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+Do you think science should influence policy? Do you wonder how to improve evidence-based decision making? Do you have a particular issue that you would like to bring to the attention of decision makers? This workshop covers how to present science in a more impactful way, how to prepare a brief, and encourages participants to meet with a decision maker to discuss the issue raised in their policy brief.
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"section": "Community analysis",
"text": "Community analysis\nBefore we dive into analysing the diversity of leaf bacterial communities on sugar maples, this is a good time to return to the biological questions we posed in the article from which the data are taken.\n\nBiological questions\nIn the study for which these data were collected, we looked at different taxonomic groups (bacteria, fungi, mycorrhizal fungi) living on sugar maple leaves and roots. Here we will concentrate just on leaf bacteria.\nOur aim was to assess the relative influence of host genotype (seed provenance) and environment (transplanted site and region) in structuring the microbiome of sugar maple seedlings. When analyzing the data, let’s keep focused on these biological questions to help guide our decisions about how to analyze the data. We can take different perspectives to respond to this question by looking at different aspects of diversity. And beyond specific tests of our biological hypotheses, it is also useful to carry out basic descriptive analysis of community structure in order to determine what organisms were living in our samples.\nIn the metadata, the variables StandType and TransplantedSite represent the region and site into which the seedlings were planted (environment), and the variables SeedSourceRegion and SeedSourceOrigin represent the region and site from which the seeds were collected (genotype).\n\n\nVisualize taxonomic composition of communities\nA fundamental question we can address using microbiome data is simply ‘who is there’? What are the abundant taxa in different samples?\n\nPhylum-level taxonomic composition of samples\nRemember that for each ASV, we have taxonomic annotations at different ranks. Here we’ll look at the relative abundance of bacterial phyla in each sample. We can repeat these analyses at different taxonomic ranks, but as we go to finer ranks there will be more ASVs with missing data because we could not confidently determine their taxonomic annotation, so there will be more unidentified taxa. Nearly all ASVs have an annotation at the phylum level. We first manipulate the ASV data to create a new data object of phylum abundances, and then we can visualize those abundances.\n\n# community data aggregation at a taxonomic level. e.g. phylum \n# take the sum of each phylum in each sample\ntaxa.agg <- aggregate(t(comm_rarfy),by=list(taxo.sub[colnames(comm_rarfy),2]),FUN=sum)\n# clean up resulting object\nrownames(taxa.agg) <- taxa.agg$Group.1\nphylum_data <- t(taxa.agg[,-1])\n# convert abundances to relative abundances\nphylum_data <- phylum_data/rowSums(phylum_data)\n# remove rare phyla\nphylum_data <- phylum_data[,colSums(phylum_data)>0.01]\n# now reshape phylum data to long format\nphylum_data <- reshape2::melt(phylum_data)\n# rename columns\ncolnames(phylum_data)[1:2] <- c('Samples','Bacteria_phylum')\n# now we can plot phylum relative abundance per sample\nggplot(phylum_data, aes(Samples, weight = value, fill = Bacteria_phylum)) +\n geom_bar(color = \"black\", width = .7, position = 'fill') +\n labs( y = 'Relative abundance (%)') +\n scale_y_continuous(expand = c(0,0)) +\n scale_fill_viridis_d(direction = -1L) +\n theme_classic() +\n coord_flip()\n\n\n\n\n\n\nPhylum-level composition for each transplant site\nIt is also useful to see how taxonomic composition varies with respect to different variables we measured. For example, how does phylum-level taxonomic composition differ among different transplant sites?\n\n# aggregate average phylum abundances per transplanted site\nphylum_data_agg <- aggregate(phylum_data$value,by=list(metadata.sub[phylum_data$Samples,]$TransplantedSite,phylum_data$Bacteria_phylum),FUN=mean)\n# rename columns\ncolnames(phylum_data_agg) <- c('TransplantedSite','Bacteria_phylum','value')\n# now we can plot phylum abundance by transplant site\nggplot(phylum_data_agg, aes(TransplantedSite, weight =value, fill = Bacteria_phylum)) +\n geom_bar(color = \"black\", width = .7, position = 'fill') +\n labs( y = 'Relative abundance (%)') +\n scale_y_continuous(expand = c(0,0)) +\n scale_fill_viridis_d(direction = -1L) +\n theme_classic()\n\n\n\n\n\n\n\nCommunity diversity (alpha diversity)\nTo look at how diversity differed among samples as a function of the different variables we are interested in, we’ll begin by looking at the alpha-diversity, or within-community diversity. Two commonly used measures of alpha diversity are ASV richness (the number of ASVs present in the sample), and the Shannon index, also sometimes referred to as Shannon diversity or Shannon diversity index, which measures both the number of taxa in the sample (ASV richness) as well as the equitability of their abundances (evenness).\n\nCalculate ASV richness and Shannon diversity of bacterial community\n\n# calculate ASV richness\n# calculate Shannon index\nShannon <- diversity(comm_rarfy)\nhist(richness_rarfy)\n\n\n\nhist(Shannon)\n\n\n\n\n\n\nCompare bacterial diversity among categories\nWe can ask how diversity differs with respect to seedling genotype and environment. Here, let’s ask specifically whether leaf bacterial alpha diversity differs between stand types. See Figure 2 of the article by De Bellis et al. 2022 for a comparable analysis.\n\n# create data frame to hold alpha diversity values by stand type\ndiv_standtype <- data.frame(richness=richness_rarfy, Shannon=Shannon, standtype=metadata.sub$StandType)\n# set up analysis to compare diversity among different pairs of stand types\nmy_comparisons <- list( c(\"Mixed\", \"Boreal\"), c(\"Mixed\", \"Temperate\"), c(\"Boreal\", \"Temperate\") )\n# plot ASV richness as a function of stand type\nggboxplot(div_standtype, x = \"standtype\", y = \"richness\", hide.ns=F,\n color = \"standtype\", palette = \"jco\",add = \"jitter\") + \n stat_compare_means(comparisons = my_comparisons,method = \"t.test\")\n\n[1] FALSE\n\n\n\n\n# plot Shannon index as a function of stand type\nggboxplot(div_standtype, x = \"standtype\", y = \"Shannon\",hide.ns=F,\n color = \"standtype\", palette = \"jco\",add = \"jitter\")+ \n stat_compare_means(comparisons = my_comparisons,method = \"t.test\")\n\n[1] FALSE\n\n\n\n\n\nThese figures indicate that ASV richness does not differ significantly among pairs of stand types. However, the Shannon index does differ somewhat between temperate and boreal stands, where the Shannon index tends to be lower in boreal stands than in temperate stands. These analyses are limited somewhat by the sample size that remains after removing low quality samples; in the original article (De Bellis et al 2022) we compared more sites which increased sample size enough that we found a significant (P<0.05) difference in the Shannon index between temperate and boreal stand types.\n\n\nBacterial diversity related to numeric variables\nWe can also ask if bacterial diversity differs with respect to numeric variables, for example with respect to latitude. To ask how richness varies with latitude, we will use a generalized linear model to take into account that richness counts are count data and thus are more likely to follow a Poisson distribution than a normal distribution.