Karyotypic characterization of the great sturgeon, Huso huso, by multiple staining techniques and fluorescent in situ hybridization

A karyotype analysis by several staining techniques was carried out on the great sturgeon, Huso huso (Linnaeus, 1758). The karyotype (2n = 118 ± 2) was composed of 42 pairs of meta-/submetacentric chromosomes and 17 pairs of acrocentrics/microchromosomes. Constitutive heterochromatin was mainly located at the centromeric regions of the acrocentric chromosomes. The biarmed chromosomes showed weak C-bands. Fluorescent staining with GC-specific chromomycin A₃ showed clearly recognizable fluorescent regions, whereas a more uniform base composition was revealed by the AT-specific 4,6-diamidino-2 phenylindole. After Ag-staining, nucleolar organizer regions could be observed on the short arms of two medium-sized submetacentrics and on two acrocentrics. Digoxigenated 28S and 5S rDNA probes, prepared from Acipenser naccarii DNA and hybridized to metaphase chromosomes, showed signals on six and two chromosomes, respectively. The telomeric sequence (TTAGGG) _n detected by FISH was located at both ends of each chromosome. Results are discussed in relation to karyotype organization and evolution in sturgeons. Received: 1 April 1998 / Accepted: 21 July 1998     

Major and trace elements of selected pedons in the USA

Few studies of soil geochemistry over large geographic areas exist, especially studies encompassing data from major pedogenic horizons that evaluate both native concentrations of elements and anthropogenically contaminated soils. In this study, pedons (n = 486) were analyzed for trace (Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Zn) and major (Al, Ca, Fe, K, Mg, Na, P, Si, Ti, Zr) elements, as well as other soil properties. The objectives were to (i) determine the concentration range of selected elements in a variety of U.S. soils with and without known anthropogenic additions, (ii) illustrate the association of elemental source and content by assessing trace elemental content for several selected pedons, and (iii) evaluate relationships among and between elements and other soil properties. Trace element concentrations in the non-anthropogenic dataset (NAD) were in the order Mn > (Zn, Cr, Ni, Cu) > (Pb, Co) > (Cd, Hg), with greatest mean total concentrations for the Andisol order. Geometric means by horizon indicate that trace elements are concentrated in surface and/or B horizons over C horizons. Median values for trace elements are significantly higher in surface horizons of the anthropogenic dataset (AD) over the NAD. Total Al, Fe, cation exchange capacity (CEC), organic C, pH, and clay exhibit significant correlations (0.56, 0.74, 0.50, 0.31, 0.16, and 0.30, respectively) with total trace element concentrations of all horizons of the NAD. Manganese shows the best inter-element correlation (0.33) with these associated total concentrations. Total Fe has one of the strongest relationships, explaining 55 and 30% of the variation in total trace element concentrations for all horizons in the NAD and AD, respectively.     

METHODOLOGIES FOR CALIBRATION AND PREDICTIVE ANALYSIS OF A WATERSHED MODEL1

ABSTRACT: The use of a fitted parameter watershed model to address water quantity and quality management issues requires that it be calibrated under a wide range of hydrologic conditions. However, rarely does model calibration result in a unique parameter set. Parameter nonuniqueness can lead to predictive nonuniqueness. The extent of model predictive uncertainty should be investigated if management decisions are to be based on model projections. Using models built for four neighboring watersheds in the Neuse River Basin of North Carolina, the application of the automated parameter optimization software PEST in conjunction with the Hydrologic Simulation Program Fortran (HSPF) is demonstrated. Parameter nonuniqueness is illustrated, and a method is presented for calculating many different sets of parameters, all of which acceptably calibrate a watershed model. A regularization methodology is discussed in which models for similar watersheds can be calibrated simultaneously. Using this method, parameter differences between watershed models can be minimized while maintaining fit between model outputs and field observations. In recognition of the fact that parameter nonuniqueness and predictive uncertainty are inherent to the modeling process, PEST's nonlinear predictive analysis functionality is then used to explore the extent of model predictive uncertainty.     

