The ecological impact of different mechanisms of chronic sub-lethal toxicity on feeding and respiratory physiology

Sub-lethal toxicity tests, such as the scope-for-growth test, reveal simple relationships between measures of contaminant concentration and effect on respiratory and feeding physiology. Simple models are presented to investigate the potential impact of different mechanisms of chronic sub-lethal toxicity on these physiological processes. Since environmental quality is variable, even in unimpacted environments, toxicants may have differentially greater impacts in poor compared to higher quality environments. The models illustrate the implications of different degrees and mechanisms of toxicity in response to variability in the quality of the feeding environment, and variability in standard metabolic rate. The models suggest that the relationships between measured degrees of toxic stress, and the maintenance ration required to maintain zero scope-for-growth, may be highly nonlinear. In addition it may be possible to define critical levels of sub-lethal toxic effect above which no environment is of sufficient quality to permit prolonged survival.

Reference-growth rate – a simple and handy parameter summarizing the influence of environmental conditions

Abstract Environmental changes may have an impact on life conditions of the fish, e.g. food supply for the fish. The prevailing environmental conditions apply evenly to all age groups of one stock. Small fish have high growth rates, whereas large fish grow with low rates. But, it can be shown on the basis of the von Bertalanffy-growth model that it is sufficient to know only the growth rate of one single age group to compute the growth rates of all other age groups. The growth rate of a reference fish GRF (e.g. a fish with a body mass of 1 kg) was introduced as a reference growth describing the current food condition of all age groups of the stock. As an example a time series of the reference-growth rate of the northern cod stock (NAFO, 3K) was computed for the time span 1979 to 1999. For the northern cod stock it can be observed that environmental conditions caused growth rates below the long-term mean for seven years in a row. After a prolonged hunger period the fish stock collapsed in 1992 also by the impact of fisheries - and this was probably not a coincidence. Now, with the reference-growth rate GRF a simple and handy parameter was found to summarize the influence of the environmental conditions on growth and other derived models and therefore makes it easier to compute the influence of environmental changes within stock assessment. Zusammenfassung Veränderungen der Umwelt können Auswirkungen auf die Lebensbedingungen der Fische haben, z. B. auf das Nahrungsangebot der Fische. Die vorherrschenden Umgebungsbedingungen wirken gleichmäßig auf alle Altersgruppen eines Bestandes, wobei typischer Weise kleineFische hohe Wachstumsraten haben, während die großen Fische mit niedrigen Raten wachsen. Auf der Grundlage des von Bertalanffy-Wachstumsmodells kann gezeigt werden, dass es ausreicht, nur die Wachstumsrate von einer einzigen Altersgruppe zu kennen, um die Wachstumsraten von allen anderen Altersgruppen berechnen zu können. Die Wachstumsrate eines Referenz-Fisches (z.B. eines Fisches mit einer Körpermasse von 1 kg) wurde als Referenz-Wachstum GRF eingeführt, die den aktuellen Zustand des Nahrungsangebots füralle Altersgruppen des Bestandes beschreibt. Als Beispiel wurde einer Zeitreihe der Referenz-Wachstumsraten des nördlichen Kabeljaubestandes (NAFO, 3K) für die Zeitsraum 1979 bis 1999 berechnet. Für diesen Kabeljaubestand war zu beobachten, dass Umgebungsbedingungen für sieben Jahre in Folge Wachstumsraten unter dem langjährigen Mittelwert verursachten. Nach einer längeren Hungerperiode kollabierte dieser Fischbestand im Jahr 1992 auch durch den Einfluß der Fischerei - und dies war sicher kein Zufall. Jetzt, mit der Referenz-Wachstumsrate GRF, ist ein einfacher und handlicher Parameter gefunden, der es gestattet den Einfluss der Umweltbedingungen auf die Wachstumsbedingungen und andere davon abgeleitete Modelle zusammenzufassen. Dies macht es einfach, den Einfluss von Umweltveränderungen innerhalb der Bestandsabschätzungen zu berechnen.

