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\usepackage{vegan} %% vegan setup
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%% FD, check Kindt reference to specaccum, check estimateR ref
\title{Vegan: ecological diversity} \author{Jari Oksanen}
\date{\footnotesize{
processed with vegan \Sexpr{packageDescription("vegan", field="Version")}
in \Sexpr{R.version.string} on \today}}
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\begin{document}
\bibliographystyle{jss}
\SweaveOpts{strip.white=true}
<>=
par(mfrow=c(1,1))
options(width=55)
figset <- function() par(mar=c(4,4,1,1)+.1)
options(SweaveHooks = list(fig = figset))
options("prompt" = "> ", "continue" = " ")
@
\maketitle
\begin{abstract}
This document explains diversity related methods in
\pkg{vegan}. The methods are briefly described, and the equations
used them are given often in more detail than in their help
pages. The methods discussed include common diversity indices and
rarefaction, families of diversity indices, species abundance
models, species accumulation models and beta diversity, extrapolated
richness and probability of being a member of the species pool. The
document is still incomplete and does not cover all diversity
methods in \pkg{vegan}.
\end{abstract}
\tableofcontents
\noindent The \pkg{vegan} package has two major components:
multivariate analysis (mainly ordination), and methods for diversity
analysis of ecological communities. This document gives an
introduction to the latter. Ordination methods are covered in other
documents. Many of the diversity functions were written by Roeland
Kindt, Bob O'Hara and P{\'e}ter S{\'o}lymos.
Most diversity methods assume that data are counts of individuals.
The methods are used with other data types, and some people argue that
biomass or cover are more adequate than counts of individuals of
variable sizes. However, this document mainly uses a data set with
counts: stem counts of trees on $1$\,ha plots in the Barro Colorado
Island. The following steps make these data available for the
document:
<<>>=
library(vegan)
data(BCI)
@
\section{Diversity indices}
Function \code{diversity} finds the most commonly used diversity
indices \citep{Hill73number}:
\begin{align}
H &= - \sum_{i=1}^S p_i \log_b p_i & \text{Shannon--Weaver}\\
D_1 &= 1 - \sum_{i=1}^S p_i^2 &\text{Simpson}\\
D_2 &= \frac{1}{\sum_{i=1}^S p_i^2} &\text{inverse Simpson}\,,
\end{align}
where $p_i$ is the proportion of species $i$, and $S$ is the number of
species so that $\sum_{i=1}^S p_i = 1$, and $b$ is the base of the
logarithm. It is most common to use natural logarithms (and then we
mark index as $H'$), but $b=2$ has
theoretical justification. The default is to use natural logarithms.
Shannon index is calculated with:
<<>>=
H <- diversity(BCI)
@
which finds diversity indices for all sites.
\pkg{Vegan} does not have indices for evenness (equitability), but
the most common of these, Pielou's evenness $J = H'/\log(S)$ is easily
found as:
<<>>=
J <- H/log(specnumber(BCI))
@
where \code{specnumber} is a simple \pkg{vegan} function to find
the numbers of species.
\pkg{vegan} also can estimate series of R\'{e}nyi and Tsallis
diversities. R{\'e}nyi diversity of order $a$ is \citep{Hill73number}:
\begin{equation}
H_a = \frac{1}{1-a} \log \sum_{i=1}^S p_i^a \,,
\end{equation}
and the corresponding Hill number is $N_a = \exp(H_a)$. Many common
diversity indices are special cases of Hill numbers: $N_0 = S$, $N_1 =
\exp(H')$, $N_2 = D_2$, and $N_\infty = 1/(\max p_i)$. The
corresponding R\'{e}nyi diversities are $H_0 = \log(S)$, $H_1 = H'$, $H_2 =
- \log(\sum p_i^2)$, and $H_\infty = - \log(\max p_i)$.
Tsallis diversity of order $q$ is \citep{Tothmeresz95}:
\begin{equation}
H_q = \frac{1}{q-1} \left(1 - \sum_{i=1}^S p^q \right) \, .
