1. Microbiome sequencing data often need to be normalized due to differences in read depths, and recommendations for microbiome analyses generally warn against using proportions or rarefying to normalize data and instead advocate alternatives, such as upper quartile, CSS, edgeR-TMM, or DESeq-VS. Those recommendations are, however, based on studies that focused on differential abundance testing and variance standardization, rather than community-level comparisons (i.e., beta diversity). Also, standardizing the within-sample variance across samples may suppress differences in species evenness, potentially distorting communitylevel patterns. Furthermore, the recommended methods use log transformations, which we expect to exaggerate the importance of differences among rare OTUs, while suppressing the importance of differences among common OTUs.2. We tested these theoretical predictions via simulations and a real-world dataset.3. Proportions and rarefying produced more accurate comparisons among communities and were the only methods that fully normalized read depths across samples. Additionally, upper quartile, CSS, edgeR-TMM, and DESeq-VS often masked differences among communities when common OTUs differed, and they produced false positives when rare OTUs differed. 4. Based on our simulations, normalizing via proportions may be superior to other commonly used methods for comparing ecological communities.

Contamination is a ubiquitous problem in microbiome research and can skew results, especially when small amounts of target DNA are available. Nevertheless, no clear solution has emerged for removing microbial contamination. To address this problem, we developed the R package microDecon (https://github.com/donaldtmcknight/microDecon), which uses the proportions of contaminant operational taxonomic units (OTUs) or amplicon sequence variants (ASVs) in blank samples to systematically identify and remove contaminant reads from metabarcoding data sets. We rigorously tested microDecon using a series of computer simulations and a sequencing experiment. We also compared it to the common practice of simply removing all contaminant OTUs/ASVs and other methods for removing contamination. Both the computer simulations and our sequencing data confirmed the utility of microDecon. In our largest simulation (100,000 samples), using microDecon improved the results in 98.1% of samples. Additionally, in the sequencing data and in simulations involving groups, it enabled accurate clustering of groups as well as the detection of previously obscured patterns. It also produced more accurate results than the existing methods for identifying and removing contamination. These results demonstrate that microDecon effectively removes contamination across a broad range of situations. It should, therefore, be widely applicable to microbiome studies, as well as to metabarcoding studies in general.

Unprecedented global climate change and increasing rates of infectious disease emergence are occurring simultaneously. Infection with emerging pathogens may alter the thermal thresholds of hosts. However, the effects of fungal infection on host thermal limits have not been examined. Moreover, the influence of infections on the heat tolerance of hosts has rarely been investigated within the context of realistic thermal acclimation regimes and potential anthropogenic climate change. We tested for effects of fungal infection on host thermal tolerance in a model system: frogs infected with the chytrid Batrachochytrium dendrobatidis. Infection reduced the critical thermal maxima (CTmax) of hosts by up to ~4 °C. Acclimation to realistic daily heat pulses enhanced thermal tolerance among infected individuals, but the magnitude of the parasitism effect usually exceeded the magnitude of the acclimation effect. In ectotherms, behaviors that elevate body temperature may decrease parasite performance or increase immune function, thereby reducing infection risk or the intensity of existing infections. However, increased heat sensitivity from infections may discourage these protective behaviors, even at temperatures below critical maxima, tipping the balance in favor of the parasite. We conclude that infectious disease could lead to increased uncertainty in estimates of species’ vulnerability to climate change.

No abstract

Aim: After environmental disasters, species with large population losses may need urgent protection to prevent extinction and support recovery. Following the 2019-2020 Australian megafires, we estimated population losses and recovery in fire-affected fauna, to inform conservation status assessments and management.Location: Temperate and subtropical Australia. Time period: 2019-2030 and beyond.Major taxa: Australian terrestrial and freshwater vertebrates; one invertebrate group. Methods:From > 1,050 fire-affected taxa, we selected 173 whose distributions substantially overlapped the fire extent. We estimated the proportion of each taxon's distribution affected by fires, using fire severity and aquatic impact mapping, and new distribution mapping. Using expert elicitation informed by evidence of responses to previous wildfires, we estimated local population responses to fires of varying severity. We combined the spatial and elicitation data to estimate overall population loss and recovery trajectories, and thus indicate potential eligibility for listing as threatened, or uplisting, under Australian legislation. Results:We estimate that the 2019-2020 Australian megafires caused, or contributed to, population declines that make 70-82 taxa eligible for listing as threatened;

No abstract

No abstract

No abstract