Introduction
This vignette is a basic guide to begin exploring the Portal data. We
load in the data (making sure that we’re using the most recent copy from
GitHub), and then explore the rodent abundances over time, with a
comparison between the “control” and “kangaroo rat exclosure”
treatments.
Package Setup
First we load several packages:
tidyverse contains several packages for data
manipulation and plotting.
cowplot contains a nicer default theme for
ggplot.
portalr is this package, which contains functions to
read in the Portal data and do some basic processing.
library(dplyr)
library(tidyr)
library(ggplot2)
library(cowplot)
library(portalr)
Retrieving the Data
Note that this package does not contain the actual Portal data, which
resides online in a GitHub
repository.
First, we try to load the data. If there isn’t a Portal folder, than
load_rodent_data will fall back to downloading the data, as
well.
check whether we already have the data. If we don’t have the data, or
if the version we have isn’t the most recent, we use the
download_observations function to download the latest copy
of the data.
portal_data_path <- tempdir() # use a temporary folder to store downloaded data
data_tables <- load_rodent_data(portal_data_path, download_if_missing = TRUE)
#> Warning in load_datafile(file.path("Rodents", "Portal_rodent.csv"), na.strings = "", : Proceeding to download data into specified path/var/folders/vd/jc3_fd0j7yjbg49g693m7yw40000gn/T//RtmpufgBUr
#> Downloading version `5.104.0` of the data...
#> Loading in data version 5.104.0
The load_rodent_data function reads in several tables
related to the rodent abundances. We won’t necessarily use all of these
tables, but loading this now gives us access later.
print(summary(data_tables))
#> Length Class Mode
#> rodent_data 31 data.frame list
#> species_table 15 data.frame list
#> trapping_table 8 data.frame list
#> newmoons_table 4 data.frame list
#> plots_table 6 data.frame list
Rodent Abundances
The first table that we loaded (data_tables$rodent_data)
is a record of whatever was found in the traps, mostly rodents, but also
a few other taxa. If we just wanted to get the rodent abundance data, we
could use the abundance function, which has default
arguments to filter out the non-rodents.
# get rodent abundance by plot
rodent_abundance_by_plot <- abundance(path = portal_data_path, time = "date", level = "plot")
#> Loading in data version 5.104.0
rodent_abundance <- rodent_abundance_by_plot %>%
gather(species, abundance, -censusdate, -treatment, -plot) %>%
count(species, censusdate, wt = abundance) %>%
rename(abundance = n)
print(summary(rodent_abundance))
#> species censusdate abundance
#> Length:9513 Min. :1979-09-22 Min. : 0.000
#> Class :character 1st Qu.:1989-12-04 1st Qu.: 0.000
#> Mode :character Median :1999-06-12 Median : 0.000
#> Mean :2000-05-21 Mean : 6.897
#> 3rd Qu.:2010-09-05 3rd Qu.: 5.000
#> Max. :2023-06-20 Max. :334.000
Let’s convert the data to long format for easier facetting. Also, we
want the scientific names instead of the two-letter species codes, so
let’s do that matching, too:
join_scientific_name <- function(rodent_abundance,
species_table = data_tables$species_table)
{
return(rodent_abundance %>%
left_join(select(species_table, "species", "scientificname"),
by = "species") %>%
rename(scientific_name = scientificname)
)
}
rodent_abundance <- join_scientific_name(rodent_abundance)
Next Steps
Our next steps would likely be to dig deeper into the rodent
abundances for different treatments, but first we want to know what the
different treatments look like, so let’s revisit the abundances
later.
Plot Treatments
A description of the experimental design and treatments can be found
in this Readme
file in the PortalDate repo.
For now, we are just going to use the Portal_plots
table file to look at how the treatments for individual plots have
changed over time. Note that this file is already loaded in as the
plots_table from the loadData function we ran
previously.
print(summary(data_tables$plots_table))
#> year month plot treatment
#> Min. :1977 Min. : 1.000 Min. : 1.00 Length:11576
#> 1st Qu.:1988 1st Qu.: 4.000 1st Qu.: 6.00 Class :character
#> Median :1999 Median : 7.000 Median :12.00 Mode :character
#> Mean :1999 Mean : 6.536 Mean :12.49
#> 3rd Qu.:2010 3rd Qu.:10.000 3rd Qu.:18.00
#> Max. :2023 Max. :12.000 Max. :24.00
#> resourcetreatment anttreatment
#> Length:11576 Length:11576
#> Class :character Class :character
#> Mode :character Mode :character
#>
#>
#>
We want a proper date variable as well as converting
plot into a factor:
plot_treatments <- data_tables$plots_table %>%
mutate(iso_date = as.Date(paste0(year, "-", month, "-", "01")),
plot = as.factor(plot)) %>%
select(iso_date, plot, treatment)
Identifying control plots
The treatments for the plots have changed over time: in some cases,
this was due to initial ramping up of the experimental protocol, in
others, exclusions of Dipodomys spectabilis were started and
then converted back later because the species went locally extinct
(e.g. plots 1, 5, 9, 24).
