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Published byDenis Dennis Modified about 1 year ago

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Mantel Tests Comparing Distance Matrices

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Spatial Autocorrelation Spatial autocorrelation means that observations that are close to one another in space are more similar to one another than to other observations farther away –Nearby sites are closer in time –Nearby sites are closer in assemblage composition

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Mantel Test Widely used in ecology to compare two distance matrices Correlation is a measure of spatial autocorrelation (pearson, spearman, or kendall’s tau Significance must be determined by permutations of the distance matrix

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Example Snodgrass site – does the presence of decorated/prestige artifacts show spatial autocorrelation? Load the data set and convert the variables to dichotomies Compute distance matrices and plot 91*90/2 = 4095 distance pairs

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> pa <- sapply(Snodgrass[,8:39], function(x) as.numeric(as.logical(x))) > Snodgrass3 <- data.frame(Snodgrass[,1:7], pa, Snodgrass[,40:41]) > library(vegan) > S.dist <-dist(Snodgrass3[,c(26, 27, 29:38)], method="binary") > A.dist <-dist(Snodgrass3$Area, method="euclidean") > G.dist <-dist(Snodgrass3[,1:2], method="euclidean") > plot(G.dist, S.dist, pch=".", cex=2) > abline(lm(S.dist~G.dist), col="red", lwd=2) > GS.man <- mantel(G.dist, S.dist) > GS.man

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Mantel statistic based on Pearson's product-moment correlation Call: mantel(xdis = G.dist, ydis = S.dist) Mantel statistic r: Significance: Empirical upper confidence limits of r: 90% 95% 97.5% 99% Based on 999 permutations > str(GS.man) List of 6 $ call : language mantel(xdis = G.dist, ydis = S.dist) $ method : chr "Pearson's product-moment correlation" $ statistic : num $ signif : num $ perm : num [1:999] $ permutations: num attr(*, "class")= chr "mantel"

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Mantel Correlogram Correlation plotted by distance to see at what distances the correlation peaks

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S.correl <- mantel.correlog(S.dist, G.dist) plot(S.correl) print(S.correl) Mantel Correlogram Analysis Call: mantel.correlog(D.eco = S.dist, D.geo = G.dist) class.index n.dist Mantel.cor Pr(Mantel) Pr(corrected) D.cl ** D.cl ** D.cl ** D.cl D.cl D.cl D.cl D.cl NA NA NA D.cl NA NA NA D.cl NA NA NA D.cl NA NA NA D.cl NA NA NA D.cl NA NA NA --- Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

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Partial Mantel Test We can control for a third variable to see if that eliminates the spatial autocorrelation

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> GSA.man <- mantel.partial(G.dist, S.dist, A.dist) > GSA.man Partial Mantel statistic based on Pearson's product-moment correlation Call: mantel.partial(xdis = G.dist, ydis = S.dist, zdis = A.dist) Mantel statistic r: Significance: Empirical upper confidence limits of r: 90% 95% 97.5% 99% Based on 999 permutations

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