Data visualization with ggplot2

Data Carpentry contributors

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.3     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Learning Objectives

  • Produce scatter plots, boxplots, and time series plots using ggplot.
  • Set universal plot settings.
  • Describe what faceting is and apply faceting in ggplot.
  • Modify the aesthetics of an existing ggplot plot (including axis labels and color).
  • Build complex and customized plots from data in a data frame.

We start by loading the required packages. ggplot2 is included in the tidyverse package.

If not still in the workspace, load the data we saved in the previous lesson.

surveys_complete <- read_csv("/cloud/project/data_output/surveys_complete.csv")

Plotting with ggplot2

ggplot2 is a plotting package that makes it simple to create complex plots from data in a data frame. It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties. Therefore, we only need minimal changes if the underlying data change or if we decide to change from a bar plot to a scatter plot. This helps in creating publication quality plots with minimal amounts of adjustments and tweaking.

ggplot2 functions like data in the ‘long’ format, i.e., a column for every dimension, and a row for every observation. Well-structured data will save you lots of time when making figures with ggplot2

ggplot graphics are built step by step by adding new elements. Adding layers in this fashion allows for extensive flexibility and customization of plots.

To build a ggplot, we will use the following basic template that can be used for different types of plots:

ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) +  <GEOM_FUNCTION>()
  • use the ggplot() function and bind the plot to a specific data frame using the data argument
ggplot(data = surveys_complete)
  • define a mapping (using the aesthetic (aes) function), by selecting the variables to be plotted and specifying how to present them in the graph, e.g. as x/y positions or characteristics such as size, shape, color, etc.
ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length))

  • add ‘geoms’ – graphical representations of the data in the plot (points, lines, bars). ggplot2 offers many different geoms; we will use some common ones today, including:

    * `geom_point()` for scatter plots, dot plots, etc.
* `geom_boxplot()` for, well, boxplots!
* `geom_line()` for trend lines, time series, etc.

To add a geom to the plot use the + operator. Because we have two continuous variables, let’s use geom_point() first:

ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length)) +
  geom_point()

The + in the ggplot2 package is particularly useful because it allows you to modify existing ggplot objects. This means you can easily set up plot templates and conveniently explore different types of plots, so the above plot can also be generated with code like this:

# Assign plot to a variable
surveys_plot <- ggplot(data = surveys_complete, 
                       mapping = aes(x = weight, y = hindfoot_length))

# Draw the plot
surveys_plot + 
    geom_point()

Notes

  • Anything you put in the ggplot() function can be seen by any geom layers that you add (i.e., these are universal plot settings). This includes the x- and y-axis mapping you set up in aes().
  • You can also specify mappings for a given geom independently of the mappings defined globally in the ggplot() function.
  • The + sign used to add new layers must be placed at the end of the line containing the previous layer. If, instead, the + sign is added at the beginning of the line containing the new layer, ggplot2 will not add the new layer and will return an error message.
# This is the correct syntax for adding layers
surveys_plot +
  geom_point()

# This will not add the new layer and will return an error message
surveys_plot
  + geom_point()

Challenge (optional)

Scatter plots can be useful exploratory tools for small datasets. For data sets with large numbers of observations, such as the surveys_complete data set, overplotting of points can be a limitation of scatter plots. One strategy for handling such settings is to use hexagonal binning of observations. The plot space is tessellated into hexagons. Each hexagon is assigned a color based on the number of observations that fall within its boundaries. To use hexagonal binning with ggplot2, first install the R package hexbin from CRAN:

Then use the geom_hex() function:

surveys_plot +
 geom_hex()
  • What are the relative strengths and weaknesses of a hexagonal bin plot compared to a scatter plot? Examine the above scatter plot and compare it with the hexagonal bin plot that you created.

Building your plots iteratively

Building plots with ggplot2 is typically an iterative process. We start by defining the dataset we’ll use, lay out the axes, and choose a geom:

ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length)) +
    geom_point()

Then, we start modifying this plot to extract more information from it. For instance, we can add transparency (alpha) to avoid overplotting:

ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length)) +
    geom_point(alpha = 0.1)

We can also add colors for all the points:

ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length)) +
    geom_point(alpha = 0.1, color = "blue")

Or to color each species in the plot differently, you could use a vector as an input to the argument color. ggplot2 will provide a different color corresponding to different values in the vector. Here is an example where we color with species_id:

ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length)) +
    geom_point(alpha = 0.1, aes(color = species_id))

We can also specify the colors directly inside the mapping provided in the ggplot() function. This will be seen by any geom layers and the mapping will be determined by the x- and y-axis set up in aes().

ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length, color = species_id)) +
    geom_point(alpha = 0.1)

Notice that we can change the geom layer and colors will be still determined by species_id

ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length, color = species_id)) +
    geom_jitter(alpha = 0.1)

Challenge

Use what you just learned to create a scatter plot of weight over species_id with the plot types showing in different colors. Is this a good way to show this type of data?

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
   geom_point(aes(color = plot_type))

Boxplot

We can use boxplots to visualize the distribution of weight within each species:

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
    geom_boxplot()

By adding points to boxplot, we can have a better idea of the number of measurements and of their distribution:

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
    geom_boxplot(alpha = 0) +
    geom_jitter(alpha = 0.3, color = "tomato")

Notice how the boxplot layer is behind the jitter layer? What do you need to change in the code to put the boxplot in front of the points such that it’s not hidden?

Challenges

Boxplots are useful summaries, but hide the shape of the distribution. For example, if the distribution is bimodal, we would not see it in a boxplot. An alternative to the boxplot is the violin plot, where the shape (of the density of points) is drawn.

In many types of data, it is important to consider the scale of the observations. For example, it may be worth changing the scale of the axis to better distribute the observations in the space of the plot. Changing the scale of the axes is done similarly to adding/modifying other components (i.e., by incrementally adding commands). Try making these modifications:

So far, we’ve looked at the distribution of weight within species. Try making a new plot to explore the distribution of another variable within each species.

  • Create a boxplot for hindfoot_length. Overlay the boxplot layer on a jitter layer to show actual measurements.

  • Add color to the data points on your boxplot according to the plot from which the sample was taken (plot_id).

