Package 'algaeClassify'

Title: Tools to Query the 'Algaebase' Online Database, Standardize Phytoplankton Taxonomic Data, and Perform Functional Group Classifications
Description: Functions that facilitate the use of accepted taxonomic nomenclature, collection of functional trait data, and assignment of functional group classifications to phytoplankton species. Possible classifications include Morpho-functional group (MFG; Salmaso et al. 2015 <doi:10.1111/fwb.12520>) and CSR (Reynolds 1988; Functional morphology and the adaptive strategies of phytoplankton. In C.D. Sandgren (ed). Growth and reproductive strategies of freshwater phytoplankton, 388-433. Cambridge University Press, New York). Versions 2.0.0 and later includes new functions for querying the 'algaebase' online taxonomic database (www.algaebase.org), however these functions require a valid API key that must be acquired from the 'algaebase' administrators. Note that none of the 'algaeClassify' authors are affiliated with 'algaebase' in any way. Taxonomic names can also be checked against a variety of taxonomic databases using the 'Global Names Resolver' service via its API (<https://resolver.globalnames.org/api>). In addition, currently accepted and outdated synonyms, and higher taxonomy, can be extracted for lists of species from the 'ITIS' database via its JSON web service API. The 'algaeClassify' package is a product of the GEISHA (Global Evaluation of the Impacts of Storms on freshwater Habitat and Structure of phytoplankton Assemblages), funded by CESAB (Centre for Synthesis and Analysis of Biodiversity) and the U.S. Geological Survey John Wesley Powell Center for Synthesis and Analysis, with data and other support provided by members of GLEON (Global Lake Ecology Observation Network). DISCLAIMER: This software has been approved for release by the U.S. Geological Survey (USGS). Although the software has been subjected to rigorous review, the USGS reserves the right to update the software as needed pursuant to further analysis and review. No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. Furthermore, the software is released on condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use.
Authors: Vijay Patil [aut, cre], Torsten Seltmann [aut], Nico Salmaso [aut], Orlane Anneville [aut], Marc Lajeunesse [aut], Dietmar Straile [aut]
Maintainer: Vijay Patil <[email protected]>
License: CC0
Version: 2.0.6
Built: 2026-05-11 11:44:53 UTC
Source: https://github.com/cran/algaeClassify

Help Index

Split a dataframe column with binomial name into genus and species columns. Plots change in species richness over time, generates species accumulation curve, and compares SAC against simulated idealized curve assuming all unique taxa have equal probability of being sampled at any point in the time series. (author Dietmar Straile)

Description

Split a dataframe column with binomial name into genus and species columns. Plots change in species richness over time, generates species accumulation curve, and compares SAC against simulated idealized curve assuming all unique taxa have equal probability of being sampled at any point in the time series. (author Dietmar Straile)

Usage

accum(
  b_data,
  phyto_name = "phyto_name",
  column = NA,
  n = 100,
  save.pdf = FALSE,
  lakename = "",
  datename = "date_dd_mm_yy",
  dateformat = "%d-%m-%y"
)

Arguments

b_data

Name of data.frame object
phyto_name

Character string: field containing phytoplankton id (species, genus, etc.)
column

column name or number for field containing abundance (biomass,biovol, etc.). Can be NA if the dataset only contains a species list for each sampling date.
n

number of simulations for randomized ideal species accumulation curve
save.pdf

TRUE/FALSE- should plots be displayed or saved to a pdf?
lakename

optional character string for adding lake name to pdf output
datename

character string name of b_data field containing date
dateformat

character string: posix format for datename column

Value

a two panel plot with trends in richness on top, and cumulative richness vs. simulated accumulation curve on bottom

Examples

data(lakegeneva)
#example dataset with 50 rows
head(lakegeneva)

accum(b_data=lakegeneva,column='biovol_um3_ml',n=10,save.pdf=FALSE)

Search algaebase for information about a genus of phytoplankton

Description

Search algaebase for information about a genus of phytoplankton

Usage

algaebase_genus_search(
  genus = NULL,
  apikey = NULL,
  handle = NULL,
  higher = TRUE,
  print.full.json = FALSE,
  newest.only = TRUE,
  long = FALSE,
  exact.matches.only = TRUE,
  return.higher.only = FALSE,
  api_file = NULL
)

