Application of phytotoxicity data to a new Australian soil quality guideline framework for biosolids

To protect terrestrial ecosystems and humans from contaminants many countries and jurisdictions have developed soil quality guidelines (SQGs). This study proposes a new framework to derive SQGs and guidelines for amended soils and uses a case study based on phytotoxicity data of copper (Cu) and zinc (Zn) from field studies to illustrate how the framework could be applied. The proposed framework uses normalisation relationships to account for the effects of soil properties on toxicity data followed by a species sensitivity distribution (SSD) method to calculate a soil added contaminant limit (soil ACL) for a standard soil. The normalisation equations are then used to calculate soil ACLs for other soils. A soil amendment availability factor (SAAF) is then calculated as the toxicity and bioavailability of pure contaminants and contaminants in amendments can be different. The SAAF is used to modify soil ACLs to ACLs for amended soils. The framework was then used to calculate soil ACLs for copper (Cu) and zinc (Zn). For soils with pH of 4-8 and OC content of 1-6%, the ACLs range from 8 mg/kg to 970 mg/kg added Cu. The SAAF for Cu was pH dependant and varied from 1.44 at pH 4 to 2.15 at pH 8. For soils with pH of 4-8 and OC content of 1-6%, the ACLs for amended soils range from 11 mg/kg to 2080 mg/kg added Cu. For soils with pH of 4-8 and a CEC from 5-60, the ACLs for Zn ranged from 21 to 1470 mg/kg added Zn. A SAAF of one was used for Zn as it concentrations in plant tissue and soil to water partitioning showed no difference between biosolids and soluble Zn salt treatments, indicating that Zn from biosolids and Zn salts are equally bioavailable to plants.

Parameter estimation and sensitivity analysis of fat deposition models in beef steers using acslXtreme.

The Davis Growth Model (a dynamic steer growth model encompassing 4 fat deposition models) is currently being used by the phenotypic prediction program of the Cooperative Research Centre (CRC) for Beef Genetic Technologies to predict P8 fat (mm) in beef cattle to assist beef producers meet market specifications. The concepts of cellular hyperplasia and hypertrophy are integral components of the Davis Growth Model. The net synthesis of total body fat (kg) is calculated from the net energy available after accounting tor energy needs for maintenance and protein synthesis. Total body fat (kg) is then partitioned into 4 fat depots (intermuscular, intramuscular, subcutaneous, and visceral). This paper reports on the parameter estimation and sensitivity analysis of the DNA (deoxyribonucleic acid) logistic growth equations and the fat deposition first-order differential equations in the Davis Growth Model using acslXtreme (Hunstville, AL, USA, Xcellon). The DNA and fat deposition parameter coefficients were found to be important determinants of model function; the DNA parameter coefficients with days on feed >100 days and the fat deposition parameter coefficients for all days on feed. The generalized NL2SOL optimization algorithm had the fastest processing time and the minimum number of objective function evaluations when estimating the 4 fat deposition parameter coefficients with 2 observed values (initial and final fat). The subcutaneous fat parameter coefficient did indicate a metabolic difference for frame sizes. The results look promising and the prototype Davis Growth Model has the potential to assist the beef industry meet market specifications.

Susceptibility of source plants to sugarcane Fiji disease virus influences the acquisition and transmission of the virus by the planthopper vector Perkinsiella saccharicida

Fiji leaf gall (FLG) caused by Sugarcane Fiji disease virus (SCFDV) is transmitted by the planthopper Perkinsiella saccharicida. FLG is managed through the identification and exploitation of plant resistance. The glasshouse-based resistance screening produced inconsistent transmission results and the factors responsible for that are not known. A series of glasshouse trials conducted over a 2-year period was compared to identify the factors responsible for the erratic transmission results. SCFDV transmission was greater when the virus was acquired by the vector from a cultivar that was susceptible to the virus than when the virus was acquired from a resistant cultivar. Virus acquisition by the vector was also greater when the vector was exposed to the susceptible cultivars than when exposed to the resistant cultivar. Results suggest that the variation in transmission levels is due to variation in susceptibility of sugarcane cultivars to SCFDV used for virus acquisition by the vector.

A novel species of mastrevirus (family Geminiviridae) isolated from Digitaria didactyla grass from Australia.

