Macroecological analysis at the species level

In another post, I have shown how to use letsR to analyze species traits at the community level. But, in many cases this type of analysis can lead to spurious patterns (click here for further discussion on this issue). An alternative can be analyzing the data at the species-level. In this post, I will show two examples on how to make macroecological analysis at the species level using the letsR package. In the first example, we will continue the test of Rapoport’s rule on Phyllomedusa frogs using species centroids. In the second example, we will summarize climate spatial data at the species level to explore how temperature correlates with Phyllomedusa species extinction risk.

To start this test we can load our example PresenceAbsence object.

Note: I recommend to use the latest version of the letsR package on GitHub

# Load the package

# Load the data

# Plot

Example 1: Species level test of Rapoport’s rule on Phyllomedusa frogs.

We first have to calculate species range size. We can do it directly on the species shapefiles for higher precision.

rangesize <- lets.rangesize(Phyllomedusa,
                            coordinates = "geographic")
rangesize <- rangesize / 1000 # Transform in km2

The second step is to calculate species geographical centroid/midpoint using the function lets.midpoint. There are several ways to calculate species geographic centroid, and this function offers several methods to do it. When species range are both circular and continuous, all of the methods will provide the same answer. However, as the shape of distributions start to become more complex, different methods will provide very different answers. For this example, we will use the default option “PC” (polygon centroid). This method will generate a polygon from the raster, and calculate the centroid of this polygon.

centroids <- lets.midpoint(PAM)
Species x y
Phyllomedusa araguari -47.50000 -19.500000
Phyllomedusa atelopoides -72.50000 -7.053571
Phyllomedusa ayeaye -46.83333 -20.833333
Phyllomedusa azurea -56.49554 -19.098214
Phyllomedusa bahiana -40.03846 -11.807692
Phyllomedusa baltea -74.50000 -9.500000
Phyllomedusa bicolor -60.87860 -3.374486
Phyllomedusa boliviana -62.11165 -15.082524
Phyllomedusa burmeisteri -43.23684 -17.912281
Phyllomedusa camba -66.03398 -11.995146
Phyllomedusa centralis -55.50000 -15.500000
Phyllomedusa coelestis -76.07143 -3.857143
Phyllomedusa distincta -48.08333 -25.500000
Phyllomedusa duellmani -77.50000 -5.500000
Phyllomedusa ecuatoriana -78.50000 -2.500000
Phyllomedusa hypochondrialis -55.69363 -9.071974
Phyllomedusa iheringii -53.59677 -31.758064
Phyllomedusa itacolomi -43.50000 -20.500000
Phyllomedusa megacephala -43.00000 -19.500000
Phyllomedusa neildi -69.50000 11.000000
Phyllomedusa nordestina -40.66216 -10.707207
Phyllomedusa oreades -48.25000 -14.750000
Phyllomedusa palliata -69.82031 -9.289062
Phyllomedusa perinesos -76.50000 0.500000
Phyllomedusa rohdei -43.83333 -21.981482
Phyllomedusa sauvagii -60.04264 -24.011628
Phyllomedusa tarsius -67.12143 -4.596429
Phyllomedusa tetraploidea -51.79630 -24.074074
Phyllomedusa tomopterna -62.56324 -4.081028
Phyllomedusa trinitatis -65.77778 10.277778
Phyllomedusa vaillantii -61.87419 -4.947312
Phyllomedusa venusta -75.23077 7.461538

We can also plot the geographical centroids.

d <- data.frame(centroids[, 2:3], 
                "Species" = centroids[, 1], 
                "Range size" = rangesize)
sp <- terra::vect(x = d, geom  = c("x", "y"))
plot(sf::st_geometry(wrld_simpl), add = TRUE)

To check the Rapoport’s rule we can plot the latitude against the range size:

data_plot <- data.frame(centroids[, 2:3], "Range size" = rangesize)
g <- ggplot(data_plot, aes(x, Range_size))
g + geom_smooth() + geom_point() + labs(x = "Latitude(x)", y = "Range size")

Again, the data indicate that Rapoport’s rule does not apply for Phyllomedusa genus. However, there seems to be an interesting pattern where range size decreases from the center towards the extremes of the group. This could be an effect of niche conservatism, where species in the extreme latitude would face very different conditions from the ancestral Phylllomedusa. Another possibility is that this pattern could be due to the shape of the continent, where extreme latitudes means smaller longitudes.

Example 2: Extinction risk correlation with temperature

To evaluate how temperature correlates with extinction risk, we first have to add the temperature variable to the PresenceAbsence object.

r <- terra::unwrap(temp)
PAM_env <- lets.addvar(PAM, r, fun = mean)

Next step is to get the average temperature values per species. The lets.summarizer can do this directly on the resulting object of lets.addvar function (note that this can only be done if onlyvar = FALSE). We only have to indicate the position of the variable in the matrix using the argument pos.