\end{equation}
These correspond to common diversity indices: $H_0 = S-1$, $H_1 = H'$,
and $H_2 = D_1$, and can be converted to Hill numbers:
\begin{equation}
N_q = (1 - (q-1) H_q )^\frac{1}{1-q} \, .
\end{equation}
We select a random subset of five sites for R\'{e}nyi diversities:
<<>>=
k <- sample(nrow(BCI), 6)
R <- renyi(BCI[k,])
@
We can really regard a site more diverse if all of its R\'{e}nyi
diversities are higher than in another site. We can inspect this
graphically using the standard \code{plot} function for the
\code{renyi} result (Fig.~\ref{fig:renyi}).
\begin{figure}
<>=
print(plot(R))
@
\caption{R\'{e}nyi diversities in six randomly selected plots. The plot
uses Trellis graphics with a separate panel for each site. The dots
show the values for sites, and the lines the extremes and median in
the data set.}
\label{fig:renyi}
\end{figure}
Finally, the $\alpha$ parameter of Fisher's log-series can be used as
a diversity index \citep{FisherEtal43}:
<<>>=
alpha <- fisher.alpha(BCI)
@
\section{Rarefaction}
Species richness increases with sample size, and differences in
richness actually may be caused by differences in sample size. To
solve this problem, we may try to rarefy species richness to the same
number of individuals. Expected number of species in a community
rarefied from $N$ to $n$ individuals is \citep{Hurlbert71}:
\begin{equation}
\label{eq:rare}
\hat S_n = \sum_{i=1}^S (1 - q_i)\,, \quad\text{where } q_i =
\frac{{N-x_i \choose n}}{{N \choose n}} \,.
\end{equation}
Here $x_i$ is the count of species $i$, and ${N \choose n}$ is the
binomial coefficient, or the number of ways we can choose $n$ from
$N$, and $q_i$ give the probabilities that species $i$ does \emph{not} occur in a
sample of size $n$. This is positive only when $N-x_i \ge n$, but for
other cases $q_i = 0$ or the species is sure to occur in the sample.
The variance of rarefied richness is \citep{HeckEtal75}:
\begin{multline}
\label{eq:rarevar}
s^2 = q_i (1-q_i) \\ + 2 \sum_{i=1}^S \sum_{j>i} \left[ \frac{{N- x_i - x_j
\choose n}}{ {N
\choose n}} - q_i q_j\right] \,.
\end{multline}
Equation~\ref{eq:rarevar} actually is of the same form as the variance
of sum of correlated variables:
\begin{equation}
\VAR \left(\sum x_i \right) = \sum \VAR (x_i) + 2 \sum_{i=1}^S
\sum_{j>i} \COV (x_i, x_j) \,.
\end{equation}
The number of stems per hectare varies in our
data set:
<<>>=
quantile(rowSums(BCI))
@
To express richness for the same number of individuals, we can use:
<<>>=
Srar <- rarefy(BCI, min(rowSums(BCI)))
@
Rarefaction curves often are seen as an objective solution for
comparing species richness with different sample sizes. However, rank
orders typically differ among different rarefaction sample sizes,
rarefaction curves can cross.
As an extreme case we may rarefy sample size to two individuals:
<<>>=
S2 <- rarefy(BCI, 2)
@
This will not give equal rank order with the previous rarefaction
richness:
<<>>=
all(rank(Srar) == rank(S2))
@
Moreover, the rarefied richness for two individuals is a finite
sample variant of Simpson's diversity index \citep{Hurlbert71}\,--\,or
more precisely of $D_1 + 1$, and these two are almost identical in
BCI:
<<>>=
range(diversity(BCI, "simp") - (S2 -1))
@
Rarefaction is sometimes presented as an ecologically meaningful
alternative to dubious diversity indices \citep{Hurlbert71}, but the
differences really seem to be small.
\section{Taxonomic and functional diversity}
Simple diversity indices only consider species identity: all different
species are equally different. In contrast, taxonomic and functional
diversity indices judge the differences of species. Taxonomic and
functional diversities are used in different fields of science, but
they really have very similar reasoning, and either could be used
either with taxonomic or functional traits of species.
