The fqar package
Introduction
The fqar packages provides tools for downloading and
analyzing floristic quality assessment (FQA) data from universalFQA.org. Two sample data
sets, chicago and missouri, are also
provided.
Functions in this package fall into four general categories: indexing functions, which produce data frames of current public databases and FQAs from various regions, downloading functions, which download the FQAs themselves, tidying functions, which convert downloaded assessments into a standard format, and analytic functions, which compare species across assessments.
Indexing functions
Each floristic quality assessment is tied to a specific databases of
native plants that has been compiled by experts in local flora. A
listing of all databases accepted by universalFQA.org can be viewed with
the index_fqa_databases() function.
databases <- index_fqa_databases()
head(databases)
#> # A tibble: 6 × 4
#> database_id region year description
#> <dbl> <chr> <dbl> <chr>
#> 1 206 "Allegheny Plateau, Glaciated" 2021 Faber-Lang…
#> 2 70 "Appalachian Mtn (EPA Ecoregions 66… 2013 Gianopulos…
#> 3 108 "Atlantic Coastal Pine Barrens (8.5… 2017 NEIWPCC FQ…
#> 4 136 "Atlantic Coastal Pine Barrens (8.5… 2018 NatureServ…
#> 5 204 "Atlantic Coastal Pine Barrens (8.5… 2021 Faber-Lang…
#> 6 1 "Chicago Region" 1994 Swink, F. …To see a listing of all public floristic quality assessments using a
given database, use the index_fqa_assessments()
function.
missouri_fqas <- index_fqa_assessments(database_id = 63)
head(missouri_fqas)
#> # A tibble: 6 × 5
#> id assessment date site practitioner
#> <dbl> <chr> <date> <chr> <chr>
#> 1 30687 Bridge School Prairie Survey 2023-09-02 Vari… Nathan Aaron
#> 2 30115 Leatherwood Hollow Survey (Up… 2023-07-13 Pion… Nathan Aaro…
#> 3 29965 chi 2023-06-28 CHI … chi
#> 4 29949 CHI List 2023-06-27 CHI … ns
#> 5 29622 Interior Woodlands Survey 2023-05-26 WS I… Nathan Aaron
#> 6 29750 Wetland B 2023-05-24 STL … Marion Well…Similarly, the index_fqa_transects() function returns a
listing of all public transect assessments using the specified
database.
missouri_transects <- index_fqa_transects(database_id = 63)
head(missouri_transects)
#> # A tibble: 6 × 5
#> id assessment date site practitioner
#> <dbl> <chr> <date> <chr> <chr>
#> 1 8434 Hawn-Array-2-2023 2023-09-04 Hawn State Park Parks
#> 2 8415 STJ-Array2-2023 2023-08-29 St. Joe State Park Parks
#> 3 8414 STJ-3-23 2023-07-20 St. Joe State Park Parks
#> 4 8347 TUCKER DNA 2023-07-06 DNA Floristic Sam… Lord/ Sutton
#> 5 8052 Golden DNA23 2023-06-28 DNA Floristic Sam… Lord/Sutton
#> 6 8053 Lindens DNA23 2023-06-28 DNA Floristic Sam… Lord/SuttonDownloading functions
Floristic quality assessments can be downloaded individually by id
number or in batches according to specified search criteria using the
download_assessment() and
download_assessment_list() functions, respectively.
The first of these accepts an assessment ID number as its sole input
and returns a data frame. For instance, the Grasshopper Hollow survey
has assessment_id = 25961 according to the listing obtained
using index_fqa_assessments(). The following code downloads
this assessment.
Multiple assessments from a specified database can be downloaded
simultaneously using download_assessment_list(), which
makes use of dplyr::filter syntax on the variables
id, assessment, date, site and practitioner.
For instance, the following code downloads all assessments performed
using the 2015 Missouri database at the Ambrose Farm site.
