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\author{Robin K. S. Hankin\\Auckland University of Technology}
\Plainauthor{Robin K. S. Hankin}
\title{Introducing \pkg{untb}, an \proglang{R} Package For Simulating
Ecological Drift Under the Unified Neutral Theory of Biodiversity}
\Plaintitle{Introducing untb, an R Package For Simulating Ecological Drift
Under the Unified Neutral Theory of Biodiversity}
\Shorttitle{Ecological Drift With \proglang{R}}
%\VignetteIndexEntry{A vignette for the untb package}
\Abstract{
The distribution of abundance amongst species with similar ways of
life is a classical problem in ecology.
The {\em unified neutral theory of biodiversity}, due to Hubbell,
states that observed population dynamics may be explained on the
assumption of per capita equivalence amongst individuals. One can
thus dispense with differences between species, and differences
between abundant and rare species: all individuals behave alike in
respect of their probabilities of reproducing and death.
It is a striking fact that such a parsimonious theory results in a
non-trivial dominance-diversity curve (that is, the simultaneous
existence of both abundant and rare species) and even more striking
that the theory predicts abundance curves that match observations
across a wide range of ecologies.
Here I introduce the \pkg{untb} package of \proglang{R}
routines, for numerical simulation of ecological drift under the
unified neutral theory. A range of visualization, analytical, and
simulation tools are provided in the package and these are presented
with examples in the paper.
This vignette is based on~\cite{hankin2007a}. For reasons of
performance, some of the more computationally expensive results are
pre-loaded. To calculate them from scratch, change
``\code{calc\_from\_scratch <- TRUE}'' to ``\code{calc\_from\_scratch <-
FALSE}'' in chunk \code{time\_saver}.
}
\Keywords{unified neutral theory, neutral theory, biodiversity, island
biogeography}
\Plainkeywords{unified neutral theory, neutral theory, biodiversity, island
biogeography}
%% publication information
%% NOTE: Typically, this can be left commented and will be filled out by the technical editor
%% \Volume{13}
%% \Issue{9}
%% \Month{September}
%% \Year{2004}
%% \Submitdate{2004-09-29}
%% \Acceptdate{2004-09-29}
%% The address of (at least) one author should be given
%% in the following format:
\Address{
Robin K. S. Hankin\\
Auckland University of Technology\\
Wakefield Street\\
Auckland, New Zealand\\
E-mail: \email{hankin.robin@gmail.com}\\
}
%% It is also possible to add a telephone and fax number
%% before the e-mail in the following format:
%% Telephone: +43/1/31336-5053
%% Fax: +43/1/31336-734
%% for those who use Sweave please include the following line (with % symbols):
%% need no \usepackage{Sweave.sty}
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\SweaveOpts{echo=FALSE}
\begin{document}
\hfill\includegraphics[width=1in]{\Sexpr{system.file("help/figures/untb.png",package="untb")}}
<>=
<>=
require("untb",quietly=TRUE)
@
<>=
calc_from_scratch <- FALSE
@
<>=
if(calc_from_scratch){
set.seed(0)
rn <- rand.neutral(5e6, theta=50)
} else {
load("rn.Rdata")
}
@
\section{Island biogeography and biodiversity}
In the field of ecological dynamics, one simple system of interest is
that of similar species, at the same trophic level, that compete for
the same or similar resources. One might choose forest ecology as the
canonical example: trees are long-lived, stationary, and easily
identified. Determining the statistical properties of such systems is
a classical problem in ecology~\citep{bell2000}; the observed range of
abundances between apparently similar species does not have a ready
explanation.
Many studies involve the tabulation of individuals, collected
according to some formal sampling method, and it is a universal
finding that the species abundances exhibit a large range: the
majority of individuals are accounted for by a relatively small number
of species, while the rarest species typically are sufficiently rare
to have been sampled only once (`singletons').
