Download In the trophic pyramid…

Management of living aquatic
resources and food security;
How can Norway contribute?
Jeppe Kolding
University of Bergen
Comfort Hotel Holberg, Bergen 17–18 September 2012
Only 2 biological questions in
terms of food security
• How much?
 = Fishing pressure
• (effort f)
• How?
 = Fishing pattern
• (catchability q)
• (selectivity s)
Catch and Fishing mortality
• Catch = Fishing mortality x Biomass
• Fishing mortality = “How much” x “How”
• “How much” = effort (fishing power)
• “How” = catchability (selectivity + efficiency)
C
F = = q ⋅ f = catchability ⋅ effort
B
What can we learn from Norway?
SSB x 10-1
SSB x 10-1
Fishers, catches and efficiency in Norway
180
4 000 000
160
3 500 000
140
3 000 000
120
2 500 000
100
Catches not
changing
80
60
40
2 000 000
1 500 000
Quantity (tons)
Fishermen (1000) / Catch per
fisherman (tons)
Effort (fishers) decreases while Catch Per Unit Effort (CPUE)
increases because catchability (q =efficiency) increases
1 000 000
500 000
20
0
0
1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Quantity (tons)
Fishermen (1000)
?
Catch (tons) per fisherman
Hersoug
Total catch, numbers of fishers, and catch per fisherman in the Norwegian
fishery(2007)
1945-2005
Fishing mortality (F)
C
F = = q ⋅ f = catchability ⋅ effort
B
Better methods
Increasing these is
Development
So while we ‘manage’
and ‘develop’ the
fishing mortality stays
the same.
Only we get fewer and
richer fishers
catchability (q)
Fishing mortality (F)
Effort (f)
More units (capacity)
Decreasing these is Management
Norway in a nutshell





Fish stocks have increased by x 3
While we have reduced our fishers with x 10
Catches have remained stable at ≈ 3 mill tonnes
We produce ≈ 1 mill tonnes farmed salmon
We import ≈ 3 mill tonnes feed

Soon we will have the largest stocks on record
.. and become net fish importers

Is that our contribution to food security?

The tragedy of our legacy:
How do global management discourses
affect small scale fisheries in the South?
Jeppe Kolding (University of Bergen)
&
Paul van Zwieten (University of Wageningen)
Forum for Development Studies 38(3): 267-297.
The history of our legacy
•
H. Scott Gordon (1954). The Economic Theory of a
Common-Property Resource: The Fishery. The
Journal of Political Economy, Vol. 62, pp. 124-142.
•
Ray J. H. Beverton and Sidney J. Holt (1957). On the
dynamics of exploited fish populations. Fish. Inv.
ser. 2, Sea Fisher. 19: 1-533.
•
Garrett Hardin (1968). The Tragedy of the Commons
. Science 13: Vol. 162, pp. 1243 - 1248
The result of our legacy
•
= Unconditional acceptance of two truisms:


•
Common Property (open access systems) will lead to overfishing (and
‘tragedy’ and destruction)
Non-selective fishing gears (small mesh sizes) will lead to overfishing
Both truisms are based on theoretical models:
Google scholar hits:
about 2,090 for "Tragedy of the
 Logistic Gordon-Schaefer model
(CPT)
commons" and overfishing
about 3,360 for "Mesh size" and
 Yield per recruit model (Y/R)
overfishing and
•
All models are based on
about 1,480 for "Mesh size" and
enforcement
assumptions!
International fishery objectives
Johannesburg 2002 Declaration § 31 (a):
«Stocks should be kept at biomass levels
that can produce maximum sustainable
yields (MSY).»
International conservation objectives
The Malawi principles for Ecosystem Approach:
«A key feature of the ecosystem approach
includes conservation of ecosystem
structure and functioning»
Our conventional collective wisdom:

“.. a fishery will yield its maximum physical
returns if all fish are allowed to grow to the
point where the rate of increase in weight
just ceases to outstrip losses due to natural
mortality and then harvested… (This is
logical, and is how a farmer would produce
meat, bearing in mind that he must leave a
breeding stock.)”
Hillis and Arnason 1995
COFI/2012/7:
International Guidelines for Securing Sustainable
Small-Scale Fisheries:
«Unselective fishing methods should be
discouraged and restricted to areas beyond
a certain distance from the shore and
destructive practices should be entirely
phased out..»
“if all fish are allowed to grow“?
Survival and reproduction
Few
More
Millions
Young
fish
are
likely
to
die,
only
a
Fish are not mammals…
fraction ( < 1%) will mature..
..but large fish have more eggs
100 cm = 16 mill eggs
50 cm = 1 mill eggs
Age at maturity
Natural mortality in fish
Master, I marvel how the fishes live
in the sea. Why, as men do a-land;
the great ones eat up the little ones.
Shakespeare, Pericles, prince of Tyre
Solution: Grow and be Big!
Age (years)
Cartoon by Frits Ahlefelt
Single species Y/R models
but every step in the food chain loose 90% energy
Growth
overfishing
Growth
Mortality
Beverton & Holt (1957)
Natural mortality in fish
Is almost exactly opposite to fishing mortality
Fishing mortality (yr-1)
Solution: Be Big!
Age (years)
Cartoon by Frits Ahlefelt
..and this is what happens:
Solution: Stay small!
Median age-at-maturation (sexes combined) of Northeast Arctic cod based
on spawning zones in otoliths (from Jørgensen, 1990).
But we know that – we even use it as a
sign of fishing
CPUE = q ⋅ B
Age and size structure changes
under selective fishing to younger
and smaller individuals.
effort
Size selection = Fishery induced evolution?
Increased mortality on:
Small
Random
V
Large
After Conover and Munch Science 2002
Mean individual weight at age for six
harvested populations after 4
generations. Circles, squares, and
triangles represent the small-,
random-, and large-harvested
populations, respectively.
Effect of size-selective fishing
Mortality on:
Small
Random
Large
Trends in average total weight harvested (A) and mean weight of harvested individuals
(B) across multiple generations of size-selective exploitation. Closed circles represent
small harvested lines, open squares are the random-harvested lines, and closed triangles
are the large-harvested lines. Conover and Munch 2002
Size selective fishing with large mesh sizes on adults
will decrease mean size and total yields
Percent changes in the long term
fishery yields for North Sea
stocks resulting from an
increase in trawl mesh size from
85 to 120 mm for the directed
fishery for cod.
Results are presented for
1) single species (but multi-fleet)
Y/R assessment.
2) MSVPA including interspecies
predation
Lower yields when food is
accounted for are due to greater
predation rates from large
predatory fish (cod, whiting,
haddock, saithe) released by the
larger mesh sizes.
Source: Anonymous 1989. Report of the
multispecies assessment working group.
Int. Counc. Explor. Sea., C.M.
1989/Asess: 20, Copenhagen.
North Sea multispecies system
Effect of mesh change from 85 to 120 mm
Multispecies
Single species
