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Perspectives and dilemmas for smallscale fisheries management in African freshwater fisheries Jeppe Kolding (University of Bergen) Small-scale fisheries - A challenge for fisheries management Experiences and lessons from developing countries and Norway. Fisheries Forum (Fiskerifaglig Forum) 4 – 5 October 2007 Background • Inland fisheries (and small-scale fisheries in general) are the ‘social security system’ in Africa – A common good! • Serves as the ‘last resort’ when everything else fail. How should they be managed? How can they be managed? 3 major dilemmas on small-scale fisheries management: • Why does the common property theory • • (CPT) only apply to man, and not to other predators? – they are also harvesting a common. Why are we controlling effort increase (f), while we at the same time refine and develop the catchability (q)? Why is man the only predator that has a harvesting pattern completely opposite all other predators? Small-scale fisheries comprise: • • • • > 30 % of total world captures > 50 % of total landings for human consumption > 90 % of all fishermen ≈ 80 % live in Asia Many ecosystems only exploitable on a small-scale • Coastal lagoons • Tidal flats, shallow shores • Estuaries • Coral reefs • Most freshwaters (contribute 25% of global production) Marine and inland capture fisheries – top 10 producers 2002 China, India and Indonesia have populations of nearly 1 billion people living below the UNDP poverty line of US$ 1 per day (Staples et al. 2004) (SOFIA 2004) Importance of fish for people The richer the less dependent on fish Relationship between the proportion of fish protein in human diets and the relative wealth (measured as GPD) of the nations they live in. From Kent (1998) Small-scale fisheries • • • • Research generally very, very small (mostly socio-economic) Most fisheries biologist are dealing with large scale industrialized fisheries Quantitative SSF data limited or nil Problems and management? copied from industrial fisheries (the ruling paradigm) • • Small-scale fisheries Mostly associated with developing countries traditional - antiquated - primitive poor - needs development unmanaged - resource depleting - challenge overfished “Tragedy of the commons” Having a negative image: Illegal and destructive gears Ignore regulations and legislation Unruly members of society Subject to “Maltusian” overfishing “Poverty trap” Unselective, indiscriminate fishing methods Small-scale fisheries Thus, plenty of arguments for: They need to be managed! 90 % of projects use one or several of the above reasons for justifications But how to manage them? when: • Little research = little knowledge • • • • Multi-species and multi-gear situations Negligible monitoring Unwillingness to abide Costly to enforce But do we understand their best ? Traditional fisheries management The present answer (panacea) seems to be: Co-management and MPAs “They must learn to understand their own good” Management paradigm The present mainstream research is focused on: • Industrial (valuable) fisheries • Single-species considerations • F, TAC, Quotas, size-limits Enhanced selective harvesting strategies Purpose: A selective kill on targeted species and sizes Result: Dominate our thinking (paradigm) and forms our perception on small-scale fisheries • EAF (ecosystem approach to fisheries) is only recent on the agenda (Johannesburg 1992) and only conceptually debated = we don’t know how! Patterns of exploitation Selectivity = rooted in all fisheries theory: • • • • Mesh size regulations Gear restrictions By-catch Destructive methods seining beat fishing barriers, weirs small mesh sizes Almost universally banned in Africa In industrial fisheries non-selectivity = BAD Result: Universally applied - also in co-management! The selectivity paradigm • FAO 2003 (Ecosystem Approach to Fisheries): "Selectivity, or lack of it, is central to many biological issues affecting fisheries. Bycatch or incidental capture is responsible for endangering and contributing to extinction of a number of non-target species…. In addition, the discarding of unwanted catch, which is particularly important in unselective fisheries, is being considered by society not only as wasteful but as unethical. The Code of Conduct dedicates a whole section to the issue (8.5). It promotes the use of