Section 1. (Surplus) production, productivity, trophic level and density dependence
Biological production (P) is the total amount of tissue generated in a population in a particular space during a given period of time. It is of central interest in the exploitation of renewable resources, since the yield (C) is a fraction F (Fishing mortality) of the mean biomass and is a fraction (x) of biological production P.
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(1) |
Production thus includes both living organisms and organisms that died within the time period. Gains in biomass are a result of individual growth, new offspring and immigration, whereas death and emigration cause losses in biomass.
‘Surplus’ production, or the net production after natural density-dependent mortality has been subtracted, is essential for any population in order to expand and/or withstand predation without declining. The ecotrophic efficiency (x in eqn. 1) is defined as the fraction of total production that is consumed by higher trophic levels. The concept of trophic level (Lindeman, 1942) means grouping of taxa or populations into discrete levels according to their place in a food chain, e.g. primary producers (plants and algae), herbivores, first-order carnivores, second-order carnivores, etc. This system is used to simplify the description of an ecosystem, but also to describe the interactions and efficiencies of energy transfers between trophic levels. Most models used in fisheries are single-species models with only two ‘trophic levels’, the stock as prey and the fisherman as the only predator.
The concept of Maximum Sustainable Yield (MSY), that is the maximum surplus production, was derived from sigmoid curve theory (Graham, 1935). The theory describes the change in biomass and production within a population. It presupposes that the regeneration of biomass, or net rate of increase, is a density-dependent function of biomass (dB/dt = g(B)) which is dome-shaped with its highest point (= MSY) at some intermediate level between 0 and a maximum density. The simplest mathematical model for self-regulating growth in populations is the well-known logistic equation (Verhulst, 1838). Although widely criticized for oversimplification (e.g. Kozlowski, 1980), it has contributed much to ecological thinking and forming of ideas. Its greatest applicability is perhaps in the illustration of the theory of density-dependent growth, which is best shown in its differential dome-shaped form
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(2) |
In this equation, K is the theoretical maximum biomass (B) that can be attained in an environment (= Bmax or B¥) also known as carrying capacity, which is mainly determined by available food and space. The parameter rm is the intrinsic rate of natural increase in biomass. It is defined as the maximum instantaneous rate of births per individual (b), minus the minimum instantaneous rate of natural deaths per individual (d) under specific environmental conditions or rm=b-d. (Fenchel, 1977). The logistic growth described by (2) starts with an exponential growth phase of a population that decreases as the relative saturation of the environment increases, until the asymptote K is reached (i.e. where K = B) and growth ceases (g(B)=0). This means that the per capita, or actual rate of increase (r) in the population is a linear function of density (B) with r = rm at the intercept, which is seen when rewriting (2):
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(3) |
If we then assume that the potential birth rate (fecundity) is constant (Ricker, 1954; Beverton and Holt, 1957) then the death rate must be a linear function of the density too, since d = b - rm. The assumption of density-independent birth rate may not be true for the full range of r. However, it is generally accepted at lower population densities, while it seems valid at least up to the inflection point (K/2 in (2)) where intra-specific competition for resources starts affecting productivity (Begon et al., 1990, p. 202).
Production is thus a density-dependent quantity expressed in kilograms or tonnes, often scaled by area or volume. Productivity is the rate or speed at which production is generated and is a function of both the individual biological regenerative characteristics of a particular species (the per capita rate of increase), and the density (B) of the stock. Productivity is the instantaneous rate of biomass production dB/dt. In the presence of fishing, the instantaneous rate of change in biomass is equal to the productivity minus the accrual rate of the yield, or, combining the basic fisheries equations C(atch)=F(ishing mortality)·B(iomass) and F=q·f =catchability·effort and (2):
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(4) |
In words, what this equation says is that, over a fixed period of time, the change in biomass is the surplus production minus the yield:
new biomass = old biomass + surplus production - catch
When biomass does not change (dB/dt = 0), then surplus is equal to output and a stock is said to be at an equilibrium. Reasoning along these lines has led to a set of important fishery models called surplus production models, of which the Schaefer (1954) model (equivalent to (2)) is an example. Another important inference is that at any constant population size, the average total death rate (Z) is equal to the intrinsic rate of increase (rm), which implies that the total mortality rate of a population is related to this important life history characteristic of the species (Kolding, 1994).
