Search

Degradation of ecosystems can occur when certain ecological thresholds are passed below which ecosystem responses remain within ‘safe ecological limits’. Ecosystems such as drylands are sensitive to both aridification and grazing, but the combined effects of such factors on the emergence of ecological thresholds beyond which ecosystem degradation occurs has yet to be quantitatively evaluated. This limits our understanding on ‘safe operating spaces’ for grazing, the main land use in drylands worldwide. Here we assessed how 20 structural and functional ecosystem attributes respond to joint changes in aridity and grazing pressure across China´s drylands. Gradual increases in aridity resulted in abrupt decreases in productivity, soil fertility and plant richness. Rising grazing pressures lowered such aridity thresholds for most ecosystem variables, thus showing how ecological thresholds can be amplified by the joint effects of these two factors. We found that 44.4% of China’s drylands are unsuitable for grazing due to climate change-induced aridification, a percentage that may increase to 50.8% by 2100. Of current dryland grazing areas, 8.9% exceeded their maximum allowable grazing pressure. Our findings provide important insights into the relationship between aridity and optimal grazing pressure and identify safe operating spaces for grazing across China’s drylands.

Background Cooling towers are a major source of large community-associated outbreaks of Legionnaires’ disease, a severe pneumonia. This disease is contracted when inhaling aerosols that are contaminated with bacteria from the genus Legionella, most importantly Legionella pneumophila. How cooling towers support the growth of this bacterium is still not well understood. As Legionella species are intracellular parasites of protozoa, it is assumed that protozoan community in cooling towers play an important role in Legionella ecology and outbreaks. However, the exact mechanism of how the eukaryotic community contributes to Legionella ecology is still unclear. Therefore, we used 18S rRNA gene amplicon sequencing to characterize the eukaryotic communities of 18 different cooling towers. The data from the eukaryotic community was then analysed with the bacterial community of the same towers in order to understand how each community could affect Legionella spp. ecology in cooling towers. Results We identified several microbial groups in the cooling tower ecosystem associated with Legionella spp. that suggest the presence of a microbial loop in these systems. Dissolved organic carbon was shown to be a major factor in shaping the eukaryotic community and may be an important factor for Legionella ecology. Network analysis, based on co-occurrence, revealed that Legionella was correlated with a number of different organisms. Out of these, the bacterial genus Brevundimonas and the ciliate class Oligohymenophorea were shown, through in vitro experiments, to stimulate the growth of L. pneumophila through direct and indirect mechanisms. Conclusion Our results suggest that Legionella ecology depends on the host community, including ciliates and on several groups of organisms that contribute to its survival and growth in the cooling tower ecosystem. These findings further support the idea that some cooling tower microbiomes may promote the survival and growth of Legionella b

Simulation has been widely used in modeling engineering systems. A metamodel is a surrogate model used to approximate a computationally expensive simulation model. Extensive research has investigated the performance of different metamodeling techniques in terms of accuracy and/or robustness and concluded no model outperforms others across diverse problem structures. Motivated by this finding, this research proposes a bi-criteria (accuracy and robustness) optimized ensemble framework to optimally identify the contributions from each metamodel (Kriging, Support Vector Regression and Radial Basis Function), where uncertainties are modeled for evaluating robustness. Twenty-eight functions from the literature are tested. It is observed for most problems, a Pareto Frontier is obtained, while for some problems only a single point is obtained. Seven geometrical and statistical metrics are introduced to explore the relationships between the function properties and the ensemble models. It is concluded that the bi-criteria optimized ensembles render not only accurate but also robust metamodels., Published in the Proceedings of the 2014 Winter Simulation Conference, held in Savannah, GA, 7-10 Dec 2014.