Table 1

Allelic richness estimated by regression, coalescent and rarefaction

Species

ID

Source data set

Estimated allelic richness


No. of loci

N

A

Subsampling

(n = 120)

ρ

(n = 120)

θEwens

(n = 120)

θcoalescent

(n = 120)

Rarefaction

(n = 120)


Microsatellites


Picea rubens

PR1

6

180

13.00

11.06

11.04

11.98

9.23

10.68

PR2

6

180

13.33

11.18

11.17

12.29

8.94

10.71

PR3

6

180

15.33

12.48

12.44

14.13

11.92

12.19

PR4

6

180

14.83

12.48

12.44

13.67

12.13

11.92


Picea glauca

PG1

6

105

22.83

21.13

21.30

23.49

35.74

20.96

PG2

6

105

22.83

20.55

20.62

23.49

51.84

20.44


Pinus strobus

PS1

13

102

9.77

9.03

9.13

10.11

17.57

9.03

PS2

13

102

9.23

8.67

8.73

9.55

15.91

8.68


Thuja occidentalis

TO1

6

100

7.83

7.18

7.17

8.14

12.26

7.17

TO2

6

100

9.67

8.95

9.00

10.05

16.28

9.09

TO3

6

100

8.83

7.86

7.95

9.18

14.06

7.95


Allozymes


Pinus strobus

PS1

15

95

3.20

2.97

2.98

3.38

3.34

2.93

PS2

15

95

3.27

3.09

3.10

3.59

4.15

3.04


Subsampling - allelic richness estimated by repeated random subsampling in pseudosimulated population data sets based on the empirical data. ρ - allelic richness predicted by the regression model (5). θEwens - Allelic richness predicted by the Ewens sampling formula (3), where θ was directly calculated from the empirical data set. θcoalescent - Allelic richness predicted by the Ewens sampling formula (3), where θ was estimated by coalescent approach from the empirical data set. Rarefaction - Allelic richness predicted by rarefaction of the source empirical data set to the sample size of n = 120.

Bashalkhanov et al. BMC Genetics 2009 10:84   doi:10.1186/1471-2156-10-84

Open Data