\subsection{The unified neutral theory of biodiversity}
One natural null hypothesis which arises in this field is that
differences between species may be ascribed to random,
species-independent, processes. The origin of such ideas may be
traced back to at least the 1960s, in which \citet{macarthur1963}
discussed the observation that island faunas tend to become
progressively impoverished with distance from the nearest landmass.
Their insight was to treat the number of species on an island as the
{\em independent} variable. \cite{hubbell2001} considers that
\citeauthor{macarthur1963}'s theory raised the possibility that
``chance {\em alone} could play a \ldots role in structuring
ecological communities'' (my italics), thus paving the way for later,
more explicitly stochastic, theories.
In the context of theoretical studies, and in particular the
\pkg{untb} package, it is standard practice to consider a
metacommunity of fixed size~$\jm=\sum_{i=1}^S n_i$ individuals where the
abundance of the $i^\mathrm{th}$ species, $1\leqslant i\leqslant S$,
is~$n_i$. The evolution of the community is then modelled as a Markov
chain with one state change corresponding to the death of a single
individual, and the simultaneous birth of another individual of the
same or a different species. Because the community size is fixed, it
is common to refer to an individual being associated with a ``site'',
thus notionally carving the ecosystem resource into discrete units.
The transition matrix may thus be described,
following~\citet{macarthur1963,macarthur1967}, in terms of the
behaviour of the~$i^\mathrm{th}$ species:
\begin{equation}\label{neutral.markoff.chain}
n_i(t+1)=
\left\{
\begin{array}{ll}
n_i(t) -1 &\mbox{with probability~$p_{-1}$}\\
n_i(t) &\mbox{with probability~$p_{0}$}\\
n_i(t) +1 &\mbox{with probability~$p_{+1}$.}\\
\end{array}\right.
\end{equation}
The three probabilities~$\left(p_{-1},p_0,p_{+1}\right)$ may be
functions of the species~$i$, and the abundances~$n_i$ of each species
present. A theory is {\em species neutral} if it assumes that the
probabilities are independent of~$i$. Thus, in a species neutral
theory, all species are equivalent. Note that this model does not
preclude abundant species being at a competitive
disadvantage\footnote{One mechanism might be increased incidence of
parasitism among an abundant species; or a rare species might enjoy
the competitive advantage of dispensing with territoriality.} or
advantage\footnote{For example, an abundant species might gain a
competitive advantage from cooperative behaviour such as swarming;
or, conversely, a rare species might suffer a competitive
disadvantage due to the difficulty of finding a mate.}.
A theory is {\em neutral} (sometimes {\em unified neutral}) if it
assumes that all individuals in a community are ``strictly equivalent
regarding their prospects of reproduction and
death''~\citep{chave2005}; a neutral theory is thus species neutral.
One consequence of this assumption is that both~$p_B(i)$ and~$p_D(i)$,
the probabilities of a birth and death respectively being an
individual of species~$i$, are both proportional to~$n_i$, the number
of individuals of that species present in the ecosystem. Then we
have~$p_D(i)=p_B(i)$ for all~$i$, and hence~$p_{-1}=p_{+1}$. Thus the
number of individuals of species~$i$---and hence that of all
species---is a Martingale.
\cite{hubbell2001,hubbell1979} discusses, motivates, and assesses the
unified neutral theory of biodiversity (henceforth UNTB) and shows
that it makes a wide variety of quantitative predictions, all of which
appear to be statistically consistent with predictions---or to be
incorrect by only a small margin, as discussed by~\cite{volkov2003},
for example.
The neutral theory also provides a useful null model against which to
compare temporal data: \cite{leigh1993}, for example, use neutral
dynamics in this way and found evidence that tree diversity on newly
isolated tropical islands declines faster than expected by neutral
models, suggesting that the discrepancy may be explained by
non-neutral animal-plant interactions.
More recent work adduces subtle analyses that cast doubt on the
verisimilitude of the UNTB. For example, \citet{nee2005} shows that
the UNTB implies that the expected age of common lineages (of trees,
in his example) is ``impossibly old''; \citet{mcgill2006} present a
hierarchy of increasingly demanding statistical tests of the UNTB and
finds it lacking at the highest levels.
