the few and oldest
deep water fisheries of the world. The fishery was reported to be as large as about 6000 t in the 1910s. This early fishery provided a smoked or salted meat product and used the livers for vitamin A and D production. The fishery was encouraged as a way to adapt to a shortage of meat during the First World War. Catches declined into the 1920s, possibly because of a reduced demand after the war. It was not until the late 1960s that catches of sablefish increased as a consequence of a Japanese fishery established outside of Canada’s exclusive fishing zone. Following the extension of the exclusive fishing zone in 1977, the fishery was exclusively Canadian and accounted for annual catches ranging from 830 t in 1978 to 5,381 t in 1989. The average commercial landings from 1978 to 2002 have been 4,071 t (Fig. 2.4). Sablefish landings fluctuated in the 1990s, with declines in recent years (Fig. 2.4). The declines in catch are related to declines in biomass as indicated by the trend in catch per unit effort (Fig. 2.5). Since 1973 the dominant fishing gear used by the fishery has been Korean conical traps (73% of the annual landings). In 1990, the fishery switched to an individual quota for each vessel in an attempt to stabilize the length of the fishing season, to improve management, to optimize the landed value, and to reduce quota over-runs. Climate and ocean effects
Potential impacts of climate change on sablefish off British Columbia are summarized in Table 2.3. Sablefish exhibit decadal-scale patterns in the relative success of year classes. By combining estimates of relative abundance of year classes determined from commercial catches and research surveys for adults and juveniles, McFarlane and Beamish (1992) and King et al. (2000) were able to reconstruct an index of year-class success. Year classes from 1960 to 1976 were generally poor, with no indication of good year-class success. The 1977 year class was exceptionally large and year classes from 1978 to 1988 were generally good or average.
42
C
0
1
2
3
4
5
6
1985 1987 1989 1991 1993 1995 1997 1999 2001
Land
ings
(100
0t)
0
10
20
30
40
Land
ed v
alue
CA
$mill
ion
Fig. 2.4 Food and Agriculture Organization landings (grey bars), Fisheries and Oceans Canada landings (white bars) and landed value (solid line) of sablefish in British Columbia fisheries.
Cat
ch p
er u
nit e
ffort
(kg/
trap)
810121416182022242628
) 1986 1988 1990 1992 1994 1996 1998 2000
sablefish
Fig. 2.5 Sablefish average catch per unit effort. Year classes following 1989 and 1990 were generally poor. McFarlane and Beamish (1986) proposed that sablefish live up to > 70 years because their ability to reproduce successfully each year was restricted by their biology and habitat. Their longevity, therefore, represented the longest period of unsuccessful reproduction over evolutionary time. If this hypothesis is valid, sablefish recruitment is closely related to specific kinds of climate-related ocean conditions. One limiting factor would be the ability of the fragile eggs to remain suspended in mid-depths, and for the larval sablefish to find copepod eggs and nauplii immediately after they begin exogenous feeding. It was observed that despite a large fecundity, strong year classes resulted from both large and small spawning biomass (McFarlane and Beamish, 1986). It was also observed that the production of strong year classes was closely associated with copepod production at a site off the west coast of Vancouver Island
(McFarlane and Beamish, 1992). The periods of above average year-class strength coincided with stronger Aleutian Lows, more frequent southwesterly winds, below average temperatures in the subarctic Pacific and warmer sea surface temperatures off the west coast of British Columbia (King et al., 2000). In general, the pattern of year-class success matches the patterns of regimes and regime shifts. This is evidence that there are trends in sablefish production that are related to climate and ocean conditions on a decadal scale. The recent declines in biomass (Fig. 2.5) reflect fishing removal and declining recruitment, which is related to the generally less productive regime in the 1990s. There is concern that fishing is reducing the number of age classes in the population, resulting in a population of relatively young fish (Beamish et al., 2006). However, because adult sablefish appear to be able to adapt to natural short-term and long-term
43
shifts in ocean conditions, it is probable that global warming will not have impacts on adult sablefish in a time frame of 50 years that will threaten the long-term dynamics of the population. This does not mean that specific global warming impacts on the survival of eggs, larvae and juveniles will not occur, but rather that the adult fish may be able to survive such adverse conditions. Also, there will be time to detect changes in the population dynamics and to consider management options, but this also does not mean that the population will be able to support current levels of exploitation, particularly at rates of around 15%. An immediate concern is the impact of fishing on the population structure and the natural ability of sablefish to survive in unfavourable conditions.
Fishing impacts over the past 30 years have reduced the percentage of older fish in the population. It is possible that resilience to long periods of unfavourable climate may be lost. If the remaining fish still have the ability to live for extended periods, this resiliency may not be lost. If the impacts of global warming are negative and reproduction is less successful or fails, it may be important to ensure that a percentage of the existing population is allowed to live to the older ages that existed prior to commercial fishing. This may be best accomplished by establishing no fishing zones that are in the most favourable spawning areas. If periods of intense Aleutian Lows increase as a consequence of global warming, there is a possibility that recruitment periods may increase relative to the past 50 years.
45
Northern California Current (British Columbia) pink (Oncorhynchus gorbuscha) and chum (O. keta) salmon production
Richard Beamish
Fisheries and Oceans Canada, Pacific Biological Station, 3190 Hammond Bay Road, Nanaimo, BC, V9T 6N7, Canada. E-mail: [email protected] Pink and chum salmon will probably respond in a similar way to a changing climate as the fry for both species spend only short periods in fresh water and enter the ocean at about the same time. In the Strait of Georgia their early growth is strongly correlated among years (Fig. 2.6). Pink salmon are the better indicator of climate impacts as their shorter life span and genetically distinct odd- and even-year runs provide more information sooner. Pink salmon biology Pink salmon (Oncorhynchus gorbuscha) are the most abundant of the Pacific salmon in British Columbia waters. They have the shortest life span, approximately two years from hatching, and are the smallest salmon species. Pink salmon form distinct spawning brood-lines, with some stocks spawning in years with even numbers (i.e., 1996) and some with odd numbers (i.e., 1997). The largest stocks of pink salmon occur in the Fraser River, where spawning takes place only in odd-numbered years. Farther north, spawning occurs in all years, with a tendency for the even-year spawning stocks to predominate.
Although pink salmon exist farther south than British Columbia, the center of distribution is north of British Columbia. Fraser River stocks, therefore, are close to the southern limit of the range. Females may produce 1,200 to 1,900 eggs, depending on the stock and the body length of the female. Spawning occurs from July through to early fall in riverbeds with coarse gravel. Pink salmon prefer to spawn in swift currents along the borders of streams or in riffle areas. Pink salmon fishery Pink salmon are not held in high esteem in British Columbia, thus catches probably are not a good indicator of abundance. This is particularly true in recent years when there has been exceptional production in some stocks with virtually no commercial fishery. In Canada, annual total catches of pink salmon averaged 19.7 thousand t or approximately 14.7 million fish from 1959 to 2000. Total catches of pink salmon increased after the 1977 regime shift, reaching a maximum in the early 1980s. Since the early 1990s, there has been a dramatic reduction in catch (Fig. 2.7A).
80
100
120
140
160
1996 1998 2000 2002 2004 2006 2008
Survey Year
Ave
rage
For
k Le
ngth
(mm
)
Chum salmonPink salmon
Fig. 2.6 Average fork lengths (mm) for juvenile chum (close circles, dashed line) and pink (open diamonds, solid line) salmon captured in July surveys in the Strait of Georgia from 1997–2007, using a mid-water trawl. The regression equation for the two datasets has an R2 value of 0.86, with an F-value of 43.40 (highly significant). Note that no survey was conducted in 2003. Juvenile pink salmon catches are very low in odd-numbered years (0 in the 2007 survey).
46
0
10
20
30
40
50
1950 1960 1970 1980 1990 2000 2010
Year
Cat
ch (t
x 1
03 )
A
0
10
20
30
40
1950 1960 1970 1980 1990 2000 2010
Year
B
Fig. 2.7 Canadian catches of (A) pink salmon and (B) chum salmon from 1950 to 2004.
0
500
1,000
1,500
2,000
2,500
3,000
1999 2002 2005Year to Sea
Cat
ch
0
1
2
3
4
Abu
ndan
ce (
x 10
6 )
Pink Catch
Coho abundance
Fig. 2.8 Strait of Georgia juvenile coho salmon abundance and pink salmon catch in July from 1997 to 2007. A recent analysis of the coast-wide production of pink salmon indicated that over the past decade, the spawning abundances may have doubled or tripled compared to numbers in the 1960s and 1970s. Pink salmon produced in the Fraser River historically account for about 60% of the total British Columbia catch, although some of this 60% is caught by the United States. Their population dynamics and the response of the fishery is probably a good indicator of the dynamics of most pink salmon stocks. As mentioned, virtually all stocks of pink salmon in the Fraser River spawn in odd-numbered years. The reason for the persistent dominance of these “odd-year stocks” and the general phenomenon of dominance among pink salmon stocks is unknown. In 2001 and 2003, there was a large return of pink salmon to the Fraser River. Management policy resulted in an exploitation rate on this return that was
very low, resulting in a spawning escapement that was approximately two times the highest estimated escapement on record and at least a magnitude larger than escapements in the 1950s. These exceptional returns were the production from one of the lowest escapements on record in 1999. Clearly, there was a dramatic increase in the marine survival of pink salmon fry entering the Strait of Georgia in 2000. Juvenile pink salmon in the Strait of Georgia compete with other juvenile salmon. Because pink salmon spawn in the Fraser River in odd-numbered years, the juveniles are abundant in the Strait of Georgia in even-numbered years. The abundance of juvenile coho salmon is frequently reduced in July in even-numbered years, compared to odd-numbered years (Fig. 2.8).
Abu
ndan
ce (x
106 )
47
Chum salmon biology Chum salmon (Oncorhynchus keta) may exist in about 800 rivers in British Columbia (Salo, 1991). There are distinct summer and fall spawning stocks, with the runs in the north being earlier than in the south. In the south, spawning can occur from October to January. In the spring, chum salmon are some of the first salmon to enter the ocean, remaining in the nearshore areas until the end of May (Healey, 1980). Chum then move into more coastal waters, and by mid-summer they leave the inshore areas and migrate offshore and into the Gulf of Alaska (Hartt and Dell, 1986). In recent years, chum salmon have remained inshore in large numbers through to mid-September (Beamish and Folkes, 1998). Most chum salmon (about 60%) spend three winters in the ocean. The remaining fish spend two or four years at sea. Very few spend five years at sea. The average size at return is about 70–75 cm. Since the late 1970s, chum salmon have been produced in hatcheries. The total production reached a maximum in the early 1990s, and in recent years has declined. Chum eggs are hatched in hatcheries, and the fry are fed in channels prior to release. In some cases, the fed fry are placed in sea pens and reared in salt water prior to release. Chum salmon fishery Chum salmon are generally the last species caught in the commercial fisheries. Most fisheries occur near river mouths, or what are called “terminal areas.” Fishing is with purse seines or gillnets. Smaller fisheries occur that use troll gear. Chum salmon that retain their “silver colour” are frequently marketed for smoking. Chum salmon that are coloured externally are valued mainly for their roe. In recent years, catches increased in the late 1980s through to the early 1990s (Fig. 2.7B). Catches were low in the mid- to late 1990s through to the present. Assessments of total production generally show that it has been stable for the past 30 years (Godbout et al., 2004; Spilsted, 2004). Climate, ocean and global warming impacts The specific factors that regulate salmon abundance in the ocean are not clearly identified, making it difficult to predict the impacts of altered marine ecosystems. Changes that could occur in fresh water as a consequence of climate change would have a major impact on Pacific salmon. These would impact on the migrations, spawning, hatching, and early rearing
phases. Physical changes in temperature, precipitation, groundwater discharge, and increased ice-free periods for lakes could affect community structure and the survival, growth, and distribution of salmon species. The Fraser River drainage in British Columbia is a major producer of Pacific salmon, accounting for 30 to 40% of all Pacific salmon produced in Canada. Because numerous stocks of the five species of salmon are at or near the southern limit of their range, the early impacts of climate change should be detectable in these stocks. We know that a warmer climate will increase water temperatures and decrease flows during spawning migrations, increasing pre-spawning mortality and reducing egg deposition (Figs. 2.9 and 2.10). A warmer climate will increase water temperatures during egg incubation stages, causing premature fry emergence and increased fry-to-smolt mortality. At the same time, a warmer climate will increase the severity and frequency of winter floods, thereby reducing egg-to-fry survival rates. The productivity of lakes will be altered, but impacts on their suitability as nursery habitats for juvenile sockeye salmon is not known.
January
July
May
March
November
September
October
August
June
April
Febuary
December
Fig. 2.9 Monthly percentage contribution to the total Fraser River flow (1913–2007) showing the increased discharge in the spring months and slightly declining discharge in the fall months.
February
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130
135
140
145
150
155
1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Year
25%
Flo
w D
ate
19-Yr smoothed
Fig. 2.10 Date that 25% of annual Fraser River total flow achieved (1913–2007), smoothed by a 19-yr running average, showing that over the last 80 years that date is about one week earlier.
It is highly probable that there will be a direct relationship between increased river temperatures and pre-spawning mortalities for all salmon. In fact, in the late 1990s, abnormally high pre-spawning mortality occurred, and one of the explanations related the mortality to changes in climate. The impact of climatic warming on winter water temperatures is uncertain. Winter water temperatures are related to groundwater base flows, lake water runoff, precipitation levels, and perhaps changes in snowmelt patterns. It is probable, however, that both summer and winter temperatures will be higher. Pacific salmon are particularly susceptible to temperature fluctuations because they have adapted to thermal regimes in both fresh and salt water. At the southern limits of salmon distribution, projected climatic changes would warm both marine and freshwater habitats, especially in the winter. High temperature has a profound effect on fishes because they cannot regulate their body temperature. Extreme temperatures may kill eggs, juveniles, or adult salmon; less extreme temperatures can affect growth, reproduction, and movement. Recommended temperatures for most Pacific salmon in fresh water range from about 7° to 16°C, with extremes from 3° to 20°C. Upper lethal temperatures are 25° to 26°C. Southern rivers could approach these higher limits under projected climatic scenarios. Stream discharge patterns have a high degree of variation, and changes in the variability of timing and the expected increase in variation could reduce
the accuracy of management and result in the need for reduced exploitation rates. Warming of fresh water in the north may also improve production. Much of the increases in total Pacific salmon abundance in the 1980s occurred in Alaska stocks, possibly indicating that warming in fresh water and coastal areas at this time was beneficial for salmon production. However, the function of northern aquatic systems has not been well documented, and large temperature increases could have unforeseen effects on Pacific salmon survival. Beamish and Noakes (2004) examined the role of climate change on the past, present and future of Pacific salmon species off the west coast of Canada. They suggested that existing stock assessment models might be inadequate to predict the dynamics of a stock in a future of climate change. They provided one scenario that predicts an increase in the total production of Pacific salmon as climate changes. This contrasts with other interpretations, such as that of Welch et al. (1998), who used estimates of sea surface temperature increases to propose that the ocean habitat available to sockeye salmon would diminish and would move farther north. Both scenarios are possible and need to be evaluated as climate changes become more extreme. Beamish and Noakes (2004) also noted that Pacific salmon in general, and pink salmon in particular, may move into the Canadian Arctic in increasing numbers. Pacific salmon are well known for their homing ability from feeding areas in the open ocean to the exact areas of their birth in coastal freshwater rivers.
