ICES Journal of Marine Science: Journal du Conseil

Reiss, Christian S.; Cossio, Anthony M.; Loeb, Valerie; Demer, David A.

doi: 10.1093/icesjms/fsn033pmid: N/A

Reiss, C. S., Cossio, A. M., Loeb, V., and Demer, D. A. 2008. Variations in the biomass of Antarctic krill (Euphausia superba) around the South Shetland Islands, 19962006. ICES Journal of Marine Science, 65: 497508.The time-series of acoustically surveyed Antarctic krill (Euphausia superba) biomass near the South Shetland Islands (SSI) between 1996 and 2006 is re-estimated using a validated physics-based model of target strength (TS), and a species-discrimination algorithm based on the length-range of krill in plankton samples to identify krill acoustically, derived from TS-model predictions. The SSI area is surveyed each austral summer by the US Antarctic Marine Living Resources Program, and the acoustic data are used to examine trends in krill biomass and to assess the potential impact of fishing to the reproductive success of land-based predators (seals and penguins). The time-series of recomputed biomass densities varies greatly from that computed using an empirical log-linear TS-model and fixed-ranges of differences in volumebackscattering strengths (Sv), conventionally used to identify krill acoustically. The new acoustic estimates of biomass are significantly correlated with both proportional recruitment and krill abundance estimated from zooplankton samples. Two distinct peaks in biomass (1996 and 2003) are in accord with recruitment events shown by net-based krill time-series. The foundation for the new TS-model and the associated krill-discrimination algorithm, coupled with the agreement between acoustic- and net-survey results, provides strong support for the use of the new analytical technique. Variable biases in the re-estimated krill biomass have been greatly reduced. However, survey variability increased as a result of the increased rejection of acoustic backscatter previously attributed to krill. Management of Southern Ocean krill stocks based on a precautionary approach may therefore result in decreased allocations of krill, given its dependence on the variability of survey estimates.

Peteiro, Laura G.; Filgueira, Ramn; Labarta, Uxo; Fernndez-Reiriz, Mara Jos

doi: 10.1093/icesjms/fsn014pmid: N/A

Peteiro, L. G., Filgueira, R., Labarta, U., and Fernndez-Reiriz, M. J. 2008. Growth and biochemical responses of the offspring of mussels directly affected by the Prestige oil spill. ICES Journal of Marine Science, 65: 509513.We investigated whether perturbations to growth and biochemistry, detected in Mytilus galloprovincialis mussel seed cultured after the Prestige oil spill (2003), were evident in its offspring (2004). Sublethal effects in the 2003 mussels or, alternatively, direct exposure to remobilization of fuel on the seabed, might have detrimental effects on mussel juveniles in 2004. However, the absence of differences in lipid composition and growth performance between mussel seed gathered from the area most affected by the spill (Pindo) and the reference population (Miranda) seems to indicate the absence of sublethal effects related to hydrocarbon exposure in the offspring of mussels directly exposed to the Prestige oil spill.

