raupcrick: Raup-Crick Dissimilarity with Unequal Sampling Densities of...

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

Description

Function finds the Raup-Crick dissimilarity which is a probability of number of co-occurring species with species occurrence probabilities proportional to species frequencies.

Usage

1
raupcrick(comm, null = "r1", nsimul = 999, chase = FALSE, ...)

Arguments

comm

Community data which will be treated as presence/absence data.

null

Null model used as the method in oecosimu.

nsimul

Number of null communities for assessing the dissimilarity index.

chase

Use the Chase et al. (2011) method of tie handling (not recommended except for comparing the results against the Chase script).

...

Other parameters passed to oecosimu.

Details

Raup-Crick index is the probability that compared sampling units have non-identical species composition. This probability can be regarded as a dissimilarity, although it is not metric: identical sampling units can have dissimilarity slightly above 0, the dissimilarity can be nearly zero over a range of shared species, and sampling units with no shared species can have dissimilarity slightly below 1. Moreover, communities sharing rare species appear as more similar (lower probability of finding rare species together), than communities sharing the same number of common species.

The function will always treat the data as binary (presence/ absence).

The probability is assessed using simulation with oecosimu where the test statistic is the observed number of shared species between sampling units evaluated against a community null model (see Examples). The default null model is "r1" where the probability of selecting species is proportional to the species frequencies.

The vegdist function implements a variant of the Raup-Crick index with equal sampling probabilities for species using exact analytic equations without simulation. This corresponds to null model "r0" which also can be used with the current function. All other null model methods of oecosimu can be used with the current function, but they are new unpublished methods.

Value

The function returns an object inheriting from dist which can be interpreted as a dissimilarity matrix.

Note

The test statistic is the number of shared species, and this is typically tied with a large number of simulation results. The tied values are handled differently in the current function and in the function published with Chase et al. (2011). In vegan, the index is the number of simulated values that are smaller or equal than the observed value, but smaller than observed value is used by Chase et al. (2011) with option split = FALSE in their script; this can be achieved with chase = TRUE in vegan. Chase et al. (2011) script with split = TRUE uses half of tied simulation values to calculate a distance measure, and that choice cannot be directly reproduced in vegan (it is the average of vegan raupcrick results with chase = TRUE and chase = FALSE).

Author(s)

The function was developed after Brian Inouye contacted us and informed us about the method in Chase et al. (2011), and the function takes its idea from the code that was published with their paper. The current function was written by Jari Oksanen.

References

Chase, J.M., Kraft, N.J.B., Smith, K.G., Vellend, M. and Inouye, B.D. (2011). Using null models to disentangle variation in community dissimilarity from variation in alpha-diversity. Ecosphere 2:art24 [doi:10.1890/ES10-00117.1]

See Also

The function is based on oecosimu. Function vegdist with method = "raup" implements a related index but with equal sampling densities of species, and designdist demonstrates its calculation.

Examples

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## data set with variable species richness
data(sipoo)
## default raupcrick
dr1 <- raupcrick(sipoo)
## use null model "r0" of oecosimu
dr0 <- raupcrick(sipoo, null = "r0")
## vegdist(..., method = "raup") corresponds to 'null = "r0"'
d <- vegdist(sipoo, "raup")
op <- par(mfrow=c(2,1), mar=c(4,4,1,1)+.1)
plot(dr1 ~ d, xlab = "Raup-Crick with Null R1", ylab="vegdist")
plot(dr0 ~ d, xlab = "Raup-Crick with Null R0", ylab="vegdist")
par(op)

## The calculation is essentially as in the following oecosimu() call,
## except that designdist() is replaced with faster code
## Not run: 
oecosimu(sipoo, function(x) designdist(x, "J", "binary"), method = "r1")

## End(Not run)

Example output

Loading required package: permute
Loading required package: lattice
This is vegan 2.4-3
oecosimu object