Nevertheless, the UNTB furnishes a broadly realistic mechanism for
reproduction and speciation~\citep{maurer2004}; it makes predictions
that are closer to field observations than one might expect for so
parsimonious a theory.
\cite{mcgill2006}, after rejecting the UNTB in favour of a
non-mechanistic alternative\footnote{The lognormal
distribution~\citep{volkov2003}}, state that the UNTB remains ``an
extraordinary and perhaps uniquely elegant ecological theory. Such
elegance is indicative that the neutral theory will have some
important role in ecology''. Similarly, \cite{nee2005} states that
the UNTB can still provide ``useful null models for the
interpretation of data''. It is in this spirit that I present
\pkg{untb}, an \proglang{R}~\citep{rcore2007} package that uses the
UNTB to simulate and analyze such datasets. The theory is then
applied to a number of oceanographic datasets.
In the context of computer simulations, it is possible to simulate
neutral ecological drift by the following algorithm. Consider an
system of~$\jm$ individuals, and a timestep sufficiently short for
only a single individual to die. Then: \label{intro}
\begin{enumerate}
\item Choose a site at random; the individual at this site dies.
\item With probability~$1-\nu$, an individual of species chosen at
random from the remaining population is born
\item With probability~$\nu$, an individual of a new species is born.
\end{enumerate}
Subsequent timesteps may be enacted using the new community as a pool
in step~2. The meaning of ``new'' species is subject to differing
interpretations: the new species might be the result of a point
mutation, or in the case of island biogeography, the successful
immigration of an individual from a metacommunity (typically the
mainland).
This system is not the only way to simulate neutral ecological drift:
it ignores, for example, changes in competitive advantage accruing to
older individuals. Also, alternative models of speciation may be
adopted, such as the random fission
model~\citep{ricklefs2003,hubbell2003}.
\subsection[Computational population ecology and the untb
package]{Computational population ecology and the \pkg{untb} package}
The \proglang{R} package \pkg{untb} associated with this paper may be
used to analyze ecosystem data in the context of Hubbell's neutral
theory. The package contains routines that summarize census data,
estimate biodiversity parameters, and generate synthetic datasets
under conditions of exact neutrality. In an \proglang{R} session,
typing {\tt library(help = "untb")} at the command line shows a list
of topics that are documented in the package.
It is envisaged that the untb package will be useful to ecologists in
both research and teaching. Many of the routines are intended to be
useful to teachers covering the neutral theory: a number of
visualization tools are included that display census datasets in a
consistent and standardized format; function \code{display.untb()}
displays a ``movie'' of a system undergoing neutral drift in a
visually striking, and hopefully memorable, manner.
Researchers will find useful functionality in the package, which can
perform a range of relevant analyses on real data. It is hoped that
the package will avoid other workers having to `reinvent the wheel' by
virtue of having all of these features together in a unified, clearly
described framework implemented in an open-source software
environment.
\section[Package untb in use]{Package \pkg{untb} in use}
\subsection{Analysis of species abundance data}
Consider the \code{saunders} dataset, which lists species abundances
of various types of marine animals living in kelp
holdfasts~\citep{saunders2007,anderson2005}. The dataset may be
examined using the functionality of \pkg{untb}:
<>=
data("saunders")
summary(saunders.tot)
@
Thus the \code{saunders.tot} dataset
comprises~\Sexpr{no.of.ind(saunders.tot)} individuals
of~\Sexpr{no.of.spp(saunders.tot)} species, of
which~\Sexpr{no.of.singletons(saunders.tot)} are
``singletons''---that is, species sufficiently rare that they have
only one representative in the entire sample. Further details of the
data are given by function \code{preston()}, which carries out the
analysis of~\cite{preston1948}:
<>=
preston(saunders.tot,n=9)
@
<>=
jj.preston <- preston(saunders.tot)
jj.summary <- summary(saunders.tot)
@
Thus there are~\Sexpr{jj.preston[1]} species with abundance 1 (that
is, singletons); \Sexpr{jj.preston[2]} species with abundance~2;
\Sexpr{jj.preston[3]} species with abundance 3-4;
\Sexpr{jj.preston[4]} species with abundance~5-8, and so on.
