Introduction
This Task View contains information about using R to analyse ecological and environmental data.
The base version of R ships with a wide range of functions for use within the field of environmetrics.
This functionality is complemented by a plethora of packages available via CRAN, which provide specialist
methods such as ordination & cluster analysis techniques. A brief overview of the available packages is
provided in this Task View, grouped by topic or type of analysis. As a testament to the popularity of R for the
analysis of environmental and ecological data, a
special volume
of
the
Journal of Statistical Software
was produced in 2007.
Those useRs interested in environmetrics should consult the
Spatial
view.
Complementary information is also available in the
Multivariate,
Phylogenetics,
Cluster, and
SpatioTemporal
task views.
If you have any comments or suggestions for additions or improvements, then please contact the
maintainer
.
A list of available packages and functions is presented below, grouped by analysis type.
General packages
These packages are general, having wide applicability to the environmetrics field.
Modelling species responses and other data
Analysing species response curves or modeling other data often involves the fitting of standard statistical models
to ecological data and includes simple (multiple) regression, Generalised Linear Models (GLM), extended regression
(e.g. Generalised Least Squares [GLS]), Generalised Additive Models (GAM), and mixed effects models, amongst
others.

The base installation of R provides
lm()
and
glm()
for fitting linear and generalised
linear models, respectively.

Generalised least squares and linear and nonlinear mixed effects models extend the simple regression model
to account for clustering, heterogeneity and correlations within the sample of observations. Package
nlme
provides functions for fitting these models. The package is supported by Pinheiro & Bates (2000)
Mixedeffects Models in S and SPLUS
, Springer, New York. An updated approach to mixed effects models,
which also fits Generalised Linear Mixed Models (GLMM) and Generalised nonLinear Mixed Models (GNLMM) is provided
by the
lme4
package, though this is currently beta software and does not yet allow correlations within
the error structure.

Recommended package
mgcv
fits GAMs and Generalised Additive Mixed Models (GAMM) with
automatic smoothness selection via generalised crossvalidation. The author of
mgcv
has
also written a companion monograph, Wood (2006)
Generalized Additive Models; An Introduction with R
Chapman Hall/CRC, which has an accompanying package
gamair.

Alternatively, package
gam
provides an implementation of the SPLUS function
gam()
that
includes LOESS smooths.

Proportional odds models for ordinal responses can be fitted using
polr()
in the
MASS
package, of Bill Venables and Brian Ripley.

A negative binomial family for GLMs to model overdispersion in count data is available in
MASS.

Models for overdispersed counts and proportions

Package
pscl
also contains several functions for dealing with overdispersed count data. Poisson or
negative binomial distributions are provided for both zeroinflated and hurdle models.

aod
provides a suite of functions to analyse overdispersed counts or proportions, plus utility
functions to calculate e.g. AIC, AICc, Akaike weights.

Detecting change points and structural changes in parametric models is well catered for in the
segmented
package and the
strucchange
package respectively.
segmented
has recently been
the subject of an R News article (
R News, volume 8 issue 1
).
Treebased models
Treebased models are being increasingly used in ecology, particularly for their ability to fit flexible models to
complex data sets and the simple, intuitive output of the tree structure. Ensemble methods such as bagging, boosting and
random forests are advocated for improving predictions from treebased models and to provide information on uncertainty
in regression models or classifiers.
Treestructured models for regression, classification and survival analysis, following the ideas in the CART book,
are implemented in

recommended package
rpart

party
provides an implementation of conditional inference trees which embed treestructured regression
models into a well defined theory of conditional inference procedures
Multivariate trees are available in

package
party
can also handle multivariate responses.
Ensemble techniques for trees:

The Random Forest method of Breiman and Cutler is implemented in
randomForest, providing classification
and regression based on a forest of trees using random inputs

Package
ipred
provides functions for improved predictive models for classification, regression and
survival problems.
Graphical tools for the visualization of trees are available in package
maptree.
Packages
mda
and
earth
implement Multivariate Adaptive Regression Splines (MARS), a technique
which provides a more flexible, treebased approach to regression than the piecewise constant functions used in
regression trees.
Ordination
R and addon packages provide a wide range of ordination methods, many of which are specialised techniques
particularly suited to the analysis of species data. The two main packages are
ade4
and
vegan.
ade4
derives from the traditions of the French school of
Analyse des Donnees
and is based on the use of the duality diagram.
vegan
follows
the approach of Mark Hill, Cajo ter Braak and others, though the implementation owes more to that presented in
Legendre & Legendre (1988)
Numerical Ecology, 2
^{
nd
}
English Edition
, Elsevier. Where the
two packages provide duplicate functionality, the user should choose whichever framework that best suits their
background.