Productivity is also used more loosely in this report for example when “changes in lake productivity” are related to “changes in fish yield”. What is meant by these expressions is that, through environmentally driven processes such as changes in water inflow in lakes and resulting water levels, the productivity at different trophic levels changes, resulting in changes in fish production and hence fish yields. Therefore, in a changing environment, the idea of a constant carrying capacity (which is the underlying assumption for (2)) is neither plausible nor necessary for conceiving density dependence or equilibrium situations. Steady state means that the actual rate of increase r = 0. In a growing population r > 0 and in a declining population r < 0. Consequently, in nature the value of r for all non-extinct populations is fluctuating around a mean value of 0. It also implies that there will always be a set of environmental conditions at which r is positive. A theoretical set even exists where r attains a maximum value (rm) at steady state conditions, leading back to equation (2). Since r depends on the age structure in the population, it is clear that any specific value of r is only valid for a particular environment and mortality regime. The frequency and amplitude of oscillations in r must then be mainly related to the variability of the environment (the extrinsic factors). In fish, the change in r is dependent both on abiotic factors, such as temperature and oxygen, and biotic factors such as food and predation.
All factors limiting population growth are considered to be density dependent (Andrewartha and Birch, 1954). In order of increasing importance these factors are:
· Shortage of resources like food and shelter,
· Inaccessibility of these resources in relation to the animal’s capacity of dispersal, and
· Shortages of time duration in which the rate of increase (r) is positive.
Climate or predators influence the fluctuations in r, the last, most important factor. Depending on the environment, one or the other has overwhelming importance. The occurrence of intermittent short spells of optimal situations might well be illustrated by the strong year-class variation we observe in most fisheries. The huge variations are considered one of the biggest obstacles in fisheries modelling where traditionally, an attempt is made to relate recruitment with stock size. Yet stock-recruitment theory, as emphasized by Rothschild (1986), is simply a theory that attempts to account for the mortality of young fish between spawning and recruitment time. Thus we may generalize to two situations: one in which population size is largely determined by climate, and another where other animals largely determine it. In both cases mortality is still the most important common denominator.
Section 2. Diversity, stability, resilience and regenerative capacity
Fishing, predation and environmental changes all causes stress, and the capacity to recover, persist, endure or ‘bounce back’ to a previous state, is theoretically associated with the two concepts: stability and resilience. According to one of the better known definitions (Holling, 1973), stability is “The ability of a system to return to an equilibrium state after a temporary disturbance, the more rapidly it returns and the less it fluctuates, the more stable it would be”. Again according to Holling, (op, cit.) resilience is: “A measure of persistence of systems and the ability to absorb change and disturbance and still maintain the same relationships and composition between populations or state variables [irrespective of relative abundance]”. In Holling’s view, instability in the sense of large fluctuations, produces a highly resilient system capable of repeating itself and persisting over time until a disturbance restarts the sequence. Thus systems can be very resilient and still fluctuate wildly. Holling (op. cit.) states that these two distinct properties alone define the behaviour of ecological systems. However, any measure of stability requires a time-perspective that must be seen in relation to the lifespan of a species. The ambiguities in the literature between stability and resilience might thus, like the equilibrium and non-equilibrium models discussed later, simply be a matter of scale (Kolding, 1997). Therefore the mortality rate and pattern represents the speed of regeneration against which stability and resilience should be measured. As shown in Figure 5.7 ‘stable’ species have on average a low intrinsic growth rate (r) and total mortality (Z) with a correspondingly longer lifespan, whereas ‘resilient’ species have a high r and Z and shorter lifespan. What determines resilience and stability depends on the combination of stress (continuous selective or discrete non-selective mortality), and the trade-off between the advantages of being big, or developing specialized behaviour, and the probability of dying with time.
The coexistence of several species in an ecosystem, so-called biodiversity, and particularly the natural regulation and maintenance of biodiversity is theoretically a challenge (Kolding, 1997). The basic unit in biodiversity is the individual species and normally the focus is on the number of species and the relative abundance and distribution of individuals within an ecosystem (alpha diversity). The more species, the more diverse is the ecosystem, and the more we tend to value it. Consequently, fisheries are facing a dilemma against the drive to conserve biodiversity. For instance, FAO (1992, p. 5) wrote: “Continued high fishing intensity will contribute to a loss of biological diversity,(...) and this may lead to more unstable, and possibly lower, catches in the long term”. From the background assumption of “The balance of nature” (Egerton, 1973), system complexity, diversity and environmental stability have traditionally been positively related to each other (Margalef, 1968, 1969; Odum, 1969). This ‘diversity-stability’ hypothesis has often led to the suggestion that highly diverse communities are particularly vulnerable to exploitation (May, 1975; Sainsbury, 1982).