For even mid-sized requests, this command may run slowly due to the
limited speed of the universalFQA.org website. For this
reason, a progress bar has been added to the
download_assessment_list() function when n ≥ 5.
As the name suggests, the output of
download_assessment_list() is a list of data frames.
Transect assessment data data stored on universalFQA.org is accessible to
analysts using the fqar package via the functions
download_transect() and
download_transect_list(), which work exactly like their
counterparts, download_assessment() and
download_assessment_list().
Tidying functions
The data frames obtained from these downloading functions are all
highly untidy, respecting the default structure of the website from
which they are obtained. The fqar package provides tools
for efficiently re-formatting these sets.
Each floristic quality assessments on universalFQA.org includes two types
of information: details about the species observed during data
collection and summary information about the assessment as a whole. The
${\tt fqar}$ functions
assessment_inventory() and assessment_glance()
extract and tidy these two types of information.
For instance, the following code creates a data frame of species found in the 2021 Grasshopper Hollow survey downloaded earlier.
grasshopper_species <- assessment_inventory(grasshopper)
glimpse(grasshopper_species)
#> Rows: 317
#> Columns: 9
#> $ scientific_name <chr> "Acer rubrum var. rubrum", "Acer saccharum…
#> $ family <chr> "Sapindaceae", "Sapindaceae", "Asteraceae"…
#> $ acronym <chr> "ACERUR", "ACESUG", "ACHMIL", "ACOCAL", "A…
#> $ nativity <chr> "native", "native", "native", "non-native"…
#> $ c <dbl> 5, 5, 1, 0, 8, 2, 5, 4, 4, 0, 2, 7, 6, 4, …
#> $ w <dbl> 0, 3, 3, -5, 3, 3, -3, 5, 3, -3, 3, -5, 3,…
#> $ physiognomy <chr> "tree", "tree", "forb", "forb", "forb", "f…
#> $ duration <chr> "perennial", "perennial", "perennial", "pe…
#> $ common_name <chr> "red maple", "sugar maple", "yarrow", "swe…A tidy summary of the assessment can be obtained with
assessment_glance(). The output is a data frame with a
single row and 53 columns, including native_mean_c,
native_species, and native_fqi.
grasshopper_summary <- assessment_glance(grasshopper)
names(grasshopper_summary)
#> [1] "title" "date"
#> [3] "site_name" "city"
#> [5] "county" "state"
#> [7] "country" "fqa_db_region"
#> [9] "fqa_db_publication_year" "fqa_db_description"
#> [11] "custom_fqa_db_name" "custom_fqa_db_description"
#> [13] "practitioner" "latitude"
#> [15] "longitude" "weather_notes"
#> [17] "duration_notes" "community_type_notes"
#> [19] "other_notes" "private_public"
#> [21] "total_mean_c" "native_mean_c"
#> [23] "total_fqi" "native_fqi"
#> [25] "adjusted_fqi" "c_value_zero"
#> [27] "c_value_low" "c_value_mid"
#> [29] "c_value_high" "native_tree_mean_c"
#> [31] "native_shrub_mean_c" "native_herbaceous_mean_c"
#> [33] "total_species" "native_species"
#> [35] "non_native_species" "mean_wetness"
#> [37] "native_mean_wetness" "tree"
#> [39] "shrub" "vine"
#> [41] "forb" "grass"
#> [43] "sedge" "rush"
#> [45] "fern" "bryophyte"
#> [47] "annual" "perennial"
#> [49] "biennial" "native_annual"
#> [51] "native_perennial" "native_biennial"The tidy format provided by assessment_glance() is most
useful when applied to multiple data sets at once, for instance in the
situation where the analyst wants to consider statistics from many
different assessments simultaneously. The
assessment_list_glance() function provides a shortcut when
those data frames are housed in a list like that returned by
download_assessment_list(). For instance, the following
code returns a data frame with 52 columns and 3 rows, one per
assessment.