more selective gear (7.6.9; 8.4.5) and calls for more international collaboration in better gear development (8.5.1; 8.5.4), as well as for the agreement on gear research standards.” SSF management = Copy+paste • Industrial fisheries are single species fisheries with single species management They are so large and valuable that research and CMS is invested for management decisions • Small-scale fisheries are multi-species, multi-gear, too small to warrant research. Our ‘understanding’ and assumptions on which we base our management is directly inherited from large-scale fisheries. Q1: Why does the common property theory (CPT) only apply to man, and not to other predators? In the ‘balance of nature’ it is generally assumed that: • • • • predation is the most important factor in natural mortality of fish (Sissenwine 1984; Vetter 1988; ICES 1988), adaptations tend to maximize fitness through optimal utilization of resources (Slobodkin 1974; Stearns 1976; Maynard-Smith 1978), predators and prey are co-evolved (Slobodkin 1974; Krebs 1985) and, there is an uni-modal response of prey productivity to predator densities: (sigmoid curve theory = logistic Gordon Schaefer model) Logistic growth –Surplus production Max = MSY No growth No surplus No stock The rate of change in biomass production as a function of the biomass is uni-modal No growth No surplus Stock = Max Logistic growth - predator-prey • • From above principles it is reasonable to assume that predation would 'maintain' prey populations close to their highest average production rate (Slobodkin 1961, 1968; Mertz & Wade 1976; Pauly 1979; Caddy & Csirke 1983; Carpenter et al. 1985). The argument follows simply from the sigmoid curve where the highest surplus production of the prey population (dN/dt = max) is the 'carrying capacity' (K) of the predator population. Predator-prey Thus predators can in theory grow to reach K (= MSYprey), but if they overshoot they will reduce prey production and consequently decline themselves. This is the background for density dependent cascade theory, and the coupled time-lagged oscillations observed between predator and prey Predator-prey: Cascading effects Inverse biomass trends illustrating trophic cascades in the Black Sea (from Daskalov 2002) What has this to do with CPT? • The big question is if effort is controlling the productivity or if the productivity is controlling the effort? Are small-scale fishermen different from other predators? • • • The answer to this dilemma is fundamental for applying CPT and co-management! If we close for open access it will have severe consequences for the ‘last resort’ option By closing open access we are in fact, closing the social security system of Africa! Productivity in African lakes • Morphology • • …of a lake, particularly area, volume, depth, and shoreline development or gradient, is of major importance to the productivity (Ryder 1978). The mean depth encapsulates several of these attributes and is considered as the most important (Rawson 1952, Ryder et al. 1974, Mehner et al. 2005). Nutrients Lakes do not maintain fertility unless continual external loading of nutrients is applied (Schindler 1978, Moss 1988, Karenge and Kolding 1995). Water inflow is a major contributor and serves as a proxy for nutrient load. Hydrology The ‘flood pulse advantage’ is the amount by which fish yield per unit mean water area is increased by a natural, predictable flood pulse (Bayley 1991). The ‘flood pulse’ keeps the environment in a stage of early succession, which means that it is dominated by biota with r-selected traits (Junk et al. 1989). The physical basis for lake productivity Climatic Latitude Altitude Morphological Edaphic Hydrologic Nutrient loading Size, duration and variability of flood pulse Not considered here Area Depth Volume Generalised effects of climatic, morphological, edaphic and hydrological factors (X-axis) on productivity (Y-axis) Relative Lake Level Fluctuation Index (RLLF) … mean lake level amplitude RLLF 100 mean depth • • • …encapsulates the morphological, edaphic and hydrological driving forces for productivity into a single quantity. … is a dynamic extension of the MEI index that only incorporated morphological and edaphic factors … builds on the ‘flood pulse’ concept (Junk et al. 1989) and the ‘flood pulse advantage’ (Bayley 1991) Hydrology and fish yields • Variability around the trend of total inland catches of the SADC countries show decadal fluctuations possibly influenced by long term climate variations (water levels) Lake levels as drivers of fish productivity • Lake Turkana 1972-1989 Lake Kariba 1982-1992 Kolding (1992) Karenge and Kolding (1995) Mean annual catch rates varies with water levels in most African fisheries. This has long been known by local fishermen, but not much investigated. Data on catch, effort and water levels • • 17 major lakes and reservoirs in Africa. Monthly time series (Min # years = 9) of lake levels from gauge readings (N = 13) or satellites (N = 4) • ESA http://earth.esa.int/riverandlake/ TOPEX-POSEIDON http://www.pecad.fas.usda.gov/crop explorer/global_reservoir/ Yield and effort estimates from 1990’s (Updated from Jul-Larsen et al. (2003) and various projects we have been involved with). Kolding and van Zwieten (2007) Data.. 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 Africa – Yield (production) is highly correlated with RLLF Productivity (annual yield/km2) vs Seasonal RLLF 35 y = 0.42x + 3.97 R2 = 0.64 30 Volta Kainji 25 20 Chilwa t/km2 Malombe 15 Mweru 1 Chiuta 10 1) 2) 3) Victoria Edward Nasser Kariba 5 1 Rukwa Tanganyika Malawi Kivu Turkana 0 0 5 10 Data too old for comparison (1970s) Oligotrophic – large areas inaccessible Unreliable records – Kapenta not incl. 3 2 Itezhi-tezhi Bangweulu 15 20 25 30 35 40 Seasonal RLLF 45 50 55 60 65 70 Similar results from Asian reservoirs.. 8 2 Mean yield (t/km /yr) 7 y = 0.038x - 1.9 r2 = 0.65 6 5 4 3 2 1 0 0 20 40 60 80 100 120 140 160 180 200 Relative lake level fluctuations (RLLF-s) From Kolding and van Zwieten (2006) Relationship between mean annual yield (t/km2) and relative seasonal lake level fluctuations (RLLF-s = %(annual draw downs/mean depth)) in 15 reservoirs of the lower Mekong countries. Data from Bernascek (1995) Africa – Fishing effort is highly correlated with RLLF… # fishers/km2 vs seasonal RLLF 16 y = 0.16x + 0.83 R2 = 0.64 14 Kainji 12 fishers/km2 10 Volta 8 Victoria Malawi Tanganyika Kivu 6 Malombe Mweru Chilwa 4 2 Edward Itezhi-tezhi Bangweulu 2 Kariba 1) 2) Nasser Turkana 1 Chiuta Data too old for comparison (1970s) Unreliable records 0 0 10 20 30 40 seasonal RLLF 50 60 70 80 … but catch rates are not correlated with RLLF Catch rates vs seasonal RLLF 6 y = 0.0046x + 2.61 R2 = 0 Victoria 5 Nasser Chilwa Kariba Chiuta ton/fisher/year 4 Edward 3 Volta Malombe Mweru Kainji 2 Tanganyika Malawi Bangweulu 1 Itezhi-tezhi Turkana Kivu 0 0 10 20 30 40 seasonal RLLF Indicating……. 50 60 70 80 …effort seems self-regulating (from CPUE) 50 45 y = 3.0417x R2 = 0.8228 Average yield per fisher is 3 ton per year irrespective of system Catch (t/Km^2/year) 40 35 Volta 30 Kainji 25 Chilwa 20 15 10 Malombe Nasser & Kariba Mweru Chiuta Is yield driven by effort or is effort driven by yield? Victoria 1990 Victoria 2002/4 Edward 5 Albert Adapted from Jul-Larsen et al. 2003 Itezhi-tezhi Victoria 1970/2 Bangweulu 0 Turkana 0 Kivu Malawi • 2 Tanganyika 4 6 8 10 12 Density (#fishers/km^2) ‘No management’ = Natures management 14 16 Does CPT apply? • Effort in African lake fisheries seems selfregulated by system productivity • Effort grows until the average catch rate per fisher reaches around 3 ton per year • Highest effort in most productive and resilient systems. Less effort in low productive vulnerable systems. is there need for co-management? Q2: Why are we afraid of effort increase (f), while we at the same time refine and develop the catchability (q)? • What are the options of management regulations? They can all be traced back to the simplest version of the so-called catch equation: • • We can regulate directly or indirectly on: Yield (Y), Fishing mortality (F) or Biomass (B). That is all. Any available or conceivable regulation can be reduced to one of the three terms. Management regulations what are the options? BMSY, Minimum SSB, MBAL, Bpa B Y FB Y MSY, TAC, ITQ F Size of capture: tc Mortality index: Z=F+M Exploitation rate: E = F/Z Effort control: f = F/q F control: F0.1, Fmed etc. Closed area Closed season Management regulations • The choice of management regulations depends on: Knowledge of the stock (research, monitoring) Control of the fishery (compliance, statistics) Management level (distribution, quotas…) • In terms of required knowledge (= management