Case study 2: Geographical variation in the relationship between range size and body size

Appendix S3

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.

Project Set Up

For a step-by-step project set-up see Case study 1.

breding_ranges = rgdal::readOGR(system.file(package = "rangeMapper",
     "extdata", "wrens", "vector_combined"), "wrens", verbose = FALSE)
d = subset(wrens, select = c('sci_name', 'body_mass') )
con = ramp("wrens.sqlite", gridSize = 2.5, spdf = breding_ranges,
             biotab = d, ID = "sci_name",metadata = rangeTraits()['Area'],
             FUN = "median", overwrite = TRUE)

Convert metadata_ranges table to a life-history BIO_ table.


Merge the newly converted metadata_ranges to the other life-history table(s) previously saved.

bio.merge(con, tableName = 'all_life_history')

Define New Functions

The newly defined function should take formula & data as arguments and should return a numeric vector of length 1. We define a simple wrapper around the rlm function in MASS package to extract a robust regression slope for each assembladge (i.e. canvas cell).

Note that:

rlm_slope = function (formula, data,...) {
    x = try(as.numeric(
        MASS::rlm(formula, data,...)$coefficients[2]), silent = TRUE)
    if(inherits(x, "try-error")) x = NA

Map the Area ~ Body Mass slope using the user-defined function, FUN = rlm_slope, biotab = "all_life_history",
    biotrait  = "body_mass_biotab",
    tableName = "rlm_slope_BM_rangeSize",
    formula   = scale(log(Area_metadata_ranges)) ~ scale(log(body_mass_biotab)),
                maxit = 20)

Additionally map the breeding range area, FUN = 'median', biotab = "all_life_history",
    biotrait  = "Area_metadata_ranges",
    tableName = "median_area")

Plot maps

m= rangeMap.fetch(con, spatial = FALSE,
        maps = c("species_richness", "median_body_mass","median_area", "rlm_slope_BM_rangeSize" ) )
plot(m, rm.outliers = TRUE)

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rangeMapper documentation built on May 29, 2017, 12:08 p.m.