Additional file 1: of An interspecific assessment of Bergmann’s rule in 22 mammalian families

All phylogenies used in analyses. R script for data extraction and analyses. Detailed results/raw output from SLOUCH. SLOUCH input data. Likelihood plots for all half-life estimations. (ZIP 2442 kb

Visual and olfactory components and total probability of being killed.

<p>Different fixed values for VC (visual conspicuousness) are plotted against OSD (olfactory signal and defence) values, showing variation in the total probability of being killed (Φ). Selective forces acting on conspicuousness undergo a shift when defence levels reach a critical value (point of intersection). Our model predicts that maximum conspicuousness is the best strategy when the individuals are maximally defended through OSD.</p

MOESM1 of Performance evaluation of a new custom, multi-component DNA isolation method optimized for use in shotgun metagenomic sequencing-based aerosol microbiome research

Additional file 1: Figure S1. Rarefaction curves with α-diversity measures: “Observed”, “Shannon”, and “Simpson” for subway air samples (N = 6) that were split and processed with the MetaSUB (N = 3) and Jiang (N = 3) or MetaSUB (N = 3) and Zymobiomics (N = 3) methods. Figure S2. Rarefaction curves with α-diversity measures: “Observed”, “Shannon”, and “Simpson” for the intermediate pellet (N = 6) and supernatant (N = 6) fractions from subway air samples (N = 6) processed separately with the MetaSUB method. Figure S3. Proportion of total DNA and 16S rRNA gene copy yield found in the supernatant fractions, referencing the total yield in the combined pellet and supernatant fractions, from subway air samples (N = 24) where the intermediate pellet and supernatant fractions were processed separately with the MetaSUB method. Figure S4. The 20 fungal species that were among the top 100 species from the random forest classification analysis of subway air samples (N = 3) that were split and processed with the MetaSUB (N = 3) and Jiang (N = 3) methods, where Z-score distributions were compared with linear models

Additional file 1: of Globally distributed Xyleborus species reveal recurrent intercontinental dispersal in a landscape of ancient worldwide distributions

Supplemental tables and figures: Table S1.—Collected data and sequencing coverage for COI and EF1α. Table S2.—Sequencing primers. Table S3.—Out and ingroup specimen collection data, with Genbank accession numbers. Table S4.—Evolutionary models and rates, and summary statistics including ESS values from the biogeographic and phylogenetic reconstruction shown in Fig. 1. Table S5.—Evolutionary models and rates, and summary statistics including ESS values from the species level biogeographic and phylogenetic reconstructions used for the SPREAD plots. Figure S1.—All specimens, for which we had coordinates, plotted on a word map. Figure S2.—A tanglegram showing the level of concordance between the two phylogenetic markers (COI and EF1α). Figure S3.—Mismatch distribution plots showing the distribution of distances between alleles expected under stable population size and the empirical data. Figure S4.—EF1α haplotype network. Figure S5.—Mantel tests of genetic and geographic distance. (PDF 2578 kb

Bark beetle damage in Norwegian forests: a study of model suitability and projected impact under climate change

Bark beetle (Ips typographus) outbreaks have the potential to damage large areas of spruce-dominated forests in Scandinavia. To define forest management strategies that will minimize the risk of bark beetle attacks, we need robust models that link forest structure and composition to the risk and potential damage of bark beetle attacks. Since data on bark beetle infestation rates and corresponding damages does not exist in Norway, we implement a previously published meta-model for estimating I. typographus damage probability and intensity. Using both current and projected climatic conditions we used the model to estimate damage inflicted by I. typographus in Norwegian spruce stands. The model produces feasible results for most of Norway’s climate and forest conditions, but a revised model tailored to Norway should be fitted to a dataset that includes older stands and lower temperatures. Based on current climate and forest conditions, the model predicts that approximately nine percent of productive forests within Norway’s main spruce-growing region will experience a loss ranging from 1.7 to 11 m3/ha of spruce over a span of five years. However, climate change is predicted to exacerbate the annual damage caused by I. typographus, potentially leading to a doubling of its detrimental effects.</p

MOESM2 of The subway microbiome: seasonal dynamics and direct comparison of air and surface bacterial communities

Additional file 1: Table S1. Type of environment, latitude and longitude for all sampled stations. Table S2. Overview of all samples included in the analyses. Table S3. PCR program for 16S rRNA gene amplicon sequencing. Table S4. The best-fit models of qPCR 16S rRNA gene copies for air samples and surface samples. Table S5. Top 20 phyla, families, and genera and species in surface samples collected on kiosks, benches, and railings. Table S6. Random forest classification models of samples collected from different surface types. Figure S1. The significant predictors of qPCR 16S rRNA gene copy yields in air samples. Figure S2. The significant predictors of qPCR 16S rRNA gene copy yields in surface samples. Figure S3. Quality profile of filtered reads. Figure S4. Rarefaction curves with observed diversity and Shannon’s Diversity Index. Figure S5. A) Relative abundances of the top 15 phyla across the three surface types and seasons. B) Heatmap of most abundant families. Figure S6. Top 20 most important genera in random forest classification analysis of samples collected in different seasons. Figure S7. Top 20 most important genera in random forest classification analysis of air and surface samples. Figure S8. Interaction effect between temperature (°C) and air/surface in the linear model of Shannon’s diversity index. Figure S9. PCoA plot of Bray Curtis dissimilarity distances with the only significant predictor (surface type) from the PERMANOVA model that included only surface-specific predictors

R scripts and input files

File contents: 1: Two BEAST input files for phylogenetic reconstructions; 2: input file, settings file and sample prob. file for BAMM analysis; 3: R script for downloading COI sequences from genbank; 4: R script for processing the 305 species tree (data extraction and pruning to genus tree); 5: R script for prepping 602 spp tree for BAMM and analyses of BAMM output files; 6: R scripts for SLOUCH analyse

Structure input data (Libyan set)

Structure input data (Libyan set

Structure 7500 SNPs input file

Structure 7500 SNPs input fil

COI sequences

COI sequence