Arguments

genus

genus name as character string
apikey

valid key for algaebase API as character string
handle

curl handle with API key. Will be created if not present.
higher

boolean should higher taxonomy be included in output?
print.full.json

boolean returns raw json output if TRUE. Default is FALSE (return R data frame)
newest.only

boolean should results be limited to the most recent matching entry in algaebase?
long

boolean return long output including full species name and authorship, and entry date from algaebase.
exact.matches.only

boolean should results be limited to exact matches?
return.higher.only

boolean should output only included higher taxonomy?
api_file

path to text file containing a valid API key

Value

data frame that may include: accepted.name (currently accepted synonym if different from input name), input.name (name supplied by user), input.match (1 if exact match, else 0), currently.accepted (1=TRUE/0=FALSE), genus.only (1=genus search/0=genus+species search),higher taxonomy (kingdom,phylum,class,order,family), genus, species (always NA for genus search), infraspecies name (always NA for genus search), long.name (includes author and date if given), taxonomic.status (currently accepted, synonym, or unverified), taxon.rank (taxonomic rank of accepted name (genus, species, infraspecies), mod.date (date when entry was last modified in algaebase).

Examples

## Not run: algaebase_genus_search("Anabaena") #not run.

Helper function for parsing output from algaebase

Description

Helper function for parsing output from algaebase

Usage

algaebase_output_parse(x, field.name)

Arguments

x

list object containing output from an algaebase query
field.name

character string

Value

selected output variable as character vector

Search algaebase for information about a list of phytoplankton names

Description

Search algaebase for information about a list of phytoplankton names

Usage

algaebase_search_df(
  df,
  apikey = NULL,
  handle = NULL,
  genus.only = FALSE,
  genus.name = "genus",
  species.name = "species",
  higher = TRUE,
  print.full.json = FALSE,
  long = FALSE,
  exact.matches.only = TRUE,
  api_file = NULL,
  sleep.time = 1
)

Arguments

df

data frame containing columns with genus and species names
apikey

valid key for algaebase API as character string
handle

curl handle with API key. Will be created if not present.
genus.only

boolean: should searches be based solely on the genus name?
genus.name

name of data.frame column that contains genus names
species.name

name of data.frame column that contains species names
higher

boolean should higher taxonomy be included in output?
print.full.json

boolean returns raw json output if TRUE. Default is FALSE (return R data frame)
long

boolean return long output including full species name and authorship, and entry date from algaebase.
exact.matches.only

boolean should results be limited to exact matches?
api_file

path to text file containing a valid API key
sleep.time

delay between algaebase queries (in seconds). Should be at least 1 second if querying more than 10 names at once.

Value

data frame that may include: accepted.name (currently accepted synonym if different from input name), input.name (name supplied by user), input.match (1 if exact match, else 0), currently.accepted (1=TRUE/0=FALSE), genus.only (1=genus search/0=genus+species search),higher taxonomy (kingdom,phylum,class,order,family), genus, species (always NA for genus search), infraspecies name (always NA for genus search), long.name (includes author and date if given), taxonomic.status (currently accepted, synonym, or unverified), taxon.rank (taxonomic rank of accepted name (genus, species, infraspecies), mod.date (date when entry was last modified in algaebase).

Examples

## Not run: 
data(lakegeneva)
#example dataset with 50 rows

new.lakegeneva <- genus_species_extract(lakegeneva,'phyto_name')
lakegeneva.algaebase<-algaebase_search_df(new.lakegeneva[1:10,],higher=TRUE,long=TRUE)
head(lakegeneva.algaebase)
## End(Not run)

Retrieve taxonomic information from the algaebase online database (www.algaebase.org) based on a user-specified genus and species name . This function requires a valid API key for algaebase.

Description

Retrieve taxonomic information from the algaebase online database (www.algaebase.org) based on a user-specified genus and species name . This function requires a valid API key for algaebase.

