# vegan FAQ

This document contains answers to some of the most frequently asked questions about R package vegan.

> This work is licensed under the Creative Commons Attribution 3.0 License.

> Copyright © 2008-2025 vegan development team

## Introduction

### What is vegan?

Vegan is an R package for community ecologists. It contains the most popular methods of multivariate analysis needed in analysing ecological communities, and tools for diversity analysis, and other potentially useful functions. Vegan is not self-contained but it must be run under R statistical environment, and it also depends on many other R packages. Vegan is distributed under GPL2 license.

### How to obtain vegan and R?

Both R and latest release version of vegan can be obtained through CRAN. Unstable development version of vegan can be obtained through GitHub. The github page gives further instructions for obtaining and installing development versions of vegan.

### What other packages are available for ecologists?

CRAN Task Views include entries like `Environmetrics`, `Multivariate` and `Spatial` that describe several useful packages and functions. If you install R package ctv, you can inspect Task Views from your R session, and automatically install sets of most important packages.

### What other documentation is available for vegan?

Vegan is a fully documented R package with standard help pages. These are the most authoritative sources of documentation (and as a last resource you can use the force and the read the source, as vegan is open source). Vegan package ships with other documents which can be read with `browseVignettes("vegan")` command. The documents included in the vegan package are

- Vegan `NEWS` that can be accessed via `news()` command.
- This document (`FAQ-vegan`).
- Short introduction to basic ordination methods in vegan (`intro-vegan`).
- Introduction to diversity methods in vegan (`diversity-vegan`).
- Discussion on design decisions in vegan (`decision-vegan`).
- Description of variance partition procedures in function `varpart` (`partitioning`).

Web documents outside the package include:

- https://github.com/vegandevs/vegan: development page.
- https://vegandevs.github.io/vegan/: vegan homepage.

### Is there a Graphical User Interface (GUI) for vegan?

Roeland Kindt has made package BiodiversityR which provides a GUI for vegan. The package is available at CRAN. It is not a mere GUI for vegan, but adds some new functions and complements vegan functions in order to provide a workbench for biodiversity analysis. You can install BiodiversityR using `install.packages("BiodiversityR")` or graphical package management menu in R. The GUI works on Windows, MacOS X and Linux.

### How to cite vegan?

Use command `citation("vegan")` in R to see the recommended citation to be used in publications.

### How to build vegan from sources?

In general, you do not need to build vegan from sources, but binary builds of release versions are available through CRAN for Windows and MacOS X. If you use some other operating systems, you may have to use source packages. Vegan is a standard R package, and can be built like instructed in R documentation. Vegan contains source files in C and FORTRAN, and you need appropriate compilers (which may need more work in Windows and MacOS X).

### Are there binaries for devel versions?

Binaries can be available from R Universe: see https://github.com/vegandevs/vegan for instructions.

### How to report a bug in vegan?

If you think you have found a bug in vegan, you should report it to vegan maintainers or developers. The preferred forum to report bugs is GitHub. The bug report should be so detailed that the bug can be replicated and corrected. Preferably, you should send an example that causes a bug. If it needs a data set that is not available in R, you should send a minimal data set as well. You also should paste the output or error message in your message. You also should specify which version of vegan you used.

Bug reports are welcome: they are the only way to make vegan non-buggy.

Please note that you shall not send bug reports to R mailing lists, since vegan is not a standard R package.

### Is it a bug or a feature?

It is not necessarily a bug if some function gives different results than you expect: That may be a deliberate design decision. It may be useful to check the documentation of the function to see what was the intended behaviour. It may also happen that function has an argument to switch the behaviour to match your expectation. For instance, function `vegdist` always calculates quantitative indices (when this is possible). If you expect it to calculate a binary index, you should use argument `binary = TRUE`.

### Can I contribute to vegan?

Vegan is dependent on user contribution. All feedback is welcome. If you have problems with vegan, it may be as simple as incomplete documentation, and we shall do our best to improve the documents.

Feature requests also are welcome, but they are not necessarily fulfilled. A new feature will be added if it is easy to do and it looks useful, or if you submit code.

If you can write code yourself, the best forum to contribute to vegan is GitHub.

## Ordination

### I have only numeric and positive data but vegan still complains

You are wrong! Computers are painfully pedantic, and if they find non-numeric or negative data entries, you really have them. Check your data! Most common reasons for non-numeric data are that row names were read as a non-numeric variable instead of being used as row names (check argument `row.names` in reading the data), or that the column names were interpreted as data (check argument `header = TRUE` in reading the data). Another common reason is that you had empty cells in your input data, and these were interpreted as missing values. See also the next question.

