Case studies (GEB_739_sm_AppendixS2-S5)

Source: vignettes/Case_studies_Valcu_et_al_2012.Rmd

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.

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 2: Geographical variation of the range size~ body size slope

1. The range size~ body size slope map


lm_slope = function (x) {
  lm(scale(log(breeding_range_area)) ~ scale(male_tarsus), x)  %>% 
  summary %>% coefficients %>% data.frame %>% .[-1, ] 
  }


rmap_save_map(con, fun = lm_slope, src='wrens', dst='slope_area_body_mass')

2. Maps: load and display

m = rmap_to_sf(con, 'slope_area_body_mass')

ggplot(m) + 
  geom_sf(aes(fill = Estimate),  size= 0.05, show.legend = TRUE) + 
  scale_fill_gradientn(colours = viridis(10, option = 'E') ) + 
  ggtitle("Range size ~ Body size slope")

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()

Case study 4: The influence of range size on the relationship between species richness and body size

1. Set parameters

quant = seq(0.05, 1, 0.1) 
Q = quantile(wrens$breeding_range_area,  probs = quant)
range_classes = data.frame(area = Q, quant =  quant )
W = 4   # size of the moving window
maxn = nrow(range_classes) - W

range_classes
#>                   area quant
#> 5%     1266.498 [km^2]  0.05
#> 15%    9479.414 [km^2]  0.15
#> 25%   37151.056 [km^2]  0.25
#> 35%   90315.966 [km^2]  0.35
#> 45%  154772.132 [km^2]  0.45
#> 55%  243138.507 [km^2]  0.55
#> 65%  405426.829 [km^2]  0.65
#> 75%  826032.781 [km^2]  0.75
#> 85% 3590834.210 [km^2]  0.85
#> 95% 5914789.134 [km^2]  0.95

2. Make subset tables for multiple range size intervals.


subsets = paste0('area_subset_',1:maxn )

for(i in 1:maxn ) {
  rmap_save_subset(con, subsets[i], 
    wrens = glue("breeding_range_area BETWEEN 
            {range_classes[i,     'area']} AND 
            {range_classes[i+W, 'area']  }") )
  }

2. Make median body mass maps for all subset tables.


maps = paste0('body_size_', subsets)

for(i in 1:maxn ) {
  rmap_save_map(con, subset = subsets[i], dst = maps[i],  
                fun = 'median', src='wrens', v = 'male_tarsus')
    }

3. Get maps and run Run assemblage body size ~ species_richness regression


m = rmap_to_sf(con, pattern = 'richness|body')  %>% setDT
m = melt(m, measure.vars = patterns("median") )

x = m[, { 
      
      fm = lm( log10(value) ~  sqrt(species_richness) )
      data.table( Estimate = coefficients(fm)[2], t( confint(fm)[2, ]) )
      
      } , by = variable]

x[, Quantiles :=  quant[1:maxn]  ]

4. Plot regression slope for different quantile-based range size intervals

ggplot(x, aes(x = Quantiles, y = Estimate) ) +
    geom_errorbar(aes(ymin = `2.5 %`, ymax = `97.5 %`), width= 0) +
    geom_line() +
    geom_point() +
    theme_bw()