Gislason, Henrik; Pope, John G.; Rice, Jake C.; Daan, Niels

doi: 10.1093/icesjms/fsn035pmid: N/A

Abstract Gislason, H., Pope, J. G., Rice, J. C., and Daan, N. 2008. Coexistence in North Sea fish communities: implications for growth and natural mortality. – ICES Journal of Marine Science, 65: 514–530. For a fish community to persist over time, all species must be able on average to replace themselves on a one-for-one basis over their lifetime. We use this principle and a size-based equilibrium model where asymptotic length is used as a functional trait to investigate how natural mortality should scale with size within and across pelagic and demersal species of North Sea teleosts. The model predicts natural mortality to scale with body length raised to a power of −1.66 at current levels of exploitation. Additionally, natural mortality of demersal species should be proportional to asymptotic length raised to a power of 0.80, so generating a higher natural mortality at a given length for large species than for small ones. The model also suggests that the exponent in the scaling of the von Bertalanffy growth parameter K with asymptotic length should be more negative for pelagic than for demersal species. We test our results by analysing independent estimates of predation mortality, the scaling of maximum recruitment per unit of spawning-stock biomass with asymptotic length, and the general relationship between K and asymptotic length for demersal and pelagic families of fish. All tests are consistent with our modelling results. Introduction Modelling exploited fish populations (Quinn and Deriso, 1999) often involves attempts to close the life cycle of the population by functionally linking numbers of recruits to numbers of spawners. This includes examining processes that regulate the number of recruits produced per unit of spawning-stock biomass (R/SSB) and processes regulating the spawning-stock biomass produced per recruit (SSB/R). In both cases, the underlying principle has been that for a population to persist, on average its members will have to replace themselves on a one-for-one basis over their lifetime. Failure to replace at least one-for-one will lead to population declines, which cannot continue indefinitely without extinction. Consistently replacing at a rate greater than one-for-one will produce population growth that cannot continue indefinitely for ecological and energetic reasons. Approaches for equilibrium and temporary non-equilibrium modelling of single populations are well established (Murdoch, 1994), with density dependence as a routine property of at least some processes, including feeding, growth, and recruitment. However, when such models are placed in a community context, additional considerations emerge. Fish communities do persist over time in the sense that sympatric species coexist over geological timescales (Daan et al., 1990). For this to happen, not only must individual populations on average maintain replacement, but competitors, predators, and prey on average must coexist. This does not mean that the populations need ever reach a static equilibrium, nor that the dynamics of species need be tightly coupled. However, it does mean that no predator population can consistently prevent its prey populations from being able to replace themselves spawner-for-spawner, or consistently fail to find sufficient prey to support replacement of itself. Density dependence remains a necessary consideration at the community scale, although the ways it is expressed requires more directed study. How populations solve the challenges of existence are reflected in their life-history strategies (Stearns, 1992), in solutions to the challenges of coexistence and coadaptation in their strategies of predation and predator avoidance (Holt and Polis, 1997), and in trade-offs among life-history processes (Kneitel and Chase, 2004). Many of the more abundant fish species of commercial interest are trophic generalists, occupying broad feeding niches (Garrison and Link, 2000; Trenkel et al., 2005). This raises questions about how multiple populations can coexist without the bulk of the biomass gradually becoming monopolized by a few species characterized by sets of life history, predation, and predator-avoidance strategies and parameters that make them more efficient at replacement, or more effective as a predator or at avoiding predation. Species coexistence in communities means that just as single-species models must close the loop for replacement from spawner to spawner (or egg to egg), multispecies models must close the life cycles of all populations in a community simultaneously, with spawner-for-spawner (or egg-for-egg) replacement on average. Otherwise, populations will gradually be lost and eventually the community will come to consist of only the few most successful species. Size has proven a powerful property for understanding and modelling the processes involved in life-history strategies leading to replacement. Basic biological processes, such as metabolism, food intake, growth, reproduction, and mortality, all scale with body size (Kooijman, 2000; Brown et al., 2004; West and Brown, 2005). For fish populations, there have been empirical observations of relationships between von Bertalanffy growth parameters, natural mortality, and size or age at first maturity (Pauly, 1980; Beverton, 1987; Charnov, 1993; Jensen, 1996). These size-based relationships have been found among as well as within species, allowing size-dependent scaling to be combined with the theory of life-history strategies to understand fish community dynamics (Charnov et al., 2001; Denney et al., 2002; Goodwin et al., 2006). Using functional traits such as asymptotic length (L∞) to provide scaling relationships across species in size-based models offers a promising avenue for identifying the trade-offs involved in the processes important for coexistence (McGill et al., 2006). The size-based multispecies models of Hall et al. (2006) and Pope et al. (2006) focused primarily on the role that size-based predation plays in fish community regulation. We confront the question of whether recruitment scales with asymptotic length (L∞) across the species in a given community. This question is fundamental for community persistence, by framing the requirement for, on average, one-for-one replacement in the setting of all the interacting populations in the community. Two pieces of evidence suggest that strong size-dependent scaling in recruitment is necessary to allow species to coexist. First, empirically there is a large and systematic difference between large and small species in the maximum R/SSB (Denney et al., 2002). Those authors found that large, slow-growing species generated significantly lower maximum R/SSB than did smaller, fast-growing species. Across 54 northeast Atlantic stocks, maximum R/SSB scaled with maximum length raised to a power close to −3, indicating that a species with L∞ = 100 cm produces 1000 times fewer R/SSB than would a species with L∞ = 10 cm (Denney et al., 2002). Second, in developing multispecies size-based predation models for Georges Bank and the North Sea, respectively, Hall et al. (2006) and Pope et al. (2006) found that for their models to match observed values for abundance, small species needed to consistently generate a higher R/SSB than larger ones. Hall et al. (2006) found that the maximum R/SSB estimated from data collected by Myers et al. (1995) scaled to asymptotic length raised to a power of −2.3. At low population sizes, a species with L∞ = 10 cm would need to produce ∼200 times more R/SSB in their model than a species with L∞ = 100 cm. Likewise, Pope et al. (2006) found it impossible to maintain both small and large species in their model simulations unless the R/SSB of the small species was three orders of magnitude higher. If on average R/SSB was constant over all species, over time only the largest species would remain in both model simulations. These apparent differences in realized R/SSB between small and large teleosts have important implications for replacement of populations in a community. Consider two species for which their L∞ differs by a factor of 10. If both species have the same SSB, spawn once annually, and produce the same number of eggs per gramme of female body mass, they should both produce approximately the same total number of eggs annually. However, because of the difference in R/SSB, these equal numbers of eggs will result in the smaller species producing 2–3 orders of magnitude more recruits. Because the biological processes producing this difference are acting on prerecruits that are of the same size, regardless of how big they will eventually grow, this difference begs for a plausible explanation. To investigate possible causes of the difference in R/SSB, we construct a simple size-based model and use it to explore the conditions required for simultaneous replacement of different sizes of coexisting fish species. Our model uses the scaling of mortality, growth, maturation, and fecundity with L∞ to calculate the net reproductive rate, R, for fish of different L∞ in the equilibrium situation. The differences in survivorship may reflect differences in predator-avoidance strategy, so the model considers demersal and pelagic habitats separately, to reflect the different potential strategies for predator avoidance in the two types of habitat. The model is parameterized with data on fishing mortality and scaling factors for growth and reproduction representative for the North Sea fish community. We use the model to estimate how natural mortality should scale with body size and L∞ to achieve replacement (i.e. R = 1) simultaneously for all species, irrespective of their L∞. We also use the model to explore what aspects of species life histories and behaviours may contribute to the estimated scaling of mortality with size. Winemiller and Rose (1992) classified fish species according to different life-history strategies. McCann (1998) used these strategies in a stage-structured model with density-dependent interactions, and was able to obtain coexistence for only two out of four strategies. Others have used differences in average body size to explain coexistence (e.g. Etienne and Olff, 2004; Scheffer and van Nes, 2006). However, before this study, the scaling of growth and reproduction with L∞ and length has not been used to explore how natural mortality should scale with body size. Nor have the implications that a large difference in R/SSB for small and large species would have for recruitment and replacement been addressed. Our model and analyses are limited by incomplete information on the reproduction and early life stages of marine teleosts. Most fish species produce eggs with a median diameter of ∼1 mm (Chambers, 1997; Appendix Table A2), and mature gonads account for ∼20% of female body weight. Nonetheless, the reproductive potential of stocks may vary systematically as a function of an individual's age and size, and changes in sex ratio, sexual maturity, and condition (Tomkiewicz et al., 2003). Likewise, estimates of the natural mortality of first-feeding post-larvae used as input to our model are uncertain, so we investigate the sensitivity of our results to changes in relative fecundity and to the natural mortality of the post-larvae. We check the model by comparing the output with estimates of natural mortality derived from multispecies virtual population analysis (MSVPA) using comprehensive stomach content data, with growth characteristics of pelagic and demersal families of fish from FishBase (Froese and Pauly, 2006), and with the scaling of maximum recruit production with L∞ reported by Denney et al. (2002). We also investigate the possibilities for coexistence in a simpler model where no distinction is made between pelagic and demersal habitats. Finally, we discuss the implications of our findings in the context of general differences in life-history characteristics of demersal and pelagic species, and for management. The model General description The model describes a fish community composed of a number of teleost populations in steady state, i.e. where growth, mortality, and recruitment are constant over time. As in the size-based model of Pope et al. (2006), where additional justification for many of the formulations that follow are presented, we represent different fish species in the community by their L∞, with one hypothetical species per 5 cm increment from the smallest to the largest species present, and use information on how growth, mortality, and reproduction scale with L∞ to model the steady-state populations in each L∞ group. However, in contrast to Pope et al. (2006), we also account for fish <5 cm and include eggs, larvae, and early juveniles. Further, we split the species into demersal and pelagic, to account for possible habitat-related differences in scaling factors. Briefly, the model calculates the number of individuals for each L∞ group (“species”) in subsequent size classes, and estimates the total number of eggs produced by the mature females. The calculations start with the same number of eggs (E0) for all species. These eggs and the subsequent yolk-sac larvae suffer from mortality. The yolk-sac larvae become post-larvae when they start feeding at a fixed length, L0. The post-larvae grow and suffer mortality until they become juveniles at a length, Lmet. Juvenile demersal species settle and may experience a change in the level of their natural mortality at settling, whereas juvenile pelagic species continue to have a natural mortality determined exclusively by their individual length. Both pelagic and demersal species are subject to fishing mortality. Females mature at a size Lmat after which they start to produce eggs. Relative fecundity is used to calculate the total lifetime egg production (E1,L∞) of a cohort for each L∞ group. The model is finally used to find the values of natural mortality (ML) producing a one-for-one replacement of eggs (R = 1) across the size range of fish species found in the fish community (i.e. a value of E1,L∞/E0 as close as possible to 1 for all L∞ groups in the range). Length is measured in cm, body weight in g, age in years, and all mortality and growth rates are annual unless otherwise specified. Egg and yolk-sac larval survival Of the E0 eggs spawned, only some will hatch to become yolk-sac larvae, and only a fraction of the yolk-sac larvae will survive to become post-larvae. If the fractions surviving the egg (Ce) and yolk-sac larval stages (Cy) are known, the initial number of post-larvae at length L0, (NL0) can be calculated from 1 Growth Many fish species undergo major ontogenetic changes during their early development (Fuiman and Higgs, 1997), and parameters derived from adult fish often