University of Michigan Wrestling Team, 1982

Green turtle somatic growth dynamics in a spatially disjunct Great Barrier Reef metapopulation

The growth dynamics of green sea turtles resident in four separate foraging grounds of the southern Great Barrier Reef genetic stock were assessed using a nonparametric regression modeling approach. Juveniles recruit to these grounds at the same size, but grow at foraging-ground-dependent rates that result in significant differences in expected size- or age-at-maturity. Mean age-at-maturity was estimated to vary from 25-50 years depending on the ground. This stock comprises mainly the same mtDNA haplotype, so geographic variability might be due to local environmental conditions rather than genetic factors, although the variability was not a function of latitudinal variation in environmental conditions or whether the food stock was seagrass or algae. Temporal variability in growth rates was evident in response to local environmental stochasticity, so geographic variability might be due to local food stock dynamics. Despite such variability, the expected size-specific growth rate function at all grounds displayed a similar nonmonotonic growth pattern with a juvenile growth spurt at 60-70 cm curved carapace length, (CCL) or 15-20 years of age. Sex-specific growth differences were also evident with females tending to grow faster than similar-sized males after the Juvenile growth spurt. It is clear that slow sex-specific growth displaying both spatial and temporal variability and a juvenile growth spurt are distinct growth behaviors of green turtles from this stock.

Monitoring green turtles (Chelonia mydas) at a coastal foraging area in Baja California, Mexico: multiple indices to describe population status

From June 1995 to August 2002 we assessed green turtle (Chelonia mydas) population structure and survival, and identified human impact, at Bahia de los Angeles, a large bay that was once the site of the greatest sea turtle harvest rates in the Gulf of California, Mexico. Turtles were captured live with entanglement nets and mortality was quantified through stranding surveys and flipper tag recoveries. A total of 14,820 netting hours (617.5 d) resulted in 255 captures of 200 green turtles. Straight-carapace length and mass ranged from 46.0-100.0 cm (mean = 74.3 +/- 0.7 cm) and 14.5-145.0 kg (mean = 61.5 +/- 1.7 kg), respectively. The size-frequency distribution remained stable during all years and among all capture locations. Anthropogenic-derived injuries ranging from missing flippers to boat propeller scars were present in 4% of captured turtles. Remains of 18 turtles were found at dumpsites, nine stranded turtles were encountered in the study area, and flipper tags from seven turtles were recovered. Survival was estimated at 0.58 for juveniles and 0.97 for adults using a joint live-recapture and dead-recovery model (Burnham model). Low survival among juveniles, declining annual catch per unit effort, and the presence of butchered carcasses indicated human activities continue to impact green turtles at this foraging area.

Thirty-year recovery trend in the once depleted Hawaiian green sea turtle stock

The green sea turtle is one of the long-lived species that comprise the charismatic marine megafauna. The green turtle has a long history of human exploitation with some stocks extinct. Here we report on a 30-year study of the nesting abundance of the green turtle stock endemic to the Hawaiian Archipelago. We show that there has been a substantial long-term increase in abundance of this once seriously depleted stock following cessation of harvesting since the 1970s. This population increase has occurred in a far shorter period of time than previously thought possible. There was also a distinct 3-4 year periodicity in annual nesting abundance that might be a function of regional environmental stochasticity that synchronises breeding behaviour throughout the Archipelago. This is one of the few reliable long-term population abundance time series for a large long-lived marine species, which are needed for gaining insights into the recovery process of long-lived marine species and long-term ecological processes. (C) 2003 Elsevier Ltd. All rights reserved.