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Less well known is their ability to stray. This straying rate can range up to 10% and provides Pacific salmon with an ability to adapt to large-scale climate change such as past periods of glaciation. The Arctic is one area that may be exhibiting early impacts of global warming. Model predictions indicate that a doubling of CO2 would reduce the extent of sea ice by 60% and the volume by 25–45% (Gordon and O’Farrell, 1997). There would also be greater freshwater runoff. During the period 1978–1996 there has been a 2.9–3.5% per decade decrease in the extent of Arctic sea ice (Cavalieri et al., 1997, Serreze et al., 2000). If such dramatic changes were to continue, conditions favorable to straying and perhaps feeding for pink and chum salmon may improve. In the ocean, we expect that the major sources of early marine mortality will become more variable and more extreme. Predation may increase as more pelagic predators such as Pacific hake and mackerel move north. Growth-based mortality (Beamish and Mahnken, 2001) may become more important and more variable. It may be possible to mitigate climate-related changes in fresh water; however, adjusting management to adapt to climate-related changes in the ocean would range between challenging and impossible. Nonetheless, if climate-related impacts could be identified quickly, it may be possible to use this information to show that
reductions in greenhouse gases are essential for the protection of Pacific salmon at their southern range. The short life span of pink salmon of two years between spawning, as well as their abundance and extensive distribution, makes pink salmon a desirable species for studies of environmental impacts such as greenhouse gas-induced climate change on the long-term population dynamics of all Pacific salmon. The increase in marine survival of this species in 2000 coincided with a major shift in the trend of climate indicators. Thus, there is evidence that pink salmon respond to climate changes in a time frame that could be used to detect the impacts of greenhouse gas-induced climate change. Pink salmon are an excellent indicator species because they are distributed throughout the subarctic Pacific and there is a long history of careful management. The single year class and the short life span facilitate associations between climate change and estimates of production. Additionally, the tendency for pink salmon to stray may also become an important indicator of factors affecting distributions. Mechanisms underlying production of Pacific salmon in British Columbia are summarized in Table 2.3. The possible impacts of global warming and a changing climate on these species are listed in Table 2.4. In general, Pacific salmon from the Fraser
Table 2.4 Potential affects of global warming on the ecology of Pacific salmon in British Columbia.
Fresh water Salt water
Earlier timing of returning adults Earlier time and size of ocean entry
Earlier time of entry into rivers Changes in predator composition
Higher river temperatures Changes in ocean productivity will affect species differently
River flow rates Changes in growth in the first marine year
Reduced access to spawning areas Changes in juvenile migratory routes in response to temperature
Earlier changes in the hatching times Increase in temperature
• Changes in the productivity of freshwater ecosystems, • Impact of species new to the ecosystem.
• Decrease in salinity, • Changes in the Aleutian Low and Pacific Decadal Oscillation
that are currently unknown.
More variability in growth Earlier timing of spring transition
Increased percentage of hatchery salmon • Changes in competitors for food, • Reduced marine growth.
Reduced ability to adapt to changes in habitat • More variability in straying rates, • Loss of ability to adapt because of the loss of wild fish.
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River stocks will suffer major impacts in fresh water and in the ocean. Pink and chum salmon from the Fraser River will be reduced in abundance as a consequence of reduced fresh water survival as juveniles and spawning adults. Pink and chum salmon stocks from the Skeena and Nass rivers and to the north could increase in abundance as a result
of improved ocean productivity. Pacific salmon probably will begin to reproduce in Arctic rivers. Basin-scale changes in growth, survival and straying rates will all indicate when large-scale changes occur. Pink salmon will be excellent indicators of climate-related change and could be used as biological indicators of the changing ecosystems.
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Northern California Current (British Columbia) ocean shrimp (Pandalus jordani) production
Caihong Fu Fisheries and Oceans Canada, Pacific Biological Station, 3190 Hammond Bay Road, Nanaimo, BC, V9T 6N7, Canada. E-mail: [email protected] See Table 2.1 for mechanisms and indicators.
Alaska salmon production
Anne Hollowed
Alaska Fisheries Science Center, NMFS, NOAA, 7600 Sand Point Way NE, Seattle , WA 98115-6349, U.S.A. E-mail: [email protected] Mechanisms underlying the production of Alaskan salmon stocks are summarized in Table 2.5. Table 2.5 Proposed mechanisms underlying salmon production in Alaska.
Index Mechanism Season Citation
Spring temperature, out-migration timing, timing of spring bloom
Ice breakup affects timing of outmigration
Spring Rogers (1988); Burgner (1991); Shotwell et al. (2006)
Timing of spring bloom, apparent growth effect
Match/mismatch; critical period Spring Cushing (1972); Beamish and Mahnken (2001); Mackas et al. (2001)
Temperature effect on predation; diet composition of predators, spatial distribution of predators, year-class strength of young-of-the-year pollock
Alternative prey for predators and daily ration for predators
Spring–Summer
Pearcy (1992); Farley et al. (2007)
Timing of spring transition Match/mismatch; critical period Spring–Summer
Logerwell et al. (2003); Peterson and Schwing (2003)
Prey availability to smolts, zooplankton abundance and species composition
Growth, foraging success Summer Cushing (1972); Willette et al. (1997); Beamish and Mahnken (2001); Peterson and Schwing (2003)
Ocean habitat volume, mixed layer depth, and fronts
Competition for prey – partitioning predators and prey
Summer Coachman (1986); McRoy et al. (1986)
Euphausiid abundance Reduced predation risk when alternative prey abundant – high prey availability leads to accelerated growth; critical size
Summer Cooney (1993); Willette et al. (1997); Beamish and Mahnken (2001)
Diet composition of predators, abundance of predators
Predation Summer–Fall
Pearcy (1992)
Winter survival Critical size/critical period; winter survival of larvae and juveniles
Winter Beamish and Mahnken (2001)
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U.S. walleye pollock (Theragra chalcogramma) production in the eastern Bering Sea and Gulf of Alaska
Kevin Bailey and Anne Hollowed
Alaska Fisheries Science Center, NMFS, NOAA, 7600 Sand Point Way NE, Seattle , WA 98115-6349, U.S.A. E-mail: [email protected] Walleye pollock, Theragra chalcogramma, is a marine fish species that is highly fecund, producing millions of eggs per individual spawner, and has highly variable mortality rates in early life stages (Bailey and Ciannelli, 2007). A consequence of this reproductive strategy (producing lots of young with high expected mortality) is fluctuating annual recruitment levels (the number of young fish entering the population each year). The instability of fluctuating year classes must be buffered by the averaging effect of many age classes in the population. Although the recruitment of walleye pollock in the Gulf of Alaska is one of the better studied processes in the world, admittedly there is still much that is not well understood. Pollock is an opportunistic species that has a broad distribution range and has adapted to different environments (Bailey et al., 1999). On the other hand, the population is limited by finding and adapting to local conditions that favor successful spawning (maximizing reproduction) and survival (minimizing mortality) of the early life stages. Local populations of pollock respond differently to shifting environmental regimes, as warming periods have seen those stocks at the southern margins of the pollock distribution falter or fail (Bailey et al., 1999). In the center of its distribution of mass in the eastern Bering Sea, pollock have been (if at all) favorably impacted by periods of environmental warming (Hollowed et al., 2001; Quinn and Niebauer, 1995). Delayed springtime blooms may be a factor negatively influencing recruitment in the Bering Sea (Mueter et al., 2006). However, another recent study correlates cool temperatures in the Bering Sea with increasing recruitment (Megrey, Alaska Fisheries Science Center, Seattle, WA, pers. comm.). In the Gulf of Alaska the situation appears more complex, as pollock have been initially favored by a warm environmental regime (e.g., stock increase in the late 1970s and mid-1980s) but negatively impacted afterwards (Hollowed et al., 2001), possibly in association with an increase of predator biomass. However, a recent study has tentatively and weakly linked cool springtime sea surface temperature (SST) with increasing recruitment (A’mar, University of
Washington, Seattle, WA, pers. comm.). These conflicting findings illustrate the difficulty in relating environmental indices near the birth of the cohort to highly variable recruitment 3–4 years later. Pollock spawn once per year, in an event that involves individual pairing and courtship (Baird and Olla, 1991), and that is highly concentrated in space and time (Kendall and Picquelle, 1990). Given the fragility of eggs and larvae to environmental conditions, and their concentration in space and time, the survival of a whole year class is vulnerable to the vagaries of the ocean and weather, such as storms passing through Shelikof Strait, the major spawning site. On the other hand, pollock dynamics are buffered partly by multiple spawning stocks, spawning in different locales, compensatory mortality and by multiple age groups in the population. Spawning in different locations moderates the effects of temporal variation in habitat suitability by taking advantage of spatial variation. While the long life span of pollock is an adaptation that tempers the high variation in year-class strength, a high abundance of predators on adults, as well as commercial fishing that removes older age groups, reduces the age span over which mean abundance is averaged (and perhaps other aspects of the contribution of older fish to the population’s viability). As a consequence, the population could be dependent on fewer age groups, hence contributing to overall stock variability (Longhurst, 2002). The spawning regions of pollock are noted for mixing of coastal and nutrient-laden oceanic waters and stratification of the water column, which leads to enhanced productivity. These conditions favor the survival of early life stages of pollock. In the Gulf of Alaska pollock typically spawn during the last week in March and first week in April in Shelikof Strait (Ciannelli et al., 2007). In this area, mixing of the Alaska Coastal Current, the Alaska Stream and coastal water, along with springtime increases in sunlight, warming and water column stratification, leads to a spring bloom and increasing zooplankton production. Zooplankton prey of pollock larvae are concentrated by
54
eddies and fronts (Napp et al., 1996), resulting in favorable feeding conditions. Larvae are advected toward favorable nursery areas, such as the waters around the Shumagin Islands. Mortality rates of pollock eggs and young larvae are high, with estimates ranging from 4 to 40% per day, but they decline as larvae develop. Larval conditions can vary from year to year and by location, and high percentages of larvae in the ocean have sometimes been observed in poor feeding condition (Theilacker et al., 1996). Egg and early larval development and survival are suboptimal at temperatures below about 0o and above 10o–13oC (Blood, 2002). Very high and low temperatures can be lethal to eggs and larvae, but generally for the Gulf of Alaska population, in the central part of the species distribution (4o–6oC springtime SST), higher temperatures may favor survival of early stages, perhaps through one or more indirect mechanisms (Bailey, 2000). Optimal prey levels for successful feeding depend on many other conditions affecting predator–prey encounters, including larval size, temperature, light levels, turbidity and turbulence (Porter et al., 2005), but generally they range between 20 and 40 prey/liter (Theilacker et al., 1996). In very high density patches, pollock may deplete their prey (Duffy-Anderson et al., 2002), leading to slower growth and higher mortality. At later stages, predation on juveniles is an important source of mortality. Large piscivorous fishes, including halibut, cod, arrowtooth flounder and flathead sole contribute significantly to mortality of juvenile pollock (Livingston, 1993). An evolving perspective of the recruitment of pollock is that it is a complex process, influenced by both high frequency changes in the environment of young fish stages and by bounding effects of low frequency changes in the ecosystem (Bailey et al., 2005). As a consequence, recruitment is caught in the push-pull between these scales. Larval mortality is highly variable and subject to many interacting high frequency factors (such as storms and prey availability), with feedback and non-linearity (Bailey et al., 2004). Larvae show sophisticated behaviors involving choice and decisions when confronted with multiple and perhaps conflicting stimuli (Olla et al., 1996). For example, they avoid turbulence by descending (Davis, 2001), taking them out of the photic zone and into colder water where growth is less optimal and prey are less abundant
(Kendall et al., 1994). Under normal circumstances, these conditions are associated with poor feeding and high mortality. However, when prey are driven deeper by turbulence and there is bright daylight, these conditions are then optimal for feeding (Porter et al., 2005). Thus, environmental factors driving recruitment are governed by complex relationships. On the other hand, although juveniles also show complicated behaviours in response to the environment (e.g., Sogard and Olla, 1996), they are less impacted by small-scale physics, and juvenile mortality seems to be more stable and predictable, occurring largely as a result of predation and density-dependent mechanisms. The role of density-dependent mechanisms also seems to be influenced by environmental factors (Ciannelli et al., 2004). Environmental and ecosystem structure shifts may also have indirect effects on pollock survival, such as causing changes in the operation of density-dependent mechanisms. For example, Ciannelli et al. (2004) found that the level of density-dependent mortality in juvenile pollock increases when water temperature and predation intensity are high. The build-up of predators in the community represents a low frequency, slowly changing pattern with lagged effects. Changes in ecosystem structure may be related to the relative stage in life history when recruitment is determined (i.e., larval versus juvenile control) (Bailey, 2000). Therefore, control points may change from year to year, and depend on longer-term changes in the environment and community structure, such as those occurring with environmental and biological regime shifts. General patterns in recruitment have been well described by models incorporating stochastic mortality related to environmental conditions during the larval period and by deterministic factors and constraints during the juvenile period (Ciannelli et al., 2004; Ciannelli et al., 2005). It should be noted that although we have a fairly good understanding of how small-scale factors affect survival of early life stages, knowing how these factors combine and interact over larger and longer space and time scales (scale up), thus determining how pollock populations respond to the environment, fluctuating and shifting prey and predator abundances, and to self-regulation, is a difficult problem. Proposed mechanisms underlying production of walleye pollock in the eastern Bering Sea are summarized in Table 2.6.
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Table 2.6 Proposed mechanisms underlying production of walleye pollock in the eastern Bering Sea.
Index Mechanism Season/stage Citation
Pelagic ocean habitat volume (Winter) – location of fronts + MLD + temperature + sea ice extent
Location of spawning Winter Proposed – NPRB BSIERP
Winter temperature, and onset of maturation
Timing of spawning Winter Blood (2002)
Timing of spring bloom Prey availability for larvae; “critical period”
Winter preconditioning–Spring
Cushing (1972); Napp et al. (1996)
Transport to the northeast (northern part of inner and middle front)
Transport to suitable nursery grounds – spatial separation from cannibalistic parents
Winter–Spring Wespestad et al. (2000)
Seasonal temperature Metabolic rates – stage duration All Bailey et al. (1996); Bailey (2000)
Predator abundance Predation (by salmon, ATF and cannibalism) of age-0 pollock
Summer Livingston (1993); Bailey (2000); Farley et al. (2007)
Pelagic ocean habitat volume MLD and location of fronts + temperature (cold pool)
Competition for prey and spatial overlap of predators and prey
Summer Ciannelli et al. (2004); Kotwicki et al. (2005); Porter et al. (2005)
Strength of MLD Partitioning predators and prey Summer Bailey (1989)
Summer productivity/wind mixing Age-0 prey availability Summer Bond and Overland (2005)
Prey availability/size at age Growth of age-0 out of cannibalism size range
Summer Foy and Paul (1999; GOA example); Brodeur et al. (1999); Swartzman et al. (2002);
Euphausiid, copepod abundance for pollock
Reduced predation risk when alternative prey abundant – high prey availability
Summer Cooney (1993); Willette et al. (1997)
Fall size at age Critical size amount of energy on board and large enough to reduce predation and continue foraging – a measure of overwintering survival
Fall Beamish and Mahnken (2001)
ATF – arrowtooth flounder, GOA – Gulf of Alaska, BSIERP – Bering Sea Integrated Ecosystem Program, NPRB – North Pacific Research Board, MLD – mixed layer depth
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U.S. groundfish production in the eastern Bering Sea
Tom Wilderbuer
Alaska Fisheries Science Center, NOAA, 7600 Sand Point Way NE, Seattle, WA 98115, U.S.A. E-mail: [email protected] An important discovery in Bering Sea fisheries oceanography in recent years was the realization that climate variability at the scale of decades may have a direct effect on the productivity of winter spawning flatfish (Wilderbuer et al., 2002). The mechanism under consideration is related to the advection/ dispersal of larvae to favorable nearshore nursery habitat. To better understand the variability in the transport of larvae to these areas, an understanding and description of the following physical oceanographic variables would be needed: • Spring winds over the eastern Bering Sea shelf
from April–June (Alaska Peninsula north to
Nunivak Island, shelf margins to inner Bristol Bay);
• Spring currents (April–June) from about 40 m depth to the surface for the same areas as described above. Starting points should vary about 56°N latitude and 165°W longitude;
• The influence and extent of the tidal cycle on the Bering Sea shelf oceanography. Flatfish are known to take advantage of selective tidal transport and this may be the mechanism that takes them to their settlement areas once they get close enough.