A more graphical representation of the same dataset is given in
figure~\ref{ranked.abundance.curve}, which shows the ranked abundance
of each species in red on the logarithmic vertical axis. The highest
red dot thus corresponds to \Sexpr{jj.summary[[5]]} with
\Sexpr{jj.summary[[4]]} individuals. On this figure, grey lines show
synthetic datasets generated using the maximum likelihood estimate
for~$\theta$ (section~\ref{parameter.estimation}).
<>=
n <- 10
J <- no.of.ind(saunders.tot)
unc <- list()
theta_hat <- optimal.theta(saunders.tot)
for(i in 1:n){
unc[[i]] <- rand.neutral(J=J, theta=theta_hat)
}
@
\begin{figure}[htbp]
\begin{center}
<>=
plot(saunders.tot,uncertainty=FALSE)
for(i in 1:n){
points(seq_along(unc[[i]]),unc[[i]],type="l",col="grey")
}
@
\caption{The\label{ranked.abundance.curve} ranked abundance curve of
the Saunders dataset showing real data (red) and~10 simulated ranked
abundance curves, generated randomly using \code{rand.neutral()}
using the maximum likelihood estimate for $\theta$ (grey)}
\end{center}
\end{figure}
\subsection{Estimation of parameters}
\label{parameter.estimation}
One distinguishing feature of Hubbell's unified neutral theory is that
it provides a mechanism for speciation: the algorithm shown in
section~\ref{intro} specifies that a birth of an individual will be a
new species with probability~$\nu$. Although~$\nu$ is small and $\jm$
large, \citeauthor{hubbell2001} (\citeyear[page 116]{hubbell2001})
observes that their product is a `moderately sized number'. The
Fundamental Biodiversity parameter~$\theta$, defined as
$\theta=2\jm\nu$, arises naturally when investigating neutral ecological
drift and in the present context may be regarded as a parameter
susceptible to statistical inference. If~$\phi_a$ is the number of
species with abundance~$a$, the ``Ewens sampling formula''
\citep{ewens1972} gives the probability of observing a
particular species abundance distribution (SAD) as
\begin{equation}\label{hubbellpage122}
\mathrm{Pr}\left\{S, n_1,n_2,\ldots,n_S|\theta\right\}=
\frac{\jm!\theta^S}{1^{\phi_1}2^{\phi_2}\cdots\jm^{\phi_{\jm}}\,
\phi_1!\phi_2!\cdots\phi_{\jm}!\,
\prod_{k=1}^{\jm}\left(\theta+k-1\right)}
\end{equation}
[\code{theta.prob()} in the package] from which it is clear that the
number of species~$S$ occurring in a fixed sample size of~$\jm$
individuals is a sufficient statistic for~$\theta$, and optimizing the
likelihood~$\mathcal{L}$ given as
\begin{equation}
\mathcal{L}=\frac{\theta^S}{\prod_{k=1}^{\jm}\theta+k-1}
\end{equation}
[\code{theta.likelihood()} in the package] over positive values
thus furnishes a maximum likelihood estimate for~$\theta$. Function
\code{optimal.theta()} carries out this procedure numerically:
<>=
optimal.theta(saunders.tot)
@
The accuracy of this estimate may be assessed by examining the
likelihood curve~\citep{alonso2004}; figure~\ref{theta.likelihood}
shows the support ($\log\mathcal{L}$) for a range of $\theta$. Using
an exchange rate of two units of support per degree of freedom
\citep{edwards1992} suggests that the true value of~$\theta$ lies in
the range 26-37.
