Transcript 07. Oceanic fisheries Nandini

Vulnerability of oceanic fisheries
to climate change
Presented by
Sri Nandini
Authors
This presentation is based on Chapter 8 ‘Vulnerability of
oceanic fisheries to climate change’ in the book
Vulnerability of Tropical Pacific Fisheries and Aquaculture
to Climate Change, edited by JD Bell, JE Johnson and AJ
Hobday and published by SPC in 2011.
The authors of Chapter 8 are: Patrick Lehodey, John
Hampton, Rich Brill, Simon Nicol, Inna Senina, Beatriz
Calmettes, Hans O. Pörtner, Laurent Bopp, Tatjana Ilyina,
Johann Bell and John Sibert
Outline
• Sensitivity of tuna
habitats to oceanic
variables
• Potential changes
and impacts
• Priority adaptations
• Conclusions
Tuna habitat – temperature
• Each tuna species has evolved with a preferred
range in temperature
• Impacts vertical &
horizontal distribution
(habitat and food) &
reproduction location
and timing
Species
Skipjack
Yellowfin
Bigeye
Albacore
Sth. bluefin
Temperature
(°C)
20-29
20-30
13-27
15-21
17-20
Range of sea surface temperature
with substantial catches
Source: Sund et al. (1981)
Tuna habitat – temperature
• Larvae are most sensitive to temperature
changes (affects spawning ground)
Yellowfin larvae (Wexler et al 2011)
 optimal range for growth is 26-31oC for Yellowfin
 low and high lethal temperatures are 21 & 33oC
• The upper lethal limit for yellowfin (33 oC) is
projected to occur more often in Western
Pacific Ocean by 2100
Tuna habitat – oxygen
Sensitive to combined effects of
SST + O2
Less tolerant to
low values
Estimated lower lethal oxygen
Species
Fork length Lower lethal O2
(cm)
levels (ml l-1)
Skipjack
50
1.87
Albacore
50
1.23
Yellowfin
50
1.14
Bigeye
50
0.40
Skipjack
Albacore
Yellowfin
Bigeye
Most tolerant
to low values
Tuna habitat – oxygen
+
0
0m
100 m Well oxygenated
Albacore
500 m
Skipjack
Yellowfin
Low oxygen
Bigeye
Typical
vertical O2
profile
Change in subsurface may have more
impact on low oxygen tolerant species
Tuna habitat – ocean production
Zooplankton
Tuna larvae
Micronekton
Primary production
Source: Rudy Kloser and Jock Young CSIRO, Australia
Better understanding of oceanography =
better expected projections
Skipjack projection
2000
2050
Adult biomass
Larval density
2000
2050
Reduced biomass in western
pacific associated with SST
overheating.
Gains & challenges faced by
PICTs EEZ, e.g. FIJI
Bigeye projection
2050
2000
Adult biomass
Larval density
2000
2050
good fishing grounds could
be displaced further
eastward & Reduced
biomass in western Pacific
Albacore projection
2050
2000
Adult biomass
Larval density
2000
No change
in O2
With modelled O2
2050
Sensative to O2 hence
distribution changes
Total Fishery catch
2035
2050
2100
2035
2050
Change in % relative to average catch
1980-2000
2100
Total Fishery FIJI
Projected changes in biomass (%) of Skipjack for FIJI EEZ
without fishing
with fishing
2035
2050
2100
2035
2050
2100
3
4
-3
1
0
-7
Total Catch
a
2,500,000
Yellowfin
Total catch (tonnes)
2,000,000
Skipjack
Bigeye
1,500,000
Albacore
1,000,000
500,000
0
What will be the future trend of fishing effort?
Status of Stocks
Last place to be
Priority adaptations
• Regional management org (WCPFC, FFA, PNA and
Te Vaka Moana groups) and national agencies
should include implications of climate change in
management objectives and strategies
• Maintain bigeye tuna stock in WCPO in a healthy
state to avoid combining high fishing pressure and
adverse environmental conditions
Priority adaptations
• Develop management systems to ensure flexibility
to cope with changing spatial distribution of fishing
effort (e.g. PNA vessel day scheme- tool that exist
to manage for climate variability and climate
change).
Socio-economic scenarios likely to drive future
fishing effort in the region need to be identified
and incorporated in modelling e.g. the increasing
demand for tuna, the likelihood of spatial changes
in fishing effort, and increasing fuel costs.
Priority adaptations
• Consider spatially-explicit management in
archipelagic areas, to monitor and assess potential
sub-regional effects.
• Fiji archipelagic waters have potential to become
more productive under CC predictions
Eg. Productivity
associated with the
Sepik-Ramu Rivers in
PNG currently provide
optimal habitat
Conclusions
• Understanding impact of climate change on tuna
depends on our capacity to explain, model and
predict the effect of natural variability and fishing
effects.
• While there is still
uncertainty about
impacts of climate
change (ENSO, pH, O2),
we know fishing has a
strong impact and will
continue to be a major
driver of stocks
Conclusions
• The model seems robust for
historical period but its forecast
skills are linked to those of the
climate models - improved climate
forcings (physics+biochemistry) are
needed to update this first risk
assessment
Resolution 2°
• Better projections of key oceanic
variables for tuna can be achieved
using an ensemble of models
Resolution 0.25 °
• work in progress for SEAPODYM
Resolution 1°