The Latest
Update in Nov. 2024: In earlier versions, diversity decomposition
(alpha, beta, gamma, and dissimilarity) was performed only for all
assemblages of datasets. In the updated version, we have added a logical
argument “by_pair” in the main function “iNEXTbeta3D” to specify whether
diversity decomposition will be performed for pairs of assemblages or
not. If “by_pair = TRUE”, alpha/beta/gamma diversity or dissimilarity
will be computed for all pairs of assemblages in the input data; if
“by_pair = FALSE”, alpha/beta/gamma diversity or dissimilarity will be
computed for K assemblages (i.e., K can be greater than two) when data
for K assemblages are provided in the input data. Default is “by_pair =
FALSE”.
The package iNEXT.beta3D (iNterpolation and
EXTrapolation with beta diversity for three dimensions of biodiversity)
is a sequel to iNEXT. The three dimensions (3D) of
biodiversity include taxonomic diversity
(TD), phylogenetic diversity (PD) and
functional diversity (FD). This document
provides an introduction demonstrating how to run
iNEXT.beta3D. An online version iNEXT.beta3D Online
is also available for users without an R background.
A unified framework based on Hill numbers and their generalizations
is adopted to quantify TD, PD and FD. TD quantifies the effective number
of species, mean-PD (PD divided by tree depth) quantifies the effective
number of lineages, and FD quantifies the effective number of virtual
functional groups (or functional “species”). Thus, TD, mean-PD, and FD
are all in the same units of species/lineage equivalents and can be
meaningfully compared; see Chao et al. (2021) for a review of the
unified framework.
For each of the three dimensions, iNEXT.beta3D focuses
on the multiplicative diversity decomposition (alpha, beta and gamma) of
orders q = 0, 1 and 2 based on sampling data. Beta diversity quantifies
the extent of among-assemblage differentiation, or the changes in
species/lineages/functional-groups composition and abundance among
assemblages. iNEXT.beta3D features standardized 3D
estimates with a common sample size (for alpha and gamma diversity) or
sample coverage (for alpha, beta and gamma diversity).
iNEXT.beta3D also features coverage-based standardized
estimates of four classes of dissimilarity measures.
Based on the rarefaction and extrapolation (R/E) method for Hill
numbers (TD) of orders q = 0, 1 and 2, Chao et al. (2023b) developed the
pertinent R/E theory for taxonomic beta diversity with applications to
real-world spatial, temporal and spatio-temporal data. An application to
Gentry’s global forest data along with a concise description of the
theory is provided in Chao et al. (2023a). The extension to phylogenetic
and functional beta diversity is generally parallel.
The iNEXT.beta3D package features two types of R/E
sampling curves:
Sample-size-based (or size-based) R/E sampling curves: This type
of sampling curve plots standardized 3D gamma and
alpha diversity with respect to sample size. Note that the
size-based beta diversity is not a statistically valid measure (Chao et
al. 2023b) and thus the corresponding sampling curve is not
provided.
Sample-coverage-based (or coverage-based) R/E sampling curves:
This type of sampling curve plots standardized 3D
gamma, alpha, and beta diversity as well as
four classes of dissimilarity measures with respect to sample coverage
(an objective measure of sample completeness).
Sufficient data are needed to run iNEXT.beta3D. If your
data comprise only a few species and their
abundances/phylogenies/traits, it is probable that the data lack
sufficient information to run iNEXT.beta3D.
HOW TO CITE iNEXT.beta3D
If you publish your work based on results from
iNEXT.beta3D, you should make reference to at least one of
the following methodology papers (2023a, b) and also cite the
iNEXT.beta3D package:
Chao, A., Chiu, C.-H., Hu, K.-H., and Zeleny, D. (2023a).
Revisiting Alwyn H. Gentry’s forest transect data: a statistical
sampling-model-based approach. Japanese Journal of Statistics and
Data Science, 6, 861-884. (https://doi.org/10.1007/s42081-023-00214-1)
Chao, A., Thorn, S., Chiu, C.-H., Moyes, F., Hu, K.-H., Chazdon,
R. L., Wu, J., Magnago, L. F. S., Dornelas, M., Zeleny, D., Colwell, R.
K., and Magurran, A. E. (2023b). Rarefaction and extrapolation with beta
diversity under a framework of Hill numbers: the iNEXT.beta3D
standardization. Ecological Monographs e1588.(https://doi.org/10.1002/ecm.1588)
Chao, A. and Hu, K.-H. (2023). The iNEXT.beta3D package:
interpolation and extrapolation with beta diversity for three dimensions
of biodiversity. R package available from CRAN.
SOFTWARE NEEDED TO RUN iNEXT.beta3D IN R
HOW TO RUN iNEXT.beta3D:
The iNEXT.beta3D package is available from CRAN and can
be downloaded from Anne Chao’s Github iNEXT.beta3D_github
using the following commands. For a first-time installation, additional
visualization extension package (ggplot2 from CRAN) and
relevant package (iNEXT.3D from CRAN) must be installed and
loaded.
## install iNEXT.beta3D package from CRAN
install.packages("iNEXT.beta3D")
## install the latest version from github
install.packages('devtools')
library(devtools)
install_github('AnneChao/iNEXT.beta3D')
## import packages
library(iNEXT.beta3D)
There are three main functions in this package:
iNEXTbeta3D: computes standardized 3D estimates
with a common sample size (for alpha and gamma diversity) or sample
coverage (for alpha, beta and gamma diversity) for default sample sizes
or coverage values. This function also computes coverage-based
standardized 3D estimates of four classes of dissimilarity measures for
default coverage values. In addition, this function also computes
standardized 3D estimates with a particular vector of user-specified
sample sizes or coverage values.
ggiNEXTbeta3D: Visualizes the output from the
function iNEXTbeta3D.