In contrast, theoretical advancement in understanding species diversity generation, notably the ‘intermediate disturbance hypothesis’ (Connell, 1978), and the ‘dynamic equilibrium hypothesis’ (Huston, 1979), build on non-equilibrium dynamic community assumptions. Both infer that frequent but irregular disruptions are a major agent in maintaining high-diversity communities. In general, the various hypotheses for the regulation of diversity can be grouped into so-called equilibrium and non-equilibrium models (Tonn and Magnuson, 1982; Petraitis et al., 1989; Begon et al., 1990). Selective, density-dependent, predator-induced mortality belongs to the first category, whereas catastrophic, non-predictive, density-independent, environment-induced mass mortality belongs to the latter. However, common to all these hypotheses is that population reduction in the form of either selective (predation) or non-selective mortality (environmental disturbances) is the main mechanism for the regulation of diversity. The logic is that individual population densities must be (and are, see section 3) kept lower than the carrying capacity to prevent the effects of strong mutual interactions, the so-called competitive exclusion principle (Hardin, 1960). Both the selective mortality based hypotheses (equilibrium models), and the hypotheses based on non-selective population reductions (non-equilibrium models) predict the highest diversity to be at an intermediate level of predation, stress or disturbances, i.e. the various populations never gain enough dominance to competitively exclude others. Thus the difference between the two groups of models can simply be reduced to a situation where the population reductions are either continuous or discrete (Kolding, 1997). In other words, the creation and maintenance of biodiversity can be considered to be regulated by the mortality pattern in the ecosystem.
In summary, we can generalize these ecological concepts and processes into two broad categories where the environment determines the prevailing mortality pattern:
The unstable environment, characterized by discrete, density-independent, non-predictive, non-selective mortality induced by physical changes
The stable environment, characterized by continuous, density-dependent, predictive, and size-selective mortality induced by the biotic community.
The two broad categories represent extremes on a gradient, and (in)stability must be seen as a time function in relation to the mean generation time of populations. Thus, even for the ‘unstable’ environment, there are two life-history strategies: either follow the fluctuations (boom-and-bust ephemeral species), or endure the disturbances (long-lived resistant species).
For the latter, the environment may even no longer be unstable, only periodic (Kolding, 1994).
Section 3. The regulation of populations and mortality as a key parameter
Fishing activity is but one of many stress factors to a population. If we can understand the adaptations and life history traits of a population to resist natural mortality factors, we can also evaluate the effect of fishing on these stocks. The diversity and abundance of natural populations is maintained and regulated through a series of interacting factors and associated fundamental concepts in population and community ecology such as: density dependence, compensatory mechanisms, stability and resilience. There is a distinction between internal processes that are regulated by the abundance of the population itself, such as density dependence (see section 1), and external processes that are controlled by the surrounding environment and community of other species. Without compensatory properties, a population in a density-controlled multi-species system exposed to long-term increased mortality from predation or fishing, would ultimately perish. Most theories on population and community ecology and life histories can be reduced to show that the processes they aim to explain are closely associated with the pattern and rate of mortality (Kolding, 1994, 1997). In essence, it is the transience of life, not life itself, that is the driving force of evolution, simply because dying is more certain than giving birth.
Probably few, if any, natural animal populations utilize or occupy their environment to carrying capacity (Andrewartha and Birch, 1954; Slobodkin et al., 1967; Stearns, 1977). Species will mostly either compete for the resources, or be predators. The influence that species have on each other is difficult to measure. On the other hand, if a competitor or predator is removed from the system and we then observe an expansion of other species, then competition or predation is demonstrated. Such multispecies interactions have been observed in the North Sea (Andersen and Ursin, 1977), in the Antarctic (May et al., 1979), in the Gulf of Thailand (Pauly, 1979), in West Africa (Gulland and Garcia, 1984) and in many freshwater fisheries (Paloheimo and Regier, 1982; Carpenter et al., 1985), where heavy fishing pressure on larger slower-growing species leads to an expansion of smaller faster-growing organisms.
