13 resultados para parameter uncertainty

em Aquatic Commons


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Fish bioenergetics models estimate relationships between energy budgets and environmental and physiological variables. This study presents a generic rockfish (Sebastes) bioenergetics model and estimates energy consumption by northern California blue rockf ish (S. mystinus) under average (baseline) and El Niño conditions. Compared to males, female S. mystinus required more energy because they were larger and had greater reproductive costs. When El Niño conditions (warmer temperatures; lower growth, condition, and fecundity) were experienced every 3−7 years, energy consumption decreased on an individual and a per-recruit basis in relation to baseline conditions, but the decrease was minor (<4% at the individual scale, <7% at the per-recruit scale) compared to decreases in female egg production (12−19% at the individual scale, 15−23% at the per-recruit scale). When mortality in per-recruit models was increased by adding fishing, energy consumption in El Niño models grew more similar to that seen in the baseline model. However, egg production decreased significantly — an effect exacerbated by the frequency of El Niño events. Sensitivity analyses showed that energy consumption estimates were most sensitive to respiration parameters, energy density, and female fecundity, and that estimated consumption increased as parameter uncertainty increased. This model provides a means of understanding rockfish trophic ecology in the context of community structure and environmental change by synthesizing metabolic, demographic, and environmental information. Future research should focus on acquiring such information so that models like the bioenergetics model can be used to estimate the effect of climate change, community shifts, and different harvesting strategies on rockfish energy demands.

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Changes in sensory and instrumental quality parameter sand in thawing drip, cooking drip and total drip loss of frozen stored Baltic cod fillets (Gadus morhua) at different storage temperatures were investigated. Cod fillets stored at –20 °C and –30 °C exhibited the lowest drip losses and obtained the highest sensory scores. Drip losses were found to be highest in cod fillets stored at –10°C and in double frozen fillets stored at –20 °C. These two experiments also gave the lowest sensory scores. The texture parameters increased during storage parallel with storage time. The waterbinding capacity was lowest at –10 °C and almost constant at –30 °C. There is a good correlation between the sensory scores for “tough” and the instrumental texture measurement for hardness and chewiness.

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“Why does overfishing persist in the face of regulation?” The author argues that over fishing,a fundamental cause of the crisis facing our oceans, is the result of the failure of our fishing management agencies (ultimately our politicians and communities) to embrace a small suite of powerful tools (more correctly strategic approaches) which have been developed to account for uncertainty. Broad success in managing fisheries to achieve sustainability goals will only come if these tools are enthusiastically applied. This will not happen until organisational cultures within fishery management agencies undergo a major shift leading to an asset-based biodiversity conservation, rather than resource exploitation, to be placed at the centre of ocean governance.This thesis examines these issues in the context of case studies covering regional, national and provincial (State) fishery management agencies. With the exception of the case study of a regional fishery (the southern ocean krill fishery) all case studies are drawn from Australian experiences. The central recommendation of the thesis is that fishery management agencies, worldwide, should be replaced by biodiversity asset management agencies.

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This research work involves the determination and modelling of water parameter such as pH, temperature, turbidity, chloride, hardness. The result of the analysis was used as important operating variables to generate a model equation of pH, hardness, temperature, turbidity and chloride. The values obtained from the model equation were compared with those from experiment. On an average bases the values were close. These parameters can be used to monitor the extent of pollution of pond water and to monitor stress and diseases of fish. The experimental data of pH was in the range of 6.7 to 6.9 while the modelled result was also between 6.7 to 7.0. The turbidity experimental value was close to the modelled value also. The chloride value for the experimental data was in the range of 25.32 to 35.0. The total hardness value ranges between 4.5 to 65.1 mg/l while the modelled result ranges between 11.025 to 68.402 mg/l. The result was within the acceptable limit of world health organization standard on water quality parameter.

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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.

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Quantifying scientific uncertainty when setting total allowable catch limits for fish stocks is a major challenge, but it is a requirement in the United States since changes to national fisheries legislation. Multiple sources of error are readily identifiable, including estimation error, model specification error, forecast error, and errors associated with the definition and estimation of reference points. Our focus here, however, is to quantify the influence of estimation error and model specification error on assessment outcomes. These are fundamental sources of uncertainty in developing scientific advice concerning appropriate catch levels and although a study of these two factors may not be inclusive, it is feasible with available information. For data-rich stock assessments conducted on the U.S. west coast we report approximate coefficients of variation in terminal biomass estimates from assessments based on inversion of the assessment of the model’s Hessian matrix (i.e., the asymptotic standard error). To summarize variation “among” stock assessments, as a proxy for model specification error, we characterize variation among multiple historical assessments of the same stock. Results indicate that for 17 groundfish and coastal pelagic species, the mean coefficient of variation of terminal biomass is 18%. In contrast, the coefficient of variation ascribable to model specification error (i.e., pooled among-assessment variation) is 37%. We show that if a precautionary probability of overfishing equal to 0.40 is adopted by managers, and only model specification error is considered, a 9% reduction in the overfishing catch level is indicated.

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Stock assessments can be problematic because of uncertainties associated with the data or because of simplified assumptions made when modeling biological processes (Rosenberg and Restrepo, 1995). For example, the common assumption in stock assessments that stocks are homogeneous and discrete (i.e., there is no migration between the stocks) is not necessarily true (Kell et al., 2004a, 2004b).

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Estimates of the growth (K), natural mortality (M), consumption/biomass (Q/B) rate and trophic level (TL) for 35 species in the upper Paraná river floodplain and the Itaipu reservoir (interconnected ecosystems) are presented. A compilation of these biological statistics is made for comparison purposes and some general trends are briefly discussed.

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Shepherd's "weekly parametric" method for estimating the parameter L sub( infinity ) and K of the von Bertalanffy growth function from length-frequency data often fails to converge, and usually overestimates K. It is shown that this is due to overcounting of the frequencies associated with large, slow growing fish, and that both of these problems can be completely overcome by a simple change in the way the scoring function is formulated.

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This contribution illustrates how modern spreadsheets aid the calculation and visualization of yield models and how the effects of uncertainties may be incorporated using Monte Carlo simulation. It is argued that analogous approaches can be implemented for other assessment models of simple to medium complexity justifying wider use of spreadsheets in fisheries analysis and training.