\begin{figure}[htbp]
\begin{center}
<>=
S <- no.of.spp(saunders.tot)
J <- no.of.ind(saunders.tot)
theta <- seq(from=25,to=39,len=55)
jj <- theta.likelihood(theta=theta,S=S,J=J,give.log=TRUE)
support <- jj-max(jj)
plot(theta,support,xlab=paste("Biodiversity parameter",expression(theta)),ylab="support")
abline(h= -2)
@
\caption{The\label{theta.likelihood} support curve for the
Saunders dataset as a function of the Fundamental Biodiversity
Parameter $\theta$}
\end{center}
\end{figure}
\subsubsection{Estimating the probability of immigration: The Etienne sampling formula}
Neutral theories may allow for the possibility of restricted
immigration to, for example, islands. Consider a partially isolated
ecosystem of~$J$ individuals, and a neutral metacommunity of~$\jm$
individuals and biodiversity parameter~$\theta$. In the algorithm
presented in section~\ref{intro}, reinterpret the birth of a new
species (an event with probability~$\nu$) as the arrival of an
organism from the metacommunity. This probability is conventionally
labelled~$m$; the probability of mutation on the island is negligible.
It is often of great interest to estimate~$m$; \citet{etienne2005}
shows that the probability of a given set of abundances~$D$ is just
equation~\ref{hubbellpage122}, modified by a factor that is a
complicated function of~$m$, the species abundances, and~$\theta$.
\citeauthor{etienne2005}'s sampling probability is given by function
\code{etienne()} in the package, and is maximized by function
\code{optimal.params()}.
From a numerical perspective, the Etienne sampling formula is
challenging: the intermediate steps involved in the calculation of
Etienne's $K(D,A)$ [\code{logkda()} in the package] involve very large
numbers. Standard IEEE floating-point arithmetic, being unable to
represent numbers larger than about~$1.7\times 10^{308}$, restricts
one to~$J\lesssim 100$. The \pkg{untb} package circumvents this
restriction in two ways: one can use either the logarithmic
representation employed by the \pkg{Brobdingnag}
package~\citep{hankin2007}; or the \proglang{PARI}/\proglang{GP}
system~\citep{batut2006}. These options are controlled by setting a
flag in function \code{logkda()}; full documentation is given in the
online help page.
\subsubsection{The Barro Colorado dataset}
The BCI dataset~\citep{condit2005}---a standard resource for
ecologists~\citep{hubbell2001,etienne2005}--- contains location and
species identity for all trees of~$\geqslant 10\,{\rm cm}$ diameter at
breast height on Barro Colorado Island for a number of years. This
dataset was used in~\citet{hankin2007a} but is not included in the
\pkg{untb} package (and therefore not used in this vignette), because
its licence appears to be inconsistent with the GPL. Further details
may be found in the package's online documentation file {\tt bci.Rd}.
\subsubsection{Estimation of parameters from field data}
Some of the difficulties of estimating~$\theta$ and~$m$ from field
data are shown in Figure~\ref{mle.theta}, which shows three ensembles
of maximum likelihood estimates for local communities of size
$J=100,1000,10000$, partially isolated ($m=0.01$) from a metacommunity
of size~$\jm=5\times 10^{6}$ with~$\theta=50$. The local communities
were allowed to evolve for sufficiently many timesteps for the results
to be unaffected by further
simulation~\citep{chisholm2004,hubbell2004}. Note the ensemble behaviour of
the maximum likelihood estimator: the
estimates are severely biased, especially for the smaller sample sizes. In general, $\theta$ is
underestimated, while~$m$ is overestimated.
Even with the largest local community, $\E(\hat{\theta})=40.9$
and~$\E(\hat{m})=0.047$, showing under- and over-estimation respectively.