Estimates of sex- and age-class-specific survival probabilities for a southern Great Barrier Reef green sea turtle population

Sex- and age-class-specific survival probabilities of a southern Great Barrier Reef green sea turtle population were estimated using a capture - mark - recapture (CMR) study and a Cormack - Jolly - Seber (CJS) modelling approach. The CMR history profiles for 954 individual turtles tagged over a 9-year period ( 1984 - 1992) were classified into three age classes ( adult, subadult, juvenile) based on somatic growth and reproductive traits. Reduced-parameter CJS models, accounting for constant survival and time-specific recapture, fitted best for all age classes. There were no significant sex-specific differences in either survival or recapture probabilities for any age class. Mean annual adult survival was estimated at 0.9482 (95% CI: 0.92 - 0.98) and was significantly higher than survival for either subadults or juveniles. Mean annual subadult survival was 0.8474 ( 95% CI: 0.79 - 0.91), which was not significantly different from mean annual juvenile survival estimated at 0.8804 ( 95% CI: 0.84 - 0.93). The time-specific adult recapture probabilities were a function of sampling effort but this was not the case for either juveniles or subadults. The sampling effort effect was accounted for explicitly in the estimation of adult survival and recapture probabilities. These are the first comprehensive sex- and age-class-specific survival and recapture probability estimates for a green sea turtle population derived from a long-term CMR program.

Modelling the effect of fibropapilloma disease on the somatic growth dynamics of Hawaiian green sea turtles

The effect of the tumour-forming disease, fibropapillomatosis, on the somatic growth dynamics of green turtles resident in the Pala'au foraging grounds (Moloka'i, Hawai'i) was evaluated using a Bayesian generalised additive mixed modelling approach. This regression model enabled us to account for fixed effects (fibropapilloma tumour severity), nonlinear covariate functional form (carapace size, sampling year) as well as random effects due to individual heterogeneity and correlation between repeated growth measurements on some turtles. Somatic growth rates were found to be nonlinear functions of carapace size and sampling year but were not a function of low-to-moderate tumour severity. On the other hand, growth rates were significantly lower for turtles with advanced fibropapillomatosis, which suggests a limited or threshold-specific disease effect. However, tumour severity was an increasing function of carapace size-larger turtles tended to have higher tumour severity scores, presumably due to longer exposure of larger (older) turtles to the factors that cause the disease. Hence turtles with advanced fibropapillomatosis tended to be the larger turtles, which confounds size and tumour severity in this study. But somatic growth rates for the Pala'au population have also declined since the mid-1980s (sampling year effect) while disease prevalence and severity increased from the mid-1980s before levelling off by the mid-1990s. It is unlikely that this decline was related to the increasing tumour severity because growth rates have also declined over the last 10-20 years for other green turtle populations resident in Hawaiian waters that have low or no disease prevalence. The declining somatic growth rate trends evident in the Hawaiian stock are more likely a density-dependent effect caused by a dramatic increase in abundance by this once-seriously-depleted stock since the mid-1980s. So despite increasing fibropapillomatosis risk over the last 20 years, only a limited effect on somatic growth dynamics was apparent and the Hawaiian green turtle stock continues to increase in abundance.

Increase of a Caribbean leatherback turtle Dermochelys coriacea nesting population linked to long-term nest protection

The leatherback turtle Dermochelys coriacea is considered to be at serious risk of global extinction, despite ongoing conservation efforts. Intensive long-term monitoring of a leatherback nesting population on Sandy Point (St. Croix, US Virgin Islands) offers a unique opportunity to quantify basic population parameters and evaluate effectiveness of nesting beach conservation practices. We report a significant increase in the number of females nesting annually from ca. 18-30 in the 1980s to 186 in 2001, with a corresponding increase in annual hatchling production from ca. 2000 to over 49,000. We then analyzed resighting data from 1991 to 2001 with an open robust-design capture-mark-recapture model to estimate annual nester survival and adult abundance for this population. The expected annual survival probability was estimated at ca. 0.893 (95% CL 0.87-0.92) and the population was estimated to be increasing ca. 13% pa since the early 1990s. Taken together with DNA fingerprinting that identify mother-daughter relations, our findings suggest that the increase in the size of the nesting population since 1991 was probably due to an aggressive program of beach protection and egg relocation initiated more than 20 years ago. Beach protection and egg relocation provide a simple and effective conservation strategy for this Northern Caribbean nesting population as long as adult survival at sea remains relatively high. (c) 2005 Elsevier Ltd. All rights reserved.