Hint: Check the class for plot_id. Consider changing the class of plot_id from integer to factor. Why does this change how R makes the graph?

Plotting time series data

Let’s calculate number of counts per year for each genus. First we need to group the data and count records within each group:

yearly_counts <- surveys_complete %>%
  count(year, genus)

Time series data can be visualized as a line plot with years on the x axis and counts on the y axis:

ggplot(data = yearly_counts, mapping = aes(x = year, y = n)) +
     geom_line()

Unfortunately, this does not work because we plotted data for all the genera together. We need to tell ggplot to draw a line for each genus by modifying the aesthetic function to include group = genus:

ggplot(data = yearly_counts, mapping = aes(x = year, y = n, group = genus)) +
    geom_line()

We will be able to distinguish genera in the plot if we add colors (using color also automatically groups the data):

ggplot(data = yearly_counts, mapping = aes(x = year, y = n, color = genus)) +
    geom_line()

Faceting

ggplot2 has a special technique called faceting that allows the user to split one plot into multiple plots based on a factor included in the dataset.

There are two types of facet functions:

  • facet_wrap() arranges a one-dimensional sequence of panels to allow them to cleanly fit on one page.
  • facet_grid() allows you to form a matrix of rows and columns of panels.

Both geometries allow to to specify faceting variables specified within vars(). For example, facet_wrap(facets = vars(facet_variable)) or facet_grid(rows = vars(row_variable), cols = vars(col_variable)).

Let’s start by using facet_wrap() to make a time series plot for each species:

ggplot(data = yearly_counts, mapping = aes(x = year, y = n)) +
    geom_line() +
    facet_wrap(facets = vars(genus))

Now we would like to split the line in each plot by the sex of each individual measured. To do that we need to make counts in the data frame grouped by year, species_id, and sex:

yearly_sex_counts <- surveys_complete %>%
  count(year, genus, sex)

We can now make the faceted plot by splitting further by sex using color (within each panel):

ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_wrap(facets =  vars(genus))

Now let’s use facet_grid() to control how panels are organised by both rows and columns:

ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_grid(rows = vars(sex), cols =  vars(genus))

You can also organise the panels only by rows (or only by columns):

# One column, facet by rows
ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_grid(rows = vars(genus))

# One row, facet by column
ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_grid(cols = vars(genus))

Note: In earlier versions of ggplot2 you need to use an interface using formulas to specify how plots are faceted (and this is still supported in new versions). The equivalent syntax is:

# facet wrap
facet_wrap(vars(genus))    # new
facet_wrap(~ genus)        # old

# grid on both rows and columns
facet_grid(rows = vars(genus), cols = vars(sex))   # new
facet_grid(genus ~ sex)                            # old

# grid on rows only
facet_grid(rows = vars(genus))   # new
facet_grid(genus ~ .)            # old

# grid on columns only
facet_grid(cols = vars(genus))   # new
facet_grid(. ~ genus)            # old

ggplot2 themes

Usually plots with white background look more readable when printed. Every single component of a ggplot graph can be customized using the generic theme() function, as we will see below. However, there are pre-loaded themes available that change the overall appearance of the graph without much effort.

For example, we can change our previous graph to have a simpler white background using the theme_bw() function:

 ggplot(data = yearly_sex_counts, 
        mapping = aes(x = year, y = n, color = sex)) +
     geom_line() +
     facet_wrap(vars(genus)) +
     theme_bw()

In addition to theme_bw(), which changes the plot background to white, ggplot2 comes with several other themes which can be useful to quickly change the look of your visualization. The complete list of themes is available at https://ggplot2.tidyverse.org/reference/ggtheme.html. theme_minimal() and theme_light() are popular, and theme_void() can be useful as a starting point to create a new hand-crafted theme.

The ggthemes package provides a wide variety of options. The ggplot2 extensions website provides a list of packages that extend the capabilities of ggplot2, including additional themes.

Challenge

Use what you just learned to create a plot that depicts how the average weight of each species changes through the years.

yearly_weight <- surveys_complete %>% group_by(year, species_id) %>% summarize(avg_weight = mean(weight)) ggplot(data = yearly_weight, mapping = aes(x=year, y=avg_weight)) + geom_line() + facet_wrap(vars(species_id)) + theme_bw()

Customization

Take a look at the ggplot2 cheat sheet, and think of ways you could improve the plot.

Now, let’s change names of axes to something more informative than ‘year’ and ‘n’ and add a title to the figure:

ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(vars(genus)) +
    labs(title = "Observed genera through time",
         x = "Year of observation",
         y = "Number of individuals") +
    theme_bw()

The axes have more informative names, but their readability can be improved by increasing the font size. This can be done with the generic theme() function:

ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(vars(genus)) +
    labs(title = "Observed genera through time",
        x = "Year of observation",
        y = "Number of individuals") +
    theme_bw() +
    theme(text=element_text(size = 16))

Note that it is also possible to change the fonts of your plots. If you are on Windows, you may have to install the extrafont package, and follow the instructions included in the README for this package.

After our manipulations, you may notice that the values on the x-axis are still not properly readable. Let’s change the orientation of the labels and adjust them vertically and horizontally so they don’t overlap. You can use a 90-degree angle, or experiment to find the appropriate angle for diagonally oriented labels:

ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(vars(genus)) +
    labs(title = "Observed genera through time",
        x = "Year of observation",
        y = "Number of individuals") +
    theme_bw() +
    theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5),
                        axis.text.y = element_text(colour = "grey20", size = 12),
          text = element_text(size = 16))

If you like the changes you created better than the default theme, you can save them as an object to be able to easily apply them to other plots you may create:

# define custom theme
grey_theme <- theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5),
                          axis.text.y = element_text(colour = "grey20", size = 12),
                          text = element_text(size = 16))

# create a boxplot with the new theme
ggplot(surveys_complete, aes(x = species_id, y = hindfoot_length)) +
    geom_boxplot() +
    grey_theme

Challenge

With all of this information in hand, please take another five minutes to either improve one of the plots generated in this exercise or create a beautiful graph of your own. Use the RStudio ggplot2 cheat sheet for inspiration. Here are some ideas:

Arranging and exporting plots

Faceting is a great tool for splitting one plot into multiple plots, but sometimes you may want to produce a single figure that contains multiple plots using different variables or even different data frames. The gridExtra package allows us to combine separate ggplots into a single figure using grid.arrange():

install.packages("gridExtra")
library(gridExtra)

spp_weight_boxplot <- ggplot(data = surveys_complete, 
                             mapping = aes(x = genus, y = weight)) +
  geom_boxplot() +
  xlab("Genus") + ylab("Weight (g)") +
  scale_y_log10() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

spp_count_plot <- ggplot(data = yearly_counts, 
                         mapping = aes(x = year, y = n, color = genus)) +
  geom_line() + 
  xlab("Year") + ylab("Abundance")

grid.arrange(spp_weight_boxplot, spp_count_plot, ncol = 2, widths = c(4, 6))

In addition to the ncol and nrow arguments, used to make simple arrangements, there are tools for constructing more complex layouts.