provide a poor description of larval growth. We follow Pepin (1991) and describe post-larval growth as an allometric function of larval length: 2 where a and b are constants. The solution to this equation is 3 Rearranging Equation (3) provides the age (tL) of a post-larva of given length L: 4 For juvenile and adult fish, we follow Pope et al. (2006) and use the von Bertalanffy equation 5 where t is age and t0 can be found from 6 where tLmet, the age at the end of the post-larval stage, is estimated by inserting Lmet in Equation (4). The growth parameter K is assumed to scale with L∞. However, the scaling may be habitat-dependent and may hence differ for pelagic and demersal species: 7 Mortality The natural mortality of juvenile and adult fish is difficult to determine empirically (Quinn and Deriso, 1999), and fisheries scientists seem divided into those who consider it to be a species-specific constant depending on life-history parameters such as asymptotic length or K (Pauly, 1980; Jensen, 1996; Griffiths and Harrod, 2007) and those who consider it to be an allometric function of individual body size (McGurk, 1986; Lorenzen, 1996). We assume that natural mortality (ML) consists of a size-dependent part reflecting predation and a constant size-independent part generated by all other causes of natural death (M1), which is identical for all species and sizes. In accord with general size-spectrum theory (Peterson and Wroblewski, 1984; Andersen and Beyer, 2006), predation is assumed to scale with individual body size, using the same exponent (n) for all species and stages. However, to explain the relationship between L∞, K, and ML identified by Pauly (1980), we also allow for potential differences in the predation mortality-at-length on different species depending on their asymptotic size. Conceptually, such a difference seems more likely for demersal species than for pelagic. Many piscivorous fish species are found in association with the seabed, and small demersal fish found at or close to the seabed are likely to run a considerable risk of being eaten (Myers and Cadigan, 1993; Van der Veer et al., 2000; Hixon and Jones, 2005; review by Juanes, 2007). Among the smaller species found on sandy or muddy seabeds, many are characterized by adaptations to reduce predation, such as spines, flattened body size, venom, and cryptic or burying behaviour, but such adaptations are largely lacking in the larger species. Species with these adaptations have been demonstrated to have a low natural mortality (Griffiths and Harrod, 2007). Hence, for demersal species, the assumption that ML may depend on asymptotic length once they have settled seems reasonable. We therefore describe their predation mortality as a power function of both individual and asymptotic lengths, with a constant rest mortality (M1) added to account for diseases and other non-size-related causes of death: 8 For pelagic species, adaptations to reduce predation seem less likely to differ systematically among small and large species, so we assume that predation mortality depends on individual length, L, only: 9 This equation is also used to describe mortality in the pelagic post-larval phase of demersal and pelagic fish alike, because empirical investigations have shown natural mortality to be a function of individual size (McGurk, 1986; Bailey and Houde, 1989; Pepin, 1991), whereas dependence on asymptotic length has not been identified so far. Natural mortality is therefore described as being habitat-dependent, using Equation (8) for demersal life stages and Equation (9) for all pelagic stages. Fishing mortality-at-length (FL) is modelled by an equation borrowed from Pope et al. (2006): 10 where δ is the length in proportion of L∞ at which 50% of the individual fish become selected by the fishery, γ a slope parameter, κ and λ the parameters expressing how fast fishing mortality changes between species in relation to their L∞, and F is the overall level of fishing mortality acting on all species. The nominator is intended to change species-specific fishing mortality linearly from small to large species, and the denominator ensures that the length at 50% of maximum exploitation of each species is reached when the individual fish have attained a fraction (δ) of their L∞. Fishing mortality is thus modelled as a function of L∞, but with the usual sigmoid relationship between exploitation rate and length. Numbers at length For each L∞-group, the population is divided into a number of equally sized length intervals of width ΔL. For post-larvae, the number of survivors at length L + ΔL is calculated from the numbers surviving to length L: 11 where the value of ML is calculated at the midpoint of the interval, and tL is the age at length L. For juvenile and adult fish, the number of survivors at the end of each length interval is calculated from the Jones (1974) equation: 12 where ZL refers to total mortality (ZL = FL + ML), estimated at the midpoint of the length interval. To ensure that the steep decline in natural mortality with size in the smallest size classes can be adequately captured, ΔL was set to 0.25 cm. Reproduction To calculate the total lifetime egg production of each L∞ group, the average number of individuals in each length interval is first calculated from 13 and the average annual biomass in the length interval is estimated by multiplying the average numbers by their body weight, WL, which is determined by 14 Survivors recruit to the spawning stock when they reach (knife-edge) maturity-at-length (Lmat), determined from 15 after which half of them (the females) start producing eggs; s and q are constants. Relative fecundity (RE) is expressed as the number of eggs produced per gramme of female, and is assumed to scale with L∞: 16 where u and v are constants. Multiplying RE by 0.5×SSB provides the total lifetime egg production. Thus, for each L∞ value, the number of eggs generated can be calculated from 17 The ratio of parent egg production and offspring egg production, the net reproductive rate, can be expressed as 18 Parameter estimation Growth and maturity data are available for many fish species, and have previously been shown to scale with L∞ (Cury and Pauly, 2000; Froese and Binohlan, 2000), whereas the parameters describing the relationship between M, L∞, and L are more difficult to estimate and are generally lacking. However, assuming that all species within a given range of L∞ should be able to replace themselves, the model can estimate the combination of M parameters that would, given empirical estimates of all other parameters and as far possible, produce a one-for-one replacement. We use the model to calculate the net reproductive rate in the equilibrium situation for every 5 cm of asymptotic length, and estimate the missing mortality parameters by minimizing the standard deviation of R over the range of L∞ values observed to coexist in the North Sea fish community, subject to the constraint that within this range, the net reproductive rate should, on average, equal 1. Because different calculations are necessary for demersal and pelagic species, the actual minimization is done by minimizing the sum of the standard deviation for demersals and pelagics: 19 subject to the constraints 20 where D is the number of 5 cm L∞ groups within the size range from the smallest (L∞,MIN) to the largest (L∞,MAX) value of L∞ for demersal species, P the number of L∞ groups for pelagics, and and the average net reproductive rates of demersal and pelagic species, respectively, over the size ranges observed. The model was implemented in Excel, and Solver was used to find the parameters generating the minimum standard deviation of the net reproductive rate for different values of L∞, so maximizing the possibilities for coexistence of species of different asymptotic lengths. An overview of the variables and constants used is provided in Appendix Table A1. Parameter values and model setup Estimates of L∞, K, Lmat, and RE for North Sea teleosts from different sources are given in Appendix Table A2. Usually, the estimates used have been derived from fish sampled in the North Sea proper. When multiple von Bertalanffy growth estimates were available for a single species, we selected those where the value of the age at zero length was <1, and where L∞ was within ±33% of the maximum length given in Froese and Pauly (2006), to avoid bias caused by questionable age readings or insufficient coverage of the length range. Overall, 45 pairs of L∞ and K estimates were available, 35 estimates of L∞ and Lmat, and 16 estimates of L∞ and RE (Figure 1). Figure 1. Open in new tabDownload slide Relationships between three life-history parameters and the natural logarithm of L∞ (asymptotic length, cm). (a) Natural logarithm of K (growth constant, year−1), (b) natural logarithm of Lmat (length at first maturity, cm), and (c) natural logarithm of RE (relative fecundity, number of eggs per gramme female body weight). Open squares, demersal species; filled squares, pelagic species. Data from Appendix Table A2. Non-linear regression was then used to estimate the scaling parameters for growth, maturity, and relative fecundity (d, e, q, s, u, and v), and their approximate confidence intervals (Table 1). The relationships for growth and maturity against L∞ are highly significant for all species combined (p < 0.0001; n = 45 and n = 35, respectively), and for demersal species (p < 0.0001; n = 41 and n = 31, respectively), but barely significant for pelagic species (p = 0.03 and p = 0.04, respectively; n = 4 in both cases). The scaling parameters for the pelagic species do not differ significantly from the parameters estimated for demersal species. The relationship between RE and L∞ is significant for all species combined (p = 0.0003, n = 16), but the exponent does not differ significantly from zero. To calculate the total egg production in the model, an average RE of 997 eggs per gramme female (s.d. = 758) is therefore used for all L∞ groups. Table 1. Estimates of parameters (with 95% CI in parenthesis) from non-linear regressions of K, Lmat, and RE against L∞. Expression . Parameter . All species combined . Demersal species . Pelagic species . K = dL∞e d 3.15 (1.44, 4.88) 3.07 (1.30, 4.85) 6.48 (−21.1, 34.1) e −0.64 (−0.82, −0.47) −0.64 (−0.83, −0.45) −0.83 (−2.18, 0.52) Lmat = qL∞s q 0.72 (0.41, 1.00) 0.64 (0.35, 0.92) 5.10 (−28.9, 39.1) s 0.93 (0.85, −1.03) 0.95 (0.86, 1.06) 0.41 (−1.48, 2.30) RE = uL∞v u 5194 (−11 924, 22 312) – – v −0.44 (−1.36, 0.47) – – Expression . Parameter . All species combined . Demersal species . Pelagic species . K = dL∞e d 3.15 (1.44, 4.88) 3.07 (1.30, 4.85) 6.48 (−21.1, 34.1) e −0.64 (−0.82, −0.47) −0.64 (−0.83, −0.45) −0.83 (−2.18, 0.52) Lmat = qL∞s q 0.72 (0.41, 1.00) 0.64 (0.35, 0.92) 5.10 (−28.9, 39.1) s 0.93 (0.85, −1.03) 0.95 (0.86, 1.06) 0.41 (−1.48, 2.30) RE = uL∞v u 5194 (−11 924, 22 312) – – v −0.44 (−1.36, 0.47) – – The data are from Appendix Table A2. Open in new tab Table 1. Estimates of parameters (with 95% CI in parenthesis) from non-linear regressions of K, Lmat, and RE against L∞. Expression . Parameter . All species combined . Demersal species . Pelagic species . K = dL∞e d 3.15 (1.44, 4.88) 3.07 (1.30, 4.85) 6.48 (−21.1, 34.1) e −0.64 (−0.82, −0.47) −0.64 (−0.83, −0.45) −0.83 (−2.18, 0.52) Lmat = qL∞s q 0.72 (0.41, 1.00) 0.64 (0.35, 0.92) 5.10 (−28.9, 39.1) s 0.93 (0.85, −1.03) 0.95 (0.86, 1.06) 0.41 (−1.48, 2.30) RE = uL∞v u 5194 (−11 924, 22 312) – – v −0.44 (−1.36, 0.47) – – Expression . Parameter . All species combined . Demersal species . Pelagic species . K = dL∞e d 3.15 (1.44, 4.88) 3.07 (1.30, 4.85) 6.48 (−21.1, 34.1) e −0.64 (−0.82, −0.47) −0.64 (−0.83, −0.45) −0.83 (−2.18, 0.52) Lmat = qL∞s q 0.72 (0.41, 1.00) 0.64 (0.35, 0.92) 5.10 (−28.9, 39.1) s 0.93 (0.85, −1.03) 0.95 (0.86, 1.06) 0.41 (−1.48, 2.30) RE = uL∞v u 5194 (−11 924, 22 312) – – v −0.44 (−1.36, 0.47) – – The data are from Appendix Table A2. Open in new tab The survival of eggs and early larvae is temperature-dependent (Pepin, 1991). With average surface temperatures in the North Sea of 5°C in March and 6°C in April (ICES, 2006), the fractions surviving the egg and yolk-sac larval stages can be estimated as 0.435 and 0.074, respectively (Pepin, 1991), corresponding to an overall total survival of 3.2% to the post-larval stage. This stage is assumed to start when the larvae commence feeding at an average length of 0.5 cm (Russell, 1976). From then on, the larvae grow in accordance with the empirical model of Pepin (1991), with an exponent b equal to 0.54 and a constant a equal to 0.064 at 10°C, the average North Sea surface temperature in the period from April to July (ICES, 2006). The post-larvae are assumed to metamorphose at a size of 4 cm, corresponding to an age of 114 d, after which their growth starts to follow the von Bertalanffy growth equation, with a growth parameter K determined by their asymptotic length, L∞. The estimated empirical relationship between K and L∞ was kept for demersal species, but for pelagic species the scaling exponent (epel) was estimated within the model because of the limited number of four observations available from the North Sea. To generate values of K for pelagic species in accord with the observations, dpel was adjusted so that the K for a pelagic species with an L∞ of 32.1 cm (the average value for the pelagic species included in the non-linear regression) remained the same (K32.1 = 0.38 year−1), irrespective of changes in epel: 21 The parameters of Equation (10) describing fishing mortality as a function of individual and asymptotic lengths were taken from Pope et al. (2006). Other natural mortality, M1, was assumed to be 0.1 year−1 for all sizes. This value corresponds to the lowest value of natural mortality used for adult fish in North Sea stock assessments. The L∞ ranges for demersal and pelagic species were set at 10–180 and 10–50 cm, respectively, in accord with the size range of the species included in Appendix Table A2. All calculations were constrained to produce replacement within these ranges. Hence, solutions were sought where These criteria were met in all runs. Initial runs produced unlikely parameter estimates or failed to converge, suggesting that the model was over-parameterized. A constraint on natural mortality was therefore introduced. Newly hatched fish larvae are reported to suffer daily mortalities in the order of 10–50% per day (McGurk, 1986; Bailey and Houde, 1989; Pepin, 1991; Houde and Zastrow, 1993). According to Pepin (1991), the daily mortality of a first-feeding post-larva is some 15.1% d−1 at 10°C. Assuming natural mortality to be 15.1% d−1 or 59.7 year−1 in the first post-larval size class (L̄ = 0.625 cm) allowed hpel to be calculated from 22 and reduced the number of parameters to be estimated to 4 (n, i, epel, and hdem; Appendix Table A1). The sensitivity of the model to changes in input parameters was examined by changing fecundity by a factor 100, 10, and 0.1, by changing ML0 by ±50%, and by halving M1. In addition to a key-run where scaling relationships were allowed to differ for the pelagic and the demersal habitats, runs with identical growth and mortality relationships and parameters were used to explore the consequences of simplifying the model. Using the scaling parameters for species combined (shown in the first column of Table 1), natural mortality was assumed to depend only on individual size, L, or to follow the formula of Pauly (1980) and hence to depend only on L∞. Results Key-run The overall exponent expressing the scaling of ML with length for both pelagic and demersal species was estimated at n = −1.66 (Table 2). Table 2. Parameter estimates from the key-run compared with estimates obtained when relative fecundity (RE) is multiplied by 100, 10, and 0.1, and when initial post-larval mortality (M0.625) is multiplied by 0.5 and 1.5. Model version . Key-run . RE changed . Post-larval M changed . Expression . Parameter . . 