Effect of permanent sea ice cover and different nutrient regimes on the phytoplankton succession of fjords of the Vestfold Hills Oasis, eastern Antarctica

Early season phytoplankton communities in both Omega and TaynayaBays are characterized by diatoms sedimenting out of the overlyingsea ice. Initial nitrate, phosphate and silicate levels arehigh and the bay waters are covered with ice and well mixed.In Taynaya Bay the ice cover is retained throughout the seasonwhile Omega Bay is ice free for 6–8 weeks. After ice breakout in Omega Bay, the phytoplankton community changes from onedominated by diatoms to one dominated by the phytoflagellates,Pyramimonas spp., Cryptomonas sp. and Gymnodinium sp. In TaynayaBay the ice remained and even though phytoflagellates becamemore common, diatoms still dominated. These differences in communitycomposition result from differences in light climate, extentof stratification and nutrient levels.     

Grazing rates of a calanoid copepod (Paralabidocera antarctica) in a continental Antarctic lake

The presence of crustaceans in some Antarctic lakes adds a level of complexity to the food webs of these environments. The grazing pressure of the crustaceans on phytoplankton and other protists, and the role they play in controlling food web structure, has not yet been examined in any detail. This paper presents the first data on grazing pressure of crustacean zooplankton in continental Antarctic lakes. In this study the grazing rates of Paralabidocera antarctica in saline Ace Lake, Vestfold Hills, were assessed using a radio-tracer method. Clearance rates ranged from approximately 1 to 8 ml ind.⁻¹ day⁻¹, depending on developmental stage. Due to their high abundance, nauplii exhibited the highest daily carbon ingestion rates. The population cleared about 3% of the water column per day, accounting for a significant fraction of primary production. P. antarctica has a considerable impact on the carbon cycle in Ace Lake and, by inference, in the other lakes in which it occurs. Accepted: 30 October 1999     

Guidelines for model adaptation: A study of the transferability of a general seagrass ecosystem Dynamic Bayesian Networks model

In general, it is not feasible to collect enough empirical data to capture the entire range of processes that define a complex system, either intrinsically or when viewing the system from a different geographical or temporal perspective. In this context, an alternative approach is to consider model transferability, which is the act of translating a model built for one environment to another less well‐known situation. Model transferability and adaptability may be extremely beneficial—approaches that aid in the reuse and adaption of models, particularly for sites with limited data, would benefit from widespread model uptake. Besides the reduced effort required to develop a model, data collection can be simplified when transferring a model to a different application context. The research presented in this paper focused on a case study to identify and implement guidelines for model adaptation. Our study adapted a general Dynamic Bayesian Networks (DBN) of a seagrass ecosystem to a new location where nodes were similar, but the conditional probability tables varied. We focused on two species of seagrass (Zostera noltei and Zostera marina) located in Arcachon Bay, France. Expert knowledge was used to complement peer‐reviewed literature to identify which components needed adjustment including parameterization and quantification of the model and desired outcomes. We adopted both linguistic labels and scenario‐based elicitation to elicit from experts the conditional probabilities used to quantify the DBN. Following the proposed guidelines, the model structure of the general DBN was retained, but the conditional probability tables were adapted for nodes that characterized the growth dynamics in Zostera spp. population located in Arcachon Bay, as well as the seasonal variation on their reproduction. Particular attention was paid to the light variable as it is a crucial driver of growth and physiology for seagrasses. Our guidelines provide a way to adapt a general DBN to specific ecosystems to maximize model reuse and minimize re‐development effort. Especially important from a transferability perspective are guidelines for ecosystems with limited data, and how simulation and prior predictive approaches can be used in these contexts.     

On the occurrence of males and production of ephippial eggs in populations of Daphniopsis studeri (Cladocera) in lakes of the Vestfold and Larsemann Hills, East Antarctica

Populations of the cladoceran Daphniopsis studeri Rühe in freshwater and brackish lakes of eastern Antarctica have been thought to consist solely of females that reproduce parthenogenetically by the production of ameiotic subitaneous eggs. This note reports the presence of male D. studeri and the production of ephippial (sexual) eggs in a number of lakes of the Vestfold and Larsemann Hills, which indicate the possibility of sexual reproduction within these populations. Received: 12 May 1997 / Accepted: 8 September 1997     

Methane and oxygen dynamics in a shallow floodplain lake: the significance of periodic stratification