Call: oecosimu(comm = sipoo, nestfun = function(x) designdist(x, "J",
"binary"), method = "r1")

nullmodel method 'r1' with 99 simulations

alternative hypothesis: statistic is less or greater than simulated values

          Svartholm L.Hogholm Ledholmen S.Hogholm Torvedsh SkataLed Flakaskar
L.Hogholm         2                                                          
Ledholmen         3         3                                                
S.Hogholm         1         1         1                                      
Torvedsh          3         3         4         2                            
SkataLed          1         3         2         2        4                   
Flakaskar         1         1         1         2        3        3          
S.farholm         3         3         4         2        4        4         2
Farholmn          1         2         1         2        2        4         2
Asplandet         1         2         1         2        3        4         3
Granlndet         2         3         3         2        5        6         3
Hanskholm         3         3         4         2        6        4         3
Ragskar           3         3         4         2        6        5         3
Trutland          3         3         5         1        5        3         2
Kaivokari         2         3         3         2        5        6         3
Mustahevo         2         2         3         2        5        5         3
Kaunissri         3         4         5         2        6        6         3
Onas              3         4         4         2        6        6         4
          S.farholm Farholmn Asplandet Granlndet Hanskholm Ragskar Trutland
L.Hogholm                                                                  
Ledholmen                                                                  
S.Hogholm                                                                  
Torvedsh                                                                   
SkataLed                                                                   
Flakaskar                                                                  
S.farholm                                                                  
Farholmn          5                                                        
Asplandet         4        4                                               
Granlndet         7        5         6                                     
Hanskholm         6        4         4         6                           
Ragskar           6        5         4         7         8                 
Trutland          6        2         4         6         7       7         
Kaivokari         6        6         5         8         7      10        7
Mustahevo         5        5         4         8         7      11        7
Kaunissri         9        6         6        10        10      11       11
Onas              7        6         5         9         9      12        9
          Kaivokari Mustahevo Kaunissri
L.Hogholm                              
Ledholmen                              
S.Hogholm                              
Torvedsh                               
SkataLed                               
Flakaskar                              
S.farholm                              
Farholmn                               
Asplandet                              
Granlndet                              
Hanskholm                              
Ragskar                                
Trutland                               
Kaivokari                              
Mustahevo        11                    
Kaunissri        13        15          
Onas             15        15        22

