get_abundance_df

internal

The “TAD” package compiled an analytical framework based on an analysis of the shape of the trait abundance distributions to better understand community assembly processes, and predict community dynamics under environmental changes. This framework mobilized a study of the relationship between the moments describing the shape of the distributions: the skewness and the kurtosis (SKR). The SKR allows the identification of commonalities in the shape of trait distributions across contrasting communities. Derived from the SKR, we developed mathematical parameters that summarise the complex pattern of distributions by assessing (i) the R², (ii) the Y-intercept, (iii) the slope, (iv) the functional stability of community (TADstab), and, (v) the distance from specific distribution families (i.e., the distance from the skew-uniform family a limit to the highest degree of evenness: TADeve).

Raphael Martin

Realize the Trait Abundance Distribution

Nathan Rondeau

Yoann Le Bagousse-Pinguet

Raphaël Martin

Lain Pavot

Pierre Liancourt

Nicolas Gross

INRAe/UREP 

get_abundance_df function

<dl><dt>weights</dt>
<dd>the dataframe of weights, one row correspond to a
series of observation</dd>
<dt>weights_factor</dt>
<dd>the dataframe which contains the different
factor linked to the weights</dd>
<dt>randomization_number</dt>
<dd>the number of random abundance matrix to
generate</dd>
<dt>abundance_file</dt>
<dd>the path and name of the RDS file to
load/save the dataframe which
contains the observed data and the generated matrix</dd>
<dt>regenerate_abundance_df</dt>
<dd>boolean to specify if the
abundance dataframe is computed again</dd>
<dt>aggregation_factor_name</dt>
<dd>vector of factor name for the
generation of random matrix</dd>
<dt>seed</dt>
<dd>the seed of the pseudo random number generator</dd></dl>

Arguments

abundance generation — get_abundance_df

<dl>

<dt>weights</dt>
<dd>the dataframe of weights, one row correspond to a
series of observation</dd>


<dt>weights_factor</dt>
<dd>the dataframe which contains the different
factor linked to the weights</dd>


<dt>randomization_number</dt>
<dd>the number of random abundance matrix to
generate</dd>


<dt>abundance_file</dt>
<dd>the path and name of the RDS file to
load/save the dataframe which
contains the observed data and the generated matrix</dd>


<dt>regenerate_abundance_df</dt>
<dd>boolean to specify if the
abundance dataframe is computed again</dd>


<dt>aggregation_factor_name</dt>
<dd>vector of factor name for the
generation of random matrix</dd>


<dt>seed</dt>
<dd>the seed of the pseudo random number generator</dd>

</dl>

abundance generation

get_abundance_df: abundance generation

Description

Usage

Arguments