Western White Pine Cone Illustration

Introduction

I recently revisited one the conifer species that occur in the Miracle Mile, but by trail running in the Trinity Alps Wilderness area in the Klamath range of Northern California. I found a number of cool species, but one recognizable one was the Western White Pine (Pinus monticola). Pinus monticola is found in many of the cool trail running spots I frequent. Here is another post about another trail run with Pinus monticola.

Load the libraries.

library(tidyverse)
library(gpx)
library(rgbif)

In-file the trail run gps data and make a data frame to use for plotting.

run <-  read_gpx('~/DATA/data/Trinity-Alps-TrailRunning-2022-10-08-route.gpx')
summary(run)

          Length Class  Mode
routes    1      -none- list
tracks    1      -none- list
waypoints 1      -none- list

TrailRun1 <- as.data.frame(run$routes)
TrailRun1$Time <- as.numeric(row.names(TrailRun1))

Make a quick plot using the latitude and longitude coordinates.

TR_p1 <- ggplot() +
          geom_point(data = TrailRun1, aes(x = Longitude, y = Latitude), color = 'black', size = 2) +
          xlab("Longitude") + ylab("Latitude")
TR_p1

Take a look at the elevation profile of the run.

TR_p2 <- ggplot() +
          geom_line(data = TrailRun1, aes(x = Time, y = Elevation), color = 'black', size = 2) +
          xlab("Time (seconds)") + ylab("Elevation (m)")
TR_p2

We can also plot the run and false color it based on the elevation.

TR_p3 <- ggplot() +
          geom_point(data = TrailRun1, aes(x = Longitude, y = Latitude, color = Elevation), size = 2) +
          scale_color_continuous(limits=c(800,2800))
TR_p3

Using the Latitude and Longitude coordinates of the area we can make a general area polygon to be used for a GBIF species observation query to the public database.

trinity_geometry <- paste('POLYGON((-122.91 40.88, -122.87 40.88, -122.87 40.96, -122.91 40.96, -122.91 40.88))')

mm_species <- c("pinus monticola") # can add multiple species here for larger query

whitepine_data <- occ_data(scientificName = mm_species, hasCoordinate = TRUE, limit = 10000,
                   geometry = trinity_geometry)
whitepine_data

Records found [5] 
Records returned [5] 
Args [hasCoordinate=TRUE, occurrenceStatus=PRESENT, limit=10000, offset=0,
     scientificName=pinus monticola, geometry=POLYGON((-122.91 40.88, -122.87
     40.88, -122.87 40.96, -122.91 40.96, -122.91 40.88))] 
# A tibble: 5 × 78
  key     scien…¹ decim…² decim…³ issues datas…⁴ publi…⁵ insta…⁶ hosti…⁷ publi…⁸
  <chr>   <chr>     <dbl>   <dbl> <chr>  <chr>   <chr>   <chr>   <chr>   <chr>  
1 396113… Pinus …    40.9   -123. cdc,c… 50c950… 28eb1a… 997448… 28eb1a… US     
2 396097… Pinus …    40.9   -123. cdc,c… 50c950… 28eb1a… 997448… 28eb1a… US     
3 338409… Pinus …    40.9   -123. cdc,c… 50c950… 28eb1a… 997448… 28eb1a… US     
4 239748… Pinus …    40.9   -123. cdc,c… 50c950… 28eb1a… 997448… 28eb1a… US     
5 254089… Pinus …    40.9   -123. cdc,c… 50c950… 28eb1a… 997448… 28eb1a… US     
# … with 68 more variables: protocol <chr>, lastCrawled <chr>,
#   lastParsed <chr>, crawlId <int>, basisOfRecord <chr>,
#   occurrenceStatus <chr>, taxonKey <int>, kingdomKey <int>, phylumKey <int>,
#   classKey <int>, orderKey <int>, familyKey <int>, genusKey <int>,
#   speciesKey <int>, acceptedTaxonKey <int>, acceptedScientificName <chr>,
#   kingdom <chr>, phylum <chr>, order <chr>, family <chr>, genus <chr>,
#   species <chr>, genericName <chr>, specificEpithet <chr>, taxonRank <chr>, …

whitepine_coords <- whitepine_data$data[ , c("decimalLongitude", "decimalLatitude", "occurrenceStatus", "coordinateUncertaintyInMeters",
  "institutionCode", "references")]

Plot the observations on the California map to see the limited polygon sampled.

maps::map(database = "state", region = "california")
points(whitepine_coords[ , c("decimalLongitude", "decimalLatitude")], pch = ".", col = "red", cex = 3)

Plot all of the observations using ggplot for the zoomed in area.

whitepine_plot1  <- ggplot(whitepine_coords, aes(x=decimalLongitude, y = decimalLatitude)) +
                             geom_point(color='red') + labs(title = "MM Zone")
whitepine_plot1

Combine trail running and Western White Pine occurrence observations.

run_plot2 <- ggplot() +
          coord_quickmap() +
          geom_point(data = TrailRun1, aes(x = Longitude, y = Latitude), color = 'black') +
          geom_point(data = whitepine_coords, aes(x = decimalLongitude, y = decimalLatitude), color = 'red') +
          xlab("Longitude") + ylab("Latitude")
run_plot2

Now to see a random sampling of what other species are also in the area of the trail run.

trinity_species <- occ_data(hasCoordinate = TRUE, limit = 5000,
                         geometry = trinity_geometry)

trinity_species_coords <- trinity_species$data[ , c("scientificName","phylum", "order", "family", "decimalLongitude", "decimalLatitude","occurrenceStatus", "coordinateUncertaintyInMeters","institutionCode", "references")]

Plot the run data, White Pine data, and other species data on the same map. I changed the size of the trail running and White Pine points so they are easier to see. I also just plotted things by Phylum as to not overload the plot by doing all the species!

run_phylum_plot <- ggplot() +
        geom_point(data = TrailRun1, aes(x = Longitude, y = Latitude), color = 'black', size = .75) +
        geom_point(data = trinity_species_coords, aes(x = decimalLongitude, y = decimalLatitude, color = phylum)) +
        geom_point(data = whitepine_coords, aes(x = decimalLongitude, y = decimalLatitude), color = 'red', size = 5, shape = 19) +
        xlab("Longitude") + ylab("Latitude")
run_phylum_plot

You can see that there are various species to explore representing Arthropods (Insects, Spiders etc.), Cordates (Animals), Bisidomycota (fungi), and Tracheophyta (vascular plants).