       statistic       SES     mean     2.5%      50% 97.5% Pr(sim.)   
  [1,]         2  1.817643  0.60606  0.00000  0.00000  2.00     0.27   
  [2,]         3  3.515120  0.59596  0.00000  0.00000  2.00     0.03 * 
  [3,]         1  1.667133  0.26263  0.00000  0.00000  1.00     0.53   
  [4,]         3  2.583701  0.90909  0.00000  1.00000  3.00     0.09 . 
  [5,]         1  0.168818  0.86869  0.00000  1.00000  3.00     1.00   
  [6,]         1  0.494518  0.68687  0.00000  1.00000  2.00     1.00   
  [7,]         3  1.804440  1.39394  0.00000  1.00000  3.00     0.19   
  [8,]         1  0.102129  0.91919  0.00000  1.00000  3.00     1.00   
  [9,]         1 -0.200991  1.19192  0.00000  1.00000  3.00     1.00   
 [10,]         2  0.104829  1.89899  0.00000  2.00000  3.00     1.00   
 [11,]         3  1.630834  1.43434  0.00000  1.00000  3.00     0.29   
 [12,]         3  1.416663  1.69697  0.00000  2.00000  3.00     0.35   
 [13,]         3  1.313352  1.85859  0.00000  2.00000  3.00     0.53   
 [14,]         2 -0.255528  2.27273  0.00000  2.00000  4.00     1.00   
 [15,]         2 -0.360041  2.33333  1.00000  2.00000  4.00     1.00   
 [16,]         3 -0.392787  3.29293  2.00000  3.00000  4.00     1.00   
 [17,]         3 -0.752101  3.48485  2.00000  4.00000  4.00     0.89   
 [18,]         3  3.405735  0.66667  0.00000  1.00000  2.00     0.03 * 
 [19,]         1  1.322676  0.32323  0.00000  0.00000  1.00     0.61   
 [20,]         3  2.934576  0.96970  0.00000  1.00000  2.00     0.03 * 
 [21,]         3  2.997374  0.85859  0.00000  1.00000  2.00     0.03 * 
 [22,]         1  0.501892  0.64646  0.00000  1.00000  2.00     1.00   
 [23,]         3  1.773160  1.38384  0.00000  1.00000  3.00     0.21   
 [24,]         2  1.412803  0.91919  0.00000  1.00000  2.55     0.39   
 [25,]         2  0.946661  1.15152  0.00000  1.00000  3.00     0.65   
 [26,]         3  1.171234  1.82828  0.00000  2.00000  4.00     0.55   
 [27,]         3  1.629512  1.48485  0.00000  2.00000  3.00     0.25   
 [28,]         3  1.287925  1.74747  0.00000  2.00000  4.00     0.39   
 [29,]         3  1.038641  2.11111  1.00000  2.00000  4.00     0.61   
 [30,]         3  0.995040  2.01010  0.00000  2.00000  4.00     0.67   
 [31,]         2 -0.241308  2.22222  1.00000  2.00000  4.00     1.00   
 [32,]         4  1.100855  3.23232  2.00000  3.00000  4.00     0.77   
 [33,]         4  0.803205  3.49495  2.00000  4.00000  4.00     1.00   
 [34,]         1  0.918705  0.45455  0.00000  0.00000  2.00     0.81   
 [35,]         4  3.005561  1.30303  0.00000  1.00000  3.00     0.01 **
 [36,]         2  0.828900  1.31313  0.00000  1.00000  3.00     0.85   
 [37,]         1  0.199147  0.84848  0.00000  1.00000  2.00     1.00   
 [38,]         4  2.385983  1.70707  0.00000  2.00000  3.55     0.07 . 
 [39,]         1 -0.267344  1.20202  0.00000  1.00000  2.00     1.00   
 [40,]         1 -0.477960  1.47475  0.00000  1.00000  3.55     1.00   
 [41,]         3  0.577918  2.35354  0.00000  3.00000  4.00     1.00   
 [42,]         4  2.119678  1.85859  0.00000  2.00000  4.00     0.09 . 
 [43,]         4  1.908853  2.10101  0.00000  2.00000  4.00     0.11   
 [44,]         5  2.305181  2.44444  0.45000  3.00000  4.00     0.03 * 
 [45,]         3  0.200440  2.76768  0.45000  3.00000  5.00     1.00   
 [46,]         3  0.214574  2.77778  1.00000  3.00000  4.55     1.00   
 [47,]         5  1.010835  4.16162  2.00000  4.00000  5.00     0.79   
 [48,]         4 -0.577281  4.39394  3.00000  4.00000  5.00     1.00   
 [49,]         2  2.527566  0.49495  0.00000  0.00000  2.00     0.11   
 [50,]         2  2.645547  0.43434  0.00000  0.00000  2.00     0.11   
 [51,]         2  3.731935  0.29293  0.00000  0.00000  1.00     0.01 **