\subsection{Taxonomic diversity: average distance of traits}
The two basic indices are called taxonomic diversity $\Delta$ and
taxonomic distinctness $\Delta^*$ \citep{ClarkeWarwick98}:
\begin{align}
\Delta &= \frac{\sum \sum_{i>=
data(dune)
data(dune.taxon)
taxdis <- taxa2dist(dune.taxon, varstep=TRUE)
mod <- taxondive(dune, taxdis)
@
\begin{figure}
<>=
plot(mod)
@
\caption{Taxonomic diversity $\Delta^+$ for the dune meadow data. The
points are diversity values of single sites, and the funnel is their
approximate confidence intervals ($2 \times$ standard error).}
\label{fig:taxondive}
\end{figure}
\subsection{Functional diversity: the height of trait tree}
In taxonomic diversity the primary data were taxonomic trees which
were transformed to pairwise distances among species. In functional
diversity the primary data are species traits which are translated to
pairwise distances among species and then to clustering trees of
species traits. The argument for using trees is that in this way a
single deviant species will have a small influence, since its
difference is evaluated only once instead of evaluating its distance
to all other species \citep{PetcheyGaston06}.
Function \code{treedive} implements functional diversity defined as
the total branch length in a trait dendrogram connecting all species,
but excluding the unnecessary root segments of the tree
\citep{PetcheyGaston02, PetcheyGaston06}. The example uses the
taxonomic distances of the previous chapter. These are first converted
to a hierarchic clustering (which actually were their original form
before \code{taxa2dist} converted them into distances)
<<>>=
tr <- hclust(taxdis, "aver")
mod <- treedive(dune, tr)
@
\section{Species abundance models}
Diversity indices may be regarded as variance measures of species
abundance distribution. We may wish to inspect abundance
distributions more directly. \pkg{Vegan} has functions for
Fisher's log-series and Preston's log-normal models, and in addition
several models for species abundance distribution.
\subsection{Fisher and Preston}
In Fisher's log-series, the expected number of species $\hat f$ with $n$
individuals is \citep{FisherEtal43}:
\begin{equation}
\hat f_n = \frac{\alpha x^n}{n} \,,
\end{equation}
where $\alpha$ is the diversity parameter, and $x$ is a nuisance
parameter defined by $\alpha$ and total number
of individuals $N$ in the site, $x = N/(N-\alpha)$. Fisher's
log-series for a randomly selected plot is (Fig.~\ref{fig:fisher}):
<<>>=
k <- sample(nrow(BCI), 1)
fish <- fisherfit(BCI[k,])
fish
@
\begin{figure}
<>=
plot(fish)
@
\caption{Fisher's log-series fitted to one randomly selected site
(\Sexpr{k}).}
\label{fig:fisher}
\end{figure}
We already saw $\alpha$ as a diversity index.
Preston's log-normal model is the main challenger to Fisher's
log-series \citep{Preston48}. Instead of plotting species by
frequencies, it bins species into frequency classes of increasing
sizes. As a result, upper bins with high range of frequencies become
more common, and sometimes the result looks similar to Gaussian
distribution truncated at the left.