Temperature, dissolved oxygen and dissolved methane profiles were measured during autumn and summer, in a shallow floodplain lake in south-eastern Australia to determine the effects of water-column stability on methane and oxygen dynamics. The water column was well mixed in autumn. Strong thermal stratification developed in the late afternoon in summer, with top-to-bottom temperature differences of up to 6 °C. Methane concentrations in surface waters varied over a daily cycle by an 18-fold range in summer, but only by a 2-fold range in autumn. The implication of short-term temporal variation is that static chambers deployed on the water surface for short times (less than a day) in summer will significantly underestimate the diffusive component of methane emissions across the water–atmosphere interface. There was a marked diel variation in dissolved oxygen concentrations in summer, with the highest oxygen values (commonly 5–8 mg l^–1) occurring in the surface waters in late afternoon; the bottom waters were then devoid of oxygen (< 0.2 mg l^–1). Because of high respiratory demands, even the surface water layers could be nearly anoxic by morning in summer. The concentration of dissolved oxygen in the surface waters was always less than the equilibrium value. When the water column became thermally stratified in summer, the dissolved oxygen and methane maxima were spatially separated, and planktonic methanotrophy would be limited to a moving zone, at variable depth, in the water column. In summer the whole-wetland rates of oxygen production and respiration, calculated from long-term (5 h) shifts in dissolved oxygen concentrations over a diel period, were approximately 6–10 and 3–6 mmol m^–3 h^–1, respectively. These values correspond to net and gross primary production rates of 0.7–1.2 and 1.0–1.9 g C m^–3 day^–1, respectively.     

Use of simulated data sets to evaluate the fidelity of metagenomic processing methods

Metagenomics is a rapidly emerging field of research for studying microbial communities. To evaluate methods presently used to process metagenomic sequences, we constructed three simulated data sets of varying complexity by combining sequencing reads randomly selected from 113 isolate genomes. These data sets were designed to model real metagenomes in terms of complexity and phylogenetic composition. We assembled sampled reads using three commonly used genome assemblers (Phrap, Arachne and JAZZ), and predicted genes using two popular gene-finding pipelines (fgenesb and CRITICA/GLIMMER). The phylogenetic origins of the assembled contigs were predicted using one sequence similarity-based (blast hit distribution) and two sequence composition-based (PhyloPythia, oligonucleotide frequencies) binning methods. We explored the effects of the simulated community structure and method combinations on the fidelity of each processing step by comparison to the corresponding isolate genomes. The simulated data sets are available online to facilitate standardized benchmarking of tools for metagenomic analysis.     

Distribution and Diversity of Soil Microfauna from East Antarctica: Assessing the Link between Biotic and Abiotic Factors

Terrestrial life in Antarctica has been described as some of the simplest on the planet, and mainly confined to soil microfaunal communities. Studies have suggested that the lack of diversity is due to extreme environmental conditions and thought to be driven by abiotic factors. In this study we investigated soil microfauna composition, abundance, and distribution in East Antarctica, and assessed correlations with soil geochemistry and environmental variables. We examined 109 soil samples from a wide range of ice-free habitats, spanning 2000 km from Framnes Mountains to Bailey Peninsula. Microfauna across all samples were patchily distributed, from complete absence of invertebrates to over 1600 specimens/gram of dry weight of soil (gdw), with highest microfauna abundance observed in samples with visible vegetation. Bdelloid rotifers were on average the most widespread found in 87% of sampled sites and the most abundant (44 specimens/gdw). Tardigrades occurred in 57% of the sampled sites with an abundance of 12 specimens/gdw. Nematodes occurred in 71% of samples with a total abundance of 3 specimens/gdw. Ciliates and mites were rarely found in soil samples, with an average abundance of 1.3 and 0.04 specimens/gdw, respectively. We found that microfaunal composition and abundance were mostly correlated with the soil geochemical parameters; phosphorus, NO₃ ⁻ and salinity, and likely to be the result of soil properties and historic landscape formation and alteration, rather than the geographic region they were sampled from. Studies focusing on Antarctic biodiversity must take into account soil geochemical and environmental factors that influence population and species heterogeneity.     

Spatial Prediction of N2O Emissions in Pasture: A Bayesian Model Averaging Analysis

Nitrous oxide (N₂O) is one of the greenhouse gases that can contribute to global warming. Spatial variability of N₂O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N₂O – environmental factors relationships. Few researches have examined the impacts of various spatial correlation structures (e.g. independence, distance-based and neighbourhood based) on spatial prediction of N₂O emissions. This study aimed to assess the impact of three spatial correlation structures on spatial predictions and calibrate the spatial prediction using Bayesian model averaging (BMA) based on replicated, irregular point-referenced data. The data were measured in 17 chambers randomly placed across a 271 m² field between October 2007 and September 2008 in the southeast of Australia. We used a Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR) model to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N₂O emissions across the study site. We compared these with a Bayesian regression model with independent errors. The three approaches resulted in different derived maps of spatial prediction of N₂O emissions. We found that incorporating spatial dependency in the model not only substantially improved predictions of N₂O emission from soil, but also better quantified uncertainties of soil parameters in the study. The hybrid model structure obtained by BMA improved the accuracy of spatial prediction of N₂O emissions across this study region.