First Day Demo

Quickly show some of the sorts of things we’ll be able to do by the end of the course
Use data from the National Ecological Observation Network NEON
Continental scale science project where systematic, quantitative data is collected repeatedly through time
Look at how the number of species at a site responds to environmental factors
We only have a few minutes, so I’m going to move quickly and use tools you won’t have installed yet, so this is the one day of the course where I won’t encourage you to code along with me.
The goal today isn’t to understand everything, it’s to see where we’re going.

Demo

First we need to load some packages to leverage code that others have written to make our lives easier

library(tidyverse)
library(maps)

Download neon-stream data from the course website
Load data

bugs <- read_csv("data/neon-stream-macros.csv")
sites <- read_csv("data/neon-stream-information.csv")

Once we’ve done this we can use tables in the database in R
Show tables
bugs: data on how many individuals of each size class (dr weight, dw) for each species are sampled at each site
sites: information on where each site is and its mean annual temperature in C (mat.c)
First, let’s convert the bugs data into total biomass at each site

biomass <- bugs %>%
  mutate(biomass = estimatedTotalCount * dw) %>%
  group_by(siteID) %>%
  summarise(tot_b = sum(biomass))

Combine our site data with our species richness data

biomass_env <- inner_join(biomass, sites)

Let’s see where our sites are located
first need to load base-map data

world <- map_data("world")
states <- map_data("state")

ggplot() +
  geom_polygon(data = world,
               aes(x = long, 
               y = lat, 
               group = group),
               color = "white",
               fill = "gray") +
  geom_polygon(data = states,
               aes(x = long,
                   y = lat,
                   group = group),
               color = "white",
               fill = "grey") +
  coord_quickmap(ylim = c(18,70),
                 xlim = c(-160,-50)) +
  theme_void()

Now we can plot our sites over the map

ggplot() +
  geom_polygon(data = world,
               aes(x = long, y = lat, group = group),
               color = "white",
               fill = "gray") +
  geom_polygon(data = states,
               aes(x = long,
                   y = lat,
                   group = group),
               color = "white",
               fill = "grey") +
  coord_quickmap(ylim = c(18,70),
                 xlim = c(-160,-50)) +
  geom_point(data = rich_env,
             aes(x = longitude,
                 y = latitude),
             size = 4) +
  theme_void()

Let’s add color to the sites to show the mean annual temperature

ggplot() +
  geom_polygon(data = world,
               aes(x = long, y = lat, group = group),
               color = "white",
               fill = "gray") +
  geom_polygon(data = states,
               aes(x = long,
                   y = lat,
                   group = group),
               color = "white",
               fill = "grey") +
  coord_quickmap(ylim = c(18,70),
                 xlim = c(-160,-50)) +
  geom_point(data = rich_env,
             aes(x = longitude,
                 y = latitude,
                 color = mat.c),
             size = 4) +
  scale_color_viridis_c(option = "plasma") +
  theme_void()

Now let’s see how biomass relates to the mean annual temperature

ggplot(biomass_env,
     aes(x = mat.c,
         y = t_b)) +
geom_point()

Note the scale on the y-axis
let’s put it on the log10 scale

ggplot(biomass_env,
       aes(x = mat.c,
           y = t_b)) +
  geom_point() +
  scale_y_log10()

It looks like there’s a pattern here, so let’s fit a linear model to it
we will also change the theme to be easier to view

ggplot(biomass_env,
       aes(x = mat.c,
           y = t_b)) +
  geom_point() +
  scale_y_log10()+
  stat_smooth(method = "lm") +
  theme_bw()

Look like total biomass on the log~10~ scale increases with temperature
This could help us with management decisions for fisheries
- i.e., “colder” sites have less biomass, so may not be able to support large fish biomass

Notes

Demo