Principal Components (PCA) is available via the
prcomp()
function.
rda()
(in package
vegan),
pca()
(in package
labdsv) and
dudi.pca()
(in
package
ade4), provide more ecologicallyorientated implementations.

Redundancy Analysis (RDA) is available via
rda()
in
vegan
and
pcaiv()
in
ade4.

Canonical Correspondence Analysis (CCA) is implemented in
cca()
in both
vegan
and
ade4.

Detrended Correspondence Analysis (DCA) is implemented in
decorana()
in
vegan.

Principal coordinates analysis (PCO) is implemented in
dudi.pco()
in
ade4,
pco()
in
labdsv,
pco()
in
ecodist, and
cmdscale()
in package
MASS.

NonMetric multiDimensional Scaling (NMDS) is provided by
isoMDS()
in package
MASS
and
nmds()
in
ecodist.
nmds(), a wrapper function for
isoMDS(),
is also provided by package
labdsv.
vegan
provides helper function
metaMDS()
for
isoMDS(), implementing random starts of the algorithm and standardised scaling of the NMDS results.
The approach adopted by
vegan
with
metaMDS()
is the recommended approach for ecological
data.

Coinertia analysis is available via
coinertia()
and
mcoa(), both in
ade4.

Cocorrespondence analysis to relate two ecological species data matrices is available in
cocorresp.

Canonical Correlation Analysis (CCoA  not to be confused with CCA, above) is available in
cancor()
in standard package stats.

Procrustes rotation is available in
procrustes()
in
vegan
and
procuste()
in
ade4, with both
vegan
and
ade4
providing functions to test the significance of
the association between ordination configurations (as assessed by Procrustes rotation) using permutation/randomisation
and Monte Carlo methods.

Constrained Analysis of Principal Coordinates (CAP), implemented in
capscale()
in
vegan,
fits constrained ordination models similar to RDA and CCA but with any any dissimilarity coefficient.

Constrained Quadratic Ordination (CQO; formerly known as Canonical Gaussian Ordination (CGO)) is a maximum likelihood
estimation alternative to CCA fit by Quadratic Reduced Rank Vector GLMs. Constrained Additive Ordination (CAO) is a
flexible alternative to CQO which uses Quadratic Reduced Rank Vector GAMs. These methods and more are provided in
Thomas Yee's
VGAM
package.

Fuzzy set ordination (FSO), an alternative to CCA/RDA and CAP, is available in package
fso.
fso
complements a recent paper on fuzzy sets in the journal
Ecology
by Dave Roberts (2008, Statistical analysis of
multidimensional fuzzy set ordinations.
Ecology
89(5)
, 12461260).

See also the
Multivariate
task view for complementary information.
Dissimilarity coefficients
Much ecological analysis proceeds from a matrix of dissimilarities between samples. A large amount of effort has
been expended formulating a wide range of dissimilarity coefficients suitable for ecological data. A selection of
the more useful coefficients are available in R and various contributed packages.
Standard functions that produce, square, symmetric matrices of pairwise dissimilarities include:

dist()
in standard package stats

daisy()
in recommended package
cluster

vegdist()
in
vegan

dsvdis()
in
labdsv

Dist()
in
amap

distance()
in
ecodist

a suite of functions in
ade4

Package
simba
provides functions for the calculation of similarity and multiple plot similarity
measures with binary data (for instance presence/absence species data)
Function
distance()
in package
analogue
can be used to calculate dissimilarity between samples
of one matrix and those of a second matrix. The same function can be used to produce pairwise dissimilarity matrices,
though the other functions listed above are faster.
distance()
can also be used to generate
matrices based on Gower's coefficient for mixed data (mixtures of binary, ordinal/nominal and continuous variables).
Function
daisy()
in package
cluster
provides a faster implementation of Gower's coefficient for
mixedmode data than
distance()
if a standard dissimilarity matrix is required. Function
gowdis()
in package
FD
also computes Gower's coefficient and impliments extensions to ordinal variables.
Cluster analysis
Cluster analysis aims to identify groups of samples within multivariate data sets. A large range of
approaches to this problem have been suggested, but the main techniques are hierarchical cluster analysis,
partitioning methods, such as
k
means, and finite mixture models or modelbased clustering. In the machine
learning literature, cluster analysis is an unsupervised learning problem.
The
Cluster
task view provides a more detailed discussion of available cluster analysis methods and
appropriate R functions and packages.
Hierarchical cluster analysis:

hclust()
in standard package stats

Recommended package
cluster
provides functions for cluster analysis following the methods
described in Kaufman and Rousseeuw (1990)
Finding Groups in data: an introduction to cluster analysis
,
Wiley, New York

hcluster()
in
amap

pvclust
is a package for assessing the uncertainty in hierarchical cluster analysis. It provides
approximately unbiased
p
values as well as bootstrap
p
values.
Partitioning methods:

kmeans()
in stats provides
k
means clustering

cmeans()
in
e1071
implements a fuzzy version of the
k
means algorithm

Recommended package
cluster
also provides functions for various partitioning methodologies.
Mixture models and modelbased cluster analysis:

mclust
and
flexmix
provide implementations of modelbased cluster analysis.

prabclus
clusters a species presenceabsence matrix object by calculating an
MDS
from the distances, and applying maximum likelihood Gaussian
mixtures clustering to the MDS points. The maintainer's, Christian Hennig, web site contains several publications in
ecological contexts that use
prabclus, especially Hausdorf & Hennig (2007;
Oikos 116 (2007), 818828
).
Ecological theory
There is a growing number of packages and books that focus on the use of R for theoretical ecological models.

vegan
provides a wide range of functions related to ecological theory, such as diversity indices
(including the
socalled
Hill's numbers [e.g. Hill's N
^{
2
}
] and rarefaction), ranked abundance diagrams,
Fisher's log series, Broken Stick model, Hubbell's abundance model, amongst others.

The
vegetarian
provides the diversity measures suggested by Jost
(
2006, Oikos 113(2), 363375
;
2007, Ecology 88(10), 24272439
).

untb
provides a collection of utilities for biodiversity data, including the simulation ecological drift
under Hubbell's Unified Neutral Theory of Biodiversity, and the calculation of various diagnostics such as Preston
curves.

primer
is a support software for Stevens
(
2009,
A Primer of Ecology with R
,
Springer
). The package provides a variety of functions for modeling ecological data and basic theoretical ecology,
including functions related to demographic matrix models, metapopulation and sourcesink models, hostparasitoid and
disease models, multiple basins of attraction, the storage effect, neutral theory, and diversity partitioning.

Package
BiodiversityR
provides a GUI for biodiversity and community ecology analysis.

Function
betadiver()
in
vegan
implements all of the diversity indices reviewed in
Koleff et al (2003;
Journal of
Animal Ecology 72(3), 367382
).
betadiver()
also provides a
plot
method to produce the cooccurrence frequency triangle plots
of the type found in Koleff et al (2003).

Function
betadisper(), also in
vegan, implements Marti Anderson's distancebased test for
homogeneity of multivariate dispersions (PERMDISP, PERMDISP2), a multivariate analogue of Levene's test (Anderson
2006;
Biometrics 62,
245253
). Anderson et al (2006;
Ecology Letters 9(6), 683693
)
demonstrate the use of this approach for measuring beta diversity.

The
FD
package computes several measures of functional diversity indices from multiple traits.
Population dynamics
Estimating animal abundance and related parameters
This section concerns estimation of population parameters (population size, density, survival probability, site occupancy
etc.) by methods that allow for incomplete detection. Many of these methods use data on marked animals, variously called
'capturerecapture', 'markrecapture' or 'capturemarkrecapture' data.

