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

The functions allows to evaluate the significance and estimate parts in variation partitioning using Moran Spectral Randomization (MSR) as a spatially-constrained null model to account for spatial autocorrelation in table X. Hence, this function provides a variation partioning adujsted for spurious correlation due to spatial autocorrelation in both the response and one explanatory matrix.

1 2 3 |

`x` |
An object generated by the |

`listwORorthobasis` |
an object of the class |

`nrepet` |
an |

`method` |
an character specifying which algorithm should be used to produce spatial replicates (see codemsr.default). |

`...` |
further arguments of the codemsr.default function. |

The function corrects the biases due to spatial autocorrelation by using MSR procedure to produce environmental predictors that preserve the spatial autocorrelation and the correlation structures of the original environmental variables while being generated independently of species distribution.

An object of class `varipart`

randomized replicates.

(s) Stephane Dray [email protected] and Sylvie Clappe [email protected]

Sylvie Clappe, Stephane Dray and Pedro R. Peres-Neto (in preparation) Beyond neutrality: using a null model to disentangle the effects of niche dynamics and spurious correlations in variation partitioning.

Wagner, H. H., and S. Dray, 2015. Generating spatially constrained null models for irregularly spaced data using Moran spectral randomization methods. Methods in Ecology and Evolution 6:1169–1178.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
library(ade4)
library(spdep)
data(mafragh)
## Performing standard variation partitioning
dudiY <- dudi.pca(mafragh$flo, scannf = FALSE, scale = FALSE)
mafragh.lw <- nb2listw(mafragh$nb)
me <- mem(mafragh.lw, MEM.autocor = "positive")
vprda <- varipart(dudiY, mafragh$env, me, type = "parametric")
## Adjust estimation and compute p-value by msr methods
vprda.msr <- msr(vprda, mafragh.lw, nrepet=99)
vprda.msr
``` |

```
Attaching package: 'ade4'
The following object is masked from 'package:adespatial':
multispati
Loading required package: sp
Loading required package: Matrix
Attaching package: 'spdep'
The following object is masked from 'package:ade4':
mstree
$test
Monte-Carlo test
Call: msr.varipart(x = vprda, listwORorthobasis = mafragh.lw, nrepet = 99)
Observation: 0.2366554
Based on 99 replicates
Simulated p-value: 0.02
Alternative hypothesis: greater
Std.Obs Expectation Variance
2.6216833285 0.1742386390 0.0005668156
$R2
a b c d
0.06295763 0.17369775 0.42438794 0.33895668
$R2.adj.msr
a b c d
-0.0139170 0.0895039 0.2565338 0.6678793
attr(,"class")
[1] "varipart" "list"
```

adespatial documentation built on Sept. 27, 2018, 5:04 p.m.

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