After creating your plot, you can save it to a file in your favorite format. The Export tab in the Plot pane in RStudio will save your plots at low resolution, which will not be accepted by many journals and will not scale well for posters.

Instead, use the ggsave() function, which allows you easily change the dimension and resolution of your plot by adjusting the appropriate arguments (width, height and dpi).

Make sure you have the fig_output/ folder in your working directory.

my_plot <- ggplot(data = yearly_sex_counts, 
                  mapping = aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(vars(species_id)) +
    labs(title = "Observed genera through time",
        x = "Year of observation",
        y = "Number of individuals") +
    theme_bw() +
    theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5),
                        axis.text.y = element_text(colour = "grey20", size = 12),
          text=element_text(size = 16))
ggsave("/cloud/project/fig_output/yearly_sex_counts.png", my_plot, width = 15, height = 10)

# This also works for grid.arrange() plots
combo_plot <- grid.arrange(spp_weight_boxplot, spp_count_plot, ncol = 2, widths = c(4, 6))
ggsave("/cloud/project/fig_output/combo_plot_abun_weight.png", combo_plot, width = 10, dpi = 300)

Note: The parameters width and height also determine the font size in the saved plot.

Error Bars

Error bars represent some measure of variability in the data. Common types of error bars are one standard deviation uncertainty, one standard error, or a 95% confidence interval.

Let’s first take a look at plotting error bars as one standard deviation from the mean. This is typically a two-step process. The first step is to calculate our standard deviation, then to add this to the plot.

surveys_sd <- surveys_complete %>% 
  group_by(sex) %>%
  summarize(mean=mean(hindfoot_length),
            sd=sd(hindfoot_length))

If you need to recall,

the calculation of the mean, {X}, looks like so:

\(\bar{X} = \frac{\sum Observations}{N}\)

and standard deviation (a measure of the distance between observations, , and the mean, {X}):

$= $

Here is what the standard deviation will look like on a plot by sex.

ggplot(surveys_sd, aes(x=sex, y=mean, fill=sex)) + 
   geom_bar(stat="identity", color="black", 
            position=position_dodge()) +
   geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,
                 position=position_dodge(.9))

It’s a little more interesting if we peek at the sex by species id interaction.

surveys_sd <- surveys_complete %>% 
  group_by(sex, species_id) %>%
  summarize(mean=mean(hindfoot_length),
            sd=sd(hindfoot_length))
## `summarise()` has grouped output by 'sex'. You can override using the `.groups`
## argument.

What do we think it means that the

ggplot(surveys_sd, aes(x=species_id, y=mean, fill=sex)) + 
   geom_bar(stat="identity", color="black", 
            position=position_dodge()) +
   geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,
                 position=position_dodge(.9))

We can also do this with the standard error of the mean. Standard error of the mean is a little different than standard deviation, typically defined as

\(se = \frac{\sigma}{\sqrt{N}}\)

We’ll need to install a package to compute the standard error:

install.packages("plotrix")
## Installing package into '/private/var/folders/54/9kd8nf1x4fnft0ymvb11qmc80000gn/T/Rtmpie5FUp/temp_libpath3b62461540c7'
## (as 'lib' is unspecified)
## 
## The downloaded binary packages are in
##  /var/folders/54/9kd8nf1x4fnft0ymvb11qmc80000gn/T//Rtmpc3JR3s/downloaded_packages

We could also write one, but for expediency today, we won’t be.

surveys_se <- surveys_complete %>% 
  group_by(sex, species_id) %>%
  summarize(mean=mean(hindfoot_length),
            se=std.error(hindfoot_length))
## `summarise()` has grouped output by 'sex'. You can override using the `.groups`
## argument.

We can think of this measure as being a measure of dispersion around a mean. Because N is in the denominator here, SE will tend to decrease as sample sizes increase. Thus, we see on this plot that SE is very low, even when standard deviation is fairly high.

ggplot(surveys_se, aes(x=species_id, y=mean, fill=sex)) + 
   geom_bar(stat="identity", color="black", 
            position=position_dodge()) +
   geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.2,
                 position=position_dodge(.9))

To explain this visually, let’s take a quick look at the barplots for these animals again. Many have fairly low variance around the mean.

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
    geom_boxplot()

We’ll discuss confidence intervals in a couple weeks when we look at regression.

Challenge

The relationship between mean, standard devitation and standard error is confusing. Look at the equations above. What information is shared between equations, and what information is different. In what types of situations do you think you’d want to use each one?

Annotation

Sometimes we want to add additional information or context to our plots. This can come in several forms: we may want to add or modify legends. We may also want to add text directly to the plot.