100 . 10 . 0.1 . 0.5 . 1.5 . K = dpelL∞epel epel −1.34 −1.17 −1.25 −1.45 −1.23 −1.38 ML = M1 + hL∞iLn n −1.66 −1.24 −1.41 −2.22 −1.11 −2.31 hpel 27.3 33.3 30.8 21 16.9 31.6 hdem 1.71 6.01 3.6 0.76 3.33 0.61 i 0.8 0.46 0.6 1.12 0.46 1.18 ML for a 50 cm fish M50,dem (L∞ = 130 cm) 0.22 0.55 0.39 0.13 0.51 0.12 M50,pel 0.14 0.36 0.23 0.1 0.32 0.1 Model version . Key-run . RE changed . Post-larval M changed . Expression . Parameter . . 100 . 10 . 0.1 . 0.5 . 1.5 . K = dpelL∞epel epel −1.34 −1.17 −1.25 −1.45 −1.23 −1.38 ML = M1 + hL∞iLn n −1.66 −1.24 −1.41 −2.22 −1.11 −2.31 hpel 27.3 33.3 30.8 21 16.9 31.6 hdem 1.71 6.01 3.6 0.76 3.33 0.61 i 0.8 0.46 0.6 1.12 0.46 1.18 ML for a 50 cm fish M50,dem (L∞ = 130 cm) 0.22 0.55 0.39 0.13 0.51 0.12 M50,pel 0.14 0.36 0.23 0.1 0.32 0.1 Open in new tab Table 2. Parameter estimates from the key-run compared with estimates obtained when relative fecundity (RE) is multiplied by 100, 10, and 0.1, and when initial post-larval mortality (M0.625) is multiplied by 0.5 and 1.5. Model version . Key-run . RE changed . Post-larval M changed . Expression . Parameter . . 100 . 10 . 0.1 . 0.5 . 1.5 . K = dpelL∞epel epel −1.34 −1.17 −1.25 −1.45 −1.23 −1.38 ML = M1 + hL∞iLn n −1.66 −1.24 −1.41 −2.22 −1.11 −2.31 hpel 27.3 33.3 30.8 21 16.9 31.6 hdem 1.71 6.01 3.6 0.76 3.33 0.61 i 0.8 0.46 0.6 1.12 0.46 1.18 ML for a 50 cm fish M50,dem (L∞ = 130 cm) 0.22 0.55 0.39 0.13 0.51 0.12 M50,pel 0.14 0.36 0.23 0.1 0.32 0.1 Model version . Key-run . RE changed . Post-larval M changed . Expression . Parameter . . 100 . 10 . 0.1 . 0.5 . 1.5 . K = dpelL∞epel epel −1.34 −1.17 −1.25 −1.45 −1.23 −1.38 ML = M1 + hL∞iLn n −1.66 −1.24 −1.41 −2.22 −1.11 −2.31 hpel 27.3 33.3 30.8 21 16.9 31.6 hdem 1.71 6.01 3.6 0.76 3.33 0.61 i 0.8 0.46 0.6 1.12 0.46 1.18 ML for a 50 cm fish M50,dem (L∞ = 130 cm) 0.22 0.55 0.39 0.13 0.51 0.12 M50,pel 0.14 0.36 0.23 0.1 0.32 0.1 Open in new tab For pelagic species, the minimum standard deviation of the net reproductive rate over the L∞ interval from 10 to 50 cm was generated with an estimated exponent in the K/L∞ relationship of epel = −1.34. The model produced a dome-shaped replacement curve with a maximum in net reproductive rate for species with an asymptotic length of ∼30 cm and a difference of less than a factor of 4 in net reproductive rate within the pelagic L∞ range of 10–50 cm (Figure 2a). The total natural mortality (ML) of a 25 cm fish was 0.23 year−1 (Figure 2b). Figure 2. Open in new tabDownload slide Comparison of the relationship between (a and c) net reproductive rate (R) and asymptotic length (L∞), and (b and d) natural mortality-at-length (ML) and length (L) for (a and b) pelagic and (c and d) demersal species. In (d), curves after metamorphosis are presented for three values of L∞ values (upper curve, 130 cm; intermediate curve, 75 cm; lower curve, 20 cm). For demersal species, the deviation in net reproductive rate over the L∞ range 10–180 cm was minimized when ML after settling was almost proportional to L∞ (i = 0.8). The differences between maximum and minimum replacement within the length range considered were again small. R increased gradually with L∞ to peak at a value of 85 cm, after which it declined (Figure 2c). A demersal fish with L∞ = 85 cm had an R ∼4 times higher than a 20 cm species, and twice as high as a 180 cm species. The ML of a 5-cm newly settled fish was in the order of 0.4% d−1 (1.33 year−1) for L∞ = 20 cm, but 1.5% d−1 (5.54 year−1) for L∞ = 130 cm (Figure 2d). The natural mortality of a 50-cm fish with L∞ = 75 cm was 0.18 year−1, whereas the value for an individual of the same length with L∞ = 130 cm was 0.22 year−1. Because of the reasonably constant replacement of fish of different size and the realistic values for natural mortality, this run was adopted as the key-run. For both demersal and pelagic species, the value of M/K at half the asymptotic size of a species was virtually independent of L∞, ranging between 1.1 and 1.5. Sensitivity Given the uncertainty in the relationship between SSB and viable egg production, as well as in larval mortality, Table 2 also provides estimates for key parameters under alternative assumptions about these factors. The scaling factors for both growth and mortality were much more sensitive to the value of post-larval mortality than to changes in fecundity: a tenfold change in fecundity produced a similar change in the parameter estimates as a twofold change in the initial natural mortality of the post-larvae. The exponent in the length–mortality relationship (n) changed substantially between the different runs, but remained within the interval −1.11 to −2.31. The exponent in the ML/L∞ relationship of demersal species after settling (i) changed from 0.46 to 1.18 when the initial daily post-larval mortality was set at 7.5% and 22.5%, respectively. The K/L∞ relationship of pelagic species changed comparatively less than the mortality parameters, and the exponent (epel) remained within the interval −1.17 to −1.38, irrespective of the large changes in fecundity and in post-larval mortality. Halving M1 resulted in a <2% change in any of the parameter values. Using a simpler model with no difference between habitats When natural mortality was assumed to scale with individual length, L, only, solutions minimizing the across-species difference in R (in the L∞ range 10–180 cm, corresponding to the range found for common North Sea species) resulted in a scaling exponent n = −1.28, and values of adult total natural mortality in the order of 0.32 year−1 for a 50-cm fish and 0.19 year−1 for a 100-cm fish. These values of M are not unlike those currently used for demersal species in North Sea stock assessments. However, the replacement curve increased monotonically as a function of L∞, suggesting that coexistence of different-sized species of fish was unattainable (Figure 3). Comparing species with an L∞ of 20 and 100 cm, the latter were predicted to produce a 927 × higher R than the former, a difference hardly conceivable to be conducive for coexistence. Figure 3. Open in new tabDownload slide Estimates of net reproductive rate (R) plotted against asymptotic length (L∞) for two models: black line, constant size-independent natural mortality estimated using Pauly's (1980) equation; grey line, natural mortality depending only on individual length (see text for more explanation). Pauly (1980) described natural mortality as a function of K, L∞, and ambient temperature. Using Pauly's formula with an ambient temperature of 12°C to calculate natural mortality from the post-larval stage on, a steadily increasing replacement curve was obtained. With a constant M (independent of individual length), R for a species with L∞ = 100 cm was 44 times larger with the model of Pauly (1980) than R for a species with L∞ = 20 cm (Figure 3). Checking the results from the key-run against independent data Recent estimates of quarterly predation mortality from a North Sea MSVPA (1963–2003; excluding seals) were kindly provided by Morten Vinther (of DTU-Aqua). The average quarterly predation mortality (M2) for cod, haddock, whiting, Norway pout, herring, sprat, and sandeel was calculated across all years and regressed against average length (Figure 4) using the model ln (M2L,j) = ln(h) + iln(L∞,j) + nln(L), where j is the species, length (L) was calculated from MSVPA weights-at-age, and the other parameters correspond to Equation (8). The model explained 51% of the total variance of the data and was highly significant [r2 = 0.51, F = 49.7, Pr(F) < 0.001, n = 98]. Introducing separate slopes and intercepts for individual species did not produce parameter estimates that differed significantly from the overall. Figure 4. Open in new tabDownload slide Quarterly estimates of ln predation mortality (M2) for seven North Sea fish species from MSVPA plotted against ln length (L, cm). The straight lines plotted are the regression lines for cod (upper), haddock (intermediate), and sandeel (lower). Fitting the model separately for demersal (cod, haddock, whiting, Norway pout, and sandeel) and pelagic species (herring and sprat), the parameter estimates for demersal species were all significantly different from zero and their confidence limits contained the theoretical values estimated by the size-based model (Table 3). The exponent i of the ML/L∞ relationship was here estimated as 0.71 compared with 0.80 in the key-run, the exponent n in the ML/L relationship was −2.11 rather than −1.66, and the parameter ln(h) was 1.45 vs. 0.54 in the key-run. For pelagic species, i was not significantly different from zero, but n was significantly different from zero (−0.99), and its confidence limits contain the −1.66 estimated in the key-run, and ln(h) (1.03) had confidence limits large enough to contain the 3.31 estimate from the key-run. Table 3. Parameter estimates (95% CI in parenthesis) obtained when fitting the model of length-dependent natural mortality to MSVPA estimates of quarterly predation mortality in the North Sea for demersal species (cod, haddock, whiting, Norway pout, and sandeel; r2 = 0.59, F = 48.5, Pr(F) < 0.0001, n = 70), and pelagic species (sprat and herring; r2 = 0.32, F = 6.0, Pr(F) < 0.0074, n = 28). Habitat . Parameter . Estimate . t-value . Pr > ||t| . Demersal ln(h) 1.45 (0.3, 2.62) 2.51 0.0145 n −2.11 (−2.58, −1.64) −8.97 <0.0001 i 0.71 (0.3, 1.12) −3.43 0.001 Pelagic ln(h) 1.03 (−2.02, 4.11) 0.70 0.492 n −0.99 (−1.03, −0.06) −2.19 0.038 i −0.19 (−1.58, 1.2) −0.29 0.777 Habitat . Parameter . Estimate . t-value . Pr > ||t| . Demersal ln(h) 1.45 (0.3, 2.62) 2.51 0.0145 n −2.11 (−2.58, −1.64) −8.97 <0.0001 i 0.71 (0.3, 1.12) −3.43 0.001 Pelagic ln(h) 1.03 (−2.02, 4.11) 0.70 0.492 n −0.99 (−1.03, −0.06) −2.19 0.038 i −0.19 (−1.58, 1.2) −0.29 0.777 Open in new tab Table 3. Parameter estimates (95% CI in parenthesis) obtained when fitting the model of length-dependent natural mortality to MSVPA estimates of quarterly predation mortality in the North Sea for demersal species (cod, haddock, whiting, Norway pout, and sandeel; r2 = 0.59, F = 48.5, Pr(F) < 0.0001, n = 70), and pelagic species (sprat and herring; r2 = 0.32, F = 6.0, Pr(F) < 0.0074, n = 28). Habitat . Parameter . Estimate . t-value . Pr > ||t| . Demersal ln(h) 1.45 (0.3, 2.62) 2.51 0.0145 n −2.11 (−2.58, −1.64) −8.97 <0.0001 i 0.71 (0.3, 1.12) −3.43 0.001 Pelagic ln(h) 1.03 (−2.02, 4.11) 0.70 0.492 n −0.99 (−1.03, −0.06) −2.19 0.038 i −0.19 (−1.58, 1.2) −0.29 0.777 Habitat . Parameter . Estimate . t-value . Pr > ||t| . Demersal ln(h) 1.45 (0.3, 2.62) 2.51 0.0145 n −2.11 (−2.58, −1.64) −8.97 <0.0001 i 0.71 (0.3, 1.12) −3.43 0.001 Pelagic ln(h) 1.03 (−2.02, 4.11) 0.70 0.492 n −0.99 (−1.03, −0.06) −2.19 0.038 i −0.19 (−1.58, 1.2) −0.29 0.777 Open in new tab Values of L∞ and K for selected families of demersal and pelagic teleosts were obtained from FishBase (Froese and Pauly, 2006). Only data from families for which >100 individual estimates were available, and with few multiple estimates for individual species, were included. Non-linear regression was used to estimate the parameters d and e (Table 4). Fitting a linear model with separate intercepts and slopes for demersal and pelagic fish to log-transformed values of L∞ and K revealed a significant difference in slope between the two groups (F = 9.7, p < 0.0019), the slope being consistently more negative for pelagic species than for demersal. Except for the Sciaenidae, the exponent was between −0.85 and −0.63 for demersal families, and between −1.57 and −1.08 for pelagic families, conforming to the difference in growth patterns suggested by the model where the estimated exponent for the pelagic species was −1.34, different from the −0.64 estimated for demersal fish. Table 4. Estimates of the parameters d and e (with 95% CI in parenthesis) in the relationship K = dL∞e for different families of fish. Habitat . Family . d . e . Pelagic Carangidae n = 252 37 (9, 66) −1.16 (−0.93, −1.39) Clupeidae n = 629 94 (80, 108) −1.57 (−1.50, −1.64) Engraulidae n = 233 22 (12, 32) −1.08 (−0.9, −1.27) Scombridae n = 375 117 (48, 187) −1.33 (−1.16, −1.51) Demersal Gadidae n = 408 5.6 (4.1, 7.1) −0.74 (−0.68, −0.81) Pleuronectidae n = 146 3.7 (−0.7, 8.1) −0.70 (−0.38, −1.02) Sparidae n = 200 5.4 (1.7, 9.1) −0.85 (−0.66, −1.04) Lutjanidae n = 219 3.2 (1.7, 4.8) −0.63 (−0.51, −0.76) Serranidae n = 123 3.0 (1.2, 4.8) −0.64 (−0.48, −0.80) Sciaenidae n = 187 22 (9, 34) −1.02 (−0.86, −1.19) Habitat . Family . d . e . Pelagic Carangidae n = 252 37 (9, 66) −1.16 (−0.93, −1.39) Clupeidae n = 629 94 (80, 108) −1.57 (−1.50, −1.64) Engraulidae n = 233 22 (12, 32) −1.08 (−0.9, −1.27) Scombridae n = 375 117 (48, 187) −1.33 (−1.16, −1.51) Demersal Gadidae n = 408 5.6 (4.1, 7.1) −0.74 (−0.68, −0.81) Pleuronectidae n = 146 3.7 (−0.7, 8.1) −0.70 (−0.38, −1.02) Sparidae n = 200 5.4 (1.7, 9.1) −0.85 (−0.66, −1.04) Lutjanidae n = 219 3.2 (1.7, 4.8) −0.63 (−0.51, −0.76) Serranidae n = 123 3.0 (1.2, 4.8) −0.64 (−0.48, −0.80) Sciaenidae n = 187 22 (9, 34) −1.02 (−0.86, −1.19) The data are from FishBase (Froese and Pauly, 2006). Open in new tab Table 4. Estimates of the parameters d and e (with 95% CI in parenthesis) in the relationship K = dL∞e for different families of fish. Habitat . Family . d . e . Pelagic Carangidae n = 252 37 (9, 66) −1.16 (−0.93, −1.39) Clupeidae n = 629 94 (80, 108) −1.57 (−1.50, −1.64) Engraulidae n = 233 22 (12, 32) −1.08 (−0.9, −1.27) Scombridae n = 375 117 (48, 187) −1.33 (−1.16, −1.51) Demersal Gadidae n = 408 5.6 (4.1, 7.1) −0.74 (−0.68, −0.81) Pleuronectidae n = 146 3.7 (−0.7, 8.1) −0.70 (−0.38, −1.02) Sparidae n = 200 5.4 (1.7, 9.1) −0.85 (−0.66, −1.04) Lutjanidae n = 219 3.2 (1.7, 4.8) −0.63 (−0.51, −0.76) Serranidae n = 123 3.0 (1.2, 4.8) −0.64 (−0.48, −0.80) Sciaenidae n = 187 22 (9, 34) −1.02 (−0.86, −1.19) Habitat . Family . d . e . Pelagic Carangidae n = 252 37 (9, 66) −1.16 (−0.93, −1.39) Clupeidae n = 629 94 (80, 108) −1.57 (−1.50, −1.64) Engraulidae n = 233 22 (12, 32) −1.08 (−0.9, −1.27) Scombridae n = 375 117 (48, 187) −1.33 (−1.16, −1.51) Demersal Gadidae n = 408 5.6 (4.1, 7.1) −0.74 (−0.68, −0.81) Pleuronectidae n = 146 3.7 (−0.7, 8.1) −0.70 (−0.38, −1.02) Sparidae n = 200 5.4 (1.7, 9.1) −0.85 (−0.66, −1.04) Lutjanidae n = 219 3.2 (1.7, 4.8) −0.63 (−0.51, −0.76) Serranidae n = 123 3.0 (1.2, 4.8) −0.64 (−0.48, −0.80) Sciaenidae n = 187 22 (9, 34) −1.02 (−0.86, −1.19) The data are from FishBase (Froese and Pauly, 2006). Open in new tab Denney et al. (2002) found an inverse relationship between the natural logarithm of the maximum R/SSB and maximum body length. Re-analysing these data (in their Table A1) revealed that maximum recruitment scaled with maximum length raised to a power of −3.03 [r2 = 0.63, F = 87, p < 0.0001, n = 54, 95% confidence intervals (−2.38, −3.69)]. Many commercially exploited North Sea species are close to or at levels of spawning–stock biomass where recruitment is impaired. In accord with Denney et al. (2002) and others (Myers et al., 1999; Barrowman and Myers, 2000), we assumed that the effect of density dependence on recruitment would be negligible in this situation and that R/SSB was close to the maximum possible. However, the maximum R/SSB reported by Denney et al. (2002) were derived from VPA and represent cohort strength before the cohorts enter the exploited phase. In the size-based model, small species are subject to fishing in their first year of life, and the maximum number of 1-year-old recruits generated per unit of SSB was hence obtained at zero fishing mortality. Changing F from its current level to zero in the key-run without changing the other parameters generates a scaling of recruitment at age 1 with L∞ with an exponent of −2.69 for demersal species and −2.96 for pelagic species. Both values fall well within the confidence intervals of the estimate derived from the data of Denney et al. (2002). Discussion Scaling relationships and coexistence The model describes a teleost community where the species share a common set of habitat-dependent scaling relationships describing how growth, maturity, and mortality depend on length and asymptotic length. We assume that these scaling relationships have evolved in response to interactions with co-occurring species, to habitat characteristics, and to phylogenetic and physiological constraints. Most of the parameters in the model can be gathered from published data on growth, maturity, and fecundity, but assuming that both small and large species are able to replace themselves, the remaining parameters describing natural mortality and, for pelagic species, the parameters linking growth to asymptotic length have to be estimated within the model. This was done by minimizing the standard deviation of the net reproductive rate (R) across the range of asymptotic lengths found in the community, subject to the constraint that, on average, R should equal 1.0 across the range of asymptotic lengths. We have used the model to explain a general scaling of SSB/R with L∞. This scaling is puzzling because of its magnitude—three orders of magnitude for a one-order change in asymptotic length—and important because of its potential consequences for species coexistence. The trivial explanation for the scaling of SSB/R that spawners of a species with large L∞ necessarily accumulate more biomass per recruit than a species with small L∞ must be placed in the fuller context of how many equally sized recruits of both species will be produced per kilogramme of spawner. M or K simultaneously has to scale in some way to produce a long-term expectation of one spawner per spawner (R = 1) for all species to persist in the community. The solutions obtained with the simpler models (with M depending only on individual length, and with M depending only on L∞) produce large differences in R among species. This demonstrates that there would be little opportunity for coexistence of large and small species of fish in a simple single-factor model where M is assumed to scale either with individual body length within species (i = 0) or with L∞ across species (n = 0). The scaling problem may be solved by allowing small species to produce more eggs than large species, by subjecting large species to a higher M than small species, or by letting small species grow more rapidly than large species. The regression of the number of eggs produced per gramme of female provided little evidence for a general scaling of relative fecundity with L∞, and a trial run where the estimates of u and v given in Table 1 were used to calculate relative fecundity did not produce results that differed significantly from the key-run. Relative fecundity may vary over time within species, but the variation seems to be relatively modest (Oskarsson et al., 2002; Yoneda and Wright, 2004; Thorsen et al., 2006). RE may also increase somewhat with size within species (Kamler, 2005), but this would only aggravate the problem by affecting large species more than small species. No evidence was found in the literature (e.g. Pepin, 1991; Houde and Zastrow, 1993) to support a scaling of larval growth and mortality with L∞. We also cannot find a plausible explanation as to why the mechanisms regulating growth and survival in the pelagic larval and early juvenile stages should work quite differently for species with different L∞, when their larval and early juvenile stages are of similar size. We therefore consider a scaling of the survival of pelagic prerecruits of the necessary magnitude to be unlikely and propose an alternative hypothesis where differences between pelagic and demersal habitats are used to explain how small and large species can coexist in the North Sea. Demersal species For demersal fish, the observed scaling of K with L∞ implies that the rate of growth immediately after metamorphosis and settling differs between small and large species. In the von Bertalanffy growth equation, the rate of growth in length approaches a maximum of L∞×K as length moves towards zero. If L∞ were exactly inversely proportional to K, all species would have the same maximum growth rate. When the exponent differs from −1, maximum growth rate is a function of L∞. For exponents <−1, the maximum growth rate of a large species is lower than the maximum growth rate of a small species. For exponents >−1, the opposite occurs. With an estimated exponent of −0.64 in the scaling of K with L∞, the initial slope of the growth curve is much steeper for large demersal species than for small. At a size of 4 cm, the growth rate of a demersal species with L∞ = 130 cm is four times the growth rate of a species with L∞ = 10 cm. Securing the food necessary to maintain a fast growth rate may entail a fish being exposed to a higher M (Sogard, 1997), in particular during and immediately after major life-history transitions such as settling. Moreover, many of the small demersal species in the North Sea seem better protected against predation than large species fish of similar size (Figure 5). They typically possess anti-predator defences such as (venomous) spines, flat body shapes, or cryptic behaviour, or they live in habitats where they can hide from predation (e.g. sandeel and species associated with structurally complex habitats such as rocky seabeds). Developing predator defences or hiding may involve a metabolic cost or reduce their food intake and affect growth. For example, hiding by fresh-water fish reduces food intake while decreasing predation mortality (Mittelbach, 1986; Werner and Hall, 1988). In their analyses of empirical estimates of the natural mortality of 173 fish species, Griffiths and Harrod (2007) found species that were cryptic, hid in burrows, or had morphological defence against predators to have the lowest natural rates of mortality. We therefore hypothesize that the scaling of M with L∞ reflects a trade-off between growth and mortality. Demersal fish with small L∞ may have a lower maximum growth rate at the larval/juvenile transition than fish with a large L∞, specifically because they adjust their behaviour or morphology to reduce predation. Figure 5. Open in new tabDownload slide Proportion of species protected by either spines, venom, bone plates, being cryptic, flattened, or buried plotted against L∞. Data are from Appendix Table A2. Our model shows that the difference in R with L∞ can be largely solved for demersal species by making M after settling a function of individual length and L∞. Many authors have identified settling as an important life-history event and have found density-dependent predation in the post-settlement phase to be important (Myers and Cadigan, 1993; Van der Veer et al., 2000; Hixon and Webster, 2002; Almany and Webster, 2006). To our knowledge, whether mortality immediately after settling generally scales with L∞ has so far not been investigated. Our results suggest that M after settling should be almost directly proportional to L∞. This proportionality obviously depends on the input parameters. Changes in larval mortality and relative fecundity alter the results substantially, but the values of the mortality parameters from the key-run are not significantly different from those generated by an independent analysis of the average predation mortalities estimated by North Sea MSVPA. For demersal species, our estimate of the exponent in the relationship between M and size is in general accord with the values reported. M and body weight or length scale with an exponent close to −0.25 or −0.75, respectively (Peterson and Wroblewski, 1984; McGurk, 1986; Brown et al., 2004). At first glance, our results may seem to suggest that M should scale with length raised to a power of −1.66 rather than with length raised to −0.75 (Table 2). However, this apparent difference can be explained by the confounding effect of the L∞ term for demersal species. Assuming that the values of M for adult fish used by the authors cited above were derived mainly from demersal fish of a length corresponding to a constant fraction (μ) of their L∞, their predation mortality should scale as h × (L∞)i × (μL∞)n, i.e. as i + n. We estimate i + n to equal −0.86 (Table 2), so the overall exponent relating M to weight is estimated to be −0.86/3 = −0.29, close to the −0.25 reported by others. Further, the exponent relating M to larval body weight is reported to be significantly more negative than it is for adults (McGurk, 1986; Houde, 1987). In our model, the exponent for the post-larvae is −1.66/3 = −0.55 on a weight basis, compared with the −0.29 estimated for adult fish. Finally, in an empirical analysis of 175 fish stocks, Pauly (1980) found that M at a given temperature was proportional to K0.654×L∞−0.28. Inserting K∼L∞−0.64 (Table 2) in this relationship, we find that natural mortality should scale to asymptotic length raised to −0.70, not far from the value of −0.86 derived above. Pelagic species For pelagic species, the model predicts that K should scale with L∞ raised to a power of −1.34. Although this result is based on very few species, an analysis of L∞ and K from FishBase resulted in exponents for four pelagic families ranging from −1.08 for Engraulidae to −1.57 for Clupeidae, significantly lower than those for most demersal families (Table 4). Hence, the growth pattern appears generally different among the two habitats. Small pelagic species seem initially to grow faster than their larger counterparts, and this may explain how the former are able to sustain the higher levels of M associated with a smaller body size, by being exposed to size-dependent M for less time at each size. Where demersal species seem to solve the apparent difference in replacement of large and small species by modifying their growth and vulnerability to predation, pelagic species may overcome the problem by changing their growth rate with asymptotic size. Ideally, we should have used estimates of L∞ and K for pelagic and demersal species from different fish communities and analysed their relationship allowing for differences in intercepts and exponents between communities. However, for pelagic species, we could not find sufficient estimates of growth parameters from separate communities to perform such analyses. By analysing the data at a family level, we may have introduced additional variation caused by differences between fish communities in, for instance, ambient temperature and exploitation. Despite the likely inflation of overall variance caused by these differences, the difference in the slope of the K/L∞ relationship between pelagic and demersal families of fish proved highly significant. Our analysis not only confirms the overall difference between pelagic and demersal species suggested by the model, but it shows that this difference is present also at a family level. Scaling of maximum R/SSB Our model can reproduce the scaling of maximum R/SSB with L∞ reported by Denney et al. (2002). Calculating the maximum survival from egg to age 1 by assuming that only M operates during this period results in numbers of R/SSB for both demersal and pelagic species that scale with L∞ raised to powers that are close to the estimate of −3.03 based on their data from the Northeast Atlantic. This scaling cannot be reproduced by a model where natural mortality depends only on individual length and where the combined scaling of L∞ and K is used to model growth. The importance of stabilizing and equalizing mechanisms for species coexistence We base our conclusions on a simple model of coexistence in which populations are assumed to be in steady state. In line with common approaches in life-history theory (Stearns, 1992; Charnov, 1993), we attempt to capture the general patterns in the average life-history characteristics of demersal and pelagic teleosts. For the sake of simplicity, we used a model with an unstable equilibrium, providing no mechanism for recovery of species with a less than one-for-one replacement. Species can coexist in variable environments if they respond differently to differing conditions and can persist through unfavourable periods or retract to favourable areas, and stage structure, resource partitioning, non-linear competition, and density-dependent biological interactions are able to promote coexistence (McCann and Yodzis, 1998; Chesson, 2000; Abrams and Holt, 2002; Bonsall and Hastings, 2004; Chase, 2005). We recognize that periods of decline or increase are common in fish populations, and that stabilizing mechanisms must be important for their long-term persistence. Still, when