Phylogeny- and Abundance-Based Metrics Allow for the Consistent Comparison of Core Gut Microbiome Diversity Indices Across Host Species

The filtering of gut microbial datasets to retain high prevalence taxa is often performed to identify a common core gut microbiome that may be important for host biological functions. However, prevalence thresholds used to identify a common core are highly variable, and it remains unclear how they affect diversity estimates and whether insights stemming from core microbiomes are comparable across studies. We hypothesized that if macroecological patterns in gut microbiome prevalence and abundance are similar across host species, then we would expect that increasing prevalence thresholds would yield similar changes to alpha diversity and beta dissimilarity scores across host species datasets. We analyzed eight gut microbiome datasets based on 16S rRNA gene amplicon sequencing and collected from different host species to (1) compare macroecological patterns across datasets, including amplicon sequence variant (ASV) detection rate with sequencing depth and sample size, occupancy-abundance curves, and rank-abundance curves; (2) test whether increasing prevalence thresholds generate universal or host-species specific effects on alpha and beta diversity scores; and (3) test whether diversity scores from prevalence-filtered core communities correlate with unfiltered data. We found that gut microbiomes collected from diverse hosts demonstrated similar ASV detection rates with sequencing depth, yet required different sample sizes to sufficiently capture rare ASVs across the host population. This suggests that sample size rather than sequencing depth tends to limit the ability of studies to detect rare ASVs across the host population. Despite differences in the distribution and detection of rare ASVs, microbiomes exhibited similar occupancy-abundance and rank-abundance curves. Consequently, increasing prevalence thresholds generated remarkably similar trends in standardized alpha diversity and beta dissimilarity across species datasets until high thresholds above 70%. At this point, diversity scores tended to become unpredictable for some diversity measures. Moreover, high prevalence thresholds tended to generate diversity scores that correlated poorly with the original unfiltered data. Overall, we recommend that high prevalence thresholds over 70% are avoided, and promote the use of diversity measures that account for phylogeny and abundance (Balance-weighted phylogenetic diversity and Weighted Unifrac for alpha and beta diversity, respectively), because we show that these measures are insensitive to prevalence filtering and therefore allow for the consistent comparison of core gut microbiomes across studies without the need for prevalence filtering

Phylogeny-and abundance-based metrics allow for the consistent comparison of core gut microbiome diversity indices across host species

The filtering of gut microbial datasets to retain high prevalence taxa is often performed to identify a common core gut microbiome that may be important for host biological functions. However, prevalence thresholds used to identify a common core are highly variable, and it remains unclear how they affect diversity estimates and whether insights stemming from core microbiomes are comparable across studies. We hypothesized that if macroecological patterns in gut microbiome prevalence and abundance are similar across host species, then we would expect that increasing prevalence thresholds would yield similar changes to alpha diversity and beta dissimilarity scores across host species datasets. We analyzed eight gut microbiome datasets based on 16S rRNA gene amplicon sequencing and collected from different host species to (1) compare macroecological patterns across datasets, including amplicon sequence variant (ASV) detection rate with sequencing depth and sample size, occupancy-abundance curves, and rank-abundance curves; (2) test whether increasing prevalence thresholds generate universal or host-species specific effects on alpha and beta diversity scores; and (3) test whether diversity scores from prevalence-filtered core communities correlate with unfiltered data. We found that gut microbiomes collected from diverse hosts demonstrated similar ASV detection rates with sequencing depth, required different sample sizes to sufficiently capture rare ASVs across the host population. This suggests that sample size rather than sequencing depth tends to limit the ability of studies to detect rare ASVs across the host population. Despite differences in the distribution and detection of rare ASVs, microbiomes exhibited similar occupancy-abundance and rank-abundance curves. Consequently, increasing prevalence thresholds generated remarkably similar trends in standardized alpha diversity and beta dissimilarity across species datasets until high thresholds above 70%. At this point, diversity scores tended to become unpredictable for some diversity measures. Moreover, high prevalence Frontiers in Microbiology. Filtering Effects on Core Microbiome thresholds tended to generate diversity scores that correlated poorly with the original unfiltered data. Overall, we recommend that high prevalence thresholds over 70% are avoided, and promote the use of diversity measures that account for phylogeny and abundance (Balance-weighted phylogenetic diversity and Weighted Unifrac for alpha and beta diversity, respectively), because we show that these measures are insensitive to prevalence filtering and therefore allow for the consistent comparison of core gut microbiomes across studies without the need for prevalence filtering.publishedVersio

Making energy efficiency pro-poor : insights from behavioural economics for policy design

