Description Usage Arguments Details Value Author(s) References See Also Examples

The function computes eight metrics of beta diversity according to an informed environmental gradient. It selects a given number of environmentally-neighborhood sites in a moving window to obtain beta diversity.

1 2 3 4 | ```
betaRegDisp(y, x, xy.coords = NULL, ws = 3,
method.1 = "jaccard", method.2 = "ruzicka",
method.3 = "ruzicka",
independent.data = FALSE, illust.plot = FALSE)
``` |

`y ` |
Response matrix, where rows are sites and columns are species. |

`x ` |
Predictor vector. A vector of the environmental gradient under study with the same number of sites as in matrix |

`xy.coords ` |
Geographical coordinates. A matrix with two columns of XY decimal degree geographical coordinates, which are used to compute euclidean distance among sites. Rows must be sites in the same order as in |

`ws ` |
Window size or number of sites to be used in the computation of the distinct beta-diversity metrics or between-site dissimilarities. It must be a positive integer higher than 2. |

`method.1 ` |
For beta-diversity metrics 1 to 3 (see details). A dissimilarity index available in the |

`method.2 ` |
For beta-diversity metrics 4 and 5 (see details). A dissimilarity index available in the |

`method.3 ` |
For beta-diversity metrics 6, 7, and 8 (see details). A multisample dissimilarity index available in the |

`independent.data ` |
Should windows not superpose each other? If |

`illust.plot ` |
Should a window plot be open and illustrate how the window moves along the gradient? |

The function computes eight beta-diversity metrics among sites included in a set (window) of length `ws`

. See details in Dala-Corte et al. (2019).

Metrics 1-3 uses dissimilarity indices available in `vegdist`

:

1. Mean pair-wise dissimilarity between sites in a window;

2. Mean dissimilarity between focal site and the other sites in a window. If an odd number is informed in `ws`

, the focal site is the central site in relation to its neighbours in the window. If an even number is informed in `ws`

, the focal site is the first site in the window;

3. Mean distance of sites to their group centroid in a Principal Coordinate (PCoA) space computed using `betadisper`

;

Metrics 4-5 uses dissimilarity indices available in `beta.div`

:

4. Total sum of squares (SS) of the window sites (Legendre and De Caceres, 2013);

5. Local contributions to beta diversity (LCBD; Legendre and De Caceres, 2013);

Metrics 6-8 uses dissimilarity indices available in `beta.multi.abund`

:

6. Total multiple-site dissimilarities for a selected window of sites;

7. Nestedness component of multiple-site dissimilarities for a selected window of sites;

8. Turnover component of multiple-site dissimilarities for a selected window of sites.

A matrix with 10 columns (or 12 if `xy.coords`

is informed). Values in columns are sorted according to the enviromental gradient, from the lowest to the highest value. Columns correspond to:

1. `grad`

- The environmental gradient (predictor vector, `x`

);

2. `mean.grad`

- Mean value of the environmental gradient of sites selected in each window;

3. `mean.diss.pairs`

- Mean pair-wise dissimilarity between sites in a selected window (metric 1);

4. `diss.focal`

- Mean dissimilarity between focal site and the other sites (metric 2);

5. `mean.dist.cent `

- Mean distance of sites to their group centroid in a Principal Coordinate (PCoA) space (metric 3);

6. `SS.group`

- Total sum of squares (SS) of the sites in a window (metric 4);

7. `SS.focal`

- Local contributions to beta diversity (LCBD), which represents how much a focal site contributed to the total window SS;

8. `beta.TOT`

- Total multiple-site dissimilarity;

9. `beta.NES`

- Nestedness component of multiple-site dissimilarity;

10. `beta.TUR`