Here are a few examples. Warm up with a quick jitter plot. Hindfoot_length vs weight.

surveys_reduced <- surveys_complete %>% 
  filter(genus == "Dipodomys")
ggplot(data = surveys_reduced, mapping = aes(x = weight, y = hindfoot_length, color = sex)) +
    geom_jitter(alpha = 0.5)

We can label individual points. I’m not sure how much I recommend this, but we want to get familiar with the concept here, not necessarily the specific use case:

ggplot(data = surveys_reduced, mapping = aes(x = weight, y = hindfoot_length, label=species_id)) +
    geom_jitter(alpha = 0.5) +
    geom_text(hjust=-1,vjust=1)

So that’s how we label points. Lets say we want to label one or a couple points:

surveys_labeled <- surveys_reduced %>% 
  mutate(TF = record_id == 257)

ggplot(data = surveys_labeled, mapping = aes(x = weight, y = hindfoot_length)) +
    geom_jitter(alpha = 0.5) +
    geom_point(data=surveys_labeled[surveys_labeled$TF == "TRUE",],color="pink",size=2)

Or you can color based on this:

ggplot(data = surveys_labeled, mapping = aes(x = weight, y = hindfoot_length)) +
    geom_point(aes(color=TF))

The difficulty with this way is that you can lose your point in overplotting.

library(ggrepel)

ggplot(data = surveys_labeled, mapping = aes(x = weight, y = hindfoot_length)) +
    geom_jitter(alpha = 0.5) +
    geom_point(data=surveys_labeled[surveys_labeled$TF == "TRUE",],color="pink",size=2) +
    geom_text_repel(data=surveys_labeled[surveys_labeled$TF == "TRUE",], aes(label=species_id, color = "pink"))

Challenge

Try labeling a cluster of points out of the whole surveys_complete. Perhaps choose a favorite species and label all the points of that species.

