Quantifying the efficiency and biases of forest Saccharomyces sampling strategies

Saccharomyces yeasts are emerging as model organisms for ecology and evolution, and researchers need environmental Saccharomyces isolates to test ecological and evolutionary hypotheses. However, methods for isolating Saccharomyces from nature have not been standardized and isolation methods may influence the genotypes and phenotypes of studied strains. We compared the effectiveness and potential biases of an established enrichment culturing method against a newly developed direct plating method for isolating forest floor Saccharomyces spp. In a European forest, enrichment culturing was both less successful at isolating S. paradoxus per sample collected and less labor intensive per isolated S. paradoxus colony than direct isolation. The two methods sampled similar S. paradoxus diversity: the number of unique genotypes sampled (i.e., genotypic diversity) per S. paradoxus isolate and average growth rates of S. paradoxus isolates did not differ between the two methods, and growth rate variances (i.e., phenotypic diversity) only differed in one of three tested environments. However, enrichment culturing did detect rare S. cerevisiae in the forest habitat, and also found two S. paradoxus isolates with outlier phenotypes. Our results validate the historically common method of using enrichment culturing to isolate representative collections of environmental Saccharomyces. We recommend that researchers choose a Saccharomyces sampling method based on resources available for sampling and isolate screening. Researchers interested in discovering new Saccharomyces phenotypes or rare Saccharomyces species from natural environments may also have more success using enrichment culturing. We include step-by-step sampling protocols in the supplemental materials

Control of seminal fluid protein expression via regulatory hubs in Drosophila melanogaster

Highly precise, yet flexible and responsive coordination of expression across groups of genes underpins the integrity of many vital functions. However, our understanding of gene regulatory networks (GRNs) is often hampered by the lack of experimentally tractable systems, by significant computational challenges derived from the large number of genes involved or from difficulties in the accurate identification and characterization of gene interactions. Here we used a tractable experimental system in which to study GRNs: the genes encoding the seminal fluid proteins that are transferred along with sperm (the ‘transferome’) in Drosophila melanogaster fruit flies. The products of transferome genes are core determinants of reproductive success and, to date, only transcription factors have been implicated in the modulation of their expression. Hence, as yet, we know nothing about the post-transcriptional mechanisms underlying the tight, responsive and precise regulation of this important gene set. We investigated this omission in the current study. We first used bioinformatics to identify potential regulatory motifs that linked the transferome genes in a putative interaction network. This predicted the presence of putative microRNA (miRNA) ‘hubs’. We then tested this prediction, that post-transcriptional regulation is important for the control of transferome genes, by knocking down miRNA expression in adult males. This abolished the ability of males to respond adaptively to the threat of sexual competition, indicating a regulatory role for miRNAs in the regulation of transferome function. Further bioinformatics analysis then identified candidate miRNAs as putative regulatory hubs and evidence for variation in the strength of miRNA regulation across the transferome gene set. The results revealed regulatory mechanisms that can underpin robust, precise and flexible regulation of multiple fitness-related genes. They also help to explain how males can adaptively modulate ejaculate composition

Optimizing passive acoustic sampling of bats in forests

Passive acoustic methods are increasingly used in biodiversity research and monitoring programs because they are cost-effective and permit the collection of large datasets. However, the accuracy of the results depends on the bioacoustic characteristics of the focal taxa and their habitat use. In particular, this applies to bats which exhibit distinct activity patterns in three-dimensionally structured habitats such as forests. We assessed the performance of 21 acoustic sampling schemes with three temporal sampling patterns and seven sampling designs. Acoustic sampling was performed in 32 forest plots, each containing three microhabitats: forest ground, canopy, and forest gap. We compared bat activity, species richness, and sampling effort using species accumulation curves fitted with the clench equation. In addition, we estimated the sampling costs to undertake the best sampling schemes. We recorded a total of 145,433 echolocation call sequences of 16 bat species. Our results indicated that to generate the best outcome, it was necessary to sample all three microhabitats of a given forest location simultaneously throughout the entire night. Sampling only the forest gaps and the forest ground simultaneously was the second best choice and proved to be a viable alternative when the number of available detectors is limited. When assessing bat species richness at the 1-km(2) scale, the implementation of these sampling schemes at three to four forest locations yielded highest labor cost-benefit ratios but increasing equipment costs. Our study illustrates that multiple passive acoustic sampling schemes require testing based on the target taxa and habitat complexity and should be performed with reference to cost-benefit ratios. Choosing a standardized and replicated sampling scheme is particularly important to optimize the level of precision in inventories, especially when rare or elusive species are expected

How robust are prediction effects in language comprehension?:Failure to replicate article-elicited N400 effects

Current psycholinguistic theory proffers prediction as a central, explanatory mechanism in language processing. However, widely-replicated prediction effects may not mean that prediction is necessary in language processing. As a case in point, C. D. Martin et al. [2013. Bilinguals reading in their second language do not predict upcoming words as native readers do. Journal of Memory and Language, 69 (4), 574 – 588. doi:10.1016/j.jml.2013.08.001] reported ERP evidence for prediction in native- but not in non-native speakers. Articles mismatching an expected noun elicited larger negativity in the N400 time window compared to articles matching the expected noun in native speakers only. We attempted to replicate these findings, but found no evidence for prediction irrespective of language nativeness. We argue that pre-activation of phonological form of upcoming nouns, as evidenced in article-elicited effects, may not be a robust phenomenon. A view of prediction as a necessary computation in language comprehension must be re-evaluated

