The Empirical Modeling of an Ecosystem

The authors have endeavored to create a verified a-posteriori model of a planktonic ecosystem. Verification of an empirically derived set of first-order, quadratic differential equations proved elusive due to the sensitivity of the model system to changes in initial conditions. Efforts to verify a similarly derived set of linear differential equations were more encouraging, yielding reasonable behavior for half of the ten ecosystem compartments modeled. The well-behaved species models gave indications as to the rate-controlling processes in the ecosystem.

Income, inequality, and criteria air pollutants in the CAMA counties

Socioeconomic factors have long been incorporated into environmental research to examine the effects of human dimensions on coastal natural resources. Boyce (1994) proposed that inequality is a cause of environmental degradation and the Environmental Kuznets Curve is a proposed relationship that income or GDP per capita is related with initial increases in pollution followed by subsequent decreases (Torras and Boyce, 1998). To further examine this relationship within the CAMA counties, the emission of sulfur dioxide and nitrogen oxides, as measured by the EPA in terms of tons emitted, the Gini Coefficient, and income per capita were examined for the year of 1999. A quadratic regression was utilized and the results did not indicate that inequality, as measured by the Gini Coefficient, was significantly related to the level of criteria air pollutants within each county. Additionally, the results did not indicate the existence of the Environmental Kuznets Curve. Further analysis of spatial autocorrelation using ArcMap 9.2, found a high level of spatial autocorrelation among pollution emissions indicating that relation to other counties may be more important to the level of sulfur dioxide and nitrogen oxide emissions than income per capita and inequality. Lastly, the paper concludes that further Environmental Kuznets Curve and income inequality analyses in regards to air pollutant levels incorporate spatial patterns as well as other explanatory variables. (PDF contains 4 pages)

Dietary carbohydrate requirement of Heterobranchus longifilis

The dietary carbohydrate requirement of Heterobranchus longifilis was evaluated in two separate experiments.In the first experiment, varying levels of carbohydrate ranging from 28, 24 to58 72% were fed to the fish of mean weight 1.83~c0.02g. Results revealed that the polynomial regression curve for the mean weight gain and the carbohydrate levels did not present a point where Y-max is equal to X-max and so the requirement was not obtained. The second experiment was therefore, conducted with lower levels of carbohydrate ranging from 17.00 to 20.86% and fed to fish with mean weight 0.49~c0.02g. Based on growth and feed efficiency data the carbohydrate requirement was determined to be 19.5%

Estimation of the growth curve parameters in Macrobrachium rosenbergii

Growth is one of the most important characteristics of cultured species. The objective of this study was to determine the fitness of linear, log linear, polynomial, exponential and Logistic functions to the growth curves of Macrobrachium rosenbergii obtained by using weekly records of live weight, total length, head length, claw length, and last segment length from 20 to 192 days of age. The models were evaluated according to the coefficient of determination (R2), and error sum off square (ESS) and helps in formulating breeders in selective breeding programs. Twenty full-sib families consisting 400 PLs each were stocked in 20 different hapas and reared till 8 weeks after which a total of 1200 animals were transferred to earthen ponds and reared up to 192 days. The R2 values of the models ranged from 56 – 96 in case of overall body weight with logistic model being the highest. The R2 value for total length ranged from 62 to 90 with logistic model being the highest. In case of head length, the R2 value ranged between 55 and 95 with logistic model being the highest. The R2 value for claw length ranged from 44 to 94 with logistic model being the highest. For last segment length, R2 value ranged from 55 – 80 with polynomial model being the highest. However, the log linear model registered low ESS value followed by linear model for overall body weight while exponential model showed low ESS value followed by log linear model in case of head length. For total length the low ESS value was given by log linear model followed by logistic model and for claw length exponential model showed low ESS value followed by log linear model. In case of last segment length, linear model showed lowest ESS value followed by log linear model. Since, the model that shows highest R2 value with low ESS value is generally considered as the best fit model. Among the five models tested, logistic model, log linear model and linear models were found to be the best models for overall body weight, total length and head length respectively. For claw length and last segment length, log linear model was found to be the best model. These models can be used to predict growth rates in M. rosenbergii. However, further studies need to be conducted with more growth traits taken into consideration

Size-composition of Annual Landings in the White Shrimp, Litopenaeus setiferus, Fishery of the Northern Gulf of Mexico, 1960–2006: Its Trend and Relationships with Other Fishery-dependent Variable