Usage

algaebase_species_search(
  genus,
  species,
  apikey = NULL,
  handle = NULL,
  higher = TRUE,
  print.full.json = FALSE,
  newest.only = TRUE,
  long = FALSE,
  exact.matches.only = TRUE,
  api_file = NULL
)

Arguments

genus

genus name as character string
species

species name as character string
apikey

valid key for algaebase API as character string
handle

curl handle with API key. Will be created if not present.
higher

boolean should higher taxonomy be included in output?
print.full.json

boolean returns raw json output if TRUE. Default is FALSE (return R data frame)
newest.only

boolean should results be limited to the most recent matching entry in algaebase?
long

boolean return long output including full species name and authorship, and entry date from algaebase.
exact.matches.only

boolean should results be limited to exact matches?
api_file

path to text file containing a valid API key

Value

data frame that may include: accepted.name (currently accepted synonym if different from input name), input.name (name supplied by user), input.match (1 if exact match, else 0), currently.accepted (1=TRUE/0=FALSE), genus.only (1=genus search/0=genus+species search),higher taxonomy (kingdom,phylum,class,order,family), genus, species (always NA for genus search), infraspecies name (always NA for genus search), long.name (includes author and date if given), taxonomic.status (currently accepted, synonym, or unverified), taxon.rank (taxonomic rank of accepted name (genus, species, infraspecies), mod.date (date when entry was last modified in algaebase).

Examples

## Not run: algaebase_species_search("Anabaena flos-aquae") #not run

fuzzy partial matching between a scientific name and a list of possible matches

Description

fuzzy partial matching between a scientific name and a list of possible matches

Usage

bestmatch(enteredName, possibleNames, maxErr = 3, trunc = TRUE)

Arguments

enteredName

Character string with name to check
possibleNames

Character vector of possible matches
maxErr

maximum number of different bits allowed for a partial match
trunc

TRUE/FALSE. if true and no match, retry with last three letters truncated

Value

a character string with the best match, or 'multiplePartialMatches'

Examples

possibleMatches=c('Viburnum edule','Viburnum acerifolia')
bestmatch(enteredName='Viburnum edulus',possibleNames=possibleMatches)

Database of functional traits for MFG classification, derived from Rimet et al. 2019

Description

Database of functional traits for MFG classification, derived from Rimet et al. 2019

Usage

data(mfgTraits)

Format

A data frame with columns:

phyto_name

binomial scientific name

genus

genus name

species

species name

SAV

surface area:volume ratio

MLD

maximum linear dimension (micrometers)

MSV

product of SAV and MLD; unitless

volume.um3

cell or colony biovolume

surface.area.um2

biological unit (cell or colony) surface area accounting for mucilage

Colonial

1/0 indicates colonial growth form

Number.of.cells.per.colony

literature-based average colony abundance

Geometrical.shape.of.the.colony

Shape descriptions. See Rimet et al. 2019 for abbreviations

traitCSR

CSR classification using traits_to_CSR function and criteria from Reynolds 2006

Transform a phytoplankton timeseries into a matrix of abundances for ordination

Description

Transform a phytoplankton timeseries into a matrix of abundances for ordination

Usage

date_mat(
  phyto.df,
  abundance.var = "biovol_um3_ml",
  summary.type = "abundance",
  taxa.name = "phyto_name",
  date.name = "date_dd_mm_yy",
  format = "%d-%m-%y",
  time.agg = c("day", "month", "year", "monthyear"),
  fun = mean_naomit
)

Arguments

phyto.df

Name of data.frame object
abundance.var

Character string: field containing abundance data. Can be NA if the dataset only contains a species list for each sampling date.
summary.type

'abundance' for a matrix of aggregated abundance,'presence.absence' for 1 (present) and 0 (absent).
taxa.name

Character string: field containing taxonomic identifiers.
date.name

Character string: field containing date.
format

Character string: POSIX format string for formatting date column.
time.agg

Character string: time interval for aggregating abundance. default is day.
fun

function for aggregation. default is mean, excluding NA's.

Value

A matrix of phytoplankton abundance, with taxa in rows and time in columns. If time.agg = 'monthyear', returns a 3dimensional matrix (taxa,month,year). If abundance.var = NA, matrix cells will be 1 for present, 0 for absent

Examples

data(lakegeneva)
#example dataset with 50 rows

geneva.mat1<-date_mat(lakegeneva,time.agg='month',summary.type='presence.absence')
geneva.mat2<-date_mat(lakegeneva,time.agg='month',summary.type='abundance')

geneva.mat1
geneva.mat2

Wrapper function for several functions in ritis:: Searches ITIS database for matches to a genus name

Description

Wrapper function for several functions in ritis:: Searches ITIS database for matches to a genus name

Usage

genus_search_itis(genus, higher = FALSE)

Arguments

genus

Character string. genus name to search for in ITIS
higher

Boolean. If TRUE, add higher taxonomic classifications to output

Value

input data.frame with matches, current accepted names, synonyms, and higher taxonomy

Examples

genus='Anabaena'
genus_search_itis(genus,higher=FALSE)

Split a dataframe column with binomial name into genus and species columns.

