Supporting information in Valcu, M., Dale, J., and Kempenaers, B. (2012). rangeMapper: a platform for the study of macroecology of life-history traits. Global Ecology and Biogeography 21, 945-951.
The example shown here is run on the wrens dataset which is part of the package. The wrens dataset has 84 species while the case study presented in the paper was run on 8434 bird species. Therefore both the settings and the results shown below are not identical with the results presented in Valcu et al 2012.
require(rangeMapper) breding_ranges = rgdal::readOGR(system.file(package = "rangeMapper", "extdata", "wrens", "vector_combined"), "wrens", verbose = FALSE) data(wrens) d = subset(wrens, select = c('sci_name', 'body_mass') ) con = ramp("wrens.sqlite", gridSize = 1, spdf = breding_ranges, biotab = d, ID = "sci_name",metadata = rangeTraits()['Area'], overwrite = TRUE)
Range size classes
mt = dbReadTable(con, "metadata_ranges") Q = quantile(log(mt$Area), probs = seq(0.05, 1, 0.1) ) rangeA = data.frame(area = exp(Q), quant = gsub("%", "", names(Q)) )
log10(median_body_mass) ~ sqrt(species_richness)
regression for each range size intervalW = 4 # size of the moving window output = vector(mode = "list", length = nrow(rangeA)) names(output) = rangeA$quant for(i in seq(1:(nrow(rangeA) - W) ) ) { # Define map subset area_subset =list(metadata_ranges = paste("Area between", rangeA[i,"area"], "and", rangeA[i+W,"area"])) # Save map rangeMap.save(con, subset = area_subset , biotab = "biotab", biotrait = "body_mass", FUN = "median", tableName = "median_body_mass", overwrite = TRUE) # Fetch map m = rangeMap.fetch( con, c("species_richness", "median_body_mass"), spatial = FALSE ) # Perform OLS regression # NOTE: In order to perform a spatial simultaneous autoregressive error # regression # see 'errorsarlm ' function and the auxiliary neighbours list functions in # 'spdep' package. fm = lm( log10(median_body_mass) ~ sqrt(species_richness), m) output[[i]] = fm } output = output[!sapply(output, is.null)]
X = lapply(output, function(x) data.frame(slope = coef(x)[2], ciu = confint(x)[2,1], cil = confint(x)[2,2]) ) X = do.call(rbind, X) X$rangeSize = as.numeric(row.names(X)) # Plot require(ggplot2) ggplot(X, aes(x = rangeSize, y = slope)) + geom_errorbar(aes(ymin = cil, ymax = ciu), width= 0) + geom_line() + geom_point() + theme_bw()
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