Dynamic changes in host-virus interactions associated with colony founding and social environment in fire ant queens (Solenopsis invicta)

Funding Information US Department of Agriculture AFRI Award. Grant Number: 2009‐35302‐05301 Marie Curie International Incoming Fellowship. Grant Number: FP7‐PEOPLE‐2013‐IIF‐625487Peer reviewedPublisher PD

Predicting Chronic Fine and Coarse Particulate Exposures Using Spatiotemporal Models for the Northeastern and Midwestern United States

Background: Chronic epidemiologic studies of particulate matter (PM) are limited by the lack of monitoring data, relying instead on citywide ambient concentrations to estimate exposures. This method ignores within-city spatial gradients and restricts studies to areas with nearby monitoring data. This lack of data is particularly restrictive for fine particles (PM with aerodynamic diameter < 2.5 μm; PM2.5) and coarse particles (PM with aerodynamic diameter 2.5–10 μm; PM10–2.5), for which monitoring is limited before 1999. To address these limitations, we developed spatiotemporal models to predict monthly outdoor PM2.5 and PM10–2.5 concentrations for the northeastern and midwestern United States. Methods: For PM2.5, we developed models for two periods: 1988–1998 and 1999–2002. Both models included smooth spatial and regression terms of geographic information system-based and meteorologic predictors. To compensate for sparse monitoring data, the pre-1999 model also included predicted PM10 (PM with aerodynamic diameter < 10 μm) and extinction coefficients (km−1). PM10–2.5 levels were estimated as the difference in monthly predicted PM10 and PM2.5, with predicted PM10 from our previously developed PM10 model. Results: Predictive performance for PM2.5 was strong (cross-validation R2 = 0.77 and 0.69 for post-1999 and pre-1999 PM2.5 models, respectively) with high precision (2.2 and 2.7 μg/m3, respectively). Models performed well irrespective of population density and season. Predictive performance for PM10–2.5 was weaker (cross-validation R2 = 0.39) with lower precision (5.5 μg/m3). PM10–2.5 levels exhibited greater local spatial variability than PM10 or PM2.5, suggesting that PM2.5 measurements at ambient monitoring sites are more representative for surrounding populations than for PM10 and especially PM10–2.5. Conclusions: We provide semiempirical models to predict spatially and temporally resolved long-term average outdoor concentrations of PM2.5 and PM10–2.5 for estimating exposures of populations living in the northeastern and midwestern United States

Grasses continue to trump trees at soil carbon sequestration following herbivore exclusion in a semiarid African savanna

Although studies have shown that mammalian herbivores often limit aboveground carbon storage in savannas, their effects on belowground soil carbon storage remain unclear. Using three sets of long‐term, large herbivore exclosures with paired controls, we asked how almost two decades of herbivore removal from a semiarid savanna in Laikipia, Kenya affected aboveground (woody and grass) and belowground soil carbon sequestration, and determined the major source (C3 vs. C4) of belowground carbon sequestered in soils with and without herbivores present. Large herbivore exclusion, which included a diverse community of grazers, browsers, and mixed‐feeding ungulates, resulted in significant increases in grass cover (~22%), woody basal area (~8 m2/ha), and woody canopy cover (31%), translating to a ~8.5 t/ha increase in aboveground carbon over two decades. Herbivore exclusion also led to a 54% increase (20.5 t/ha) in total soil carbon to 30‐cm depth, with ~71% of this derived from C4 grasses (vs. ~76% with herbivores present) despite substantial increases in woody cover. We attribute this continued high contribution of C4 grasses to soil C sequestration to the reduced offtake of grass biomass with herbivore exclusion together with the facilitative influence of open sparse woody canopies (e.g., Acacia spp.) on grass cover and productivity in this semiarid system

fullfact: an R package for the analysis of genetic and maternal variance components from full factorial mating designs

Full factorial breeding designs are useful for quantifying the amount of additive genetic, nonadditive genetic, and maternal variance that explain phenotypic traits. Such variance estimates are important for examining evolutionary potential. Traditionally, full factorial mating designs have been analyzed using a two-way analysis of variance, which may produce negative variance values and is not suited for unbalanced designs. Mixed-effects models do not produce negative variance values and are suited for unbalanced designs. However, extracting the variance components, calculating significance values, and estimating confidence intervals and/or power values for the components are not straightforward using traditional analytic methods. We introduce fullfact an R package that addresses these issues and facilitates the analysis of full factorial mating designs with mixed-effects models. Here, we summarize the functions of the fullfact package. The observed data functions extract the variance explained by random and fixed effects and provide their significance. We then calculate the additive genetic, nonadditive genetic, and maternal variance components explaining the phenotype. In particular, we integrate nonnormal error structures for estimating these components for nonnormal data types. The resampled data functions are used to produce bootstrap-t confidence intervals, which can then be plotted using a simple function. We explore the fullfact package through a worked example. This package will facilitate the analyses of full factorial mating designs in R, especially for the analysis of binary, proportion, and/or count data types and for the ability to incorporate additional random and fixed effects and power analyses

Using evolutionary demography to link life history theory, quantitative genetics and population ecology

1. There is a growing number of empirical reports of environmental change simultaneously influencing population dynamics, life history and quantitative characters. We do not have a well-developed understanding of links between the dynamics of these quantities