Spatiotemporal variability of wet atmospheric nitrogen deposition to the Neuse River Estuary, North Carolina

Excessive nitrogen (N) loading to N-sensitive waters such as the Neuse River estuary (North Carolina) has been shown to promote changes in microbial and algal community composition and function (harmful algal blooms), hypoxia and anoxia, and fish kills. Previous studies have estimated that wet atmospheric deposition of nitrogen (WAD-N), as deposition of dissolved inorganic nitrogen (DIN: NO3-, NH3/NH4+) and dissolved organic nitrogen, may contribute at least 15% of the total externally supplied or "new" N flux to the coastal waters of North Carolina. In a 3-yr study from June 1996 to June 1999, we calculated the weekly wet deposition of inorganic and organic N at eleven sites on a northwest-southeast transect in the watershed. The annual mean total (wet DIN + wet organics) WAD-N flux for the Neuse River watershed was calculated to be 956 mg N/m2/yr (15026 Mg N/yr). Seasonally, the spring (March-May) and summer (June-August) months contain the highest total weekly N deposition; this pattern appears to be driven by N concentration in precipitation. There is also spatial variability in WAD-N deposition; in general, the upper portion of the watershed receives the lowest annual deposition and the middle portion of the watershed receives the highest deposition. Based on a range of watershed N retention and in-stream riverine processing values, we estimate that this flux contributes approximately 24% of the total "new" N flux to the estuary.     

Rechts- und naturwissenschaftliche Kooperation im Umweltrecht am Beispiel des Chemikalien- und Naturschutzrechts

Der Beitrag ist aus einem Vortrag hervorgegangen, den der Verfasser am 23. 9. 2009 bei der Jahrestagung der Gesellschaft Deutscher Chemiker, Fachgruppe Umweltchemie und ?kotoxikologie, in Trier gehalten hat. Wenngleich gewichtige Sachgründe für eine fachübergreifende rechts- und naturwissenschaftliche Kooperation sprechen, so stellt sie doch in der gegenw?rtigen universit?ren Forschungslandschaft eher eine Rarit?t dar. Zu den Ursachen hierfür geh?rt, dass eine derartige Kooperation mit erheblichen Schwierigkeiten verbunden ist, die aus der zunehmenden Spezialisierung der Wissenschaft und der damit einhergehenden Herausbildung divergierender F?cherkulturen resultieren. Hinzu kommt, dass die aktuelle Drittmittelf?rderung, die nicht zuletzt auf die Initiierung und Intensivierung von Interdisziplinarit?t zielt, auf Rechtswissenschaftler nur eine vergleichsweise geringe Anreizwirkung ausübt. Doch mehren sich die Anzeichen dafür, dass sich hier alsbald ein Wandel vollzieht. Das Chemikalienrecht bietet jedenfalls (ebenso wie beispielsweise das Naturschutzrecht) etliche Ansatzpunkte für sachlich ertragreiches kooperatives Zusammenwirken von Rechts- und Naturwissenschaftlern.     

Global sensitivity analysis of a process-based model for ammonia emissions from manure storage and treatment structures

The development of process-based models to estimate ammonia emissions from animal feeding operations (AFOSs) is sought to replace costly and time-consuming direct measurements. Critical to process-based model development is conducting sensitivity analysis to determine the input parameters and their interactions that contribute most to the variance of the model output. Global and relative sensitivity analyses were applied to a process-based model for predicting ammonia emissions from the surface of anaerobic lagoons for treating and storing manure. The objectives were to compare global sensitivity analysis (GSA) to relative (local) sensitivity analysis (RSA) on a process-based model for ammonia emissions. Based on the first-order coefficient, both GSA and RSA showed the model input parameters in order of importance in process model for ammonia emissions from lagoon surfaces were: (i) pH, (ii) lagoon liquid temperature, (iii) wind speed above the lagoon surface, and (iv) the concentration of ammoniacal nitrogen in the lagoon. The GSA revealed that interactions between model parameters accounted for over two-thirds of the model variance, a result that cannot be achieved using traditional RSA. Also, the GSA showed that parameter interactions involving liquid pH had more impact on the model output variance than the single parameters: (i) temperature, (ii) wind speed, or (iii) total ammoniacal nitrogen. This study demonstrates that GSA provides a more complete analysis of model input parameters and their interactions on the model output compared to RSA. A comprehensive tutorial regarding the application of GSA to a process model is presented.