 [52,]         2  1.842759  0.77778  0.00000  1.00000  2.00     0.27   
 [53,]         2  2.774377  0.41414  0.00000  0.00000  2.00     0.09 . 
 [54,]         2  1.989873  0.66667  0.00000  1.00000  2.00     0.23   
 [55,]         2  1.687789  0.78788  0.00000  1.00000  2.00     0.35   
 [56,]         2  1.839639  0.78788  0.00000  1.00000  2.00     0.27   
 [57,]         2  1.893740  0.75758  0.00000  1.00000  2.00     0.25   
 [58,]         1 -0.092981  1.06061  0.00000  1.00000  2.00     1.00   
 [59,]         2  1.361407  1.11111  0.00000  1.00000  2.00     0.55   
 [60,]         2  1.069329  1.28283  0.00000  1.00000  2.00     0.81   
 [61,]         2  0.624806  1.71717  1.00000  2.00000  2.00     1.00   
 [62,]         2  0.332411  1.86869  1.00000  2.00000  2.00     1.00   
 [63,]         4  3.258578  1.24242  0.00000  1.00000  3.00     0.01 **
 [64,]         3  3.509236  0.76768  0.00000  1.00000  2.00     0.01 **
 [65,]         4  1.905159  2.00000  0.00000  2.00000  4.00     0.17   
 [66,]         2  0.707557  1.32323  0.00000  1.00000  3.00     0.83   
 [67,]         3  1.235700  1.74747  0.00000  2.00000  3.55     0.49   
 [68,]         5  1.775376  2.69697  0.00000  3.00000  5.00     0.19   
 [69,]         6  3.338042  2.24242  0.00000  2.00000  4.00     0.01 **
 [70,]         6  3.010334  2.40404  0.00000  2.00000  5.00     0.01 **
 [71,]         5  1.982252  2.86869  1.00000  3.00000  5.00     0.11   
 [72,]         5  1.812934  2.96970  1.00000  3.00000  5.00     0.19   
 [73,]         5  1.469862  3.33333  1.00000  3.00000  5.00     0.33   
 [74,]         6  1.248159  4.74747  3.00000  5.00000  6.00     0.47   
 [75,]         6  0.924673  5.18182  3.00000  5.00000  6.00     0.85   
 [76,]         3  2.091606  1.03030  0.00000  1.00000  3.00     0.13   
 [77,]         4  1.775855  2.18182  0.00000  2.00000  4.00     0.21   
 [78,]         4  2.770045  1.38384  0.00000  1.00000  3.00     0.01 **
 [79,]         4  2.145736  1.77778  0.00000  2.00000  4.00     0.11   
 [80,]         6  3.020112  2.70707  1.00000  3.00000  4.55     0.01 **
 [81,]         4  1.925850  2.25253  1.00000  2.00000  4.00     0.21   
 [82,]         5  2.344439  2.44444  1.00000  2.00000  5.00     0.09 . 
 [83,]         3 -0.044063  3.05051  1.00000  3.00000  5.00     1.00   
 [84,]         6  2.247203  3.33333  1.00000  3.00000  6.00     0.09 . 
 [85,]         5  1.485939  3.46465  1.45000  4.00000  5.00     0.31   
 [86,]         6  1.095491  4.89899  3.00000  5.00000  6.00     0.65   
 [87,]         6  0.926178  5.22222  3.00000  5.00000  6.00     0.89   
 [88,]         2  0.740554  1.39394  0.00000  1.00000  3.00     0.89   
 [89,]         2  1.440800  0.90909  0.00000  1.00000  2.00     0.45   
 [90,]         3  2.162006  1.17172  0.00000  1.00000  3.00     0.13   
 [91,]         3  1.448291  1.65657  0.00000  2.00000  3.00     0.35   
 [92,]         3  1.787215  1.42424  0.00000  1.00000  3.00     0.25   
 [93,]         3  1.371511  1.78788  0.00000  2.00000  3.00     0.47   
 [94,]         2  0.124722  1.88889  0.00000  2.00000  3.55     1.00   
 [95,]         3  0.903659  2.10101  0.00000  2.00000  4.00     0.75   
 [96,]         3  0.910055  2.13131  0.00000  2.00000  4.00     0.63   
 [97,]         3 -0.265521  3.21212  1.45000  3.00000  4.00     1.00   
 [98,]         4  0.762539  3.48485  2.00000  4.00000  4.00     1.00   
 [99,]         5  2.523460  2.19192  0.00000  2.00000  4.00     0.03 * 
[100,]         4  1.104999  2.71717  1.00000  3.00000  5.00     0.47   
[101,]         7  1.982558  4.28283  2.00000  4.00000  7.00     0.15   
[102,]         6  1.848986  3.42424  1.00000  3.00000  6.00     0.17   
[103,]         6  1.708111  3.95960  2.00000  4.00000  6.55     0.19   