costs) then: B > Y > F, where for the latter f > q expensive cheap • SSF = ‘q’ - management For fisheries where little or nothing is known, management regulations are always based on regulating catchability q (in particular selectivity): Mesh size Size of capture Gear regulations (e.g beach seines…) Closed area or season (e.g. MPAs) • • • Find one example where one or several of these do not apply When nothing is known these regulations are based on assumptions (often based on model results). Next step is effort (f) control, then TAC etc. Each new step requires exponential increase in research and monitoring. Co-management • • • • Introduced because of the failures of enforcing existing management regulations Based on the same assumptions as conventional management (CPT, i.e. avoiding the ‘tragedy’) ‘Tragedy’ can be avoided if the ‘common’ (read open access) is removed → fishers become responsible for the resources Regulations are the same (always q-based) but Who are the ‘fishers’? Who will control access? Who will benefit? Fishing mortality (F) Better methods Increasing these is development efficiency catchability (q) So while we ‘manage’ and ‘develop’ the fishing mortality stays the same. Fishing mortality (F) Effort (f) Number of units Who are we helping? More of the same Decreasing these is management Catchability vs. effort • Increased efficiency (q) requires increased • • investments Decreased effort (f) requires increased control But the exploitation pressure on the fish stocks will often be the same - or even higher with investment (q) driven development (exit is no longer easy) • Only difference is a few rich vs. many ‘poor’ fishermen – but that is not a biological issue!! • The conclusion of Jul-Larsen et al. (2003) was that investment driven growth (q), was much more dangerous than population driven growth (f) • But this is exactly what we promote!! Q3: Why is man the only predator that has a harvesting pattern completely opposite all other predators? • Related to previous question • Per definition then: F = q = s when effort = 1 Fishing mortality = catchability = selectivity for one effort unit • Harvesting pattern is how the fishing mortality, catchability, or selectivity is aimed at the target species (prey) over its lifetime Predation vs fishing mortality.. Instantaneous rate of mortality .. is almost exactly opposite Predation mortality Fishing mortality Age (years) From ICES (1997). ..and this is what happens: 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 Age and size structure changes under selective fishing to younger and smaller individuals. effort Age and size structure As age and size structure changes under selective fishing to younger and smaller individuals, there will be a decrease in: • size (age) of maturity • fecundity, • egg quality • egg volume, • larval size at hatch, • larval viability, • food consumption rate, • conversion efficiency, • growth rate. So, is this inevitable? Life history and natural selection Dying is more certain than giving birth! Most ecological processes and life history traits can be related to the prevailing mortality pattern: • The unstable environment: characterised by discrete, density independent, non-predictive, nonselective mortality induced by physical changes • The stable environment: characterised by continuous, density-dependent, predictive, and sizeselective mortality induced by the biotic community. Mean size of organisms Cope’s rule Stable period Stable period Stable period Stable period Geological time Cope's rule states that evolution tends to increase body size over geological time in a lineage of populations. But the precondition is geological stability. During unstable periods with mass extinctions the large lineages are more susceptible. Investment in age (size) is investment in future. Life history: r-K selection • r-selected species: Small Rapid growth Early maturation No parental care Opportunistic Colonisers Unstable environment Resilient • K-selected species Large Slow growth Late maturation Parental care Specialised Competitors Stable environment Vulnerable Logistic growth: r-K selection Carrying capacity = B∞ = K • r-selected species: Small Rapid growth Early maturation No parental care Opportunistic Colonisers Unstable environment Resilient • K-selected species Large Slow growth Late maturation Parental care