This paper reviews the current state of behavioural economics and its applications to energy efficiency in developing countries. Taking energy efficient lighting in Ghana, Uganda and Rwanda as empirical examples, this paper develops hypotheses on how behavioural factors can improve energy efficiency policies directed towards poor populations. The key argument is that different types of affordability exist that are influenced by behavioural factors to varying degrees. Using a qualitative approach, this paper finds that social preferences, framing and innovative financing solutions that acknowledge people’s mental accounts can provide useful starting points. Behavioural levers are only likely to work in a policy package that addresses wider technical, market and institutional barriers to energy efficiency. More research, carefully designed pre-tests and stakeholder debates are required before introducing policies based on behavioural insights. This is imperative to avoid the dangers of nudging

A farewell to the sum of Akaike weights: The benefits of alternative metrics for variable importance estimations in model selection

Galipaud M, Gillingham MAF, Dechaume-Moncharmont F-X. A farewell to the sum of Akaike weights: The benefits of alternative metrics for variable importance estimations in model selection. METHODS IN ECOLOGY AND EVOLUTION. 2017;8(12):1668-1678.1. In a previous article, we advocated against using the sum of Akaike weights (SW) as a metric to distinguish between genuine and spurious variables in Information Theoretic (IT) statistical analyses. A recent article (Giam & Olden, Methods in Ecology and Evolution, 2016, 7, 388) criticises our finding and instead argues in favour of SW. It points out that (1) we performed a biased data-generation procedure and (2) we erroneously evaluated SW on its capacity to estimate the proportion of variance in the data explained by a variable. We here respond to these points. 2. Giam and Olden's first concern is unfounded. When using the data-generating code they proposed, SW remains very imprecise. To respond to their second concern, we first list the meanings taken by a variable's importance in the context of IT. Although, SW is presented as an estimate of variable relative importance in methodological textbooks (i.e. a variable's rank in importance or its relative contribution to the variance in the data), it is also used as a metric of variable absolute importance (i.e. a variable's absolute effect size or its statistical significance). We then compare SW to alternative metrics on its ability to estimate variable absolute or relative importance. 3. SW values have low repeatability across analyses. As a result, based on SW, it is hard to distinguish between variables with weak and large effects. For estimations of variable absolute importance, experimenters should prefer model-averaged parameter estimates and/or compare nested models based on evidence ratios. Sum of Akaike weights is also a poor metric of variable relative importance. We showed that correct variable ranking in importance was generally more frequent when using model-averaged standardised parameter estimates, than when using SW. 4. To avoid recurrent errors in ecology and evolution, we therefore warn against the use of SW for estimations of variable absolute and relative importance and we propose that experimenters should instead use model-averaged standardised parameter estimates for statistical inferences

DataPlosOne

Capture mark recapture data (from summer 1995 to winter 2010) for greater flamingos captured as fledglings and ringed between 1995 and 1998 which were genotyped for microsatellite multi-locus heterozygosity. Body condition of fledgling also provided

Data from: Home-made cost effective preservation buffer is a better alternative to commercial preservation methods for microbiome research

The investigation of wildlife gastrointestinal microbiomes by next-generation sequencing approaches is a growing field in microbial ecology and conservation. Such studies often face difficulties in sample preservation if neither freezing facilities nor liquid nitrogen (LQN) are readily available. Thus, in order to prevent microbial community changes because of bacterial growth after sampling, preservation buffers need to be applied to samples. However, the amount of microbial community variation attributable to the different preservation treatments and potentially affecting biological interpretation is hardly known. Here, we sampled feces of 11 sheep (Ovis aries sp.) by using swabs and analyzed the effect of air-drying, an inexpensive self-made nucleic acid preservation buffer (NAP), DNA/RNA Shield™, and RNAlater®, each together with freezing (for 10 days) or storing at room temperature (for 10 days) prior to 16S rRNA gene high-throughput sequencing to determine bacterial communities. Results revealed that the proportions of operational taxonomic units (OTUs) belonging to a bacterial phylum were affected by the preservation treatments, and that alpha diversities (observed OTUs, Shannon index, and phylogenetic diversity (PD)) were lower in all preservation treatments than in samples taken by forensic swabs and immediately frozen which is considered as the favored preservation treatment in the absence of any logistic constraints. Overall, NAP had better preservation qualities than RNAlater® and DNA/RNA Shield™ making this self-made buffer a valuable solution in wildlife microbiome studies

Estudio de filtros cerámicos impregnados con plata coloidal, como un sistema de tratamiento de agua para procesos agroindustriales

