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

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

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

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

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

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

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

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

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

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

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

Description

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

Usage

gnr_df(
  df,
  name.column,
  sourceid = NULL,
  best_match = TRUE,
  fuzzy_uninomial = TRUE,
  canonical = TRUE,
  with_context = TRUE,
  higher = FALSE
)

Arguments

Value

new data.frame original names (input_name), 1/0 flag for an exact match,the best match (match_name, 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/)

Description

Provides convienent output with a single result, using a variety of criteria for the best match

Usage

gnr_simple(
  name,
  sourceid = NULL,
  best_match = TRUE,
  fuzzy_uninomial = TRUE,
  canonical = TRUE,
  with_context = TRUE,
  higher = FALSE
)

Arguments

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,195 for Algaebase
gnr_simple(name,sourceid=3) #search for ITIS matches
gnr_simple(name,sourceid=NULL) #search for 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

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)
#example dataset

new.lakegeneva <- genus_species_extract(lakegeneva[1,],'phyto_name')
new.lakegeneva$genus_species <- trimws(paste(new.lakegeneva$genus,
new.lakegeneva$species))
#checking for genus-only name matches in ITIS, and extracting higher taxonomy
#flagging names with imperfect or no matches

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

Value

the mean value

Examples

data(lakegeneva)
#example dataset with 50 rows

mean_naomit(lakegeneva$biovol_um3_ml)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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)