之前也介绍过几个计算多样性的包，包括vegan，iNEXT,fossil等。见
物种数量及多样性的外推
SpadeR是2016年发表的较新的R包，汇集了几乎所有常见的多样性计算和估计的方法，计算基于个体(丰度)数据或基于采样单元(发生率)数据的各种生物多样性指数和相关相似性指标。 里面有很多对原始方法的改进值得注意。如仅chao2就补充了Chao2-bc和ichao2两种更新的改进方法。
安装
代码语言：javascript复制1install.packages("SpadeR")
2library(SpadeR)
包含6个主要函数
1.ChaoSpecies，估计群落物种多样性
代码语言：javascript复制 1data(ChaoSpeciesData)
2ChaoSpecies(ChaoSpeciesData$Abu,"abundance",k=10,conf=0.95)
3#k为稀有物种的丰度阈值，用于计算ACE和ICE。conf为置信区间。
4#结果包括三部分。（1）是基本信息，（2）为各种多样性指标，（3）为各种指标的说明。
5(1) BASIC DATA INFORMATION:
6
7 Variable Value
8 Sample size n 1996
9 Number of observed species D 25
10 Coverage estimate for entire dataset C 0.998
11 CV for entire dataset CV 1.916
12 Cut-off point k 10
13
14 Variable Value
15 Number of observed individuals for rare group n_rare 53
16 Number of observed species for rare group D_rare 11
17 Estimate of the sample coverage for rare group C_rare 0.943
18 Estimate of CV for rare group in ACE CV_rare 0.629
19 Estimate of CV1 for rare group in ACE-1 CV1_rare 0.74
20 Number of observed individuals for abundant group n_abun 1943
21 Number of observed species for abundant group D_abun 14
22
23NULL
24
25
26(2) SPECIES RICHNESS ESTIMATORS TABLE:
27
28 Estimate s.e. 95%Lower 95%Upper
29 Homogeneous Model 25.660 0.954 25.082 30.295
30 Homogeneous (MLE) 25.000 0.975 25.000 28.500
31 Chao1 (Chao, 1984) 27.249 3.394 25.266 44.030
32 Chao1-bc 25.999 1.817 25.094 35.673
33 iChao1 (Chiu et al. 2014) 27.249 3.394 25.266 44.030
34 ACE (Chao & Lee, 1992) 26.920 2.367 25.292 37.639
35 ACE-1 (Chao & Lee, 1992) 27.399 3.163 25.336 42.153
36 1st order jackknife 27.998 2.449 25.739 37.171
37 2nd order jackknife 28.998 4.240 25.730 46.915
38
39
40(3) DESCRIPTION OF ESTIMATORS/MODELS:
41
42Homogeneous Model: This model assumes that all species have the same incidence or detection probabilities. See Eq. (3.2) of Lee and Chao (1994) or Eq. (12a) in Chao and Chiu (2016b).
43
44Chao2 (Chao, 1987): This approach uses the frequencies of uniques and duplicates to estimate the number of undetected species; see Chao (1987) or Eq. (11a) in Chao and Chiu (2016b).
45
46Chao2-bc: A bias-corrected form for the Chao2 estimator; see Chao (2005).
47
48iChao2: An improved Chao2 estimator; see Chiu et al. (2014).
49
50ICE (Incidence-based Coverage Estimator): A non-parametric estimator originally proposed by Lee and Chao (1994) in the context of capture-recapture data analysis. The observed species are separated as frequent and infrequent species groups;>in the infrequent group are used to estimate the number of undetected species. The estimated CV for species in the infrequent group characterizes the degree of heterogeneity among species incidence probabilities. See Eq. (12b) of Chao and Chiu (2016b), which is an improved version of Eq. (3.18) in Lee and Chao (1994). This model is also called Model(h) in capture-recapture literature where h denotes "heterogeneity".
51
52ICE-1: A modified ICE for highly-heterogeneous cases.
53
541st order jackknife: It uses the frequency of uniques to estimate the number of undetected species; see Burnham and Overton (1978).
55
562nd order jackknife: It uses the frequencies of uniques and duplicates to estimate the number of undetected species; see Burnham and Overton (1978).
