nestedtemp {vegan} | R Documentation |

Patches or local communities are regarded as nested if they all could be subsets of the same community. In general, species poor communities should be subsets of species rich communities, and rare species should only occur in species rich communities.

nestedchecker(comm) nestedn0(comm) nesteddisc(comm) nestedtemp(comm, ...) nestednodf(comm, order = TRUE) ## S3 method for class 'nestedtemp': plot(x, kind = c("temperature", "incidence"), col=rev(heat.colors(100)), names = FALSE, ...)

`comm` |
Community data. |

`x` |
Result object for a `plot` . |

`col` |
Colour scheme for matrix temperatures. |

`kind` |
The kind of plot produced. |

`names` |
Label columns and rows in the plot using names in `comm` .
If it is a logical vector of length 2, row and column labels are
returned accordingly. |

`order` |
Order rows and columns by frequencies. |

`...` |
Other arguments to functions. |

The nestedness functions evaluate alternative indices of nestedness.
The functions are intended to be used together with Null model
communities and used as an argument in `oecosimu`

to analyse
the nonrandomness of results.

Function `netstedchecker`

gives the number of checkerboard units,
or 2x2 submatrices where both species occur once but on different
sites (Stone & Roberts 1990).

Function `nestedn0`

implements
nestedness measure N0 which is the number of absences from the sites
which are richer than the most pauperate site species occurs
(Patterson & Atmar 1986).

Function `nesteddisc`

implements
discrepancy index which is the number of ones that should be shifted
to fill a row with ones in a table arranged by species frequencies
(Brualdi & Sanderson 1999). The original definition arranges species
(columns) by their frequencies, but did not have any method of
handling tied frequencies.

The `nesteddisc`

function tries to
order tied columns to minimize the discrepancy statistic but this is
rather slow, and with a large number of tied columns there is no
guarantee that the best ordering was found. In that case a warning
of tied columns will be issued.

Function `nestedtemp`

finds the matrix temperature which is
defined as the sum of “surprises” in arranged matrix. In
arranged unsurprising matrix all species within proportion given by
matrix fill are in the upper left corner of the matrix, and the
surprise of the absence or presences is the diagonal distance from the
fill line (Atmar & Patterson 1993). Function tries to pack species and
sites to a low temperature (Rodríguez-Gironés
& Santamaria 2006), but this is an iterative procedure, and the
temperatures usually vary among runs. Function `nestedtemp`

also
has a `plot`

method which can display either incidences or
temperatures of the surprises. Matrix temperature was rather vaguely
described (Atmar & Patterson 1993), but
Rodríguez-Gironés & Santamaria (2006) are
more explicit and their description is used here. However, the results
probably differ from other implementations, and users should be
cautious in interpreting the results. The details of calculations are
explained in the `vignette`

*Design decisions and
implementation* that you can read using functions
`vignette`

or `vegandocs`

. Function
`nestedness`

in the bipartite package is
a direct port of the BINMATNEST programme of
Rodríguez-Gironés & Santamaria (2006).

Function `nestednodf`

implements a nestedness metric based on
overlap and decreasing fill (Almeida-Neto et al., 2008). Two basic
properties are required for a matrix to have the maximum degree of
nestedness according to this metric: (1) complete overlap of 1's from
right to left columns and from down to up rows, and (2) decreasing
marginal totals between all pairs of columns and all pairs of
rows. The nestedness statistic is evaluated separately for columns
(`N columns`

) for rows (`N rows`

) and combined for the whole
matrix (`NODF`

). If you set `order = FALSE`

, the statistic
is evaluated with the current matrix ordering allowing tests of other
meaningful hypothesis of matrix structure than ordering by row and
column totals (see Almeida-Neto et al. 2008).

The result returned by a nestedness function contains an item called
`statistic`

, but the other components differ among functions. The
functions are constructed so that they can be handled by
`oecosimu`

.

Jari Oksanen and Gustavo Carvalho (`nestednodf`

).

Almeida-Neto, M., Gumarães, P.,
Gumarães, P.R., Loyola, R.D. & Ulrich, W. (2008). A
consistent metric for nestedness analysis in ecological systems:
reconciling concept and measurement. *Oikos* 117, 1227–1239.

Atmar, W. & Patterson, B.D. (1993). The measurement of order and
disorder in the distribution of species in fragmented
habitat. *Oecologia* 96, 373–382.

Brualdi, R.A. & Sanderson, J.G. (1999). Nested species subsets, gaps,
and discrepancy. *Oecologia* 119, 256–264.

Patterson, B.D. & Atmar, W. (1986). Nested subsets and the structure
of insular mammalian faunas and archipelagos. *Biol. J. Linnean
Soc.* 28, 65–82.

Rodríguez-Gironés, M.A. & Santamaria, L.
(2006). A new algorithm to calculate the nestedness temperature of
presence-absence matrices. *J. Biogeogr.* 33, 924–935.

Stone, L. & Roberts, A. (1990). The checkerboard score and species
distributions. *Oecologia* 85, 74–79.

Wright, D.H., Patterson, B.D., Mikkelson, G.M., Cutler, A. & Atmar,
W. (1998). A comparative analysis of nested subset patterns of species
composition. *Oecologia* 113, 1–20.

In general, the functions should be used with `oecosimu`

which generates Null model communities to assess the nonrandomness of
nestedness patterns.

data(sipoo) ## Matrix temperature out <- nestedtemp(sipoo) out plot(out) plot(out, kind="incid") ## Use oecosimu to assess the nonrandomness of checker board units nestedchecker(sipoo) oecosimu(sipoo, nestedchecker, "quasiswap") ## Another Null model and standardized checkerboard score oecosimu(sipoo, nestedchecker, "r00", statistic = "C.score")

[Package *vegan* version 1.16-32 Index]