Description

Split a dataframe column with binomial name into genus and species columns.

Usage

genus_species_extract(phyto.df, phyto.name)

Arguments

phyto.df

Name of data.frame object
phyto.name

Character string: field in phyto.df containing species name.

Value

A data.frame with new character fields 'genus' and 'species'

Examples

data(lakegeneva)
#example dataset with 50 rows

head(lakegeneva) #need to split the phyto_name column
new.lakegeneva=genus_species_extract(lakegeneva,'phyto_name')

head(new.lakegeneva)

Get value of algaebase API key from Environment variable Return an error if variable not set.

Description

Get value of algaebase API key from Environment variable Return an error if variable not set.

Usage

get_apikey()

Value

api key as character string (invisibly)

Get value of algaebase API key from a file

Description

Get value of algaebase API key from a file

Usage

get_apikey_fromfile(keyfile)

Arguments

keyfile

path to text file

Value

api key as character string (invisibly)

Examples

## Not run: apikey<-get_apikey_fromfile("keyfile.txt")

Wrapper function to apply gnr_simple across a data.frame or list of species names. Modified on 11/18/2025 by Vijay Patil ([email protected]) for algaeClassify v2.0.5 (pending approval on CRAN).

Description

Provides convienent output with a row per name, to streamline merging with original data.

Usage

gnr_df(
  df,
  name.column,
  sourceid = NULL,
  fuzzy_uninomial = TRUE,
  name_type = "canonical_full",
  higher = FALSE
)

Arguments

df

data.frame containing names to check
name.column

integer or character string with column name containing species names
sourceid

integer vector with data source ids. see https://resolver.globalnames.org/sources/
fuzzy_uninomial

boolean. Use fuzzy matching for uninomial names?
name_type

Specify format of matched names. Options are 'canonical_simple' (canonical binomial name), 'canonical_full' (with subspecies or subgenera), or 'with_context' (with author and year appended).
higher

boolean: Return higher taxonomic classifications?

Value

new data.frame original names (input_name), information about match type,the best match (match_name), taxonomic status, and other output from gnr_simple(). Will contain a row of NAs if no matches were found for a name.

Examples

data(lakegeneva)
#example dataset with 50 rows

lakegeneva<- genus_species_extract(lakegeneva,'phyto_name')
lakegeneva$genus_species <- trimws(paste(lakegeneva$genus,
lakegeneva$species))

#checking for matches from all GNRS sources, first 5 rows:
lakegeneva.namematches <- gnr_df(lakegeneva,"genus_species")
lakegeneva.namematches

checks species names against a variety of online databases supports fuzzy partial matching, using the Global Names Resolver (https://resolver.globalnames.org/). Modified on 11/18/2025 by Vijay Patil ([email protected]) for algaeClassify v2.0.5 (pending approval on CRAN).

Description

checks species names against a variety of online databases supports fuzzy partial matching, using the Global Names Resolver (https://resolver.globalnames.org/). Modified on 11/18/2025 by Vijay Patil ([email protected]) for algaeClassify v2.0.5 (pending approval on CRAN).

Usage

gnr_simple(
  name,
  sourceid = NULL,
  best_match = TRUE,
  fuzzy_uninomial = TRUE,
  name_type = "canonical_full",
  higher = FALSE
)

Arguments

name

character string binomial scientific name to resolve
sourceid

integer vector with data source ids. see https://resolver.globalnames.org/sources/
best_match

boolean. Should the best match be returned based on score?
fuzzy_uninomial

boolean. Use fuzzy matching for uninomial names?
name_type

Specify format of matched names. Options are 'canonical_simple' (canonical binomial name), 'canonical_full' (with subspecies or subgenera), or 'with_context' (with author and year appended).
higher

boolean: Return higher taxonomic classifications?

