Species Diversity¶

EcoPy contains several methods for estimating species diversity:

diversity()

rarefy()

diversity(x, method='shannon', breakNA=True)¶

Calculate species diversity for every site in a site x species matrix

Parameters

x: numpy.ndarray or pandas.DataFrame (required)

A site x species matrix, where sites are rows and columns are species.

shannon: Calculates Shannon’s H

$H = -\sum_1^k p_k \log p_k$

where $p_k$ is the relative abundance of species k

simpson: Calculates Simpson’s D

$D = 1 - \sum_1^k p_k^2$

invSimpson: Inverse of Simpson’s D

dominance: Dominance index. $\max p_k$

spRich: Species richness (# of non-zero columns)

even: Evenness of a site. Shannon’s H divided by log of species richness.

breakNA: [True | False]

Whether null values should halt the process. If False, then null values are removed from all calculations.

Example

Calculate Shannon diversity of the ‘varespec’ dataset from R:

import ecopy as ep
varespec = ep.load_data('varespec')
shannonH = ep.diversity(varespec, 'shannon')

rarefy(x, method='rarefy', size=None, breakNA=True)¶

Returns either rarefied species richness or draws a rarefaction curve for each row. Rarefied species richness is calculated based on the smallest sample (default) or allows user-specified sample sizes.

Parameters

x: numpy.ndarray or pandas.DataFrame (required)

A site x species matrix, where sites are rows and columns are species.

method: [‘rarefy’ | ‘rarecurve’]

rarefy: Returns rarefied species richness.

$S = \sum_1^i 1 - \frac{\binom{N-N_i}{size}}{\binom{N}{size}}$

where N is the total number of individuals in the site, $N_i$ is the number of individuals of species i, and size is the sample size for rarefaction

rarecurve: Plots a rarefaction curve for each site (row). The curve is calculated as

$S_n - \frac{\sum_1^i \binom{N-N_i}{size}}{\binom{N}{size}}$

where $S_n$ is the total number of species in the matrix and size ranges from 0 to the total number of individuals in each site.

Example

Calculate rarefied species richness for the BCI dataset:

import ecopy as ep
varespec = ep.load_data('BCI')
rareRich = ep.rarefy(varespec, 'rarefy')

Show rarefaction curves for each site:

ep.rarefy(varespec, 'rarecurve')