the species in a community manage to persist over long time-spans, their net rates of population growth are bound to be close to zero (Hubbell, 2001). This can only be realized if their growth, mortality, and reproductive characteristics on average generate spawner-for-spawner or egg-for-egg replacement, irrespective of the nature of the mechanisms involved. Although long-term persistence will be achieved by both fitness-equalizing and -stabilizing mechanisms (Chesson, 2000; Adler et al., 2007), there is no obvious reason why stabilizing mechanisms should result in a relationship between R/SSB and L∞, producing a three orders of magnitude difference in recruitment for a single order change in L∞. Rather, stabilizing mechanisms alone are unlikely to be responsible for more than a single-order change in replacement. In their analysis of >700 time-series of spawners and recruitment for different populations and species of fish, Myers et al. (1999) found that the maximum relative growth rate of fish populations is typically around 1–7 replacement spawners per spawner per year, suggesting that a factor of 10 is the likely maximum difference in average replacement that stabilizing mechanisms could effectively buffer against. The net reproductive rate is the proper fitness measure to use when populations are regulated by density-dependence early in life (Mylius and Diekmann, 1995). Still, by neglecting other stabilizing mechanisms and assuming populations to be in steady state, we cannot be completely sure that the model will generate life-history strategies that are evolutionarily stable. Therefore, investigating the consequences of our model assumptions in an evolutionary context is a clear target for further research. Model limitations and applicability Using a simple scaling model to capture variations in life-history parameters across a multitude of species makes it likely that some species rely on life-history features not adequately accounted for by the model to compensate for insufficient replacement. This is, for instance, reflected by the spread of the observations of L∞ and K around a log-linear relationship (Figure 1a). It is therefore unrealistic to expect general scaling relationships to produce an exact one-for-one replacement at any one time. Additional trade-offs between growth, mortality, and reproductive investment should allow species with identical L∞ to achieve a one-for-one replacement, e.g. if they counteract a reduction in growth rate with an increase in fecundity or a reduction in M (Gunderson, 1997). Species of similar L∞ may be found on a continuum from sit-and-wait strategists to active swimmers hunting for food or use parental care to increase egg survival, as several of the smaller North Sea demersal species do. Our model is primarily aimed at describing marine fish communities typical of temperate continental shelves with sandy and muddy seabeds, and it mimics the basic life-history features of determinate spawners with pelagic eggs. Although we applied information from North Sea species, the model could be applied easily to other shelf areas to test our prediction that for a given individual length, large demersal species after settling should suffer greater mortality than small demersal species. Size-based scaling of life-history parameters across species may also be used to compare replacement and to explore trade-offs among life histories in other types of communities, such as in deep-water fish communities where the light intensity shapes the trade-offs between feeding and predation (Strand et al., 2002; Fiksen et al., 2005), or in reef-fish communities where species diversity is linked to habitat complexity (Gratwicke and Speight, 2005). These may be particularly interesting systems for studying size-based trade-offs, because superimposed on the processes considered here, complex habitats offer the various species a multitude of possibilities for composing their own particular life-history strategy to secure their persistence, constrained by size as a factor in securing shelter and reduced predation mortality. However, where shelter favours certain sizes, it may not suffice to model predation mortality by an expression where a single exponent is used to relate predation mortality to length. The surprisingly good fit to the estimated predation mortalities from the North Sea MSVPA calls for further investigations of our model's ability to fit existing estimates of M for various species and sizes of fish, and for a revision of the formula for estimating M derived by Pauly (1980). This formula ignores the within-species length dependence frequently reported for fish (Lorenzen, 1996; Sogard, 1997; Bax, 1998). It has been widely used to estimate M for fish populations in situations where research costs would prohibit empirical observations, but many of the empirical values used in its derivation refer to inferred values (such as 0.1 or 0.2 for many temperate commercial fish stocks) rather than independent estimates. 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Patterns of life history diversification in North American fishes: implications for population regulation , Canadian Journal of Fisheries and Aquatic Sciences , 1992 , vol. 49 (pg. 2196 - 2218 ) Google Scholar Crossref Search ADS WorldCat Yoneda M. , Wright P. J. . Temporal and spatial variation in reproductive investment of Atlantic cod Gadus morhua in the northern North Sea and Scottish west coast , Marine Ecology Progress Series , 2004 , vol. 276 (pg. 237 - 248 ) Google Scholar Crossref Search ADS WorldCat Appendix Table A1. Variables and constants used in the model. Type . Parameter . Description . Unit . Value . Source . Independent parameter L∞ Asymptotic length cm – – Internal variables L Length cm – Equations (3) and (5) tL Age at length year – Equation (4) NL Numbers at length – – Equations (1), (11), and (12) N̄L, L+ΔL Average annual number of individuals in length interval from L to L+ΔL – – Equation (13) WL Body weight at length g – Equation (14) K von Bertalanffy growth parameter year−1 – Equation (7) Lmat Length at first maturity cm – Equation (15) t0 Parameter in the von Bertalanffy growth equation generating a fish of length Lmet at age tLmet year – Equation (6) FL Fishing mortality at length year−1 – Equation (10) ML Natural mortality at length year−1 – Equations (8) and (9) RE Relative fecundity g−1 – Equation (16) E1,L∞ Total lifetime egg production – – Equation (17) RL∞ Net reproductive rate – – Equation (18) Constants estimated by the model n Exponent relating predation mortality to L – – – i Exponent relating predation mortality to L∞ in the demersal habitat – – – hdem Constant used to link predation mortality to L∞ and L in the demersal habitat cm−(i+n) year−1 – – epel Exponent used to scale K to L∞ for pelagic species – – – dpel Constant used to scale K to L∞ for pelagic species cm−e year−1 – Estimated from epel and K32.1. Equation (21) hpel Constant used to link predation mortality to L in the pelagic habitat cm−n year−1 – Estimated from n, M1, and ML0. Equation (22) User-specified constants M1 Mortality attributable to non-size-related causes of death year−1 0.1 See text ΔL Width of length interval cm 0.25 See text Lmet Length at end of post-larval stage cm 4.0 See text E0 Initial number of eggs – 106 See text Ce Fraction of eggs surviving to hatch – 0.435 Pepin (1991) Cy Fraction of yolk-sac larvae surviving to post-larval stage – 0.074 Pepin (1991) L0 Length of post-larvae at first feeding cm 0.5 Russell (1976) ML0 Daily mortality of post-larvae in initial length class year−1 59.7 Pepin (1991) a Constant in post-larval growth equation cm(1−b) year−1 0.064 Pepin (1991) b Exponent in post-larval growth equation – 0.54 Pepin (1991) edem Exponent used to scale K to L∞ for demersal species – −0.64 Estimated from Table A2 ddem Constant in equation used for scaling K to L∞ cm−e year−1 3.15 Estimated from Table A2 F General overall level of fishing mortality year−1 0.7 Pope et al. (2006) δ Proportion of L∞ at 50% gear selection – 0.33 Pope et al. (2006) γ Constant used to generate the slope of the exploitation curve with respect to length cm−1 0.2 Pope et al. (2006) λ Constant used to link the level of fishing mortality to L∞ cm 70 Pope et al. (2006) κ Constant expressing how fast the level of fishing mortality changes with respect to L∞ cm−1 0.0035 Pope et al. (2006) q Constant used to scale Lmat to L∞ cm(1−s) 0.72 Estimated from Table A2 s Exponent used to scale Lmat to L∞ – 0.93 Estimated from Table A2 u Constant used to scale relative fecundity to L∞ cm(1−v) 977 Estimated from Table A2 v Exponent used to scale relative fecundity to L∞ – 0.0 Estimated from Table A2 Type . Parameter . Description . Unit . Value . Source . Independent parameter L∞ Asymptotic length cm – – Internal variables L Length cm – Equations (3) and (5) tL Age at length year – Equation (4) NL Numbers at length – – Equations (1), (11), and (12) N̄L, L+ΔL Average annual number of individuals in length interval from L to L+ΔL – – Equation (13) WL Body weight at length g – Equation (14) K von Bertalanffy growth parameter year−1 – Equation (7) Lmat Length at first maturity cm – Equation (15) t0 Parameter in the von Bertalanffy growth equation generating a fish of length Lmet at age tLmet year – Equation (6) FL Fishing mortality at length year−1 – Equation (10) ML Natural mortality at length year−1 – Equations (8) and (9) RE Relative fecundity g−1 – Equation (16) E1,L∞ Total lifetime egg production – – Equation (17) RL∞ Net reproductive rate – – Equation (18) Constants estimated by the model n Exponent relating predation mortality to L – – – i Exponent relating predation mortality to L∞ in the demersal habitat – – – hdem Constant used to link predation mortality to L∞ and L in the demersal habitat cm−(i+n) year−1 – – epel Exponent used to scale K to L∞ for pelagic species – – – dpel Constant used to scale K to L∞ for pelagic species cm−e year−1 – Estimated from epel and K32.1. Equation (21) hpel Constant used to link predation mortality to L in the pelagic habitat cm−n year−1 – Estimated from n, M1, and ML0. Equation (22) User-specified constants M1 Mortality attributable to non-size-related causes of death year−1 0.1 See text ΔL Width of length interval cm 0.25 See text Lmet Length at end of post-larval stage cm 4.0 See text E0 Initial number of eggs – 106 See text Ce Fraction of eggs surviving to hatch – 0.435 Pepin (1991) Cy Fraction of yolk-sac larvae surviving to post-larval stage – 0.074 Pepin (1991) L0 Length of post-larvae at first feeding cm 0.5 Russell (1976) ML0 Daily mortality of post-larvae in initial length class year−1 59.7 Pepin (1991) a Constant in post-larval growth equation cm(1−b) year−1 0.064 Pepin (1991) b Exponent in post-larval growth equation – 0.54 Pepin (1991) edem Exponent used to scale K to L∞ for demersal species – −0.64 Estimated from Table A2 ddem Constant in equation used for scaling K to L∞ cm−e year−1 3.15 Estimated from Table A2 F General overall level of fishing mortality year−1 0.7 Pope et al. (2006) δ Proportion of L∞ at 50% gear selection – 0.33 Pope et al. (2006) γ Constant used to generate the slope of the exploitation curve with respect to length cm−1 0.2 Pope et al. (2006) λ Constant used to link the level of fishing mortality to L∞ cm 70 Pope et al. (2006) κ Constant expressing how fast the level of fishing mortality changes with respect to L∞ cm−1 0.0035 Pope et al. (2006) q Constant used to scale Lmat to L∞ cm(1−s) 0.72 Estimated from Table A2 s Exponent used to scale Lmat to L∞ – 0.93 Estimated from Table A2 u Constant used to scale relative fecundity to L∞ cm(1−v) 977 Estimated from Table A2 v Exponent used to scale relative fecundity to L∞ – 0.0 Estimated from Table A2 Open in new tab Table A1. Variables and constants used in the model. Type . Parameter . Description . Unit . Value . Source . Independent parameter L∞ Asymptotic length cm – – Internal variables L Length cm – Equations (3) and (5) tL Age at length year – Equation (4) NL Numbers at length – – Equations (1), (11), and (12) N̄L, L+ΔL Average annual number of individuals in length interval from L to L+ΔL – – Equation (13) WL Body weight at length g – Equation (14) K von Bertalanffy growth parameter year−1 – Equation (7) Lmat Length at first maturity cm – Equation (15) t0 Parameter in the von Bertalanffy growth equation generating a fish of length Lmet at age tLmet year – Equation (6) FL Fishing mortality at length year−1 – Equation (10) ML Natural mortality at length year−1 – Equations (8) and (9) RE Relative fecundity g−1 – Equation (16) E1,L∞ Total lifetime egg production – – Equation (17) RL∞ Net reproductive rate – – Equation (18) Constants estimated by the model n Exponent relating predation mortality to L – – – i Exponent relating predation mortality to L∞ in the demersal habitat – – – hdem Constant used to link predation mortality to L∞ and L in the demersal habitat cm−(i+n) year−1 – – epel Exponent used to scale K to L∞ for pelagic species – – – dpel Constant used to scale K to L∞ for pelagic species cm−e year−1 – Estimated from epel and K32.1. Equation (21) hpel Constant used to link predation mortality to L in the pelagic habitat cm−n year−1 – Estimated from n, M1, and ML0. Equation (22) User-specified constants M1 Mortality attributable to non-size-related causes of death year−1 0.1 See text ΔL Width of length interval cm 0.25 See text Lmet Length at end of post-larval stage cm 4.0 See text E0 Initial number of eggs – 106 See text Ce Fraction of eggs surviving to hatch – 0.435 Pepin (1991) Cy Fraction of yolk-sac larvae surviving to post-larval stage – 0.074 Pepin (1991) L0 Length of post-larvae at first feeding cm 0.5 Russell (1976) ML0 Daily mortality of post-larvae in initial length class year−1 59.7 Pepin (1991) a Constant in post-larval growth equation cm(1−b) year−1 0.064 Pepin (1991) b Exponent in post-larval growth equation – 0.54 Pepin (1991) edem Exponent used to scale K to L∞ for demersal species – −0.64 Estimated from Table A2 ddem Constant in equation used for scaling K to L∞ cm−e year−1 3.15 Estimated from Table A2 F General overall level of fishing mortality year−1 0.7 Pope et al. (2006) δ Proportion of L∞ at 50% gear selection – 0.33 Pope et al. (2006) γ Constant used to generate the slope of the exploitation curve with respect to length cm−1 0.2 Pope et al. (2006) λ Constant used to link the level of fishing mortality to L∞ cm 70 Pope et al. (2006) κ Constant expressing how fast the level of fishing mortality changes with respect to L∞ cm−1 0.0035 Pope et al. (2006) q Constant used to scale Lmat to L∞ cm(1−s) 0.72 Estimated from Table A2 s Exponent used to scale Lmat to L∞ – 0.93 Estimated from Table A2 u Constant used to scale relative fecundity to L∞ cm(1−v) 977 Estimated