Transcript 05 Vulnerability of oceanic fisheries

Vulnerability of oceanic fisheries
to climate change
Presented by
Patrick Lehodey
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
• It changes with age
(size) and life stage
according species
thermoregulation
capacity
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
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 and
Central Pacific Ocean by 2100
Tuna habitat – oxygen
Estimated lower lethal oxygen
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
Less tolerant to
low values
Species
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
Fishing
CO2
Integrated modelling: SEAPODYM
Skipjack projection
2000
2050
Adult biomass
Larval density
2000
2050
Projected changes in
abundance using average
fishing effort for 1980-2000
Bigeye projection
2050
2000
Adult biomass
Larval density
2000
2050
Projected changes in
abundance using average
fishing effort for 1980-2000
Albacore projection
Evaluation of model
skills by comparison of
predicted and observed
catch and size
frequencies of catch
Albacore projection
2050
2000
Adult biomass
Larval density
2000
No change
in O2
With modelled O2
2050
Projected changes in
abundance using average
fishing effort for 1980-2000
Projected change by region and EEZ
2050
2100
b
120E
140E
160E
180
160W
140W
30N
40N
10N
20N
Change in % relative
to average catch
1980-2000
2035
2100
0
10S
2050
Tuna catch 2008
(tonnes)
100,000
50,000
10,000
20S
30S
2035
130E
40S
Albacore
Bigeye
Skipjack
Yellow fin
150E
170E
170W
150W
Tuna fishing
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?
Tuna fishing
Priority adaptations
• Regional management frameworks (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 (e.g. transferability
within PNA vessel days scheme) to ensure flexibility
to cope with changing spatial distribution of fishing
effort

Socio-economic scenarios likely to drive future fishing
effort in the region need to be identified and incorporated
in modelling
Purse seine fishing effort
in 2010 and PNA EEZs
Priority adaptations
• Spatially-explicit management in archipelagic
areas, currently beyond the mandate of WCPFC, is
needed to monitor and assess potential subregional effects
For example, there are
possible effects of better
conditions for tuna in
PNG due to increased
run-off from Sepik-Ramu
Rivers
Conclusions
• Understanding impact of climate change on tuna
depends on our capacity to explain, model and
predict the effect of natural variability (e.g., ENSO)
in recent past
• While there is still
uncertainty about
impacts of climate
change, 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
• Better projections of key oceanic
variables for tuna can be achieved
using an ensemble of models
Resolution 2°
Resolution 1°
Resolution 0.25 °