The fqar package also provides functions for handling
transect assessment data. transect_inventory(),
transect_glance(), and transect_list_glance()
work just like their counterparts, assessment_inventory(),
assessment_glance(), and
assessment_list_glance().
rock_garden_species <- transect_inventory(rock_garden)
rock_garden_summary <- transect_glance(rock_garden)
golden_summary <- transect_list_glance(golden)Additionally, transect assessments usually include physiognometric
metrics like relative frequency and relative coverage. These can be
extracted with the trasect_phys() function.
rock_garden_phys <- transect_phys(rock_garden)
glimpse(rock_garden_phys)
#> Rows: 6
#> Columns: 6
#> $ physiognomy <chr> "Native forb", "Native g…
#> $ frequency <dbl> 115, 53, 20, 6, 4, 1
#> $ coverage <dbl> 628, 413, 180, 125, 78, 1
#> $ relative_frequency_percent <dbl> 51.6, 23.8, 9.0, 2.7, 1.…
#> $ relative_coverage_percent <dbl> 26.1, 17.2, 7.5, 5.2, 3.…
#> $ relative_importance_value_percent <dbl> 38.9, 20.5, 8.3, 4.0, 2.…Analytic functions
The fqar package provides tools for analyzing species
co-occurrence across multiple floristic quality assessments. A typical
workflow consists of downloading a list of assessments, extracting
inventories from each, then enumerating and summarizing co-occurrences
of species of interest.
# Obtain a tidy data frame of all co-occurrences in the 1995 Southern Ontario database:
ontario <- download_assessment_list(database = 2)
# Extract inventories as a list:
ontario_invs <- assessment_list_inventory(ontario)
# Enumerate all co-occurrences in this database:
ontario_cooccurrences <- assessment_cooccurrences(ontario_invs)
# Summarize co-occurrences in this database, one row per target species:
ontario_cooccurrences <- assessment_cooccurrences_summary(ontario_invs)Of particular note is the species_profile() function,
which returns the frequency distribution of C-values of co-occurring
species for a given target species. Users may specify the optional
native argument to include only native species in the
profile. The species_profile_plot() function takes
identical arguments but returns an elegant plot instead of a data
frame
For instance, Aster lateriflorus (C=3) has the following native profile in the Southern Ontario database.
aster_profile <- species_profile("Aster lateriflorus",
ontario_invs,
native = TRUE)
aster_profile
#> # A tibble: 11 × 4
#> species target_c cospecies_c cospecies_n
#> <chr> <dbl> <dbl> <dbl>
#> 1 Aster lateriflorus 3 0 176
#> 2 Aster lateriflorus 3 1 58
#> 3 Aster lateriflorus 3 2 139
#> 4 Aster lateriflorus 3 3 209
#> 5 Aster lateriflorus 3 4 212
#> 6 Aster lateriflorus 3 5 186
#> 7 Aster lateriflorus 3 6 127
#> 8 Aster lateriflorus 3 7 83
#> 9 Aster lateriflorus 3 8 26
#> 10 Aster lateriflorus 3 9 9
#> 11 Aster lateriflorus 3 10 15
species_profile_plot("Aster lateriflorus",
ontario_invs,
native = TRUE)
Data sets
Two tidy data sets of floristic quality data, chicago
and missouri, are included with the fqar
package. Produced with assessment_list_glance(), these show
summary information for every floristic quality assessment that used
databases 63 and 149, respectively, prior to August 14, 2022. These sets
may be useful for visualization or machine-learning purposes. For
instance, one might consider the relationship between richness and
native mean C in sites assessed using the 2015 Missouri database:
ggplot(missouri, aes(x = native_species,
y = native_mean_c)) +
geom_point() +
geom_smooth() +
scale_x_continuous(trans = "log10") +
labs(x = "Native Species (logarithmic scale)",
y = "Native Mean C") +
theme_minimal()