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

Similarities among two subsamples, each one obtained randomly from the same community dataset. Curves are otained for all subsample sizes from 1 up to half the number of sample units in the dataset. Autosimilarity curves can be used to evaluate sample suficiency when sample size is expressed as number of sampling units such as traps or quadrats. This is particularly suitable when the study involves similarities or dissimilarities among samples such as agglomerative clustering, ordination, Mantel test.

1 |

`comm ` |
Dataframe or matrix with samples in rows and species in columns. |

`method ` |
Similarity index obtained from |

`binary ` |
Should data be transformed to presence/absence? |

`log.transf ` |
Transformation |

`simi ` |
Similarity or dissimilarity curve. |

`permutations ` |
Number of curves randomly calculated and from which the mean autosimilairty curve is obtained. |

The function selects randomly and without replacement an even number of sampling units (n = 2,4,6, ...) from the total sampling units. Next, the first n/2 sampling units are pooled (summed) to create a subsample, and the other n/2 sampling units to create another subsample. If `binary=TRUE`

, the two subsamples are transformed to presence/absence. If `log.transf=TRUE`

, `log(x+1)`

of each value is obtained. The similarity between the two subsamples is calculated and stored. The procedure is repeated for larger subsample sizes until half the size of the full dataset (or up to the integer quotient in the case of odd numbers of sample units). The procedure is then repeated for the requested number of curves. The output is the average curve. This function is a different implementation of the procedures described in Schneck & Melo (2010).

A dataframe containing subsample sizes and average similarity values.

Adriano Sanches Melo

Cao, Y., D.P. Larsen & R.M. Hughes. 2001. Evaluating sampling sufficiency in fish assemblage surveys: a similarity-based approach. Canadian Jounal of Fisheries and Aquatic Sciences 58: 1782-1793.

Schneck, F. & A.S. Melo. 2010. Reliable sample sizes for estimating similarity among macroinvertebrate assemblages in tropical streams. Annales de Limnologie 46: 93-100.

Weinberg, S. 1978. Minimal area problem in invertebrate communities of Mediterranean rocky substrata. Marine Biology 49: 33-40.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
x<-matrix(0,4,4)
diag(x)<-1
x4<-rbind(x,x,x,x)
x4
autosimi(x4, binary=TRUE)
plot(autosimi(x4, binary=TRUE))
data(BCI)
simi<-autosimi(BCI, binary=TRUE, permutations=5)
simi
plot(simi, ylim=c(0.5,1)) # maintain the plot window open for the next curve
simi.log<-autosimi(BCI, binary=FALSE, log.transf=TRUE, permutations=5)
points(simi.log, col="red")
``` |

```
Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.5-4
Loading required package: ape
Loading required package: picante
Loading required package: nlme
[,1] [,2] [,3] [,4]
[1,] 1 0 0 0
[2,] 0 1 0 0
[3,] 0 0 1 0
[4,] 0 0 0 1
[5,] 1 0 0 0
[6,] 0 1 0 0
[7,] 0 0 1 0
[8,] 0 0 0 1
[9,] 1 0 0 0
[10,] 0 1 0 0
[11,] 0 0 1 0
[12,] 0 0 0 1
[13,] 1 0 0 0
[14,] 0 1 0 0
[15,] 0 0 1 0
[16,] 0 0 0 1
sample.size bray
1 1 0.1600000
2 2 0.3700000
3 3 0.5220000
4 4 0.6619048
5 5 0.7720000
6 6 0.8447619
7 7 0.9200000
8 8 0.9771429
sample.size bray
1 1 0.6685417
2 2 0.7692059
3 3 0.7992732
4 4 0.8275900
5 5 0.8479456
6 6 0.8570635
7 7 0.8809040
8 8 0.8892465
9 9 0.8799334
10 10 0.9050134
11 11 0.8989042
12 12 0.9020819
13 13 0.9015693
14 14 0.9028228
15 15 0.9210156
16 16 0.9007667
17 17 0.9114355
18 18 0.9136586
19 19 0.9118472
20 20 0.9085888
21 21 0.9031140
22 22 0.9100698
23 23 0.9124416
24 24 0.9198143
25 25 0.9146023
```

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