Spatial patterns of sub-tidal seagrasses and their tissue nutrients in the Torres Strait,northern Australia: Implications for management

The distribution and nutritional profiles of sub-tidal seagrasses from the Torres Strait were surveyed and mapped across an area of 31,000 km². Benthic sediment composition, water depth, seagrass species type and nutrients were sampled at 168 points selected in a stratified representative pattern. Eleven species of seagrass were present at 56 (33.3%) of the sample points. Halophila spinulosa, Halophila ovalis, Cymodocea serrulata and Syringodium isoetifolium were the most common species and these were nutrient profiled. Sub-tidal seagrass distribution (and associated seagrass nutrient concentrations) was generally confined to northern-central and south-western regions of the survey area (<longitude 142.60), where mean water depth was relatively shallow (approximately 13 m below MSL) and where sediments were comprised primarily muddy sand to gravelly sand. Seagrass nitrogen and starch content, the most important nutrients for marine herbivores, were significantly correlated with species and with the plant component (above or below ground). For all seagrass species, the above-ground component (shoots and leaves) possessed greater nitrogen concentrations than the below-ground component (roots and rhizomes), which possessed greater starch concentrations. S. isoetifolium had the highest total nitrogen concentrations (1.40±0.05% DW). However, it also had higher fibre concentrations (38.2±0.68% DW) relative to the other four species. H. ovalis possessed the highest starch concentrations (2.76±0.12% DW) and highest digestibility (83.24±0.66% DW) as well as the lowest fibre (27.2±0.66% DW). The high relative abundance (found at 55% of the sites that had seagrass) and nutrient quality characteristics of H. ovalis make it an important source of energy to marine herbivores that forage sub-tidally in the Torres Strait. There were two regions in Torres Strait (north-central and south-western) where sub-tidal seagrass meadows were prevalent and of relatively higher nutritional value. This spatial and nutritional information can be used by local agencies to manage and to protect the ecological, economic and cultural values of the sub-tidal seagrass ecosystems and associated fisheries of the Torres Strait.     

Uncertainty as the overlap of alternate conditional distributions

An important task in modern geostatistics is the assessment and quantification of resource and reserve uncertainty. This uncertainty is valuable support information for many management decisions. Uncertainty at specific locations and uncertainty in the global resource is of interest. There are many different methods to build models of uncertainty, including Kriging, Cokriging, and Inverse Distance. Each method leads to different results. A method is proposed to combine local uncertainties predicted by different models to obtain a combined measure of uncertainty that combines good features of each alternative. The new estimator is the overlap of alternate conditional distributions.     

Response of Nereis diversicolor population (Polychaeta,Nereididae) to the pollution impact – Authie and Seine estuaries (France)

A survey within the French National Programme of Ecotoxicology was carried out in 2002, 2003 and 2004 to study the response of Nereis diversicolor populations (Polychaeta, Nereididae) to the impact of pollution in the Authie estuary (non-contaminated site) and in the Seine estuary (contaminated site). In the period studied, the density varied from 672 ind. m⁻² to 3584 ind. m⁻² in the Authie estuary and from 80 ind. m⁻² to 920 ind. m⁻² in the Seine estuary. Biomass varied from 3.94 g m⁻² (dry weight) in February 2004 to 38.0 g m⁻² in August 2003 in the Authie estuary and from 3.4 g m⁻² in February 2002 to 0.6 g m⁻² in February 2004 in the Seine estuary. Density and biomass of the populations of N. diversicolor were consistently lower in the Seine estuary than in the Authie estuary. Size frequency histograms permit the analysis of the cohorts as well as the elaboration of the growth curves. For the individuals from the Authie estuary, the relation between dry weight (DW) and length L3 (prostomium, peristomium and chaetiger 1) was DW = 4.2205 L3^2.9832. For those from the Seine estuary, the relation between dry weight and L3 was DW = 0.4697e^1.7209L3. The individuals of N. diversicolor should belong to eight cohorts in Authie estuary (two cohorts each year) instead of six cohorts for those from the Seine estuary. These differences can be attributed to the effect of pollution on the population of N. diversicolor.     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models: An Empirical Validation of ISWRidge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models： An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p<0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to check the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1) and potential energy (X2) significantly impact (p<0.0001) the amplitude-based reflected rate; the P-values for the deviance and Pearson are all >0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height (X1) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model.Investigation of 6 predictive powers (R2, Max-rescaled R2, Somers'D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

Hydraulic roughness over simple subaqueous dunes

Detailed studies of flow over subaqueous dunes in laboratory flumes were used to suggest a virtual near-bed layer of twice the dune height in which the mean velocity is accelerated towards the crest by contraction. The mean flow velocity in this layer above the crest, transformed into friction velocity by means of the surface skin roughness, is shown to give values consistent with measured values. The resulting dimensionless shear stress due to skin friction is depth-independent, in contrast to that derived by means of often cited traditional methods. As a result of the relationship between dune height and the thickness of the near-bed layer, an expression for the expansion loss behind dunes was formulated and used to relate form resistance directly to dune height.     

Graded-Density Reservoirs for Accessing High Stress Low Temperature Material States

In recently developed laser-driven shockless compression experiments an ablatively driven shock in a primary target is transformed into a ramp compression wave in a secondary target via unloading followed by stagnation across an intermediate vacuum gap. Current limitations on the achievable peak longitudinal stresses are limited by the ability of shaping the temporal profile of the ramp compression pulse. We report on new techniques using graded density reservoirs for shaping the loading profile and extending these techniques to high peak pressures.     

Transformation of Residuals to Avoid Artifacts in Geostatistical Modelling with a Trend

Trend modelling is an important part of natural resource characterization. A common approach to account for a variable with a trend is to decompose it into a relatively smoothly varying trend and a more variable residual component. Then, the residuals are stochastically modelled independent of the trend. This decomposition can result in values outside the plausible range of variability, such as grades below zero or ratios that exceed 1.0. We transform the residuals conditional to the trend component to explicitly remove these complex features prior to geostatistical modelling. Back transformation of the modelled residual values allows the complex relations to be reproduced. A petroleum-related application shows the robustness of the proposed transformation. Furthermore, a mining application shows that when this conditional transformation is applied to the original variable, instead of the residual, simulated values are assured to be nonnegative.     

Combining pixel and object based image analysis of ultra-high resolution multibeam bathymetry and backscatter for habitat mapping in shallow marine waters

Habitat mapping data are increasingly being recognised for their importance in underpinning marine spatial planning. The ability to collect ultra-high resolution (cm) multibeam echosounder (MBES) data in shallow waters has facilitated understanding of the fine-scale distribution of benthic habitats in these areas that are often prone to human disturbance. Developing quantitative and objective approaches to integrate MBES data with ground observations for predictive modelling is essential for ensuring repeatability and providing confidence measures for habitat mapping products. Whilst supervised classification approaches are becoming more common, users are often faced with a decision whether to implement a pixel based (PB) or an object based (OB) image analysis approach, with often limited understanding of the potential influence of that decision on final map products and relative importance of data inputs to patterns observed. In this study, we apply an ensemble learning approach capable of integrating PB and OB Image Analysis from ultra-high resolution MBES bathymetry and backscatter data for mapping benthic habitats in Refuge Cove, a temperate coastal embayment in south-east Australia. We demonstrate the relative importance of PB and OB seafloor derivatives for the five broad benthic habitats that dominate the site. We found that OB and PB approaches performed well with differences in classification accuracy but not discernible statistically. However, a model incorporating elements of both approaches proved to be significantly more accurate than OB or PB methods alone and demonstrate the benefits of using MBES bathymetry and backscatter combined for class discrimination.