rangeMapper (version 0.0-6)

rangeMapper: rangeMapper: A platform for the study of macroecology of life history traits.

Description

rangeMapper is a front end platform for the study of macroecology of life history traits at both inter-specific and assemblage levels.

Getting Started

For a quick start open the graphical user interface (gui) by typing rangeMapper(). Mouse over the buttons to see further notes (tool-tips) regarding each step. A tutorial can be accessed from Help/Get started under the gui or by browsing the doc package directory. See also the example below in the examples section on how to use rangeMapper from the command line.

The rangeMapper pipeline

• Initiate a new project (an empty sqlite database) using a pre-defined template.
• Extract/define the global bounding box of all selected range maps.
• Generate a regular grid (the canvas) using the previously constructed global bounding box.
• Perform polygon-grid overlay of all selected range maps.
• Optionally compute pre-defined or user-defined range structure indexes.
• Optionally import non-spatial data to be mapped at each grid cell (data are saved as a 'BIO' table to database).
• Optionally import georeferenced raster files.
• Compute a chosen statistical model at each grid cell optionally using complex subsets.
• Save, plot, export the MAP-s.

Details

The package uses species range (extent-of-occurrence) vector polygons and life history traits datasets to build up maps (e.g. species richness, mean body mass, ...). rangeMapper comes with an user-friendly platform-independent tcltk graphical user interface.

References

Valcu, M., Dale, J. and Kempenaers, B. (in prep) rangeMapper: A platform for the study of macroecology of life history traits.

Examples

#########################################

require(rangeMapper)

# Start or open a project
wd = tempdir()

# open the GUI, messages will be re-directed here
if(interactive() ) rangeMapper()

dbcon = rangeMap.start(file = "wrens.sqlite", 
	overwrite = TRUE, dir = tempdir() )

# To connect latter to the project created with rangeMap.start use
dbcon = rangeMap.open(path = 
	paste(tempdir(), "wrens.sqlite" , sep = .Platform$file.sep) )

# Project summary
summary(new("rangeMap", CON = dbcon) )

# Breeding range vector files location
f = system.file(package = "rangeMapper", 
	"extdata", "wrens", "vector")

# There are several alternatives to save the global bounding box, 
# a few are  exemplified below
# The default creates a global unprojected bounding box
global.bbox.save(con = dbcon)

# Creating a global bbox using the  breeding range vector files

# manually remove all data from the bbox  and proj4string table
RMQuery(dbcon, 'DELETE from bbox') 
RMQuery(dbcon, 'DELETE from proj4string')

global.bbox.save(con = dbcon, bbox = f) 

plot( global.bbox.fetch(dbcon) , axes = TRUE)
	
# Creating a global bbox using the  breeding range vector files 
# and re-projecting
# manually remove all data from the bbox table
RMQuery(dbcon, 'DELETE from bbox') 
RMQuery(dbcon, 'DELETE from proj4string')

global.bbox.save(con = dbcon,bbox = f, 
	p4s =  
	CRS("+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +ellps=WGS84 +units=m +no_defs"))

	
# upload grid size using the default
gridSize.save(dbcon)  

# upload grid size to 100000 sqm
RMQuery(dbcon, 'DELETE from gridSize')
gridSize.save(dbcon, gridSize = 100000) 

#  save canvas 
canvas.save(dbcon) 
summary(canvas.fetch(dbcon) )

# Process ranges & BIO tables

# first define a new range structure function

APratio = function(x) {
# returns 1 for a circle polygon
	# area
	A = sapply(slot(x, "polygons"), 
		function(x) slot(x, "area") )
	
	# perimeter
	a = lapply(slot(x, "polygons"),
		function(x) slot(x, "Polygons") )
	b = do.call(c, a)
	a1 = lapply(b, function(x) 
		Line(coordinates(x)))
	L = sapply(a1, LineLength)
	
	# A-P ratio
	2*(sqrt(pi*sum(A)))/sum(L)
	
}


# the APratio function will also be computed together with the pre-defined functions
processRanges(dir = f, con =  dbcon, metadata = TRUE, APratio)


# Manage BIO tables
csvfile = system.file(package="rangeMapper", "data", "wrens.csv")
bio.save(con = dbcon, loc = csvfile ,  ID = "sci_name")

rm.rangeMapper(con = dbcon, tablePrefix = "BIO")

data(wrens)
bio.save(con = dbcon, loc = wrens,  ID = "sci_name")

# metadata_ranges table can be merged with the wrens table
metadata2bio(dbcon)
bio.merge(dbcon, tableNam = "wrensRanges", 
		"wrens", "metadata_ranges")


# Save MAPs
rangeMap.save(dbcon) #species richness is the default

#Using sqlite aggregate functions
.sqlAggregate()
rangeMap.save(dbcon, FUN = "median" , biotab = "wrens", 
	biotrait = "body_size", tableName = "body_size")

rangeMap.save(dbcon, FUN = "median" , 
	biotab = "metadata_ranges", biotrait = "V1", tableName = "APratio")

#Using an  R function which does not require a formula argument
rangeMap.save(dbcon, FUN = mean , biotab = "wrens", 
	biotrait = "clutch_size", tableName = "clutch_size", 
		na.rm = TRUE, overwrite = TRUE)

#Using an user defined R function which has a formula argument
lmSlope = function (formula, data) {
    fm = try(lm(formula, data = data), silent = TRUE)
	if (inherits(fm, "try-error")) 
		res = NA else 
			res = coef(fm)[2]
    as.numeric(res)
}

# Map of log(body_mass) ~ clutch_size only for species richness > 10
rangeMap.save(dbcon, FUN = lmSlope, biotab = "wrens", 
	biotrait = "body_mass", 
	tableName = "slope_body_mass_clutchSize", 
	formula = log(body_mass) ~ clutch_size , overwrite = TRUE, 
	list(MAP_species_richness = "species_richness > 5")
	)

	
# rangeMap.save can be also used to  import raster maps the current project
r = system.file(package = "rangeMapper", 
	"extdata", "etopo1", "etopo1_Americas.tif")

rangeMap.save(dbcon, path = r, tableName = 'meanAltitude', 
	FUN = mean , overwrite = TRUE)

# rangeMap.save takes a subset argument on both 
# metadata_ranges, MAP and BIO tables

# clutch size of the highland species
rangeMap.save(dbcon, biotab = "wrens", biotrait = "clutch_size", 
	tableName = "highlandClutch", overwrite = TRUE, 
	subset = list(MAP_meanAltitude = "meanAltitude > 2000") )

# clutch size of restricted range lowland species 
rangeMap.save(dbcon, biotab = "wrens", biotrait = "clutch_size", 
	tableName = "endemicsLowlandClutch", overwrite = TRUE, 
		subset = list(MAP_meanAltitude = "meanAltitude < 1000",
					 metadata_ranges = "Area < 100000000000") )


# Fetch and plot Maps
	
plot(rangeMap.fetch(dbcon, 'species_richness') ,n = 10, style = "fisher")		

plot(rangeMap.fetch(dbcon, "APratio"), style = "equal" )
plot(rangeMap.fetch(dbcon, 'meanAltitude'),  style = "quantile")

clutch = rangeMap.fetch(dbcon, 
	c('clutch_size', 'slope_body_mass_clutchSize', 'body_size') )
plot(clutch, style = "fisher")

plot(rangeMap.fetch(dbcon, 'highlandClutch'), n = 4)	


image(rangeMap.fetch(dbcon, 'meanAltitude') )	
image(rangeMap.fetch(dbcon, 'endemicsLowlandClutch') , 
	add = TRUE, col = "blue")	

setwd(wd)