| Type: | Package |
| Title: | Process NEON Plant Data for Ecological Analysis |
| Version: | 1.6.1 |
| Date: | 2024-05-30 |
| Maintainer: | Adam Mahood <admahood@gmail.com> |
| Description: | Downloading and organizing plant presence and percent cover data from the National Ecological Observatory Network https://www.neonscience.org. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | neonUtilities, vegan, ggplot2, data.table, dtplyr, dplyr, lubridate, sf, stringr, tibble, tidyr, ggpubr, utils |
| RoxygenNote: | 7.3.1 |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| URL: | https://github.com/admahood/neonPlantEcology |
| BugReports: | https://github.com/admahood/neonPlantEcology/issues |
| NeedsCompilation: | no |
| Packaged: | 2024-05-30 17:03:30 UTC; Adam.Mahood |
| Author: | Adam Mahood [aut, cre], Jacob Macdonald [ctb], Ranjan Muthukrishnan [ctb] |
| Depends: | R (≥ 3.5.0) |
| Repository: | CRAN |
| Date/Publication: | 2024-05-30 17:30:02 UTC |
Plant Presence and Percent Cover Data for Domain 14
Description
This includes Jornada Experimental Range and Santa Rita Experimental Range
Usage
D14
Format
## 'D14' A list with 8 items, 2 of which are used by neonPlantEcology
Source
<https://doi.org/10.48443/9579-a253>
<https://data.neonscience.org/data-products/DP1.10058.001>
Change the native status code for a particular taxon at a particular site
Description
Sometimes even though a particular species identity is not known, the end user can still determine its native status. For example, maybe the taxon was identified to the genus level, and the local flora confirms that all plants in that genus are native at that particular site. This function allows for post-hoc modification of the native status code for cases like this.
Usage
npe_change_native_status(df, taxon, site, new_code)
Arguments
df |
is the data frame returned by npe_longform |
taxon |
is the taxonID column in the data frame |
site |
is the identity of the NEON site (e.g. "JORN") |
new_code |
is the NativeStatusCode value to change to |
Value
a data frame
Examples
data("D14")
lf_div <- npe_longform(D14)
modified_lf_div <- npe_change_native_status(lf_div, "ABUTI", "JORN", "N")
Get plot information from a community matrix
Description
The npe_community_matrix() function is designed to work with the vegan package, and one of the requirements of vegan functions is that there are only numeric columns in community matrices. Therefore, all of the metatdata is collapsed into the rownames. This function allows you to extract that very basic metadata back out to a more easily interpretable data frame.
Usage
npe_cm_metadata(comm)
Arguments
comm |
the community matrix object created by npe_community_matrix() |
Value
a data frame
Examples
data("D14")
npe_community_matrix(D14) |> npe_cm_metadata()
Create a species abundance or occurrence matrix
Description
npe_community_matrix creates a wide matrix of species cover or binary (presence/absence) values with the plot/subplot/year as rownames. This is useful for the vegan package, hence the name.
Usage
npe_community_matrix(
x,
scale = "plot",
trace_cover = 0.5,
timescale = "annual",
input = "neon_div_object",
binary = FALSE
)
Arguments
x |
Input object. See input argument help for more details. |
scale |
what level of aggregation? This can be "1m", "10m", "100m", "plot", which is the default, or "site". |
trace_cover |
cover value for subplots where only occupancy was recorded |
timescale |
what temporal resolution? can be "subannual", which is really only applicable at sites where there are multiple bouts per year, "annual" or "all", which dissolves together the entire time series. |
input |
by default, longform dataframe is calculated from the diversity object and then converted to a community matrix, set this option to "lf" to use a longform data frame that was created separately (and perhaps modified). Another option is input = "divStack", which is using the output from the divStack function in the neonPlants package. Using a premade longform data frame or a divStack output will use the spatial and temporal scale of that input data separately |
binary |
should the matrix be converted from percent cover to binary? |
neon_div_object |
the raw diversity data downloaded using neonPlantEcology::download_plant_div() or the function neonUtilities::loadByProduct() with the dpID arguement set to "DP1.10058.001". |
Value
a data frame with each row a site aggregated at the spatial and temporal scales chosen by the user. Each column is a single species, and cell values can be either cover (a value between 0 and 100) or occurrence (1 or 0)
Examples
data("D14")
comm <- npe_community_matrix(D14)
Data downloader
Description
A wrapper function to download data from the NEON API using neonUtilities::loadByProduct. Some commonly used products are provided as plain language options, otherwise the user can enter the product ID number (dpID). Downloads Plant Presence and Percent Cover by default (DP1.10058.001).
Usage
npe_download(
sites = "JORN",
dpID = NA,
token = NA,
verbose = TRUE,
product = "plant_diversity",
...