\n\n# plot richness versus latitude\nplot(richness_rarfy~metadata.sub$TransplantedSiteLat,xlab='Latitude',ylab='ASV richness')\n# add best fit line\nabline(glm(richness_rarfy~metadata.sub$TransplantedSiteLat), family=\"poisson\")\n\n\n\n# generalized linear model of richness vs. latitude\nsummary(glm(richness_rarfy~metadata.sub$TransplantedSiteLat, family=\"poisson\"))\n\n\nCall:\nglm(formula = richness_rarfy ~ metadata.sub$TransplantedSiteLat, \n family = \"poisson\")\n\nDeviance Residuals: \n Min 1Q Median 3Q Max \n-8.3234 -2.7974 0.4008 2.0747 8.9199 \n\nCoefficients:\n Estimate Std. Error z value Pr(>|z|) \n(Intercept) 7.98399 0.59571 13.403 < 2e-16 ***\nmetadata.sub$TransplantedSiteLat -0.06996 0.01246 -5.615 1.97e-08 ***\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for poisson family taken to be 1)\n\n Null deviance: 409.78 on 26 degrees of freedom\nResidual deviance: 378.60 on 25 degrees of freedom\nAIC: 555.3\n\nNumber of Fisher Scoring iterations: 4\n\n\nASV richness of leaf bacteria decreases with increasing latitude.\nWhat about the Shannon index? Here we’ll fit a linear model since we expect the data to be normally distributed.\n\n# plot Shannon index versus latitude\nplot(Shannon~metadata.sub$TransplantedSiteLat,xlab='Latitude',ylab='Shannon diversity')\n# add best fit line\nabline(lm(Shannon~metadata.sub$TransplantedSiteLat))\n\n\n\n# linear model of Shannon index vs. latitude\nsummary(lm(Shannon~metadata.sub$TransplantedSiteLat))\n\n\nCall:\nlm(formula = Shannon ~ metadata.sub$TransplantedSiteLat)\n\nResiduals:\n Min 1Q Median 3Q Max \n-1.56982 -0.27830 0.07116 0.35948 1.11564 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 7.8728 3.6067 2.183 0.0387 *\nmetadata.sub$TransplantedSiteLat -0.1030 0.0752 -1.370 0.1829 \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 0.5839 on 25 degrees of freedom\nMultiple R-squared: 0.06983, Adjusted R-squared: 0.03262 \nF-statistic: 1.877 on 1 and 25 DF, p-value: 0.1829\n\n\nThe Shannon index of leaf bacteria decreases with increasing latitude, but the relationship is not statistically significant.\n\n\n\nCommunity composition (beta diversity)\nWe can now look at the beta-diversity of the samples, which measures the between-community differences in composition.\nBeta diversity approaches to studying composition involve calculating a measure of the compositional difference between pairs of communities (= beta diversity, dissimilarity, or distance). Then these distance metrics can be analysed using approaches such as ordination (to summarize the overall trends in community composition among samples) or PERMANOVA (to test whether groups of samples differ significantly in their composition).\nThere are a huge number of different beta diversity/dissimilarity metrics that have been used in ecology. There is an ongoing debate about the relative advantages and disadvantages of these beta diversity measures and approaches. There are several different beta diversity metrics and ordination approaches that work well based on simulation studies and empirical analysis. For those wanting to learn more about multivariate analysis and beta diversity approaches for the analysis of ecological communities, the book “Numerical Ecology with R” by Borcard, Gillet and Legendre is an excellent reference. Dr. Pierre Legendre also maintains a website with links to several excellent courses that serve as a complete introduction to multivariate methods in ecology.\nIn this workshop we will focus on a few different beta diversity metrics and ordination approaches that have been shown to perform well in theory and practise when applied to ecological community data. How should you choose which of these approaches to your own data? This is not an easy question to answer, as long as you are using a method that works well with ecological community data, there are different reasons to prefer one method over another. Ultimately, it can be useful to try different approaches and see if you obtain similar results with your data.\n\nOrdination - Principal Components Analysis (PCA)\nPrincipal components analysis (PCA) is a commonly used approach to analysing multivariate data. PCA uses eigenanalysis of Euclidean distances among samples computed from ASV abundance data to identify the axes of correlated variation that explain the most possible variation in your multivariate data. These axes correspond to major gradients of changes in community composition. We typically focus on the first few axes of the PCA ordination, since these axes should capture the majority of the variation in community composition among samples.\nIt’s important to note that PCA should never be used to analyze untranformed ecological community data. PCA is based on the Euclidean distance among samples. Ecological community data violate many of the assumptions of PCA - recall that there are many zeroes in our matrix of ASV abundances, and the distribution of abundance values among ASVs is very non-normal. Legendre and Gallagher (2001) showed that several transformations including the Chord and Hellinger transformation allow ecological community data matrices to be analyzed using PCA. Here we will use the Hellinger transformation to transform ASV abundances prior to analyzing them with a PCA ordination.\n\n# Create Hellinger-transformed version of rarefied community data\ncomm_hel <- decostand(comm_rarfy,method='hellinger')\n# PCA analysis of Hellinger-transformed community data\ncomm_hel_PCA <- prcomp(comm_hel)\n# Summarize variance in beta diversity explained by PCA axes\nsummary(comm_hel_PCA)\n\nImportance of components:\n PC1 PC2 PC3 PC4 PC5 PC6 PC7\nStandard deviation 0.2599 0.20969 0.19519 0.1718 0.16095 0.15289 0.14424\nProportion of Variance 0.1497 0.09746 0.08445 0.0654 0.05742 0.05181 0.04612\nCumulative Proportion 0.1497 0.24712 0.33157 0.3970 0.45439 0.50620 0.55232\n PC8 PC9 PC10 PC11 PC12 PC13 PC14\nStandard deviation 0.13835 0.1327 0.12813 0.12268 0.12067 0.11446 0.11003\nProportion of Variance 0.04242 0.0390 0.03639 0.03336 0.03228 0.02904 0.02684\nCumulative Proportion 0.59474 0.6338 0.67014 0.70350 0.73577 0.76481 0.79165\n PC15 PC16 PC17 PC18 PC19 PC20 PC21\nStandard deviation 0.10440 0.10234 0.09813 0.09351 0.09258 0.09083 0.08976\nProportion of Variance 0.02416 0.02321 0.02134 0.01938 0.01900 0.01829 0.01786\nCumulative Proportion 0.81581 0.83902 0.86037 0.87975 0.89875 0.91704 0.93490\n PC22 PC23 PC24 PC25 PC26 PC27\nStandard deviation 0.08279 0.08176 0.07641 0.07181 0.06955 2.431e-16\nProportion of Variance 0.01519 0.01482 0.01294 0.01143 0.01072 0.000e+00\nCumulative Proportion 0.95009 0.96491 0.97785 0.98928 1.00000 1.000e+00\n\n# Plot PCA results\nordiplot(comm_hel_PCA, display = 'sites', type = 'text',cex=0.8, main=\"PCA on Hellinger transformed data\")\n# Add ellipses around samples from different stand types\nordiellipse(comm_hel_PCA, metadata.sub$StandType, label=TRUE)\n\n\n\n\nThe PCA ordination diagram indicates the overall compositional similarity of samples. Samples that are close together in the ordination space contain similar ASVs with similar abundances. We can see that the gradient in community composition among different stand types is visible along the first two axes. Samples from each stand type tend to contain compositionally similar leaf bacterial communities.\nRecall that when we were first looking at the composition of communities in our samples (prior to subsetting and rarefaction), we obtained a similar looking result. As noted, because we used a rarefaction threshold that was sufficient for the rarefaction curves of the samples to reach a plateau in ASV richness, we do not expect major differences between the analysis of rarefied versus non-rarefied data. However, by analyzing the rarefied data we are now confident that the differences in composition among the samples are due to true differences in community composition and not due to differences in library size among samples.