pos <- which(colnames(PAM_env) == "bio1_mean")
temp_mean <- lets.summarizer(PAM_env, pos)
Species bio1_mean
Phyllomedusa araguari 207.5278
Phyllomedusa atelopoides 257.3056
Phyllomedusa ayeaye 201.4074
Phyllomedusa azurea 235.1530
Phyllomedusa bahiana 230.6512
Phyllomedusa baltea 253.9167
Phyllomedusa bicolor 260.1406
Phyllomedusa boliviana 236.0925
Phyllomedusa burmeisteri 220.2652
Phyllomedusa camba 248.1014
Phyllomedusa centralis 238.5556
Phyllomedusa coelestis 247.5556
Phyllomedusa distincta 189.9268
Phyllomedusa duellmani 214.6944
Phyllomedusa ecuatoriana 146.5556
Phyllomedusa hypochondrialis 249.4254
Phyllomedusa iheringii 179.0449
Phyllomedusa itacolomi 196.7222
Phyllomedusa megacephala 212.2639
Phyllomedusa neildi 249.5278
Phyllomedusa nordestina 240.2558
Phyllomedusa oreades 237.2083
Phyllomedusa palliata 251.3197
Phyllomedusa perinesos 221.5556
Phyllomedusa rohdei 208.1575
Phyllomedusa sauvagii 223.1641
Phyllomedusa tarsius 254.4805
Phyllomedusa tetraploidea 204.5278
Phyllomedusa tomopterna 257.5273
Phyllomedusa trinitatis 255.5166
Phyllomedusa vaillantii 257.8461
Phyllomedusa venusta 243.0564

Following our example, we need to obtain the IUCN extinction risk data. Previous version of the package included functions to do this, but they are no longer supported. Luckily, there is a new package called rredlist that can do this for us. Yet, for now, we can use the example data in the letsR package called IUCN.

Species Family Status Criteria Population Description_Year Country
Phyllomedusa araguari HYLIDAE DD Unknown 2007 Brazil
Phyllomedusa atelopoides HYLIDAE LC Unknown 1988 Bolivia, Brazil, Peru
Phyllomedusa ayeaye HYLIDAE CR B1ab(iii)+2ab(iii) Unknown 1966 Brazil
Phyllomedusa azurea HYLIDAE DD Unknown 1862 Argentina, Bolivia, Brazil, Paraguay
Phyllomedusa bahiana HYLIDAE DD Unknown 1925 Brazil
Phyllomedusa baltea HYLIDAE EN B1ab(iii)+2ab(iii) Decreasing 1979 Peru
Phyllomedusa bicolor HYLIDAE LC Stable 1772 Bolivia, Brazil, Colombia, French Guiana, Guyana, Peru, Suriname, Venezuela
Phyllomedusa boliviana HYLIDAE LC Stable 1902 Argentina, Bolivia, Brazil
Phyllomedusa burmeisteri HYLIDAE LC Stable 1882 Brazil
Phyllomedusa camba HYLIDAE LC Stable 2000 Bolivia, Brazil, Peru
Phyllomedusa centralis HYLIDAE DD Unknown 1965 Brazil
Phyllomedusa coelestis HYLIDAE LC Unknown 1874 Colombia, Ecuador, Peru
Phyllomedusa distincta HYLIDAE LC Decreasing 1950 Brazil
Phyllomedusa duellmani HYLIDAE DD Unknown 1982 Peru
Phyllomedusa ecuatoriana HYLIDAE EN B1ab(iii) Decreasing 1982 Ecuador
Phyllomedusa hypochondrialis HYLIDAE LC Stable 1800 Argentina, Bolivia, Brazil, Colombia, French Guiana, Guyana, Paraguay, Suriname, Venezuela
Phyllomedusa iheringii HYLIDAE LC Unknown 1885 Brazil, Uruguay
Phyllomedusa itacolomi HYLIDAE DD Unknown 2006 Brazil
Phyllomedusa megacephala HYLIDAE DD Unknown 1926 Brazil
Phyllomedusa neildi HYLIDAE DD Unknown 2006 Venezuela
Phyllomedusa nordestina HYLIDAE DD Unknown 2006 Brazil
Phyllomedusa oreades HYLIDAE DD Unknown 2002 Brazil
Phyllomedusa palliata HYLIDAE LC Stable 1873 Bolivia, Brazil, Ecuador, Peru
Phyllomedusa perinesos HYLIDAE DD Unknown 1973 Colombia, Ecuador
Phyllomedusa rohdei HYLIDAE LC Stable 1926 Brazil
Phyllomedusa sauvagii HYLIDAE LC Stable 1882 Argentina, Bolivia, Brazil, Paraguay
Phyllomedusa tarsius HYLIDAE LC Stable 1868 Brazil, Colombia, Ecuador, Peru, Venezuela
Phyllomedusa tetraploidea HYLIDAE LC Stable 1992 Argentina, Brazil, Paraguay
Phyllomedusa tomopterna HYLIDAE LC Stable 1868 Bolivia, Brazil, Colombia, Ecuador, French Guiana, Guyana, Peru, Suriname, Venezuela
Phyllomedusa trinitatis HYLIDAE LC Stable 1926 Trinidad and Tobago, Venezuela
Phyllomedusa vaillantii HYLIDAE LC Stable 1882 Bolivia, Brazil, Colombia, Ecuador, French Guiana, Guyana, Peru, Suriname, Venezuela
Phyllomedusa venusta HYLIDAE LC Decreasing 1967 Colombia, Panama

Finally, we can verify the relationship between temperature and extinction risk.

level_order <- c("DD", "LC",  "EN", "CR") # ordering for the plot
data <- data.frame("Status" = factor(IUCN$Status, levels = level_order),
                   "Temperature" = temp_mean[, 2] / 10)
g <- ggplot(data, aes(Status, Temperature))
g + geom_boxplot() + coord_flip()

The graph indicate that there is a tendency for threatened Phyllomedusa species to occur in colder regions. Still, further statistical analysis should be done to confirm this pattern.

To cite letsR in publications use: Bruno Vilela and Fabricio Villalobos (2015). letsR: a new R package for data handling and analysis in macroecology. Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.12401