Species richness and beta-diversity of aquatic macrophytes assemblages in three floodplain tropical lagoons: evaluating the effects of sampling size and depth gradients

Using aquatic macrophyte data gathered in three lagoons of the Paraná River floodplain we showed a strong effect of sample size on species richness. Incidence-based species richness estimators (Chao 2, jackknife 1, jackknife 2, incidence-based coverage estimator and bootstrap) were compared to evaluate their performance in estimating the species richness throughout transect sampling rnethod. Our results suggest that the best estimate of the species richness was gave by jackknife 2 estimator. Nevertheless, the transect sampling design was considered inappropriate to estimate aquatic macrophytes species richness. Depth gradient was not a good predictor of the species richness and species turnover (beta diversity). The dynamics of these environments, subject to high water-level fluctuation prevents the formation and permanence of a clear floristic depth-related gradient

Comparison of the metabolism of two floodplain lakes of the Trombetas River (Pará, Brazil) based on a study of diel variation

The diel variation of temperature, pH, electrical conductivity, dissolved oxygen concentration and chlorophyll-a was investigated in Batata and Mussurá Lakes on the Trombetas River floodplain. The diel variation of temperature was distinct in both lakes. The water column of Batata lake was completely mixed after 22ºº hour and Mussurá lake developed a well stablished gradient of temperature (differences up to 5.6 °C between surface and depth) which persisted all over the period studied. The thermal behavior determined the diet variation of the other parameters studied, which presented a homogenous vertical distribution in Batata Lake and remained stratified in Mussurá Lake. Chlorophyll-a concentrations were considerably lower in Batata Lake (1.8 µg/l) than in Mussurá Lake (10.8 µg/l) and resulted in production values (measured by oxygen diel variation) of ca. 2.6 g O2*m⁻²*d⁻¹ the first and 18.2 g O2*m⁻²*d⁻¹ in the former one

Análise de logs do sistema Agritempo por meio do log do PHPNuke e WebAlizer.

O foco deste trabalho é a análise dos logs do sistema Agritempo, um sistema de monitoramento agrometeorológico que disponibiliza informações meteorológicas e agrometeorológicas de diferentes regiões brasileiras gratuitamente na internet3. O Agritempo possui um amplo público-alvo: produtores, extensionistas, consultores, agentes do governo, estudantes e professores universitários, além da iniciativa privada

Bugs as Features (Part II): A Perspective on Enriching Microbiome-Gut-Brain Axis Analyses with Multidisciplinary Techniques

The microbiome-gut-brain-axis field is multidisciplinary, benefiting from the expertise of microbiology, ecology, psychiatry, computational biology, and epidemiology amongst other disciplines. As the field matures and moves beyond a basic demonstration of its relevance, it is critical that study design and analysis are robust and foster reproducibility. In this companion piece to Bugs as Features (Part 1), we present techniques from adjacent and disparate fields to enrich and inform the analysis of microbiome-gut-brain-axis data. Emerging techniques built specifically for the microbiome-gut-brain axis are also demonstrated. All of these methods are contextualised to inform several common challenges: how do we establish causality? How can we integrate data from multiple 'omics techniques? How might we account for the dynamicism of host-microbiome interactions? This perspective is offered to experienced and emerging microbiome scientists alike, to assist with these questions and others, at the study conception, design, analysis and interpretation stages of research.Comment: For main text: 20 pages, 2 figures; for supplementary analysis: 31 pages and 6 figures. Supplementary analysis generated using Rmarkdown by Thomaz F. S. Bastiaanssen. arXiv admin note: substantial text overlap with arXiv:2207.1247

Bugs as Features (Part I): Concepts and Foundations for the Compositional Data Analysis of the Microbiome-Gut-Brain Axis

There has been a growing acknowledgement of the involvement of the gut microbiome - the collection of microbes that reside in our gut - in regulating our mood and behaviour. This phenomenon is referred to as the microbiome-gut-brain axis. While our techniques to measure the presence and abundance of these microbes have been steadily improving, the analysis of microbiome data is non-trivial. Here, we present a perspective on the concepts and foundations of data analysis and interpretation of microbiome experiments with a focus on the microbiome-gut-brain axis domain. We give an overview of foundational considerations prior to commencing analysis alongside the core microbiome analysis approaches of alpha diversity, beta diversity, differential feature abundance and functional inference. We emphasize the compositional data analysis (CoDA) paradigm. Further, this perspective features an extensive and heavily annotated microbiome analysis in R in the supplementary materials, as a resource for new and experienced bioinformaticians alike.Comment: For main text: 23 pages, 3 figures; for supplementary demonstration analysis: 31 pages and 12 figures. Supplementary demonstration analysis generated using Rmarkdown by Thomaz F. S. Bastiaanssen. Part I of a two-part piec