fqar

Floristic Quality Assessment (FQA) is a standardized method for rating the ecological value of natural areas based on the plant species found within them. The ${\tt fqar}$ package provides tools to download and analyze floristic quality assessments from universalfqa.org, an online database maintained by Openlands.

Installation

The ${\tt fqar}$ package is available on CRAN.

install.packages("fqar")

Alternatively, the development version can be installed from GitHub.

devtools::install_github("equitable-equations/fqar")

Usage

The ${\tt fqar}$ package consists of four categories of functions: indexing, downloading, tidying, and analytic functions. ${\tt fqar}$ also includes two sample data sets.

Indexing functions

At the simplest level, fqar allows users to obtain specific information about the databases, assessments, and transect assessments available from universalfqa.org.

# download a list of all fqa databases:
databases <- index_fqa_databases()

# download a list of all assessments in a specific database:
chicago_fqas <- index_fqa_assessments(database_id = 149) 

# download a list of all transect assessments in a specific database:
chicago_transects <- index_fqa_transects(database_id = 149)

Downloading functions

Floristic quality assessments can be downloaded individually by ID number or collectively using dplyr::filter syntax.

# download a single assessment using the `assessment_id` assigned by
# [universalfqa.org](https://universalfqa.org/). These identifiers 
# can be found using `index_fqa_assessments`.
woodland <- download_assessment(assessment_id = 25640)

# download multiple assessments:
mcdonald_fqas <- download_assessment_list(database_id = 149, 
                                          site == "McDonald Woods")

${\tt fqar}$ also provides functions for downloading transect assessments.

# download a single transect assessment:
rock_garden <- download_transect(transect_id = 6875)

# download multiple transect assessments:
lord_fqas <- download_transect_list(database = 63,
                                    practitioner == "Sam Lord")

Unfortunately, the universalfqa.org server is often slow, and downloads (especially for transect assessments) may take some time.

Tidying functions

Data sets obtained from universalfqa.org are quite messy. ${\tt fqar}$ provides tools for converting such sets into a more convenient tidy format.

# obtain a data frame with species data for a downloaded assessment:
woodland_species <- assessment_inventory(woodland)

# obtain a data frame with summary information for a downloaded assessment:
woodland_summary <- assessment_glance(woodland)

# obtain a data frame with summary information for multiple downloaded assessments:
mcdonald_summary <- assessment_list_glance(mcdonald_fqas)

Similar functions are provided for handling transect assessments. For those sets, physiognometric information can also be extracted.

# obtain a data frame with species data for a downloaded transect assessment:
survey_species <- transect_inventory(rock_garden)

# obtain a data frame with physiognometric data for a downloaded transect assessment:
survey_phys <- transect_phys(rock_garden)

# obtain a data frame with summary information for a downloaded transect assessment:
rock_garden_summary  <- transect_glance(rock_garden)

# obtain a data frame with summary information for multiple downloaded transect assessments:
lord_summary <- transect_list_glance(lord_fqas)

Analytic functions

As of version 0.3.0, ${\tt fqar}$ includes 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 the 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)

# Sumamrize 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.

aster_profile <- species_profile("Aster lateriflorus", ontario_invs)

Learn More

• Read the ${\tt fqar}$ vignette to learn how to download and analyze FQAs with fqar.
• View the help files of any function in the ${\tt fqar}$ package for more examples.

Contribute

To contribute to ${\tt fqar}$ you can fork this repository and create pull requests to add features you think will be useful for users. You can also open an issue if you find a bug or wish to make a suggestion.