\n\n\nOrdination - Non-metric Multidimensional Scaling (NMDS)\nAnother commonly used ordination approach in ecology is non-metric multidimensional scaling (NMDS). NMDS can be used with any beta diversity distance metric. Unlike PCA, NMDS is not based on eigenanalysis of the distance metric. Rather, NMDS uses an algorithm to find an ordination of samples in a few dimensions that represents as best as possible the rank transformed pairwise distances among samples measured with the original distance metric. When carrying out a NMDS analysis, rather than obtaining many PCA axes, the user specifies how many axes to identify. NMDS analysis is based on a heuristic algorithm that may give slightly different results when run multiple times on the same data, whereas PCA has a unique analytical solution.\nNMDS can be used with any distance metric. Commonly in microbial ecology it is used with the Bray-Curtis distance. The Bray-Curtis distance, like the Hellinger distance, has been shown to perform well compared with other distance measures (Faith et al. 1987, Gallagher and Legendre 2001).\nHere we’ll calculate a NMDS ordination using Bray-Curtis distance.\n\n# NMDS ordination based on Bray-Curtis distances\ncomm_NMDS <- metaMDS(comm_hel, distance=\"bray\", trace=FALSE)\nordiplot(comm_NMDS, cex = 0.5, type = 'text', display='sites')\nordiellipse(comm_NMDS, metadata.sub$StandType, label=TRUE)\n# overlay the direction of latitude effect on bacteria community composition\nef <- envfit(comm_NMDS, metadata.sub$TransplantedSiteLat)\nrownames(ef$vectors$arrows)='Latitude'\nplot(ef)\n\n\n\n\nHere we can see that the NMDS ordination based on Bray-Curtis distances among samples looks quite similar to the PCA ordination, with community composition differing among different stand types. We have also added an arrow indicating the correlation between latitude and the ordination axes, which also supports the idea that communities vary as we move from boreal stands in the north to temperate stands in the south.\n\n\nPERMANOVA\nOrdination analyses allow us to visualize how the composition of communities differs among samples and how it relates to different variables in a qualitative way. For example, we can see from the ordination diagrams above that it seems that community composition differs among stand types. We can statistically test whether stand types differ in composition using permutational multivariate analysis of variance (PERMANOVA). A PERMANOVA works in a way similar to an ANOVA, but with multivariate compositional data. PERMANOVA tests indicate whether community composition (beta diversity) differs among groups of samples.\nHere we can first use a PERMANOVA to test whether community composition differs among stand types. As with ordination methods, a PERMANOVA can be run on any distance metric. Let’s try computing a PERMANOVA using both Hellinger distance (used for the PCA) and Bray-Curtis distance (used for the NMDS).\n\n# set number of permutations for PERMANOVA\nperm <- how(nperm = 999)\n# PERMANOVA on Hellinger-transformed Euclidean distances\nadonis2(formula = dist(comm_hel) ~ StandType, data = metadata.sub, permutations = perm)\n\nPermutation test for adonis under reduced model\nTerms added sequentially (first to last)\nPermutation: free\nNumber of permutations: 999\n\nadonis2(formula = dist(comm_hel) ~ StandType, data = metadata.sub, permutations = perm)\n Df SumOfSqs R2 F Pr(>F) \nStandType 2 1.8390 0.15678 2.2311 0.001 ***\nResidual 24 9.8911 0.84322 \nTotal 26 11.7301 1.00000 \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n# PERMANOVA on Bray-Curtis distances\nadonis2(formula = vegdist(comm_rarfy, method=\"bray\") ~ StandType, data = metadata.sub, permutations = perm)\n\nPermutation test for adonis under reduced model\nTerms added sequentially (first to last)\nPermutation: free\nNumber of permutations: 999\n\nadonis2(formula = vegdist(comm_rarfy, method = \"bray\") ~ StandType, data = metadata.sub, permutations = perm)\n Df SumOfSqs R2 F Pr(>F) \nStandType 2 0.9716 0.17856 2.6085 0.001 ***\nResidual 24 4.4697 0.82144 \nTotal 26 5.4413 1.00000 \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n\nBoth of these analyses indicate that community composition differs significantly among stand types, explaining around 16-18% of variation in the distance metric.\nPERMANOVA can also be used to test more complex experimental designs. Here, let’s replicate the analyses by De Bellis et al. (2022). We used PERMANOVA on Bray-Curtis distances to measure the relative importance of transplanted region and site (environment) versus origin region and site (genotype) in determining the composition of leaf bacterial communities. Site is nested within region for both variable types, which we represent using “/” in the formula (region/site).\n\npermanova.bc <- adonis2(formula = vegdist(comm_rarfy, method=\"bray\") ~ StandType/TransplantedSite * SeedSourceRegion/SeedSourceOrigin, data = metadata.sub, permutations = perm)\nprint(permanova.bc)\n\nPermutation test for adonis under reduced model\nTerms added sequentially (first to last)\nPermutation: free\nNumber of permutations: 999\n\nadonis2(formula = vegdist(comm_rarfy, method = \"bray\") ~ StandType/TransplantedSite * SeedSourceRegion/SeedSourceOrigin, data = metadata.sub, permutations = perm)\n Df SumOfSqs\nStandType 2 0.9716\nSeedSourceRegion 2 0.2277\nStandType:TransplantedSite 3 0.6410\nStandType:SeedSourceRegion 3 0.5562\nStandType:TransplantedSite:SeedSourceRegion 3 0.7350\nStandType:TransplantedSite:SeedSourceRegion:SeedSourceOrigin 5 1.1043\nResidual 8 1.2056\nTotal 26 5.4413\n R2 F\nStandType 0.17856 3.2236\nSeedSourceRegion 0.04184 0.7554\nStandType:TransplantedSite 0.11780 1.4177\nStandType:SeedSourceRegion 0.10222 1.2302\nStandType:TransplantedSite:SeedSourceRegion 0.13507 1.6256\nStandType:TransplantedSite:SeedSourceRegion:SeedSourceOrigin 0.20294 1.4654\nResidual 0.22157 \nTotal 1.00000 \n Pr(>F) \nStandType 0.001 ***\nSeedSourceRegion 0.835 \nStandType:TransplantedSite 0.058 . \nStandType:SeedSourceRegion 0.191 \nStandType:TransplantedSite:SeedSourceRegion 0.019 * \nStandType:TransplantedSite:SeedSourceRegion:SeedSourceOrigin 0.031 * \nResidual \nTotal \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n\nThis analysis shows that, as mentioned by De Bellis et al. (2022), stand type of planting was the most important variable explaining variation in leaf bacterial community composition. There was a marginally significant variation among transplanted sites. There were also significant interactions among transplant region and site as a function of seed source region and seed source site of origin, indicating that the leaf bacterial communities of some genotypes respond differently to different environments. Taken together, this analysis indicates that environment (transplant region and site) have a greater impact on the composition of leaf bacterial communities than genotype, although there is also an interaction between genotype and environment.\n\n\n\nDifferentially abundant taxa\nBy analyzing alpha and beta diversity, we have determined that some groups of samples differ in their diversity and composition. But measures of diversity look at the entire community. We may also be interested in knowing which individual taxa differ in abundance among groups of samples. To address this question we can use differential abundance analysis. There are many methods that can be used for differential abundance analysis, and as for other ecological analysis there is active debate about which methods should be used to detect differences in taxa abundances between groups of samples.\nDifferential abundance analysis allows you to identify differentially abundant taxa between two groups. There are many methods for this type of analysis such as ALDEx2, ANCOM-BC, and DESeq2. Several recent articles have compared the performance of these differential abundance approaches when applied to microbiome data (Calgaro et al. (2020), Nearing et al. (2022)), finding that some methods perform better than others, but also finding that different methods are sensitive to different aspects of data structure and with differing statistical power and sensitivity. Here we will compare two different methods for detecting differentially abundant taxa.