from Table A2 v Exponent used to scale relative fecundity to L∞ – 0.0 Estimated from Table A2 Type . Parameter . Description . Unit . Value . Source . Independent parameter L∞ Asymptotic length cm – – Internal variables L Length cm – Equations (3) and (5) tL Age at length year – Equation (4) NL Numbers at length – – Equations (1), (11), and (12) N̄L, L+ΔL Average annual number of individuals in length interval from L to L+ΔL – – Equation (13) WL Body weight at length g – Equation (14) K von Bertalanffy growth parameter year−1 – Equation (7) Lmat Length at first maturity cm – Equation (15) t0 Parameter in the von Bertalanffy growth equation generating a fish of length Lmet at age tLmet year – Equation (6) FL Fishing mortality at length year−1 – Equation (10) ML Natural mortality at length year−1 – Equations (8) and (9) RE Relative fecundity g−1 – Equation (16) E1,L∞ Total lifetime egg production – – Equation (17) RL∞ Net reproductive rate – – Equation (18) Constants estimated by the model n Exponent relating predation mortality to L – – – i Exponent relating predation mortality to L∞ in the demersal habitat – – – hdem Constant used to link predation mortality to L∞ and L in the demersal habitat cm−(i+n) year−1 – – epel Exponent used to scale K to L∞ for pelagic species – – – dpel Constant used to scale K to L∞ for pelagic species cm−e year−1 – Estimated from epel and K32.1. Equation (21) hpel Constant used to link predation mortality to L in the pelagic habitat cm−n year−1 – Estimated from n, M1, and ML0. Equation (22) User-specified constants M1 Mortality attributable to non-size-related causes of death year−1 0.1 See text ΔL Width of length interval cm 0.25 See text Lmet Length at end of post-larval stage cm 4.0 See text E0 Initial number of eggs – 106 See text Ce Fraction of eggs surviving to hatch – 0.435 Pepin (1991) Cy Fraction of yolk-sac larvae surviving to post-larval stage – 0.074 Pepin (1991) L0 Length of post-larvae at first feeding cm 0.5 Russell (1976) ML0 Daily mortality of post-larvae in initial length class year−1 59.7 Pepin (1991) a Constant in post-larval growth equation cm(1−b) year−1 0.064 Pepin (1991) b Exponent in post-larval growth equation – 0.54 Pepin (1991) edem Exponent used to scale K to L∞ for demersal species – −0.64 Estimated from Table A2 ddem Constant in equation used for scaling K to L∞ cm−e year−1 3.15 Estimated from Table A2 F General overall level of fishing mortality year−1 0.7 Pope et al. (2006) δ Proportion of L∞ at 50% gear selection – 0.33 Pope et al. (2006) γ Constant used to generate the slope of the exploitation curve with respect to length cm−1 0.2 Pope et al. (2006) λ Constant used to link the level of fishing mortality to L∞ cm 70 Pope et al. (2006) κ Constant expressing how fast the level of fishing mortality changes with respect to L∞ cm−1 0.0035 Pope et al. (2006) q Constant used to scale Lmat to L∞ cm(1−s) 0.72 Estimated from Table A2 s Exponent used to scale Lmat to L∞ – 0.93 Estimated from Table A2 u Constant used to scale relative fecundity to L∞ cm(1−v) 977 Estimated from Table A2 v Exponent used to scale relative fecundity to L∞ – 0.0 Estimated from Table A2 Open in new tab Table A2. Growth parameters (L∞, K), length at first maturity (Lmat), egg diameter (EggD), relative fecundity (RE) in eggs g female body weight−1, and predator defence (Pdef) of North Sea teleosts. Lmat, EggD, and Pdef from FishBase (Pl, plates; B, bury; F, flat; S, spiny; V, venomous; N, nearshore; Blank, no apparent predator defence). Species . L∞ (cm) . K (year−1) . Sourcea . Demersal/Pelagic . Lmat (cm) . EggD (mm) . RE . Sourcea . Pdef . Agonus cataphractus 15.0 0.48 1 D 9.2 1.9 – – Pl Ammodytes marinus 21.8 0.89 2 D 16.3 1.0 790 2 B Anarhichas lupus 115.0 0.11 3 D 55.0 6.0 – – – Arnoglossus laterna 15.8 0.84 4 D – 0.7 – – F Aspitrigla cuculus 41.7 0.46 5 D 25.0 1.6 – – S Brosme brosme 89.0 0.08 6 D 50.0 1.3 – – – Buglossidium luteum 11.7 0.54 4 D 7.0 0.8 – – F Callionymus lyra 17.5 0.55 7 D 13.3 0.9 – – S, B Chelidonichthys lucernus 48.4 0.33 5 D – 1.4 – – S Clupea harengus 30.0 0.38 8 P 25.7 1.2 310 33 – Dicentrarchus labrax 72.3 0.14 9 D 42.0 1.2 – – – Echiichthys vipera 15.0 0.33 10 D – 1.2 – – V, S, B Enchelyopus cimbrius 36.0 0.20 11 D 14.0 0.8 – – – Eutrigla gurnardus 46.0 0.16 12 D 23.0 1.3 – – S Gadus morhua 132.0 0.20 8 D 69.7 1.4 500 33 – Glyptocephalus cynoglossus 46.1 0.20 13 D 20.0 1.2 – – F Gobius niger 9.5 0.43 14 D – 1.5 – – N Hippoglossoides platessoides 25.0 0.34 15 D 15.1 2.3 1300 33 F Labrus bergylta 44.4 0.10 16 D – 1.0 – – – Lepidorhombus whiffiagonis 61.1 0.13 17 D 25.3 1.1 – – F Limanda limanda 27.0 0.26 18 D 13.8 1.0 3300 33 F Lophius piscatorius 137.0 0.16 19 D 75.0 2.7 – – S Lumpenus lampretaeformis 47.6 0.21 19 D – – – – B Melanogrammus aeglefinus 63.5 0.26 20 D 31.5 1.5 500 33 – Merlangius merlangus 40.0 0.29 21 D 20.2 1.2 1700 33 – Merluccius merluccius 103.6 0.11 13 D 50.0 1.0 – – – Microchirus variegatus 20.7 0.37 4 D 14.0 1.3 – – F Micromesistius poutassou 37.1 0.23 22 D 25.1 1.1 – – – Microstomus kitt 37.1 0.42 23 D 27.0 – 470 33 F Molva dipterygia 155.0 0.13 24 D 85.0 – – – – Molva molva 183.0 0.12 24 D – – – – – Myoxocephalus scorpius 26.7 0.54 25 D 15.0 2.2 – – S Platichthys flesus 46.5 0.30 26 D – 1.0 – – F Pleuronectes platessa 70.0 0.08 8 D 26.6 1.9 265 33 F Pollachius pollachius 85.6 0.19 27 D – 1.2 – – – Pollachius virens 107.0 0.19 28 D 55.4 1.1 750 33 – Pomatoschistus minutus 9.2 0.93 29 D – 1.2 – – N Psetta maxima 64.8 0.26 30 D 46.0 1.0 1100 33 F Scomber scombrus 39.0 0.43 19 P 26.2 1.2 1275 33 – Solea vulgaris 39.0 0.40 8 D 24.8 1.2 750 33 F Sprattus sprattus 16.0 0.65 19 P 13.0 1.1 – – – Trachinus draco 38.3 0.15 31 D 16.0 1.0 593 31 V, S, B Trachurus trachurus 43.4 0.18 32 P 18.5 0.9 1655 33 – Trisopterus esmarki 22.6 0.52 23 D 18.6 1.0 700 33 – Trisopterus minutus 20.0 0.51 23 D 13.0 1.0 – – – Species . L∞ (cm) . K (year−1) . Sourcea . Demersal/Pelagic . Lmat (cm) . EggD (mm) . RE . Sourcea . Pdef . Agonus cataphractus 15.0 0.48 1 D 9.2 1.9 – – Pl Ammodytes marinus 21.8 0.89 2 D 16.3 1.0 790 2 B Anarhichas lupus 115.0 0.11 3 D 55.0 6.0 – – – Arnoglossus laterna 15.8 0.84 4 D – 0.7 – – F Aspitrigla cuculus 41.7 0.46 5 D 25.0 1.6 – – S Brosme brosme 89.0 0.08 6 D 50.0 1.3 – – – Buglossidium luteum 11.7 0.54 4 D 7.0 0.8 – – F Callionymus lyra 17.5 0.55 7 D 13.3 0.9 – – S, B Chelidonichthys lucernus 48.4 0.33 5 D – 1.4 – – S Clupea harengus 30.0 0.38 8 P 25.7 1.2 310 33 – Dicentrarchus labrax 72.3 0.14 9 D 42.0 1.2 – – – Echiichthys vipera 15.0 0.33 10 D – 1.2 – – V, S, B Enchelyopus cimbrius 36.0 0.20 11 D 14.0 0.8 – – – Eutrigla gurnardus 46.0 0.16 12 D 23.0 1.3 – – S Gadus morhua 132.0 0.20 8 D 69.7 1.4 500 33 – Glyptocephalus cynoglossus 46.1 0.20 13 D 20.0 1.2 – – F Gobius niger 9.5 0.43 14 D – 1.5 – – N Hippoglossoides platessoides 25.0 0.34 15 D 15.1 2.3 1300 33 F Labrus bergylta 44.4 0.10 16 D – 1.0 – – – Lepidorhombus whiffiagonis 61.1 0.13 17 D 25.3 1.1 – – F Limanda limanda 27.0 0.26 18 D 13.8 1.0 3300 33 F Lophius piscatorius 137.0 0.16 19 D 75.0 2.7 – – S Lumpenus lampretaeformis 47.6 0.21 19 D – – – – B Melanogrammus aeglefinus 63.5 0.26 20 D 31.5 1.5 500 33 – Merlangius merlangus 40.0 0.29 21 D 20.2 1.2 1700 33 – Merluccius merluccius 103.6 0.11 13 D 50.0 1.0 – – – Microchirus variegatus 20.7 0.37 4 D 14.0 1.3 – – F Micromesistius poutassou 37.1 0.23 22 D 25.1 1.1 – – – Microstomus kitt 37.1 0.42 23 D 27.0 – 470 33 F Molva dipterygia 155.0 0.13 24 D 85.0 – – – – Molva molva 183.0 0.12 24 D – – – – – Myoxocephalus scorpius 26.7 0.54 25 D 15.0 2.2 – – S Platichthys flesus 46.5 0.30 26 D – 1.0 – – F Pleuronectes platessa 70.0 0.08 8 D 26.6 1.9 265 33 F Pollachius pollachius 85.6 0.19 27 D – 1.2 – – – Pollachius virens 107.0 0.19 28 D 55.4 1.1 750 33 – Pomatoschistus minutus 9.2 0.93 29 D – 1.2 – – N Psetta maxima 64.8 0.26 30 D 46.0 1.0 1100 33 F Scomber scombrus 39.0 0.43 19 P 26.2 1.2 1275 33 – Solea vulgaris 39.0 0.40 8 D 24.8 1.2 750 33 F Sprattus sprattus 16.0 0.65 19 P 13.0 1.1 – – – Trachinus draco 38.3 0.15 31 D 16.0 1.0 593 31 V, S, B Trachurus trachurus 43.4 0.18 32 P 18.5 0.9 1655 33 – Trisopterus esmarki 22.6 0.52 23 D 18.6 1.0 700 33 – Trisopterus minutus 20.0 0.51 23 D 13.0 1.0 – – – aSources: 1, Le Gall (1969); 2, Macer (1966); 3, Liao and Lucas (2000); 4, Deniel (1990); 5, Baron (1985); 6, Bergstad (1988); 7, Chang (1951); 8, Beverton and Holt (1959); 9, Holden and William (1974); 10, Creutzberg and Witte (1989); 11, Albert (1993); 12, Damm (1987); 13, Greenstreet and Rogers (2006); 14, Nash (1984); 15, Ntiba (1989); 16, Dipper et al. (1977); 17, Robb (1973); 18, Rijnsdorp et al. (1992); 19, Pauly (1978); 20, Jones (1983); 21, Knudsen (1964); 22, Jennings et al. (1998); 23, Jennings et al. (1999); 24, Joenoes (1961); 25, Hass (1937); 26, Saeger (1974); 27, Moreau (1964); 28, Beverton and Holt (1959); 29, Fonds (1973); 30, Jones (1974); 31, Bagge (2004); 32, Sahrhage (1970); 33, Knijn et al. (1993). Open in new tab Table A2. Growth parameters (L∞, K), length at first maturity (Lmat), egg diameter (EggD), relative fecundity (RE) in eggs g female body weight−1, and predator defence (Pdef) of North Sea teleosts. Lmat, EggD, and Pdef from FishBase (Pl, plates; B, bury; F, flat; S, spiny; V, venomous; N, nearshore; Blank, no apparent predator defence). Species . L∞ (cm) . K (year−1) . Sourcea . Demersal/Pelagic . Lmat (cm) . EggD (mm) . RE . Sourcea . Pdef . Agonus cataphractus 15.0 0.48 1 D 9.2 1.9 – – Pl Ammodytes marinus 21.8 0.89 2 D 16.3 1.0 790 2 B Anarhichas lupus 115.0 0.11 3 D 55.0 6.0 – – – Arnoglossus laterna 15.8 0.84 4 D – 0.7 – – F Aspitrigla cuculus 41.7 0.46 5 D 25.0 1.6 – – S Brosme brosme 89.0 0.08 6 D 50.0 1.3 – – – Buglossidium luteum 11.7 0.54 4 D 7.0 0.8 – – F Callionymus lyra 17.5 0.55 7 D 13.3 0.9 – – S, B Chelidonichthys lucernus 48.4 0.33 5 D – 1.4 – – S Clupea harengus 30.0 0.38 8 P 25.7 1.2 310 33 – Dicentrarchus labrax 72.3 0.14 9 D 42.0 1.2 – – – Echiichthys vipera 15.0 0.33 10 D – 1.2 – – V, S, B Enchelyopus cimbrius 36.0 0.20 11 D 14.0 0.8 – – – Eutrigla gurnardus 46.0 0.16 12 D 23.0 1.3 – – S Gadus morhua 132.0 0.20 8 D 69.7 1.4 500 33 – Glyptocephalus cynoglossus 46.1 0.20 13 D 20.0 1.2 – – F Gobius niger 9.5 0.43 14 D – 1.5 – – N Hippoglossoides platessoides 25.0 0.34 15 D 15.1 2.3 1300 33 F Labrus bergylta 44.4 0.10 16 D – 1.0 – – – Lepidorhombus whiffiagonis 61.1 0.13 17 D 25.3 1.1 – – F Limanda limanda 27.0 0.26 18 D 13.8 1.0 3300 33 F Lophius piscatorius 137.0 0.16 19 D 75.0 2.7 – – S Lumpenus lampretaeformis 47.6 0.21 19 D – – – – B Melanogrammus aeglefinus 63.5 0.26 20 D 31.5 1.5 500 33 – Merlangius merlangus 40.0 0.29 21 D 20.2 1.2 1700 33 – Merluccius merluccius 103.6 0.11 13 D 50.0 1.0 – – – Microchirus variegatus 20.7 0.37 4 D 14.0 1.3 – – F Micromesistius poutassou 37.1 0.23 22 D 25.1 1.1 – – – Microstomus kitt 37.1 0.42 23 D 27.0 – 470 33 F Molva dipterygia 155.0 0.13 24 D 85.0 – – – – Molva molva 183.0 0.12 24 D – – – – – Myoxocephalus scorpius 26.7 0.54 25 D 15.0 2.2 – – S Platichthys flesus 46.5 0.30 26 D – 1.0 – – F Pleuronectes platessa 70.0 0.08 8 D 26.6 1.9 265 33 F Pollachius pollachius 85.6 0.19 27 D – 1.2 – – – Pollachius virens 107.0 0.19 28 D 55.4 1.1 750 33 – Pomatoschistus minutus 9.2 0.93 29 D – 1.2 – – N Psetta maxima 64.8 0.26 30 D 46.0 1.0 1100 33 F Scomber scombrus 39.0 0.43 19 P 26.2 1.2 1275 33 – Solea vulgaris 39.0 0.40 8 D 24.8 1.2 750 33 F Sprattus sprattus 16.0 0.65 19 P 13.0 1.1 – – – Trachinus draco 38.3 0.15 31 D 16.0 1.0 593 31 V, S, B Trachurus trachurus 43.4 0.18 32 P 18.5 0.9 1655 33 – Trisopterus esmarki 22.6 0.52 23 D 18.6 1.0 700 33 – Trisopterus minutus 20.0 0.51 23 D 13.0 1.0 – – – Species . L∞ (cm) . K (year−1) . Sourcea . Demersal/Pelagic . Lmat (cm) . EggD (mm) . RE . Sourcea . Pdef . Agonus cataphractus 15.0 0.48 1 D 9.2 1.9 – – Pl Ammodytes marinus 21.8 0.89 2 D 16.3 1.0 790 2 B Anarhichas lupus 115.0 0.11 3 D 55.0 6.0 – – – Arnoglossus laterna 15.8 0.84 4 D – 0.7 – – F Aspitrigla cuculus 41.7 0.46 5 D 25.0 1.6 – – S Brosme brosme 89.0 0.08 6 D 50.0 1.3 – – – Buglossidium luteum 11.7 0.54 4 D 7.0 0.8 – – F Callionymus lyra 17.5 0.55 7 D 13.3 0.9 – – S, B Chelidonichthys lucernus 48.4 0.33 5 D – 1.4 – – S Clupea harengus 30.0 0.38 8 P 25.7 1.2 310 33 – Dicentrarchus labrax 72.3 0.14 9 D 42.0 1.2 – – – Echiichthys vipera 15.0 0.33 10 D – 1.2 – – V, S, B Enchelyopus cimbrius 36.0 0.20 11 D 14.0 0.8 – – – Eutrigla gurnardus 46.0 0.16 12 D 23.0 1.3 – – S Gadus morhua 132.0 0.20 8 D 69.7 1.4 500 33 – Glyptocephalus cynoglossus 46.1 0.20 13 D 20.0 1.2 – – F Gobius niger 9.5 0.43 14 D – 1.5 – – N Hippoglossoides platessoides 25.0 0.34 15 D 15.1 2.3 1300 33 F Labrus bergylta 44.4 0.10 16 D – 1.0 – – – Lepidorhombus whiffiagonis 61.1 0.13 17 D 25.3 1.1 – – F Limanda limanda 27.0 0.26 18 D 13.8 1.0 3300 33 F Lophius piscatorius 137.0 0.16 19 D 75.0 2.7 – – S Lumpenus lampretaeformis 47.6 0.21 19 D – – – – B Melanogrammus aeglefinus 63.5 0.26 20 D 31.5 1.5 500 33 – Merlangius merlangus 40.0 0.29 21 D 20.2 1.2 1700 33 – Merluccius merluccius 103.6 0.11 13 D 50.0 1.0 – – – Microchirus variegatus 20.7 0.37 4 D 14.0 1.3 – – F Micromesistius poutassou 37.1 0.23 22 D 25.1 1.1 – – – Microstomus kitt 37.1 0.42 23 D 27.0 – 470 33 F Molva dipterygia 155.0 0.13 24 D 85.0 – – – – Molva molva 183.0 0.12 24 D – – – – – Myoxocephalus scorpius 26.7 0.54 25 D 15.0 2.2 – – S Platichthys flesus 46.5 0.30 26 D – 1.0 – – F Pleuronectes platessa 70.0 0.08 8 D 26.6 1.9 265 33 F Pollachius pollachius 85.6 0.19 27 D – 1.2 – – – Pollachius virens 107.0 0.19 28 D 55.4 1.1 750 33 – Pomatoschistus minutus 9.2 0.93 29 D – 1.2 – – N Psetta maxima 64.8 0.26 30 D 46.0 1.0 1100 33 F Scomber scombrus 39.0 0.43 19 P 26.2 1.2 1275 33 – Solea vulgaris 39.0 0.40 8 D 24.8 1.2 750 33 F Sprattus sprattus 16.0 0.65 19 P 13.0 1.1 – – – Trachinus draco 38.3 0.15 31 D 16.0 1.0 593 31 V, S, B Trachurus trachurus 43.4 0.18 32 P 18.5 0.9 1655 33 – Trisopterus esmarki 22.6 0.52 23 D 18.6 1.0 700 33 – Trisopterus minutus 20.0 0.51 23 D 13.0 1.0 – – – aSources: 1, Le Gall (1969); 2, Macer (1966); 3, Liao and Lucas (2000); 4, Deniel (1990); 5, Baron (1985); 6, Bergstad (1988); 7, Chang (1951); 8, Beverton and Holt (1959); 9, Holden and William (1974); 10, Creutzberg and Witte (1989); 11, Albert (1993); 12, Damm (1987); 13, Greenstreet and Rogers (2006); 14, Nash (1984); 15, Ntiba (1989); 16, Dipper et al. (1977); 17, Robb (1973); 18, Rijnsdorp et al. (1992); 19, Pauly (1978); 20, Jones (1983); 21, Knudsen (1964); 22, Jennings et al. (1998); 23, Jennings et al. (1999); 24, Joenoes (1961); 25, Hass (1937); 26, Saeger (1974); 27, Moreau (1964); 28, Beverton and Holt (1959); 29, Fonds (1973); 30, Jones (1974); 31, Bagge (2004); 32, Sahrhage (1970); 33, Knijn et al. (1993). Open in new tab © 2008 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited © 2008 The Author(s)