<>=
f <- function(local_size,gens){
jj <- isolate(rn,size=local_size)
a <- untb(start=jj, prob=0.01, D=local_size, gens=gens, meta=rn)
optimal.params(a)
}
if(calc_from_scratch){
x100 <- t(replicate(100,f(100 ,999)))
x1000 <- t(replicate(100,f(1000 ,999)))
x10000 <- t(replicate(100,f(10000 ,999)))
} else {
load("dat.Rdata")
x100 <- dat[,,1]
x1000 <- dat[,,2]
x10000 <- dat[,,3]
}
@
\begin{figure}[htbp]
\begin{center}
<>=
plot(x100,log="xy",xlim=c(1,80),ylim=c(0.001,1),col="black",pch=1,
main=paste("Maximum likelihood estimates of ", expression(m), " and ",expression(theta))
)
points(x1000,col="red",pch=2)
points(x10000,col="blue",pch=3)
points(50,0.01,pch=4,lwd=3,cex=2)
legend( "bottomleft", c("100","1000","10000"),col=c("black","red","blue"), pch=1:3, title="Local community size")
@
\caption{Ensemble of~100 maximum\label{mle.theta} likelihood estimates
of~$m$ and~$\theta$ for three different sizes of local community.
Here, the metacommunity is of size~$\jm=5\times 10^6$, the
biodiversity parameter~$\theta$ is~50, and the immigration
probability~$m$ is~0.01. The large diagonal cross corresponds to
the true values $(50,0.01)$. Note the systematic bias present for
each~$J$, being most severe for the smallest ($J=100$; black
circles)}
\end{center}
\end{figure}
In particular, note that many of the ensembles give rise to estimates
for~$m$ very close to~$1$. This represents an incorrect assessment of
(almost) no dispersal limitation, as in fact~$m=0.01$. The
probability of~$\hat{m}$ being greater than, say, 0.95 decreases with
increasing local community size~$J$ but is definitely appreciable even
when~$J=10000$. In cases
where~$\hat{m}\simeq 1$, inspection of the likelihood surface in
the~$\left(\theta,m\right)$ plane shows that there is no extremum
(that is, $\partial{\mathcal L}/\partial\theta=\partial{\mathcal
L}/\partial m=0$) anywhere in the half-plane~$m\leqslant 1$. Such
cases are challenging for the method of maximum likelihood because the
numerically determined maximum will fall on the line~$m=1$.
In practice, this means that~$\hat{m}$ being close to 1 is not
inconsistent with a much smaller value of~$m$ unless~$J\gg 10000$, at
least for~$\theta\sim 50$. Such considerations may be relevant to any
work in which~$\hat{m}\simeq 1$; rather than
using~$\left(\hat{\theta},\hat{m}\right)$ as a basis for making
inferences, one might adopt the Bayesian approach and use the
posterior joint distribution of~$\left(\theta,m\right)$.
\subsection[Exact combinatorial analysis for small $J_M$]{Exact
combinatorial analysis for small $\boldmath{J}_{\!\boldmath{M}}$}
The neutral theory may be used to produce various analytical results,
but many are tractable only for small~$\jm$; here I determine exact
expected abundances for~$\jm=20$. \citeauthor{hubbell2001}
(\citeyear[page 122]{hubbell2001}) shows that the expected abundance
of the $i^\mathrm{th}$ most abundant species in a sample is
\begin{equation}
\sum_{k=1}^C r_i(k)\mathrm{Pr}\left\{
\underbrace{S,r_1,\ldots,r_S,0,\ldots ,0}_{\jm}
\right\}
\end{equation}
where~$r_i(k)$ is the abundance of the $i^\mathrm{th}$ species under
configuration~$k$, and summation extends over partitions of~$\jm$ in the
sense of~\cite{hankin2006} (\citeauthor{hubbell2001}'s
``configurations'' are our ``partitions''). Function
\code{expected.abundance()} in the package calculates this, as shown
in Figure~\ref{exp.ab} which plots expected abundance as a function of
species rank~$R$. The very small expected abundances for, say,
$R>10$, are consistent with the overwhelming majority of $\jm=20$,
$\theta=2$ ecosystems comprising fewer than 11 species (thus giving
any species with $R>10$ a zero abundance). As an extreme case, the
value of about~$10^{-11}$ for~$R=20$ is the probability of the
ecosystem comprising only singletons.