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U.S. crab production in the eastern Bering Sea
Gordon H. Kruse
University of Alaska Fairbanks, 11120 Glacier Highway, Juneau, AK 99801-8677, U.S.A. E-mail: [email protected] The general state of knowledge on the mechanisms linking climate to crab production in the eastern Bering Sea can be summarized as follows: • Good progress has been made to compare the
geographic distributions of crabs with respect to topography, bottom temperature, and life stage.
• Reconstructed time series of abundance and recruitment have been generated by length-based population estimation models.
• Hypotheses have been generated, but testing of hypotheses is very limited.
• Forecasts of future crab abundances under global climate change using IPCC model outputs is difficult until further research is conducted to identify and confirm evidence for hypothesized recruitment mechanisms.
Tanner crab (Chionoecetes bairdi) • A workshop of Tanner crab experts was convened
to generate a list of hypothesized biophysical mechanisms by life stage as well as a conceptual model of recruitment mechanisms (Tyler and Kruse, 1997). Recruitment is only weakly related to stock size; residuals suggest a 13- to 14-year cycle likely driven by environmental factors (Zheng and Kruse, 2003).
• Statistical testing of a few hypotheses has been completed, with results pointing toward adverse effects of cold bottom temperatures on gametogenesis and favourable effects of northeasterly winds on larval advection and/or prey production. However, regression models with these two variables explained only 50% of the variability (Rosenkranz et al., 1998, 2001). An update of the wind relationship explains even less variability than previously identified.
• There is no relationship between Pacific cod biomass and Tanner crab recruitment, but Zheng and Kruse (2006) found some evidence that spatial distributions of predator and prey are more important that predator abundance.
Other mechanisms need more thorough investigation through statistical testing and simulation modeling.
These include the positive effect of warmer sea surface temperatures on production of copepod nauplii, thus enhancing larval Tanner crab feeding success (Tyler and Kruse, 1997; Rosenkranz et al., 2001). Table 2.7 lists six potential mechanisms linking climate and Tanner crab production. Many more could be examined (Tyler and Kruse, 1997). There is a high level of uncertainty about mechanisms affecting Tanner crab recruitment, but work to date sets a good foundation for additional research into these mechanisms. Development of a recruitment model for Tanner crab is currently being proposed to the North Pacific Research Board. A length-based model, already developed for previous management strategy evaluations (Zheng and Kruse, 1999, 2000), could form the basis for such projections once the climate–recruitment linkages become better elucidated. Red king crab (Paralithodes camtschaticus) • A workshop of red king crab experts was
convened to generate a list of hypothesized biophysical mechanisms by life stage, as well as a conceptual model of recruitment mechanisms (Tyler and Kruse, 1996). A Ricker stock-recruit relationship is indicated, but autocorrelated recruitment trends are also consistent with decadal climate shifts (Zheng and Kruse, 2003).
• Limited correlation analysis points toward potential relationships between recruitment and the strength of the Aleutian Low (perhaps through a wind-mixing, prey (Thalassiosira diatom) availability mechanism (Tyler and Kruse, 1996; Zheng and Kruse, 2000, 2006). Also, red king crab recruitment is negatively correlated with the biomass of both Pacific cod and yellowfin sole (Zheng and Kruse, 2006). Correlations with barometric pressure, cod abundance and yellowfin sole abundance explain only 36%, 36%, and 69% of the variability, respectively, but comprehensive statistical testing of hypotheses has not been conducted.
• Northeastward shifts in red king crab brood stock are associated with increased bottom shelf
60
temperatures in the late 1970s and early 1980s, possibly reducing the successful drift of larvae to juvenile nursery areas in nearshore areas of Bristol Bay (Loher and Armstrong, 2005).
• Another recruitment mechanism may be that reduced sea ice leads to better matching of the spring bloom with larval hatching times (Kruse, 2007), which may be opposite for snow crab that may benefit in cold years (Orensanz et al., 2004). It has been proposed that increased heat in the ocean causes a shift of energy flow away from benthic invertebrates to pelagic species (Grebmeier et al., 2006).
• All mechanisms require more thorough investigation through statistical testing and simulation modeling.
Table 2.8 lists six potential mechanisms linking climate and red king crab production. Many more could be examined (see Tyler and Kruse, 1996). There is a high level of uncertainty about mechanisms affecting red king crab recruitment. Work to date has focussed primarily on the generation of hypotheses and the cursory examination of evidence for a handful of them. A more thorough retrospective analysis of red king crab recruitment mechanisms has not yet been conducted. A length-based model, already developed for management strategy evaluations (Zheng et al., 1997a,b), could perhaps form the basis for such projections once the climate–recruitment linkages become better elucidated.
Table 2.7 Summary of proposed mechanisms linking climate to Tanner crab production.
Index Mechanism Season Citation
Minimum bottom temperature by T/V Oshoro Maru at 56–58°N and 164–166°W
Cold temperatures interrupt or delay gametogenesis.
Annual minimum Rosenkranz et al. (2001)
Mid-shelf sea surface temperature (possibly use a quadrangle centered at 55°N, 165°W)
Warm temperatures promote production of copepod nauplii.
May–June (full range, April–October)
Rosenkranz et al. (2001)
Wind from the northeast (60°) Winds alongshore the north side of the Alaska Peninsula promote coastal upwelling and production of copepod nauplii.
May–June (full range, April–October)
Rosenkranz et al. (1998); Rosenkranz et al. (2001)
Wind from the northeast (60°) Northeast winds promote retention of larvae in offshore, deep-water, and fine sediment nursery habitats.
May–June (full range, April–October)
Rosenkranz et al. (1998); Rosenkranz et al. (2001); Zheng and Kruse (2006)
ROMS output of Tanner crab advection relative to cold pool at 55.5–58°N, 165–174°W
Larval settlement in cold pool adversely affects survival.
May–June (full range, April–October)
Tyler and Kruse (1997)
ROMS output of Tanner crab advection relative to Pacific cod geographic distribution in area (55.5–58°N, 165–174°W)
Larval settlement in areas occupied by cod adversely affects survival.
May–June (full range, April–October)
Tyler and Kruse (1997); Zheng and Kruse (2006)
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Table 2.8 Summary of proposed mechanisms linking climate to red king crab production.
Index Mechanism Season Citation
North Pacific Index or Aleutian Low Pressure Index
Stronger winds, associated with deeper Aleutian Lows, increase water column mixing, reducing the proportion of Thalassiosira diatoms (prey of crab larvae).
December–March Tyler and Kruse (1996); Zheng and Kruse (2000, 2006) based on APPRISE studies in 1980s in Auke Bay (Bienfang and Ziemann (1995); Ziemann et al. (1991))
Winds in Bristol Bay (55–58°N, 158–164°W)
Same mechanism as above, but direct measure of wind
April–June (full range, April–October)
Same as above
Bottom temperature in Bristol Bay (55–58°N, 158–164°W)
Distribution of brood stock shifts to northeast with warming.
Annual (summer proxy) Loher (2001); Loher and Armstrong (2005)
ROMS drift tracks from start locations within 55.5–57.5°N, 161–163°W
Survival depends on successful advection to nearshore nursery areas in Bristol Bay.
April–June (full range, April–October)
Hsu (1987); Loher (2001); Loher and Armstrong (2005); Zheng and Kruse (2006)
Timing of spring bloom Match/mismatch of crab larvae and diatoms
April–May (full range, April–October)
Many citations, including Tyler and Kruse (1996)
Biomass of Pacific cod, yellowfin sole
Predation of ages 0–3 years Annual Zheng and Kruse (2006)
Snow crab (Chionoecetes opilio) • A workshop of snow crab experts was convened
to generate a list of hypothesized biophysical mechanisms by life stage, as well as a conceptual model of recruitment mechanisms (Kruse et al., 2007).
• Analysis of stock and recruitment data does not suggest a density-dependent relationship; residuals indicate favorable environmental conditions in the 1980s and poor conditions in the 1990s (Zheng and Kruse, 2003).
• Snow crab brood stock shifted to the northwest after the 1970s (Zheng et al., 2001, Orensanz et al., 2004, 2005; Zheng and Kruse, 2006). The shift north may be related to warming bottom temperatures in the late 1970s; return shifts may be prevented by predation by cod (Orensanz et al., 2004).
• Snow crab nursery habitat is located in the northeast shallow waters of the eastern Bering Sea (Somerton, 1981; Zheng et al., 2001; Zheng and Kruse, 2006; Ernst et al., 2005). Crabs appear to follow temperature gradients during their cross-shelf, ontogenetic migrations with age/size (Ernst et al., 2005).
• Recruitment may be driven by larval advection from hatching locations to nursery areas. Recruitment success may be affected by changing brood stock location driven by bottom temperature, advection by ocean currents, duration of pelagic stage dependent upon development rates driven by upper water column temperature, and survival affected by cod predation (Orensanz et al., 2004; Zheng and Kruse, 2006). Examination of these processes is ongoing by the National Marine Fisheries Service and University of Washington scientists (Parada et al., pers. comm.).
• Recruitment may be driven partly by the match between snow crab larvae and the spring bloom, which may be timed best in cold years (Orensanz et al., 2004).
• Cannibalism among year classes is likely an important density-dependent survival mechanism, with successful older year classes suppressing young of the year (Lovrich and Sainte-Marie, 1997; Sainte-Marie and Lafrance, 2002).
• To date, work has focussed on hypothesis generation rather than testing. Ongoing research using an individual-based model incorporating ROMS output is the first substantive effort to examine recruitment processes for snow crabs in the eastern Bering Sea.
62
Table 2.9 Summary of proposed mechanisms linking climate to snow crab production.
Index Mechanism Season Citation Bottom temperature in eastern Bering Sea shelf (56.5–62°N, 168–175°W)
Distribution of brood stock shifts to northeast with warming
Annual (summer proxy)
Orensanz et al. (2004)
ROMs drift tracks from start locations in area (56.5–62°N, 168–175°W)
Survival depends on successful advection to northeast, shallower waters
May–October Orensanz et al. (2004); Parada et al. (pers. comm.)
Sea surface temperature (56.5–62°N, 168–175°W)
Temperature in upper water column affects rate of development and settlement time
May–October Kon (1970)
ROMS outputs of snow crab advection relative to Pacific cod geographic distribution in area (56.5–62°N, 168–175°W)
Larval settlement in areas occupied by cod adversely affects survival
May–October Orensanz et al. (2004); Zheng and Kruse (2006); Kruse et al. (2007); Parada et al. (pers. comm.)
Timing of spring bloom Match/mismatch of crab larvae and spring bloom
April–May Orensanz et al. (2004)
Abundance of immature snow crabs
Settling snow crabs are cannibalized by juveniles already occupying the nursery areas
Annual Lovrich and Sainte-Marie (1997); Sainte-Marie and Lafrance (2002)
Table 2.9 lists six potential mechanisms linking climate and snow crab production. Many more could be examined (see Tyler and Kruse, 1996). There is a high level of uncertainty about mechanisms affecting snow crab recruitment. Retrospective analyses have been constrained by the lack of a length-based population model for snow crabs. Recent
development of this model now provides a recruitment time series and could give the framework for projections of future recruitment. However, analyses of linkages between climate and recruitment may need to be concluded before projections could be reasonably attempted.
63
Forecasting Japanese commercially exploited species
Shin-ichi Ito1, Kazuaki Tadokoro1 and Yasuhiro Yamanaka2 1 Tohoku National Fisheries Research Institute, Fisheries Research Agency, 3-27-5 Shinhama-cho, Shiogama,
Miyagi 985-0001, Japan. E-mail: [email protected] 2 Hokkaido University, N10W5, Kita-ku, Sapporo, Hokkaido 060-0810, Japan In this short paper, we briefly answer the requests sent by the workshop convenors in advance of the workshop. Request 1. Oceanographers and climatologists should identify what techniques they recommend for forecasting oceanographic responses to climate change. To make better predictions, we must have knowledge of the performance of the models, i.e., “which model can properly represent the target area”. There is a good example, described by Overland and Wang (2007), that only 10 of 22 tested Atmosphere–Ocean Coupled General Circulation Models (AOGCM) can reproduce an appropriate Pacific Decadal Oscillation (PDO) pattern in the 20th century. Therefore, the first step is to select models that are capable of being applied to the target regions (space) and phenomena (space and time). To make better predictions, we should also have a comprehensive understanding of the mechanisms that link environments and ecosystem responses. However, even the environmental data are usually limited in time and space. Therefore, we recommend testing model performance by reconstructing past environments using the data assimilation method. Especially for physical models, adjoint methods can fit the model to observational data while conserving model dynamics. For example, to examine bottom-up forcing, coupled bio-physical models would be used to predict lower trophic productivity. Output from these models could be compared to observed patterns of plankton variability to assess model performance. We cannot eliminate the uncertainties in forecasting. Uncertainties arise not only from the models but also from the scenarios. Therefore, we recommend ensemble forecasts using several models under several scenarios. It is obvious that computer power is limited, even in the current IT era. The horizontal resolution of AOGCMs is becoming finer, but usually ¼° is the
limit. One way to save the computational costs of contemporary models is to nest higher resolution regional models into coarser resolution global models. Another possibility is to run higher resolution regional models under the predicted atmospheric conditions. However, in those cases, the selection of the horizontal boundary conditions becomes an issue. In summary, the following steps are recommended: • verification of model performance (bias +
variability); • reconstruction of past environments by lower
trophic level model simulation on the data assimilated physical model outputs;
• ensemble forecasts; • nesting model or simple regional model run. Request 2. Fisheries scientists should provide reports on the current state of knowledge regarding mechanisms linking climate forcing and fish production, and the uncertainty associated with these mechanisms. Walleye pollock
Spawning is restricted to a very narrow area with the main spawning ground occurring in Hidaka Bay, Hokkaido. The environmental conditions of Hidaka Bay and the flow field inside and outside of the bay is the most important factor for determining recruitment (see the VENFISH special issue in Fisheries Oceanography, Vol. 13, Suppl. 1, 2004). Pacific cod
The landings of Pacific cod in the Tohoku area (northern part of Honshu) have a good correlation with the recruitment of the 1+ year class. This suggests the importance of recruitment to the total biomass. A recent study by Shimizu and Narimatsu (2006) showed that sea surface temperature in June is a critical control of recruitment. The mechanism is still unclear but June is the season when the life stage of Pacific cod changes from pelagic to demersal.
64
Pacific herring
The abundance of herring remains low in Japan. Several local herring increases have occurred since the late 1990s. Recently Ookouchi et al. (2008) showed that this increase was associated with the southward shift of the Oyashio in summer. Sardine, anchovy, jack mackerel, Pacific saury
Small pelagic species alternate with an inter-decadal time scale. The cycle changes from “sardine” to “saury and anchovy and horse mackerel” to “jack mackerel”, correlating well with the PDO (Yatsu et al., 2005). Takasuka et al. (2007) and subsequent studies simply explained this alternation by an “optimal growth temperature hypothesis”. We guess this means that the larvae always meet with prey limitations, then higher temperatures decrease the growth by higher metabolism. Another possibility is predator migration dependency on temperature. An additional important aspect of these small pelagic fish is migration. They spawn in the subtropical ocean and make feeding migrations to the subarctic region. Therefore, the transportation of larvae by the Kuroshio and migration mechanism is a key factor for their survival (e.g., Ito et al., 2007). Pacific salmon
Pacific salmon utilize fresh water, coastal and open ocean habitats so the background ecosystems are totally different from each other. Therefore, we must take into account not only marine life stages but also freshwater stages (e.g., Rand et al., 2006). Azumaya and Ishida (2004) and Kamezawa et al. (2007) applied a model of Pacific salmon, and their results showed that summer conditions are most important to salmon growth. Request 3. Fisheries scientists should identify what physical or bio-physical drivers or environmental thresholds would be needed to forecast future fish or shellfish production. These requests should specify the location (latitude and longitude) and time period for the requested physical variable. Based on the mechanisms mentioned in Request 2, we declare the drivers we need to know for the following species:
Walleye pollock
• Temperature and salinity structures in Hidaka Bay and its offshore region;
• Circulation in Hidaka Bay and its offshore region with 1/108° resolution;
• Primary and secondary production in Hidaka Bay and its offshore region (krill and benthos biomass around the Hokkaido coast).