Pretty tables 

transform_table <- surveys_complete %>%
  filter(year ==2000 ) %>% 
  filter(genus == "Dipodomys") %>% 
  group_by(sex, species_id)
pretty_table <- knitr::kable(transform_table, caption = "Observations of Dipodomys in 2000")
pretty_table
Observations of Dipodomys in 2000
record_id month day year plot_id species_id sex hindfoot_length weight genus species taxa plot_type
31215 8 25 2000 2 DM F 36 43 Dipodomys merriami Rodent Control
31386 9 30 2000 2 DM F 35 45 Dipodomys merriami Rodent Control
31515 11 25 2000 2 DM F 35 43 Dipodomys merriami Rodent Control
31632 12 22 2000 2 DM F 34 44 Dipodomys merriami Rodent Control
30171 1 8 2000 2 DO M 36 52 Dipodomys ordii Rodent Control
30173 1 8 2000 2 DO F 35 54 Dipodomys ordii Rodent Control
30303 2 5 2000 2 DO F 36 52 Dipodomys ordii Rodent Control
30305 2 5 2000 2 DO M 37 54 Dipodomys ordii Rodent Control
30612 4 30 2000 2 DO F 34 42 Dipodomys ordii Rodent Control
30739 6 3 2000 2 DO M 34 31 Dipodomys ordii Rodent Control
30196 1 8 2000 17 DM M 37 52 Dipodomys merriami Rodent Control
30197 1 8 2000 17 DM F 34 43 Dipodomys merriami Rodent Control
30328 2 5 2000 17 DM F 34 42 Dipodomys merriami Rodent Control
30331 2 5 2000 17 DM M 34 36 Dipodomys merriami Rodent Control
30470 3 4 2000 17 DM F 34 41 Dipodomys merriami Rodent Control
30471 3 4 2000 17 DM F 36 44 Dipodomys merriami Rodent Control
30472 3 4 2000 17 DM M 36 49 Dipodomys merriami Rodent Control
30473 3 4 2000 17 DM M 37 33 Dipodomys merriami Rodent Control
30475 3 4 2000 17 DM M 36 49 Dipodomys merriami Rodent Control
30477 3 4 2000 17 DM M 36 40 Dipodomys merriami Rodent Control
30604 4 30 2000 17 DM F 32 33 Dipodomys merriami Rodent Control
30605 4 30 2000 17 DM M 37 52 Dipodomys merriami Rodent Control
30606 4 30 2000 17 DM F 33 29 Dipodomys merriami Rodent Control
30647 4 30 2000 17 DM M 37 46 Dipodomys merriami Rodent Control
30648 4 30 2000 17 DM F 36 46 Dipodomys merriami Rodent Control
30767 6 3 2000 17 DM F 34 38 Dipodomys merriami Rodent Control
30768 6 3 2000 17 DM M 37 43 Dipodomys merriami Rodent Control
30772 6 3 2000 17 DM F 35 43 Dipodomys merriami Rodent Control
30969 7 1 2000 17 DM M 36 48 Dipodomys merriami Rodent Control
30974 7 1 2000 17 DM F 36 43 Dipodomys merriami Rodent Control
30978 7 1 2000 17 DM M 36 45 Dipodomys merriami Rodent Control
30979 7 1 2000 17 DM F 34 43 Dipodomys merriami Rodent Control
31134 7 22 2000 17 DM F 34 42 Dipodomys merriami Rodent Control
31136 7 22 2000 17 DM M 37 46 Dipodomys merriami Rodent Control
31137 7 22 2000 17 DM M 37 45 Dipodomys merriami Rodent Control
31141 7 22 2000 17 DM F 37 51 Dipodomys merriami Rodent Control
31260 8 25 2000 17 DM M 37 43 Dipodomys merriami Rodent Control
31261 8 25 2000 17 DM M 37 47 Dipodomys merriami Rodent Control
31263 8 25 2000 17 DM F 35 38 Dipodomys merriami Rodent Control
31267 8 25 2000 17 DM F 36 45 Dipodomys merriami Rodent Control
31411 9 30 2000 17 DM F 34 42 Dipodomys merriami Rodent Control
31412 9 30 2000 17 DM M 35 43 Dipodomys merriami Rodent Control
31413 9 30 2000 17 DM F 36 43 Dipodomys merriami Rodent Control
31415 9 30 2000 17 DM F 37 34 Dipodomys merriami Rodent Control
31416 9 30 2000 17 DM M 37 42 Dipodomys merriami Rodent Control
31419 9 30 2000 17 DM M 37 47 Dipodomys merriami Rodent Control
31420 9 30 2000 17 DM M 36 47 Dipodomys merriami Rodent Control
31536 11 25 2000 17 DM M 37 47 Dipodomys merriami Rodent Control
31538 11 25 2000 17 DM M 38 45 Dipodomys merriami Rodent Control
31539 11 25 2000 17 DM F 34 42 Dipodomys merriami Rodent Control
31540 11 25 2000 17 DM F 36 42 Dipodomys merriami Rodent Control
31541 11 25 2000 17 DM F 35 42 Dipodomys merriami Rodent Control
31542 11 25 2000 17 DM M 36 49 Dipodomys merriami Rodent Control
31645 12 22 2000 17 DM M 36 50 Dipodomys merriami Rodent Control
31646 12 22 2000 17 DM F 36 42 Dipodomys merriami Rodent Control
31647 12 22 2000 17 DM F 35 43 Dipodomys merriami Rodent Control
31648 12 22 2000 17 DM M 36 47 Dipodomys merriami Rodent Control
31650 12 22 2000 17 DM M 36 48 Dipodomys merriami Rodent Control
31652 12 22 2000 17 DM M 36 45 Dipodomys merriami Rodent Control
30199 1 8 2000 17 DO F 35 54 Dipodomys ordii Rodent Control
30329 2 5 2000 17 DO F 35 54 Dipodomys ordii Rodent Control
30602 4 30 2000 17 DO M 34 39 Dipodomys ordii Rodent Control
30603 4 30 2000 17 DO M 33 38 Dipodomys ordii Rodent Control
30649 4 30 2000 17 DO F 36 59 Dipodomys ordii Rodent Control
30769 6 3 2000 17 DO M 35 24 Dipodomys ordii Rodent Control
30776 6 3 2000 17 DO F 36 55 Dipodomys ordii Rodent Control
30970 7 1 2000 17 DO M 34 51 Dipodomys ordii Rodent Control
31138 7 22 2000 17 DO M 34 47 Dipodomys ordii Rodent Control
30179 1 8 2000 12 DM M 36 60 Dipodomys merriami Rodent Control
30313 2 5 2000 12 DM F 36 47 Dipodomys merriami Rodent Control
30316 2 5 2000 12 DM F 33 33 Dipodomys merriami Rodent Control
30453 3 4 2000 12 DM F 35 33 Dipodomys merriami Rodent Control
30614 4 30 2000 12 DM F 36 40 Dipodomys merriami Rodent Control
30615 4 30 2000 12 DM M 34 18 Dipodomys merriami Rodent Control
30621 4 30 2000 12 DM F 36 45 Dipodomys merriami Rodent Control
30742 6 3 2000 12 DM F 36 47 Dipodomys merriami Rodent Control
30743 6 3 2000 12 DM M 35 32 Dipodomys merriami Rodent Control