Rcapture
fits loglinear models to estimate population size and survival rate from capturerecapture data as
described by
Baillargeon and Rivest (2007)
.

mra
estimates survival by fitting the CormackJollySeber open population model, using a flexible formulabased
approach for covariates (see also RMark).
mra
also fits Huggin's closed population model and computes HorvitzThompson
estimates of openpopulation size from CJS models.

secr
estimates population density given spatially explicit capturerecapture data from traps, passive DNA
sampling, automatic cameras, sound recorders etc. Models are fitted by maximum likelihood. The detection function may be
halfnormal, exponential, cumulative gamma etc. Density surfaces may be fitted. Covariates of density and detection parameters are
specified via formulae.

SPACECAP
provides a graphical interface for fitting a spatially explicit capturerecapture model to photographic
'capture' data by the Bayesian method described in Royle et al. (
2009,
Ecology 90: 32333244
).

DSpat
provides analyses of linetransect distance sampling data in which the density surface and the detection function
are estimated simultaneously (
Johnson
et al. 2009
).

unmarked
fits hierarchical models of occurrence and abundance to data collected on species subject to imperfect detection.
Examples include single and multiseason occupancy models, binomial mixture models, and hierarchical distance sampling models. The data
can arise from survey methods such temporally replicated counts, removal sampling, doubleobserver sampling, and distance sampling.
Parameters governing the state and observation processes can be modeled as functions of covariates.

Package
RMark
provides a formulabased R interface for the MARK package which fits a wide variety of capturerecapture
models. See the
RMark website
and a
NOAA report
(pdf) for further details.

Package
marked
provides a framework for handling data and analysis for markrecapture.
marked
can fit CormackJollySeber (CJS)and JollySeber (JS) models via maximum likelihood and the CJS model via MCMC. Maximum likelihood estimates for the CJS model can be obtained using R or via a link to the Automatic Differentiation Model Builder software. A
description of the package
was published in Methods in Ecology and Evolution.

mrds
fits detection functions to point and line transect distance sampling survey data (for both single and double observer surveys). Abundance can be estimated using HorvitzThompsontype estimators.

Distance
is a simpler interface to
mrds
for single observer distance sampling surveys.

dsm
fits
density surface models
to spatiallyreferenced distance sampling data. Count data are corrected using detection function models fitted using
mrds
or
Distance. Spatial models are constructed as in
mgcv.
Packages
secr
and
DSpat
can also be used to simulate data from their respective models.
See also the
SpatioTemporal
task view for analysis of animal tracking data under
Moving objects, trajectories
.
Modelling population growth rates:

Package
popbio
can be used to construct and analyse age or stagespecific matrix population models.
Environmental time series

Time series objects in R are created using the
ts()
function, though see
tseries,
zoo
and
its
below for alternatives.

Classical time series functionality is provided by the
ar(), and
arima()
functions in
standard package stats for autoregressive (AR), moving average (MA), autoregressive moving average (ARMA) and
integrated ARMA (ARIMA) models.

The
forecast
package provides methods and tools for displaying and analysing univariate time series
forecasts including exponential smoothing via state space models and automatic ARIMA modelling

The
dse
package provide a variety of more advanced estimation methods
and multivariate time series analysis.

Packages
tseries
and
zoo
provide general handling and analysis of time series data.

Irregular time series can be handled using packages
zoo
and
its, as well as by
irts()
in package
tseries.

pastecs
provides functions specifically tailored for the analysis of spacetime ecological series.

strucchange
allows for testing, dating and monitoring of structural change in linear regression
relationships.

Detecting change points in time series data  see
segmented
above
.

The
surveillance
package implements statistical methods for the modeling of and changepoint detection
in time series of counts, proportions and categorical data. Focus is on outbreak detection in count data time series.