There are two alternative functions for the log-normal model:
\code{prestonfit} and \code{prestondistr}. Function \code{prestonfit}
uses traditionally binning approach, and is burdened with arbitrary
choices of binning limits and treatment of ties. It seems that Preston
split ties between adjacent octaves: only half of the species observed
once were in the first octave, and half were transferred to the next
octave, and the same for all species at the octave limits occurring 2,
4, 8, 16\ldots times \citep{WilliamsonGaston05}. Function
\code{prestonfit} can either split the ties or keep all limit cases in
the lower octave. Function \code{prestondistr} directly maximizes
truncated log-normal likelihood without binning data, and it is the
recommended alternative. Log-normal models usually fit poorly to the
BCI data, but here our random plot (number \Sexpr{k}):
<<>>=
prestondistr(BCI[k,])
@
\subsection{Ranked abundance distribution}
An alternative approach to species abundance distribution is to plot
logarithmic abundances in decreasing order, or against ranks of
species \citep{Whittaker65}. These are known as ranked abundance
distribution curves, species abundance curves, dominance--diversity
curves or Whittaker plots. Function \code{radfit} fits some of the
most popular models \citep{Bastow91} using maximum likelihood
estimation:
\begin{align}
\hat a_r &= \frac{N}{S} \sum_{k=r}^S \frac{1}{k} &\text{brokenstick}\\
\hat a_r &= N \alpha (1-\alpha)^{r-1} & \text{preemption} \\
\hat a_r &= \exp \left[\log (\mu) + \log (\sigma) \Phi \right]
&\text{log-normal}\\
\hat a_r &= N \hat p_1 r^\gamma &\text{Zipf}\\
\hat a_r &= N c (r + \beta)^\gamma &\text{Zipf--Mandelbrot}
\end{align}
In all these, $\hat a_r$ is the expected abundance of species at rank $r$, $S$
is the number of species, $N$ is the number of individuals, $\Phi$ is
a standard normal function, $\hat p_1$ is the estimated proportion of
the most abundant species, and $\alpha$, $\mu$, $\sigma$, $\gamma$,
$\beta$ and $c$ are the estimated parameters in each model.
It is customary to define the models for proportions $p_r$ instead of
abundances $a_r$, but there is no reason for this, and \code{radfit}
is able to work with the original abundance data. We have count data,
and the default Poisson error looks appropriate, and our example data
set gives (Fig.~\ref{fig:rad}):
<<>>=
rad <- radfit(BCI[k,])
rad
@
\begin{figure}
<>=
print(radlattice(rad))
@
\caption{Ranked abundance distribution models for a random plot
(no. \Sexpr{k}). The best model has the lowest \textsc{aic}.}
\label{fig:rad}
\end{figure}
Function \code{radfit} compares the models using alternatively
Akaike's or Schwartz's Bayesian information criteria. These are based
on log-likelihood, but penalized by the number of estimated
parameters. The penalty per parameter is $2$ in \textsc{aic}, and
$\log S$ in \textsc{bic}. Brokenstick is regarded as a null model and
has no estimated parameters in \pkg{vegan}. Preemption model has
one estimated parameter ($\alpha$), log-normal and Zipf models two
($\mu, \sigma$, or $\hat p_1, \gamma$, resp.), and Zipf--Mandelbrot
model has three ($c, \beta, \gamma$).
Function \code{radfit} also works with data frames, and fits models
for each site. It is curious that log-normal model rarely is the
choice, although it generally is regarded as the canonical model, in
particular in data sets like Barro Colorado tropical forests.
\section{Species accumulation and beta diversity}
Species accumulation models and species pool models study collections
of sites, and their species richness, or try to estimate the number of
unseen species.
\subsection{Species accumulation models}
Species accumulation models are similar to rarefaction: they study the
accumulation of species when the number of sites increases. There are
several alternative methods, including accumulating sites in the order
they happen to be, and repeated accumulation in random order. In
addition, there are three analytic models. Rarefaction pools
individuals together, and applies rarefaction equation (\ref{eq:rare})
to these individuals. Kindt's exact accumulator resembles rarefaction
\citep{UglandEtal03}:
\begin{multline}
\label{eq:kindt}
\hat S_n = \sum_{i=1}^S (1 - p_i), \,\quad \text{where }
p_i = \frac{{N- f_i \choose n}}{{N \choose n}} \,,
\end{multline}
and $f_i$ is the frequency of species $i$. Approximate variance
estimator is:
\begin{multline}
\label{eq:kindtvar}
s^2 = p_i (1 - p_i) \\ + 2 \sum_{i=1}^S \sum_{j>i} \left( r_{ij}
\sqrt{p_i(1-p_i)} \sqrt{p_j (1-p_j)}\right) \,,
\end{multline}
where $r_{ij}$ is the correlation coefficient between species $i$ and
$j$. Both of these are unpublished: eq.~\ref{eq:kindt} was developed
by Roeland Kindt, and eq.~\ref{eq:kindtvar} by Jari Oksanen. The third
analytic method was suggested by \citet{Coleman82}:
\begin{equation}
\label{eq:cole}
S_n = \sum_{i=1}^S (1 - p_i), \quad \text{where } p_i = \left(1 -
\frac{1}{n}\right)^{f_i} \,,
\end{equation}
and the suggested variance is $s^2 = p_i (1-p_i)$ which ignores the
covariance component. In addition, eq.~\ref{eq:cole} does not
properly handle sampling without replacement and underestimates the
species accumulation curve.