Cod
Whiting
Saithe
Mackerel
Haddock
Herring
Sprat
Norway pout
Sand eel
Total
-30
-20
-10
0
10
20
30
Percent change in long-term yield
40
The food web is size structured…
Top predators
Biomass
Tertiary consumers
Secondary consumers
Primary consumers
Primary producers
Size
..abundance is inversely correlated with size
Community size spectrum
Biomass
The distribution of biomass by body size
follows regular patterns
phytoplankton
zooplankton
Small fish
Large fish
Size
Under conventional selective fishing slope and
intercept will change
Changes in the North Sea
Biomass
Unfished
1983–1987
1998–2002
Size
Garcia et al. 2012
Science 2. March 2012
Balanced harvesting…
Biomass
It will reconcile objectives
by maintaining community
structure while returning
highest yields
Size
.. is fishing as many sizes and species as possible in
proportion to natural productivity
Lake Kariba
Lake Kariba
Non regulated and non enforced
Yield = 6000 tonnes yr-1
Regulated and enforced
Yield = 1000 tonnes yr-1
Lake Kariba
Zimbabwe - Catch rates
Zambia - Catch rates
14
Artisanal CPUE (kg/45m net) from CAS surveys
12
Experimental CPUE (kg/45m)
Controlled effort
8
Artisanal CPUE (Kg/net) from CAS surveys
Open access
Experimental CPUE (Kg/45 m net set)
Artisanal CPUE (Kg/net/night) from Scholtz 1993
CPUE
CPUE
10
10
9
8
7
6
5
4
3
2
1
0
6
4
2
0
1962
1967
1972
1977
1982
1987
1992
1997
1959 1964 1969 1974 1979 1984 1989 1994 1999
Average mesh size (mm stretched), Zambia
thesedecrease
symptoms of
As Are
CPUE
overfishing?effort,
with increasing
Is there cause for
so does concern?
mesh sizes to
maintain catch rates
180
160
R2 = 0.86
140
120
100
80
60
1960
Kolding et al. (2003)
1965
1970
1975
1980
1985
1990
1995
Kolding et al. in prep
Kolding et al. in prep
Slopes are parallel,
species and size
composition identical,
but everything is less
Kolding et al. in prep
The Zambian IUU fishing has reconciled our
objectives…
Johannesburg 2002 Declaration § 31 (a):
«Stocks should be kept at biomass levels that can
produce maximum sustainable yields (MSY).»
CBD Malawi principles for Ecosystem Approach:
«A key feature of the ecosystem approach
includes conservation of ecosystem structure and
functioning»
Bangweulu swamps Northern Zambia
Mesh size
(mm)
3
4
6
8
10
25
38
50
63
76
89
102
114
127
140
Total:
%
% legal
Mesh size
Total number of gear by type
seines weir traps cum %
gillnets
kusikila
3,869
13
8,358
41
2,322
49
387
50
534
6,719
4,233
1,260
554
136
255
13,691
46
22
17
68
135
643
74
-
53
178
49
52
75
90
97
99
99
100
937
3
280
1
14,936
50
29,844
100
Kolding et al. 2003
Species and size composition by gear
Legal
Only largest species in legal gillnets!
are technically overexploited
0.8
0.7
Only legal catches overexploited
Mean exploitation rate (F/Z)
0.6
R2 = 0.77
0.5
Large predators
0.4
0.3
C. gariepinus and S. robustus
in seines
medium sized cichlids
0.2
0.1
small species
0.0
0
5
10
15
20
25
30
size range of exploitation (L00-L50%, cm)
35
40
45
Kolding et al. 2003
50
By fishing illegally they fish balanced
and reconcile our objectives
Kolding 2011
Balanced Harvest has been
explored theoretically
> 4 times higher yields
than size-at-entry
Balanced
Size-at-entry (increasing)
Is the Norwegian Fishery in balance?