30925 7 1 2000 12 DM M 36 39 Dipodomys merriami Rodent Control
30926 7 1 2000 12 DM M 35 28 Dipodomys merriami Rodent Control
31113 7 22 2000 12 DM M 36 39 Dipodomys merriami Rodent Control
31115 7 22 2000 12 DM M 37 36 Dipodomys merriami Rodent Control
31119 7 22 2000 12 DM F 36 49 Dipodomys merriami Rodent Control
31217 8 25 2000 12 DM F 35 49 Dipodomys merriami Rodent Control
31218 8 25 2000 12 DM M 36 45 Dipodomys merriami Rodent Control
31393 9 30 2000 12 DM M 37 41 Dipodomys merriami Rodent Control
31396 9 30 2000 12 DM F 36 47 Dipodomys merriami Rodent Control
31522 11 25 2000 12 DM F 36 47 Dipodomys merriami Rodent Control
31526 11 25 2000 12 DM M 36 38 Dipodomys merriami Rodent Control
31527 11 25 2000 12 DM M 36 44 Dipodomys merriami Rodent Control
31637 12 22 2000 12 DM M 37 45 Dipodomys merriami Rodent Control
30177 1 8 2000 12 DO F 32 54 Dipodomys ordii Rodent Control
30180 1 8 2000 12 DO M 36 42 Dipodomys ordii Rodent Control
30181 1 8 2000 12 DO M 35 55 Dipodomys ordii Rodent Control
30182 1 8 2000 12 DO M 36 63 Dipodomys ordii Rodent Control
30314 2 5 2000 12 DO M 33 54 Dipodomys ordii Rodent Control
30318 2 5 2000 12 DO F 35 56 Dipodomys ordii Rodent Control
30622 4 30 2000 12 DO M 36 51 Dipodomys ordii Rodent Control
30748 6 3 2000 12 DO M 36 47 Dipodomys ordii Rodent Control
30752 6 3 2000 12 DO M 35 53 Dipodomys ordii Rodent Control
30921 7 1 2000 12 DO M 35 62 Dipodomys ordii Rodent Control
30931 7 1 2000 12 DO M 36 56 Dipodomys ordii Rodent Control
31112 7 22 2000 12 DO M 36 58 Dipodomys ordii Rodent Control
31221 8 25 2000 12 DO M 36 55 Dipodomys ordii Rodent Control
31397 9 30 2000 12 DO M 36 51 Dipodomys ordii Rodent Control
31528 11 25 2000 12 DO M 35 55 Dipodomys ordii Rodent Control
31636 12 22 2000 12 DO M 35 54 Dipodomys ordii Rodent Control
30241 1 10 2000 11 DM M 35 43 Dipodomys merriami Rodent Control
30242 1 10 2000 11 DM M 35 44 Dipodomys merriami Rodent Control
30244 1 10 2000 11 DM M 35 44 Dipodomys merriami Rodent Control
30246 1 10 2000 11 DM F 33 54 Dipodomys merriami Rodent Control
30379 2 6 2000 11 DM F 34 46 Dipodomys merriami Rodent Control
30382 2 6 2000 11 DM M 36 46 Dipodomys merriami Rodent Control
30529 3 5 2000 11 DM M 37 50 Dipodomys merriami Rodent Control
30681 4 31 2000 11 DM M 37 49 Dipodomys merriami Rodent Control
30683 4 31 2000 11 DM F 33 55 Dipodomys merriami Rodent Control
30845 6 4 2000 11 DM M 35 35 Dipodomys merriami Rodent Control
30848 6 4 2000 11 DM M 33 47 Dipodomys merriami Rodent Control
30850 6 4 2000 11 DM M 37 48 Dipodomys merriami Rodent Control
31021 7 2 2000 11 DM M 35 43 Dipodomys merriami Rodent Control
31024 7 2 2000 11 DM M 37 53 Dipodomys merriami Rodent Control
31314 8 26 2000 11 DM M 36 53 Dipodomys merriami Rodent Control
31315 8 26 2000 11 DM F 36 41 Dipodomys merriami Rodent Control
31465 9 31 2000 11 DM F 35 41 Dipodomys merriami Rodent Control
31578 11 26 2000 11 DM F 35 37 Dipodomys merriami Rodent Control
31685 12 23 2000 11 DM F 36 39 Dipodomys merriami Rodent Control
31686 12 23 2000 11 DM M 36 50 Dipodomys merriami Rodent Control
30243 1 10 2000 11 DO M 35 51 Dipodomys ordii Rodent Control
30210 1 8 2000 22 DM M 38 56 Dipodomys merriami Rodent Control
30215 1 8 2000 22 DM F 34 28 Dipodomys merriami Rodent Control
30341 2 5 2000 22 DM F 35 32 Dipodomys merriami Rodent Control
30344 2 5 2000 22 DM M 36 57 Dipodomys merriami Rodent Control
30347 2 5 2000 22 DM F 34 42 Dipodomys merriami Rodent Control
30630 4 30 2000 22 DM M 37 56 Dipodomys merriami Rodent Control
30792 6 3 2000 22 DM M 36 54 Dipodomys merriami Rodent Control
31245 8 25 2000 22 DM M 36 56 Dipodomys merriami Rodent Control
31427 9 30 2000 22 DM M 35 52 Dipodomys merriami Rodent Control
31432 9 30 2000 22 DM M 37 42 Dipodomys merriami Rodent Control
30271 1 10 2000 14 DM M 34 41 Dipodomys merriami Rodent Control
30273 1 10 2000 14 DM M 35 37 Dipodomys merriami Rodent Control
30409 2 6 2000 14 DM M 34 53 Dipodomys merriami Rodent Control
30556 3 5 2000 14 DM M 35 26 Dipodomys merriami Rodent Control
30557 3 5 2000 14 DM M 36 49 Dipodomys merriami Rodent Control
30558 3 5 2000 14 DM M 37 53 Dipodomys merriami Rodent Control
30560 3 5 2000 14 DM F 35 43 Dipodomys merriami Rodent Control
30563 3 5 2000 14 DM F 36 48 Dipodomys merriami Rodent Control
30564 3 5 2000 14 DM M 36 47 Dipodomys merriami Rodent Control
30565 3 5 2000 14 DM F 36 41 Dipodomys merriami Rodent Control
30711 4 31 2000 14 DM M 35 44 Dipodomys merriami Rodent Control
30712 4 31 2000 14 DM M 35 28 Dipodomys merriami Rodent Control
30713 4 31 2000 14 DM M 35 40 Dipodomys merriami Rodent Control
30714 4 31 2000 14 DM M 37 48 Dipodomys merriami Rodent Control
30880 6 4 2000 14 DM M 36 43 Dipodomys merriami Rodent Control
30881 6 4 2000 14 DM M 34 24 Dipodomys merriami Rodent Control
30882 6 4 2000 14 DM F 35 47 Dipodomys merriami Rodent Control
31070 7 2 2000 14 DM M 37 45 Dipodomys merriami Rodent Control
31074 7 2 2000 14 DM M 36 47 Dipodomys merriami Rodent Control
31076 7 2 2000 14 DM F 34 29 Dipodomys merriami Rodent Control
31349 8 26 2000 14 DM M 36 31 Dipodomys merriami Rodent Control
31351 8 26 2000 14 DM F 35 39 Dipodomys merriami Rodent Control
31352 8 26 2000 14 DM M 26 45 Dipodomys merriami Rodent Control