\nHaving seen a clear difference in leaf bacterial community composition between temperate and boreal forest stand types, we may ask which bacterial ASVs are differentially abundant between temperate versus boreal forest stand types.\n\nDESeq2\nThe DESeq2 approach (Love et al. 2014. Genome Biol 15:550) models taxa abundances using a negative binomial distribution to detect taxa that are differentially abundant between groups. This approach was initially developed for gene expression analyses, but it is commonly used in the analysis of differential taxa abundances in microbial ecology.\n\n# conduct DESeq analysis of ASV abundances between temperate and boreal stand types\n# We add a pseudocount (+1) to each abundance value to avoid zeroes\ncountdata <- data.frame(ASV=colnames(comm_rarfy),t(comm_rarfy+1))\nmetas <- metadata.sub\nmetas$StandType <- relevel(as.factor(metas$StandType),ref='Boreal')\ndds <- DESeqDataSetFromMatrix(countData=countdata, \n colData=metas,\n design=~StandType, tidy = TRUE)\n\nconverting counts to integer mode\n\ndds2 <- DESeq(dds, fitType=\"local\", quiet=TRUE)\nres <- results(dds2, name=\"StandType_Temperate_vs_Boreal\")\n#coef of differently abundant ASV between temperate and boreal forest\nhead(res[order(res$padj),])\n\nlog2 fold change (MLE): StandType Temperate vs Boreal \nWald test p-value: StandType Temperate vs Boreal \nDataFrame with 6 rows and 6 columns\n baseMean log2FoldChange lfcSE stat pvalue padj\n \nASV_8 102.71993 6.31596 1.070810 5.89830 3.67260e-09 1.31686e-06\nASV_37 16.25372 5.24749 0.906315 5.78992 7.04201e-09 1.31686e-06\nASV_94 8.43527 4.85485 0.860202 5.64385 1.66291e-08 2.07309e-06\nASV_26 29.32340 5.88306 1.086821 5.41309 6.19468e-08 5.79203e-06\nASV_22 36.19921 5.19054 0.994117 5.22126 1.77713e-07 1.32929e-05\nASV_44 20.59886 -5.09305 1.001330 -5.08629 3.65139e-07 2.27603e-05\n\n#Number of significantly different ASVs (adjusted_p_value<0.05)\ndim(res[is.na(res$padj)==F&res$padj<0.05,])[1]\n\n[1] 77\n\nsig_des <- rownames(res[is.na(res$padj)==F&res$padj<0.05,])\n# show the distribution of these marker ASV in a heatmap\nmetatoshow <- subset(metas,!metas$StandType%in%'Mixed')\ndatatoshow <- comm_rarfy[rownames(metatoshow),rownames(res[order(res$padj),])[1:20]]\nannotation_col = data.frame(standtype = as.factor(metatoshow$StandType))\nrownames(annotation_col)=rownames(datatoshow)\npheatmap(t(datatoshow),scale= \"row\", annotation_col = annotation_col,cluster_cols = F)\n\n\n\n\n\n\nANCOM-BC\nThe ANCOM-BC approach (Lin and Peddada. 2020. Nat. Comm. 35:14) detects differentially abundant taxa by analysis of compositions of microbiomes with bias correction. Nearing et al. (2022) found this method to be among the best-performing methods for detection of differentially abundant taxa.\n\n# conduct ANCOM-BC analysis of differentially abundant taxa between boreal/temperate stand types\nASV <- otu_table(countdata[,-1],taxa_are_rows<-T)\nTAX <- tax_table(as.matrix(taxo.sub))\nMETA <- sample_data(metas)\nphyloseqobj = phyloseq(ASV, TAX, META)\nancob <- ancombc(phyloseq=phyloseqobj,formula='StandType',p_adj_method = \"holm\", zero_cut = 0.90, lib_cut = 1000,\n group = 'StandType', struc_zero = TRUE, neg_lb = F,\n tol = 1e-5, max_iter = 100, conserve = TRUE,\n alpha = 0.05, global = TRUE)\nres_ancob <- ancob$res\n#show coefficients of differential abundance test between temperate vs. boreal\ncoef_ancob <- data.frame(beta=res_ancob$beta$StandTypeTemperate,se=res_ancob$se$StandTypeTemperate,W=res_ancob$W$StandTypeTemperate,\n p_val=res_ancob$p_val$StandTypeTemperate,p_adj=res_ancob$q_val$StandTypeTemperate,sign_dif=res_ancob$diff_abn$StandTypeTemperate,\n row.names = rownames(res_ancob$beta))\ncoef_ancob <- coef_ancob[order(coef_ancob$p_adj),]\nhead(coef_ancob)\n\n beta se W p_val p_adj sign_dif\nASV_44 -2.2721929 0.4081187 -5.567481 2.584489e-08 2.101189e-05 TRUE\nASV_22 2.5469174 0.5179469 4.917333 8.773119e-07 7.123773e-04 TRUE\nASV_11 2.2662741 0.4980824 4.549998 5.364645e-06 4.350727e-03 TRUE\nASV_101 -1.8558870 0.4108073 -4.517658 6.252726e-06 5.064708e-03 TRUE\nASV_37 2.3964439 0.5694741 4.208170 2.574473e-05 2.082749e-02 TRUE\nASV_129 0.4651886 0.1182296 3.934619 8.332862e-05 6.732952e-02 FALSE\n\n#Number of significantly different ASVs (adjusted p_value<0.05)\ndim(coef_ancob[coef_ancob$p_adj<0.05,])\n\n[1] 5 6\n\nsig_ancob <- rownames(coef_ancob[1:dim(coef_ancob[coef_ancob$p_adj<0.05,])[1],])\n# show the significant ASVs\nmetatoshow <- subset(metas,!metas$StandType%in%'Mixed')\ndatatoshow <- comm_rarfy[rownames(metatoshow),sig_ancob]\nannotation_col = data.frame(standtype = as.factor(metatoshow$StandType))\nrownames(annotation_col)=rownames(datatoshow)\npheatmap(t(datatoshow),scale= \"row\", annotation_col = annotation_col,cluster_cols = F)\n\n\n\n\n\n\nComparing differentially abundant taxa among methods\nIn a comparison between boreal and temperate forest stand types, ANCOM-BC and DESeq2 detected different numbers of differentially abundant ASVs. As noted earlier, there is still debate about which methods perform best for differentially abundant taxa detection. While DESeq2 and ANCOM identified differentially abundant taxa that are potentially unique to each method, there may be some ASVs that were detected as differentially abundant using both methods. This type of cross-method comparison can help us to identify statistically robust results - if taxa are differentially abundant enough to be detected by multiple methods, they should differ strongly in their abundance between groups. Let’s look at the distribution of these differentially abundant ASVs identified by both methods.\n\n# identify significantly differentially abundant ASVs according to both methods\nsig_both <- sig_des[sig_des%in%sig_ancob]\nsig_both\n\n[1] \"ASV_11\" \"ASV_22\" \"ASV_37\" \"ASV_44\" \"ASV_101\"\n\n# show the distribution of these differentially abundant ASVs\nmetatoshow <- subset(metas,!metas$StandType%in%'Mixed')\ndatatoshow <- comm_rarfy[rownames(metatoshow),sig_both]\nannotation_col = data.frame(standtype = as.factor(metatoshow$StandType))\nrownames(annotation_col)=rownames(datatoshow)\npheatmap(t(datatoshow),scale= \"row\", annotation_col = annotation_col,cluster_cols = F)\n\n\n\n\nWe can also inspect the taxonomic annotations of these differentially abundant ASVs.\n\ntaxo_rarfy[sig_both,]\n\n Kingdom Phylum Class Order \nASV_11 \"Bacteria\" \"Proteobacteria\" \"Alphaproteobacteria\" \"Rhizobiales\" \nASV_22 \"Bacteria\" \"Bacteroidota\" \"Bacteroidia\" \"Cytophagales\" \nASV_37 \"Bacteria\" \"Proteobacteria\" \"Alphaproteobacteria\" \"Sphingomonadales\"\nASV_44 \"Bacteria\" \"Proteobacteria\" \"Gammaproteobacteria\" \"Enterobacterales\"\nASV_101 \"Bacteria\" \"Proteobacteria\" \"Alphaproteobacteria\" \"Rhizobiales\" \n Family Genus Species \nASV_11 \"Beijerinckiaceae\" \"Methylobacterium-Methylorubrum\" NA \nASV_22 \"Hymenobacteraceae\" \"Hymenobacter\" NA \nASV_37 \"Sphingomonadaceae\" \"Sphingomonas\" NA \nASV_44 \"Shewanellaceae\" \"Shewanella\" \"algae/haliotis\"\nASV_101 \"Beijerinckiaceae\" \"Methylocella\" NA \n\n\n\n\n\nFinal steps - clean up and save data objects and workspace\nWe have now completed our ecological analyses of leaf bacterial communities. You may want to save the R workspace containing all the different data objects so that you can reload it in the future without having to re-run the analyses.\n\n## Save entire R workspace to file\nsave.image(\"Microbiome-ecological-analysis-workspace.RData\")"
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+ "text": "This training session was presented by Sally Otto, professor in the Department of Zoology at the University of British Columbia and BIOS² co-PI. Pr. Sarah P. Otto has strong expertise on quantitative analysis and mathematical modeling for biologists, and a lot of experience communicating theory to policy decision makers.\nThe workshop was held in four 1h30 sessions in English on March 7, 14, 21 and 28."
