Select( X.Sample, percent_N, percent_C) % >% # Get delta C:N for all pairs CN.meta1 = ame(sample_data( )) % >% There is no significant correlation between βNTI and differenc in SOC inįirst lets see if βNTI is correlated with the difference in C:N ratio # Plot = ggplot(, aes( x = delta, y = bNTI)) + P = c( ag.SOC.mantel $ signif, m.SOC.mantel $ signif, f.SOC.mantel $ signif), = ame( r = c( ag.SOC.mantel $ statistic, m.SOC.mantel $ statistic, f.SOC.mantel $ statistic), M.SOC.mantel = ( % >% filter( ecosystem = "meadow "), seed = 72)į.SOC.mantel = ( % >% filter( ecosystem = "forest "), seed = 72) # Run mantel test to see if there is a correlation ag.SOC.mantel = ( % >% filter( ecosystem = "agriculture "), seed = 72) Rename( Sample_2 = X.Sample, SOC_2 = organic_content_perc)ī = inner_join( bNTI.df, SOC.meta1) % >% Rename( Sample_1 = X.Sample, SOC_1 = organic_content_perc) Select( X.Sample, organic_content_perc) % >% # Get delta % SOM for all pairs SOC.meta1 = ame(sample_data( )) % >% Organic matter between sites within each land use.
Soil organic matterįirst lets see if βNTI is correlated with the difference in percent soil In pH between sites for all three land uses. There is a significant positive correlation between βNTI and difference Geom_hline( yintercept = - 2, linetype = 2) + Geom_hline( yintercept = 2, linetype = 2) + # Plot bNTI.pH.plot = ggplot( bNTI.pH.df, aes( x = delta, y = bNTI)) + P = c( ag.pH.mantel $ signif, m.pH.mantel $ signif, f.pH.mantel $ signif),Įcosystem = c( "agriculture ", "meadow ", "forest ")) PH.ef = ame( r = c( ag.pH.mantel $ statistic, m.pH.mantel $ statistic, f.pH.mantel $ statistic), M.pH.mantel = ( bNTI.pH.df % >% filter( ecosystem = "meadow "), seed = 72)į.pH.mantel = ( bNTI.pH.df % >% filter( ecosystem = "forest "), seed = 72) # Run mantel test to see if there is a correlation ag.pH.mantel = ( bNTI.pH.df % >% filter( ecosystem = "agriculture "), seed = 72) PH.meta2 = ame(sample_data( )) % >%īNTI.pH.df = inner_join( bNTI.df, pH.meta1) % >% "density" for density plot or "ecdf" for cumulative plot.# Get delta pH for all pairs pH.meta1 = ame(sample_data( bulk.physeq)) % >% Only if type="geo" and only applied to the plot. If not provided all variables included in modeldomain are used. variablesĬharacter vector defining the predictor variables used if type="feature. Use sampling = "Fibonacci" for global applications.
#Geodist plot how to
How to draw prediction samples? See spsample. How many prediction samples should be used? Only required if modeldomain is a raster (see Details) samplingĬharacter.
object of class sf: Data used for independent validation samplesize If cvtrain is null but cvfolds is not, all samples but those included in cvfolds are used as training data testdata List of row indices of x to fit the model to in each CV iteration. ?createFolds or ?createSpaceTimeFolds cvtrain List of row indices of x that are held back in each CV iteration. Should the distance be computed in geographic space or in the normalized multivariate predictor space (see Details) cvfolds Raster or sf object defining the prediction area (see Details) type Object of class sf, training data locations modeldomain
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