Comparing these observations with the tenets that:
predation is believed to be the most important factor for natural mortality in 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);
there is an uni-modal response of prey productivity to predator densities (sigmoid curve theory, section.1),
it is reasonable to presume that predation in the long term would ‘maintain’ prey populations close to their highest average surplus production rate (Slobodkin, 1961, 1968; Mertz and Wade, 1976; Pauly, 1979; Caddy and Csirke, 1983; Carpenter et al., 1985). The argument follows simply from the sigmoid curve where the highest sustainable surplus production of the prey population (dB/dt = max = MSY) is also the ‘carrying capacit’ (K) of the predator population. The predators can in theory grow to reach K (= MSYprey), but if they overshoot, they will reduce the net prey production and consequently decline themselves from starvation. Stable ‘equilibria’ in such cybernetic density-controlled predator-prey relations are theoretically only possible up to the inflexion point of the sigmoid growth curve of the prey where dB/dt is maximized.
Any additional mortality at this stage (as in time-lagged predator-prey oscillations), however, requires a change in the life history strategy if the prey is not to perish (Slobodkin, 1974). In other words, when a population adapted to a relatively stable environment is submitted to more long-term changes in the external mortality forces, it must somehow respond by increasing the intrinsic growth rate (r) (Roff, 1984). This requires stress response or compensatory mechanisms (intrinsic changes) which again are related to phenotypic plasticity, a trait that is particularly prominent in fish (Stearns, 1977; Stearns and Crandall, 1984).
r- K selection and size-specific mortality
In evolutionary terms, changes in the survival rate are less efficient in improving r than increasing the turnover rate by decreasing the generation time. Empirical studies have shown that there is a strong inverse correlation between age at maturity and mortality, which can be considered as a trade-off between the advantages of being big and the probability of dying with time (Adams, 1980; Gunderson, 1980; Hoenig, 1983; Roff, 1984; Gunderson and Dygert, 1988). Traditionally, the explanation of this phenomenon was based on the well-known theory of r- and K selection (MacArthur and Wilson, 1967; Pianka, 1970, 1972; Southwood et al., 1974; Boyce, 1984). This theory was associated with the environmental stability, or rather the degree of ‘saturation’ (density) a population can reach in relation to fluctuating resources. However, considering the indefinable relationship between the carrying capacity (K) and life history traits (Stearns, 1977; Kozlowski, 1980), the original interpretation of the r-K selection is in many ways an inadequate explanation. Other authors (Murphy, 1968; Schaffer, 1974; Wilbur et al., 1974; Stearns, 1977; Horn, 1978) have therefore suggested that the different life-history styles should be considered a function of relative size-specific mortalities. In essence: abiotic mortality, caused by the physical instability of the environment, is generally considered to influence the whole age structure of the population of a species. Thus a low-somatic and high-reproductive allocation of energy indicates that continued existence of the individual beyond the first reproduction is not profitable due to the risk of dying from physical disturbances. On the other hand, biotic mortality (mainly predation) is considered the factor most affecting the small/young individuals in a population (Cushing, 1974; Ware, 1975; Bailey and Houde, 1989; Caddy, 1991). Hence, if mortality is reduced with increasing size, it is advantageous to initially invest more in growth relative to reproduction. Empirically, this is corroborated by ‘Copes rule’ which states that in the evolution of relatively stable ecosystems, there will be a tendency towards the development of larger sizes within the food-chains (Pianka, 1970; Dickie, 1972; Begon et al., 1990). In conclusion, the balance between reproduction and growth, in an optimal life history, seems determined by the relation between adult and juvenile survival (Charnov and Schaffer, 1973; Horn, 1978).
Figure 1 Selection pressures. A theoretical illustration of the ‘r-K selection principle’ as a compensatory mechanism for size-specific mortality pressures only. The slopes of the lines are equal to total mortality (Z) assuming simple exponential decay. Under steady-state conditions, the intrinsic rate of increase (rm) equals Z equals P/B ratio. Arrows indicate the two selection directions and resulting ‘strategy’. A high Z-value (steep slope) thus represents a high r-value and a low Z equals a low r.
Reproduced from Kolding (1993).
From this interpretation of the r-K selection principle, then theoretically, even for ‘K-selected’ species, a compensatory strategy against increased mortality on the adult stages would be to increase the turnover rate (P/B=Z) by reducing the generation time (Figure 5.7). This has been corroborated by empirical studies for American plaice (Pitt, 1975), Lake trout (Power and Gregoire, 1978), Northeast Arctic Cod (Jørgensen, 1990) and Nile tilapia (Kolding, 1993a) that all showed a decreased age of maturity at increased adult mortality. Thus density (section 1), individual size, generation time, and changes in these attributes over time, are all seemingly close functions of death rates in a population. The product of density and size gives the biomass, and the integration of biomass over time gives the production. A further condensation of biomass and production into the P/B ratio then directly reflects the mortality rate and vice versa.
