Introduction
The \({\tt fqar}\) 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 descr…¹
#> <dbl> <chr> <dbl> <chr>
#> 1 206 "Allegheny Plateau, Glaciated" 2021 Faber-…
#> 2 70 "Appalachian Mtn (EPA Ecoregions 66,67,68,69) of KY… 2013 Gianop…
#> 3 108 "Atlantic Coastal Pine Barrens (8.5.4) " 2017 NEIWPC…
#> 4 136 "Atlantic Coastal Pine Barrens (8.5.4)" 2018 Nature…
#> 5 204 "Atlantic Coastal Pine Barrens (8.5.4)" 2021 Faber-…
#> 6 1 "Chicago Region" 1994 Swink,…
#> # … with abbreviated variable name ¹description
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 pract…¹
#> <dbl> <chr> <date> <chr> <chr>
#> 1 28176 Nodaway River 2022-12-08 Nodaway River CHESTE…
#> 2 27647 MLTNS Prairie 2022-10-27 MLTNS Nathan…
#> 3 28275 Schmidt Prairie Species List - 2022 2022-10-27 Schmidt Prairie Justin…
#> 4 27618 US 60 2022-10-25 DuckettCreekTP4 Alex T…
#> 5 27518 Seed list 2022-10-20 Shaw Nature Rese… Mike S…
#> 6 26735 Oakwood Bottoms Survey 2022-08-18 Oakwood Bottoms Jonath…
#> # … with abbreviated variable name ¹practitioner
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 7753 Pinery Plot 91 - 2022 2022-09-13 Pinery (MTNF) Justin Thomas
#> 2 7755 Pinery Plot 8 - 2022 2022-09-13 Pinery (MTNF) Justin Thomas
#> 3 7756 Pinery Plot 10 - 2022 2022-09-13 Pinery (MTNF) Justin Thomas
#> 4 7757 Pinery Plot 97 - 2022 2022-09-13 Pinery (MTNF) Justin Thomas
#> 5 7758 Pinery Plot 58 - 2022 2022-09-13 Pinery (MTNF) Justin Thomas
#> 6 7762 Pinery Plot 87 - 2022 2022-09-13 Pinery (MTNF) Justin Thomas
Downloading 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.
grasshopper <- download_assessment(assessment_id = 25961)
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.
ambrose <- download_assessment_list(database_id = 63,
site == "Ambrose Farm")
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 \(n\ge 5\).
As the name suggests, the output of
download_assessment_list() is a list of data frames.
class(ambrose)
#> [1] "list"
length(ambrose)
#> [1] 3
Transect assessment data data stored on universalFQA.org is accessible to
analysts using the \({\tt fqar}\)
package via the functions download_transect() and
download_transect_list(), which work exactly like their
counterparts, download_assessment() and
download_assessment_list().
rock_garden <- download_transect(transect_id = 6875)
golden <- download_transect_list(database_id = 63,
site == "Golden Prairie")
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 \({\tt
fqar}\) 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 subsp. sacc…
#> $ family <chr> "Sapindaceae", "Sapindaceae", "Asteraceae", "Acoraceae…
#> $ acronym <chr> "ACERUR", "ACESUG", "ACHMIL", "ACOCAL", "ACTPAC", "AGE…
#> $ nativity <chr> "native", "native", "native", "non-native", "native", …
#> $ c <dbl> 5, 5, 1, 0, 8, 2, 5, 4, 4, 0, 2, 7, 6, 4, 5, 4, 8, 5, …
#> $ w <dbl> 0, 3, 3, -5, 3, 3, -3, 5, 3, -3, 3, -5, 3, 0, 0, 3, 5,…
#> $ physiognomy <chr> "tree", "tree", "forb", "forb", "forb", "forb", "forb"…
#> $ duration <chr> "perennial", "perennial", "perennial", "perennial", "p…
#> $ common_name <chr> "red maple", "sugar maple", "yarrow", "sweet flag", "w…
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.
ambrose_summary <- assessment_list_glance(ambrose)
The \({\tt fqar}\) package also
provides functions for handling transect assessment data.
transect_inventory(),
transect_glance(), andtransect_list_glance()work just like their counterparts,assessment_inventory(),assessment_glance(), andassessment_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 grass", "Nati…
#> $ 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, 1.8, 0.4
#> $ relative_coverage_percent <dbl> 26.1, 17.2, 7.5, 5.2, 3.2, 0.0
#> $ relative_importance_value_percent <dbl> 38.9, 20.5, 8.3, 4.0, 2.5, 0.2
Analytic functions
The \({\tt fqar}\) 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)
#> Downloading...
# 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 \({\tt fqar}\) 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()
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