Specialised Competitors Stable environment Vulnerable Abundance (Log N) r-K selection as a function of mortality pattern Increased juvenile mortality = K-selection = Z ↓ Slope = total mortality rate Z = rmax Increased adult mortality = r-selection = Z ↑ Age (size) Kolding (1993) K-selection: Stable environment, biotic mortality (predation) – predictive r-selection: Unstable environment, abiotic mortality – non-predictive Evidence: Size selection = genetic changes Increased mortality on: Small Random Large After Conover and Munch 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 Size selective fishing with large mesh sizes on adults individuals (B) across multiple generations of size-selective exploitation. Cope’slines, rulesquares in reverse. Circles represent small=harvested are the random-harvested lines, and triangles are the large-harvested lines. Conover and Munch 2002 We are deliberately inducing r-selection on the stocks. Instantaneous rate of mortality Man has a harvesting pattern that is opposite to what most fish stock are naturally adapted to High juvenile mortality = K-selection large fish High adult mortality = r-selection small fish Predation mortality Fishing mortality Age (years) When yields are declining our prescription is even larger mesh sizes, which will only make matters worse, and which we already know is wrong… North Sea multispecies system 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) MSVPA including interspecies predation and 2) single species (but multi-fleet) assessment. Lower yields in the MSVPA results 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. 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 Is non-selective fishing bad? • • • • There is no empirical evidence The notion comes from a theoretical model (Y/R) which is 100% synthetic and biologically wrong (constant parameters + no density dependence) On the contrary we know that selective fishing is bad, but we still advocate it! But how do we impose gear-, mesh-, and size restrictions in a multi-species fishery? Multi-species community How should it be harvested? What should be minimum mesh-size? Biomass-size distributions Selective fishing will change the slope Size spectra Abundance The distribution of biomass by body mass follows regular patterns phytoplankton zooplankton small fish big fish Body mass slope steepens when large fish removed Jennings & Blanchard, 2004 Fishing effects on community size-structure Trends in size-spectrum slopes of the North Sea -5 Slope -6 -7 -8 -9 -10 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 Year Rice & Gislason (1996) Rotate the size spectra and.. Quaternary consumers Tertiary consumers Secondary consumers Primary consumers Primary producers … we get a Lindeman trophic pyramid EAF: What is the ‘right’ fishing pattern? How do we A non-selective manage a multiharvesting pattern is species fishery? what they are criticised for What is the right But a gears non-selective and mesh harvesting pattern is sizes? ecosystem conserving. The system remains unchanged,How except ? everything is less. How much ? Small-scale fisheries are often ‘non-selective’ ! Can we fish everything proportionally? Example from lake Kariba, Zambia where fishers are using illegal small-meshed nets Parallel slopes, only intercept lower Kolding et al. 2003 • The system remains unchanged, except everything is less Conclusions • • • The ‘Tragedy of the commons’ is the tragedy of our current management thinking. Can we universally apply notions that are: Developed for single species fisheries Mostly theoretical Often dubious On SSF - of which we know so little? We are mammals and we apply all our models and concepts based on “mammal biology”. But fish – in their breeding strategy – are closer to insects or trees!! Final questions • We try our best – but are we doing it right? • Is our theory and paradigms appropriate? • How much do we know? • Why are we in the ‘management mode’ when we have hardly started our ‘research mode’? • Most SSF in Africa are still largely unmanaged, but that is not a challenge - it is an opportunity! For studying the impact of fishing and learn! Because… Thank you for your attention • For SSF the real challenge for management is: How can we evolve our theories? Evolution of fisheries management Slightly modified from Non Sequitur: Herald Tribune 11-12/8- 2007