Influence of nutrient supply on plankton microbiome biodiversity and distribution in a coastal upwelling region.

The ecological and oceanographic processes that drive the response of pelagic ocean microbiomes to environmental changes remain poorly understood, particularly in coastal upwelling ecosystems. Here we show that seasonal and interannual variability in coastal upwelling predicts pelagic ocean microbiome diversity and community structure in the Southern California Current region. Ribosomal RNA gene sequencing, targeting prokaryotic and eukaryotic microbes, from samples collected seasonally during 2014-2020 indicate that nitracline depth is the most robust predictor of spatial microbial community structure and biodiversity in this region. Striking ecological changes occurred due to the transition from a warm anomaly during 2014-2016, characterized by intense stratification, to cooler conditions in 2017-2018, representative of more typical upwelling conditions, with photosynthetic eukaryotes, especially diatoms, changing most strongly. The regional slope of nitracline depth exerts strong control on the relative proportion of highly diverse offshore communities and low biodiversity, but highly productive nearshore communities.

Intraseasonal predictability of natural phytoplankton population dynamics.

It is difficult to make skillful predictions about the future dynamics of marine phytoplankton populations. Here, we use a 22-year time series of monthly average abundances for 198 phytoplankton taxa from Station L4 in the Western English Channel (1992-2014) to test whether and how aggregating phytoplankton into multi-species assemblages can improve predictability of their temporal dynamics. Using a non-parametric framework to assess predictability, we demonstrate that the prediction skill is significantly affected by how species data are grouped into assemblages, the presence of noise, and stochastic behavior within species. Overall, we find that predictability one month into the future increases when species are aggregated together into assemblages with more species, compared with the predictability of individual taxa. However, predictability within dinoflagellates and larger phytoplankton (>12 µm cell radius) is low overall and does not increase by aggregating similar species together. High variability in the data, due to observational error (noise) or stochasticity in population growth rates, reduces the predictability of individual species more than the predictability of assemblages. These findings show that there is greater potential for univariate prediction of species assemblages or whole-community metrics, such as total chlorophyll or biomass, than for the individual dynamics of phytoplankton species.

Circularity in fisheries data weakens real world prediction.

The systematic substitution of direct observational data with synthesized data derived from models during the stock assessment process has emerged as a low-cost alternative to direct data collection efforts. What is not widely appreciated, however, is how the use of such synthesized data can overestimate predictive skill when forecasting recruitment is part of the assessment process. Using a global database of stock assessments, we show that Standard Fisheries Models (SFMs) can successfully predict synthesized data based on presumed stock-recruitment relationships, however, they are generally less skillful at predicting observational data that are either raw or minimally filtered (denoised without using explicit stock-recruitment models). Additionally, we find that an equation-free approach that does not presume a specific stock-recruitment relationship is better than SFMs at predicting synthesized data, and moreover it can also predict observational recruitment data very well. Thus, while synthesized datasets are cheaper in the short term, they carry costs that can limit their utility in predicting real world recruitment.

Short-term effects of multiple outdoor environmental factors on risk of asthma exacerbations: Age-stratified time-series analysis.

BACKGROUND: Although the different age groups had differences in sensitivity of asthma exacerbations (AEs) to environmental factors, no comprehensive study has examined the age-stratified effects of environmental factors on AEs. OBJECTIVE: We sought to examine the short-term effects in age-stratified groups (infants, preschool children, school-aged children, adults, and the elderly) of outdoor environmental factors (air pollutants, weather conditions, aeroallergens, and respiratory viral epidemics) on AEs. METHODS: We performed an age-stratified analysis of the short-term effects of 4 groups of outdoor environmental factors on AEs in Seoul Metropolitan City (Korea) from 2008 and 2012. The statistical analysis used a Poisson generalized linear regression model, with a distributed lag nonlinear model for identification of lagged and nonlinear effects and convergent cross-mapping for identification of causal associations. RESULTS: Analysis of the total population (n = 10,233,519) indicated there were 28,824 AE events requiring admission to an emergency department during the study period. Diurnal temperature range had significant effects in pediatric (infants, preschool children, and school-aged children) and elderly (relative risk [RR], 1.056-1.078 and 1.016, respectively) subjects. Tree and weed pollen, human rhinovirus, and influenza virus had significant effects in school-aged children (RR, 1.014, 1.040, 1.042, and 1.038, respectively). Tree pollen and influenza virus had significant effects in adults (RR, 1.026 and 1.044, respectively). Outdoor air pollutants (particulate matter of ≤10 µm in diameter, nitrogen dioxide, ozone, carbon monoxide, and sulfur dioxide) had significant short-term effects in all age groups (except for carbon monoxide and sulfur dioxide in infants). CONCLUSION: These findings provide a need for the development of tailored strategies to prevent AE events in different age groups.