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- "text": "Order By\n Default\n \n Date - Oldest\n \n \n Date - Newest\n \n \n Author\n \n \n \n\n\n\n\n \n\n\n\n\nIntroduction to Microbiome Analysis\n\n\n\n\n\n\n\nTechnical\n\n\nEN\n\n\n\n\nThis workshop will give an overview of the theory and practice of using metabarcoding approaches to study the diversity of microbial communities. The workshop will give participants an understanding of 1) the current methods for microbiome diversity quantification using metabarcoding/amplicon sequencing approaches and 2) the normalization and diversity analysis approaches that can be used to quantify the diversity of microbial communities.\n\n\n\n\n\n\nMay 19, 2022\n\n\nSteven Kembel, Zihui Wang, Salix Dubois\n\n\n\n\n\n\n \n\n\n\n\nIntroduction to Generalized Additive Models (GAMs)\n\n\n\n\n\n\n\nTechnical\n\n\nEN\n\n\n\n\nTo address the increase in both quantity and complexity of available data, ecologists require flexible, robust statistical models, as well as software to perform such analyses. This workshop will focus on how a single tool, the R mgcv package, can be used to fit Generalized Additive Models (GAMs) to data from a wide range of sources.\n\n\n\n\n\n\nNov 2, 2021\n\n\nEric Pedersen\n\n\n\n\n\n\n \n\n\n\n\nIntroduction to Shiny Apps\n\n\n\n\n\n\n\nTechnical\n\n\nFellow contributed\n\n\nEN\n\n\n\n\nIntroduction to interactive app development with R Shiny.\n\n\n\n\n\n\nJun 22, 2021\n\n\nKatherine Hébert, Andrew MacDonald, Jake Lawlor, Vincent Bellavance\n\n\n\n\n\n\n \n\n\n\n\nGeneralized Linear Models for Community Ecology\n\n\n\n\n\n\n\nTechnical\n\n\nEN\n\n\n\n\nIn this workshop we will explore, discuss, and apply generalized linear models to combine information on species distributions, traits, phylogenies, environmental and landscape variation. We will also discuss inference under spatial and phylogenetic autocorrelation under fixed and random effects implementations. We will discuss technical elements and cover implementations using R.\n\n\n\n\n\n\nMay 17, 2021\n\n\nPedro Peres-Neto\n\n\n\n\n\n\n \n\n\n\n\nBuilding R packages\n\n\n\n\n\n\n\nTechnical\n\n\nEN\n\n\n\n\nThis practical training will cover the basics of modern package development in R with a focus on the following three aspects: (1) how to turn your code into functions, (2) how to write tests and documentation, and (3) how to share your R package on GitHub..\n\n\n\n\n\n\nMay 4, 2021\n\n\nAndrew MacDonald\n\n\n\n\n\n\n \n\n\n\n\nPoint-count Data Analysis\n\n\n\n\n\n\n\nTechnical\n\n\nEN\n\n\n\n\nAnalysis of point-count data in the presence of variable survey methodologies and detection error offered by Péter Sólymos to BIOS2 Fellows in March 2021.\n\n\n\n\n\n\nMar 25, 2021\n\n\nPéter Sólymos\n\n\n\n\n\n\n \n\n\n\n\nIntroduction to EDI Concepts in a Scientific Context\n\n\n\n\n\n\n\nTransversal competencies\n\n\nFR\n\n\nEN\n\n\n\n\nA short introduction to EDI concepts in a scientific context.\n\n\n\n\n\n\nJan 22, 2021\n\n\nAgathe Riallan, Marie-José Naud\n\n\n\n\n\n\n \n\n\n\n\nSpatial Statistics in Ecology\n\n\n\n\n\n\n\nFR\n\n\nEN\n\n\nTechnical\n\n\n\n\nTraining session about statistical analysis of spatial data in ecology, hosted by Philippe Marchand (UQAT). | Session de formation sur l’analyse statistique des données spatiales en écologie, animée par Pr. Philippe Marchand (UQAT).\n\n\n\n\n\n\nJan 12, 2021\n\n\nPhilippe Marchand\n\n\n\n\n\n\n \n\n\n\n\nMaking Websites with HUGO\n\n\n\n\n\n\n\nTechnical\n\n\nTransversal competencies\n\n\nEN\n\n\n\n\nThis workshop provides a general introduction to HUGO, a popular open source framework for building websites without requiring a knowledge of HTML/CSS or web programming.\n\n\n\n\n\n\nDec 7, 2020\n\n\nDominique Gravel, Guillaume Larocque\n\n\n\n\n\n\n \n\n\n\n\nData Visualization\n\n\n\n\n\n\n\nTechnical\n\n\nFellow contributed\n\n\nEN\n\n\n\n\nGeneral principles of visualization and graphic design, and techniques of tailored visualization. This training was developed and delivered by Alex Arkilanian and Katherine Hébert on September 21st and 22nd, 2020.\n\n\n\n\n\n\nSep 21, 2020\n\n\nAlex Arkilanian, Katherine Hébert\n\n\n\n\n\n\n \n\n\n\n\nScience Communication\n\n\n\n\n\n\n\nCareer\n\n\nFellow contributed\n\n\nEN\n\n\n\n\nRecordings, content and handouts from a 6-hour Science Communication workshop held over two days on 15 and 16 June 2020.\n\n\n\n\n\n\nJun 15, 2020\n\n\nGracielle Higino, Katherine Hébert\n\n\n\n\n\n\n \n\n\n\n\nSensibilisation aux réalités autochtones et recherche collaborative\n\n\n\n\n\n\n\nTransversal competencies\n\n\nFR\n\n\n\n\nSérie de deux webinaires sur la sensibilisation aux réalités autochtones et la recherche en collaboration avec les Autochtones, offert du 28 au 30 avril 2020 par Catherine-Alexandra Gagnon, PhD.\n\n\n\n\n\n\nApr 28, 2020\n\n\nCatherine-Alexandra Gagnon\n\n\n\n\n\n\n \n\n\n\n\nMathematical Modeling in Ecology and Evolution\n\n\n\n\n\n\n\nTechnical\n\n\nEN\n\n\n\n\nThis workshop will introduce participants to the logic behind modeling in biology, focusing on developing equations, finding equilibria, analyzing stability, and running simulations.Techniques will be illustrated with the software tools, Mathematica and Maxima. This workshop was held in two parts: January 14 and January 16, 2020.\n\n\n\n\n\n\nJan 14, 2020\n\n\nSarah P. Otto\n\n\n\n\n\n\nNo matching items"
+ "text": "Order By\n Default\n \n Date - Oldest\n \n \n Date - Newest\n \n \n Author\n \n \n \n\n\n\n\n \n\n\n\n\nBridging the Science-Policy Void\n\n\n\n\n\n\n\nTransversal competencies\n\n\nCareer\n\n\nEN\n\n\n\n\nDo you think science should influence policy? Do you wonder how to improve evidence-based decision making? Do you have a particular issue that you would like to bring to the attention of decision makers? This workshop covers how to present science in a more impactful way, how to prepare a brief, and encourages participants to meet with a decision maker to discuss the issue raised in their policy brief.\n\n\n\n\n\n\nJul 3, 2022\n\n\nSarah P. Otto\n\n\n\n\n\n\n \n\n\n\n\nIntroduction to Microbiome Analysis\n\n\n\n\n\n\n\nTechnical\n\n\nEN\n\n\n\n\nThis workshop will give an overview of the theory and practice of using metabarcoding approaches to study the diversity of microbial communities. The workshop will give participants an understanding of 1) the current methods for microbiome diversity quantification using metabarcoding/amplicon sequencing approaches and 2) the normalization and diversity analysis approaches that can be used to quantify the diversity of microbial communities.