TesisEl presente trabajo de investigación tuvo como objetivo evaluar la factibilidad de la utilización de filtros cerámicos, conformados por jipi de quinua (Chenopodium quinoa Willd.) y arcilla, impregnadas con plata coloidal, como alternativa para la potabilización de agua cruda de pozo para procesos agroindustriales. En el análisis microbiológico se utilizaron los métodos de Número Más Probable y Unidades Formadoras de Colonia, en el análisis fisicoquímico se utilizaron los métodos electrométrico, colorimétrico y volumétrico y para la determinación del caudal se utilizó el método volumétrico. De acuerdo a la caracterización microbiológica y fisicoquímica del agua cruda de pozo, se tiene que no cumplen con los límites máximos permisibles exigidos por el Ministerio de Salud. Para su tratamiento se elaboraron filtros a diferentes proporciones de jipi de quinua y arcilla (20/80; 30/70; 40/60) % e impregnados con plata coloidal a diferentes concentraciones (20; 35; 55) ppm. La evaluación estadística de la efectividad de los filtros en la eliminación de carga microbiológica y variación de características fisicoquímicas del agua cruda de pozo, fue contrastado bajo el diseño completamente al azar, basado en un arreglo factorial de 3x3x3, haciendo 27 observaciones con un nivel de significancia al 5%. Donde se optimizaron los tratamientos con el análisis de varianza, que tuvo diferencia estadística altamente significativa y el método de comparación múltiple Duncan nos indica el mejor tratamiento: tratamiento 1 (filtro 01 con 20% jipi de quinua y 80% arcilla y concentración de plata coloidal 55 ppm) con un caudal de 0.474 L/min, el cual presenta la mayor eliminación de carga microbiológica: coliformes totales 0 NMP/100 ml, coliformes fecales 0 NMP/100 ml, bacterias heterotróficas 0 UFC/ml y a la vez tiene una mayor variación de características fisicoquímicas: Conductividad eléctrica (900 µS/cm), Turbiedad (0.036 NTU), pH (6.87), Dureza Total CaCO_3 (201.4 mg/L), Calcio Ca^(++) (12.67mg/L), Sólidos Totales (420.0mg/L), Alcalinidad (50.01mg/L), Cloruro Cl^- (123.40 mg/L), Sulfatos SO=4 (49.90 mg/L), Hierro (0.041 mg/L), Manganeso (0.038 mg/L), todo estos resultados cumplen con los límites máximos permisibles para la calidad de agua que el Ministerio de Salud exige. Por lo tanto se concluye que es factible utilizar filtros cerámicos, conformados por jipi de quinua (Chenopodium quinoa Willd.) y arcilla e impregnados con plata coloidal, para el proceso de tratamiento del agua cruda de pozo, convirtiéndolo en apto para consumo y uso en procesos agroindustriales

Data from: Evidence for an association between post-fledging dispersal and microsatellite multilocus heterozygosity in a large population of greater flamingos

Dispersal can be divided into three stages: departure, transience and settlement. Despite the fact that theoretical studies have emphasized the importance of heterozygosity on dispersal strategies, empirical evidence of its effect on different stages of dispersal is lacking. Here, using multi-event capture-mark-recapture models, we show a negative association between microsatellite multilocus heterozygosity (MLH; 10 loci; n = 1023) and post-fledging dispersal propensity for greater flamingos, Phoenicopterus roseus, born in southern France. We propose that the negative effects of inbreeding depression affects competitive ability and therefore more homozygous individuals are more likely to disperse because they are less able to compete within the highly saturated natal site. Finally, a model with the effect of MLH on propensity of post-fledgling dispersers to disperse to the long-distance sites of Africa was equivalent to the null model, suggesting that MLH had low to no effect on dispersal distance. Variations in individual genetic quality thus result in context-dependent heterogeneity in dispersal strategies at each stage of dispersal. Our results have important implications on fitness since sites visited early in life are known to influence site selection later on in life and future survival

Ecologists overestimate the importance of predictor variables in model averaging: a plea for cautious interpretations.

9 pagesInternational audienceInformation-theory procedures are powerful tools for multimodel inference and are now standard methods in ecology. When performing model averaging on a given set of models, the importance of a predictor variable is commonly estimated by summing the weights of models where the variable appears, the so-called sum of weights (SW). However, SWs have received little methodological attention and are frequently misinterpreted. We assessed the reliability of SW by performing model selection and averaging on simulated data sets including variables strongly and weakly correlated to the response variable and a variable unrelated to the response. Our aim was to investigate how useful SWs are to inform about the relative importance of predictor variables. SW can take a wide range of possible values, even for predictor variables unrelated to the response. As a consequence, SW with intermediate values cannot be confidently interpreted as denoting importance for the considered predictor variable. Increasing sample size using an alternative information criterion for model selection or using only a subset of candidate models for model averaging did not qualitatively change our results: a variable of a given effect size can take a wide range of SW values. Contrary to what is assumed in many ecological studies, it seems hazardous to define a threshold for SW above which a variable is considered as having a statistical effect on the response and SW is not a measure of effect size. Although we did not consider every possible condition of analysis, it is likely that in most situations, SW is a poor estimate of variable's importance