### Can I use tibbles in ordination?

Yes. In most cases they behave like normal data frames. For environmental data there should be no problem with tibbles. Problems can emerge with community data. Community data frames must be numeric, but if tibble has character variables (such as a column of row names), data are non-numeric with guaranteed failure in ordination (see previous question). All character columns should be removed from community data in ordination.

### Can I analyse binary or cover class data?

Yes. Most vegan methods can handle binary data or cover abundance data. Most statistical tests are based on permutation, and do not make distributional assumptions. There are some methods (mainly in diversity analysis) that need count data. These methods check that input data are integers, but they may be fooled by cover class data.

### Why dissimilarities in vegan differ from other sources?

Most commonly the reason is that other software use presence–absence data whereas vegan used quantitative data. Usually vegan indices are quantitative, but you can use argument `binary = TRUE` to make them presence–absence. However, the index name is the same in both cases, although different names usually occur in literature. For instance, Jaccard index actually refers to the binary index, but vegan uses name `"jaccard"` for the quantitative index, too.

Another reason may be that indices indeed are defined differently, because people use same names for different indices.

### I cannot get repeated solutions in `metaMDS`

The first (try 0) run of `metaMDS` starts from the metric scaling solution and is usually good, and most sofware only return that solution. However, `metaMDS` tries to see if that standard solution can be repeated, or improved and the improved solution still repeated. In all cases, it will return the best solution found, and there is no burning need to do anything if you get the message that the solution could not be repeated. If you are keen to know that the solution really is the global optimum, you may follow the instructions in the `metaMDS` help section “Results Could Not Be Repeated” and try harder.

### I get zero stress but no repeated solutions in `metaMDS`

Most common reason is that you have too few observations for your NMDS. For \(n\) observations (points) and \(k\) dimensions you need to estimate \(nk\) parameters (ordination scores) using \(n(n-1)/2\) dissimilarities. For \(k\) dimensions you must have \(n > 2k + 1\), or for two dimensions at least six points. In some degenerate situations you may need even a larger number of points. If you have a lower number of points, you can find an undefined number of perfect (stress is zero) but different solutions. Conventional wisdom due to Kruskal is that you should have \(n > 4k + 1\) points for \(k\) dimensions. A typical symptom of insufficient data is that you have (nearly) zero stress but no repeated solutions. In those cases you should reduce the number of dimensions (\(k\)) and with very small data sets you should not use `NMDS`, but rely on metric methods (`pco` and `wcmdscale` in vegan, `cmdscale` in base R).

### Zero dissimilarities in isoMDS

Function `metaMDS` uses function `monoMDS` as its default method for NMDS, and this function can handle zero dissimilarities. Alternative function `isoMDS` cannot handle zero dissimilarities. If you want to use `isoMDS`, you can use argument `zerodist = "add"` in `metaMDS` to handle zero dissimilarities. With this argument, zero dissimilarities are replaced with a small positive value, and they can be handled in `isoMDS`. This is a kluge, and some people do not like this. A more principal solution is to remove duplicate sites using R command `unique`. However, after some standardizations or with some dissimilarity indices, originally non-unique sites can have zero dissimilarity, and you have to resort to the kluge (or work harder with your data). Usually it is better to use `monoMDS`.

### I have heard that you cannot fit environmental vectors or surfaces to NMDS results which only have rank-order scores

Claims like this have indeed been at large in the Internet, but they are based on grave misunderstanding and are plainly wrong. NMDS ordination results are strictly metric, and in vegan `metaMDS` and `monoMDS` they are even strictly Euclidean. The method is called “non-metric” because there is non-metric relation from input dissimilarities to the Euclidean ordination space. You can inspect this non-linear step function using function `stressplot` in vegan. Because the ordination scores are strictly Euclidean, it is correct to use vegan functions `envfit` and `ordisurf` with NMDS results.

### Where can I find numerical scores of ordination axes?

Normally you can use function `scores` to extract ordination scores for any ordination method. The `scores` function can also find ordination scores for many non-vegan functions such as for `prcomp` and `princomp` and for some ade4 functions.

In some cases the ordination result object stores raw scores, and the axes are also scaled appropriate when you access them with `scores`. For instance, in `cca` and `rda` the ordination object has only so-called normalized scores, and they are scaled for ordination plots or for other use when they are accessed with `scores`.

### How the RDA results are scaled?

The scaling or RDA results indeed differ from most other software packages. The scaling of RDA is such a complicated issue that it cannot be explained in this FAQ, but it is explained in a separate pdf document on “Design decision and implementation details in vegan” that you can read with command `browseVignettes("vegan")`.