\n\n\n\n\n\n\nMay 19, 2022\n\n\nSteven Kembel, Zihui Wang, Salix Dubois\n\n\n\n\n\n\n \n\n\n\n\nIntroduction to Generalized Additive Models (GAMs)\n\n\n\n\n\n\n\nTechnical\n\n\nEN\n\n\n\n\nTo address the increase in both quantity and complexity of available data, ecologists require flexible, robust statistical models, as well as software to perform such analyses. This workshop will focus on how a single tool, the R mgcv package, can be used to fit Generalized Additive Models (GAMs) to data from a wide range of sources.\n\n\n\n\n\n\nNov 2, 2021\n\n\nEric Pedersen\n\n\n\n\n\n\n \n\n\n\n\nIntroduction to Shiny Apps\n\n\n\n\n\n\n\nTechnical\n\n\nFellow contributed\n\n\nEN\n\n\n\n\nIntroduction to interactive app development with R Shiny.\n\n\n\n\n\n\nJun 22, 2021\n\n\nKatherine Hébert, Andrew MacDonald, Jake Lawlor, Vincent Bellavance\n\n\n\n\n\n\n \n\n\n\n\nGeneralized Linear Models for Community Ecology\n\n\n\n\n\n\n\nTechnical\n\n\nEN\n\n\n\n\nIn this workshop we will explore, discuss, and apply generalized linear models to combine information on species distributions, traits, phylogenies, environmental and landscape variation. We will also discuss inference under spatial and phylogenetic autocorrelation under fixed and random effects implementations. We will discuss technical elements and cover implementations using R.\n\n\n\n\n\n\nMay 17, 2021\n\n\nPedro Peres-Neto\n\n\n\n\n\n\n \n\n\n\n\nBuilding R packages\n\n\n\n\n\n\n\nTechnical\n\n\nEN\n\n\n\n\nThis practical training will cover the basics of modern package development in R with a focus on the following three aspects: (1) how to turn your code into functions, (2) how to write tests and documentation, and (3) how to share your R package on GitHub..\n\n\n\n\n\n\nMay 4, 2021\n\n\nAndrew MacDonald\n\n\n\n\n\n\n \n\n\n\n\nPoint-count Data Analysis\n\n\n\n\n\n\n\nTechnical\n\n\nEN\n\n\n\n\nAnalysis of point-count data in the presence of variable survey methodologies and detection error offered by Péter Sólymos to BIOS2 Fellows in March 2021.\n\n\n\n\n\n\nMar 25, 2021\n\n\nPéter Sólymos\n\n\n\n\n\n\n \n\n\n\n\nIntroduction to EDI Concepts in a Scientific Context\n\n\n\n\n\n\n\nTransversal competencies\n\n\nFR\n\n\nEN\n\n\n\n\nA short introduction to EDI concepts in a scientific context.\n\n\n\n\n\n\nJan 22, 2021\n\n\nAgathe Riallan, Marie-José Naud\n\n\n\n\n\n\n \n\n\n\n\nSpatial Statistics in Ecology\n\n\n\n\n\n\n\nFR\n\n\nEN\n\n\nTechnical\n\n\n\n\nTraining session about statistical analysis of spatial data in ecology, hosted by Philippe Marchand (UQAT). | Session de formation sur l’analyse statistique des données spatiales en écologie, animée par Pr. Philippe Marchand (UQAT).\n\n\n\n\n\n\nJan 12, 2021\n\n\nPhilippe Marchand\n\n\n\n\n\n\n \n\n\n\n\nMaking Websites with HUGO\n\n\n\n\n\n\n\nTechnical\n\n\nTransversal competencies\n\n\nEN\n\n\n\n\nThis workshop provides a general introduction to HUGO, a popular open source framework for building websites without requiring a knowledge of HTML/CSS or web programming.\n\n\n\n\n\n\nDec 7, 2020\n\n\nDominique Gravel, Guillaume Larocque\n\n\n\n\n\n\n \n\n\n\n\nData Visualization\n\n\n\n\n\n\n\nTechnical\n\n\nFellow contributed\n\n\nEN\n\n\n\n\nGeneral principles of visualization and graphic design, and techniques of tailored visualization. This training was developed and delivered by Alex Arkilanian and Katherine Hébert on September 21st and 22nd, 2020.\n\n\n\n\n\n\nSep 21, 2020\n\n\nAlex Arkilanian, Katherine Hébert\n\n\n\n\n\n\n \n\n\n\n\nScience Communication\n\n\n\n\n\n\n\nCareer\n\n\nFellow contributed\n\n\nEN\n\n\n\n\nRecordings, content and handouts from a 6-hour Science Communication workshop held over two days on 15 and 16 June 2020.\n\n\n\n\n\n\nJun 15, 2020\n\n\nGracielle Higino, Katherine Hébert\n\n\n\n\n\n\n \n\n\n\n\nSensibilisation aux réalités autochtones et recherche collaborative\n\n\n\n\n\n\n\nTransversal competencies\n\n\nFR\n\n\n\n\nSérie de deux webinaires sur la sensibilisation aux réalités autochtones et la recherche en collaboration avec les Autochtones, offert du 28 au 30 avril 2020 par Catherine-Alexandra Gagnon, PhD.\n\n\n\n\n\n\nApr 28, 2020\n\n\nCatherine-Alexandra Gagnon\n\n\n\n\n\n\n \n\n\n\n\nMathematical Modeling in Ecology and Evolution\n\n\n\n\n\n\n\nTechnical\n\n\nEN\n\n\n\n\nThis workshop will introduce participants to the logic behind modeling in biology, focusing on developing equations, finding equilibria, analyzing stability, and running simulations.Techniques will be illustrated with the software tools, Mathematica and Maxima. This workshop was held in two parts: January 14 and January 16, 2020.\n\n\n\n\n\n\nJan 14, 2020\n\n\nSarah P. Otto\n\n\n\n\n\n\nNo matching items"
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- "text": "About this blog\n\n\n\n\n \n \n \n Order By\n Default\n \n Date - Oldest\n \n \n Date - Newest\n \n \n Author\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\nBIOS² Education resources\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n \n\n\n\n\nBiodiversity Modelling 2022\n\n\n\n\n The 2022 edition of the Biodiversity Modelling Summer School was on the theme: Biodiversity changes and data visualization. The course took the form of a workshop during which the students, in collaboration with local organizations involved in biodiversity monitoring, developed a web platform for visualizing biodiversity changes.\n\n\n\n\n\n\nAug 22, 2022\n\n\nDominique Gravel, Vincent Beauregard\n\n\n\n\n\n\n \n\n\n\n\nBuilding R packages\n\n\n\n\nThis practical training will cover the basics of modern package development in R with a focus on the following three aspects: (1) how to turn your code into functions, (2) how to write tests and documentation, and (3) how to share your R package on GitHub..\n\n\n\n\n\n\nMay 4, 2021\n\n\nAndrew MacDonald\n\n\n\n\n\n\n \n\n\n\n\nData Visualization\n\n\n\n\nGeneral principles of visualization and graphic design, and techniques of tailored visualization. This training was developed and delivered by Alex Arkilanian and Katherine Hébert on September 21st and 22nd, 2020.\n\n\n\n\n\n\nSep 21, 2020\n\n\nAlex Arkilanian, Katherine Hébert\n\n\n\n\n\n\n \n\n\n\n\nGeneralized Linear Models for Community Ecology\n\n\n\n\nIn this workshop we will explore, discuss, and apply generalized linear models to combine information on species distributions, traits, phylogenies, environmental and landscape variation. We will also discuss inference under spatial and phylogenetic autocorrelation under fixed and random effects implementations. We will discuss technical elements and cover implementations using R.\n\n\n\n\n\n\nMay 17, 2021\n\n\nPedro Peres-Neto\n\n\n\n\n\n\n \n\n\n\n\nIntroduction to EDI Concepts in a Scientific Context\n\n\n\n\nA short introduction to EDI concepts in a scientific context.