Foxtail Pine Cone Illustration

Introduction

My partner and I recently revisited one the conifer species that occur in the Miracle Mile, but by trail running up to the top of Mount Eddy (9,037 ft; 2,754 m) in the Klamath Range. The final sub-alpine zone near the top of the mountain is a large grove of Foxtail Pine (Pinus balfouriana). I paid another visit to that same grove in another recent post, but did not go up to the higher elevations above 8000 ft.

Load the libraries.

library(tidyverse)
library(gpx)
library(rayshader)
library(rgbif)
library(data.table)
library(maps)

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

run <-  read_gpx('~/DATA/data/mount_eddy_trail_run.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. We can pass this plot to the plot_gg() function in the rayshader package to make a 3D plot of the run. The code for that is not run on the website because it takes too long to render, so I am showing the rendered snapshot as an external image instead.

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

# Not run for website rendering purposes, but you should!
# plot_gg(TR_p3, width = 15, height = 15, multicore = TRUE, scale = 1000,
#         zoom = .7, theta = 10, phi = 20, windowsize = c(3000, 3000))
# Sys.sleep(0.2)
# render_snapshot(filename = "mt-eddy-run-elevation-plot3.png", clear = TRUE)

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.

norcal_geometry <- paste('POLYGON((-122.6 41.35, -122.6 41.25, -122.4 41.25, -122.35 41.25, -122.35 41.35, -122.6 41.35))')

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

foxtail_data <- occ_data(scientificName = mm_species, hasCoordinate = TRUE, limit = 10000,
                   geometry = norcal_geometry)
foxtail_data

Records found [74] 
Records returned [74] 
Args [hasCoordinate=TRUE, occurrenceStatus=PRESENT, limit=10000, offset=0,
     scientificName=pinus balfouriana, geometry=POLYGON((-122.6 41.35, -122.6
     41.25, -122.4 41.25, -122.35 41.25, -122.35 41.35, -122.6 41.35))] 
# A tibble: 74 × 124
   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 41657… Pinus …    41.3   -122. cdc,c… 50c950… 28eb1a… 997448… 28eb1a… US     
 2 41657… Pinus …    41.3   -122. cdc,c… 50c950… 28eb1a… 997448… 28eb1a… US     
 3 44002… Pinus …    41.3   -122. cdc,c… 50c950… 28eb1a… 997448… 28eb1a… US     
 4 43998… Pinus …    41.3   -122. cdc,c… 50c950… 28eb1a… 997448… 28eb1a… US     
 5 44000… Pinus …    41.3   -122. cdc,c… 50c950… 28eb1a… 997448… 28eb1a… US     
 6 44001… Pinus …    41.3   -122. cdc,c… 50c950… 28eb1a… 997448… 28eb1a… US     
 7 43999… Pinus …    41.3   -122. cdc,c… 50c950… 28eb1a… 997448… 28eb1a… US     
 8 44004… Pinus …    41.3   -122. cdc    50c950… 28eb1a… 997448… 28eb1a… US     
 9 44049… Pinus …    41.3   -122. cdc,c… 50c950… 28eb1a… 997448… 28eb1a… US     
10 44363… Pinus …    41.3   -122. cdc,c… 50c950… 28eb1a… 997448… 28eb1a… US     
# … with 64 more rows, 114 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>, …

foxtail_coords <- foxtail_data$data[ , c("decimalLongitude", "decimalLatitude",
 "individualCount", "occurrenceStatus", "coordinateUncertaintyInMeters",
  "institutionCode", "references")]

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

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

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

foxtail_plot1  <- ggplot(foxtail_coords, aes(x=decimalLongitude, y = decimalLatitude)) +
                             geom_point(color='red') + labs(title = "MM Zone")
foxtail_plot1

Combine trail running and foxtail pine occurrence observations.

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