Package
dynlm
provides a convenient interface to fitting time series regressions via ordinary least
squares

Package
dyn
provides a different approach to that of
dynlm, which allows time series data to
be used with any regression function written in the style of lm such as
lm(),
glm(),
loess(),
rlm()
and
lqs()
from
MASS,
randomForest()
(package
randomForest),
rq()
(package
quantreg) amongst
others, whilst preserving the time series information.

Package
wq
provides functions to assist in the processing and exploration of data from monitoring programs
for aquatic ecosystems, with a focus on time series data for physical and chemical properties of water, and for the
plankton.

The
openair
provides numerous tools to analyse, interpret and understand air pollution time series data

The
bReeze
package is a collection of widely used methods to analyse, visualise, and interpret wind data. Wind resource analyses can subsequently be combined with characteristics of wind turbines to estimate the potential energy production.
Additionally, a fuller description of available packages for time series analysis can be found in the
TimeSeries
task view.
Spatial data analysis
See the
Spatial
CRAN Task View for an overview of spatial analysis in R.
Extreme values
ismev
provides functions for models for extreme value statistics and is support software for Coles (2001)
An Introduction to Statistical Modelling of Extreme Values
, Springer, New York. Other packages for extreme value
theory include:
Phylogenetics and evolution
Packages specifically tailored for the analysis of phylogenetic and evolutionary data include:
The
Phylogenetics
task view provides more detailed coverage of the subject area and related functions
within R.
UseRs may also be interested in Paradis (2006)
Analysis of Phylogenetics and Evolution with R
, Springer,
New York, a book in the new UseR series from Springer.
Soil science
Several packages are now available that implement R functions for widelyused methods and approaches in pedology.

soiltexture
provides functions for soil texture plot, classification and transformation.

aqp
contains a collection of algorithms related to modeling of soil resources, soil classification,
soil profile aggregation, and visualization.

Package
HydroMe
estimates the parameters in infiltration and water retention models by curvefitting
method.

The Soil Water project on rforge.rproject.net provides packages providing soil water retention functions,
soil hydraulic conductivity functions and pedotransfer functions to estimate their parameter from easily available soil
properties. Two packages form the project:

soilwaterfun

soilwaterptf
Hydrology and Oceanography
A growing number of packages are available that implement methods specifically related to the fields of hydrology and
oceanography. Also see the
Extreme Value
and the
Climatology
sections
for related packages.

Package
HydroMe
estimates the parameters in infiltration and water retention models by curvefitting
method.

hydroTSM
is a package for management, analysis, interpolation and plotting of time series used in hydrology
and related environmental sciences.

hydroGOF
is a package implementing both statistical and graphical goodnessoffit measures between
observed and simulated values, mainly oriented to be used during the calibration, validation, and application of
hydrological/environmental models. Related packages are
tiger, which allows temporally resolved groups of
typical differences (errors) between two time series to be determined and visualized, and
qualV
which
provides quantitative and qualitative criteria to compare models with data and to measure similarity of patterns

hydroPSO
is a modelindependent global optimization tool for calibration of environmental and other realworld models that need to be executed from the system console.
hydroPSO
implements a stateoftheart
PSO
(SPSO2011 and SPSO2007 capable), with several finetuning options. The package is parallelcapable, to alleviate the computational burden of complex models.

EcoHydRology
provides a flexible foundation for scientists, engineers, and policy makers to base
teaching exercises as well as for more applied use to model complex ecohydrological interactions.

topmodel
is a set of hydrological functions including an R implementation of the hydrological model
TOPMODEL, which is based on the 1995 FORTRAN version by Keith Beven. New functionality is being developed as part of
the
RHydro
package on RForge.

dynatopmodel
is a native R implementation and enhancement of the Dynamic TOPMODEL, Beven and Freers' (2001) extension to the semidistributed hydrological model TOPMODEL (Beven and Kirkby, 1979).

wasim
provides tools for data processing and visualisation of results of the hydrological model WASIMETH

Package
seacarb
provides functions for calculating parameters of the seawater carbonate system.

Stephen Sefick's
StreamMetabolism
package contains function for calculating stream metabolism
characteristics, such as GPP, NDM, and R, from single station diurnal Oxygen curves.