P/B ratio and turnover rate
Production processes are usually associated with the rates at which biological tissue moves between trophic levels, and are thus dynamic quantities, which can rarely be measured directly. The production/biomass ratio (P/B), however, is one way of envisioning the timescale, by indicating the turnover rate and thus the speed of the biomass regeneration. Dickie (1972) emphasized the central importance of this concept for understanding ecological and production efficiencies in relation to fishing pressure. The P/B ratio tends to decrease from one trophic level to another with distance from the primary production level, and also tends to have a general non-linear relationship with the sizes of organisms involved. This means that changes in size-composition of a population from human exploitation or predation will be reflected in the P/B ratio by a relative change in the generation time. The P/B ratio is thus an extremely useful parameter to comparatively characterize different systems, species or trophic levels within a system (Le Cren and Lowe-McConnell, 1980). Allen (1971) examined the P/B ratio for a number of mathematical models expressing mortality and growth. He found that for any growth model (except simple exponential), and with a simple exponential death rate, the P/B ratio is equal to the total instantaneous mortality rate (Z). Thus, the gross production per unit time, P = BZ, is entirely a function of the mean biomass and average mortality rate.
Section 4. The optimal exploitation rate and exploitation pattern
In a fish community with several trophic levels, the amount of production, the speed at which it is generated, and the way it is dispersed through the food-web, determine the production that can be harvested. For fisheries management, the most important implication of density-dependent limitations to growth is that a fishery must substitute one form of mortality for another if the abundance is to remain stable, as the yield is simply the fished fraction of the total deaths. Consequently, in the traditional single-species production models (section 1), a reduction of stock size (from the theoretical B¥ at the carrying capacity K) is the prerequisite for increasing the ‘surplus’, and calculations are aimed at estimating the point of highest net regeneration rate (MSY). However in a multispecies situation, if natural predation is already harvesting the resource close to this rate (section 3), then a fishery is an additional uncompensated source of mortality and the population is driven to collapse. Fortunately, predation mortality is in practice simultaneously alleviated, as few fisheries are focused on one single species and the predators are also being harvested. In fact, the top levels in an ecosystem are often the first to be exploited intensively (Regier, 1977; Beddington, 1984). Management questions are then:
how much of the production can be harvested (the exploitation rate)?
what is a rational harvesting strategy or exploitation pattern on the community, that is, what rates should be applied to each stock?
These proportions (the optimal exploitation rate and pattern) are complicated in a multispecies situation (Dickie, 1972; May et al., 1979; Beddington and May, 1982; Caddy and Csirke, 1983). This is because the fishing mortality on one species will not only affect the target species, but also cascade through the system by either increase the lower trophic levels or decrease the higher trophic levels (see section 3 and Chapter 5, Figure 5.6). The proportion of the total generated production that can be considered as surplus, that is the part which is not used to maintain the population at a given level, is extremely difficult to define, and in fish stock assessments mostly depends on the mathematical model chosen.
In ecology, the ecotrophic coefficient is defined as the proportion of the production over a period of time by trophic level (n) available as ‘yield’ (consumption) to the next trophic level (n+1). Dickie (1972) deduced, based on theoretical considerations, that the ecotrophic coefficient in nature is unlikely to exceed a value of 0.5, meaning that a maximum of half of the production (P) is available as MSY. In fisheries theory the exploitation rate (E) is defined as:
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(5) |
In single-species models, where man is the only predator, the exploitation rate also has a general recommended maximum value of 0.5, but derived from the principle that F should be equal to M giving Z=2M. In a multispecies fishery situation, however, the ecotrophic coefficient is the fraction that should be shared between fishers and the fish predators, implying that the exploitation rate should be equal or less than the ecotrophic coefficient (Kolding, 1993b).