Evaluating trends in abundance of immature green turtles, Chelonia mydas, in the Greater Caribbean

Many long-lived marine species exhibit life history traits. that make them more vulnerable to overexploitation. Accurate population trend analysis is essential for development and assessment of management plans for these species. However, because many of these species disperse over large geographic areas, have life stages inaccessible to human surveyors, and/or undergo complex developmental migrations, data on trends in abundance are often available for only one stage of the population, usually breeding adults. The green turtle (Chelonia mydas) is one of these long-lived species for which population trends are based almost exclusively on either numbers of females that emerge to nest or numbers of nests deposited each year on geographically restricted beaches. In this study, we generated estimates of annual abundance for juvenile green turtles at two foraging grounds in the Bahamas based on long-term capture-mark-recapture (CMR) studies at Union Creek (24 years) and Conception Creek (13 years), using a two-stage approach. First, we estimated recapture probabilities from CMR data using the Cormack-Jolly-Seber models in the software program MARK; second, we estimated annual abundance of green turtles. at both study sites using the recapture probabilities in a Horvitz-Thompson type estimation procedure. Green turtle abundance did not change significantly in Conception Creek, but, in Union Creek, green turtle abundance had successive phases of significant increase, significant decrease, and stability. These changes in abundance resulted from changes in immigration, not survival or emigration. The trends in abundance on the foraging grounds did not conform to the significantly increasing trend for the major nesting population at Tortuguero, Costa Rica. This disparity highlights the challenges of assessing population-wide trends of green turtles and other long-lived species. The best approach for monitoring population trends may be a combination of (1) extensive surveys to provide data for large-scale trends in relative population abundance, and (2) intensive surveys, using CMR techniques, to estimate absolute abundance and evaluate the demographic processes' driving the trends.

Age and growth in olive ridley seaturtles (Lepidochelys olivacea) from the north-central Pacific: a skeletochronological analysis

The olive ridley is the most abundant seaturtle species in the world but little is known of the demography of this species. We used skeletochronological data on humerus diameter growth changes to estimate the age of North Pacific olive ridley seaturtles caught incidentally by pelagic longline fisheries operating near Hawaii and from dead turtles washed ashore on the main Hawaiian Islands. Two age estimation methods [ranking, correction factor (CF)] were used and yielded age estimates ranging from 5 to 38 and 7 to 24 years, respectively. Rank age-estimates are highly correlated (r = 0.93) with straight carapace length (SCL), CF age estimates are not (r = 0.62). We consider the CF age-estimates as biologically more plausible because of the disassociation of age and size. Using the CF age-estimates, we then estimate the median age at sexual maturity to be around 13 years old (mean carapace size c. 60 cm SCL) and found that somatic growth was negligible by 15 years of age. The expected age-specific growth rate function derived using numerical differentiation suggests at least one juvenile growth spurt at about 10–12 years of age when maximum age-specific growth rates, c. 5 cm SCL year−1, are apparent.

Preliminary crystallographic characterization of an RNA helicase from Kunjin virus

Kunjin virus is a member of the Flavivirus genus and is an Australian variant of West Nile virus. The C-terminal domain of the Kunjin virus NS3 protein displays helicase activity. The protein is thought to separate daughter and template RNA strands, assisting the initiation of replication by unwinding RNA secondary structure in the 3' nontranslated region. Expression, purification and preliminary crystallographic characterization of the NS3 helicase domain are reported. It is shown that Kunjin virus helicase may adopt a dimeric assembly in absence of nucleic acids, oligomerization being a means to provide the helicases with multiple nucleic acid-binding capability, facilitating translocation along the RNA strands. Kunjin virus NS3 helicase domain is an attractive model for studying the molecular mechanisms of flavivirus replication, while simultaneously providing a new basis for the rational development of anti-flaviviral compounds.

