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rangeMapper

Packages and data

require(rangeMapper)
require(sf)
require(data.table)
require(ggplot2)
require(viridis)

data(dem)
wrens = read_wrens()
wrens$breeding_range_area = st_area(wrens)

A bare bone rangeMapper project

The project contains nothing but several system tables.

# path is not specified so an in-memory file is created.
con = rmap_connect()

Wrens breeding ranges are imported.

rmap_add_ranges(con, x = wrens, ID = 'sci_name')
rmap_prepare(con, 'hex', cellsize = 500)
rmap_add_bio(con, wrens, 'sci_name')

Do larger wren species have smaller clutches ?

lm(clutch_size~log(body_mass), wrens)  %>%  summary
#> 
#> Call:
#> lm(formula = clutch_size ~ log(body_mass), data = wrens)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -2.41154 -1.00417 -0.02835  0.99885  2.94120 
#> 
#> Coefficients:
#>                Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)      5.8573     1.2496   4.687 2.49e-05 ***
#> log(body_mass)  -0.8291     0.4183  -1.982   0.0534 .  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 1.303 on 46 degrees of freedom
#>   (36 observations deleted due to missingness)
#> Multiple R-squared:  0.0787, Adjusted R-squared:  0.05868 
#> F-statistic:  3.93 on 1 and 46 DF,  p-value: 0.05343

Does the slope clutch size ~ body mass vary spatially?

First we save a species richness map.

rmap_save_map(con)

Then we construct a subset table with all assemblages with a richness of at least 10 species.

rmap_save_subset(con,'sset1', species_richness = 'species_richness > 10')

Now we can construct a clutch size ~ body mass map with assemblages containing at least 10 species.

linmod = function(x) {
  lm(clutch_size ~ log(body_mass), x) %>% 
    summary %>% coefficients %>% data.table %>% .[-1] }

rmap_save_map(con, fun= linmod, subset= 'sset1', src='wrens', dst='slope_clutch_size')

We get the map as a sf data.frame and plot it with ggplot.

x = rmap_to_sf(con)

ggplot() + 
  geom_sf(data = x, aes(fill = Estimate),  size= 0.05) + 
  scale_fill_gradientn(colours =  viridis(10, option = 'E'), na.value= 'grey80') + 
  theme_bw()

Here is the “answer” to the question above.

xy = st_centroid(x)  %>% st_coordinates
x = cbind(x, xy )

ggplot(x , aes(y = Estimate, x = Y) ) + 
  geom_point() + 
  geom_smooth() +
  theme_bw() + 
  ylab('Clutch size ~ Body mass slope') + 
  xlab('Distance from equator (km)')

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.