Pacific cod
• Circulation around Japan, especially the Oyashio variability with 1/12° resolution;
• Temperature and salinity structure around Japan; • Primary and secondary production around Japan
(krill and benthos biomass along the coast of Japan). Pacific herring
• Circulation around Japan, especially the Oyashio variability with 1/12° resolution; if local stocks are considered, 1/216° resolution will be needed;
• Temperature and salinity structure around Japan; • Sea ice distribution; • Primary and secondary production around Japan. Sardine, anchovy, jack mackerel, Pacific saury
• Circulation around Japan, especially the Kuroshio variability with 1/12° resolution; if fluctuations in spawning grounds are considered, 1/108° resolution will be needed;
• Temperature and salinity structure around Japan; • Primary and secondary production around Japan; • For saury, circulation, T, S, production are needed
for the Pacific Basin; • Predator index; • Migration index. Chum salmon
• Circulation in the North Pacific including coastal areas with 1/108° resolution; if local stocks are considered, 1/216° resolution will be needed;
• Temperature and salinity structure in the North Pacific;
• Sea ice distribution; • Primary and secondary production in the North
Pacific; • Freshwater systems (lakes, ponds, rivers).
65
Russian fish production in the Japan/East Sea
Yury Zuenko, Vladimir Nuzhdin and Natalia Dolganova
Pacific Research Institute of Fisheries and Oceanography, TINRO-Center, 4 Shevchenko Alley, Vladivostok, Russia E-mail: [email protected] The recent commercial fishery in the Russian sector of the Japan/East Sea (JES) is based on two fish species: Japanese sardine, Sardinops melanostictus, and walleye pollock, Theragra chalcograma, with landings exceeding 10,000 tonnes/year. The catches of other pelagic and groundfish species such as mackerels, herring, flounders, salmon, greenlings, cod, and smelts, as well as bottom invertebrates are considerably lower (although valuable for some crabs, shrimps, and sea urchins). Russian fisheries for Pacific saury and squids are almost absent in the JES, in spite of their high abundance. Annual catches of both sardine and pollock fluctuate greatly (Fig. 2.11), due mainly to variability in environ-mental conditions. Japanese sardine Fluctuations of the sardine stock and catch in the JES are synchronous with the stock and catch fluctuations in the Pacific Ocean, and have a decadal scale. Both “sardine disasters” in the 20th century were initiated by climate shifts but the directions of environmental changes were opposite. The early 1940s were characterized by ocean cooling, but the more recent cooling in the early 1990s occurred after ocean
temperatures increased. The apparent contradiction becomes clear under the match-mismatch hypothesis (Cushing, 1972) and its relevance to larval survival. The sardine has a very early (in late winter) spawn timing in the southeastern JES, with timing dependent on winter temperatures. The spring bloom in these spawning grounds also begins very early (in early spring) but its timing depends on spring temperature conditions. So, to match the larval hatch with the spring bloom, a thermal regime should be stable, as occurs with a warm-spring-after-warm-winter or cold-spring-after-cold-winter. The former was observed in the 1930s and the last one occurred in the late 1970s–1980s. Stability was disrupted by sudden winter warming in the late 1980s, causing a mismatch of timing, and consequently, low survival of the sardine larvae. Sardine reproduction is also determined by parental stock value (adults in the range of age from 3+ to 6+), individual fecundity, and population density. Moreover, the adult survival rate is important. Therefore, the commercial stock of this species in year j can be roughly estimated by the following multiple regressive model:
0
20
40
60
80
100
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160
1960
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1990
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annu
al c
atch
of p
ollo
ck, 1
03 t
0
200
400
600
800
1000
1200
1400
1600
annu
al c
atch
of s
ardi
ne, 1
03 t
pollock sardine
Fig. 2.11 Annual all-nation catch of Japanese sardine and Russian catch of walleye pollock in the JES.
66
( )[ ]∑=
−−− ⋅−−⋅=6
3i
iijDijMijj sSkMkfSS ,
where f is a fecundity parameter, M is the match-mismatch factor (M = 0 in the case of stable winter–spring sea surface temperature (SST anomalies), s is the annual adult survival, and kM, kD are empirical coefficients. This model generates stock abundance forecasts for at least 3 years in advance. To predict the climate change consequences, we need to couple the model with long-term forecasts of SST. Walleye pollock Walleye pollock is a long-lived species so its stock is made up of several generations. That is why its
catch does not respond clearly to climate changes. However, the relative abundance of cohorts does. Year-to-year fluctuations have a strong correlation with zooplankton biomass in the spawning grounds during spring–summer when the pollock larvae and fingerlings develop (Fig. 2.12). Zooplankton abundance in the JES depends on thermal conditions in the subsurface and intermediate layers where large-sized adult copepods have their dormancy and maturation. On decadal scales, cohort abundance is negatively correlated with the thermal regime (Fig. 2.13), in spite of the positive correlation between the subsurface temperature and zooplankton.
0
50
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gene
ratio
ns a
bund
ance
, mln
ind.
0
200
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600
800
1000
zoop
lank
ton
biom
ass,
mg/
m3
pollock generations zooplankton biomass Fig. 2.12 Interannual variation of walleye pollock cohort abundance in the northwestern JES. In the last two decades abundance was correlated positively with zooplankton biomass in the main spawning grounds in Peter the Great Bay (no zooplankton data for early years).
-0.1
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subs
urfa
ce te
mpe
ratu
re a
nom
aly,
o C
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gene
ratio
ns a
bund
ance
, 106 in
d.
subsurface temperature pollock generations Fig. 2.13 Decadal-scale changes of walleye pollock abundance in the northwestern JES (9-year running mean smoothing) in relation to mean decadal temperature anomalies in the thermocline–200 m layer on the section 41°30′–42°20′ N, 132°00′ E.
67
These statistical links are explained by simple ecological mechanisms: (i) the larvae mortality dependence on prey abundance and (ii) appearance–disappearance of competitive warm-water species (sardine or common squid) on the biogeographic boundary where Peter the Great Bay is situated. Thus, these links could be used for pollock stock and catch forecasting with lead times of up to 3 years. Long-range forecasts are available on the basis of subsurface temperature predictions. Forecasting of environmental factors important for fish populations An Intermediate Water mass is formed in the JES at the Polar (Subarctic) Front in winter, and its temperature is determined by winter SST. The spreading of this water is a mechanism to transfer climate change impacts into the subsurface and intermediate layers of the JES, which are so important for zooplankton and walleye pollock. We suppose that this spreading has an anticyclonic route, and estimate that the transfer time of the impacts from the sea surface to the subsurface layer is 0–3 years (the maximal delay is observed in the northwestern part of the JES).
Winter SST in the JES is strongly correlated (negatively) with the development of the Siberian High: lower air temperatures arise from higher atmospheric pressures in Siberia, which create strong winter monsoon winds and lower SSTs (Fig. 2.14). Thus, the consequences of climate change for SST, subsurface temperature, and zooplankton abundance could be modeled using significant statistical links, at least until the system works. Temporal lags between climate shifts and zooplankton responses can be as long as 5–6 years, mainly because of the time taken for Intermediate Water to be transported to the most distant areas of the JES. Spatial and temporal scales of environmental variables useful for fish production forecasting The location and time period of physical predictors for fish production forecasting are determined by the mechanisms of influence and are presented in Table 2.10. In as much as some populations depend on prey abundance, zooplankton biomass is included as one of the subjects for forecasting.
-3
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-1
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1
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SST
anom
aly,
оС
1020
1025
1030
1035
SHI,
hPa
winter SST in the southern Japan Sea (35-38 N)Siberian High Index
Fig. 2.14 Siberian High Index (mean surface pressure in the area 40–65°N, 80–120°E in December–February, after Panagiotopoulos et al., 2005) and winter SST anomalies in the southern JES relative to 1970–2000 (JMA data averaged within 35–38°N for December–February).
68
Table 2.10 Proposed mechanisms underlying fish production in the JES and their spatio-temporal scales.
Species, group Index Mechanism Season Location
Japanese sardine SST Match/mismatch of larvae and spring bloom
Winter–Spring Spawning grounds in the southeastern JES
Walleye pollock Zooplankton biomass; subsurface temperature
1) Survival of larvae and fingerlings dependent on prey ability,
2) Competition with warm-water species.
• Spring–Summer, • The whole life span.
• Spawning grounds at Primorye and Hamgen coasts,
• The whole northern and central JES.
Pacific saury Zooplankton biomass; subsurface temperature
Survival of larvae and fingerlings dependent on prey ability
Spring–Summer Wide spawning area in the coastal areas of southern and central JES
Arabesque greenling SST Unclear, possibly related to larvae survival
Winter Peter the Great Bay
Saffron cod SST; Siberian High Index; ice cover
Spawning duration depended on ice cover period
Winter Coastal areas of Peter the Great Bay
Zooplankton biomass in deep-water areas (mostly large cold-water copepods)
Subsurface temperature
Optimal window for maturation in the period of dormancy
Summer–Fall–Winter Deep-water areas
Zooplankton biomass in the coastal zone (mostly copepods)
Winds at Primorye coast; NPI
Wind-induced advection into coastal zone
Spring, Summer, Fall (depending on species)
Primorye shelf
SST – sea surface temperature, JES – Japan/East Sea, NPI – North Pacific Index
69
Chum salmon (Oncorhynchus keta) production in Korea
Sukyung Kang1, Suam Kim2 and Hyunju Seo3 1 National Fisheries Research and Development Institute, 408-1 Sirang-ri, Gijang-eup, Gijang-gun, Busan 619-905,
Korea. E-mail: [email protected] 2 Pukyong National University, 559-1 Daeyeon-3-dong, Nam-gu, Busan 608-737, Korea 3 Hokkaido University, 3-1-1 Minato-cho, Hokkaido 041-8611, Japan Biology Chum salmon (Oncorhynchus keta) are distributed widely across the North Pacific Ocean and in the Bering Sea, generally north of about 40–45°N latitude (Salo, 1991). Chum salmon production in Korea hinges on the release of fry salmon from hatcheries. The released fishes are thought to migrate from coastal areas to the Bering Sea and subarctic North Pacific (Fig. 2.15). Release of fingerlings by artificial stocking programs occurs annually from mid-February to late March. There is very little information about the ocean distribution and migration of Korean chum salmon in the North Pacific Ocean, but for now we assume that it is similar to that of chum salmon from Japanese hatcheries that are released from western Honshu and western Hokkaido. The juveniles remain in the Okhotsk Sea from summer to late autumn and overwinter in the western North Pacific Ocean, and the immature salmon (age 2 to 5) then migrate back and forth between the Bering Sea and the North
Pacific seasonally (Azumaya and Ishida, 2001; Urawa et al., 2001). Spawning adults return to their natal rivers on the Korean Peninsula between mid-September and mid-December. Main diets of chum salmon caught from the Subarctic Current and Alaskan Gyre areas consist of zooplankton such as copepods, pteropods, amphipods, euphausiids, and jellyfish (Kaeriyama et al., 2004). Fishery Escapement of chum salmon to Korean streams was very poor until the late 1980s. Since 1990, catches from the set net fisheries were included in these statistics so that an abrupt increase appears in 1990 (Fig. 2.16). The proportion of salmon catch by set net fisheries in total catch was about 70~80% for the 1990s and decreased to 50~70% in the 2000s. Salmon catch was seriously reduced from 553 million t in 1997 to 51 million t in 2000. It increased slightly in 2002 though it was still less than 200 million t.
Fig. 2.15 Schematic migration model of chum salmon released in Korea (modified from Urawa et al., 2001) (Seo et al., 2006).
70
0
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300
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500
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1970 1974 1978 1982 1986 1990 1994 1998 2002Year
Cat
ch (x
mt)
Fig. 2.16 Annual catches of chum salmon in Korean waters, 1991–2003. Open circles represent the catch in Korean rivers. Dark circles mean total catch from rivers and the set net fishery.
-6.0
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ocean entry year
Tem
pera
ture
(C)
-0.6
-0.3
0.0
0.3
0.6
Return rate(%
)
May SST Return rate (%)
Fig. 2.17 Interannual variability in water temperature at river mouths and the return rate to Namdae-cheon, a major release site in Korea.
Fig. 2.18 Interannual variation in return timing of Korean chum salmon.
71
Climate and ocean effects The return rates of chum salmon to Korean waters were less than 0.4% for the broods released in the mid- and late 1980s, but escapement increased after the set net fishery was allowed in 1990. Return rates ranged from 1.0 to 1.5% and suddenly dropped to 0.1% for the 1997 brood stock which is still struggling to recover. The return rate has a negative correlation with the mean water temperature of the coastal waters in April and May (r = –0.485, p < 0.05 in April; r = –0.599, p < 0.01 in May) (Fig. 2.17). High water temperature after late April could affect the survival of juvenile chum salmon and might be a reason for their mass mortality. Maturing chum salmon returned in mid-November in the 1980s, in early November and/or in late October in the 1990s
and in 2000s. The return timing in 2003 was almost 2 weeks earlier than in the 1980s (Fig. 2.18). Zooplankton biomass in the coastal waters of Korea in the year of release was not correlated with the return rate of Korean chum salmon. However, early growth of chum salmon did correspond with trends of zooplankton biomass. In river mouths and coastal areas, growth rates of fingerling salmon were higher in the 1990s than in the 1980s. On the east coast of Korea, zooplankton abundance increased after the late 1980s, which may have caused favorable growth conditions for young salmon in the 1990s (Seo et al., 2006). They reported that food availability is more important than seawater temperature in chum salmon growth in the North Pacific, although it is not easy to decouple the effects of these factors on fish growth.
73
Jack mackerel (Trachurus japonicus) production in Korea
Jae Bong Lee1 and Chang-Ik Zhang2 1 National Fisheries Research and Development Institute, 408-1 Sirang-ri, Gijang-eup, Gijang-gun, Busan 619-905,
Korea. E-mail: [email protected] 2 Pukyong National University, 559-1 Daeyeon-3-dong, Nam-gu, Busan 608-737, Korea Biology Jack mackerel (Trachurus japonicus) migrate to the northern area of the East China Sea during January–March, and to the spawning grounds near the western Kyushu Islands and areas between Jeju Island and Tsushima Island during April–May (Lee, 1970; Nakashima, 1982). Jack mackerel spawn in water temperatures from 16° to 22°C. In the East China Sea, jack mackerel larvae are mainly distributed in the Kuroshio frontal area along the continental margin in winter and spring. They usually feed on zooplankton, including the larvae of anchovy (Engraulis japonicus, E. mordax) and bigeye sardine (Etrumeus teres). As the fish grow older, they move to deeper water and feed on larger prey such as Amphipoda and Mysidacea, but usually not Copepoda (Cha, 1991). There is evidence that the recruitment of jack mackerel is determined by the degree to which eggs and larvae are affected by environmental factors. These include seawater temperature, salinity, volume transport of the Kuroshio Current, and zooplankton biomass in the Korean waters (Zhang and Lee, 2001). Increases in salinity in April, volume transport of the Kuroshio Current, and zooplankton biomass were significantly correlated with increased recruitment of jack mackerel in the following year (Table 2.11).
Fishery Jack mackerel are found throughout the Yellow Sea, the East China Sea, and the southern East/Japan Sea (Kim, 1970). They are caught with large purse seines, bottom trawls, and drift gill nets in Korean waters. About 80% of the total catches are from the large purse seine fishery, mostly in the Korean region of the East China Sea. The history of catches is characterized by a cyclic behavior at a relatively low frequency (30 years) with a high coefficient of variation (CV = 0.74). Annual catch reached the highest level of 48 thousand t in 1956, and then declined below 10 thousand t in the late 1960s. In the early 1980s, the annual catch increased and it has been about 28 thousand t in recent years, 2002–2006 (Fig. 2.19). Climate and ocean effects The volume transport of the Kuroshio increased after 1977. This intensified the Tsushima Warm Current connected with the Kuroshio in the inshore waters of southern Korea. Warm saline waters have a positive correlation with the density of jack mackerel distribution (Cho, 1981). Therefore, the increase in salinity of the East China Sea may have triggered the increases in recruitment and biomass of jack mackerel in the early 1980s, resulting in the
Table 2.11 Correlation coefficient matrix between recruitment of jack mackerel and environmental factors (Zhang and Lee, 2001).