Disruption prediction at JET [Joint European Torus]

The sudden loss of the plasma magnetic confinement, known as disruption, is one of the major issue in a nuclear fusion machine as JET (Joint European Torus), Disruptions pose very serious problems to the safety of the machine. The energy stored in the plasma is released to the machine structure in few milliseconds resulting in forces that at JET reach several Mega Newtons. The problem is even more severe in the nuclear fusion power station where the forces are in the order of one hundred Mega Newtons. The events that occur during a disruption are still not well understood even if some mechanisms that can lead to a disruption have been identified and can be used to predict them. Unfortunately it is always a combination of these events that generates a disruption and therefore it is not possible to use simple algorithms to predict it. This thesis analyses the possibility of using neural network algorithms to predict plasma disruptions in real time. This involves the determination of plasma parameters every few milliseconds. A plasma boundary reconstruction algorithm, XLOC, has been developed in collaboration with Dr. D. Ollrien and Dr. J. Ellis capable of determining the plasma wall/distance every 2 milliseconds. The XLOC output has been used to develop a multilayer perceptron network to determine plasma parameters as ?i and q? with which a machine operational space has been experimentally defined. If the limits of this operational space are breached the disruption probability increases considerably. Another approach for prediction disruptions is to use neural network classification methods to define the JET operational space. Two methods have been studied. The first method uses a multilayer perceptron network with softmax activation function for the output layer. This method can be used for classifying the input patterns in various classes. In this case the plasma input patterns have been divided between disrupting and safe patterns, giving the possibility of assigning a disruption probability to every plasma input pattern. The second method determines the novelty of an input pattern by calculating the probability density distribution of successful plasma patterns that have been run at JET. The density distribution is represented as a mixture distribution, and its parameters arc determined using the Expectation-Maximisation method. If the dataset, used to determine the distribution parameters, covers sufficiently well the machine operational space. Then, the patterns flagged as novel can be regarded as patterns belonging to a disrupting plasma. Together with these methods, a network has been designed to predict the vertical forces, that a disruption can cause, in order to avoid that too dangerous plasma configurations are run. This network can be run before the pulse using the pre-programmed plasma configuration or on line becoming a tool that allows to stop dangerous plasma configuration. All these methods have been implemented in real time on a dual Pentium Pro based machine. The Disruption Prediction and Prevention System has shown that internal plasma parameters can be determined on-line with a good accuracy. Also the disruption detection algorithms showed promising results considering the fact that JET is an experimental machine where always new plasma configurations are tested trying to improve its performances.

Online Genetic Algorithms

This paper present a technique based on genetic algorithms for generating online adaptive services. Online adaptive systems provide flexible services to a mass of clients/users for maximising some system goals, they dynamically adapt the form and the content of the issued services while the population of clients evolve over time. The idea of online genetic algorithms (online GAs) is to use the online clients response behaviour as a fitness function in order to produce the next generation of services. The principle implemented in online GAs, “the application environment is the fitness”, allow modelling highly evolutionary domains where both services providers and clients change and evolve over time. The flexibility and the adaptive behaviour of this approach seems to be very relevant and promising for applications characterised by highly dynamical features such as in the web domain (online newspapers, e- markets, websites and advertising engines). Nevertheless the proposed technique has a more general aim for application environments characterised by a massive number of anonymous clients/users which require personalised services, such as in the case of many new IT applications.

A Two Layered Model for Evolving Web Resources

In this paper the key features of a two-layered model for describing the semantic of dynamical web resources are introduced. In the current Semantic Web proposal [Berners-Lee et al., 2001] web resources are classified into static ontologies which describes the semantic network of their inter-relationships [Kalianpur, 2001][Handschuh & Staab, 2002] and complex constraints described by logical quantified formula [Boley et al., 2001][McGuinnes & van Harmelen, 2004][McGuinnes et al., 2004], the basic idea is that software agents can use techniques of automatic reasoning in order to relate resources and to support sophisticated web application. On the other hand, web resources are also characterized by their dynamical aspects, which are not adequately addressed by current web models. Resources on the web are dynamical since, in the minimal case, they can appear or disappear from the web and their content is upgraded. In addition, resources can traverse different states, which characterized the resource life-cycle, each resource state corresponding to different possible uses of the resource. Finally most resources are timed, i.e. they information they provide make sense only if contextualised with respect to time, and their validity and accuracy is greatly bounded by time. Temporal projection and deduction based on dynamical and time constraints of the resources can be made and exploited by software agents [Hendler, 2001] in order to make previsions about the availability and the state of a resource, for deciding when consulting the resource itself or in order to deliberately induce a resource state change for reaching some agent goal, such as in the automated planning framework [Fikes & Nilsson, 1971][Bacchus & Kabanza,1998].