<>=
n <- 20
x <- expected.abundance(J=n, theta=3)
e.low <- expected.abundance(J=n,theta=4)
e.high <- expected.abundance(J=n,theta=2)
@
\begin{figure}[htbp]
\begin{center}
<>=
plot(x)
segments(x0=1:n,x1=1:n,y0=e.low,y1=e.high)
@
\caption{The\label{exp.ab} expected abundances for the
$i^{\mathrm{th}}$ ranked species with $\jm=20$ and~$\theta=3$ for
$1\leqslant i\leqslant 20$. Red dots show exact expected abundances
and the vertical black lines show the range
if~$2\leqslant\theta\leqslant 4$.}
\end{center}
\end{figure}
The abundance of each ranked species will exhibit variance; it is
possible to build up a histogram of the abundance of, say, the third
ranked species by repeatedly generating a neutral ecosystem and
recording the abundance of the third most abundant species amongst
the~$\jm$ individuals present. Figure~\ref{hist.abund} shows such a
histogram generated, again using~$\jm=20$ and~$\theta=2$. Note the
absence of zero abundance: in none of the~1000 replicates did the
ecosystem comprise only two species (thus rendering the third ranked
species nonexistent). Also note the impossibility of it being~7 or
greater: if the third ranked species had abundance~7, then the first
and second ranked species would have abundances~$\geqslant 7$, so the
total number of individuals would exceed~20.
<>=
rank3 <- table(replicate(1000,rand.neutral(J=20,theta=2)[3]))
@
\begin{figure}[htbp]
\begin{center}
<>=
plot(rank3,xlab="abundance of third ranked species",ylab="frequency")
@
\caption{Abundance of third\label{hist.abund} ranked species of an
ecosystem of $\jm=20$ individuals and a biodiversity parameter
$\theta=2$: 1000 replicates}
\end{center}
\end{figure}
\subsection{Creation and analysis of synthetic datasets}
Package \pkg{untb} includes a number of functions that generate
synthetic datasets in the context of visualization or verification of
predictions of the neutral theory.
Function \code{untb()} generates random neutral ecosystems using the
system specified in equations~\ref{neutral.markoff.chain}. A sample
output is shown graphically in Figure~\ref{punctuated.equilibrium},
which exhibits sudden overturning events during which the dominant
species suffers a drastic reduction in abundance, to be replaced by a
different species. This is a direct numerical verification of the
neutral theory's prediction of punctuated
equilibrium~\citep[page~233]{hubbell2001}.
<>=
if(calc_from_scratch){
set.seed(0);
synthetic.spp <- species.table(untb(start=rep(1,60),prob=0.002, gens=40000, keep=TRUE))
} else {
load("synthetic_spp.Rdata")
}
@
\begin{figure}[htb]
\begin{center}
<>=
plot(1:10,xlim=c(0,40000),ylim=c(0,60),type="n",xlab="time (generation)",ylab="abundance")
"showabundance" <- function(x, ...){
jj <- rle(x)
x <- cumsum(jj$lengths)
y <- jj$values
segments(x0=c(1,x),y0=y,x1=c(x,x[length(x)]),y1=y, ...) #horizontal ones
segments(x0=x[-length(x)],y0=y[-1],x1=x[-length(x)],y1=y[-length(y)], ...) # vertical ones
}
showabundance(synthetic.spp[,1],col="black")
showabundance(synthetic.spp[,2],col="red")
showabundance(synthetic.spp[,3],col="green")
showabundance(synthetic.spp[,4],col="blue")
showabundance(synthetic.spp[,5],col="cyan")
showabundance(synthetic.spp[,6],col="magenta")
if(FALSE){matplot(synthetic.spp,type="l",lty=1,xlab="time (generation)",ylab="abundance")}
@
\caption{Synthetic dataset generated using neutral
dynamics.\label{punctuated.equilibrium} Lines show the abundance of
each species in time; different colours correspond to different
(equivalent) species. A sudden displacement of the initially
monodominant species (black line) with another species (red line) occurs
at $t\simeq 17000$; a similar overturning event occurs at around time
35000}
\end{center}
\end{figure}
Function~\code{display.untb()} displays an animation of successive
iterations of function \code{untb()} using different coloured dots to
represent the different species; Figure~\ref{display.untb} shows
stills from this function at various points in time under differing
values of~$\theta$. Take the top row, which uses~\mbox{$\theta=0$}.
