Evaluating practical macrophyte control tools on small agricultural waterways in Canterbury,New Zealand

Excessive macrophytes can cause significant problems in agricultural waterways requiring active management. Conventional control techniques can have a range of adverse effects. We investigated several control tools in two experiments: firstly, we tested eight treatments at a small-scale (2?m?×?2?m). We found intensive hand weeding, weed mat and herbicide spraying to be effective treatments, reducing macrophyte cover to <5%. Hand weeding and weed mat immediately reduced cover, while dieback from herbicide took two months. Weed mat was a novel and effective control mechanism along stream banks. Secondly, we tested the impact of shading on macrophyte growth. Macrophyte growth was enhanced under partially shaded conditions, but with 80% effective shading over the entire channel, cover was reduced to 17%. Once treatments ceased, macrophytes grew back within 3–5 months. Long-term, control methods will require combinations of tools but will need to include optimal shading for the target species.     

The planet formation imager

The Planet Formation Imager (PFI, www.planetformationimager.org) is a next-generation infrared interferometer array with the primary goal of imaging the active phases of planet formation in nearby star forming regions. PFI will be sensitive to warm dust emission using mid-infrared capabilities made possible by precise fringe tracking in the near-infrared. An L/M band combiner will be especially sensitive to thermal emission from young exoplanets (and their disks) with a high spectral resolution mode to probe the kinematics of CO and H₂O gas. In this paper, we give an overview of the main science goals of PFI, define a baseline PFI architecture that can achieve those goals, point at remaining technical challenges, and suggest activities today that will help make the Planet Formation Imager facility a reality.     

Decision Support Modeling: Data Assimilation,Uncertainty Quantification,and Strategic Abstraction

We present a framework for design and deployment of decision support modeling based on metrics which have their roots in the scientific method. Application of these metrics to decision support modeling requires recognition of the importance of data assimilation and predictive uncertainty quantification in this type of modeling. The difficulties of implementing these procedures depend on the relationship between data that is available for assimilation and the nature of the prediction(s) that a decision support model is required to make. Three different data/prediction contexts are identified. Unfortunately, groundwater modeling is generally aligned with the most difficult of these. It is suggested that these difficulties can generally be ameliorated through appropriate model design. This design requires strategic abstraction of parameters and processes in a way that is optimal for the making of one particular prediction but is not necessarily optimal for the making of another. It is further suggested that the focus of decision support modeling should be on the ability of a model to provide receptacles for decision-pertinent information rather than on its purported ability to simulate environmental processes. While models are compromised in both of these roles, this view makes it clear that simulation should serve data assimilation and not the other way around. Data assimilation enables the uncertainties of decision-critical model predictions to be quantified and maybe reduced. Decision support modeling requires this.     

Sedimentary fabric characterized by X-ray tomography: A case-study from tsunami deposits on the Marquesas Islands,French Polynesia

X-ray tomography is used to analyse the grain size and sedimentary fabric of two tsunami deposits in the Marquesas Islands (French Polynesia, Pacific Ocean) which are particularly exposed to trans-Pacific tsunamis. One site is located on the southern coast of Nuku Hiva Island (Hooumi) and the other one is on the southern coast of Hiva Oa Island (Tahauku). Results are compared with other techniques such as two-dimensional image analysis on bulk samples (particle analyser) and anisotropy of magnetic susceptibility. The sedimentary fabric is characterized through three-dimensional stacks of horizontal slices (following a vertical step of 2·5 mm along the cores), while grain-size distribution is estimated from two-dimensional vertical slices (following a step of 2 mm). Four types of fabric are distinguished: (a) moderate to high angle (15 to 75°); (b) bimodal low-angle (<15°); (c) low to high angle with at least two different orientations; and (d) dispersed fabric. The fabric geometry in a tsunami deposit is not only controlled by the characteristics of the flow itself (current strength, flow regime, etc.) but also sediment concentration, deposition rate and grain-size distribution. There is a notable correlation between unimodal high-angle fabric – type (a) – and finely-skewed grain-size distribution. The two tsunami deposits studied represent two different scenarios of inundation. As demonstrated here, X-ray tomography is an essential method for characterizing past tsunamis from their deposits. The method can be applied to many other types of sediments and sedimentary rocks.     