57
5895% Confidence interval: A log-transformation is used for all estimators so that the lower bound of the resulting interval is at least the number of observed species. See Chao (1987).2.Diversity，计算richness, Shannon diversity and Simpson diversity
代码语言：javascript复制 1data(DiversityData)
2Diversity(DiversityData$Abu,"abundance",q=c(0,0.5,1,1.5,2))
3#q为多样性阶数
4#结果分5部分
5(1) BASIC DATA INFORMATION:
6 Variable Value
7 Sample size n 557
8 Number of observed species D 69
9 Estimated sample coverage C 0.957
10 Estimated CV CV 2.237
11
12(2) ESTIMATION OF SPECIES RICHNESS (DIVERSITY OF ORDER 0):
13
14 Estimate s.e. 95%Lower 95%Upper
15 Chao1 (Chao, 1984) 104.9 20.3 81.8 169.9
16 Chao1-bc 99.6 16.9 80.1 153.2
17 iChao1 113.9 12.7 95.1 146.4
18 ACE (Chao & Lee, 1992) 92.1 10.2 79.1 121.8
19 ACE-1 (Chao & Lee, 1992) 100.4 15.7 81.4 148.1
20
21 Descriptions of richness estimators (See Species Part)
22
23(3a) SHANNON ENTROPY:
24
25 Estimate s.e. 95%Lower 95%Upper
26 MLE 3.193 0.065 3.067 3.320
27 Jackknife 3.280 0.070 3.143 3.417
28 Chao & Shen 3.308 0.071 3.168 3.447
29 Chao et al. (2013) 3.293 0.072 3.152 3.433
30
31 MLE: empirical or observed entropy.
32 Jackknife: see Zahl (1977).
33 Chao & Shen: based>2003).
34 see Chao and Shen (2003).
35 Chao et al. (2013): A nearly optimal estimator of Shannon entropy; see Chao et al. (2013).
36 Estimated standard error is computed based>37
38(3b) SHANNON DIVERSITY (EXPONENTIAL OF SHANNON ENTROPY):
39
40 Estimate s.e. 95%Lower 95%Upper
41 MLE 24.372 1.539 21.355 27.388
42 Jackknife 26.573 1.805 23.035 30.111
43 Chao & Shen 27.320 1.895 23.606 31.034
44 Chao et al. (2013) 26.917 1.870 23.251 30.583
45
46(4a) SIMPSON CONCENTRATION INDEX:
47
48 Estimate s.e. 95%Lower 95%Upper
49 MVUE 0.08328 0.00714 0.06929 0.09728
50 MLE 0.08493 0.00713 0.07096 0.09890
51
52 MVUE: minimum variance unbiased estimator; see Eq. (2.27) of Magurran (1988).
53 MLE: maximum likelihood estimator or empirical index; see Eq. (2.26) of Magurran (1988).
54
55(4b) SIMPSON DIVERSITY (INVERSE OF SIMPSON CONCENTRATION):
56
57 Estimate s.e. 95%Lower 95%Upper
58 MVUE 12.00729 0.96804 10.10992 13.90465
59 MLE 11.77460 0.92959 9.95262 13.59659
60
61(5) CHAO AND JOST (2015) ESTIMATES OF HILL NUMBERS
62
63 q ChaoJost 95%Lower 95%Upper Empirical 95%Lower 95%Upper
64 1 0.0 104.935 7.476 202.394 69.000 61.625 76.375
65 2 0.5 53.093 38.499 67.687 41.565 37.267 45.863
66 3 1.0 26.917 23.475 30.359 24.372 21.420 27.324
67 4 1.5 16.411 13.936 18.886 15.806 13.481 18.131
68 5 2.0 12.007 10.006 14.008 11.775 9.854 13.696
69
70 ChaoJost: diversity profile estimator derived by Chao and Jost (2015).
71 Empirical: maximum likelihood estimator (observed index).3.ChaoShared，计算两群落共有的物种
代码语言：javascript复制1data(ChaoSharedData)
2ChaoShared(ChaoSharedData$Abu,"abundance",se=TRUE,nboot=200,conf=0.95)
3#结果太多不放了4.SimilartyPair，计算两群落的相似性指数
代码语言：javascript复制1data(SimilarityPairData)
2SimilarityPair(SimilarityPairData$Abu,"abundance",nboot=200)
3#结果也很丰富，包括了除Jaccard and Sorensen以外其他多种指标5.SimilarityMult，计算多个群落的相似性指数
6.Genetics，计算基因数据的等位基因不相似性
感兴趣可以自己试用一下~
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