)
Arguments
sites |
a vector of NEON site abbreviations. Defaults to "JORN" |
dpID |
if you need a data product not given as one of the product options, set the data product ID here (e.g. "DP1.10023.001"). |
token |
a token from neonscience.org |
verbose |
if true, prints which sites are being downloaded |
product |
a plain language vector of the data product to be downloaded. Can be "plant_diversity", "litterfall", "woody_veg_structure", "belowground_biomass", "herbaceous_clip", "coarse_downed_wood", or "soil_microbe_biomass" |
... |
additional arguments can be passed to neonUtilities::loadByProduct see ?neonUtilites::loadByProduct for more details |
Value
a list
Examples
diversity_object <- npe_download(sites = "JORN")
fix errors in the eventID column
Description
neonPlantEcology is a house of cards that rests delicately upon the eventID column being in the site.bout-number.year format, and if there is any deviation from that format all hell breaks loose. This function converts any NA or non-standard eventID rows to the desired format.
Usage
npe_eventID_fixer(x, verbose = FALSE)
Arguments
x |
raw list data from NEON api |
verbose |
if true, prints details of which eventID errors were fixed into the console |
Value
the same list object but with repaired eventIDs
Examples
data("D14")
x <- npe_eventID_fixer(D14)
Get ground cover and other variables
Description
Get ground cover and other variables
Usage
npe_groundcover(
neon_div_object,
scale = "plot",
verbose = FALSE,
pc_na_value = 0.5,
timescale = "annual"
)
Arguments
neon_div_object |
the raw diversity data downloaded using neonPlantEcology::download_plant_div() or the function neonUtilities::loadByProduct() with the dpID arguement set to "DP1.10058.001". |
scale |
the spatial scale of aggregation. Can be "1m", "10m", "100m", "plot" or "site". default is "plot". |
verbose |
if true, prints details of which eventID errors were fixed into the console |
pc_na_value |
sometimes the raw data from neon will have NA's in the percent cover cells. This is assumed to be a data entry error and is set to 0.5 by default. |
timescale |
The temporal scale of aggregation. Can be "all", "annual" or "subannual" in the case of multiple sampling bouts per year. Defaults to "annual". |
Value
a data frame with each row a single observation of ground cover at the spatial and temporal scale chosen by the user.
Examples
data("D14")
groundcover <- npe_groundcover(D14)
Get heights
Description
Get heights
Usage
npe_heights(
neon_div_object,
scale = "plot",
verbose = FALSE,
timescale = "annual"
)
Arguments
neon_div_object |
the raw diversity data downloaded using neonPlantEcology::download_plant_div() or the function neonUtilities::loadByProduct() with the dpID arguement set to "DP1.10058.001". |
scale |
the spatial scale of aggregation. Can be "1m", "10m", "100m", "plot" or "site". default is "plot". |
verbose |
if true, prints details of which eventID errors were fixed into the console |
timescale |
The temporal scale of aggregation. Can be "all", "annual" or "subannual" in the case of multiple sampling bouts per year. Defaults to "annual". |
Value
a data frame with each row a single observation of species height at the spatial and temporal scale chosen by the user.
Examples
data("D14")
heights <- npe_heights(D14)
Convert raw NEON diversity object to longform plant cover data frame
Description
The diversity data from NEON comes as a list containing 2 data frames of data that need to be combined, among other things. Here, we take those two data frames and combine them into a longform data frame that can then be further modified for analysis. Most of the unneccessary information from the raw data has been removed. Column names that remain are plotID, subplotID, year, taxonID, cover, scientificName, nativeStatusCode, family, and site.
Usage
npe_longform(
neon_div_object,
trace_cover = 0.5,
pc_na_value = 0.5,
scale = "plot",
verbose = FALSE,
timescale = "annual"
)
Arguments
neon_div_object |
the raw diversity data downloaded using neonPlantEcology::download_plant_div() or the function neonUtilities::loadByProduct() with the dpID arguement set to "DP1.10058.001". |
trace_cover |
cover value for subplots where only occupancy was recorded |
pc_na_value |
sometimes the raw data from neon will have NA's in the percent cover cells. This is assumed to be a data entry error and is set to 0.5 by default. |
scale |
what level of spatial aggregation? This can be "1m", "10m", "100m", "plot", which is the default, or "site". |
verbose |
if true, prints details of which eventID errors were fixed into the console |
timescale |
what level of temporal aggregation? can be "subannual", which is only important for sites with multiple sampling bouts per year, "annual" or "all" for the full time series. |
Value
a data frame with each row a single observation of species cover at the spatial and temporal scale chosen by the user.
Examples
data("D14")
lf <- npe_longform(D14)
Download and join spatial information to a neonPlantEcology output data frame
Description
Download and join spatial information to a neonPlantEcology output data frame
Usage
npe_plot_centroids(
df,
type = "latlong",
spatial_only = TRUE,
input = "community_matrix"
)
Arguments
df |
a neonPlantEcology-produced data frame |
type |
what type of ancillary data structure you want joined. Can be "spatial", which will turn the data frame into an sf data frame, or "latlong", which will add the latitudes and longitudes and other ancillary data as columns only. |
spatial_only |
set to TRUE if you only want the coordinates and none of the ancillary variables. |
input |
to what kind of neonPlantEcology product are you appending? Can be "community_matrix", "longform_cover", or "summary_info". |
Value
a data frame
Get site ids
Description
This returns a list of 4 letter site ID codes to feed into npe_download. It can return all 47 siteID codes, or a subset based on site type, aridity index, Koppen-Geiger Climate region, or NEON domain.