Heavy metal toxicity of kidney and bone tissues in South Australian adult bottlenose dolphins (Tursiops aduncus)

Metallothioneins (MT) concentration, renal damage, and bone malformations were investigated in 38 adult Tursiops aduncus carcasses to determine any associations with cadmium, copper, zinc, mercury, lead and selenium. Significantly higher concentrations of cadmium, copper, and zinc in the liver were observed in dolphins showing evidence of more advanced renal damage. No significant differences in metal or selenium concentrations in the liver were observed between groups differing in level of bone malformations. Some dolphins displayed evidence of toxicity and knowledge of metal toxicity pathways were used to elucidate the cause of these abnormalities. Two dolphins had high metal burdens, high MT concentrations, renal damage, and evidence of bone malformations, indicating possible severe and prolonged metal toxicity. One dolphin showed evidence of renal damage, but the lack of any other symptoms suggests that this was unlikely to be caused by metal toxicity. We recommend examining a range of metal toxicity symptoms simultaneously to aid in distinguishing metal toxicity from unrelated aetiologies.     

Eco‐physiological repercussions of dietary arachidonic acid in cell membranes of active tissues of the Gray whale

The radiation of the mammalian land species that became the baleen whales happened about 27–34 Mya. Mammals require omega 6 fatty acids for reproduction. With this long exposure to the omega 3‐rich marine food chain, the Gray whale (Eschrichtius robustus) might be expected to have lost its requirement for omega 6 fatty acids. We report an unexpectedly high content of omega 6 arachidonic acid (ArA) in the Gray whale liver and muscle lipids. This whale migrates 10,000 km from the cold polar, omega 3 oil‐rich food chain to that of the breeding lagoons of the tropical waters. The food web of tropical waters is a source of omega 6 fatty acids, which are hardly present in the cold polar food web. We suggest the reason for this longest of migrations from cold to warm waters is to meet the requirement for omega 6 fatty acids for mammalian reproduction and brain growth. This extreme conservation of omega 6 fatty acids in Gray whale biology has critical implications for mammalian biology and especially for whale conservation.     

Marine genetic resources: A review of scientific and commercial interest

Marine genetic resources both within and beyond national jurisdiction have been the focus of international negotiations in a range of forums in recent years. One recurrent theme throughout these discussions has been the absence of detailed information upon which policy responses to the emerging issue of the conservation and sustainable use of marine genetic resources (especially in areas beyond national jurisdiction) could be based. In an effort to address some of these knowledge gaps, this article examines the level and nature of scientific and commercial interest in marine genetic resources, including in areas beyond national jurisdiction. It also examines the changing perspectives of the scientific community in relation to the potential of marine genetic resources.     

Effects of hydrostatic pressure on microbial alteration of sinking fecal pellets

We used a new experimental device called PASS (PArticle Sinking Simulator) during MedFlux to simulate changes in in situ hydrostatic pressure that particles experience sinking from mesopelagic to bathypelagic depths. Particles, largely fecal pellets, were collected at 200 m using a settling velocity NetTrap (SV NetTrap) in Ligurian Sea in April 2006 and incubated in high-pressure bottles (HPBs) of the PASS system under both atmospheric and continuously increasing pressure conditions, simulating the pressure change experienced at a sinking rate of 200 m d⁻¹. Chemical changes over time were evaluated by measuring particulate organic carbon (POC), carbohydrates, transparent exopolymer particles (TEP), amino acids, lipids, and chloropigments, as well as dissolved organic carbon (DOC) and dissolved carbohydrates. Microbial changes were evaluated microscopically, using diamidinophenylindole (DAPI) stain for total cell counts and catalyzed reporter deposition-fluorescence in situ hybridization (CARD-FISH) for phylogenetic distinctions. Concentrations (normalized to POC) of particulate chloropigments, carbohydrates and TEP decreased under both sets of incubation conditions, although less under the increasing pressure regime than under atmospheric conditions. By contrast, dissolved carbohydrates (normalized to DOC) were higher after incubation and significantly higher under atmospheric conditions, suggesting they were produced at the expense of the particulate fraction. POC-normalized particulate wax/steryl esters increased only under pressure, suggesting biochemical responses of prokaryotes to the increasing pressure regime. The prokaryotic community initially consisted of 43% Bacteria, 12% Crenarchaea and 11% Euryarchaea. After incubation, Bacteria dominated (90%) the prokaryote community in all cases, with γ-Proteobacteria comprising the greatest fraction, followed by the Cytophaga–Flavobacter cluster and α-Proteobacteria group. Using the PASS system, we obtained chemical and microbial evidence that degradation by prokaryotes associated with fecal pellets sinking through mesopelagic waters is limited by the increasing pressure they experience.     

