accum

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)

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currently accepted and outdated synonyms, and higher taxonomy, can be extracted for lists of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 using wrapper functions for the ritis package.
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.

Vijay Patil

algaeClassify

Tools to Query the 'Algaebase' Online Database, Standardize
Phytoplankton Taxonomic Data, and Perform Functional Group
Classifications

Torsten Seltmann

Nico Salmaso

Orlane Anneville

Marc Lajeunesse

Dietmar Straile

accum function

<dl><dt>b_data</dt>
<dd>Name of data.frame object</dd>
<dt>phyto_name</dt>
<dd>Character string: field containing phytoplankton id (species, genus, etc.)</dd>
<dt>column</dt>
<dd>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.</dd>
<dt>n</dt>
<dd>number of simulations for randomized ideal species accumulation curve</dd>
<dt>save.pdf</dt>
<dd>TRUE/FALSE- should plots be displayed or saved to a pdf?</dd>
<dt>lakename</dt>
<dd>optional character string for adding lake name to pdf output</dd>
<dt>datename</dt>
<dd>character string name of b_data field containing date</dd>
<dt>dateformat</dt>
<dd>character string: posix format for datename column</dd></dl>

Arguments

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) — accum

<dl>

<dt>b_data</dt>
<dd>Name of data.frame object</dd>


<dt>phyto_name</dt>
<dd>Character string: field containing phytoplankton id (species, genus, etc.)</dd>


<dt>column</dt>
<dd>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.</dd>


<dt>n</dt>
<dd>number of simulations for randomized ideal species accumulation curve</dd>


<dt>save.pdf</dt>
<dd>TRUE/FALSE- should plots be displayed or saved to a pdf?</dd>


<dt>lakename</dt>
<dd>optional character string for adding lake name to pdf output</dd>


<dt>datename</dt>
<dd>character string name of b_data field containing date</dd>


<dt>dateformat</dt>
<dd>character string: posix format for datename column</dd>

</dl>

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

Usage

Value

Arguments

Examples