The infrabuccal pellet piles of fungus-growing ants

Fungus-growing ants (Attini) live in an obligate mutualism with the fungi they cultivate for food. Because of the obligate nature of this relationship, the success of the ants is directly dependent on their ability to grow healthy fungus gardens. Attine ants have evolved complex disease management strategies to reduce their gardens exposure to potential parasitic microbes, to prevent the establishment of infection in their gardens, and to remove infected garden sections. The infrabuccal pocket, a filtering device located in the oral cavity of all ants, is an integral part of the mechanisms that leaf-cutter ants use to prevent the invasion and spread of general microbial parasites and the specific fungal-garden parasite Escovopsis. Fungus-growing ants carefully groom their garden, collecting general debris and pathogenic spores of Escovopsis in their infrabuccal pocket, the contents of which are later expelled in dump chambers inside the nest or externally. In this study we examined how a phylogenetically diverse collection of attine ants treat their infrabuccal pellets. Unlike leaf-cutters that deposit their infrabuccal pellets directly in refuse piles, ants of the more basal attine lineages stack their infrabuccal pellets in piles located close to their gardens, and a separate caste of workers is devoted to the construction, management, and eventual disposal of these piles.     

Batch methanogenic fermentation experiments of wastewater from a brown coal low-temperature coke plant

Coke plant effluents with high contents of organic compounds are mainly treated by biological aerobic fermentation after physical pre-treatment.In this study,a brown coal condensate wastewater from a low temperature coking process was fermented under methanogenic conditions in discontinuous experiments.By this fermentation,acetate,propionate,and the main polyphenolic compounds(catechol,resorcinol and hydroquinone) were degraded to a level below the detection limit.The COD was reduced by 72% with a residual ...     

The Baltic Sea spring phytoplankton bloom in a changing climate: an experimental approach

The response of the Baltic Sea spring bloom was studied in mesocosm experiments, where temperatures were elevated up to 6°C above the present-day sea surface temperature of the spring bloom season. Four of the seven experiments were carried out at different light levels (32–202?Wh?m^?2 at the start of the experiments) in the different experimental years. In one further experiment, the factors light and temperature were crossed, and in one experiment, the factors density of overwintering zooplankton and temperature were crossed. Overall, there was a slight temporal acceleration of the phytoplankton spring bloom, a decline of peak biomass and a decline of mean cell size with warming. The temperature influence on phytoplankton bloom timing, biomass and size structure was qualitatively highly robust across experiments. The dependence of timing, biomass, and size structure on initial conditions was tested by multiple regression analysis of the y-temperature regressions with the candidate independent variables initial light, initial phytoplankton biomass, initial microzooplankton biomass, and initial mesozooplankton (=copepod) biomass. The bloom timing predicted for mean temperatures (5.28°C) depended on light. The peak biomass showed a strong positive dependence on light and a weaker negative dependence on initial copepod density. Mean phytoplankton cell size predicted for the mean temperature responded positively to light and negatively to copepod density. The anticipated mismatch between phytoplankton supply and food demand by newly hatched copepod nauplii occurred only under the combination of low light and warm temperatures. The analysis presented here confirms earlier conclusions about temperature responses that are based on subsets of our experimental series. However, only the comprehensive analysis across all experiments highlights the importance of the factor light.     

Null model analysis of species associations using abundance data

The influence of negative species interactions has dominated much of the literature on community assembly rules. Patterns of negative covariation among species are typically documented through null model analyses of binary presence/absence matrices in which rows designate species, columns designate sites, and the matrix entries indicate the presence (1) or absence (0) of a particular species in a particular site. However, the outcome of species interactions ultimately depends on population-level processes. Therefore, patterns of species segregation and aggregation might be more clearly expressed in abundance matrices, in which the matrix entries indicate the abundance or density of a species in a particular site. We conducted a series of benchmark tests to evaluate the performance of 14 candidate null model algorithms and six covariation metrics that can be used with abundance matrices. We first created a series of random test matrices by sampling a metacommunity from a lognormal species abundance distribution. We also created a series of structured matrices by altering the random matrices to incorporate patterns of pairwise species segregation and aggregation. We next screened each algorithm-index combination with the random and structured matrices to determine which tests had low Type I error rates and good power for detecting segregated and aggregated species distributions. In our benchmark tests, the best-performing null model does not constrain species richness, but assigns individuals to matrix cells proportional to the observed row and column marginal distributions until, for each row and column, total abundances are reached. Using this null model algorithm with a set of four covariance metrics, we tested for patterns of species segregation and aggregation in a collection of 149 empirical abundance matrices and 36 interaction matrices collated from published papers and posted data sets. More than 80% of the matrices were significantly segregated, which reinforces a previous meta-analysis of presence/absence matrices. However, using two of the metrics we detected a significant pattern of aggregation for plants and for the interaction matrices (which include plant-pollinator data sets). These results suggest that abundance matrices, analyzed with an appropriate null model, may be a powerful tool for quantifying patterns of species segregation and aggregation.     

Comparison of methods used to calculate typical threshold values for potentially toxic elements in soil

The environmental quality of land can be assessed by calculating relevant threshold values, which differentiate between concentrations of elements resulting from geogenic and diffuse anthropogenic sources and concentrations generated by point sources of elements. A simple process allowing the calculation of these typical threshold values (TTVs) was applied across a region of highly complex geology (Northern Ireland) to six elements of interest; arsenic, chromium, copper, lead, nickel and vanadium. Three methods for identifying domains (areas where a readily identifiable factor can be shown to control the concentration of an element) were used: k-means cluster analysis, boxplots and empirical cumulative distribution functions (ECDF). The ECDF method was most efficient at determining areas of both elevated and reduced concentrations and was used to identify domains in this investigation. Two statistical methods for calculating normal background concentrations (NBCs) and upper limits of geochemical baseline variation (ULBLs), currently used in conjunction with legislative regimes in the UK and Finland respectively, were applied within each domain. The NBC methodology was constructed to run within a specific legislative framework, and its use on this soil geochemical data set was influenced by the presence of skewed distributions and outliers. In contrast, the ULBL methodology was found to calculate more appropriate TTVs that were generally more conservative than the NBCs. TTVs indicate what a “typical” concentration of an element would be within a defined geographical area and should be considered alongside the risk that each of the elements pose in these areas to determine potential risk to receptors.     

The influence of initial conditions on the propagation of smouldering fires in dust accumulations

A mathematical model is presented which allows one to treat the combined phenomena of heat, mass and species transfer by diffusion as they occur within smouldering fires in accumulations of dust or other solid bulk materials. The model was applied to predict self-ignition temperatures of five different dusts, where it could be shown that computed and experimental self-ignition temperatures coincide within an error margin of ±5%.

For smouldering fires, if initiated by either self-ignition or an ignition source, it could be shown that the temperature and the velocity at which the reaction front propagates both depend on the volume of the dust accumulation. In addition, the propagation velocity increases when the initial temperature of the dust accumulation is increased and decreases when the initial moisture content of the dust accumulation is increased.

Comparisons of the numerical model with experiments show that the smouldering propagation is mirrored qualitatively, while the accuracy of the computations strongly depends on the accuracy of the input parameters, namely on the apparent activation energy.