The recommended is Kindt's exact method (Fig.~\ref{fig:sac}):
<>=
sac <- specaccum(BCI)
plot(sac, ci.type="polygon", ci.col="yellow")
@
\begin{figure}
<>=
<>
@
\caption{Species accumulation curve for the BCI data; exact method.}
\label{fig:sac}
\end{figure}
\subsection{Beta diversity}
\citet{Whittaker60} divided diversity into various components. The
best known are diversity in one spot that he called alpha diversity,
and the diversity along gradients that he called beta diversity. The
basic diversity indices are indices of alpha diversity. Beta diversity
should be studied with respect to gradients \citep{Whittaker60}, but
almost everybody understand that as a measure of general heterogeneity
\citep{Tuomisto10a, Tuomisto10b}: how many more species do you have in
a collection of sites compared to an average site.
The best known index of beta diversity is based on the ratio of total
number of species in a collection of sites $S$ and the average
richness per one site $\bar \alpha$ \citep{Tuomisto10a}:
\begin{equation}
\label{eq:beta}
\beta = S/\bar \alpha - 1 \,.
\end{equation}
Subtraction of one means that $\beta = 0$ when there are no excess
species or no heterogeneity between sites. For this index, no specific
functions are needed, but this index can be easily found with the help
of \pkg{vegan} function \code{specnumber}:
<<>>=
ncol(BCI)/mean(specnumber(BCI)) - 1
@
The index of eq.~\ref{eq:beta} is problematic because $S$ increases
with the number of sites even when sites are all subsets of the same
community. \citet{Whittaker60} noticed this, and suggested the index
to be found from pairwise comparison of sites. If the number of shared
species in two sites is $a$, and the numbers of species unique to each
site are $b$ and $c$, then $\bar \alpha = (2a + b + c)/2$ and $S =
a+b+c$, and index~\ref{eq:beta} can be expressed as:
\begin{equation}
\label{eq:betabray}
\beta = \frac{a+b+c}{(2a+b+c)/2} - 1 = \frac{b+c}{2a+b+c} \,.
\end{equation}
This is the S{\o}rensen index of dissimilarity, and it can be found
for all sites using \pkg{vegan} function \code{vegdist} with
binary data:
<<>>=
beta <- vegdist(BCI, binary=TRUE)
mean(beta)
@
There are many other definitions of beta diversity in addition to
eq.~\ref{eq:beta}. All commonly used indices can be found using
\code{betadiver} \citep{KoleffEtal03}. The indices in \code{betadiver}
can be referred to by subscript name, or index number:
<<>>=
betadiver(help=TRUE)
@
Some of these indices are duplicates, and many of them are well known
dissimilarity indices.
One of the more interesting indices is based
on the Arrhenius species--area model
\begin{equation}
\label{eq:arrhenius}
\hat S = c X^z\,,
\end{equation}
where $X$ is the area (size) of the patch or site, and $c$ and $z$ are
parameters. Parameter $c$ is uninteresting, but $z$ gives the
steepness of the species area curve and is a measure of beta
diversity. In islands typically $z \approx 0.3$. This kind of
islands can be regarded as subsets of the same community, indicating
that we really should talk about gradient differences if $z \gtrapprox 0.3$. We
can find the value of $z$ for a pair of plots using function
\code{betadiver}:
<<>>=
z <- betadiver(BCI, "z")
quantile(z)
@
The size $X$ and parameter $c$ cancel out, and the index gives the
estimate $z$ for any pair of sites.