The impact of fishing on a fish community can now be illustrated by combining the fisheries and ecological concepts defined in sections 1 to 3. In summary: The yield or catch is a fraction of the production and defined as (section 1 eqn.1). From the P/B ratio, production can be defined as P = ZB (which shows why C/P = F/Z in eqn. 5). The Maximum Sustainable Yield (MSY), which is the carrying capacity of the next trophic level, has a theoretical maximum value of around half the total production, thus:
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(6) |
The answer to the question how to share the MSY depends on how one wants the fish community to be composed. In the absence of other information, a conservative exploitation rate of 0.5 on top-level predators and 0.25 on lower levels could be used, which means that man becomes the new top predator and otherwise share the rest fifty-fifty. Such a fishing pattern will in theory keep the relative abundance of fish in the community unaltered, but will lower the overall biomass. This principle, together with the impact of different fishing patterns, is illustrated in Figure 2, which for simplicity, assumes a steady state community under logistic conditions (eqn 2) where MSY = 0.5BZ at B¥/2. The system is closed, the primary production finite, and we require that the original species composition (in this case 3 stocks at 3 trophic levels) should be conserved. Under unfished ‘virgin’ conditions (Figure 2A) the energy source of each trophic level is defined by the maximum ‘yield’ from the level below. We then start exploiting the system from the top level (Figure2B) by harvesting the MSY (i.e. reducing the ‘virgin’ biomass by half). This will decrease consumption by half and thus release half the ‘yield’ from the second level (MSY/2) for human exploitation, but in theory no ‘surplus’ is made available from the first level. In Figure 2C, exploitation starts from the bottom level. Removing a proportion of the MSY from the first level will reduce the ‘carrying capacity’ of the next level and thus reduce its ‘virgin’ steady state biomass to a new value: B¥* (B¥*< B¥). This reduction will cascade up through the system and also affect the potential yields (MSYn*< MSYn), but in theory the system will find a new balance under the new carrying capacities. As the lower trophic levels are having the highest productivity (highest P/B ratio), the fishing pattern sketched in Figure 2C (equivalent to “hunting all that moves” in Figure 5.5) seems the most rational solution to exploiting the whole system (i.e. maximizing the output) without causing deep disturbances (Kolding, 1994). In theory, due to gear selectivity as described above, such an exploitation pattern in a multispecies community can only be achieved by employing a multitude of fishing gears, which in combination can generate a size-specific fishing mortality that is proportional to the natural size-specific mortality pattern. Incidentally, in contrast with most fisheries theory based on single-species considerations, effort in such a fishing pattern should in most systems be highest on the smaller sizes to match the prevailing natural mortality pattern (see Figure 1).
Figure 2 A simplified fish community of three trophic levels. Each box represents the biomass of each level (not to scale; see Figure 5.5) relative to the ‘virgin’ biomass (B8) of each level, under logistic conditions. Arrows indicate the flow of energy (net production) through the system. See text for explanation. Redrawn from Kolding (1994)
Section 5. Scale of operation and variability in daily catch rates
Variability in the day-to-day catch of a fisherman depends in the first place on the fishing pattern. The type of gear used is strongly related to its selectivity, while the scale at which it is operated determines both the magnitude of the effect it has on the stock and the variability in the day-to-day catch. Increasing the fished space - larger or more gillnets, hauls of longer duration in trawls - and increasing efficiency in finding fish by auxiliary devices will decrease variability. The first aspect in fact is simply an effect of aggregation of fish over space. The last aspect, the probability of encountering fish, in the first place depends on investments in mobility, that is boats. Increased mobility with paddles, sails or engines will increase the spatial allocation of effort or the freedom of movement to choose between fishing grounds and the operation of the gear. The possibility of increasing the encounter rate with fish (+ efficiency), and decreasing variability, also depends both on the knowledge a fisherman has about the spatial distribution of the fish (local knowledge) and on his means to invest in auxiliary technology, such as fish-finding devices.
Besides the choice of fishing gear, variability in catch also depends on the resource character. In Chapter 5 and in the previous sections of this Appendix resource character has been discussed extensively in the light of susceptibility of species and fish communities to fishing under annual flood pulses. What was not discussed was that patterns of spatial distribution and dispersal typical for a species or even a particular fish community, will determine the probability of encounter. Catches of schooling and aggregated species with low stock densities, distributed and moving around over large areas, for example many pelagics, are more variable compared to catches of species that are more evenly distributed over space with limited movement and/or with high stock densities (Densen, 2001). Lastly variability in day-to-day catches will increase with decreasing stock size (Figure 3 indicated by “- stock size”).
Figure 3 Conceptual model classifying fishing methods based on their scale of operation and the re-sulting variability in catch probability (CV). The grey area indicates the range of daily varia-bility of a particular gear. Efficiency includes both technological de-velopment of gears and fishermen’s knowledge of the spatial behaviour of the fish. Decreasing stock size is a result of fishing, environment or migration.