Value

new data.frame with name matches, column indicating match type and scores from Global Names Resolver (https://resolver.globalnames.org/). Will contain a row of NAs if no matches found

Examples

#Visit https://resolver.globalnames.org/data_sources to see all possible
#data sources for name checking.
name<-"Aphanazomenon flos-aquae"
#sourceid=3 for ITIS database
gnr_simple(name,sourceid=3) #search for best match from ITIS
gnr_simple(name,sourceid=NULL,best_match=FALSE) #search for all matches from any source

Wrapper function for applying genus_search_itis and species_search_itis to a whole data.frame containing scientific names

Description

Wrapper function for applying genus_search_itis and species_search_itis to a whole data.frame containing scientific names

Usage

itis_search_df(df, namecol = NA, higher = FALSE, genus.only = FALSE)

Arguments

df

data.frame containing names to check
namecol

integer or character string with column name containing species or genus names
higher

Boolean. If TRUE, add higher taxonomic classifications to output
genus.only

boolean If TRUE, search for matches with just the genus name using genus_search_itis

Value

data.frame with submitted names (orig.name), matched names (matched.name), 1/0 flag indicating that original name is currently accepted (orig.name.accepted), 1/0 flag indicating if search was genus_only (for distinguishing genus_search_itis and species_search_itis results), synonyms if any, and higher taxonomy (if higher=TRUE)

Examples

data(lakegeneva)
new.lakegeneva <- genus_species_extract(lakegeneva[1,],'phyto_name')
new.lakegeneva$genus_species <- trimws(paste(new.lakegeneva$genus,
new.lakegeneva$species))
lakegeneva.genus.itischeck <-
         itis_search_df(new.lakegeneva,"genus_species")
lakegeneva.genus.itischeck

example dataset from lake Geneva, Switzerland

Description

example dataset from lake Geneva, Switzerland

Usage

data(lakegeneva)

Format

A data frame with columns:

lake

lake name

phyto_name

phytoplankton species name

month

month of sampling

year

year of sampling

date_dd_mm_yy

date of sampling

biovol_um3_ml

biovolume

Compute mean value while ignoring NA's

Description

Compute mean value while ignoring NA's

Usage

mean_naomit(x)

Arguments

x

A numeric vector that may contain NA's

Value

the mean value

Examples

data(lakegeneva)
#example dataset with 50 rows

mean_naomit(lakegeneva$biovol_um3_ml)

Returns a CSR classification based on Morphofunctional group (MFG). Correspondence based on Salmaso et al. 2015 and Reynolds et al. 1988

Description

Returns a CSR classification based on Morphofunctional group (MFG). Correspondence based on Salmaso et al. 2015 and Reynolds et al. 1988

Usage

mfg_csr_convert(mfg)

Arguments

mfg

Character string with MFG name, following Salmaso et al. 2015

Value

A character string with values 'C','S','R','CR','SC','SR', or NA

Examples

mfg_csr_convert("11a-NakeChlor")

Returns a CSR classification based on Morphofunctional group (MFG). Correspondence based on Salmaso et al. 2015 and Reynolds et al. 1988

Description

Returns a CSR classification based on Morphofunctional group (MFG). Correspondence based on Salmaso et al. 2015 and Reynolds et al. 1988

Usage

mfg_csr_convert_df(phyto.df, mfg)

Arguments

phyto.df

dataframe containing a character field containing MFG classifications
mfg

Character string with MFG name, following Salmaso et al. 2015

Value

A dataframe with an additional field named CSR, containing CSR classifications or NA

Examples

data(lakegeneva)
lakegeneva<-genus_species_extract(lakegeneva,'phyto_name')
lakegeneva<-species_to_mfg_df(lakegeneva)
lakegeneva<-mfg_csr_convert_df(lakegeneva,mfg='MFG')
head(lakegeneva)

MFG-CSR correspondence based on CSR-trait relationships in Reynolds et al. 1988 and MFG-trait relationships in Salmaso et al. 2015

Description

MFG-CSR correspondence based on CSR-trait relationships in Reynolds et al. 1988 and MFG-trait relationships in Salmaso et al. 2015

Usage

data(mfg_csr_library)

Format

A data frame with columns:

MFG

full MFG name from Salmaso et al. 2015

CSR

CSR classification including intermediate classes

Functional Trait Database derived from Rimet et al.