Function \code{betadisper} can be used to analyse beta diversities
with respect to classes or factors \citep{Anderson06, AndersonEtal06}.
There is no such classification available for the Barro Colorado
Island data, and the example studies beta diversities in the
management classes of the dune meadows (Fig.~\ref{fig:betadisper}):
<<>>=
data(dune)
data(dune.env)
z <- betadiver(dune, "z")
mod <- with(dune.env, betadisper(z, Management))
mod
@
\begin{figure}
<>=
boxplot(mod)
@
\caption{Box plots of beta diversity measured as the average steepness
($z$) of the species area curve in the Arrhenius model $S = cX^z$ in
Management classes of dune meadows.}
\label{fig:betadisper}
\end{figure}
\section{Species pool}
\subsection{Number of unseen species}
Species accumulation models indicate that not all species were seen in
any site. These unseen species also belong to the species pool.
Functions \code{specpool} and \code{estimateR} implement some
methods of estimating the number of unseen species. Function
\code{specpool} studies a collection of sites, and
\code{estimateR} works with counts of individuals, and can be used
with a single site. Both functions assume that the number of unseen
species is related to the number of rare species, or species seen only
once or twice.
The incidence-based functions group species by their number of
occurrences $f_i = f_0, f_1, \ldots, f_N$, where $f$ is the number of
species occurring in exactly $i$ sites in the data: $f_N$ is the number
of species occurring on every $N$ site, $f_1$ the number of species
occurring once, and $f_0$ the number of species in the species pool
but not found in the sample. The total number of species in the pool
$S_p$ is
\begin{equation}
S_p = \sum_{i=0}^N f_i = f_0+ S_o \,,
\end{equation}
where $S_o = \sum_{i>0} f_i$ is the observed number of species. The
sampling proportion $i/N$ is an estimate for the commonness of the
species in the community. When species is present in the community but
not in the sample, $i=0$ is an obvious under-estimate, and
consequently, for values $i>0$ the species commonness is
over-estimated \citep{Good53}. The models for the pool size estimate
the number of species missing in the sample $f_0$.
Function \code{specpool} implements the following models to estimate
the number of missing species $f_0$. Chao estimator is \citep{Chao87, ChiuEtal14}:
\begin{equation}
\label{eq:chao}
\hat f_0 = \begin{cases}
\frac{f_1^2}{2 f_2} \frac{N-1}{N} &\text{if } f_2 > 0 \\
\frac{f_1 (f_1 -1)}{2} \frac{N-1}{N} & \text{if } f_2 = 0 \,.
\end{cases}
\end{equation}
The latter case for $f_2=0$ is known as the bias-corrected
form. \citet{ChiuEtal14} introduced the small-sample correction term
$\frac{N}{N-1}$, but it was not originally used \citep{Chao87}.
The first and second order jackknife estimators are
\citep{SmithVanBelle84}:
\begin{align}
\hat f_0 &= f_1 \frac{N-1}{N} \\
\hat f_0 & = f_1 \frac{2N-3}{N} + f_2 \frac{(N-2)^2}{N(N-1)} \,.
\end{align}
The bootstrap estimator is \citep{SmithVanBelle84}:
\begin{equation}
\hat f_0 = \sum_{i=1}^{S_o} (1-p_i)^N \,.
\end{equation}
The idea in jackknife seems to be that we missed about as many species
as we saw only once, and the idea in bootstrap that if we repeat
sampling (with replacement) from the same data, we miss as many
species as we missed originally.
The variance estimaters only concern the estimated number of missing
species $\hat f_0$, although they are often expressed as they would
apply to the pool size $S_p$; this is only true if we assume that
$\VAR(S_o) = 0$. The variance of the Chao estimate is \citep{ChiuEtal14}:
\begin{multline}
\label{eq:var-chao-basic}
\VAR(\hat f_0) = f_1 \left(A^2 \frac{G^3}{4} + A^2 G^2 + A \frac{G}{2} \right),\\
\text{where } A = \frac{N-1}{N}\;\text{and } G = \frac{f_1}{f_2} \,.