Fecundity estimates for Maryland Striped Bass

Contemporary striped bass population modeling efforts on coastal stocks point to a reduced population fecundity in Chesapeake Bay being partially responsible for declining reproduction (Anonymous 1985; Boreman and Goodyear 1984). Fecundity values used in these models were based on earlier work by jackson and tiller (1952), lewis and Bonner (1966), Hollis (1967) and Holland and Yelverton (1973). An important feature to the Boreman and Goodyear (1985) model (FSIM) is an accurate determination of the fecundity weight regression equation used to determine the rate of egg deposition over time. Egg deposition models in turn can be used to determine how reproductive potential is changing over time in response to various management actions, i.e. reducing fishing mortality rates. thus it is imperative to follow population stock structure in the Bay system and to develop a contemporary fecundity relationship for striped bass. This report deals with the gonadal material collected in 1986 and 1987 from a coordinated Maryland field program. Samples were obtained from drift gill net collections during the spawning season from four localities: Potomac Estuary, Upper Bay, Chesapeake and Delaware Canal, and the Choptank Estuary (Figure 1).

Interactions of age-dependent mortality and selectivity functions in age-based stock assessment models

The natural mortality rate (M) of fish varies with size and age, although it is often assumed to be constant in stock assessments. Misspecification of M may bias important assessment quantities. We simulated fishery data, using an age-based population model, and then conducted stock assessments on the simulated data. Results were compared to known values. Misspecification of M had a negligible effect on the estimation of relative stock depletion; however, misspecification of M had a large effect on the estimation of parameters describing the stock recruitment relationship, age-specific selectivity, and catchability. If high M occurs in juvenile and old fish, but is misspecified in the assessment model, virgin biomass and catchability are often poorly estimated. In addition, stock recruitment relationships are often very difficult to estimate, and steepness values are commonly estimated at the upper bound (1.0) and overfishing limits tend to be biased low. Natural mortality can be estimated in assessment models if M is constant across ages or if selectivity is asymptotic. However if M is higher in old fish and selectivity is dome-shaped, M and the selectivity cannot both be adequately estimated because of strong interactions between M and selectivity.

Natural mortality rate, annual fecundity, and maturity at length for Greenland halibut (Reinhardtius hippoglossoides) from the northeastern Pacific Ocean

Mortality, fecundity, and size at maturity are important life history traits, and their interactions determine the evolution of life history strategies (Roff, 1992; Stearns, 1992; Charnov, 2002). These same traits are also important for population dynamics models (Hunter et al., 1992; Clark, 1999). It is increasingly important to accurately determine Greenland halibut (Reinhardtius hippoglossoides) life history traits and to correctly assess the status of its stocks because low recruitment or low biomass estimates have led to catch restrictions in the Bering Sea and Aleutian Islands (Ianelli et al.1), the Northeastern Arctic (Ådlandsvik et al., 2004), and the Northwest Atlantic (Bowering and Nedreaas, 2000).

Estimates of growth and comparisons of growth rates determined from length- and age-based models for populations of purple wrasse (Notolabrus fucicola)

Growth of a temperate reefa-ssociated fish, the purple wrasse (Notolabrus fucicola), was examined from two sites on the east coast of Tasmania by using age- and length-based models. Models based on the von Bertalanffy growth function, in the standard and a reparameterized form, were constructed by using otolith-derived age estimates. Growth trajectories from tag-recaptures were used to construct length-based growth models derived from the GROTAG model, in turn a reparameterization of the Fabens model. Likelihood ratio tests (LRTs) determined the optimal parameterization of the GROTAG model, including estimators of individual growth variability, seasonal growth, measurement error, and outliers for each data set. Growth models and parameter estimates were compared by bootstrap confidence intervals, LRTs, and randomization tests and plots of bootstrap parameter estimates. The relative merit of these methods for comparing models and parameters was evaluated; LRTs combined with bootstrapping and randomization tests provided the most insight into the relationships between parameter estimates. Significant differences in growth of purple wrasse were found between sites in both length- and age-based models. A significant difference in the peak growth season was found between sites, and a large difference in growth rate between sexes was found at one site with the use of length-based models.

In search of climate effects on Atlantic Croaker (Micropogonias undulatus) stock off the U.S. Atlantic coast with Bayesian state-space biomass dynamic models