Effects of salinity on sublethal toxicity of atrazine to mummichog (Fundulus heteroclitus) larvae

In coastal marshes, fish larvae may be exposed simultaneously to extreme salinities and to atrazine, a widely used herbicide. To assess the effects of salinity on the toxicity of atrazine, newly-hatched mummichog (Fundulus heteroclitus) were exposed to atrazine (0, 5, 50 and 500 microg/L) at three salinities (3, 15 and 35PSU). Whole body cortisol was measured after 24 h. Body length, condition factor, whole body proteins, lipids, residual masses and water contents were assessed after 96 h. Significants effects were found for both condition factor and water content. Condition factors were lower at salinity 3 and 35 compared to near isoosmotic salinity, 15PSU. In addition, atrazine decreased condition factor at 500 microg/L. Reduction in condition was likely due to retardation in axial growth since body length, percentages of proteins or lipids were not affected. In the absence of atrazine, salinity had no effect on the prevalence of dehydrated (81% water) or hyperhydrated (85% water) larvae. In larvae exposed to atrazine, the prevalence of hyperhydrated larvae increased at 3PSU and 5 microg/L atrazine and that of dehydrated larvae increased at 15 and 35PSU and 5 microg/L atrazine. Severity of dehydratation increased with atrazine concentration at salinity 35PSU. Thus, a short-term exposure to environmentally realistic concentrations of atrazine affects osmotic control in mummichog larvae with possible effects on buoyancy, survival and recruitment.     

Development of a regional neural network for coastal water level predictions

This paper presents the development of a Regional Neural Network for Water Level (RNN_WL) predictions, with an application to the coastal inlets along the South Shore of Long Island, New York. Long-term water level data at coastal inlets are important for studying coastal hydrodynamics sediment transport. However, it is quite common that long-term water level observations may be not available, due to the high cost of field data monitoring. Fortunately, the US National Oceanographic and Atmospheric Administration (NOAA) has a national network of water level monitoring stations distributed in regional scale that has been operating for several decades. Therefore, it is valuable and cost effective for a coastal engineering study to establish the relationship between water levels at a local station and a NOAA station in the region. Due to the changes of phase and amplitude of water levels over the regional coastal line, it is often difficult to obtain good linear regression relationship between water levels from two different stations. Using neural network offers an effective approach to correlate the non-linear input and output of water levels by recognizing the historic patterns between them. In this study, the RNN_WL model was developed to enable coastal engineers to predict long-term water levels in a coastal inlet, based on the input of data in a remote NOAA station in the region. The RNN_WL model was developed using a feed-forwards, back-propagation neural network structure with an optimized training algorithm. The RNN_WL model can be trained and verified using two independent data sets of hourly water levels.The RNN_WL model was tested in an application to Long Island South Shore. Located about 60–100 km away from the inlets there are two permanent long-term water level stations, which have been operated by NOAA since the1940s. The neural network model was trained using hourly data over a one-month period and validated for another one-month period. The model was then tested over year-long periods. Results indicate that, despite significant changes in the amplitudes and phases of the water levels over the regional study area, the RNN_WL model provides very good long-term predictions of both tidal and non-tidal water levels at the regional coastal inlets. In order to examine the effects of distance on the RNN_WL model performance, the model was also tested using water levels from other remote NOAA stations located at longer distances, which range from 234 km to 591 km away from the local station at the inlets. The satisfactory results indicate that the RNN_WL model is able to supplement long-term historical water level data at the coastal inlets based on the available data at remote NOAA stations in the coastal region.