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.     

Argo floats revealing bimodality of large-scale mid-depth circulation in the North Atlantic

Analysis of Argo float trajectories at 1 000 m and temperature at 950 m in the North Atlantic between November 2003 and January 2005 demonstrates the existence of two different circulation modes with fast transition between them. Each mode has a pair of cyclonic - anticyclonic gyres. The difference is the location of the cyclonic gyre. The cyclonic gyre stretches from southeast to northwest in the first mode and from the southwest to the northeast in the second mode. The observed modes strongly affect the heat and salt transport in the North Atlantic. In particular, the second mode slows down the westward transport of the warm and saline water from the Mediterranean Sea.     

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.     

Ex situ cultivation of Aplysinaaerophoba close to in situ conditions: ecological, biochemical and histological aspects

Sponges provide the largest number of biologically active natural products known from the marine environment and continue to be a very well studied phylum of marine fauna. The Mediterranean sponge Aplysina aerophoba accumulates brominated isoxazoline alkaloids such as Aplysinamisin-1, Aerophobin-2, Isofistularin-3 and the biotransformation product Aeroplysinin-1, which possesses, for example, antibiotic and cytotoxic properties. Until now, it is still being discussed which organisms – the sponge itself or associated microorganisms – are responsible for metabolite production. For cultivating Aplysina individuals under ex situ conditions, we surveyed relevant ecological factors in situ and controlled them in our aquarium system. We maintained A. aerophoba for more than 9 months and analysed changes of metabolite content and composition, microbial association as well as morphology in situ and ex situ under different light exposure. Although sponges showed slight reduction during maintenance, ex situ cultivation similar to in situ conditions provides a promising method to keep sponges and obtain their bioactive metabolites.     

A climatology of mesoscale model simulated low-level wind jets over Cook Inlet and Shelikof Strait, Alaska

Weather in the North Gulf of Alaska is characterized by a high frequency of deep synoptic-scale low-pressure systems, especially during the cold season. The strong pressure gradients of these storms interact with the extremely rugged terrain of the coastal mountains to produce a variety of channeled flows. These surface wind regimes are not well documented in the scientific community, due to the paucity of observations. Modeling of these phenomena in regions of complex terrain is of great interest to those working with hydrodynamic, wave, and pollutant transport models in coastal and shelf areas. Such models, when coupled with ocean and coastal-ecology counterparts, give a broad view of the role surface winds play in shaping local coastal marine ecosystem in this region. This paper presents a climatology of simulated low-level wind jets over the domain of Cook Inlet and Shelikof Strait along Alaska's south-central coast. Daily simulations using the RAMS model were conducted in a 36-h forecast mode for the cold-season period 10/1/03 to 3/31/04. Systematic analysis of the resulting simulated low-level wind field makes it possible to characterize these jets and gap flows in spatial and temporal detail. The comparison between the RAMS winds and the Synthetic Aperture Radar (SAR)-derived winds when available verifies the existence of these wind jets and the capability of the model to simulate these cases. Clearly, the results of a study in this region depend on the fidelity of the model at these scales (O[5 km]). The SAR comparisons attempt to help establish this. From the 6 months of simulations over Cook Inlet and Shelikof Strait, the low-level wind jets are classified into 10 different regimes by location and orientation. These regimes are categorized into four more general groups: cross-channel westerly, easterly, and up and down Inlet flows. The nature of a particular regime is largely a function of pressure gradient orientation and local topography. Jets in the same group have a similar occurrence distribution with time. Some form of jet occurred in the study region almost daily each month of the period, with December 2003 having the highest frequency of wind jets.     

Exponential leap-forward gradient scheme for determining the isothermal layer depth from profile data

Two distinct layers usually exist in the upper ocean. The first has a near-zero vertical gradient in temperature (or density) from the surface and is called the isothermal layer (or mixed layer). Beneath that is a layer with a strong vertical gradient in temperature (or density), called the thermocline (or pycnocline). The isothermal layer depth (ILD) or mixed layer depth (MLD) for the same profile varies depending on the method used to determine it. Also, whether they are subjective or objective, existing methods of determining the ILD do not estimate the thermocline (pycnocline) gradient. Here, we propose a new exponential leap-forward gradient (ELG) method of determining the ILD that retains the strengths of subjective (simplicity) and objective (gradient change) methods and avoids their weaknesses (subjective methods are threshold-sensitive and objective methods are computationally intensive). This new method involves two steps: (1) the estimation of the thermocline gradient G _th for an individual temperature profile, and (2) the computation of the vertical gradient by averaging over gradients using exponential leap-forward steps. Such averaging can filter out noise in the profile data. Five existing methods of determining the ILD (difference, gradient, maximum curvature, maximum angle, and optimal linear fitting methods) as well as the proposed ELG method were verified using global expendable bathythermograph (XBT) temperature and conductivity–temperature–depth (CTD) datasets. Among all the methods considered, the ELG method yielded the highest skill score and the lowest Shannon information entropy (i.e., the lowest uncertainty).     