Recruitment Temperature Salinity Volume transport Zooplankton biomass
Recruitment 1.000 – – – – Temperature 0.043 1.000 – – – Salinity 0.529* 0.153 1.000 – – Volume transport 0.487* – 0.264 0.230 1.000 – Zooplankton biomass 0.547* – 0.288 0.248 0.399** 1.000
* P < 0.01, ** P < 0.05
74
0
10
20
30
40
50
60
1926 1936 1946 1956 1966 1976 1986 1996 2006Year
Cat
ch (x
103 t)
Fig. 2.19 Annual catches of jack mackerel in Korean waters, 1926–2006.
Fig. 2.20 Diagram showing the effects of the early 1980s environmental shift of the jack mackerel population based on the bottom-up hypothesis (Zhang and Lee, 2001). increased fish catches. The effects of the early 1980s environmental shift are hypothesized to have affected the productivity of the jack mackerel population (Fig. 2.20). As the volume transport of the Tsushima Warm Current increases, resulting in higher salinity in the East China Sea, spawning grounds for jack mackerel become optimal. Consequently, high concentrations of the spawning stock were observed in this area. As the intensity of the Tsushima Warm Current increased, the variation
of zooplankton biomass in the Korean waters of the East China Sea was expected to correspond to the variation in the stratification of the ocean. Since the thermocline in the East China Sea is formed at 30– 50 m depth, and the water is relatively shallow (75–150 m) (NFRDI, 2005) where photosynthesis can be active in all seasons, the increase in zooplankton biomass may not be directly related to the primary production in the early 1980s in the Korean waters of the East China Sea. The increase of recruitment and
75
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67
Shifts in the mean for Recruitment, 1968-2004Probability = 0.05, cutoff length = 10
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Fig. 2.21 Ocean surface currents from spawning ground (rectangle) in the East China Sea (left) and variation in recruitment of jack mackerel (right) in Korean waters, 1968–2004 (Lee, 2005). biomass in plankton-feeding jack mackerel stock after the early 1980s can be attributed to the aggregation of spawning stock and the increase of prey organisms in the East China Sea (Zhang and Lee, 2001). Jack mackerel share 63.4% of their habitat with chub mackerel in Korean waters (Zhang and Lee, 2001). Jack mackerel share 36.6% of their habitat with both chub mackerel and Japanese sardine, and further, they were not found to co-exist with Japanese sardine exclusively during the period of 1980–1998 (Fig. 2.19). The 1988 climatic regime shift affected the habitat of jack mackerel by shifting their distribution southward to 27oN. After 1988, the distributional overlap of jack mackerel and chub mackerel decreased. These shifts in the habitats of both species resulted in Pacific sardine occupying a habitat area separated from the shared mackerel distributions (Zhang et al., 2004). An important discovery in Korean fisheries oceanography in recent years is the realization that climate variability, at the scale of decades, may have a direct effect on the productivity of winter spawning jack mackerel (Fig. 2.21). The mechanism under consideration is the advection/dispersal of larvae to favorable nearshore nursery habitats.
To better understand the variability in the transport of larvae to these favorable areas, an understanding and description of the following physical oceanographic variables are needed: • Springtime winds over the southern East China
Sea from February–June. Future winds from IPCC models have been provided by Dr. Nicholas Bond.
• Springtime currents (February–June) from about 50 m depth to the surface for the same areas as described above. Starting points should vary about 26°N latitude and 123°E longitude.
Recruitment is related to successful settlement in nursery grounds transported by the Kuroshio. Within a “touchdown zone” where larval jack mackerel settle out of the planktonic larval stage within 32°–35°N and 125°–131°E, environment data needed are: • temperatures and salinities at the surface and
50 m layer depth, • zooplankton biomass during April–June, • number of days of ocean current trajectory within
the zone during April–June. Possible impacts of changing climate on jack mackerel are listed in Table 2.14.
77
Chub mackerel (Scomber japonicus) production in Korea
Jae Bong Lee1, Sukyung Kang1, Suam Kim2, Chang-Ik Zhang2 and Jin Yeong Kim1 1 National Fisheries Research and Development Institute, 408-1 Sirang-ri, Gijang-eup, Gijang-gun, Busan 619-905,
Korea. E-mail: [email protected] 2 Pukyong National University, 559-1 Daeyeon-3-dong, Nam-gu, Busan 608-737, Korea Biology Chub mackerel (Scomber japonicus) that migrate throughout the warm water are widely distributed and caught in the western, southern, and southeastern seas around the Korean peninsula, in the East China Sea, and around Japan. This species is distributed on the continental shelf from the surface to 300 m depth in the Yellow Sea and the East China Sea (Fig. 2.22). Chub mackerel spawn from February to May when the water temperature ranges from 15° to 23°C, and migrate to wintering grounds between Jeju Island and Tsushima Island in the East China Sea during
December–February. The fecundity of chub mackerel ranges from 112,000 to 570,000 eggs, and the fork length of 50% mature females is 28.7 cm (Choi, 2003). Mean length of chub mackerel has tended to decrease since the 1970s, and the proportion of small mackerel has increased (Fig. 2.23). The fork length was about 32 cm in the early 1970s, but it has continuously decreased, with a mean fork length in 2002 of 29.2 cm. Some biological parameters such as natural mortality, maturity length, and recruitment age have been estimated (Table 2.12).
Fig. 2.22 Seasonal migration route of chub mackerel near the Korean Peninsula.
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Table 2.12 Estimated biological parameters of chub mackerel.
Parameter Estimate Unit
Natural mortality 0.6 year–1
Maturity length (50%) 24.0 fork length, cm
Recruitment age 0.5 year
Mean length (2006) 29.3 fork length, cm
Mean age (2006) 2 year
22
24
26
28
30
32
34
1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006
Fork
leng
th (m
ean;
cm
)
20
30
40
50
60
70
80
1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006
Year
Perc
enta
ge
Fig. 2.23 (a) Long-term variation of mean length of captured chub mackerel, and (b) percentage of fish smaller than the 50% maturity length to the total catch of chub mackerel from Korean waters, 1970–2006.
0
100
200
300
400
500
1926 1936 1946 1956 1966 1976 1986 1996 2006Year
Cat
ch (x
103 t)
Fig. 2.24 Annual catches of chub mackerel in Korean waters, 1926–2006.
(a)
(b)
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Fishery About 70–80% of the global catch of chub mackerel by countries, including Korea, Japan, and China, occurs in the northwestern Pacific Ocean (FAO, 2002). Korean annual catches were below 100 thousand t until the mid-1980s, and increased to 150 thousand t in 1988 and 1989 (Fig. 2.24). The annual catch was just 100 thousand t in the early 1990s. However, it started to increase to over 150 thousand t from the mid-1990s and reached the highest level of 415 thousand t in 1996. Currently, the catch is about 170 thousand t. This species is usually caught by large purse seines, drift gill nets, and set nets in Korean waters. About 90% of total chub mackerel catches are from the large purse seine fishery, mainly in the East China Sea and in the western and southeastern seas of Korea. Climate and ocean effects Recruitment is correlated with salinity (r = 0.454, P < 0.05), with zooplankton biomass (r = 0.692,
P < 0.01), and with copepod biomass (r = 0.815, P < 0.01) (Choi et al., 2000). Chub mackerel catches were highly correlated with sea surface temperature (SST) in December, Chl-a in June, and large zooplankton in the fall and early winter (Table 2.13). Chub mackerel share 35.7% of their habitat with both jack mackerel and Pacific sardine, and 28.6% with jack mackerel or 3.1% with Pacific sardine in Korean waters (Zhang et al., 2004). The 1988 climatic regime shift affected the habitat of chub mackerel by widening and moving it to the west of 128°E (Fig. 2.25). After 1988, the distributional overlap of chub mackerel and jack mackerel decreased. These shifts in the habitats of jack mackerel and chub mackerel resulted in Japanese sardine occupying a habitat area separated from the shared mackerel distributions. Replacement in biomass between chub mackerel and Japanese sardine stocks came from the continuous competition of prey and space among major small pelagics in Korean waters (Zhang et al., 2000).
Table 2.13 Selected correlation coefficients, which were statistically significant for fish catch vs. their biotic and abiotic environmental factors (Kim and Kang, 2000).
Variable Anchovy
Engraulis japonica Mackerel
Scomber japonicus Sardine
Sardinops melanosticta Anchovy 1.0 0.790** 0.453*
Mackerel 0.790** 1.0 0.602**
Sardine 0.453* 0.602** 1.0 SST (December) 0.419* 0.436* 0.327 Chl-a (April) 0.186 0.019 0.561**
Chl-a (June) 0.635** 0.523** 0.264 Chl-a (August) 0.442* 0.377 0.276 Zooplankton (February) – 0.559** – 0.406* – 0.339 Zooplankton (April) – 0.304 – 0.408* – 0.291 Copepods (April) 0.563* 0.434 – 0.398 Copepods (June) 0.121 0.571* – 0.042 Copepods (December) 0.635* 0.477 – 0.277 Chaetognaths (April) 0.647** 0.307 – 0.499 Chaetognaths (October) 0.728** 0.512* – 0.321 Chaetognaths (December) 0.558* 0.129 – 0.427 Euphausiids (June) 0.349 0.356 – 0.550*
Euphausiids (December) 0.768** 0.603* – 0.492 Amphipods (April) 0.713** 0.504* – 0.395 Amphipods (December) 0.712** 0.616* – 0.423
Sampling month in parenthesis; * indicates that the correlation is significant at the 0.05 level, and ** at the 0.01 level.
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Fig. 2.25 Joint confidence regions in the habitat of major small pelagics in Korean waters during the periods of (a) pre- and (b) post-1988 climatic regime shift (Zhang et al., 2004).
Table 2.14 Potential impacts of climate changes on the population ecology of mackerels in Korean waters.
Species Impact factor Impacts of climate and ocean condition Chub mackerel
• Seawater temperature, Kuroshio/Tsushima Warm Current,
• Phytoplankton bloom, • Zooplankton biomass
Chub mackerel abundances increased in the mid-1990s, a period of high SST in December due to ENSO seemed to cause high growth and good year classes in Korean waters (Kim and Kang, 2000; Zhang et al., 2004).
Jack mackerel
• Kuroshio/Tsushima Warm Current, • SST, • Phytoplankton bloom, • Zooplankton biomass
As the Kuroshio transports eggs and larvae of jack mackerel to the fishing grounds, recruitment of jack mackerel increased. Increase in zooplankton biomass contributed to increase in spawning biomass of jack mackerel (Lee, 2005; Zhang et al., 2004).
The possible impacts of global warming and a changing climate on mackerels are listed in Table 2.14. In general, these fishes will be
influenced by seawater temperature and the Kuroshio/Tsushima Warm Current in Korean waters.
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References Alderdice, D.F. and Forrester, C.R. 1971. Effects of
salinity, temperature, and dissolved oxygen on early development of the Pacific cod (Gadus macrocephalus). J. Fish. Res. Bd. Can. 28: 883–902.
Azumaya, T. and Ishida, Y. 2001. Effects of ocean currents on juvenile chum salmon migration. NPAFC Tech. Rep. 2, pp. 17–19.
Azumaya, T. and Ishida, Y. 2004. An evaluation of the potential influence of SST and currents on the oceanic migration of juvenile and immature chum salmon (Oncorhynchus keta) by a simulation model. Fish. Oceanogr. 13: 10–23.
Bailey, K.M. 1989. Interaction between the vertical distribution of juvenile walleye pollock Theragra chalcogramma in the eastern Bering Sea, and cannibalism. Mar. Ecol. Prog. Ser. 53: 205–213.
Bailey, K.M. 2000. Shifting control of recruitment of walleye pollock (Theragra chalcogramma) after a major climate and ecosystem change. Mar. Ecol. Prog. Ser. 198: 215–224.
Bailey, K.M. and Ciannelli, L. 2007. Walleye pollock. pp. 85–93, in Long Term Ecological Change in the Northern Gulf of Alaska, edited by R.B. Spies, Elsevier, Oxford, UK.
Bailey, K.M., Brodeur, R.D. and Hollowed, A.B. 1996. Cohort survival patterns of walleye pollock, Theragra chalcogramma, in Shelikof Strait, Alaska: a critical factor analysis. Fish. Oceanogr. 5 (supplement 1): 179–188.
Bailey, K.M., Hollowed, A.B. and Wooster, W.S. 2004. Complexity of marine fisheries dynamics and climate interactions in the northeast Pacific Ocean. In Marine Ecosystems and Climate Variation – The North Atlantic. A Comparative Perspective, edited by N. Stenseth, G. Ottersen, J.W. Hurrell, Oxford University Press.
Bailey, K.M., Quinn, T., Grant, W.S. and Bentzen, P. 1999. Population structure and dynamics of walleye pollock, Theragra chalcogramma. Advances Mar. Biol. 37: 179–255.
Bailey, K.M., Ciannelli, L., Bond, N., Belgrano, A. and Stenseth, N.C. 2005. Recruitment of walleye pollock in a complex physical and biological ecosystem. Prog. Oceanogr. 67: 24–42.
Baird, T.A. and Olla, B.L. 1991. Social and reproductive behaviour of a captive group of walleye pollock, Theragra chalcogramma. Envir. Biol. Fishes 30: 295–301.
Beamish, R.J. and Folkes, M. 1998. Recent changes in the marine distribution of juvenile chum salmon off Canada. N. Pac. Anadr. Fish Comm. Bull. 1: 443–453.
Beamish, R.J. and Mahnken, C. 2001. A critical size and period hypothesis to explain natural regulation of salmon abundance and the linkage to climate and climate change. Prog. Oceanogr. 49: 423–438.
Beamish, R.J. and Noakes, D.J. 2004. Global warming, aquaculture, and commercial fisheries. pp. 25–47, in
Stock Enhancement and Sea Ranching: Developments, Pitfalls and Opportunities, edited by K.M. Leber, S. Kitada, H.L. Blankenship and T. Svasand, Blackwell Publ. Ltd. Oxford.
Beamish, R.J., McFarlane, G.A. and Benson, A. 2006. Longevity overfishing. Prog. Oceanogr. 68: 289–302.
Bienfang, P.K. and Ziemann, D.A. 1995. APPRISE: a multi-year investigation of environmental variation and its effects on larval recruitment. pp. 483–487, in Climate Change and Northern Fish Populations, edited by R.J. Beamish, Can. Spec. Publ. Fish. Aquat. Sci. 121.
Blood, D.M. 2002. Low-temperature incubation of walleye pollock (Theragra chalcogramma) eggs from the southeast Bering Sea shelf and Shelikof Strait, Gulf of Alaska. Deep Sea Res. II 49: 6095–6108.
Bond, N.A. and Overland, J.E. 2005. The importance of episodic weather events to the ecosystem of the Bering Sea shelf. Fish. Oceanogr. 14: 97–111.
Brodeur, R.D., Wilson, M.T., Walters, G.E. and Melnikov, I.V. 1999. Forage fishes in the Bering Sea: Distribution, species associations, and biomass trends. pp. 509–536 in Dynamics of the Bering Sea, edited by T.R. Loughlin and K. Ohtani, University of Alaska Sea Grant, AK-SG-99-03, Fairbanks, AK.
Burgner, R.L. 1991. Life history of sockeye salmon (Onchorhyncus nerka). pp. 3–117, in Pacific Salmon Life Histories, edited by C. Groot and L. Margolis, University of British Columbia Press, Vancouver.
Cavalieri, D.J., Gloersen, P., Parkinson, C.L., Cow, J.C. and Zwally, H.J. 1997. Observed hemispheric asym-metry in global sea ice changes. Science 278: 1104–1106.
Cha, B.Y. 1991. Study on the feeding ecology of horse mackerel (Trachurus japonicus). Doctoral dissertation, Pukyong National University, Seoul, Korea, 33 pp. (in Korean with English abstract).