Description

Functional Trait Database derived from Rimet et al.

Usage

data(mfgTraits)

Format

A data frame with columns:

phyto_name

binomial scientific name

genus

genus name

species

species name

Mobility.apparatus

1/0 indicates presence/absence of flagella or motility

Size

character values 'large' or 'small'; based on 35 micrometer max linear dimension

Colonial

1/0 indicates typical colonial growth form or not

Filament

1/0 indicates filamentous growth form or not

Centric

1/0 indicates diatoms with centric growth form

Gelatinous

1/0 indicates presence/absence of mucilage

Aerotopes

1/0 indicates presence/absence of aerotopes

Class

Taxonomic class

Order

Taxonomic order

MFG.fromtraits

MFG classification using traits_to_mfg function

Aggregate phytoplankton timeseries based on abundance. Up to 3 grouping variables can be given: e.g. genus, species, stationid, depth range. If no abundance var is given, will aggregate to presence/absence of grouping vars.

Description

Aggregate phytoplankton timeseries based on abundance. Up to 3 grouping variables can be given: e.g. genus, species, stationid, depth range. If no abundance var is given, will aggregate to presence/absence of grouping vars.

Usage

phyto_ts_aggregate(
  phyto.data,
  DateVar = "date_dd_mm_yy",
  SummaryType = c("abundance", "presence.absence"),
  AbundanceVar = "biovol_um3_ml",
  GroupingVar1 = "phyto_name",
  GroupingVar2 = NA,
  GroupingVar3 = NA,
  remove.rare = FALSE,
  fun = sum,
  format = "%d-%m-%y"
)

Arguments

phyto.data

data.frame
DateVar

character string: field name for date variable. character or POSIX data.
SummaryType

'abundance' for a matrix of aggregated abundance,'presence.absence' for 1 (present) and 0 (absent).
AbundanceVar

character string with field name containing abundance data Can be NA if data is only a species list and aggregated presence/absence is desired.
GroupingVar1

character string: field name for first grouping variable. defaults to spp.
GroupingVar2

character string: name of additional grouping var field
GroupingVar3

character string: name of additional grouping var field
remove.rare

TRUE/FALSE. If TRUE, removes all instances of GroupingVar1 that occur < 5 of time periods.
fun

function used to aggregate abundance based on grouping variables
format

character string: format for DateVar POSIXct conversion

Value

a data.frame with grouping vars, date_dd_mm_yy, and abundance or presence/absence

Examples

data(lakegeneva)
lakegeneva<-genus_species_extract(lakegeneva,'phyto_name')
lg.genera=phyto_ts_aggregate(lakegeneva,SummaryType='presence.absence',
                             GroupingVar1='genus')
head(lg.genera)

Visually assess change in sampling effort over time (author: Dietmar Straile)

Description

Visually assess change in sampling effort over time (author: Dietmar Straile)

Usage

sampeff(
  b_data,
  column,
  save.pdf = FALSE,
  lakename = "",
  datecolumn = "date_dd_mm_yy",
  dateformat = "%d-%m-%y"
)

Arguments

b_data

Name of data.frame object
column

column name or number for field containing abundance (biomass,biovol, etc.) can be NA for presence absence
save.pdf

TRUE/FALSE Should the output plot be saved to a file? defaults to FALSE
lakename

Character string for labeling output plot
datecolumn

Character String or number specifying dataframe field with date information
dateformat

Character string specifying POSIX data format

Value

a time-series plot of minimum relative abundance over time. This should change systematically with counting effort.

Examples

data(lakegeneva)
#example dataset with 50 rows

sampeff(lakegeneva,column=6) #column 6 contains biovolume

Add algaebase API key to curl handle

Description

Add algaebase API key to curl handle

Usage

set_algaebase_apikey_header(apikey = NULL)

Arguments

apikey

character string with valid key

Value

curl handle object

Trait-based MFG classifications for common Eurasion/North American phytoplankton species. See accompanying manuscript for sources

Description

Trait-based MFG classifications for common Eurasion/North American phytoplankton species. See accompanying manuscript for sources

Usage

data(species_mfg_library)