### Variance explained by ordination axes.

In general, vegan does not directly give any statistics on the “variance explained” by ordination axes or by the constrained axes. This is a design decision: I think this information is normally useless and often misleading. In community ordination, the goal typically is not to explain the variance, but to find the “gradients” or main trends in the data. The “total variation” often is meaningless, and all proportions of meaningless values also are meaningless. Often a better solution explains a smaller part of “total variation”. For instance, in non-standardized principal components analysis most of the variance is generated by a small number of most abundant species, and they are easy to “explain” because data really are not very multivariate. If you standardize your data, all species are equally important. The first axes explains much less of the “total variation”, but now they explain all species equally, and results typically are much more useful for the whole community. Correspondence analysis uses another measure of variation (which is not variance), and again it typically explains a “smaller proportion” than principal components but with a better result. Detrended correspondence analysis and nonmetric multidimensional scaling even do not try to “explain” the variation, but use other criteria. All methods are incommensurable, and it is impossible to compare methods using “explanation of variation”.

If you still want to get “explanation of variation” (or a deranged editor requests that from you), it is possible to get this information for some methods:

- Eigenvector methods: Functions `rda`, `cca`, `dbrda` and `capscale` give the variation of conditional (partialled), constrained (canonical) and residual components. Function `eigenvals` extracts the eigenvalues, and `summary(eigenvals(ord))` reports the proportions explained in the result object `ord`, and also works with `decorana` and `wcmdscale`. Function `RsquareAdj` gives the R-squared and adjusted R-squared (if available) for constrained components. Function `goodness` gives the same statistics for individual species or sites. In addition, there is a special function `varpart` for unbiased partitioning of variance between up to four separate components.

- Nonmetric multidimensional scaling. NMDS is a method for nonlinear mapping, and the concept of of variation explained does not make sense. However, 1 - stress^2 transforms nonlinear stress into quantity analogous to squared correlation coefficient. Function `stressplot` displays the nonlinear fit and gives this statistic.

### Can I have random effects in constrained ordination or in `adonis`?

No. Strictly speaking, this is impossible. However, you can define models that respond to similar goals as random effects models, although they strictly speaking use only fixed effects.

Constrained ordination functions `cca`, `rda` and `dbrda` can have `Condition()` terms in their formula. `Condition()` defines partial terms that are fitted before other constraints and can be used to remove the effects of background variables, and their contribution to decomposing inertia (variance) is reported separately. These partial terms are often regarded as similar to random effects, but they are still fitted in the same way as other terms and strictly speaking they are fixed terms.

Function `adonis2` can evaluate terms sequentially. In a model with right-hand-side `~ A + B` the effects of `A` are evaluated first, and the effects of `B` after removing the effects of `A`. Sequential tests are also available in `anova` function for constrained ordination results by setting argument `by = "term"`. In this way, the first terms can serve in a similar role as random effects, although they are fitted in the same way as all other terms, and strictly speaking they are fixed terms.

All permutation tests in vegan are based on the permute package that allows constructing various restricted permutation schemes. For instance, you can set levels of `plots` or `blocks` for a factor regarded as a random term.

A major reason why real random effects models are impossible in most vegan functions is that their tests are based on the permutation of the data. The data are given, that is fixed, and therefore permutation tests are basically tests of fixed terms on fixed data. Random effect terms would require permutations of data with a random component instead of the given, fixed data, and such tests are not available in vegan.

### Is it possible to have passive points in ordination?

Vegan does not have a concept of passive points, or a point that should only little influence the ordination results. However, you can add points to eigenvector methods using `predict` functions with `newdata`. You can first perform an ordination without some species or sites, and then you can find scores for all points using your complete data as `newdata`. The `predict` functions are available for basic eigenvector methods in vegan (`cca`, `rda`, `decorana`, for an up-to-date list, use command `methods("predict")`).

You can add new points NMDS with function `MDSaddpoints`.

### Class variables and dummies

You should define a class variable as an R `factor`, and vegan will automatically handle them.

R (and vegan) knows both unordered and ordered factors. Unordered factors are internally coded as dummy variables, but one redundant level is removed or aliased. With default contrasts, the removed level is the first one. Ordered factors are expressed as polynomial contrasts. Both of these contrasts are explained in standard R documentation.

### How are environmental arrows scaled?

The printed output of `envfit` gives the direction cosines which are the coordinates of unit length arrows. For plotting, these are scaled by their correlation (square roots of column `r2`). You can see the scaled lengths of `envfit` arrows using command `scores`.