The potential for growth overfishing in the white shrimp, Litopenaeus setiferus, fishery of the northern Gulf of Mexico appears to have been of limited concern to Federal or state shrimp management entities, following the cataclysmic drop in white shrimp abundance in the 1940’s. As expected from surplus production theory, a decrease in size of shrimp in the annual landings accompanies increasing fishing effort, and can eventually reduce the value of the landings. Growth overfishing can exacerbate such decline in value of the annual landings. We characterize trends in size-composition of annual landings and other annual fishery-dependent variables in this fishery to determine relationships between selected pairs of these variables and to determine whether growth overfishing occurred during 1960–2006. Signs of growth overfishing were equivocal. For example, as nominal fishing effort increased, the initially upward, decelerating trend in annual yield approached a local maximum in the 1980’s. However, an accelerating upward trend in yield followed as effort continued to increase. Yield then reached its highest point in the time series in 2006, as nominal fishing effort declined due to exogenous factors outside the control of shrimp fishery managers. The quadratic relationship between annual yield and nominal fishing effort exhibited a local maximum of 5.24(107) pounds (≈ MSY) at a nominal fishing effort level of 1.38(105) days fished. However, annual yield showed a continuous increase with decrease in size of shrimp in the landings. Annual inflation-adjusted ex-vessel value of the landings peaked in 1989, preceded by a peak in annual inflation-adjusted ex-vessel value per pound (i.e. price) in 1983. Changes in size composition of shrimp landings and their economic effects should be included among guidelines for future management of this white shrimp

Growth, mortality, and hatchdate distributions of larval and juvenile spotted seatrout (Cynoscion nebulosus) in Florida Bay, Everglades National Park

Life history aspects of larval and, mainly, juvenile spotted seatrout (Cynoscion nebulosus) were studied in Florida Bay, Everglades National Park, Florida. Collections were made in 1994−97, although the majority of juveniles were collected in 1995. The main objective was to obtain life history data to eventually develop a spatially explicit model and provide baseline data to understand how Everglades restoration plans (i.e. increased freshwater flows) could influence spotted seatrout vital rates. Growth of larvae and juveniles (<80 mm SL) was best described by the equation loge standard length = –1.31 + 1.2162 (loge age). Growth in length of juveniles (12–80 mm SL) was best described by the equation standard length = –7.50 + 0.8417 (age). Growth in wet weight of juveniles (15–69 mm SL) was best described by the equation loge wet-weight = –4.44 + 0.0748 (age). There were no significant differences in juvenile growth in length of spotted seatrout in 1995 between three geographical subdivisions of Florida Bay: central, western, and waters adjacent to the Gulf of Mexico. We found a significant difference in wet-weight for one of six cohorts categorized by month of hatchdate in 1995, and a significant difference in length for another cohort. Juveniles (i.e. survivors) used to calculate weekly hatchdate distributions during 1995 had estimated spawning times that were cyclical and protracted, and there was no correlation between spawning and moon phase. Temperature influenced otolith increment widths during certain growth periods in 1995. There was no evidence of a relationship between otolith growth rate and temperature for the first 21 increments. For increments 22–60, otolith growth rates decreased with increasing age and the extent of the decrease depended strongly in a quadratic fashion on the temperature to which the fish was exposed. For temperatures at the lower and higher range, increment growth rates were highest. We suggest that this quadratic relationship might be influenced by an environmental factor other than temperature. There was insufficient information to obtain reliable inferences on the relationship of increment growth rate to salinity.

An evaluation of back-calculation methodology using simulated otolith data

I simulated somatic growth and accompanying otolith growth using an individual-based bioenergetics model in order to examine the performance of several back-calculation methods. Four shapes of otolith radius-total length relations (OR-TL) were simulated. Ten different back-calculation equations, two different regression models of radius length, and two schemes of annulus selection were examined for a total of 20 different methods to estimate size at age from simulated data sets of length and annulus measurements. The accuracy of each of the twenty methods was evaluated by comparing the back-calculated length-at-age and the true length-at-age. The best back-calculation technique was directly related to how well the OR-TL model fitted. When the OR-TL was sigmoid shaped and all annuli were used, employing a least squares linear regression coupled with a log-transformed Lee back-calculation equation (y-intercept corrected) resulted in the least error; when only the last annulus was used, employing a direct proportionality back-calculation equation resulted in the least error. When the OR-TL was linear, employing a functional regression coupled with the Lee back-calculation equation resulted in the least error when all annuli were used, and also when only the last annulus was used. If the OR-TL was exponentially shaped, direct substitution into the fitted quadratic equation resulted in the least error when all annuli were used, and when only the last annulus was used. Finally, an asymptotically shaped OR-TL was best modeled by the individually corrected Weibull cumulative distribution function when all annuli were used, and when only the last annulus was used.