Format

A data frame with columns:

genus

genus name

species

species name

MFG

corresponding MFG classification based on Salmaso et al. 2015

source

literature or online source for MFG classification

References

Algaebase https://www.algaebase.org

Phycokey https://www.cfb.unh.edu/phycokey/phycokey.htm

Western Diatoms of North America https://diatoms.org

CyanoDB 2 http://www.cyanodb.cz/

Nordic Microalgae https://nordicmicroalgae.org

Phytopedia https://phytoplankton.eoas.ubc.ca/

Kapustin, D., Sterlyagova, I. and Patova, E., 2019. Morphology of Chrysastrella paradoxa stomatocysts from the Subpolar Urals (Russia) with comments on related morphotypes. Phytotaxa, 402(6), pp.295-300.

Wrapper function for several functions in ritis:: Searches ITIS database for matches to a binomial scientific name outputs matches, current accepted names, synonyms, and higher taxonomy

Description

Wrapper function for several functions in ritis:: Searches ITIS database for matches to a binomial scientific name outputs matches, current accepted names, synonyms, and higher taxonomy

Usage

species_search_itis(genspp, higher = FALSE)

Arguments

genspp

Character string. Binomial scientific name with space between genus and species.
higher

Boolean. If TRUE, add higher taxonomic classifications to output

Value

data.frame with submitted name (orig.name), matched name (matched.name), 1/0 flag indicating that original name is currently accepted (orig.name.accepted), 1/0 flag indicating if search was genus_only (for distinguishing genus_search_itis and species_search_itis results), synonyms if any, and higher taxonomy (if higher=TRUE)

Examples

species="Aphanizomenon flosaquae"
species_search_itis(species,higher=FALSE)

Conversion of a single genus and species name to a single MFG. Uses species.mfg.library

Description

Conversion of a single genus and species name to a single MFG. Uses species.mfg.library

Usage

species_to_mfg(genus, species = "", flag = 1, mfgDbase = NA)

Arguments

genus

Character string: genus name
species

Character string: species name
flag

Resolve ambiguous mfg: 1 = return(NA),2= manual selection
mfgDbase

data.frame of species MFG classifications. Defaults to the supplied species.mfg.library data object

Value

a data frame with MFG classification and diagnostic information. ambiguous.mfg=1 if multiple possible mfg matches genus.classification=1 if no exact match was found with genus + species name partial.match=1 if mfg was based on fuzzy matching of taxonomic name.

Examples

species_to_mfg('Scenedesmus','bijuga')
#returns "11a-NakeChlor"

Wrapper function to apply species_phyto_convert() across a data.frame

Description

Wrapper function to apply species_phyto_convert() across a data.frame

Usage

species_to_mfg_df(phyto.df, flag = 1, mfgDbase = NA)

Arguments

phyto.df

Name of data.frame. Must have character fields named 'genus' and 'species'
flag

Resolve ambiguous MFG: 1 = return(NA), 2 = manual selection
mfgDbase

specify library of species to MFG associations.

Value

input data.frame with a new character column of MFG classifications and diagnostic information

Examples

data(lakegeneva)
#example dataset with 50 rows

new.lakegeneva <- genus_species_extract(lakegeneva,'phyto_name')
new.lakegeneva <- species_to_mfg_df(new.lakegeneva)
head(new.lakegeneva)

surface/volume ratio and max linear dimension criteria for CSR From Reynolds 1988 and Reynolds 2006

Description

surface/volume ratio and max linear dimension criteria for CSR From Reynolds 1988 and Reynolds 2006

Usage

data(traitranges)

Format

A data frame with columns:

Measurement

measurement type

C.min

minimum value for C

S.min

minimum value for S

R.min

minimum value for R

C.max

maximum value for C

S.max

maximum value for S

R.max

maximum value for R

units

units of measurement

source

source for criteria

Assign phytoplankton species to CSR functional groups, based on surface to volume ratio and maximum linear dimension ranges proposed by Reynolds et al. 1988;2006

Description

Assign phytoplankton species to CSR functional groups, based on surface to volume ratio and maximum linear dimension ranges proposed by Reynolds et al. 1988;2006

Usage

traits_to_csr(
  sav,
  msv,
  msv.source = "Reynolds 2006",
  traitrange = algaeClassify::traitranges
)

Arguments

sav

numeric estimate of cell or colony surface area /volume ratio
msv

numeric product of surface area/volume ratio and maximum linear dimension
msv.source

character string with reference source for distinguishing criteria
traitrange

data frame with trait criteria for c,s,r groups. The included table can be replaced with user-defined criteria if desired. Measurements are: Surface area/volume ratio (sav), maximum linear dimension (mld) and mld*sav (msv).