Package
oce
supports the analysis of Oceanographic data, including ADP measurements, CTD measurements,
sectional data, sealevel time series, and coastline files.

The
nsRFA
package provides collection of statistical tools for objective (nonsupervised) applications
of the Regional Frequency Analysis methods in hydrology.

The
boussinesq
package is a collection of functions implementing the onedimensional Boussinesq Equation
(groundwater).

rtop
is a package for geostatistical interpolation of data with irregular spatial support such as runoff
related data or data from administrative units.
Climatology
Several packages related to the field of climatology.

seas
implements a number of functions for analysis and graphics of seasonal data.

RMAWGEN
is set of S3 and S4 functions for spatial multisite stochastic generation of daily
time series of temperature and precipitation making use of Vector Autoregressive Models.

Interpol.T
makes hourly interpolation of daily minimum and maximum temperature series for example
when hourly time series must be downscaled from the daily information.
Palaeoecology and stratigraphic data
Several packages now provide speciailist functionality for the import, analysis, and plotting of
palaeoecological data.

Transfer function models including weighted averaging (WA), modern analogue technique (MAT), Locallyweighted WA, &
maximum likelihood (aka Gaussian logistic) regression (GLR) are provided by some or all of the
rioja,
and
analogue
packages.

Import of common, legacy, palaeodata formats are provided by package
vegan
(cornell format) and
rioja
(cornell and Tilia format). In addition,
rioja
also allows for import of C2 model files.

Stratigraphic data plots can be drawn using
Stratiplot()
function in
analogue
and functions
strat.plot()
and
strat.plot.simple
in the
rioja
package.

analogue
provides extensive support for developing and interpretting MAT transfer function models,
including ROC curve analysis. Summary of stratigraphic data is supported via principal curves in the
prcurve()
function.

Constrained clustering of stratigraphic data is provided by function
chclust()
in the form of constrained
hierarchical clustering in
rioja.
Other packages
Several other relevant contributed packages for R are available that do not fit under nice headings.

adehabitat
complements
ade4
and provides a collection of tools for the analysis of habitat
selection by animals.

diveMove
provides tools to represent, visualize, filter, analyse, and summarize timedepth recorder (TDR) data
for research on animal diving and movement behaviour.

latticeDensity
implements methods for density estimation and nonparametric regression on irregular regions.
A useful alternative to kernel density estimation for e.g. estimating animal densities and home ranges in regions with
irregular boundaries or holes.

Andrew Robinson's
equivalence
package provides some statistical tests and graphics for assessing
tests of equivalence. Such tests have similarity as the alternative hypothesis instead of the null. The package
contains functions to perform two onesided ttests (TOST) and paired ttests of equivalence.

Thomas Petzoldt's
simecol
package provides an object oriented framework and tools to simulate
ecological (and other) dynamic systems within R. See the
simecol website
and a
R News
article
on the package for further information.

Functions for circular statistics are found in
CircStats
and
circular.

Package
eco
fits Bayesian models of ecological inference to 2 x 2 contingency tables.

Package
e1071
provides functions for latent class analysis, short time Fourier transform, fuzzy clustering,
support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, and more...

Package
pgirmess
provides a suite of miscellaneous functions for data analysis in ecology.

mefa
provides functions for handling and reporting on multivariate count data in ecology and
biogeography.

Sensitivity analysis of models is provided by packages
sensitivity
and
fast.
sensitivity
contains a collection of functions for factor screening and global sensitivity analysis of model
output.
fast
is an implementation of the Fourier Amplitude Sensitivity Test (FAST), a method to determine
global sensitivities of a model on parameter changes with relatively few model runs.

Functions to analyze coherence, boundary clumping, and turnover following the patternbased metacommunity analysis of
Leibold and Mikkelson (2002)
are provided in the
metacom
package.

Growth curve estimation via noncrossing and nonparametric regression quantiles is implemented in package
quantregGrowth. A supporting paper is
Muggeo et al.
(2013)
.

The
siplab
package provides an R platform for experimenting with spatially explicit individualbased vegetation models. A supporting paper is
GarcĂa, O. (2014)
.