Cho, K.D. 1981. Studies on the distribution and fluctuation of the purse-seine fishing grounds in relation to oceanographic conditions in the East China Sea. 1. The distribution of mackerels and jack mackerel fishing grounds. Bull. Korean Fish. Soc. 14: 239–252 (in Korean with English abstract).
Choi, Y.M. 2003. Stock assessment and management implications of chub mackerel, Scomber japonicus in Korean waters. Doctoral dissertation, Pukyong National University, Seoul, Korea, 130 pp. (in Korean with English abstract).
Choi, Y.M., Lee, J.B., Zhang, C.I., Baik, C.I. and Park, J.H. 2000. Assessment and management of common mackerel (Scomber japonicus) in Korean waters, based on the relationship between recruitment and the ocean environmental factors. Abstracts of Korea–Japan Joint GLOBEC Symposium: Long-term Variation in the Northwestern Pacific Ecosystems, 52 pp.
Ciannelli, L., Chan, K.-S., Bailey, K.M. and Stenseth, N.C. 2004. Nonadditive effects of the environment on
82
the survival of a large marine fish population. Ecology 85: 3418–3427.
Ciannelli, L., Bailey, K.M., Chan, K.-S., Belgrano, A. and Stenseth, N.C. 2005. Climate change causing phase transitions of walleye pollock (Theragra chalcogramma) recruitment dynamics. Proc. R. Soc. B, 272: 1735–1743, doi: 10.1098/rspb.2005.3136.
Ciannelli, L., Bailey, K.M., Chan, K.-S. and Stenseth, N.C. 2007. Phenological and geographical patterns of walleye pollock (Theragra chalcogramma) spawning in the western Gulf of Alaska. Can. J. Fish. Aquat. Sci. 64: 713–722.
Coachman, R.T. 1986. Circulation, water masses, and fluxes on the southeastern Bering Sea shelf. Cont. Shelf Res. 5: 23–108.
Cooney, R.T. 1993. A theoretical evaluation of the carrying capacity of Prince William Sound, Alaska, for juvenile Pacific salmon. Fish. Res. 18: 77–87.
Cushing, D.H. 1972. The production cycle and the numbers of marine fish. Symp. Zool. Soc. Lond. 29: 213–232.
Davis, M.W. 2001. Behavioral responses of walleye pollock, Theragra chalcogramma, larvae to experimental gradients of sea water flow: implications for vertical distribution. Envir. Biol. Fishes 61: 253–260.
Duffy-Anderson, J.T., Bailey, K.M. and Ciannelli, L. 2002. Consequences of superabundance of larval walleye pollock Theragra chalcogramma in the Gulf of Alaska in 1981. Mar. Ecol. Prog. Ser. 243: 179–190.
Ernst, B., Orensanz, J.M. and Armstrong, D.A. 2005. Spatial dynamics of female snow crab (Chionoecetes opilio) in the eastern Bering Sea. Can. J. Fish. Aquat. Sci. 62: 250–268.
FAO. 2002. FAO yearbook of fishery statistics. Vol. 93, Food and Agriculture Organization.
Farley, E., Murphy, J., Adkison, M. and Eisner, L. 2007. Juvenile sockeye salmon distribution, size, condition and diet during years with warm and cool spring sea temperatures along the eastern Bering Sea shelf. J. Fish Biol. 71: 1145–1158.
Foy, R.J. and Paul, A.J. 1999. Winter feeding and changes in somatic energy content of age-0 Pacific herring in Prince William Sound, Alaska. Trans. Am. Fish. Soc. 128: 1193–1200.
Godbout, L., Irvine, J.R., Bailey, D., Van Will, P. and McConnell, C. 2004. Stock status of wild chum salmon (Oncorhynchus keta Walbaum) returning to British Columbia’s Central Coast and Johnstone and Georgia Straits (excluding the Fraser River). Canadian Science Advisory Secretariat Research Document 2004/007, 45 pp.
Gordon, H.B. and O’Farrell, S.P. 1997. Transient climate change in the CSIRO coupled model with dynamic sea ice. Mon. Wea. Rev. 25: 875–907.
Grebmeier, J.M., Overland, J.E., Moore, S.E., Farley, E.V., Carmack, E.C., Cooper, L.W., Frey, K.E., Helle, J.H., McLaughlin, F.A. and McNutt, S.L. 2006. A major ecosystem shift in the northern Bering sea. Science 311: 1461–1464.
Hartt, A.C. and Dell, M.B. 1986. Early oceanic migrations and growth of juvenile Pacific salmon and steelhead trout. Int. N. Pac. Fish Comm. Bull. 46: 105 pp.
Healey, M.C. 1980. The ecology of juvenile salmon in Georgia Strait, British Columbia. pp. 203–229 in Salmonid Ecosystems of the North Pacific, edited by W.J. McNiel and D.C. Himsworth, Oregon State University Press and Oregon State University Sea Grant College Program, Corvallis, OR.
Hollowed, A.B., Hare, S.R. and Wooster, W.S. 2001. Pacific basin climate variability and patterns of Northeast Pacific marine fish production. Prog. Oceanogr. 49: 257–282.
Hsu, C.-C. 1987. Spatial and temporal distribution patterns of female red king crabs in the southeastern Bering Sea. Ph.D. dissertation, University of Washington, Seattle, WA., 300 pp.
Ito, S.-I., Megrey, B.A., Kishi, M.J., Mukai, D., Kurita, Y., Ueno, Y. and Yamanaka, Y. 2007. On the interannual variability of the growth of Pacific saury (Cololabis saira): A simple 3-box model using NEMURO.FISH. Ecol. Model. 202: 174–183.
Kamezawa, Y., Azumaya, T., Nagasawa, T. and Kishi, M.J. 2007. Bioenergetics model of the Japanese chum salmon (Oncorhynchus keta) growth. NPAFC Tech. Rep. No. 7, pp. 95–98.
Kaeriyama, M., Nakamura, M., Edpalina, R., Bower, J.R., Yamaguchi, H., Walker, R.V. and Myers, K.W. 2004. Change in feeding ecology and trophic dynamics of Pacific salmon (Oncorhynchus spp.) in the central Gulf of Alaska in relation to climate events. Fish. Oceanogr. 13: 197–207.
Kendall, Jr., A.W. and Picquelle, S.J. 1990. Egg and larval distributions of walleye pollock Theragra chalcogramma in Shelikof Strait, Gulf of Alaska. Fish. Bull. 88: 133–154.
Kendall, Jr., A.W., Incze, L.S., Ortner, P.B., Cummings, S.R. and Brown, P.K. 1994. The vertical distribution of eggs and larvae of walleye pollock, Theragra chalcogramma, in Shelikof Strait, Gulf of Alaska. Fish. Bull. 92: 540–554.
Kim, K.J. 1970. Studies on the interspecific relations between common mackerel and horse mackerel. 1. Analysis of fluctuations in their abundance over a long period. Bull. Korean Fish. Soc. 3: 149–153 (in Korean with English abstract).
Kim, S. and Kang, S. 2000. Ecological variations and El Niño effects off the southern coast of the Korean Peninsula during the last three decades. Fish. Oceanogr. 9: 239–247.
King, J.R., McFarlane, G.A. and Beamish, R.J. 2000. Decadal scale patterns in the relative year class success of sablefish, Anoplopoma fimbria. Fish. Oceanogr. 9: 62–70.
Kon, T. 1970. Fisheries biology of the Tanner crab. III. The duration of the planktonic stages estimated by rearing experiments of larvae. Bull. Jpn. Soc. Sci. Fish. 36: 219–224 (English translation by Fish. Res. Bd. Can., Transl. Ser. No. 1603).
83
Kosro, P.M., Peterson, W.T., Hickey, B.M., Shearman, R.K. and Pierce, S.D. 2006. Physical versus biological spring transition: 2005. Geophys. Res. Lett. 33: L22S03, doi:10.1029/2006GL027072.
Kotwicki, S., Buckley, T.W., Honkalehto, T. and Walters, G. 2005. Variation in the distribution of walleye pollock (Theragra chalcogramma) with temperature and implications for seasonal migration. Fish. Bull. 103: 574–587.
Kruse, G.H. 2007. Impacts of climate change on red king crabs in the eastern Bering Sea. Submitted to Industrial Economics, Incorporated, Funded by the Environmental Protection Agency under Contract EP-D-04-006, Subcontract Agreement 8033-UALASKA. University of Alaska Fairbanks, School of Fisheries and Ocean Sciences, Report SFOS-Fisheries Division 07-007, 35 pp.
Kruse, G.H., Tyler, A.V., Sainte-Marie, B. and Pengilly, D. 2007. A workshop on mechanisms affecting year-class strength formation of snow crabs (Chionoecetes opilio) in the eastern Bering Sea. Alaska Fish. Res. Bull. 12: 277–290.
Lee, B.H. 1970. Growth and spawning of horse mackerel. Bull. Fish. Res. Develop. Agency 8: 49–62 (in Korean).
Lee, J.B. 2005. Stock-recruitment relationships incor-porating environmental factors and optimal harvest rates of jack mackerel (Trachurus japonicus) around Korean waters. Ph.D. thesis, University–Research Institute Interdisciplinary Program of Fisheries Oceanography, Pukyong National University, Korea, 97 pp.
Livingston, P.A. 1993. The importance of predation by groundfish, marine mammals, and birds on walleye pollock, Theragra chalcogramma, and Pacific herring, Clupea pallasi, in the eastern Bering Sea. Mar. Ecol. Prog. Ser. 102: 205–215.
Logerwell, E.A, Mantua, N., Lawson, P.W., Francis, R.C. and Agostini, V.N. 2003. Tracking environmental processes in the coastal zone for understanding and predicting Oregon coho (Oncorhynchus kisutch) marine survival. Fish. Oceanogr. 12: 554–568.
Loher, T. 2001. Recruitment variability in southeast Bering Sea red king crab (Paralithodes camtschaticus): The roles of early juvenile habitat requirements, spatial population structure, and physical forcing mechanisms. Ph.D. dissertation, University of Washington, Seattle, 436 pp.
Loher, T. and Armstrong, D.A. 2005. Historical changes in the abundance and distribution of ovigerous red king crabs (Paralithodes camtschaticus) in Bristol Bay (Alaska), and potential relationship with bottom temperature. Fish. Oceanogr. 14: 292–306.
Longhurst, A. 2002. Murphy’s Law revisited: longevity as a factor in recruitment to fish populations. Fish. Res. 56: 125–131.
Lovrich, G.A. and Sainte-Marie, B. 1997. Cannibalism in the snow crab, Chionoecetes opilio (O. Fabricius) (Brachyura: Majidae), and its potential importance to recruitment J. Exper. Mar. Bio. Ecol. 211: 225–245.
Mackas, D., Thomson, R. and Galbraith, M. 2001. Changes in the zooplankton community of the British
Columbia continental margin, 1984–1999, and their covariation with oceanographic conditions. Can. J. Fish. Aquat. Sci. 58: 685–702.
Mackas, D.L., Peterson, W.T., Ohman, M.D. and Lavaniegos, B.E. 2006. Zooplankton anomalies in the California Current system before and during the warm ocean conditions of 2005. Geophys. Res. Lett. 33: L22S07, doi:10.1029/2006GL027930.
McFarlane, G.A. and Beamish, R.J. 1986. Production of strong year classes of sablefish (Anoplopoma fimbria) off the west coast of Canada. Int. N. Pac. Fish Comm. Bull. 47: 191–202.
McFarlane, G.A. and Beamish, R.J. 1992. Climatic influence linking copepod production with strong year classes in sablefish, Anoplopoma fimbria. Can. J. Fish. Aquat. Sci. 49: 743–753.
McRoy, C.P., Hood, D.W., Coachman, L.K., Walsh, J.J. and Goering, J.J. 1986. Processes and resources of the Bering Sea shelf (PROBES): The development and accomplishments of the project. Cont. Shelf Res. 5: 5–22.
Mueter, F.J., Ladd, C., Palmer, M.C. and Norcross, B.L. 2006. Bottom-up and top-down controls of walleye pollock (Theragra chalcogramma) on the Eastern Bering Sea shelf. Prog. Oceanogr. 68: 152–183.
Nakashima, J. 1982. On the growth and age of three populations of jack mackerel, Trachurus japonicus, in the western Seas of Japan. Bull. Seikai Region. Fish. Res. Lab. 57: 47–57.
Napp, J.M., Incze, L.S., Ortner, P.B., Siefert D.L.W. and Britt L. 1996. The plankton of Shelikof Strait, Alaska: standing stock, production, mesoscale variability and their relevance to larval fish survival. Fish. Oceanogr. 5 (Suppl. 1): 19–38.
NFRDI. 2005. Ecology and fishing grounds of major fish species in the Korean coastal and offshore area. Yemunsa Publ. Co., National Fisheries Research and Development Institute of Korea. Pusan, 304 pp. (in Korean).
Olla, B.L., Davis, M.W., Ryer, C.H. and Sogard, S.M. 1996. Behavioral determinants of distribution and survival in early stages of walleye pollock, Theragra chalcogramma: a synthesis of experimental studies. Fish. Oceanogr. 5 (Suppl. 1): 167–178.
Ookouchi H., Yamane, S. and Aritaki, M. 2008. Migration ecology of the herring Clupea pallasii after spawning in Miyako Bay and homing in the following year. Nippon Suisan Gakkaisi 74: 389–394 (in Japanese with English abstract).
Orensanz, J., Ernst, B., Armstrong, D.A., Stabeno, P. and Livingston, P. 2004. Contraction of the geographic range of distribution of snow crab (Chionecetes opilio) in the eastern Bering Sea: An environmental rachet? CalCOFI Rep. 45: 65–79.
Orensanz, J.M., Ernst, B., Armstrong, D.A. and Parma, A.M. 2005. Detecting early warnings of recruitment overfishing in male-only crab fisheries: an example from the snow crab fishery. pp. 267–288, in Fisheries Assessment in Data-Limited Situations, edited by G.H. Kruse, V.F. Gallucci, D.E. Hay, R.I. Perry, R.M.
84
Peterman, T.C. Shirley, P.D. Spencer, B. Wilson and D. Woodby, Alaska Sea Grant AK-SG-05-02, 958 pp.
Overland, J.E. and Wang, M. 2007. Future regional Arctic Sea ice declines. Geophys. Res. Lett. 34: L17705, doi: 10.1029/2007GL030808.
Panagiotopoulos, F., Shahgedanova, M., Hannachi A. and Stephenson, D.B. 2005. Observed trends and tele-connections of the Siberian High: a recently declining center of action. J. Climate 18: 1411–1422.
Pearcy, W.G. 1992. Ocean Ecology of North Pacific Salmonids. Books in Recruitment Fishery Oceanography. Washington Sea Grant Program. University of Washington, Seattle, 176 pp.
Peterson, W.T. and Schwing, F.B. 2003. A new climate regime in northeast Pacific ecosystems. Geophys. Res. Lett. 30: 1896, doi:10.1029/2003GL017528
Porter, S.M. Ciannelli, L., Hillgruber, N., Bailey, K.M., Chan, K.S., Canino, M.F. and Haldorson, L.J. 2005. Analysis of factors influencing larval walleye pollock Theragra chalcogramma feeding in Alaskan waters. Mar. Ecol. Prog. Ser. 302: 207–217.
Quinn, T.J. and Niebauer, H.J. 1995. Relation of eastern Bering Sea walleye pollock (Theragra chalcogramma) recruitment to environmental and oceanographic variables. pp. 497–507, in Climate Change and Northern Fish Populations, edited by R.J. Beamish, Can. Spec. Publ. Fish. Aquat. Sci. 121.
Rand, P.S., Hinch, S.G., Morrison, J., Foreman, M.G.G., MacNutt, M.J., Macdonald, J.S., Healey, M.C., Farrell, A.P. and Higgs, D.A. 2006. Effects of river discharge, temperature, and future climates on energetics and mortality of adult migrating Fraser River sockeye salmon. Trans. Am. Fish. Soc. 135: 655–667.
Rogers, D.E. 1988. Bristol Bay smolt migrations: timing and size composition and the effects on distribution and survival at sea. pp 87–101 in Salmon Production Management, and Allocation, edited by W.J. McNeil, Oregon State University Press, Corvallis, OR.