\n\n\n\n\n\n\nJan 22, 2021\n\n\nAgathe Riallan, Marie-José Naud\n\n\n\n\n\n\n \n\n\n\n\nIntroduction to Generalized Additive Models (GAMs)\n\n\n\n\nTo address the increase in both quantity and complexity of available data, ecologists require flexible, robust statistical models, as well as software to perform such analyses. This workshop will focus on how a single tool, the R mgcv package, can be used to fit Generalized Additive Models (GAMs) to data from a wide range of sources.\n\n\n\n\n\n\nNov 2, 2021\n\n\nEric Pedersen\n\n\n\n\n\n\n \n\n\n\n\nIntroduction to Microbiome Analysis\n\n\n\n\nThis workshop will give an overview of the theory and practice of using metabarcoding approaches to study the diversity of microbial communities. The workshop will give participants an understanding of 1) the current methods for microbiome diversity quantification using metabarcoding/amplicon sequencing approaches and 2) the normalization and diversity analysis approaches that can be used to quantify the diversity of microbial communities.\n\n\n\n\n\n\nMay 19, 2022\n\n\nSteven Kembel, Zihui Wang, Salix Dubois\n\n\n\n\n\n\n \n\n\n\n\nIntroduction to Shiny Apps\n\n\n\n\nIntroduction to interactive app development with R Shiny.\n\n\n\n\n\n\nJun 22, 2021\n\n\nKatherine Hébert, Andrew MacDonald, Jake Lawlor, Vincent Bellavance\n\n\n\n\n\n\n \n\n\n\n\nMaking Websites with HUGO\n\n\n\n\nThis workshop provides a general introduction to HUGO, a popular open source framework for building websites without requiring a knowledge of HTML/CSS or web programming.\n\n\n\n\n\n\nDec 7, 2020\n\n\nDominique Gravel, Guillaume Larocque\n\n\n\n\n\n\n \n\n\n\n\nMathematical Modeling in Ecology and Evolution\n\n\n\n\nThis workshop will introduce participants to the logic behind modeling in biology, focusing on developing equations, finding equilibria, analyzing stability, and running simulations.Techniques will be illustrated with the software tools, Mathematica and Maxima. This workshop was held in two parts: January 14 and January 16, 2020.\n\n\n\n\n\n\nJan 14, 2020\n\n\nSarah P. Otto\n\n\n\n\n\n\n \n\n\n\n\nPoint-count Data Analysis\n\n\n\n\nAnalysis of point-count data in the presence of variable survey methodologies and detection error offered by Péter Sólymos to BIOS2 Fellows in March 2021.\n\n\n\n\n\n\nMar 25, 2021\n\n\nPéter Sólymos\n\n\n\n\n\n\n \n\n\n\n\nScience Communication\n\n\n\n\nRecordings, content and handouts from a 6-hour Science Communication workshop held over two days on 15 and 16 June 2020.\n\n\n\n\n\n\nJun 15, 2020\n\n\nGracielle Higino, Katherine Hébert\n\n\n\n\n\n\n \n\n\n\n\nSensibilisation aux réalités autochtones et recherche collaborative\n\n\n\n\nSérie de deux webinaires sur la sensibilisation aux réalités autochtones et la recherche en collaboration avec les Autochtones, offert du 28 au 30 avril 2020 par Catherine-Alexandra Gagnon, PhD.\n\n\n\n\n\n\nApr 28, 2020\n\n\nCatherine-Alexandra Gagnon\n\n\n\n\n\n\n \n\n\n\n\nSpatial Statistics in Ecology\n\n\n\n\nTraining session about statistical analysis of spatial data in ecology, hosted by Philippe Marchand (UQAT). | Session de formation sur l’analyse statistique des données spatiales en écologie, animée par Pr. Philippe Marchand (UQAT).\n\n\n\n\n\n\nJan 12, 2021\n\n\nPhilippe Marchand\n\n\n\n\n\n\nNo matching items"
+ "text": "About this blog\n\n\n\n\n \n \n \n Order By\n Default\n \n Date - Oldest\n \n \n Date - Newest\n \n \n Author\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\nBIOS² Education resources\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n \n\n\n\n\nBiodiversity Modelling 2022\n\n\n\n\n The 2022 edition of the Biodiversity Modelling Summer School was on the theme: Biodiversity changes and data visualization. The course took the form of a workshop during which the students, in collaboration with local organizations involved in biodiversity monitoring, developed a web platform for visualizing biodiversity changes.\n\n\n\n\n\n\nAug 22, 2022\n\n\nDominique Gravel, Vincent Beauregard\n\n\n\n\n\n\n \n\n\n\n\nBridging the Science-Policy Void\n\n\n\n\nDo you think science should influence policy? Do you wonder how to improve evidence-based decision making? Do you have a particular issue that you would like to bring to the attention of decision makers? This workshop covers how to present science in a more impactful way, how to prepare a brief, and encourages participants to meet with a decision maker to discuss the issue raised in their policy brief.\n\n\n\n\n\n\nJul 3, 2022\n\n\nSarah P. Otto\n\n\n\n\n\n\n \n\n\n\n\nBuilding R packages\n\n\n\n\nThis practical training will cover the basics of modern package development in R with a focus on the following three aspects: (1) how to turn your code into functions, (2) how to write tests and documentation, and (3) how to share your R package on GitHub..\n\n\n\n\n\n\nMay 4, 2021\n\n\nAndrew MacDonald\n\n\n\n\n\n\n \n\n\n\n\nData Visualization\n\n\n\n\nGeneral principles of visualization and graphic design, and techniques of tailored visualization. This training was developed and delivered by Alex Arkilanian and Katherine Hébert on September 21st and 22nd, 2020.\n\n\n\n\n\n\nSep 21, 2020\n\n\nAlex Arkilanian, Katherine Hébert\n\n\n\n\n\n\n \n\n\n\n\nGeneralized Linear Models for Community Ecology\n\n\n\n\nIn this workshop we will explore, discuss, and apply generalized linear models to combine information on species distributions, traits, phylogenies, environmental and landscape variation. We will also discuss inference under spatial and phylogenetic autocorrelation under fixed and random effects implementations. We will discuss technical elements and cover implementations using R.\n\n\n\n\n\n\nMay 17, 2021\n\n\nPedro Peres-Neto\n\n\n\n\n\n\n \n\n\n\n\nIntroduction to EDI Concepts in a Scientific Context\n\n\n\n\nA short introduction to EDI concepts in a scientific context.\n\n\n\n\n\n\nJan 22, 2021\n\n\nAgathe Riallan, Marie-José Naud\n\n\n\n\n\n\n \n\n\n\n\nIntroduction to Generalized Additive Models (GAMs)\n\n\n\n\nTo address the increase in both quantity and complexity of available data, ecologists require flexible, robust statistical models, as well as software to perform such analyses. This workshop will focus on how a single tool, the R mgcv package, can be used to fit Generalized Additive Models (GAMs) to data from a wide range of sources.\n\n\n\n\n\n\nNov 2, 2021\n\n\nEric Pedersen\n\n\n\n\n\n\n \n\n\n\n\nIntroduction to Microbiome Analysis\n\n\n\n\nThis workshop will give an overview of the theory and practice of using metabarcoding approaches to study the diversity of microbial communities. The workshop will give participants an understanding of 1) the current methods for microbiome diversity quantification using metabarcoding/amplicon sequencing approaches and 2) the normalization and diversity analysis approaches that can be used to quantify the diversity of microbial communities.