Which plots have always been control plots?
always_control_plots <- plot_treatments %>%
group_by(plot) %>%
summarize(always_control = all(treatment == "control")) %>%
filter(always_control)
print(always_control_plots)
#> # A tibble: 4 × 2
#> plot always_control
#> <fct> <lgl>
#> 1 4 TRUE
#> 2 11 TRUE
#> 3 14 TRUE
#> 4 17 TRUE
Note, however, that this excludes several plots for which the
treatment was changed ~2015. We can include these plots by first
filtering by date before testing for the “control” treatment:
mostly_control_plots <- plot_treatments %>%
filter(iso_date < "2015-01-01") %>%
group_by(plot) %>%
summarize(mostly_control = all(treatment == "control")) %>%
filter(mostly_control)
print(mostly_control_plots)
#> # A tibble: 8 × 2
#> plot mostly_control
#> <fct> <lgl>
#> 1 2 TRUE
#> 2 4 TRUE
#> 3 8 TRUE
#> 4 11 TRUE
#> 5 12 TRUE
#> 6 14 TRUE
#> 7 17 TRUE
#> 8 22 TRUE
And to identify the datespan over which these plots have been
controls:
date_span <- plot_treatments %>%
filter(plot %in% mostly_control_plots$plot) %>%
group_by(iso_date) %>%
summarize(all_control = all(treatment == "control")) %>%
filter(all_control)
print(date_span)
#> # A tibble: 411 × 2
#> iso_date all_control
#> <date> <lgl>
#> 1 1977-07-01 TRUE
#> 2 1977-08-01 TRUE
#> 3 1977-09-01 TRUE
#> 4 1977-10-01 TRUE
#> 5 1977-11-01 TRUE
#> 6 1977-12-01 TRUE
#> 7 1978-01-01 TRUE
#> 8 1978-02-01 TRUE
#> 9 1978-03-01 TRUE
#> 10 1978-04-01 TRUE
#> # ℹ 401 more rows
Abundances over control plots
We are now ready to plot abundances just over the control plots and
the time span in 1977-07-01 to 2015-03-01. We do this by retrieving the
abundance data by plot, and then filtering accordingly:
rodent_abundance_by_plot %>%
filter(censusdate >= min(date_span$iso_date),
censusdate <= max(date_span$iso_date),
plot %in% mostly_control_plots$plot) %>%
select(-treatment, -plot) %>%
gather(species, abundance, -censusdate) %>%
count(censusdate, species, wt = abundance) %>%
rename(abundance = n) %>%
join_scientific_name() %>%
{.} -> rodent_abundance_control
rodent_abundance_control %>%
make_abundance_plot_over_time() %>%
print()
Monthly abundance of rodents (control
plots)
Abundances over rodent exclosures
We can do the same with the “exclosure” condition. First, which
plots:
mostly_exclosure_plots <- plot_treatments %>%
filter(iso_date > as.Date("1989-01-01"),
iso_date < "2015-01-01") %>%
group_by(plot) %>%
summarize(mostly_exclosure = all(treatment == "exclosure")) %>%
filter(mostly_exclosure)
print(mostly_exclosure_plots)
#> # A tibble: 8 × 2
#> plot mostly_exclosure
#> <fct> <lgl>
#> 1 3 TRUE
#> 2 6 TRUE
#> 3 13 TRUE
#> 4 15 TRUE
#> 5 18 TRUE
#> 6 19 TRUE
#> 7 20 TRUE
#> 8 21 TRUE
Then, the datespan:
date_span <- plot_treatments %>%
filter(plot %in% mostly_exclosure_plots$plot) %>%
group_by(iso_date) %>%
summarize(all_exclosure = all(treatment == "exclosure")) %>%
filter(all_exclosure)
print(date_span)
#> # A tibble: 297 × 2
#> iso_date all_exclosure
#> <date> <lgl>
#> 1 1988-01-01 TRUE
#> 2 1988-02-01 TRUE
#> 3 1988-03-01 TRUE
#> 4 1988-04-01 TRUE
#> 5 1988-05-01 TRUE
#> 6 1988-06-01 TRUE
#> 7 1988-07-01 TRUE
#> 8 1988-08-01 TRUE
#> 9 1988-09-01 TRUE
#> 10 1988-10-01 TRUE
#> # ℹ 287 more rows
Finally, the figure:
rodent_abundance_by_plot %>%
filter(censusdate >= min(date_span$iso_date),
censusdate <= max(date_span$iso_date),
plot %in% mostly_exclosure_plots$plot) %>%
select(-treatment, -plot) %>%
gather(species, abundance, -censusdate) %>%
count(censusdate, species, wt = abundance) %>%
rename(abundance = n) %>%
join_scientific_name() %>%
{.} -> rodent_abundance_exclosure
rodent_abundance_exclosure %>%
make_abundance_plot_over_time() %>%
print()
Monthly abundance of rodents (exclosure
plots)
Since these data have the same number of plots as the previous
figure, we can directly compare abundances. Note the decreased numbers
for kangaroo rats (Dipodomys spp.) and increased numbers for
some other taxa.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
