General project setup
We setup a project using a hexagonal canvas with a cell size of 500 km. The project is set in-memory but for a real case study you would like to set path to a persistent location on disk.
We’ll use the wrens dataset which is part of the package.
require(rangeMapper)
require(sf)
require(data.table)
require(glue)
require(ggplot2)
require(viridis)
wrens = read_wrens()
wrens$breeding_range_area = st_area(wrens)
con = rmap_connect()
rmap_add_ranges(con, x = wrens, ID = 'sci_name')
rmap_prepare(con, 'hex', cellsize = 500)
rmap_add_bio(con, wrens, 'sci_name')
Raw data: wrens breeding range distribution and life history
ggplot() +
geom_sf(data = rmap_to_sf(con, 'wkt_canvas') , color = 'grey80', fill = NA) +
geom_sf(data = wrens, fill = NA)
head(wrens,3)
#> Simple feature collection with 3 features and 12 fields
#> geometry type: MULTIPOLYGON
#> dimension: XY
#> bbox: xmin: -10184.52 ymin: 2019.465 xmax: -8391.563 ymax: 3447.125
#> CRS: +proj=moll +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=km +no_defs
#> ID sci_name com_name subspecies clutch_size
#> 1 1 Campylorhynchus_jocosus boucard's wren 1 3.5
#> 2 2 Campylorhynchus_gularis spotted wren 1 4.0
#> 3 3 Campylorhynchus_yucatanicus yucatan wren 1 3.0
#> male_wing female_wing male_tarsus female_tarsus body_mass data_src
#> 1 73.10 70.30 22.9 22.2 27.6 1,1,1,1,1,1,3
#> 2 74.00 71.75 24.0 24.0 30.1 1,2,1,1,1,1,3
#> 3 76.55 71.35 25.1 23.6 35.5 1,2,1,1,1,1,3
#> geometry breeding_range_area
#> 1 MULTIPOLYGON (((-9589.923 2... 68459.65 [km^2]
#> 2 MULTIPOLYGON (((-9469.687 2... 237890.51 [km^2]
#> 3 MULTIPOLYGON (((-8391.563 2... 11365.78 [km^2]
Case study 1: Different biodiversity hotspots and their congruence.
We describe biodiversity hotspots based on of three avian diversity parameters: total species richness, endemic species richness and relative body mass diversity.
1. Set parameters
P_richness = 0.75 # species richness quantile
P_bodymass = 0.50 # CV body mass quantile
P_endemics = 0.35 # endemic species richness quantile
2. Primary maps and thresholds
rmap_save_map(con)
# rmap_save_map with no arguments other than `con` saves a species_richness map.
CV_Mass <- function(x) (sd(log(x),na.rm = TRUE)/mean(log(x),na.rm = TRUE))
rmap_save_map(con, fun = CV_Mass, src='wrens',v = 'body_mass', dst='CV_Mass')
Thresholds are computed using the parameters defined in 1 and the maps saved at 2.
sr = rmap_to_sf(con, "species_richness")
sr_threshold = quantile(sr$species_richness, probs = P_richness, na.rm = TRUE)
es_threshold = quantile(wrens$breeding_range_area, probs = P_endemics, na.rm = TRUE)
bmr = rmap_to_sf(con, "CV_Mass")
bmr_threshold = quantile(bmr$V1_body_mass, probs = P_bodymass, na.rm = TRUE)
3. Congruence subsets and congruence maps
3.1 Subsets
rmap_save_subset(con,'sr_threshold', species_richness = paste('species_richness >', sr_threshold) )
rmap_save_subset(con,'es_threshold', wrens = paste('breeding_range_area <=', es_threshold) )
rmap_save_subset(con,'bmr_threshold', CV_Mass = paste('V1_body_mass >=', bmr_threshold))
rmap_save_subset(con, "cumul_congruence_threshold",
species_richness = paste('species_richness >', sr_threshold),
wrens = paste('breeding_range_area <=', es_threshold),
CV_Mass = paste('body_mass >=', bmr_threshold)
)
3.2 Threshold Maps
rmap_save_map(con, subset = 'sr_threshold', dst = 'Species_richness_hotspots')
rmap_save_map(con, subset = 'es_threshold', dst = 'Endemics_hotspots')
rmap_save_map(con, subset = 'bmr_threshold', dst = 'Body_mass_diversity_hotspots')
rmap_save_map(con, subset = 'cumul_congruence_threshold', dst = 'Cumul_congruence_hotspots')
4. Maps: load and display
study_area = rmap_to_sf(con, 'species_richness') %>% st_union
bmr = rmap_to_sf(con, pattern = 'hotspots') %>%
melt(id.vars = c('geometry', 'cell_id') ) %>%
st_as_sf
bmr$variable = bmr$variable %>% gsub('species_richness_|_hotspots', '', .)
ggplot() +
facet_wrap(~variable) +
geom_sf(data = study_area ) +
geom_sf(data = bmr, aes(fill = value), size= 0.05) +
scale_fill_gradientn(colours = viridis(10, option = 'E'), na.value= 'grey80') +
guides(fill=guide_legend(title='Wren\nspecies')) +
ggtitle("Hotspots") +
theme_bw()
Case study 3: The influence of cell size on body size ~ species richness slope
1. assemblage level median body size ~ species richness slope for varying cell sizes.
cellSizes = seq(from = 700, to = 1500, length.out = 5)
FUN = function(g) {
options(rmap.verbose = FALSE)
con = rmap_connect()
rmap_add_ranges(con, x = wrens, ID = 'sci_name')
rmap_prepare(con, 'hex', cellsize=g)
rmap_add_bio(con, wrens, 'sci_name')
rmap_save_map(con)
rmap_save_map(con, fun = 'median', src='wrens', v = 'male_tarsus', dst='median_male_tarsus')
m = rmap_to_sf(con)
# lm at assemblage level
o = lm(scale(log(median_male_tarsus)) ~ sqrt(species_richness), m) %>%
summary %>% coefficients %>% data.frame %>% .[-1, ]
o$cell_size = g
options(rmap.verbose = TRUE)
o
}
o = lapply(cellSizes, FUN) %>% rbindlist
2. Plot regression parameters for different cell sizes
Most of the variation here is due to spatial autocorrelation, a proper analysis requires a spatial model.
ggplot(o, aes(x = cell_size, y = Estimate)) +
geom_point() +
theme_bw()