Here, species count decreases monotonically\footnote{Hubbell shows
that the probability of having just one species tends to 1 as
time~$t\longrightarrow\infty$ for~$\theta=0$. Running the
\code{display.untb()} function illustrates just how long it takes to
get to a monoculture.}, with only three species remaining
at~$t=100$. The other rows show an equilibrium being approached, the
species richness of which depends on magnitude of $\theta$. Also note
the first column, showing the the ecosystem after~$t=20$: the four
plots are visually indistinguishable, illustrating the difficulty of
estimating~$\theta$ in ecosystems far from equilibrium (after a
disturbance, for example).
\begin{figure}[htbp]
\begin{center}
<>=
set.seed(0)
f <- function(gens,p){
display.untb(start=sample(as.census(untb(start=1:100,gens=gens,D=100,prob=p))),gens=0,main="",cex=1.7, asp=1)
}
g <- function(u="title", ...){
par(srt=0)
par(mai=c(0,0,0,0))
plot.new()
text(0.5,0.5,u,...)
}
h <- function(u="title", ...){
par(mai=c(0,0,0,0))
par(srt=90)
plot.new()
text(0.5,0.5,u, ...)
}
nf <- layout(matrix(
c(00,01,00,02,00,03,
04,05,00,06,00,07,
00,00,00,00,00,00,
08,09,00,10,00,11,
00,00,00,00,00,00,
12,13,00,14,00,15,
00,00,00,00,00,00,
16,17,00,18,00,19),8,6,byrow=TRUE),
c(1,4, 1,4, 1,4),
c(1,4, 1,4, 1,4, 1,4),
TRUE)
g(expression(t==10))
g(expression(t==50))
g(expression(t==100))
h(expression(theta==0))
f(10,0)
f(50,0)
f(100,0)
h(expression(theta==0.1))
f(10,0.001)
f(50,0.001)
f(100,0.001)
h(expression(theta==1))
f(10,0.01)
f(50,0.01)
f(100,0.01)
h(expression(theta==10))
f(10,0.1)
f(50,0.1)
f(100,0.1)
@
\caption{Simulated ecosystems of size~$\jm=100$ for varying
parameters, \label{display.untb} with each organism represented by a
dot, the different colours representing different species; spatial
locations are functionally identical. Initial conditions are a
system of maximal diversity (i.e. 100 singletons)}
\end{center}
\end{figure}
\clearpage
\section{Conclusions}
This paper presents Hubbell's Unified Neutral Theory of Biodiversity %'
from a computational ecology perspective, and introduces the
\pkg{untb} package of \proglang{R} routines for numerical simulation
of neutral ecosystem dynamics, analysis of field data, and
visualization of real and synthetic datasets.
Examples taken from oceanographic datasets are presented and
discussed using the package. A number of synthetic datasets are
generated and analyzed using the package's simulation functions and %'
analyzed using the visualization suite.
\section*{Acknowledgements}
I would like to acknowledge the many stimulating and helpful comments
made on the \proglang{R}-help list while preparing this paper; and the
helpful reviews of two anonymous referees which considerably improved
the clarity of an earlier draft. The manuscript was prepared with the
help of the \pkg{weaver} package~\citep{falcon2007}.
\bibliography{untb}
\end{document}