Rosenkranz, G.E., Tyler, A. and Kruse, G. 2001. Effects of water temperature and wind on year-class success of Tanner crabs in Bristol Bay, Alaska. Fish. Oceanogr. 10: 1–12.
Rosenkranz, G.E., Tyler, A.V., Kruse, G.H. and Niebauer, H.J. 1998. Relationship between wind and year class strength of Tanner crabs in the Southeastern Bering Sea. Alaska Fish. Res. Bull. 5: 18–24.
Sainte-Marie, B. and Lafrance, M. 2002. Growth and survival of recently settled snow crab Chionoecetes opilio in relation to intra- and intercohort competition and cannibalism: a laboratory study. Mar. Ecol. Prog. Ser. 244: 191–203.
Salo, E.O. 1991. Life history of chum salmon (Oncorhynchus keta). pp. 231–309 in Pacific Salmon Life Histories, edited by C. Groot and L. Margolis, UBC Press, Vancouver, British Columbia, 564 pp.
Schirripa, M.J. and Colbert, J.J. 2006. Interannual changes in sablefish (Anoplopoma fimbria) recruitment in relation to oceanographic conditions within the California Current System. Fish. Oceanogr. 14: 1–12.
Seo, H., Kim, S., Seong, K. and Kang, S. 2006. Variability in scale growth rates of chum salmon (Oncorhynchus keta) in relation to climate changes in the late 1980s. Prog. Oceanogr. 68: 205–216.
Serreze, M.C., Walsh, J.E., Chapin III, F.S., Osterkamp, T., Dyurgerov, M., Romanovsky, V., Oechel, W.C., Morison, J., Zhang, T. and Barry, R.G. 2000. Observational evidence of recent change in the northern high latitude environment. Climate Change 46: 159–207.
Shotwell, S.K., Adkison, M.D. and Hanselman, D.H. 2006. Accounting for climate variability in forecasting Pacific salmon in data – limited situations. pp. 871–900 in Fisheries Assessment and Management in Data Limited Situations, edited by G.H. Kruse, V.F. Gallucci, D.E. Hay, R.I. Perry, R.M. Peterman, T.C. Shirley, P.D. Spencer, B. Wilson and D. Woodby, Alaska Sea Grant College Proram, AK-SG-05-02, 958 pp.
Sogard, S.M. and Olla, B.L. 1996. Food deprivation affects vertical distribution and activity of a marine fish in a thermal gradient: potential energy-conserving mechanisms. Mar. Ecol. Prog. Ser. 133: 43–55.
Somerton, D.A. 1981. Life history and population dynamics of two species of Tanner crab, Chionoecetes bairdi and C. opilio, in the eastern Bering Sea with implications for the management of the commercial harvest. Ph.D. dissertation, University of Washington, Seattle, 220 pp.
Spilsted, B.P. 2004. Trends in abundance for northern British Columbia chum salmon. Canadian Science Advisory Secretariat Research Document 2004/013.
Swartzman, G., Napp, J., Brodeur, R., Winter, A. and Ciannelli, L. 2002. Spatial patterns of pollock and zooplankton distribution in the Pribilof Islands, Alaska nursery area and their relationship to pollock recruitment. ICES J. Mar. Sci. 59: 1167–1186.
Takasuka, A., Oozeki, Y. and Aoki, I. 2007. Optimal growth temperature hypothesis: Why do anchovy flourish and sardine collapse or vice versa under the same ocean regime? Can. J. Fish. Aquat. Sci. 64: 768–776.
Theilacker, G., Bailey, K.M., Canino, M. and Porter, S. 1996. Variations in larval walleye pollock feeding and condition: a synthesis. Fish. Oceanogr. 5: 112–123.
Tyler, A.V. and Kruse, G.H. 1996. Conceptual modeling of brood strength of red king crabs in the Bristol Bay region of the Bering Sea. pp. 511–543 in High Latitude Crabs: Biology Management and Economics, Alaska Sea Grant College Program, AK-SG-96-02.
Tyler, A.V. and Kruse, G.H. 1997. Modeling workshop on year-class strength of Tanner crab, Chionoecetes bairdi. Alaska Department of Fish and Game, Commercial Fisheries Management and Development Division, Regional Information Report 5J97-02, Juneau, AK.
Urawa, S., Ueno, Y., Ishida, Y., Seeb, L.W., Crane, P.A., Abe, S. and Davis, N.D. 2001. A migration model of Japanese chum salmon during early ocean life. NPAFC Tech. Rep. 2, pp. 45–46.
Welch, D.W., Ishida, Y. and Nagasawa, K. 1998. Thermal limits and ocean migrations of sockeye salmon
85
(Oncorhynchus nerka): long-term consequences of ocean warming. Can. J. Fish. Aquat. Sci. 55: 937–948.
Wespestad, V.G., Fritz, L.W., Ingrham, W.J. and Megrey, B.A. 2000. On relationships between cannibalism, climate variability, physical transport, and recruitment success of Bering Sea walleye pollock (Theragra chalcogramma). ICES J. Mar. Sci. 57: 272–278.
Westrheim, S.J. 1987. The rockfish fisheries off western Canada 1860–1985. pp. 43–49, in Proceedings of the international rockfish symposium, October 1986, edited by B.R. Metleff, Alaska Sea Grant Program Report 87-2.
Wilderbuer, T.K., Hollowed, A.B., Ingraham, Jr., W.J., Spencer, P.D., Conners, M.E., Bond, N.A. and Walters, G.E. 2002. Flatfish recruitment response to decadal climatic variability and ocean conditions in the eastern Bering Sea. Prog. Oceanogr. 55: 235–247.
Willette, M., Sturdevant, M. and Jewett, S. 1997. Prey resource partitioning among several species of forage fishes in Prince William Sound, Alaska. pp. 11–29 in Forage Fishes in Marine Ecosystems, Proceedings of the International Symposium on the Role of Forage Fishes in Marine Ecosystems. Alaska Sea Grant College Program Report No. 97-01, University of Alaska Fairbanks, AK.
Yatsu, A. Watanabe, T., Ishida, M., Sugisaki, H. and Jacobson, L. 2005. Environmental effects on recruitment and productivity of Japanese sardine Sardinops melanostictus and chub mackerel Scomber japonicus with recommendations for management. Fish. Oceanogr. 14: 263–278.
Zhang, C.I. and Lee, J.B. 2001. Stock assessment and management implications of horse mackerel (Trachurus japonicus) in Korean waters, based on the relationships between recruitment and the ocean environment. Prog. Oceanogr. 49: 513–537.
Zhang, C.I., Lee, J.B., Kim, S. and Oh, J.-H. 2000. Climatic regime shifts and their impacts on marine
ecosystem and fisheries resources in Korean waters. Prog. Oceanogr. 47: 171–190.
Zhang, C.I., Lee, J.B., Seo, Y.I., Yoon, S.C. and Kim, S. 2004. Variations in the abundance of fisheries resources and ecosystem structure in the Japan/East Sea. Prog. Oceanogr. 61: 245–265.
Zheng, J. and Kruse, G.H. 1999. Evaluation of harvest strategies for Tanner crab stocks that exhibit periodic recruitment. J. Shellfish Res. 18: 667–679.
Zheng, J. and Kruse, G.H. 2000. Recruitment patterns of Alaskan crabs in relation to decadal shifts in climate and physical oceanography ICES J. Mar. Sci. 57: 438–451.
Zheng, J. and Kruse, G.H. 2003. Stock-recruitment relationships for three major Alaskan crab stocks. Fish. Res. 65: 103–121.
Zheng, J. and Kruse, G.H. 2006. Recruitment variation of eastern Bering Sea crabs: climate forcing or top-down effects? Prog. Oceanogr. 68: 184–204.
Zheng, J., Kruse, G.H. and Ackley, D.R. 2001. Spatial distribution and recruitment patterns of snow crabs in the eastern Bering Sea. pp. 233–255, in Spatial Processes and Management of Marine Populations, edited by G.H. Kruse, N. Bez, A. Booth, M.W. Dorn, S. Hills, R.N. Lipcius, D. Pelletier, C. Roy, S.J. Smith and D. Witherell, University of Alaska Sea Grant, Report AK-SG-01-02, Fairbanks, AK.
Zheng, J., Murphy, M.C. and Kruse, G.H. 1997a. Analysis of the harvest strategies for red king crab, Paralithodes camtschaticus, in Bristol Bay, Alaska. Can. J. Fish. Aquat. Sci. 54: 1121–1134.
Zheng, J., Murphy, M.C. and Kruse, G.H. 1997b. Alternative rebuilding strategies for the red king crab Paralithodes camtschaticus fishery in Bristol Bay, Alaska. J. Shellfish Res. 16: 205–217.
Ziemann, D.A., Conquest, L.D., Olaizola, M. and Bienfang, P.J. 1991. Interannual variability in the spring zooplankton bloom in Auke Bay. Mar. Biol. 109: 321–334.
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Appendix 2.1 List of Participants
Victoria, Canada October 30, 2007
CANADA
Richard J. Beamish Fisheries and Oceans Canada Pacific Biological Station 3190 Hammond Bay Road Nanaimo, BC, V9T 6N7 Canada [email protected] Stock assessment Caihong Fu Fisheries and Oceans Canada Pacific Biological Station 3190 Hammond Bay Road Nanaimo, BC, V9T 6N7 Canada [email protected] Coupled models
Gordon McFarlane Fisheries and Oceans Canada Pacific Biological Station 3190 Hammond Bay Road Nanaimo, BC, V9T 6N7 Canada [email protected] Stock assessment
JAPAN
Shin-ichi Ito Tohoku National Fisheries Research Institute Fisheries Research Agency 3-27-5 Shinhama-cho Shiogama, Miyagi 985-0001 Japan [email protected] Coupled models Kazuaki Tadokoro Tohoku National Fisheries Research Institute Fisheries Research Agency 3-27-5 Shinhama-cho Shiogama, Miyagi 985-0001 Japan [email protected] Lower trophic level response
Yasuhiro Yamanaka Hokkaido University N10W5, Kita-ku Sapporo, Hokkaido 060-0810 Japan [email protected] Coupled models Akihiko Yatsu Seikai National Fisheries Research Institute Fisheries Research Agency 1551-8, Taira-machi Nagasaki 851-2213 Japan [email protected] Fisheries science
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REPUBLIC OF KOREA
Suam Kim Pukyong National University 559-1 Daeyeon-3-dong, Nam-gu Busan 608-737 Republic of Korea [email protected] Oceanography Jae Bong Lee National Fisheries Research and Development Institute 408-1 Sirang-ri, Gijang-eup, Gijang-gun Busan 619-905 Republic of Korea [email protected] Stock assessment
In-Ja Yeon West Sea Fisheries Research Institute, NFRDI 707 Eulwang-dong, Jung-gu Inchon 400-420 Republic of Korea [email protected] Stock assessment
RUSSIA
Yury I. Zuenko Pacific Research Institute of Fisheries and Oceanography (TINRO-Center) 4 Shevchenko Alley Vladivostok, Primorsky 690950 Russia [email protected] Fisheries science
U.S.A.
Steven J. Bograd Southwest Fisheries Science Center NMFS, NOAA 1352 Lighthouse Avenue Pacific Grove, CA 93950 U.S.A. [email protected] CC, Oceanography Melissa Haltuch Northwest Fisheries Science Center NMFS, NOAA 2725 Montlake Boulevard, East Seattle, WA 98112 U.S.A. [email protected] CC, Stock assessment models
Christopher J. Harvey Northwest Fisheries Science Center NMFS, NOAA 2725 Montlake Blvd E Seattle, WA 98112 U.S.A. [email protected] CC, Coupled models Albert J. Hermann Joint Institute for the Study of the Atmosphere and
Ocean Pacific Marine Environmental Laboratory 7600 Sand Point Way NE Seattle, WA 98115 U.S.A. [email protected] CC and AK, Circulation models
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Anne B. Hollowed Alaska Fisheries Science Center NMFS, NOAA 7600 Sand Point Way NE Seattle , WA 98115-6349 U.S.A. [email protected] AK, Stock assessment models Nathan J. Mantua University of Washington Box 55020 Seattle, WA 98195-5020 U.S.A. [email protected] North CC, Climatology/Oceanography James E. Overland Alaska Fisheries Science Center NMFS, NOAA 7600 Sand Point Way NE Seattle , WA 98115-6349 U.S.A. [email protected] AK, Climatology/POC
Michael J. Schirripa Northwest Fisheries Science Center NMFS, NOAA Hatfield Marine Science Center 2032 SE OSU Drive Newport, OR 97365 U.S.A. [email protected] CC, Stock assessment models William J. Sydeman Farallon Institute for Advanced Ecosystem Research P.O. Box 750756 Petaluma, CA 94975 U.S.A. [email protected] Seabirds Muyin Wang University of Washington 7600 Sand Point Way NE Seattle, WA 98115 U.S.A. [email protected] AK, Climatology/Oceanography/WG20
NORWAY
Kenneth Drinkwater Institute of Marine Research Box 1870, Nordnes Bergen N-5817 Norway ESSAS, ICES/PICES Climate Change meeting
Phase 3
Summary of the PICES Workshop on Linking Global Climate Model Output to (a) Trends in Commercial Species Productivity and
(b) Changes in Broader Biological Communities in the World’s Oceans
May 18, 2008 Gijón, Spain
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A workshop entitled Linking Global Climate Model Output to (a) Trends in Commercial Species Productivity and (b) Changes in Broader Biological Communities in the World’s Oceans was convened on May 18, 2008, at the ICES/PICES/IOC International Symposium on the “Effects of Climate Change on the World’s Oceans” in Gijón, Spain. The workshop had the ultimate goal of facilitating a coordinated international research effort to forecast climate change impacts on the distribution and production of the world’s major fisheries, and on the biological communities in which these fisheries are embedded. It was attended by 33 people from 13 nations (see Appendix 3.1) and there was consensus that this group could initiate a coordinated international collaboration to advance research in marine climate impacts. Two originally proposed workshops entitled, “Linking Climate to Trends in Productivity of Key Commercial Species in the World’s Oceans” and “Screening Approaches and Linking Global Climate Model Output with Ecosystem and Population Models” were combined by the convenors because they were complementary. In retrospect, blending the two “schools” was fortuitous as it placed us in a better-than-expected position to initiate an effective international collaboration. The specific objectives were to: • review the activities of existing programs within
each nation; • examine evidence for climate impacts on
production of commercial fish species and other marine life;
• discuss the feasibility of developing medium- to long-term forecasts of climate impacts;
• discuss possible responses of commercial fisheries, human communities, and governments to climate-driven changes in marine life; and
• identify common or standard approaches to forecasting climate change impacts on commercial species and marine communities and ecosystems.
Workshop participants discussed climate scenarios to use in forecasting and the tools required for predicting climate impacts on commercial fish production and broader marine ecosystems. The workshop provided a forum to discuss four components needed to complete the forecasts in a timely and coordinated fashion. These included Intergovernmental Panel on Climate Change (IPCC) scenarios, predictions of oceanographic impacts,
modeling approaches, and regional scenarios for natural resource use and enhancement. The ecosystem component of the workshop surveyed a wide variety of approaches such as vulnerability assessments for informing location choices for ecosystem modeling efforts and management prioritization, trophodynamic fishery ecosystem modeling (i.e., Ecopath with Ecosim), climate envelope modeling, statistical approaches, and three- dimensional high-resolution biogeochemical ecosystem modeling (i.e., CCCC-NEMURO). The workshop began with an introduction by Anne Hollowed (U.S.A.) who proposed: (1) an overarching goal of producing quantitative estimates of climate change effects on the marine ecosystem – biology – in the next 5 years; (2) a review of all the related international efforts in a paper that would lay out a path for collaboration development; (3) initiation of a coordinated international effort — broader than one basin; and (4) production of a special journal issue for showcasing forecasting approaches that are available and are being developed. She discussed three broad approaches representing different levels of advancement in the science of climate impacts forecasting, listed in increasing order of sophistication: 1. IPCC scenarios downscaled to local regions and
ecosystem indicators used to project future fish production using detailed management strategy evaluations;
2. IPCC scenarios downscaled to local regions and coupled to bio-physical models with higher trophic level feedbacks;
3. Fully coupled bio-physical models that operate at time and space scales relevant to coastal domains.
The main program of the workshop (Appendix 3.2) started with a round-table discussion of existing national or international projects developing forecasting initiatives, including Quest-FISH (Jason Holt), Fisheries and the Environment (FATE; Anne Hollowed), PICES’ Forecasting and Understanding Trends, Uncertainty and Responses of North Pacific Marine Ecosystems (FUTURE; Michael Foreman), North Pacific Research Board Bering Sea Integrated Ecosystem Research Program (NPRB BSIERP; Clarence Pautzke), Climate Impacts on Oceanic Top Predators (CLIOTOP; Alistair Hobday), Ecosystem Studies of Sub-Arctic Seas (ESSAS; Harald Loeng), Evidencias e Impacto do Cambio Climático en Galicia (CLIGAL; Antonio Bode) and initiatives by the New Zealand Ministry of Fisheries (Mary Livingston).
