In Class Activity

Araptus attenuatus the enigmatic Sonoran Desert Bark Beetle.

This is a bit quick but is designed to make sure that:

  1. To make sure everyone can get their computer up-to-date with the proper set of libraries so that we can work with geospatial data, and
  2. Give you some practice turning normal numerical data into geospatial coordinates and performing basic operations.

Spatial Libraries

The following code defines the set of necessary libraries for the next few weeks and then figures out which (potential) subset you need to install. It then installs them all in one call. It will not load them into your session, you’ll have to do that later in your code chunks.

needed_libraries <- c("sf",
                      "maps",
                      "units",
                      "GGally",
                      "raster",
                      "mapproj",
                      "leaflet",
                      "rnaturalearth",
                      "rnaturalearthdata"
                      )
# Determines which libraries you do not have
#   already installed.
need_inst <- setdiff( needed_libraries, 
                      rownames( installed.packages()) )
# Instrall the neccessary ones.
if( length( need_inst ) ) {
  install.packages( need_inst, dependencies = TRUE )
}

The Data - Preprocessing

The data for this is some site-level data from the Sonoran Desert bark beetle. The URL is in the next chunk, and is currently being hosted on my class Github repository. Load it in.

library( tidyverse )
url <- "https://raw.githubusercontent.com/dyerlab/ENVS-Lectures/master/data/Araptus_Disperal_Bias.csv"
read_csv( url ) -> data 
Rows: 31 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Site
dbl (8): Males, Females, Suitability, MFRatio, GenVarArapat, GenVarEuphli, L...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
summary( data )
     Site               Males          Females       Suitability    
 Length:31          Min.   : 9.00   Min.   : 5.00   Min.   :0.0563  
 Class :character   1st Qu.:16.00   1st Qu.:15.50   1st Qu.:0.2732  
 Mode  :character   Median :21.00   Median :21.00   Median :0.3975  
                    Mean   :25.68   Mean   :23.52   Mean   :0.4276  
                    3rd Qu.:31.50   3rd Qu.:29.00   3rd Qu.:0.5442  
                    Max.   :64.00   Max.   :63.00   Max.   :0.9019  
    MFRatio        GenVarArapat     GenVarEuphli       Latitude    
 Min.   :0.5938   Min.   :0.0500   Min.   :0.0500   Min.   :23.29  
 1st Qu.:0.8778   1st Qu.:0.1392   1st Qu.:0.1777   1st Qu.:24.95  
 Median :1.1200   Median :0.2002   Median :0.2171   Median :26.64  
 Mean   :1.1598   Mean   :0.2006   Mean   :0.2203   Mean   :26.44  
 3rd Qu.:1.3618   3rd Qu.:0.2592   3rd Qu.:0.2517   3rd Qu.:27.78  
 Max.   :2.2000   Max.   :0.3379   Max.   :0.5122   Max.   :29.33  
   Longitude     
 Min.   :-114.3  
 1st Qu.:-113.1  
 Median :-112.0  
 Mean   :-112.0  
 3rd Qu.:-110.8  
 Max.   :-109.3

Interactive Map

Use the leaflet library and make an interactive map. If you can, add a label to each marker with the ID of the site. You can look at the provider tiles here and use on in the addProviderTiles() function.

There is a great tutorial here on how to customize the leaflet display. You may want to go look at it and see what you can use to make a better display.

# make a quick leaflet map here to get an idea of 

library( leaflet )

data |> 
  leaflet() |> 
  addMarkers( ~Longitude, ~Latitude ) |>
  addTiles()

Converting to sf Objects

Convert the spatial data in the beetle data.frame into an sf object. Make sure you set the Coordinate Reference System to 4326.

# convert raw data to simple features in your data.frame
library( sf )
Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE
data |>
  st_as_sf(coords = c("Longitude","Latitude"), crs=4326 ) -> data 

head( data )
Simple feature collection with 6 features and 7 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -114.2935 ymin: 24.00789 xmax: -109.327 ymax: 29.32541
Geodetic CRS:  WGS 84
# A tibble: 6 × 8
  Site  Males Females Suitability MFRatio GenVarArapat GenVarEuphli
  <chr> <dbl>   <dbl>       <dbl>   <dbl>        <dbl>        <dbl>
1 32       40      27      0.0563    1.48        0.144       0.219 
2 73       11       5      0.146     2.2         0.137       0.253 
3 93       25      21      0.163     1.19        0.163       0.133 
4 const    18      11      0.174     1.64        0.280       0.235 
5 159      22      15      0.188     1.47        0.160       0.0809
6 88       23      18      0.219     1.28        0.266       0.176 
# ℹ 1 more variable: geometry <POINT [°]>

Questions

For these questions, you may want to look at the sf cheat sheet here to find the appropriate geospatial function.

  1. How far apart (in km) are the sites Constitución (const) and San Francisquito (sfran)?
library( units )
udunits database from /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/units/share/udunits/udunits2.xml
data |> 
  filter( Site %in% c("const", "sfran" ) ) |>
  st_distance() |>
  set_units( km ) -> pw 

distConstSFran <- format( as.numeric( pw[2,1] ), digits=4 )

My sampling locations were 290.1 km apart.

  1. Is there a correlation between habitat suitability and the ratio of males to female beetles sampled at each locale?

  2. Make a plot using ggplot of the data with suitability as the size of the points and the sex ratio as the fill color.

data |>
  ggplot( aes( size=Suitability, color = MFRatio ) ) + 
  geom_sf() + 
  theme_minimal()

  1. Reproject the data and plot it again using an EPSG code defined for Mexico UTM zone 12N (see epgs.io for projections).