Balanced harvest = catches proportional to
production
Norwegian Sea and the Barents Sea:
ECOPATH model (1997-2001)
3 million km2
Skaret og Pitcher, in press
The whole ecosystem in 58 functional
groups from whales to plankton
FG
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Common name Latin name
Minke whale Balaenoptera acutorostrata
Sperm whale Physeter macrocephalus
Killer whale Orcinus orca
Other toothed whales
Northern bottlenose whale Hyperoodon ampullatus
White beaked dolphin Lagenorhynchus albirostri
Harbour porpoise Phocoena phocoena
Other baleen whales
Fin whale Balaenoptera physalus
Humpback whale Megaptera novaengliae
Blue whale Balaenoptera musculus
Harp seal (0) Phoca groenlandica
Harp seal (1+) Phoca groenlandica
Other seals (0)
Other seals (1+)
HarbourSeals Phoca vitulina
GreySeals Halichoerus grypus
HoodedSeals Cystophora cristata
Atlantic puffin Fratercula arctica
a
Other seabirds
Brünnich's guillemot Uria lomvia
Northern fulmar Fulmarus glacialis
Black-legged kittiwake Rissa tridactyla
NE Arctic cod (0-2) Gadus morhua
NE Arctic cod (3+) Gadus morhua
Coastal cod (0-2) Gadus morhua
Coastal cod (3+) Gadus morhua
Haddock (0-2) Melanogrammus aeglefinus
Haddock (3+) Melanogrammus aeglefinus
Saithe (3+) Pollachius virens
Saithe (0-2) Pollachius virens
Flatfishes and rays
European plaice Pleuronectes platessa
Long rough dab Hippoglossoides platessoides
Thornback ray Raja clavata
European flounder Platichthys flesus
Common dab Limanda limanda
Brill Scophthalmus rhombus
FG
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Common name Latin name
Other benthic fish
Cusk Brosme brosme
Ling Molva molva
Pollack Pollachius pollachius
Monkfish Lophius piscatorius
Whiting Merlangius merlangus
Eel Anguilla anguilla
European hake Merluccius merluccius
Atlantic halibut Hippoglossus hippoglossus
Blue ling Molva dypterygia
Greenland halibut (0-4) Reinhardtius hippoglossoides
Greenland halibut (5+) Reinhardtius hippoglossoides
Deep-sea redfish (0-4) Sebastes mentella
Deep-sea redfish (5+) Sebastes mentella
Golden redfish (0-4) Sebastes marinus
Golden redfish (5+) Sebastes marinus
Blue whiting (0-1) Micromesistius poutassou
Blue whiting (2+) Micromesistius poutassou
Mackerel Scomber scombrus
Herring (0) Clupea harengus
FG
43
Herring (1-2) Clupea harengus
Herring (3+) Clupea harengus
Polar cod Boreogadus saida
Capelin (0) Mallotus villosus
Capelin (1) Mallotus villosus
Capelin (2+) Mallotus villosus
Basking shark Cetorhinus maximus
Other sharks
Spiny dogfish Squalus acanthias
Porbeagle Lamna nasus
Atlantic salmon Salmo salar
Lumpsucker Cyclopterus lumpus
Small pelagic fish
Greater silver smelt Argentina silus
Horse mackerel Trachurus trachurus
Norway pout Trisopterus esmarkii
Sprat Sprattus sprattus
51
44
45
46
47
48
49
50
52
53
54
55
56
57
58
Common name Latin name
Mesopelagic fish
Pearlside Maurolicus muelleri
Glacier lanternfish Benthosema glaciale
Arctozenus risso
Squid Gonatus fabricii
Edible crabs and lobster
Edible crab Cancer pagurus
European lobster Homarus gamarus
Red king crab Paralithodes camtschaticus
Corals Lophelia pertusa
Other macrobenthos
Prawns Pandalus borealis
Krill
Meganyctiphanes norvegica
Thysanoessa inermis
Thysanoessa longicaudata
Pelagic amphipods
Themisto libellula
Themisto abyssorum
Themisto compressa
Calanus
Calanus finmarchicus
Calanus hyperboreus
Zooplankton 2mm+
Zooplankton 0-2mm
Jellyfish Periphylla periphylla
Seaweeds
Wolffishes
Common Anarhicus lupus
Spotted Anarhicus minor
Northern Anarhicus denticulatus
Phytoplankton
Detritus
Skaret og Pitcher, in press
Average catches 1997-2001
Is the Norwegian fishery in balance?
Annual production (kg/km2)
in 58 functional groups
How about the North Sea (1991)
Mackinson & Daskalov (2007)
Balanced harvest = catches proportional to production
Norwegian and Barents Sea
North Sea
Balanced harvest = flat exploitation rate
Norwegian and Barents Sea
North Sea
Food security and Sustainability