31353 8 26 2000 14 DM M 35 45 Dipodomys merriami Rodent Control
31356 8 26 2000 14 DM M 35 25 Dipodomys merriami Rodent Control
31357 8 26 2000 14 DM F 34 39 Dipodomys merriami Rodent Control
31487 9 31 2000 14 DM F 35 42 Dipodomys merriami Rodent Control
31488 9 31 2000 14 DM F 37 46 Dipodomys merriami Rodent Control
31492 9 31 2000 14 DM F 34 42 Dipodomys merriami Rodent Control
31493 9 31 2000 14 DM M 35 49 Dipodomys merriami Rodent Control
31600 11 26 2000 14 DM M 34 47 Dipodomys merriami Rodent Control
31601 11 26 2000 14 DM M 36 40 Dipodomys merriami Rodent Control
31603 11 26 2000 14 DM F 32 39 Dipodomys merriami Rodent Control
31604 11 26 2000 14 DM M 34 35 Dipodomys merriami Rodent Control
31605 11 26 2000 14 DM F 37 41 Dipodomys merriami Rodent Control
31704 12 23 2000 14 DM M 35 52 Dipodomys merriami Rodent Control
31705 12 23 2000 14 DM F 35 39 Dipodomys merriami Rodent Control
31576 11 26 2000 6 DO F 36 50 Dipodomys ordii Rodent Short-term Krat Exclosure
30227 1 10 2000 4 DM M 34 45 Dipodomys merriami Rodent Control
30366 2 6 2000 4 DM F 38 42 Dipodomys merriami Rodent Control
30369 2 6 2000 4 DM M 35 50 Dipodomys merriami Rodent Control
30509 3 5 2000 4 DM F 33 41 Dipodomys merriami Rodent Control
30510 3 5 2000 4 DM F 38 40 Dipodomys merriami Rodent Control
30676 4 31 2000 4 DM F 33 47 Dipodomys merriami Rodent Control
30823 6 4 2000 4 DM M 35 23 Dipodomys merriami Rodent Control
30826 6 4 2000 4 DM M 36 46 Dipodomys merriami Rodent Control
31000 7 2 2000 4 DM M 36 33 Dipodomys merriami Rodent Control
31003 7 2 2000 4 DM M 36 39 Dipodomys merriami Rodent Control
31190 7 23 2000 4 DM M 32 32 Dipodomys merriami Rodent Control
31191 7 23 2000 4 DM M 37 40 Dipodomys merriami Rodent Control
31288 8 26 2000 4 DM M 36 43 Dipodomys merriami Rodent Control
31289 8 26 2000 4 DM M 36 44 Dipodomys merriami Rodent Control
31450 9 31 2000 4 DM M 35 46 Dipodomys merriami Rodent Control
31451 9 31 2000 4 DM M 36 43 Dipodomys merriami Rodent Control
31452 9 31 2000 4 DM M 35 41 Dipodomys merriami Rodent Control
31453 9 31 2000 4 DM M 35 29 Dipodomys merriami Rodent Control
31454 9 31 2000 4 DM F 35 39 Dipodomys merriami Rodent Control
31566 11 26 2000 4 DM M 37 43 Dipodomys merriami Rodent Control
31567 11 26 2000 4 DM F 35 44 Dipodomys merriami Rodent Control
31568 11 26 2000 4 DM M 36 43 Dipodomys merriami Rodent Control
31571 11 26 2000 4 DM M 36 44 Dipodomys merriami Rodent Control
31673 12 23 2000 4 DM M 35 54 Dipodomys merriami Rodent Control
31674 12 23 2000 4 DM M 35 48 Dipodomys merriami Rodent Control
30981 7 1 2000 24 DO F 42 46 Dipodomys ordii Rodent Rodent Exclosure
31269 8 25 2000 24 DO M 36 59 Dipodomys ordii Rodent Rodent Exclosure
30162 1 8 2000 1 DM M 36 50 Dipodomys merriami Rodent Spectab exclosure
30292 2 5 2000 1 DM F 37 46 Dipodomys merriami Rodent Spectab exclosure
30293 2 5 2000 1 DM M 36 50 Dipodomys merriami Rodent Spectab exclosure
30427 3 4 2000 1 DM F 37 49 Dipodomys merriami Rodent Spectab exclosure
30725 6 3 2000 1 DM M 35 49 Dipodomys merriami Rodent Spectab exclosure
30900 7 1 2000 1 DM M 35 38 Dipodomys merriami Rodent Spectab exclosure
31101 7 22 2000 1 DM M 36 39 Dipodomys merriami Rodent Spectab exclosure
31195 8 25 2000 1 DM M 34 42 Dipodomys merriami Rodent Spectab exclosure
31369 9 30 2000 1 DM F 36 38 Dipodomys merriami Rodent Spectab exclosure
31370 9 30 2000 1 DM M 37 39 Dipodomys merriami Rodent Spectab exclosure
31374 9 30 2000 1 DM M 35 42 Dipodomys merriami Rodent Spectab exclosure
31509 11 25 2000 1 DM M 36 40 Dipodomys merriami Rodent Spectab exclosure
31512 11 25 2000 1 DM M 36 41 Dipodomys merriami Rodent Spectab exclosure
31620 12 22 2000 1 DM M 36 38 Dipodomys merriami Rodent Spectab exclosure
31621 12 22 2000 1 DM M 37 41 Dipodomys merriami Rodent Spectab exclosure
31622 12 22 2000 1 DM F 36 42 Dipodomys merriami Rodent Spectab exclosure
31624 12 22 2000 1 DM M 37 43 Dipodomys merriami Rodent Spectab exclosure
30160 1 8 2000 1 DO M 35 53 Dipodomys ordii Rodent Spectab exclosure
30167 1 8 2000 1 DO M 35 41 Dipodomys ordii Rodent Spectab exclosure
30289 2 5 2000 1 DO F 35 48 Dipodomys ordii Rodent Spectab exclosure
30290 2 5 2000 1 DO F 36 65 Dipodomys ordii Rodent Spectab exclosure
30294 2 5 2000 1 DO F 34 49 Dipodomys ordii Rodent Spectab exclosure
30295 2 5 2000 1 DO M 35 59 Dipodomys ordii Rodent Spectab exclosure
30422 3 4 2000 1 DO F 36 46 Dipodomys ordii Rodent Spectab exclosure
30423 3 4 2000 1 DO M 35 46 Dipodomys ordii Rodent Spectab exclosure
30425 3 4 2000 1 DO F 64 35 Dipodomys ordii Rodent Spectab exclosure
30582 4 30 2000 1 DO M 34 53 Dipodomys ordii Rodent Spectab exclosure
30583 4 30 2000 1 DO F 36 49 Dipodomys ordii Rodent Spectab exclosure
30727 6 3 2000 1 DO F 34 50 Dipodomys ordii Rodent Spectab exclosure
30728 6 3 2000 1 DO M 35 54 Dipodomys ordii Rodent Spectab exclosure
30906 7 1 2000 1 DO F 36 53 Dipodomys ordii Rodent Spectab exclosure
31196 8 25 2000 1 DO M 35 51 Dipodomys ordii Rodent Spectab exclosure
31375 9 30 2000 1 DO M 33 50 Dipodomys ordii Rodent Spectab exclosure
31506 11 25 2000 1 DO M 35 51 Dipodomys ordii Rodent Spectab exclosure
31616 12 22 2000 1 DO M 34 52 Dipodomys ordii Rodent Spectab exclosure