Usage
npe_site_ids(by, domain = NA, type = NA, aridity = NA, koppen = NA)
Arguments
by |
How to select sites? Can be "all", "domain", "ai", "koppen", or "type". |
domain |
can be one or more domain codes, as a character vector, or as a number. e.g. domain = c("D01", "D14"), or domain = c(3, 14), can also be a mix: domain = c(3, "D04). |
type |
can be "Core Terrestrial" or "Relocatable Terrestrial" |
aridity |
can be "Hyper-Arid", "Arid", "Dry sub-humid", or "Humid" |
koppen |
can be any 3 letter Koppen-Geiger code, or one of "Equatorial", "Arid", "Temperate", "Boreal", "Polar" |
Value
a vector of four letter site identification codes.
Examples
all_sites <- npe_site_ids(by = "all")
npe_site_ids(by = "domain", domain = c("Northeast", "Mid-Atlantic"))
npe_site_ids(by = "domain", domain = c("D02", 15))
Get plant biodiversity information for NEON plots
Description
npe_summary calculates various biodiversity and cover indexes at the plot or subplot scale at each timestep for each plot. Outputs a data frame with number of species, percent cover, relative percent cover (relative to the cover of the other plants), and shannon diversity, for natives, exotics, "notexotics", unknowns, and all species. "notexotic" refers to all species with native or unknown origin status. Also calculates all of these metrics for the families of your choice.
Usage
npe_summary(
neon_div_object,
scale = "plot",
trace_cover = 0.5,
timescale = "annual",
betadiversity = FALSE,
families = NA
)
Arguments
neon_div_object |
the raw vegan::diversity data downloaded using neonPlantEcology::download_plant_div() or #' the function neonUtilities::loadByProduct() with the dpID arguement set to "DP1.10058.001". |
scale |
what level of aggregation? This can be "1m", "10m", "100m", "plot" or "site". "plot" is the default. |
trace_cover |
cover value for subplots where only occupancy was recorded |
timescale |
by default npe_summary groups everything by year. The user may set this argument to "all" to have the function aggregate the years together and then calculate diversity and cover indexes, or "subannual" for bout-level. |
betadiversity |
If evaluating at the plot or site level, should beta diversity (turnover and nestedness) be calculated. If scale = plot, it will calculate betadiversity within each plot, using the combined species presences within the 1 and 10 m subplots, and so it's calcuated from 8 subplots before 2020, 6 after. if scale = site, it calculates the betadiversity between plots. |
families |
Which specific families should the metrics be calculated for? This can be a concatenated vector if the user want more than one family. |
Value
a data frame of higher-level summary information. Number of species, Shannon-Weiner alpha diversity, cover, relative cover, for all species together and grouped by nativeStatusCode.
Examples
data("D14")
plot_level <- neonPlantEcology::npe_summary(neon_div_object = D14, scale = "plot")
Change subplot names
Description
The 2024 release features a change in subplot names. This function changes subplot names of the Plant Presence and Percent Cover raw list object from the old format to the new format, to ensure backwards compatibility. This is mostly an internal helper function
Usage
npe_update_subplots(neon_div_object)
Arguments
neon_div_object |
a list downloaded using npe_download containing Plant Presence and Percent Cover data |
Value
a
Examples
data("D14")
D14_updated <- npe_update_subplots(D14)
Plot centroids for the entire NEON network
Description
Plot centroids for the entire NEON network
Usage
plot_centroids
Format
## 'plot_centroids' A simple feature collection with 3842 features and 36 fields
Source
<https://www.neonscience.org>
Polygons of National Ecological Observatory Network Core and Relocatable Terrestrial Sites
Description
Note: Some sites have more than one polygon. There are 59 polygons and 47 total sites.
Usage
site_polygons
Format
## 'site_polygons' A simple feature collection with 59 features and 8 fields
- domainNumb
Domain Number
- domainName
Domain Name
- siteType
Site type. Core or Relocatable
- siteName
Site name
- siteID
Four letter site ID. Used in npe_download()
- siteHost
Organization hosting the site
- areaKm2
Area of the site in square kilometers
- acres
Area of the site in acres
- geometry
list column containing geometry information for each polygon
Source
<https://www.neonscience.org>
National Ecological Observatory Network Core and Relocatable Terrestrial Sites
Description
Note: Some sites have more than one polygon. There are 59 polygons and 47 total sites.
Usage
sites
Format
## 'sites' data frame with 47 features and 8 fields
- domainNumb
Domain Number
- domainName
Domain Name
- siteType
Site type. Core or Relocatable
- siteID
Four letter site ID. Used in npe_download()
- koppen_fine
Koppen-Geiger climate classification from Beck et al 2023
- koppen_coarse
Coarsest category of K-G climate classification from Beck et al 2023
- ai
Annual aridity index from Zomer & Trabucco 2022
- ai_class
Climate classification based on the aridity index from Zomer & Trabucco 2022
Source
<https://www.neonscience.org>
<https://doi.org/10.6084/m9.figshare.7504448.v5>
<https://doi.org/10.1038/s41597-023–02549‑6>