rangeMapper (version 0.0-4)

rangeMapper: rangeMapper, a package to manipulate species range (extent-of-occurrence) maps.

Description

rangeMapper performs range maps interpolation with a chosen regular grid, then computes at each grid cell a chosen statistic and displays the resulting map.

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 the global bounding box of all selected range maps.
• Generate a regular grid (the canvas) onto the previously constructed global bounding box.
• Perform polygon-grid overlay of all selected range maps.
• Optionally import non-spatial data to be mapped at each grid cell (data are saved as a 'BIO' table to database).
• Optionally select a subset based on an existing 'MAP', 'BIO' or 'metadata' table.)
• Compute a chosen statistic (richness, average, median or any user-defined statistic). at each grid cell (data are saved as a 'MAP' table to the database).
• Extract, save and/or plot the MAP.

Details

rangeMapper is a package built around RSQLite, sp, rgdal and maptools for manipulating species range (extent-of-occurrence) maps of e.g. species breeding distribution ranges. rangeMapper comes with an user-friendly platform-independent tcltk graphical user interface.

References

Jetz, W; Sekercioglu, Cagan H.; Boehning-Gaese, K(2008) The Worldwide Variation in Avian Clutch Size across Species and Space PLoS Biology, 6-12 Hurlbert, AH; Jetz, W (2007) Species richness, hotspots, and the scale dependence of range maps in ecology and conservation, PNAS, 13384-13389 Graves, GR; Rahbek, C(2005) Source pool geometry and the assembly of continental avifaunas, PNAS 102-22, 7871-7876 Orme, CDL; Davies, RG; Burgess, M; et al.(2005) Global hotspots of species richness are not congruent with endemism or threat, NATURE, 436-7053,1016-1019

Examples

## These cannot be run by examples() but should be OK when pasted
## into an interactive R session 

	require(rangeMapper)
# INITIATE A NEW PROJECT
	setwd(tempdir())
	dbcon = dbConnect(dbDriver("SQLite"), dbname= "test.sqlite")
	db.ini(dbcon) 

# GET DATA
	# Range data
	f= system.file(package="rangeMapper", "extdata", "wrens", "vector")
	ranges = selectShpFiles(f, ogr = FALSE, polygons.only = TRUE)
	# Life-history data
	data(wrens)

# PREPARE PROJECT
	# upload bounding box to database
	global.bbox.save(ranges, dbcon) 
	# upload grid size
	gridSize.save(dbcon, gridSize = 200000)  
	#  save canvas to database
	canvas.save(dbcon) 

# PROCESS RANGES
	ranges = selectShpFiles(f, ogr = TRUE, polygons.only = TRUE)
	processRanges(ranges, dbcon, metadata = TRUE)

# SAVE BIO TABLE
	bio.save(dbcon, dat = wrens, common_id = "species_name") 

# MAKE MAPS
	#species richness.
	rangeMap.save(dbcon, table.nam = "richness",  biotrait = richness~1 , show.sql = FALSE)

	# Body mass map; no subset
	# mean
	rangeMap.save(dbcon, FUN = mean, biotab = "wrens", biotrait = body_mass~1, table.nam = "mean_body_mass")
	# cv
	rangeMap.save(dbcon, FUN = function(x) sd(x)/mean(x), biotab = "wrens", biotrait = body_mass~1, table.nam = "cv_body_mass")
	
	# Clutch size map; subsseting by BIO table field
	rangeMap.save(dbcon, FUN = mean, biotab = "wrens", biotrait = clutch_size ~1, table.nam = "mean_clutch_size_bm15", 
				subset =  list(BIO_wrens = "body_mass > 15") )
	
	# Body mass map; subsseting by BIO table field AND MAP table
	rangeMap.save(dbcon, FUN = mean, biotab = "wrens", biotrait = body_mass ~1, table.nam = "mean_body_mass_subset1", 
						subset = list(BIO_wrens   = "body_mass > 10", 
						             MAP_richness =  "species_richness BETWEEN 2 AND 5") )

	# Body mass map; subsseting by a BIO table, a MAP table AND  the metadata_ranges table
	rangeMap.save(dbcon, FUN = mean, biotab = "wrens", biotrait = body_mass ~ 1, table.nam = "mean_body_mass_subset2", 
						subset = list(BIO_wrens = "body_mass > 10", 
						              MAP_richness = "species_richness BETWEEN 2 AND 5",
						              metadata_ranges = "Area > 55000000000") )
	
# FETCH MAPS
	richness          = rangeMap.fetch(dbcon, "richness")
	bodyMass          = rangeMap.fetch(dbcon, "mean_body_mass")
	bodyMassSubset1   = rangeMap.fetch(dbcon, "mean_body_mass_subset1")
	bodyMassSubset2   = rangeMap.fetch(dbcon, "mean_body_mass_subset2")
	clutchSize        = rangeMap.fetch(dbcon, "mean_clutch_size_bm15")
	
	summary(richness)
	summary(clutchSize)
	
# PLOT MAPS
	# choose a brewer pal palette
	tkColorPalette(pal = brewer.pal.get(), name  = "SpectralPalette")

	# plot species richness
	rangeMap.plot(richness, SpectralPalette, ncols = 6, style = "equal")
	
	# plot body mass maps
	rangeMap.plot(bodyMass, SpectralPalette, ncols = 20, style = "fisher")
	rangeMap.plot(bodyMassSubset1, SpectralPalette, ncols = 20, style = "fisher")
	rangeMap.plot(bodyMassSubset2, SpectralPalette, ncols = 20, style = "fisher")
	
# plot clutch size subset for body_mass
	rangeMap.plot(clutchSize, SpectralPalette, ncols = 20, style = "fisher")