Atlantic Croaker (Micropogonias undulatus) production dynamics along the U.S. Atlantic coast are regulated by fishing and winter water temperature. Stakeholders for this resource have recommended investigating the effects of climate covariates in assessment models. This study used state-space biomass dynamic models without (model 1) and with (model 2) the minimum winter estuarine temperature (MWET) to examine MWET effects on Atlantic Croaker population dynamics during 1972–2008. In model 2, MWET was introduced into the intrinsic rate of population increase (r). For both models, a prior probability distribution (prior) was constructed for r or a scaling parameter (r0); imputs were the fishery removals, and fall biomass indices developed by using data from the Multispecies Bottom Trawl Survey of the Northeast Fisheries Science Center, National Marine Fisheries Service, and the Coastal Trawl Survey of the Southeast Area Monitoring and Assessment Program. Model sensitivity runs incorporated a uniform (0.01,1.5) prior for r or r0 and bycatch data from the shrimp-trawl fishery. All model variants produced similar results and therefore supported the conclusion of low risk of overfishing for the Atlantic Croaker stock in the 2000s. However, the data statistically supported only model 1 and its configuration that included the shrimp-trawl fishery bycatch. The process errors of these models showed slightly positive and significant correlations with MWET, indicating that warmer winters would enhance Atlantic Croaker biomass production. Inconclusive, somewhat conflicting results indicate that biomass dynamic models should not integrate MWET, pending, perhaps, accumulation of longer time series of the variables controlling the production dynamics of Atlantic Croaker, preferably including winter-induced estimates of Atlantic Croaker kills.

Deriving acceptable biological catch from the overfishing limit: implications for assessment models

The recently revised Magnuson–Stevens Fishery Conservation and Management Act requires that U.S. fishery management councils avoid overfishing by setting annual catch limits (ACLs) not exceeding recommendations of the councils’ scientific advisers. To meet that requirement, the scientific advisers will need to know the overfishing limit (OFL) estimated in each stock assessment, with OFL being the catch available from applying the limit fishing mortality rate to current or projected stock biomass. The advisers then will derive ‘‘acceptable biological catch’’ (ABC) from OFL by reducing OFL to allow for scientific uncertainty, and ABC becomes their recommendation to the council. We suggest methodology based on simple probability theory by which scientific advisers can compute ABC from OFL and the statistical distribution of OFL as estimated by a stock assessment. Our method includes approximations to the distribution of OFL if it is not known from the assessment; however, we find it preferable to have the assessment model estimate the distribution of OFL directly. Probability-based methods such as this one provide well-defined approaches to setting ABC and may be helpful to scientific advisers as they translate the new legal requirement into concrete advice.

Estimating long-term growth-rate changes of southern bluefin tuna (Thunnus maccoyii) from two periods of tag-return data

Southern bluefin tuna (SBT) (Thunnus maccoyii) growth rates are estimated from tag-return data associated with two time periods, the 1960s and 1980s. The traditional von Bertalanffy growth model (VBG) and a two-phase VBG model were fitted to the data by maximum likelihood. The traditional VBG model did not provide an adequate representation of growth in SBT, and the two-phase VBG yielded a significantly better fit. The results indicated that significant change occurs in the pattern of growth in relation to a VBG curve during the juvenile stages of the SBT life cycle, which may be related to the transition from a tightly schooling fish that spends substantial time in near and surface shore waters to one that is found primarily in more offshore and deeper waters. The results suggest that more complex growth models should be considered for other tunas and for other species that show a marked change in habitat use with age. The likelihood surface for the two-phase VBG model was found to be bimodal and some implications of this are investigated. Significant and substantial differences were found in the growth for fish spawned in the 1960s and in the 1980s, such that after age four there is a difference of about one year in the expected age of a fish of similar length which persists over the size range for which meaningful recapture data are available. This difference may be a density-dependent response as a consequence of the marked reduction in the SBT population. Given the key role that estimates of growth have in most stock assessments, the results indicate that there is a need both for the regular monitoring of growth rates and for provisions for changes in growth over time (possibly related to changes in abundance) in the stock assessment models used for SBT and other species.

Cumulative inbreeding rate in hatchery-reared indian major carps of Karnataka and Maharashtra states

The state fisheries department hatcheries are the major suppliers of seed to the farmers in Karnataka and Maharashtra. The brood stocks of these hatcheries are genetically closed units. In the present study, effective population size and cumulative inbreeding rates were estimated. The cumulative inbreeding rates ranged from 2.69 to 13.75, 8.63 to 15.21 and 3.02 to 5.88 per cent for catla, mrigal and rohu, respectively, in Karnataka state hatcheries. In Maharashtra, the cumulative inbreeding rates for catla ranged from 7.81 to 39.34 per cent and it was 5.84 to 14.09 and 2.46 to 10.20 per cent for mrigal and rohu, respectively. To estimate the inbreeding rates in future generations, predictive models were developed using linear regression, and polynomial and power equations separately for each hatchery. Their multiple correlation and standard errors suggested that simple linear regression can predict the future inbreeding rate efficiently.