Estimating the number of species in a community is important for assessments of biodiversity. Previous species richness estimators are mainly based on nonparametric approaches. Although parametric asymptotic models have been applied, they received limited attention due to specific limitations. Here, we introduce parametric models fitting the probability-based rarefied species richness curve that allow us to estimate the "Total Expected Species" (TES) in a community based on species' abundance data. We develop two approaches to calculate TES (termed "TESa" and "TESb"), based on two slightly different mathematical assumptions regarding Expected Species (ES) models. We provide R functions to calculate both these estimation approaches and their standard deviation. The function also enables users to visualize the estimation. We test the performance of TESa, TESb, and their average (TESab) across simulated and empirical data, and compare their bias, precision, and accuracy with other commonly used, nonparametric species richness estimators: the bias-corrected (bc-)Chao1 and the abundance-based coverage estimator (ACE).Simulation reveals that in small samples TESa shows a tendency to overestimate and TESb to underestimate overall species richness. TESab performs well in bias, precision, and accuracy when compared with (bc-)Chao1 and ACE estimators. Results from empirical data show that the variance generated from TES estimates is comparable with that for (bc-)Chao1 and ACE.Our study demonstrates that rarefaction theory in combination with parametric approximation models provides a valuable new approach to estimate the species richness of incompletely sampled communities. Robust estimates are likely to be obtained where the observed number of species is greater than half of the TES estimation. When the ratio of TESa to the observed richness is )2, we suggest the use of TESb or TESab. Although more comprehensive comparisons with other estimators are suggested, we encourage researchers to consider the TES approach in their biodiversity studies as a complement to current existing estimators.

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Background Characteristics of airway microbiota might influence asthma status or asthma phenotype. Identifying the airway microbiome can help to investigate its role in the development of asthma phenotypes or small airway function. Methods Bacterial microbiota profiles were analyzed in induced sputum from 31 asthma patients and 12 healthy individuals from Beijing, China. Associations between small airway function and airway microbiomes were examined. Results Composition of sputum microbiota significantly changed with small airway function in asthma patients. Two microbiome-driven clusters were identified and characterized by small airway function and taxa that had linear relationship with small airway functions were identified. Conclusions Our findings confirm that airway microbiota was associated with small airway function in asthma patients.

Abstract. Airborne aerosols reduce surface solar radiation through light scattering and absorption (aerosol direct effects, ADEs), influence regional meteorology, and further affect atmospheric chemical reactions and aerosol concentrations. The inhibition of turbulence and the strengthened atmospheric stability induced by ADEs increases surface primary aerosol concentration, but the pathway of ADE impacts on secondary aerosol is still unclear. In this study, the online coupled meteorological and chemistry model (WRF–CMAQ; Weather Research and Forecasting–Community Multiscale Air Quality) with integrated process analysis was applied to explore how ADEs affect secondary aerosol formation through changes in atmospheric dynamics and photolysis processes. The meteorological condition and air quality in the Jing-Jin-Ji area (denoted JJJ, including Beijing, Tianjin, and Hebei Province in China) in January and July 2013 were simulated to represent winter and summer conditions, respectively. Our results show that ADEs through the photolysis pathway inhibit sulfate formation during winter in the JJJ region and promote sulfate formation in July. The differences are attributed to the alteration of effective actinic flux affected by single-scattering albedo (SSA). ADEs through the dynamics pathway act as an equally or even more important route compared with the photolysis pathway in affecting secondary aerosol concentration in both summer and winter. ADEs through dynamics traps formed sulfate within the planetary boundary layer (PBL) which increases sulfate concentration in winter. Meanwhile, the impact of ADEs through dynamics is mainly reflected in the increase of gaseous-precursor concentrations within the PBL which enhances secondary aerosol formation in summer. For nitrate, reduced upward transport of precursors restrains the formation at high altitude and eventually lowers the nitrate concentration within the PBL in winter, while such weakened vertical transport of precursors increases nitrate concentration within the PBL in summer, since nitrate is mainly formed near the surface ground.

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