The scaled environmental vectors from `envfit` and the arrows for continuous environmental variables in constrained ordination (`cca`, `rda`, `dbrda`) are adjusted to fill the current graph. The lengths of arrows do not have fixed meaning with respect to the points (species, sites), but they can only compared against each other, and therefore only their relative lengths are important.

If you want change to the scaling of the arrows, you can use `text` (plotting arrows and text) or `points` (plotting only arrows) functions for constrained ordination. These functions have argument `arrow.mul` which sets the multiplier. The `plot` function for `envfit` also has the `arrow.mul` argument to set the arrow multiplier.

### I cannot see all variables in constrained ordination

You have some variables that have no use and information. These variables are correlated with other constraining variables and have no independent information. This can also concern only some levels of factor variables. For instance, in vegan data `dune.env` we know that `Manure` level is 0 in `Management` level `NM` and only there. If both are used as constraints (or conditions), one level of `Manure` or one level of `Management` is redundant. These variables or levels are aliased, and vegan informs about this during fitting the model and permanently when the short information of the result is printed.

Vegan function `alias` gives the defining equations for aliased variables, or optionally their names.

### Plotting aliased variables

You can fit vectors or class centroids for aliased variables using `envfit` function. The `envfit` function uses weighted fitting, and the fitted vectors are identical to the vectors in correspondence analysis. In constrained analysis you must fit the centroids or vectors to the constrained scores (LC scores), and use argument `display = "lc"` in `envfit`.

### Restricted permutations in vegan

Vegan uses permute package in all its permutation tests. The permute package will allow restricted permutation designs for time series, line transects, spatial grids and blocking factors. The construction of restricted permutation schemes is explained in the manual page `permutations` in vegan and in the documentation of the permute package.

### Why restricted permutation does not influence adonis results?

The permutation scheme influences the permutation distribution of the statistics and significance levels, but does not influence the calculation of the statistics.

### How to use different plotting symbols in ordination graphics?

Ordination objects typically have many different kind of scores, and there may be limitations of changing the way how they are displayed. However, `plot` for constrained ordination results (`cca`, `rda`, `dbrda`) can be fully configured since vegan 2.7-1. See their help. Sometimes it is easier to compose the graph by layers using pipes (`|>`) to add `text` or `points` of different scores. See the help of ordination plot functions for examples.

### How to avoid cluttered ordination graphs?

If there is a really high number of species or sites, the graphs often are congested and many labels are overwritten. It may be impossible to have complete readable graphics with some data sets. Below we give a brief overview of tricks you can use. Gavin Simpson’s blog From the bottom of the heap has a series of articles on “decluttering ordination plots” with more detailed discussion and examples.

Often the easiest solution is to use only `points`. If you need text to identify items, most `text` and `plot` functions have argument `optimize` and `bg`. With `optimize = TRUE` the exact location of the score is marked with a point, and text label is written next to the point trying to find a position that minimizes over-writing other labels. This is impossible in very congested areas, but often helps in moderate cases. Argument `bg` defines a background colour of labels. For instance, `bg = "white"` will write text on white background. These labels will cover other text below them, but at least the uppermost are readable instead of both being messed up. These arguments can be combined, and this often gives the best result. These arguments can also be used in `text` functions in pipe (`|>`). Finally, `text` functions take argument `labels` which allows editing plotted text, and argument `select` to drop completely some cases.

There is a special function `orditorp` (torp for text-or-point) function that will use text only if it is not covered by other text labels and points otherwise (with argument `priority` to define which items are printed first).

Companion CRAN package vegan3d has function `orditkplot` that provides editable plot where the points are fixed at their ordination scores, but you can move text labels with mouse.

### Can I flip an axis in ordination diagram?

Use `xlim` or `ylim` with flipped limits. If you have model `mod <- cca(dune)` you can flip the first axis with `plot(mod, xlim = c(3, -2))`.

### Can I zoom into an ordination plot?

You can use `xlim` and `ylim` arguments in `plot` or `ordiplot` to zoom into ordination diagrams. Normally you must set both `xlim` and `ylim` because ordination plots will keep the equal aspect ratio of axes, and they will fill the graph so that the longer axis will fit.

Dynamic zooming can be done with function `orditkplot` in CRAN package vegan3d. You can directly save the edited `orditkplot` graph in various graphic formats, or you can export the graph object back to R session and use `plot` to display the results.

## Other analysis methods

### Is there TWINSPAN?

TWINSPAN for R is available in github.