Molecular weight and chemical reactivity of dissolved trace metals (Cd,Cu, Ni) in surface waters from the Mississippi River to Gulf of Mexico

It is generally assumed that estuarine mixing is continuous for metals from terrestrial sources, gradually decreasing towards the open ocean endmember. Here we show that, chemical reactivity, determined by ion exchange method, and molecular weight distributions, obtained using cross-flow ultrafiltration, of dissolved Cd, Cu, and Ni in the surface waters of the Gulf of Mexico varied systematically across the estuarine mixing zone of the Mississippi River. Most size or chemical affinity fractions of dissolved metals (<0.4 μm) were linearly related to salinity (10.8–36.6), suggesting that the distribution of these elements was mainly controlled by continuous mixing processes. Dissolved concentrations across the salinity gradient ranged for Cd: 87–187 pM; Cu: 1.4–18.3 nM; and Ni: 2.6–18.8 nM, with highest values near the Mississippi river mouth, and lowest concentrations in a warm core ring in the Gulf of Mexico. Dissolved Cd was mostly present as a truly dissolved (<10 kDa, 97 ± 1%) and cationic fraction (Chelex-100 extractable, 94 ± 4%). A novel observation across the estuarine mixing zone was that colloidal metal concentrations were identical to either inert (for Cu, Ni) or AMPG-labile anionic (Cu, Cd) fractions. The difference in behavior between Cu and the other two metals might indicate differences in the biopolymeric nature of the metal–organic chelates. In particular, the anionic-organic Cd fractions accounted for just 3 ± 1%, on average. However, for Cu, it was 24 ± 4%, and for Ni, it was 9 ± 6%. The fractions of the total dissolved metal fractions that were “inert” averaged 31 ± 10% for Cu and 29 ± 12% for Ni. Small but noticeable amounts (6 ± 3%) of dissolved inert Cd fractions were also present. Apparent non-local transport processes, likely associated with cross-shelf sediment resuspension processes, could have been responsible for the relatively high concentrations of ‘inert’ and ‘anionic’ metal fractions in high salinity coastal waters, and accounting for the persistence of metals bound to humic substances in the Gulf of Mexico.     

Seasonal variation and tidal influences on estuarine use by bottlenose dolphins (Tursiops aduncus)

In order to show that dolphins use estuary habitats differently depending on the season and tidal state, possibly in response to prey distribution, temperature, risk of stranding and accessibility, Indo-Pacific bottlenose dolphins (Tursiops aduncus) were observed year-round during a 3-year study in the Clarence River estuary (CR) and Richmond River estuary (RR) in northern New South Wales, Australia. Peak dolphin sightings occurred during the spring season and one or 2 h prior to high tide. The spatial distribution of the dolphins in each estuary was analysed using the distance in kilometres that the dolphins travelled upstream with seasons and tidal phase as determinants. A General Linear Model showed that in the CR the dolphin spatial distribution in the estuary was not determined by season (F = 0.434, df = 3, P = 0.729) but was by tidal phase (F = 9.943, df = 3, P < 0.001) and the interaction between season and tidal phase (F = 3.398, df = 9, P < 0.002). However, in the RR the spatial distribution of the dolphin use of the estuary was not determined by either season (F = 1.647, df = 3, P = 0.194) or tidal phase (F = 0.302, df = 3, P = 0.824). In the CR, the spatial distribution of the dolphins was largest on high and flood tides. This pattern of spatial distribution may occur because the CR is a relatively shallow estuary and this increased spatial distribution may reflect a lower stranding risk and an increase in accessibility of shallow areas during periods of higher tide. These areas could also provide access to their preferred prey items of sea mullet (Mugil cephalus) and sand whiting (Sillago ciliata).     

Integrating Uncertainty Theories with GIS for Modeling Coastal Hazards of Climate Change

Prediction of coastal hazards due to climate change is fraught with uncertainty that stems from complexity of coastal systems, estimation of sea level rise, and limitation of available data. In-depth research on coastal modeling is hampered by lack of techniques for handling uncertainty, and the available commercial geographical information systems (GIS) packages have only limited capability of handling uncertain information. Therefore, integrating uncertainty theory with GIS is of practical and theoretical significance. This article presents a GIS-based model that integrates an existing predictive model using a differential approach, random simulation, and fuzzy set theory for predicting geomorphic hazards subject to uncertainty. Coastal hazard is modeled as the combined effects of sea-level induced recession and storm erosion, using grid modeling techniques. The method is described with a case study of Fingal Bay Beach, SE Australia, for which predicted responses to an IPCC standard sea-level rise of 0.86 m and superimposed storm erosion averaged 12 m and 90 m, respectively, with analysis of uncertainty yielding maximum of 52 m and 120 m, respectively. Paradoxically, output uncertainty reduces slightly with simulated increase in random error in the digital elevation model (DEM). This trend implies that the magnitude of modeled uncertainty is not necessarily increased with the uncertainties in the input parameters. Built as a generic tool, the model can be used not only to predict different scenarios of coastal hazard under uncertainties for coastal management, but is also applicable to other fields that involve predictive modeling under uncertainty.