- Turnover component of multiple-site dissimilarity;

11. `mean.geodist`

- If `xy.coords`

is provided, the mean linear euclidean distance between sites in the a window is returned.

12. `focal.geodist`

- If `xy.coords`

is provided, the mean linear euclidean distance of the focal site in relation to its neighbours in the window is returned.

Luciano F. Sgarbi, Renato B. Dala-Corte and Adriano S. Melo

Anderson, M.J., K.E. Ellingsen and B.H. McArdle. 2006. Multivariate dispersion as a measure of beta diversity. Ecology Letters 9: 683-693.

Baselga, A. 2010. Partitioning the turnover and nestedness components of beta diversity. Global Ecology and Biogeography 19: 134-143.

Baselga, A. 2017. Partitioning abundance-based multiple-site dissimilarity into components: balanced variation in abundance and abundance gradients. Methods in Ecology and Evolution 8: 799-808.

Dala-Corte, R.B., L.F. Sgarbi, F.G. Becker and A.S. Melo. 2019. Beta diversity of stream fish communities along anthropogenic environmental gradients at multiple spatial scales. Environmental Monitoring and Assessment 191:288.

Legendre, P. and M. De Caceres. 2013. Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. Ecology Letters 16: 951-963.

`vegdist`

, `betadisper`

, `beta.div`

, `beta.multi`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | ```
## Example 1. A simmulated community matrix with a known structure of increasing
## beta diversity by turnover
# n is the total sample sites
# LocS is the number of spp per site
# MaxS is the total number of spp in the matrix
# All samples will contain LocS species. The first sample will contain presences
# for the first LocS species. The subsequent samples will contain LocS presences
# spread over a increasing set of species. The assignment of presences for the
# second sample to the last sample is done randomly. The last sample will
# contain LocS presences assigned randomly to the MaxS species. Thus, for a
# window size of 3 (ws=3) and a dataset of 10 samples, beta diversity for the
# samples 1-3 will be much lower than for samples 8-10.
SimComm <- function(n = 21, MaxS = 30, LocS = 10){
s <- seq (LocS, MaxS, length.out = n)
mat <- matrix(0, n, MaxS, dimnames =
list(paste("site", 1:n, sep = "_"),
paste("sp", 1:MaxS, sep = "_")))
for(i in 1:n){
mat[i, sample(1:s[i], LocS)] <- 1
}
mat <- mat[, colSums(mat)!=0]
return(mat)
}
mat <- SimComm(n = 21, MaxS = 30, LocS = 10)
#Creating an environmental gradient:
grad <- 1:nrow(mat)
b.resu <- betaRegDisp(y = mat, x = grad, xy.coord = NULL, ws = 3,
method.1 = "jaccard",
method.2 = "ruzicka",
method.3 = "ruzicka",
independent.data = FALSE, illust.plot = FALSE)
##Ploting all the output of the object for the simmulated community
op <- par(no.readonly = TRUE)
par(mfrow = c(5, 2), oma = c(1, 0, 1, 0.1), mar = c(1.5, 3, .1, .1), cex = 1, las = 0)
for(i in 1:ncol(b.resu)){
plot(b.resu[, 1], b.resu[, i], ylab = colnames(b.resu)[i], cex.lab = .9,
cex.axis = 0.9, tcl = -0.2, mgp = c(1.5, .2, 0), pch = 15, col = "grey")
}
mtext("Environmental gradient", cex = 1.3, 1, -0.1, outer = TRUE)
par(op)
## Not run:
## Not run:
##Example 2
data(varespec)
data(varechem)
grad <- varechem[, "Baresoil"]
resu <- betaRegDisp(y = varespec, x = grad, ws = 3, method.1 = "jaccard",
method.2 = "ruzicka", method.3 = "ruzicka",
independent.data = FALSE, illust.plot = FALSE)
#Plotting all the outputs of the function:
op <- par(no.readonly = TRUE)
par(mfrow = c(5, 2), oma = c(1, 0, 1, 0.1), mar = c(1.5, 3, .1, .1), cex = 1, las = 0)
for(i in 1:ncol(resu)){
plot(resu[, 1], resu[, i], ylab = colnames(resu)[i], cex.lab = .9,
cex.axis = 0.9, tcl = -0.2, mgp = c(1.5, .2, 0), pch = 15, col = "grey")
}
mtext("Environmental gradient", cex = 1.3, 1, 0, outer = TRUE)
par(op)
## End(Not run)
## End(Not run)
``` |

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