[104,]         6  1.250563  4.39394  2.00000  4.00000  7.00     0.45   
[105,]         6  0.788095  4.98990  3.00000  5.00000  7.00     0.65   
[106,]         5 -0.432260  5.58586  3.00000  6.00000  8.00     0.95   
[107,]         9  0.957030  7.71717  5.00000  8.00000 10.00     0.49   
[108,]         7 -1.793108  8.64646  7.00000  9.00000 10.00     0.21   
[109,]         4  2.272249  1.73737  0.00000  2.00000  4.00     0.13   
[110,]         5  1.903690  2.81818  1.00000  3.00000  5.00     0.11   
[111,]         4  1.755976  2.16162  0.00000  2.00000  4.00     0.19   
[112,]         5  2.102507  2.67677  1.00000  3.00000  4.55     0.07 . 
[113,]         2 -0.668663  2.81818  0.45000  3.00000  5.00     0.79   
[114,]         6  2.493512  3.17172  1.00000  3.00000  5.00     0.05 * 
[115,]         5  1.353937  3.48485  2.00000  4.00000  5.55     0.35   
[116,]         6  1.285942  4.96970  3.00000  5.00000  6.00     0.53   
[117,]         6  0.982807  5.25253  4.00000  5.00000  6.00     0.85   
[118,]         6  1.852104  3.48485  1.00000  3.00000  6.00     0.15   
[119,]         4  0.991012  2.72727  0.45000  3.00000  5.00     0.59   
[120,]         4  0.703399  3.08081  1.00000  3.00000  5.00     0.81   
[121,]         4  0.320755  3.52525  1.00000  3.00000  6.00     0.97   
[122,]         5  0.704324  3.95960  1.00000  4.00000  6.00     0.73   
[123,]         4 -0.057234  4.08081  1.45000  4.00000  7.00     1.00   
[124,]         6 -0.311300  6.32323  4.45000  6.00000  8.00     1.00   
[125,]         5 -2.200414  6.89899  5.00000  7.00000  8.00     0.11   
[126,]         6  1.080346  4.45455  2.00000  4.00000  7.00     0.47   
[127,]         7  1.253298  5.10101  2.45000  5.00000  8.00     0.39   
[128,]         6  0.329366  5.55556  3.00000  6.00000  8.00     1.00   
[129,]         8  1.209777  6.33333  4.00000  6.00000  9.00     0.39   
[130,]         8  0.714667  6.75758  3.00000  7.00000 10.00     0.77   
[131,]        10 -0.187295 10.24242  8.00000 10.00000 12.55     1.00   
[132,]         9 -1.764598 11.06061  9.00000 11.00000 13.00     0.21   
[133,]         8  3.084507  4.11111  1.00000  4.00000  6.00     0.01 **
[134,]         7  1.538724  4.78788  2.00000  5.00000  8.00     0.27   
[135,]         7  1.322415  5.22222  2.45000  5.00000  8.00     0.27   
[136,]         7  0.901068  5.56566  3.00000  6.00000  8.00     0.57   
[137,]        10  1.649916  8.00000  5.00000  8.00000 10.00     0.19   
[138,]         9  0.506304  8.43434  6.00000  9.00000 10.00     1.00   
[139,]         7  1.096162  5.49495  3.00000  5.00000  8.00     0.53   
[140,]        10  3.029523  5.81818  2.45000  6.00000  8.00     0.01 **
[141,]        11  3.178976  6.11111  3.00000  6.00000  9.00     0.01 **
[142,]        11  1.181489  9.52525  7.00000 10.00000 11.00     0.55   
[143,]        12  1.577920 10.07071  7.45000 10.00000 12.00     0.21   
[144,]         7  0.064148  6.89899  4.00000  7.00000  9.55     1.00   
[145,]         7 -0.270320  7.41414  4.00000  8.00000 10.55     0.99   
[146,]        11 -0.022061 11.03030  9.00000 11.00000 14.00     1.00   
[147,]         9 -2.172582 11.82828  9.00000 12.00000 14.00     0.09 . 
[148,]        11  1.649133  8.52525  6.00000  8.00000 11.00     0.27   
[149,]        13  0.533069 12.25253 10.00000 12.00000 15.00     0.85   
[150,]        15  1.373913 13.13131 10.00000 13.00000 15.00     0.29   
[151,]        15  1.262834 12.95960  9.45000 13.00000 15.55     0.41   
[152,]        15  0.643627 14.04040 11.00000 14.00000 16.55     0.85   
[153,]        22 -0.879350 23.37374 21.00000 23.00000 26.55     0.59   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

vegan documentation built on Aug. 24, 2017, 5:21 p.m.