\end{multline}
%% The variance of bias-corrected Chao estimate can be approximated by
%% replacing the terms of eq.~\ref{eq:var-chao-basic} with the
%% corresponding terms of the bias-correcter form of in eq.~\ref{eq:chao}:
%% \begin{multline}
%% \label{eq:var-chao-bc}
%% s^2 = A \frac{f_1(f_1-1)}{2} + A^2 \frac{f_1(2 f_1+1)^2}{(f_2+1)^2}\\
%% + A^2 \frac{f_1^2 f_2 (f_1 -1)^2}{4 (f_2 + 1)^4}
%% \end{multline}
For the bias-corrected form of eq.~\ref{eq:chao} (case $f_2 = 0$), the variance is
\citep[who omit small-sample correction in some terms]{ChiuEtal14}:
\begin{multline}
\label{eq:var-chao-bc0}
\VAR(\hat f_0) = \tfrac{1}{4} A^2 f_1 (2f_1 -1)^2 + \tfrac{1}{2} A f_1
(f_1-1) \\- \tfrac{1}{4}A^2 \frac{f_1^4}{S_p} \,.
\end{multline}
The variance of the first-order jackknife is based on the number of
``singletons'' $r$ (species occurring only once in the data) in sample
plots \citep{SmithVanBelle84}:
\begin{equation}
\VAR(\hat f_0) = \left(\sum_{i=1}^N r_i^2 - \frac{f_1}{N}\right)
\frac{N-1}{N} \,.
\end{equation}
Variance of the second-order jackknife is not evaluated in
\code{specpool} (but contributions are welcome).
The variance of bootstrap estimator is\citep{SmithVanBelle84}:
\begin{multline}
\VAR(\hat f_0) = \sum_{i=1}^{S_o} q_i (1-q_i) \\ +2 \sum_{i \neq
j}^{S_o} \left[(Z_{ij}/N)^N - q_i q_j \right] \\
\text{where } q_i = (1-p_i)^N \, ,
\end{multline}
and $Z_{ij}$ is the number of sites where both species are absent.
The extrapolated richness values for the whole BCI data are:
<<>>=
specpool(BCI)
@
If the estimation of pool size really works, we should get the same
values of estimated richness if we take a random subset of a half of
the plots (but this is rarely true):
<<>>=
s <- sample(nrow(BCI), 25)
specpool(BCI[s,])
@
\subsection{Pool size from a single site}
The \code{specpool} function needs a collection of sites, but there
are some methods that estimate the number of unseen species for each
single site. These functions need counts of individuals, and species
seen only once or twice, or other rare species, take the place of
species with low frequencies. Function \code{estimateR} implements
two of these methods:
<<>>=
estimateR(BCI[k,])
@
In abundance based models $a_i$ denotes the number of species with $i$
individuals, and takes the place of $f_i$ of previous models.
Chao's method is similar as the bias-corrected model
eq.~\ref{eq:chao} \citep{Chao87, ChiuEtal14}:
\begin{equation}
\label{eq:chao-bc}
S_p = S_o + \frac{a_1 (a_1 - 1)}{2 (a_2 + 1)}\,.
\end{equation}
When $f_2=0$, eq.~\ref{eq:chao-bc} reduces to the bias-corrected form
of eq.~\ref{eq:chao}, but quantitative estimators are based on
abundances and do not use small-sample correction. This is not usually
needed because sample sizes are total numbers of individuals, and
these are usually high, unlike in frequency based models, where the
sample size is the number of sites \citep{ChiuEtal14}.
A commonly used approximate variance estimator of eq.~\ref{eq:chao-bc} is:
\begin{multline}
\label{eq:var-chao-bc}
s^2 = \frac{a_1(a_1-1)}{2} + \frac{a_1(2 a_1+1)^2}{(a_2+1)^2}\\
+ \frac{a_1^2 a_2 (a_1 -1)^2}{4 (a_2 + 1)^4} \,.