What about the “how much” question?

Will open access lead to overfishing?
Small-scale fisheries and
Livelihoods
• Supports directly > 100 mill people
• Most are components of a diversified
livelihood
• Often only source of protein
• Undervalued, underestimated, and
under-reported
Small-scale fisheries and
Poverty alleviation
• Small-scale fisheries in many developing
countries are functioning as the ‘social
security system’– A common good!
• Serves as the ‘last resort’ when
everything else fail.
• This requires open access.
Internal or external assumptions
• If a system is in internal “balance” (like
most models assume) and all processes
are driven by internal factors (predation,
competition, fishing..) then we can
influence by ‘managing’ the factors.
Fisheriesadapting
models – to
and
• But, if a system is constantly
fisheries
external drivers (climate,
rain,management
nutrient
always
have internal
inputs…), which we cannot
‘manage’,
then
assumptions only!
we have problems.
Biomass
Internal or external drivers?
Decline attributed to over fishing
Under fishing ?
Overall no
Stocks fluctuate and long
significant
change
time series are
needed
Environmentalvariation rarely considered
Lake levels as drivers of fish productivity
•
Lake Turkana 1972-1989
Lake Kariba 1982-1992
Kolding (1992)
Karenge and Kolding (1995)
Direct correlation between lake level fluctuations and fish
productivity. This has long been known by local
fishermen, but not much investigated.
Common property theory (CPT)
(open access)
dB
= Yield
dt
Max = MSY
MEY
Costs
This is whatProfit
we =fear
0
Profit = max
Stock collapse
Overfishing
B∞
Effort
But this is what we see
System
Yieldyield
Yield
or
CPUE
Biomass
A decrease in Yield is
a sign of overfishing!
CPUE
Biomass
A decrease in biomass is
NOT a sign of overfishing
Fishing effort
Jul-Larsen et al. (2003)
Costs
?
No data
Boerema and Gulland (1973)
Predator-prey
MSYprey = Carrying Capacity predator
Who controls who?
Is catch a
function of effort?
Or is effort a
function of catch?
Are small-scale fishers controlled by
the resources?
• In African freshwaters we know that rain,
river flow or lake level changes drives the
productivity (Kolding and van Zwieten 2012).
Data on catch, effort and water levels
•
•
•
17 major lakes and
reservoirs in Africa.
Time series of lake levels
from gauge readings (N =
13) or satellites (N = 4)/
Yield and effort estimates
from 1990’s
Kolding and van Zwieten (2012)
Relative Lake Level Fluctuations (RLLF)
Lake
Area
km2
Tanganyika
32600
580
240
73000
40000
0.04
0.14
2699
240
40
315
2868
0.06
0.14
Malawi
30800
290
545
28000
27296
0.10
0.30
Victoria
68800
40
288
571000
105000
0.60
1.10
Edward
2325
17
16031
5443
1.43
5.60
Turkana
7570
31
47
1500
1500
2.12
3.73
Kariba
5364
30
48
30311
7060
4.32
9.65
390
5.5
90
7500
2371
6.00
20.40
Volta
8500
18.8
121
250000
71861
7.02
19.49
Nasser
5248
Fluctuating
25.2
50
30000
6000
7.14
23.63
Mweru
2700
8
94
42000
15791
7.20
25.70
Bangweulu
5170
3.5
68
10900
10240
7.39
34.34
Kainji
1270
11
40
38246
17998
8.78
69.41
Rukwa
2300
3
17
9879
13.79
31.97
Chilwa
750
3
13
Chiuta
113
2.5
Itezhi-tezhi
370
15
Kivu
Malombe
Mean depth
m
Stable
#Species
Yield
ton/yr
#Fishermen
RLLF
annual
RLLF
seasonal
Highly fluctuating
15000
3485
17.80
39.70
40
1400
350
19.53
59.30
24
1200
1250
21.16
54.47
Yield (production) is exponetially
correlated with RLLF
1000
y = 37.774x 0.3173
Yield
100
r 2 = 0.49
10
1
0.1
1
Effort
0.1
10
100
Relative lake level changes
y = 0.0187x 0.2385
r 2 = 0.42
0.01
0.001
Relative lake level changes
Kolding and van Zwieten (2012)
Yield and effort is exponetially correlated
with RLLF
1000
y = 37.774x 0.3173
Yield
100
r 2 = 0.49
10
1
0.1
1
Effort
0.1
10
100
Relative lake level changes
y = 0.0187x 0.2385
r 2 = 0.42
0.01
0.001
Relative lake level changes
Kolding and van Zwieten (2012)
Catch per area (ton per km2 per year)
Effort seems self-regulated (from productivity)
Average yield per fisher is 3 ton per year
irrespective of system
Effort density (Number of fishers per km2)
Kolding and van Zwieten 2011
Catch per area (ton per km2 per year)
Effort seems self-regulated (from productivity)
Average yield per fisher is 3 ton per year
irrespective of system
Result of ”management”
Effort density (Number of fishers per km2)
Kolding and van Zwieten 2011
Effort as a function of productivity ?
Catch per unit effort
Are the stocks
OK at this limit?
V
Min. acceptable limit
≈ 3 tonnes per year per
fisher in Africa
Low
Productivity
Low
resilience
High
Productivity
High
resilience
effort
Conclusions

Under low technology conditions, closures and
effort regulations are not an issue – it will be
regulated by the productivity.

Closing open access is therefore pointless and
will close the de facto social security system in
many developing countries.

Costs of closing to society have never been
calculated
What is Norway’s advantage in development terms?

Norway’s knowledge-based management of
fisheries is based on:

Size limitations (minimum legal sizes)
Capacity limitations (effort and TAC)



How do we know that these paradigms is what
the world needs in terms of food security?
What do we know about many of these fisheries
in general?
How can Norway contribute?




The Norwegian fishery is highly efficient and
purely economically driven.
We have transformed our traditional small-scale
fisher/farmers along the coast into an industrial
enterprise ‘owned’ by 10 000 wealthy fishers.
It has little to do with ecology or food security. We
have not increased the catches in the past 50
years – we cannot even feed our salmon industry.
Norway has a lot to offer! But Norway should also
realize that their fisheries policies are not suited
to feed the world.
Thank you for your attention
Jeppe Kolding