Mastreviruses (family Geminiviridae) that infect monocotyledonous plants occur throughout the temperate and tropical regions of Asia, Africa, Europe and Australia. Despite the identification of a very diverse array of mastrevirus species whose members infect African monocots, few such species have been discovered in other parts of the world. For example, the sequence of only a single monocot-infecting mastrevirus, Chloris striate mosaic virus (CSMV), has been reported so far from Australia, even though earlier biological and serological studies suggested that other distinct mastreviruses were present. Here, we have obtained the complete nucleotide sequence of a virus from the grass Digitaria didactyla originating from Australia. Analysis of the sequence shows the virus to be a typical mastrevirus, with four open reading frames, two in each orientation, separated by two non-coding intergenic regions. Although it showed the highest levels of sequence identity to CSMV (68.7%), their sequences are sufficiently diverse for the virus to be considered a member of a new species in the genus Mastrevirus, based on the present species demarcation criteria. We propose that the name first used during the 1980s be used for this species, Digitaria didactyla striate mosaic virus (DDSMV).

Light thresholds derived from seagrass loss in the coastal zone of the northern Great Barrier Reef, Australia

There is a world-wide trend for deteriorating water quality and light levels in the coastal zone, and this has been linked to declines in seagrass abundance. Localized management of seagrass meadow health requires that water quality guidelines for meeting seagrass growth requirements are available. Tropical seagrass meadows are diverse and can be highly dynamic and we have used this dynamism to identify light thresholds in multi-specific meadows dominated by Halodule uninervis in the northern Great Barrier Reef, Australia. Seagrass cover was measured at similar to 3 month intervals from 2008 to 2011 at three sites: Magnetic Island (MI) Dunk Island (DI) and Green Island (GI). Photosynthetically active radiation was continuously measured within the seagrass canopy, and three light metrics were derived. Complete seagrass loss occurred at MI and DI and at these sites changes in seagrass cover were correlated with the three light metrics. Mean daily irradiance (I-d) above 5 and 8.4 mol m(-2) d(-1) was associated with gains in seagrass at MI and DI, however a significant correlation (R = 0.649, p < 0.05) only occurred at MI. The second metric, percent of days below 3 mol m(-2) d(-1), correlated the most strongly (MI, R = -0.714, p < 0.01 and DI, R = -0.859, p = <0.001) with change in seagrass cover with 16-18% of days below 3 mol m(-2) d(-1) being associated with more than 50% seagrass loss. The third metric, the number of hours of light saturated irradiance (H-sat) was calculated using literature-derived data on saturating irradiance (E-k). H-sat correlated well (R = 0.686, p <0.01; and DI, R = 0.704, p < 0.05) with change in seagrass abundance, and was very consistent between the two sites as 4 H-sat was associated with increases in seagrass abundance at both sites, and less than 4 H-sat with more than 50% loss. At the third site (GI), small seasonal losses of seagrass quickly recovered during the growth season and the light metrics did not correlate (p > 0.05) with change in percent cover, except for I-d which was always high, but correlated with change in seagrass cover. Although distinct light thresholds were observed, the departure from threshold values was also important. For example, light levels that are well below the thresholds resulted in more severe loss of seagrass than those just below the threshold. Environmental managers aiming to achieve optimal seagrass growth conditions can use these threshold light metrics as guidelines; however, other environmental conditions, including seasonally varying temperature and nutrient availability, will influence seagrass responses above and below these thresholds. (C) 2012 Published by Elsevier Ltd.

Bycatch and strandings programs as ecological indicators for data-limited cetaceans

An integrated approach of using strandings and bycatch data may provide an indicator of long-term trends for data-limited cetaceans. Strandings programs can give a faithful representation of the species composition of cetacean assemblages, while standardised bycatch rates can provide a measure of relative abundance. Comparing the two datasets may also facilitate managing impacts by understanding which species, sex or sizes are the most vulnerable to interactions with fisheries gear. Here we apply this approach to two long-term datasets in East Australia, bycatch in the Queensland Shark Control Program (QSCP, 1992–2012) and strandings in the Queensland Marine Wildlife Strandings and Mortality Program (StrandNet, 1996–2012). Short-beaked common dolphins, Delphinus delphis, were markedly more frequent in bycatch than in the strandings dataset, suggesting that they are more prone to being incidentally caught than other cetacean species in the region. The reverse was true for humpback whales, Megaptera novaeangliae, bottlenose dolphins, Tursiops spp.; and species predominantly found in offshore waters. QSCP bycatch was strongly skewed towards females for short-beaked common dolphins, and towards smaller sizes for Australian humpback dolphins, Sousa sahulensis. Overall, both datasets demonstrated similar seasonality and a similar long-term increase from 1996 until 2008. Analysis on a species-by-species basis was then used to explore potential explanations for long-term trends, which ranged from a recovering stock (humpback whales) to a shift in habitat use (short-beaked common dolphins).