31046 7 2 2000 8 DM M 34 53 Dipodomys merriami Rodent Control
31329 8 26 2000 8 DM M 35 37 Dipodomys merriami Rodent Control
31476 9 31 2000 8 DM M 36 43 Dipodomys merriami Rodent Control
30261 1 10 2000 8 DO M 36 49 Dipodomys ordii Rodent Control
30396 2 6 2000 8 DO M 34 51 Dipodomys ordii Rodent Control
30399 2 6 2000 8 DO M 37 51 Dipodomys ordii Rodent Control
30401 2 6 2000 8 DO M 36 57 Dipodomys ordii Rodent Control
30540 3 5 2000 8 DO F 36 43 Dipodomys ordii Rodent Control
30543 3 5 2000 8 DO M 35 50 Dipodomys ordii Rodent Control
30548 3 5 2000 8 DO F 35 43 Dipodomys ordii Rodent Control
30688 4 31 2000 8 DO M 35 23 Dipodomys ordii Rodent Control
30689 4 31 2000 8 DO F 36 47 Dipodomys ordii Rodent Control
30693 4 31 2000 8 DO M 37 60 Dipodomys ordii Rodent Control
30696 4 31 2000 8 DO M 35 49 Dipodomys ordii Rodent Control
30697 4 31 2000 8 DO F 35 45 Dipodomys ordii Rodent Control
30700 4 31 2000 8 DO M 35 51 Dipodomys ordii Rodent Control
30864 6 4 2000 8 DO M 36 35 Dipodomys ordii Rodent Control
30867 6 4 2000 8 DO M 35 50 Dipodomys ordii Rodent Control
30869 6 4 2000 8 DO F 36 52 Dipodomys ordii Rodent Control
30870 6 4 2000 8 DO M 37 32 Dipodomys ordii Rodent Control
31049 7 2 2000 8 DO M 36 39 Dipodomys ordii Rodent Control
31054 7 2 2000 8 DO F 36 50 Dipodomys ordii Rodent Control
31334 8 26 2000 8 DO M 35 51 Dipodomys ordii Rodent Control
31335 8 26 2000 8 DO M 36 49 Dipodomys ordii Rodent Control
31478 9 31 2000 8 DO M 36 46 Dipodomys ordii Rodent Control
31479 9 31 2000 8 DO M 35 51 Dipodomys ordii Rodent Control
31480 9 31 2000 8 DO M 36 46 Dipodomys ordii Rodent Control
31591 11 26 2000 8 DO M 34 45 Dipodomys ordii Rodent Control
31593 11 26 2000 8 DO M 34 49 Dipodomys ordii Rodent Control
31594 11 26 2000 8 DO F 38 49 Dipodomys ordii Rodent Control
31697 12 23 2000 8 DO M 35 47 Dipodomys ordii Rodent Control
31698 12 23 2000 8 DO M 36 46 Dipodomys ordii Rodent Control
30248 1 10 2000 9 DM M 34 50 Dipodomys merriami Rodent Spectab exclosure
30249 1 10 2000 9 DM M 36 43 Dipodomys merriami Rodent Spectab exclosure
30250 1 10 2000 9 DM M 35 27 Dipodomys merriami Rodent Spectab exclosure
30251 1 10 2000 9 DM F 34 46 Dipodomys merriami Rodent Spectab exclosure
30252 1 10 2000 9 DM M 35 52 Dipodomys merriami Rodent Spectab exclosure
30254 1 10 2000 9 DM M 36 43 Dipodomys merriami Rodent Spectab exclosure
30384 2 6 2000 9 DM M 34 32 Dipodomys merriami Rodent Spectab exclosure
30386 2 6 2000 9 DM F 35 46 Dipodomys merriami Rodent Spectab exclosure
30387 2 6 2000 9 DM M 34 45 Dipodomys merriami Rodent Spectab exclosure
30388 2 6 2000 9 DM M 36 47 Dipodomys merriami Rodent Spectab exclosure
30391 2 6 2000 9 DM M 34 49 Dipodomys merriami Rodent Spectab exclosure
30533 3 5 2000 9 DM M 35 47 Dipodomys merriami Rodent Spectab exclosure
30534 3 5 2000 9 DM F 35 46 Dipodomys merriami Rodent Spectab exclosure
30535 3 5 2000 9 DM M 36 50 Dipodomys merriami Rodent Spectab exclosure
30538 3 5 2000 9 DM M 35 30 Dipodomys merriami Rodent Spectab exclosure
30662 4 31 2000 9 DM F 34 52 Dipodomys merriami Rodent Spectab exclosure
30665 4 31 2000 9 DM F 35 44 Dipodomys merriami Rodent Spectab exclosure
30666 4 31 2000 9 DM M 34 50 Dipodomys merriami Rodent Spectab exclosure
30667 4 31 2000 9 DM F 34 39 Dipodomys merriami Rodent Spectab exclosure
30854 6 4 2000 9 DM M 36 46 Dipodomys merriami Rodent Spectab exclosure
30858 6 4 2000 9 DM M 35 45 Dipodomys merriami Rodent Spectab exclosure
31027 7 2 2000 9 DM F 36 45 Dipodomys merriami Rodent Spectab exclosure
31028 7 2 2000 9 DM M 35 48 Dipodomys merriami Rodent Spectab exclosure
31033 7 2 2000 9 DM M 35 51 Dipodomys merriami Rodent Spectab exclosure
31042 7 2 2000 9 DM M 35 40 Dipodomys merriami Rodent Spectab exclosure
31317 8 26 2000 9 DM F 36 44 Dipodomys merriami Rodent Spectab exclosure
31318 8 26 2000 9 DM M 36 46 Dipodomys merriami Rodent Spectab exclosure
31320 8 26 2000 9 DM F 36 39 Dipodomys merriami Rodent Spectab exclosure
31322 8 26 2000 9 DM M 36 51 Dipodomys merriami Rodent Spectab exclosure
31323 8 26 2000 9 DM M 36 46 Dipodomys merriami Rodent Spectab exclosure
31469 9 31 2000 9 DM F 35 41 Dipodomys merriami Rodent Spectab exclosure
31471 9 31 2000 9 DM M 35 46 Dipodomys merriami Rodent Spectab exclosure
31472 9 31 2000 9 DM M 36 42 Dipodomys merriami Rodent Spectab exclosure
31582 11 26 2000 9 DM F 36 39 Dipodomys merriami Rodent Spectab exclosure
31583 11 26 2000 9 DM M 37 42 Dipodomys merriami Rodent Spectab exclosure
31584 11 26 2000 9 DM F 36 43 Dipodomys merriami Rodent Spectab exclosure
31588 11 26 2000 9 DM M 36 42 Dipodomys merriami Rodent Spectab exclosure
31688 12 23 2000 9 DM M 35 43 Dipodomys merriami Rodent Spectab exclosure
31690 12 23 2000 9 DM M 35 44 Dipodomys merriami Rodent Spectab exclosure
31691 12 23 2000 9 DM M 33 43 Dipodomys merriami Rodent Spectab exclosure
31692 12 23 2000 9 DM F 35 41 Dipodomys merriami Rodent Spectab exclosure
30857 6 4 2000 9 DO M 35 44 Dipodomys ordii Rodent Spectab exclosure
31325 8 26 2000 9 DO M 35 46 Dipodomys ordii Rodent Spectab exclosure
31613 11 26 2000 16 DM F 35 38 Dipodomys merriami Rodent Rodent Exclosure
30576 3 5 2000 16 DO M 35 41 Dipodomys ordii Rodent Rodent Exclosure
30578 3 5 2000 16 DO M 33 41 Dipodomys ordii Rodent Rodent Exclosure