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.
For a step-by-step project set-up see Case study 1.
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 = 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.
metadata2bio(con)
Merge the newly converted metadata_ranges
to the other life-history table(s) previously saved.
bio.merge(con, tableName = 'all_life_history')
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:
...
in rangeMap.save
allows for further arguments to be passed to rlm()
try()
allows the computation to move on to the next canvas cell without stopping the entire process.
the function will return NA if rlm
does not return a coefficient.
rlm_slope = function (formula, data,...) { x = try(as.numeric( MASS::rlm(formula, data,...)$coefficients[2]), silent = TRUE) if(inherits(x, "try-error")) x = NA return(x) }
Map the Area ~ Body Mass slope using the user-defined function
rangeMap.save(con, 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
rangeMap.save(con, 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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