\n\n\n\n\n\n\nMay 19, 2022\n\n\nSteven Kembel, Zihui Wang, Salix Dubois\n\n\n\n\n\n\n \n\n\n\n\nIntroduction to Shiny Apps\n\n\n\n\nIntroduction to interactive app development with R Shiny.\n\n\n\n\n\n\nJun 22, 2021\n\n\nKatherine Hébert, Andrew MacDonald, Jake Lawlor, Vincent Bellavance\n\n\n\n\n\n\n \n\n\n\n\nMaking Websites with HUGO\n\n\n\n\nThis workshop provides a general introduction to HUGO, a popular open source framework for building websites without requiring a knowledge of HTML/CSS or web programming.\n\n\n\n\n\n\nDec 7, 2020\n\n\nDominique Gravel, Guillaume Larocque\n\n\n\n\n\n\n \n\n\n\n\nMathematical Modeling in Ecology and Evolution\n\n\n\n\nThis workshop will introduce participants to the logic behind modeling in biology, focusing on developing equations, finding equilibria, analyzing stability, and running simulations.Techniques will be illustrated with the software tools, Mathematica and Maxima. This workshop was held in two parts: January 14 and January 16, 2020.\n\n\n\n\n\n\nJan 14, 2020\n\n\nSarah P. Otto\n\n\n\n\n\n\n \n\n\n\n\nPoint-count Data Analysis\n\n\n\n\nAnalysis of point-count data in the presence of variable survey methodologies and detection error offered by Péter Sólymos to BIOS2 Fellows in March 2021.\n\n\n\n\n\n\nMar 25, 2021\n\n\nPéter Sólymos\n\n\n\n\n\n\n \n\n\n\n\nScience Communication\n\n\n\n\nRecordings, content and handouts from a 6-hour Science Communication workshop held over two days on 15 and 16 June 2020.\n\n\n\n\n\n\nJun 15, 2020\n\n\nGracielle Higino, Katherine Hébert\n\n\n\n\n\n\n \n\n\n\n\nSensibilisation aux réalités autochtones et recherche collaborative\n\n\n\n\nSérie de deux webinaires sur la sensibilisation aux réalités autochtones et la recherche en collaboration avec les Autochtones, offert du 28 au 30 avril 2020 par Catherine-Alexandra Gagnon, PhD.\n\n\n\n\n\n\nApr 28, 2020\n\n\nCatherine-Alexandra Gagnon\n\n\n\n\n\n\n \n\n\n\n\nSpatial Statistics in Ecology\n\n\n\n\nTraining session about statistical analysis of spatial data in ecology, hosted by Philippe Marchand (UQAT). | Session de formation sur l’analyse statistique des données spatiales en écologie, animée par Pr. Philippe Marchand (UQAT).\n\n\n\n\n\n\nJan 12, 2021\n\n\nPhilippe Marchand\n\n\n\n\n\n\nNo matching items"
}
]
\ No newline at end of file
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index c3eb2e4..c143028 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -2,66 +2,70 @@
https://bios2.github.io/posts/2020-04-28-sensibilisation-aux-ralits-autochtones-et-recherche-collaborative/index.html
- 2022-12-16T19:04:23.792Z
+ 2022-12-16T19:47:21.217Z
https://bios2.github.io/posts/2020-09-21-data-visualization/index.html
- 2022-12-16T19:04:25.126Z
+ 2022-12-16T19:47:22.974Z
https://bios2.github.io/posts/2021-11-02-introduction-to-gams/index.html
- 2022-12-16T19:04:25.993Z
+ 2022-12-16T19:47:23.852Z
https://bios2.github.io/posts/2020-01-14-mathematical-modeling-in-ecology-and-evolution/index.html
- 2022-12-16T19:04:26.595Z
+ 2022-12-16T19:47:24.490Z
https://bios2.github.io/posts/2020-06-15-science-communication/index.html
- 2022-12-16T19:04:27.129Z
+ 2022-12-16T19:47:25.079Z
https://bios2.github.io/posts/2021-06-22-introduction-to-shiny-apps/index.html
- 2022-12-16T19:04:28.237Z
+ 2022-12-16T19:47:26.281Z
https://bios2.github.io/posts/2021-05-04-building-r-packages/index.html
- 2022-12-16T19:04:29.050Z
+ 2022-12-16T19:47:27.287Z
https://bios2.github.io/posts/2021-01-12-4-day-training-in-spatial-statistics-with-philippe-marchand/index.html
- 2022-12-16T19:04:34.884Z
+ 2022-12-16T19:47:33.450Z
https://bios2.github.io/posts/2022-05-19-introduction-to-microbiome-analysis/index.html
- 2022-12-16T19:05:22.138Z
+ 2022-12-16T19:47:35.767Z
+
+
+ https://bios2.github.io/posts/2022-03-07-bridging-the-science-policy-void/index.html
+ 2022-12-16T19:47:36.334Z
https://bios2.github.io/posts/2020-12-07-making-websites-with-hugo/index.html
- 2022-12-16T19:04:37.979Z
+ 2022-12-16T19:47:37.221Z
https://bios2.github.io/posts/2021-01-22-introduction-aux-concepts-edi-en-contexte-scientifique/index.html
- 2022-12-16T19:04:38.457Z
+ 2022-12-16T19:47:37.737Z
https://bios2.github.io/posts/2021-03-25-point-count-data-analysis/index.html
- 2022-12-16T19:04:39.244Z
+ 2022-12-16T19:47:38.594Z
https://bios2.github.io/posts/2021-07-19-glm-community-ecology/index.html
- 2022-12-16T19:04:42.308Z
+ 2022-12-16T19:47:41.701Z
https://bios2.github.io/summer-schools/BiodiversityModelling2022.html
- 2022-12-16T19:04:42.735Z
+ 2022-12-16T19:47:42.106Z
https://bios2.github.io/index.html
- 2022-12-16T19:05:07.213Z
+ 2022-12-16T19:47:42.792Z
https://bios2.github.io/about.html
- 2022-12-16T19:04:43.939Z
+ 2022-12-16T19:47:43.457Z
diff --git a/posts/2022-03-07-bridging-the-science-policy-void/documents/503335a.pdf b/posts/2022-03-07-bridging-the-science-policy-void/documents/503335a.pdf
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+---
+title: "Bridging the Science-Policy Void"
+description: "Do you think science should influence policy? Do you wonder how to improve evidence-based decision making? Do you have a particular issue that you would like to bring to the attention of decision makers? This workshop covers how to present science in a more impactful way, how to prepare a brief, and encourages participants to meet with a decision maker to discuss the issue raised in their policy brief."
+author:
+ - name: "Sarah P. Otto"
+ affiliation: University of British Columbia
+date: "07-03-2022"
+image: image.jpg
+categories: [Transversal competencies, Career, EN]
+toc: true
+number-sections: true
+number-depth: 1
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+## Overview
+
+This training session was presented by Sally Otto, professor in the Department of Zoology at the University of British Columbia and BIOS² co-PI. Pr. Sarah P. Otto has strong expertise on quantitative analysis and mathematical modeling for biologists, and a lot of experience communicating theory to policy decision makers.
+
+The workshop was held in four 1h30 sessions in English on March 7, 14, 21 and 28.
+
+# Why bridge the science-policy void?
+
+# Developing your message
+
+# Writing a brief
+
+# Connecting
+
+# Resources
+
+The [Federal Recruitment of Policy Leaders program](https://www.canada.ca/en/public-service-commission/jobs/services/recruitment/graduates/recruitment-policy-leaders.html) mentioned by Katie Gibbs. This program takes leaders of all ilks, not just PhDs.
+
+Sutherland et al. article about what policy makers should know about science ([link](https://www.nature.com/articles/503335a) and [PDF](documents/503335a.pdf)), and the response letter about the [Top 20 things scientists need to know about policy-making](https://www.theguardian.com/science/2013/dec/02/scientists-policy-governments-science).