CSIRO Environmental Modelling Suite (EMS): scientific description of the optical and biogeochemical models (vB3p0)

Abstract. Since the mid-1990s, Australia's Commonwealth Science Industry and Research Organisation (CSIRO) has been developing a biogeochemical (BGC) model for coupling with a hydrodynamic and sediment model for application in estuaries, coastal waters and shelf seas. The suite of coupled models is referred to as the CSIRO Environmental Modelling Suite (EMS) and has been applied at tens of locations around the Australian continent. At a mature point in the BGC model's development, this paper presents a full mathematical description, as well as links to the freely available code and user guide. The mathematical description is structured into processes so that the details of new parameterisations can be easily identified, along with their derivation. In EMS, the underwater light field is simulated by a spectrally resolved optical model that calculates vertical light attenuation from the scattering and absorption of 20+ optically active constituents. The BGC model itself cycles carbon, nitrogen, phosphorous and oxygen through multiple phytoplankton, zooplankton, detritus and dissolved organic and inorganic forms in multiple water column and sediment layers. The water column is dynamically coupled to the sediment to resolve deposition, resuspension and benthic–pelagic biogeochemical fluxes. With a focus on shallow waters, the model also includes detailed representations of benthic plants such as seagrass, macroalgae and coral polyps. A second focus has been on, where possible, the use of geometric derivations of physical limits to constrain ecological rates. This geometric approach generally requires population-based rates to be derived from initially considering the size and shape of individuals. For example, zooplankton grazing considers encounter rates of one predator on a prey field based on summing relative motion of the predator with the prey individuals and the search area; chlorophyll synthesis includes a geometrically derived self-shading term; and the bottom coverage of benthic plants is calculated from their biomass using an exponential form derived from geometric arguments. This geometric approach has led to a more algebraically complicated set of equations when compared to empirical biogeochemical model formulations based on populations. But while being algebraically complicated, the model has fewer unconstrained parameters and is therefore simpler to move between applications than it would otherwise be. The version of EMS described here is implemented in the eReefs project that delivers a near-real-time coupled hydrodynamic, sediment and biogeochemical simulation of the Great Barrier Reef, northeast Australia, and its formulation provides an example of the application of geometric reasoning in the formulation of aquatic ecological processes. </jats:p

Hydrodynamic connectivity, water temperature, and salinity are major drivers of piscirickettsiosis prevalence and transmission among salmonid farms in Chile

Piscirickettsiosis is one of the most important diseases affecting farmed salmonid in Chile. Several studies have demonstrated the survival of Piscirickettsia salmonis in seawater and the horizontal transmission from infected to non-infected fish; however, the extent of waterborne transmission between farms has not been quantified. In this study, we used a stochastic hydrodynamic connectivity-based disease spread model to determine the role of hydrodynamic connectivity and the effect of seawater temperature and salinity on the dynamics of piscirickettsiosis in the Los Lagos region of Chile. Results demonstrate that environmental dynamics play a major role in disease prevalence. The strongest determinants of piscirickettsiosis prevalence were the number of infected farms in upstream waters and the extent of disease outbreaks in upstream waters (total mortality), followed by seawater salinity and temperature. In farms downstream from infected farms, observed disease prevalence 25 wk into the farming cycle was close to 100 %, while in farms with little or no exposure to upstream, infected farms, prevalence reached only -10 % by the end of the farming cycle (Week 56). No previous studies have quantified the scales of connectivity associated with piscirickettsiosis or provided risk metrics of waterborne transmission of the disease among farms; these are a novel aspect of this research. The above knowledge regarding the use of the epidemiological model will allow industry and regulators to better target disease control strategies for more effective control of piscirickettsiosis in the study area

PCR primers and conditions.

<p>PCR primers and conditions.</p

High-Throughput Analysis of Ammonia Oxidiser Community Composition via a Novel, <i>amoA</i>-Based Functional Gene Array

<div><p>Advances in microbial ecology research are more often than not limited by the capabilities of available methodologies. Aerobic autotrophic nitrification is one of the most important and well studied microbiological processes in terrestrial and aquatic ecosystems. We have developed and validated a microbial diagnostic microarray based on the ammonia-monooxygenase subunit A (<i>amoA</i>) gene, enabling the in-depth analysis of the community structure of bacterial and archaeal ammonia oxidisers. The <i>amoA</i> microarray has been successfully applied to analyse nitrifier diversity in marine, estuarine, soil and wastewater treatment plant environments. The microarray has moderate costs for labour and consumables and enables the analysis of hundreds of environmental DNA or RNA samples per week per person. The array has been thoroughly validated with a range of individual and complex targets (<i>amoA</i> clones and environmental samples, respectively), combined with parallel analysis using traditional sequencing methods. The moderate cost and high throughput of the microarray makes it possible to adequately address broader questions of the ecology of microbial ammonia oxidation requiring high sample numbers and high resolution of the community composition.</p></div

Evaluation with environmental samples – Agricultural soil example.

<p>A) Phylogenetic tree showing clades detected by clone library sequencing. Numbers within boxes representing clades indicate the number of sequences comprising clades. B) Coverage of probes found positive. C) Microarray results; only the section with positive results shown. D) Full microarray results. E) Side bar indicating colour coding (red: maximum signal, 100%; blue: no signal, 0%).</p

Validation with pure reference targets.

<p>Range of strain coverage for the oligonucleotide probe set targeting <i>amoA</i> genes of AOAs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051542#pone-0051542-g002" target="_blank">Fig. 2A</a>) and AOBs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051542#pone-0051542-g002" target="_blank">Fig. 2B</a>). A similar table in which over 20,000 sequences were considered (without hybridisation results) is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051542#pone.0051542.s006" target="_blank">Supporting Information S6</a>. Black fill indicates expected positive results, grey fill indicates positive results not predicted and thick black framing indicates negative results where hybridisation was predicted. Numbers indicate the number of weighted mismatches as described in the relevant section of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051542#s4" target="_blank">Experimental Procedures</a>. Reference signal values (% of that of positive controls) obtained with full match targets are indicated (‘Ref. values’). NOTE: unpredicted positives left are either from broad specificity probes where signal is still preferred or from probes not yet validated with a perfect match reference target where the signal intensity for positive call is undetermined (i.e., probes with higher than average binding capacities). Details are readable once the figure is magnified to A3 size.</p