Fraser, Helen M.; Greenstreet, Simon P. R.; Fryer, Rob J.; Piet, Gerjan J.

doi: 10.1093/icesjms/fsn036pmid: N/A

Fraser, H. M., Greenstreet, S. P. R., Fryer, R. J., and Piet, G. J. 2008. Mapping spatial variation in demersal fish species diversity and composition in the North Sea: accounting for species- and size-related catchability in survey trawls. ICES Journal of Marine Science, 65: 531538.The paper maps spatial patterns of groundfish species diversity. It considers how the catchability of different fish species in two different types of bottom trawls, the IBTS GOV and the 8-m beam trawl, influences the estimates of species diversity. Maps of groundfish species diversity derived from these two survey trawls are compared to determine the extent to which the maps of spatial variation in groundfish species diversity are influenced by gear type. Catchability-at-length coefficients were applied to the IBTS data to raise the observed catches to estimates of actual numbers of fish present in the path of the trawl, which are then used to produce maps of actual species diversity across the North Sea. Finally, these raised maps of actual groundfish species diversity are shown to be more explainable based on physical environmental parameters such as depth. We suggest that species diversity maps that take account of catchability provide more reliable information on which to base management decisions than gear-biased views. The implications for management are discussed, with particular emphasis on using closed areas to conserve marine biodiversity.

Tsagarakis, K.; Machias, A.; Giannoulaki, M.; Somarakis, S.; Karakassis, I.

doi: 10.1093/icesjms/fsn023pmid: N/A

Tsagarakis, K., Machias, A., Giannoulaki, M., Somarakis, S., and Karakassis, I. 2008. Seasonal and temporal trends in metrics of fish community for otter-trawl discards in a Mediterranean ecosystem. ICES Journal of Marine Science, 65: 539550.Trends in discard to marketed ratios, size spectra, diversities, and trophic levels of the demersal fish community were examined using data from a seasonally closed commercial trawl fishery in the eastern Mediterranean Sea (Ionian Sea), over a period of about 10 years. Trends were also examined for the artificial fractions derived from the discarding process (the marketed, the discarded, and the non-marketed clusters of the catch), as well as for the big and small fractions (defined by the size at which 50% of all specimens were discarded). The ratio of the discarded/marketed catch fluctuated greatly. Two commercial (Merluccius merluccius, and Mullus barbatus) and two non-marketed species (Lepidotrigla cavillone, and Argentina sphyraena) were the characteristic species of the fractions. A declining trend with time was observed for the examined time-series for species richness (S), Margalefs d, and average taxonomic distinctness (), whereas the variation in taxonomic distinctness () increased. The composition and/or trophic level of discards in relation to the marketed catch seemed to be indicative of the exploitation state of the demersal community: differences between the discarded and marketed fractions were high at the beginning of the fishing season (autumn), but the values of the indices converged at the end of the fishing season (spring). These changes could be attributed to alternative discarding strategies for certain species in response to increased cumulative fishing mortality towards the end of the period.

Barrento, Sara; Marques, Antnio; Pedro, Snia; Vaz-Pires, Paulo; Nunes, Maria Leonor

doi: 10.1093/icesjms/fsn037pmid: N/A

Barrento, S., Marques, A., Pedro, S., Vaz-Pires, P., and Nunes, M. L. 2008. The trade of live crustaceans in Portugal: space for technological improvements. ICES Journal of Marine Science, 65: 551559.In Portuguese coastal areas, crabs and lobsters maintained alive until prepared for the table are commercially and economically very important. The trade in live crustaceans, mostly imported animals, is an interlinked and complex chain, from fishing, collection, holding facilities, and transportation, to the end-consumer, the various facilities playing a key role. Along the chain, animals can be affected by several stressors, inducing high mortality with consequent economic loss, and contributing to unsustainable exploitation of the resource. A survey was developed to characterize storage, transportation, and handling issues affecting various crustaceans at Portuguese holding facilities. In all, 22 facilities were identified and categorized by activity and water supply system. Despite the wide variation in their infrastructure, there were no major differences in mortality rate of crustaceans between importers, wholesalers, and exclusively retailers. At all facilities, Necora puber, Cancer pagurus, and Carcinus maenas had higher rates of mortality and shorter duration of captivity than Maja spp., Homarus sp., Panulirus regius, or Palinurus sp. Overall, the main problems identified were technical issues related to careless handling, high animal density, and the varying physiological needs of each species.

Arkhipkin, Alexander I.; Baumgartner, Nina; Brickle, Paul; Laptikhovsky, Vladimir V.; Pompert, Joost H. W.; Shcherbich, Zhanna N.

doi: 10.1093/icesjms/fsn015pmid: N/A

Arkhipkin, A. I., Baumgartner, N., Brickle, P., Laptikhovsky, V. V., Pompert, J. H. W., and Shcherbich, Z. N. 2008. Biology of the skates Bathyraja brachyurops and B. griseocauda in waters around the Falkland Islands, Southwest Atlantic. ICES Journal of Marine Science, 65: 560570.The distributions and length compositions of two large, abundant skates, broadnose skate Bathyraja brachyurops and grey-tail skate B. griseocauda, were studied using an extensive dataset (48 000 skates) collected from the commercial fishery and research cruises on the southeastern Patagonian Shelf around the Falkland Islands between 1992 and 2006. Bathyraja brachyurops mostly inhabit the shelf at depths shallower than 250 m, whereas B. griseocauda are found deeper (>250 m), off the shelf break and slope. Small individuals of both species are most segregated spatially. Growth increments on caudal thorns and vertebral centra revealed that B. brachyurops grow slower and have a shorter lifespan, attaining a maximum total length of 125 cm at 20 years, compared with B. griseocauda (150 cm at 28 years). Maturity is attained at 810 years for male and female B. brachyurops, and at 15 and 17 years for B. griseocauda, respectively. Spawning takes place on spatially segregated spawning grounds, B. brachyurops reproducing above the shelf break, and B. griseocauda just below the shelf break throughout the year, with a smaller proportion of females of both species laying eggs in winter.

Michalsen, Kathrine; Johannesen, Edda; Bogstad, Bjarte

doi: 10.1093/icesjms/fsn019pmid: N/A

Michalsen, K., Johannesen, E., and Bogstad, B. 2008. Feeding of mature cod (Gadus morhua) on the spawning grounds in Lofoten. ICES Journal of Marine Science, 65: 571580.Many authors state that cod (Gadus morhua) do not feed during the spawning period. However, this more or less established fact has rarely been investigated in the field. Here, the content of stomachs from Northeast Arctic cod (NEAC) and Norwegian coastal cod (NCC) sampled from the spawning ground in Lofoten were examined over a 10-year period (19962006). The occurrence of food in the stomachs of spawning cod, stomach fullness, diet composition, and variation in these variables between NEAC and NCC, year, and sex were analysed and compared. The analysis shows that cod do feed, even when they are in a spawning state. NCC had a lower proportion of empty stomachs and the stomachs were fuller than those from NEAC. Females had a lower proportion of empty stomachs than males and their stomachs were in general fuller. Herring (Clupea harengus) dominated the diet of cod. However, cod consumption of herring on the spawning grounds seems to be a minor source of mortality on herring. Although spawning cod do feed, the proportion of empty stomachs was higher and stomach fullness was lower than in stomachs of NEAC from the Barents Sea.

Lundgren, Bo; Nielsen, J. Rasmus

doi: 10.1093/icesjms/fsn031pmid: N/A

Lundgren, B., and Nielsen, J. R. 2008. A method for the possible species discrimination of juvenile gadoids by broad-bandwidth backscattering spectra vs. angle of incidence. ICES Journal of Marine Science, 65: 581593.Measurements were made of the broad-bandwidth (80220 kHz) acoustic backscattering from free-swimming juvenile gadoids at various orientations and positions in an acoustic beam, under controlled conditions. The experimental apparatus consisted of a stereo-video camera system, a broad-bandwidth echosounder and echo-processor system, a narrowband 120 kHz split-beam echosounder, a large tank, and a fishnet cage. The net cage was centred on the acoustic beams and was virtually transparent, both acoustically and optically. Accurate three-dimensional positions and angular orientations of individual fish were estimated from stereo-images captured synchronously when broad-bandwidth echoes were received from passing fish. Fish positions were also estimated from data collected with a synchronized split-beam echosounder. Software was developed for image analysis and modelling, including calibration, alignment of acoustic and optical-reference frames, and automatic position-fitting of fish models to manually marked fix-points on fish images. The software also performs Fourier spectrum analysis and pulse-shape analysis of broad-bandwidth echoes. Therefore, several measurement series on free-swimming juvenile gadoids were evaluated. The method and data may be used to improve the acoustic identification of fish species and sizes, and thereby improve investigations of spatial preypredator relationships, and the accuracy and efficiency of acoustic surveys.

Pea, Hector

doi: 10.1093/icesjms/fsn043pmid: N/A

Pea, H. 2008. In situ target-strength measurements of Chilean jack mackerel (Trachurus symmetricus murphyi) collected with a scientific echosounder installed on a fishing vessel. ICES Journal of Marine Science 65: 594604.The use of commercial fishing vessels for scientific purposes has increased worldwide in the past 10 years. Many such studies have involved the collection of acoustic data from both uncalibrated and calibrated echosounders. However, few studies have involved investigations of in situ target strength (TS). During August/September 2003, in situ TS data on Chilean jack mackerel (Trachurus symmetricus murphyi) were collected on board a commercial purse-seiner during normal fishing operations off Chile, using a 38 kHz, Simrad EK60 scientific echosounder. The single-target detections of Chilean jack mackerel were filtered by depth, off-axis beam angle, and beam-compensation criteria to improve the quality of the data used for the TS calculations. Two methods, using raw data and tracked data, were employed to calculate the mean acoustic-backscattering cross section (bs) and mean TS of Chilean jack mackerel. The results of the two approaches gave similar results, with a strong mode in the mean TS distribution between 35 and 37 dB for fish lengths ranging from 26 to 34 cm, indicating a b20 value of 66 dB. These results agreed well with most results published for this species and others of the same genus (i.e. T. trachurus, Trachurus t. capensis, and T. japonicus).