\end{multline}
However, \pkg{vegan} does not use this, but instead the following more
exact form which was directly derived from eq.~\ref{eq:chao-bc}
following \citet[web appendix]{ChiuEtal14}:
\begin{multline}
s^2 = \frac{1}{4} \frac{1}{(a_2+1)^4 S_p} [a_1 (S_p a_1^3
a_2 + 4 S_p a_1^2 a_2^2 \\+ 2 S_p a_1 a_2^3 + 6 S_p a_1^2 a_2 + 2 S_p
a_1 a_2^2 -2 S_p a_2^3 \\+ 4 S_p a_1^2 + S_p a_1 a_2 -5 S_p a_2^2 - a_1^3 - 2
a_1^2 a_2\\ - a_1 a_2^2 - 2 S_p a_1 - 4 S_p a_2 - S_p ) ]\,.
\end{multline}
The variance estimators only concern the number of unseen species like previously.
The \textsc{ace} is estimator is defined as \citep{OHara05}:
\begin{equation}
\begin{split}
S_p &= S_\mathrm{abund} + \frac{S_\mathrm{rare}}{C_\mathrm{ACE}} +
\frac{a_1}{C_\mathrm{ACE}} \gamma^2\, , \quad \text{where}\\
C_\mathrm{ACE} &= 1 - \frac{a_1}{N_\mathrm{rare}}\\
\gamma^2 &= \frac{S_\mathrm{rare}}{C_\mathrm{ACE}} \sum_{i=1}^{10} i
(i-1) a_1 \frac{N_\mathrm{rare} - 1}{N_\mathrm{rare}}\,.
\end{split}
\end{equation}
Now $a_1$ takes the place of $f_1$ above, and means the number of
species with only one individual.
Here $S_\mathrm{abund}$ and $S_\mathrm{rare}$ are the numbers of
species of abundant and rare species, with an arbitrary upper limit of
10 individuals for a rare species, and $N_\mathrm{rare}$ is the total
number of individuals in rare species. The variance estimator uses
iterative solution, and it is best interpreted from the source code or
following \citet{OHara05}.
The pool size
is estimated separately for each site, but if input is a data frame,
each site will be analysed.
If log-normal abundance model is appropriate, it can be used to
estimate the pool size. Log-normal model has a finite number of
species which can be found integrating the log-normal:
\begin{equation}
S_p = S_\mu \sigma \sqrt{2 \pi} \,,
\end{equation}
where $S_\mu$ is the modal height or the expected number of species at
maximum (at $\mu$), and $\sigma$ is the width. Function
\code{veiledspec} estimates this integral from a model fitted either
with \code{prestondistr} or \code{prestonfit}, and fits the latter
if raw site data are given. Log-normal model may fit poorly, but we
can try:
<<>>=
veiledspec(prestondistr(BCI[k,]))
veiledspec(BCI[k,])
@
\subsection{Probability of pool membership}
Beals smoothing was originally suggested as a tool of regularizing data
for ordination. It regularizes data too strongly,
but it has been suggested as a method of estimating which of the
missing species could occur in a site, or which sites are suitable for
a species. The probability for each species at each site is assessed
from other species occurring on the site.
Function \code{beals} implement Beals smoothing \citep{McCune87,
DeCaceresLegendre08}:
<<>>=
smo <- beals(BCI)
@
We may see how the estimated probability of occurrence and observed
numbers of stems relate in one of the more familiar species. We study
only one species, and to avoid circular reasoning we do not include
the target species in the smoothing (Fig.~\ref{fig:beals}):
<>=
j <- which(colnames(BCI) == "Ceiba.pentandra")
plot(beals(BCI, species=j, include=FALSE), BCI[,j],
ylab="Occurrence", main="Ceiba pentandra",
xlab="Probability of occurrence")
@
\begin{figure}
<>=
<>
@
\caption{Beals smoothing for \emph{Ceiba pentandra}.}
\label{fig:beals}
\end{figure}
\bibliography{vegan}
\end{document}