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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