Value

a character string with one of 5 return values: C,CR,S,R, or SR. CR and SR groups reflect overlap between criteria for the 3 main groups.

See Also

<https://powellcenter.usgs.gov/geisha> for project information

Examples

traits_to_csr(sav=0.2,msv=10,msv.source='Reynolds 2006',traitrange=traitranges)

Add CSR functional group classifications to a dataframe of phytoplankton species, based on surface to volume ratio and maximum linear dimension ranges proposed by Reynolds et al. 1988;2006

Description

Add CSR functional group classifications to a dataframe of phytoplankton species, based on surface to volume ratio and maximum linear dimension ranges proposed by Reynolds et al. 1988;2006

Usage

traits_to_csr_df(
  df,
  sav,
  msv,
  msv.source = "Reynolds 2006",
  traitrange = algaeClassify::traitranges
)

Arguments

df

name of dataframe
sav

character string with name of column that contains surface to volume ratio values
msv

character string with name of column that contains maximum linear dimension * surface to volume ratio values
msv.source

character string with reference source for distinguishing criteria
traitrange

data frame with trait criteria for c,s,r groups. The included table can be replaced with user-defined criteria if desired. Measurements are: Surface area/volume ratio (sav), maximum linear dimension (mld) and mld*sav (msv).

Value

a character string with one of 5 return values: C,CR,S,SR, or R

Examples

csr.df<-data.frame(msv=10,sav=1)

csr.df$CSR<-traits_to_csr_df(csr.df,'msv','sav')

print(csr.df)

Assign MFG based on binary functional traits and taxonomy (Class and Order)

Description

Assign MFG based on binary functional traits and taxonomy (Class and Order)

Usage

traits_to_mfg(
  flagella = NA,
  size = NA,
  colonial = NA,
  filament = NA,
  centric = NA,
  gelatinous = NA,
  aerotopes = NA,
  class = NA,
  order = NA
)

Arguments

flagella

1 if flagella are present, 0 if they are absent.
size

Character string: 'large' or 'small'. Classification criteria is left to the user.
colonial

1 if typically colonial growth form, 0 if typically unicellular.
filament

1 if dominant growth form is filamentous, 0 if not.
centric

1 if diatom with centric growth form, 0 if not. NA for non-diatoms.
gelatinous

1 mucilagenous sheath is typically present, 0 if not.
aerotopes

1 if aerotopes allowing buoyancy regulation are typically present, 0 if not.
class

Character string: The taxonomic class of the species
order

Character string: The taxonomic order of the species

Value

A character string of the species' morphofunctional group

Examples

traits_to_mfg(flagella = 1,size = "large",colonial = 1,filament = 0,centric = NA,gelatinous = 0,
               aerotopes = 0,class = "Euglenophyceae",order = "Euglenales")

Assign morphofunctional groups to a dataframe of functional traits and higher taxonomy

Description

Assign morphofunctional groups to a dataframe of functional traits and higher taxonomy

Usage

traits_to_mfg_df(
  dframe,
  arg.names = c("flagella", "size", "colonial", "filament", "centric", "gelatinous",
    "aerotopes", "class", "order")
)

Arguments

dframe

An R dataframe containing functional trait information and higher taxonomy
arg.names

Character string of column names corresponding to arguments for traits_to_mfg()

Value

A character vector containing morpho-functional group (MFG) designations

Examples

#create a two-row example dataframe of functional traits
func.dframe=data.frame(flagella=1,size=c("large","small"),colonial=0,filament=0,centric=NA,
                       gelatinous=0,aerotopes=0,class="Euglenophyceae",order="Euglenales",
                       stringsAsFactors=FALSE)

#check the dataframe
print(func.dframe)

#run the function to produce a two-element character vector
func.dframe$MFG<-traits_to_mfg_df(func.dframe,c("flagella","size","colonial",
                                 "filament","centric","gelatinous",
                                 "aerotopes","class","order"))

print(func.dframe)