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The rest of the morning was devoted to eight presentations of projects that linked Global Climate Model (GCM) output to trends in commercial species productivity. Nicholas Bond (U.S.A.) presented “A method for using IPCC model simulations to project changes in marine ecosystems” in which he compared ensembles of hindcasted atmosphere–ocean model output to observed measurements, and used a tiered statistical approach to select a subset of models that performed well in representing regional oceano-graphic projections. This work indicated that different models have different strengths, so a particular question should use a tailored subset of models. Mary Livingston (New Zealand) presented “Climate change, oceanic response and possible effects on fish stocks in New Zealand waters” in which she described how climate change-related ecological trends have been equivocal in New Zealand during the last 50 years due to its oceanographic and ecological uniqueness and complexity and the paucity of long time series. Some of New Zealand’s marine life might be quite vulnerable to climate and oceanographic changes due to a variety of factors, and thus there are plans to integrate climate impacts studies with marine fisheries research and management. Jae Bong Lee (Republic of Korea) presented “Forecasting climate change impacts on distribution and abundance of jack mackerel around Korean waters” in which he illustrated how variations in ocean conditions and warming of ocean water around Korea has influenced the distributions of jack mackerel in terms of their seasonal visitation to Korean waters from the East China Sea, and suggested that continued warming by 2100 may have considerable effects on these stocks around Korea. Future sea surface temperature, ocean drift and other oceanographic variables projected with GCMs will be incorporated into a stock projection model to forecast future production scenarios. Sukyung Kang (Republic of Korea) presented “Techniques for forecasting climate-induced variation in the distribution and abundance of mackerels in the Northwestern Pacific” in which she described an exploration of the positive relationship between mackerel production and warm ocean conditions and progress in forecasting the impact of
climate change on mackerel production by downscaling forecasts of atmosphere–ocean conditions from GCMs to drive stock projection models. Adriaan Rijnsdorp (The Netherlands) presented “Effects of climate change on sole and plaice: Timing of spawning, length of the growth period and rate of growth” in which he reviewed how increased temperatures since 1989 in coastal nursery grounds in the southeastern North Sea has had a negative impact on plaice and a positive effect on sole thus causing a shifting species composition as their habitat quality changes. Implications of physiological trade-offs in this changing system will make forecasting challenging. Z. Teresa A’mar (U.S.A.) presented “The impact on management performance of including indicators of environmental variability in management strategies for the Gulf of Alaska walleye pollock fishery” in which she provided her management strategy evaluation (MSE) of the Gulf of Alaska walleye pollock fishery, with multiple indices of climate forcing incorporated into her overall modelling framework. The best performing management strategies were ones that were more responsive to fluctuations in productivity due to environmental influences. Michael Schirripa (U.S.A.) presented “Simulation testing two methods of including environmental data into stock assessments” in which he described the development of environmental indicators of fish stock recruitment and provided both modelling and a statistical examples of how such indicators could be used in stock assessments and forecasting. Sea surface height (SSH) was the best predictor of recruitment in this analysis, as low SSH occurs when the California Current and upwelling are both strong, and this is associated with high productivity. Alan Haynie (U.S.A.) presented “Climate change and changing fisher behavior in the Bering Sea pollock fishery” in which he discussed how fishermen will respond to changes in fish abundance driven by climate change, and that this will, in turn, have an impact on the ecosystem. The fisheries we observe today result from current stock distributions, abundances, and prices — all of these will change with climate. Spatial and market regulations that consider the relationship between fishermen and the
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environment as the climate changes will be most effective. The morning session concluded with a discussion of the presentations and the outlook for forecasting commercial fisheries. Thomas Okey, Pew Fellow in Marine Conservation, introduced the afternoon session by providing a framework highlighting complementary modelling approaches that could be used to explore climate impacts on marine biota and ecosystems. He described conceptual and qualitative models that are useful for proactive decision-making as a segue to the more quantitative approaches to linking GCM output to changes in broader marine communities. Jorge Sarmiento (U.S.A.) presented “Modeling response of ocean biology to climate warming using an empirical approach” in which he compared global warming simulations from six climate models and the physical changes projected for six ocean biomes. All six models indicated increases in primary production at high latitudes, but the models did not agree with direction of change at mid-latitudes. Taketo Hashioka (Japan) presented “Future ecosystem changes projected by a 3-D high-resolution ecosystem model” in which he described efforts to develop a high-resolution ecosystem model by linking COCO (CCSR Ocean Component Models) to NEMURO and NEMURO.FISH models. Projections included a 30% decrease in the Kuroshio, 10–30% decreases in Chl-a, a shift from diatoms to small phytoplankton, spring bloom 10 days earlier, decreases in phytoplankton biomass by 25%, and a 2° shift in the distribution of sardines. William Cheung (Canada) presented a “Dynamic bio-climate envelope model to predict climate-induced changes in distribution of marine fishes and invertebrates” in which he provided a global assessment of climate-induced range shifts of 1066 commercial species throughout the world’s oceans from changing temperature, habitat characteristics, and other mediators of dispersal and range occupation. Alistair Hobday (Australia) presented “Informing location choices for ecosystem model development using a vulnerability index” as an Australian example of a quantitative vulnerability assessment that is used to identify the ecosystems, habitats,
biological components, and human values most vulnerable to projected climate change so that climate impacts modelling and monitoring can be prioritized and targeted efficiently. The CSIRO Mk 3.5 model projections to 2070 provided indicators of climate change, while non-climate indicators were derived from other Australian data sets. Simone Libralato (Italy) presented “Towards the integration of biogeochemical and food web models for a comprehensive description of marine ecosystem dynamics” in which he reviewed the progress and outlooks for achieving end-to-end modelling (e.g., from viruses to fishes, from nutrients to fisheries, including climatic changes) by linking biogeo- chemical models with trophodynamic models. He also summarized outcomes of the 2007 Trieste (Italy) workshop on “Biogeochemical processes and fish dynamics in food web models for end-to-end conceptualisation of marine ecosystems: theory and use of Ecopath with Ecosim”. Steven Mackinson (U.K.) presented “Which forcing factors fit? Using ecosystem models to investigate the relative influence of fishing and primary productivity on the dynamics of marine ecosystems” in which he described dynamic fitting with Ecopath with Ecosim models to identify the main driving forces of fish stocks and marine ecosystems (e.g., fishing mortalities or proxies of primary production), to assess the relative importance of these factors across regions, and to evaluate whether similar groups in different ecosystems respond similarly. Sheila Heymans (U.K.) presented “The effects of climate change on the northern Benguela ecosystem” in which she simulated the effect of global warming on the northern Benguela Current system by fitting a 1956 Ecopath with Ecosim model to 2000 conditions and then simulating 50 years of sea surface temperature rise. The ecological effects were evaluated by indices of ecosystem function and commercial gain. The case studies presented during this workshop indicated the variety of approaches (and variations on similar approaches) for evaluating the impacts of climate change on marine life, biological communities, and ecosystem functions. Although the approaches appeared to be coordinated within communities of modellers, coordination was lacking at the global level. Most, if not all, of the presenters expressed the need to develop these approaches
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further, and there appeared to be consensus among participants that an international collaboration would be a good way to do this. A global coordination of teams and collaborators may prove to be a critical vehicle to use the increasingly refined physical and
chemical projections from GCMs and regional models to evaluate impacts of climate change on the world’s marine fisheries and ecosystems. The workshop described here may have been a key first step toward such a global collaboration.
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Appendix 3.1 List of Participants
Gijón, Spain
May 18, 2008
AUSTRALIA
Christopher J. Brown CSIRO Marine and Atmospheric Research GPO Box 1538 Hobart, Tasmania 7001 Australia [email protected] Ming Feng CSIRO Marine and Atmospheric Research Underwood Avenue Floreat, Western Australia 6014 Australia [email protected] Alistair J. Hobday CSIRO Marine and Atmospheric Research GPO Box 1538 Hobart, Tasmania 7001 Australia [email protected]
Elvira S. Poloczanska CSIRO Marine and Atmospheric Research GPO Box 1538 Hobart, Tasmania 7001 Australia [email protected] Bayden Russell The University of Adelaide Adelaide, South Australia 5005 Australia [email protected] Evan J. Weller University of Tasmania/CSIRO Marine and
Atmospheric Research Private Bag 78 Hobart, Tasmania 7001 Australia [email protected]
CANADA
William W.L. Cheung University of British Columbia 2202 Main Mall Vancouver, BC, V6T 1Z4 Canada [email protected] Michael G. Foreman Fisheries and Oceans Canada Institute of Ocean Sciences P.O. Box 6000 Sidney, BC, V8L 4B2 Canada [email protected]
Thomas A. Okey University of Victoria/Bamfield Marine Station P.O. Box 100 Bamfield, BC, V0R 1B0 Canada [email protected]
DENMARK
Keith Brander DTU-Aqua Charlottenlund, DK-2920 Denmark [email protected]
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GREECE
Eugenia Lefkadition Institute of Marine Biological Resources Ag. Kosmas, Helliniko Athens 16777 Greece [email protected]
ITALY
Simone Libralato OGS-Italian National Inst. Oceanography and Geophysics Borgo Grotta Gigante Brisciki 42/c Sgonico, Trieste 34010 Italy [email protected]
JAPAN
Taketo Hasioka JAMSTEC 3173-25 Showa-maci, Kanazawa-ku Yokohama 236-0001 Japan [email protected]
Kazuaki Tadokoro Tohoku National Fisheries Research Institute Fisheries Research Agency 3-27-5 Shinhama-cho Shiogama, Miyagi 985-0001 Japan [email protected]
NEW ZEALAND
Mary E. Livingston Ministry of Fisheries 101-103 The Terrace Wellington P.O. Box 1020 New Zealand [email protected]
NORWAY
Harold Loeng Institute of Marine Research P.O. Box 1870 Nordes N-5817 Bergen Norway [email protected]
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REPUBLIC OF KOREA
Sukyung Kang National Fisheries Research and Development Institute 408-1 Sirang-ri, Gijang-eup, Gijang-gun Busan 619-905 Republic of Korea [email protected]
Jae Bong Lee National Fisheries Research and Development Institute 408-1 Sirang-ri, Gijang-eup, Gijang-gun Busan 619-905 Republic of Korea [email protected]
SPAIN
Antonio Bode Centro Oceanográfico de A Coruña Instituto Español de Oceanografía Apdo. 130, A Coruña, E-15080 Spain [email protected]
Ángela M. Caballero-Alfonso Las Palmas de Gran Canaria University Las Palmas de Gran Canaria Las Palmas 35017 Spain [email protected]
THE NETHERLANDS
Adriaan D. Rijnsdorp Department of Fisheries Wageningen IMARES Haringkade 1 P.O. Box 68 Ijmuiden 1970 AB The Netherlands [email protected]
U.S.A.
Z. Teresa A’mar University of Washington Box 355020 SAFS Seattle, WA 98195-5020 U.S.A. [email protected] Nicholas A. Bond NOAA/PMEL, JISAO 7600 Sand Point Way, NE Seattle, WA 98115-6349 U.S.A. [email protected] Alan Haynie Alaska Fisheries Science Center NOAA 7600 Sand Point Way NE Seattle, WA 98115 U.S.A. [email protected]
Anne B. Hollowed Alaska Fisheries Science Center NMFS, NOAA 7600 Sand Point Way NE Seattle, WA 98115-6349 U.S.A. [email protected] Gordon H. Kruse University of Alaska Fairbanks 11120 Glacier Highway Juneau, AK 99801-8677 U.S.A. [email protected] Clarence Pautzke North Pacific Research Board 1107 W 3rd Avenue, Suite 100 Anchorage, AK 99501 U.S.A. [email protected]
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Jeffrey J. Polovina Pacific Islands Fisheries Science Center NMFS, NOAA 2570 Dole Street Honolulu, HI 96734 U.S.A. [email protected] Jorge L. Sarmiento Princeton University 300 Forrestal Road, Sayre Hall Princeton, NJ 08544 U.S.A. [email protected]
Michael J. Schirripa Northwest Fisheries Science Center NMFS, NOAA Hatfield Marine Science Center 2032 SE OSU Drive Newport, OR 97365 U.S.A. [email protected]
UNITED KINGDOM
Jason Holt Proudman Oceanographic Laboratory Bronlow Street Liverpool L3 5DA United Kingdom [email protected] Shelia J.J. Heymans Scottish Association for Marine Science Dunstaffnage Marine Laboratory Dunbeg, Argyll PA371QA United Kingdom [email protected]
Steven Mackinson Centre for Environment, Fisheries and Aquaculture
Science Pakefield Road Lowestoft, Suffolk NR34 0LH United Kingdom [email protected]
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Appendix 3.2
Agenda for Workshops 2/3 Linking Global Climate Model Output to (a) Trends in Commercial Species Productivity and
(b) Changes in Broader Biological Communities in the World’s Oceans
May 18, 2008 9:40 Round Table Discussion of Existing or Planned Research: Quest-FISH (Holt), FATE (Hollowed), PICES
FUTURE (Foreman), NPRB BSIERP (Pautzke), Ministry of Fisheries New Zealand (Livingston), IMBER (TBD) 10:40 Coffee Break 11:00 Nicholas A. Bond
A method for using IPCC model simulations to project changes in marine ecosystems 11:30 Mary E. Livingston
Climate change, oceanic response and possible effects on fish stocks in New Zealand waters 11:45 Jae Bong Lee
Forecasting climate change impacts on distribution and abundance of jack mackerel around Korean waters 12:00 Sukyung Kang
Techniques for forecasting climate-induced variation in the distribution and abundance of mackerels in the Northwestern Pacific
12:15 Adriaan Rijnsdorp Effects of climate change on sole and plaice: Timing of spawning, length of the growth period and rate of growth
12:30 Z. Teresa A’mar The impact on management performance of including indicators of environmental variability in management strategies for the Gulf of Alaska walleye pollock fishery
12:45 Michael J. Schirripa Simulation testing two methods of including environmental data into stock assessments
13:00 Alan Haynie Climate change and changing fisher behavior in the Bering Sea pollock fishery
13:30 Lunch 14:30 Introduction to the afternoon and discussion 15:00 Jorge L. Sarmiento
Modeling response of ocean biology to climate warming using an empirical approach 15:15 Taketo Hashioka
Future ecosystem changes projected by a 3-D high-resolution ecosystem model 15:30 William W. L. Cheung
Dynamic bioclimate envelope model to predict climate-induced changes in distribution of marine fishes and invertebrates
15:45 Alistair J. Hobday Informing location choices for ecosystem model development using a vulnerability index
16:00 Coffee Break 16:30 Simone Libralato
Towards the integration of biogeochemical and food web models for a comprehensive description of marine ecosystem dynamics
16:45 Steven Mackinson Which forcing factors fit? Using ecosystem models to investigate the relative influence of fishing and primary productivity on the dynamics of marine ecosystems
17:00 Sheila J.J. Heymans The effects of climate change on the northern Benguela ecosystem
17:15 Discussion of common approaches and workshop synthesis