DataInfobeta3D: Provides basic data information
for (1) the reference sample in each assemblage, (2) the gamma reference
sample in the pooled assemblage, and (3) the alpha reference sample in
the joint assemblage.
MAIN FUNCTION: iNEXTbeta3D()
We first describe the main function iNEXTbeta3D() with
default arguments:
iNEXTbeta3D(data, diversity = "TD", q = c(0, 1, 2), datatype = "abundance",
base = "coverage", level = NULL, nboot = 10, conf = 0.95,
PDtree = NULL, PDreftime = NULL, PDtype = "meanPD",
FDdistM = NULL, FDtype = "AUC", FDtau = NULL, FDcut_number = 30,
by_pair = FALSE)
The arguments of this function are briefly described below, and will
be explained in more details by illustrative examples in later text. By
default (with the standardization base = “coverage”), this
function computes coverage-based standardized 3D gamma, alpha, beta
diversity, and four dissimilarity indices for coverage up to one (for q
= 1, 2) or up to the coverage of double the reference sample size (for q
= 0). If users set the standardization base to base=“size”,
this function computes size-based standardized 3D gamma and alpha
diversity estimates up to double the reference sample size in each
dataset. In addition, this function also computes standardized 3D
estimates with a particular vector of user-specified sample sizes or
coverage values.
data
|
- For
datatype = “abundance”, species abundance data for
a single dataset can be input as a matrix/data.frame
(species-by-assemblage); data for multiple datasets can be input as a
list of matrices/data.frames, with each matrix
representing a species-by-assemblage abundance matrix for one of the
datasets.
- For
datatype = “incidence_raw”, data for a single
dataset with N assemblages can be input as a list of
matrices/data.frames, with each matrix representing a
species-by-sampling-unit incidence matrix for one of the assemblages;
data for multiple datasets can be input as multiple lists.
|
diversity
|
selection of diversity type: diversity = “TD” =
Taxonomic diversity, diversity =
“PD” = Phylogenetic diversity, and
diversity = “FD” = Functional
diversity.
|
q
|
a numerical vector specifying the diversity orders. Default is
c(0, 1, 2).
|
datatype
|
data type of input data: individual-based abundance data (datatype
= “abundance”) or species by sampling-units incidence matrix
(datatype = “incidence_raw”) with all entries being 0
(non-detection) or 1 (detection).
|
base
|
standardization base: coverage-based rarefaction and extrapolation for
gamma, alpha, beta diversity, and four classes of dissimilarity indices
(base = “coverage”), or sized-based rarefaction and
extrapolation for gamma and alpha diversity (base =
“size”). Default is base = “coverage”.
|
level
|
A numerical vector specifying the particular values of sample
coverage (between 0 and 1 when base = “coverage”) or sample
sizes (base = “size”) that will be used to compute
standardized diversity/dissimilarity. Asymptotic diversity estimator can
be obtained by setting level = 1 (i.e., complete coverage
for base = “coverage”).
By default (with base = “coverage”), this function
computes coverage-based standardized 3D gamma, alpha, beta diversity,
and four dissimilarity indices for coverage from 0.5 up to one (for
q = 1, 2) or up to the coverage of double the reference
sample size (for q = 0), in increments of 0.025. The
extrapolation limit for beta diversity is defined as that for alpha
diversity.
If users set base = “size”, this function computes
size-based standardized 3D gamma and alpha diversity estimates based on
40 equally-spaced sample sizes/knots from sample size 1 up to double the
reference sample size.
|
nboot
|
a positive integer specifying the number of bootstrap replications when
assessing sampling uncertainty and constructing confidence intervals.
Bootstrap replications are generally time consuming. Set
nboot = 0 to skip the bootstrap procedures. Default is
nboot = 10. If more accurate results are required, set
nboot = 100 (or nboot = 200).
|
conf
|
a positive number < 1 specifying the level of confidence interval.
Default is conf = 0.95.
|
PDtree
|
(required argument for diversity = “PD”), a phylogenetic
tree in Newick format for all observed species in the pooled assemblage.
|
PDreftime
|
(argument only for diversity = “PD”), a numerical value
specifying reference time for PD. Default is
PDreftime=NULL. (i.e., the age of the root of
PDtree)
|
PDtype
|
(argument only for diversity = “PD”), select PD type:
PDtype = “PD” (effective total branch length) or
PDtype = “meanPD” (effective number of equally divergent
lineages). Default is PDtype = “meanPD”, where
meanPD = PD/tree depth.
|
FDdistM
|
(required argument for diversity = “FD”), a species
pairwise distance matrix for all species in the pooled assemblage.
|
FDtype
|
(argument only for diversity = “FD”), select FD type:
FDtype = “tau_value” for FD under a specified threshold
value, or FDtype = “AUC” (area under the curve of
tau-profile) for an overall FD which integrates all threshold values
between zero and one. Default is FDtype = “AUC”.
|
FDtau
|
(argument only for diversity = “FD” and
FDtype=“tau_value”),
a numerical value between 0 and 1 specifying the tau value (threshold
level) that will be used to compute FD. If FDtau = NULL
(default), then the threshold level is set to be the mean distance
between any two individuals randomly selected from the pooled dataset
(i.e., quadratic entropy).
|
FDcut_number
|
(argument only for diversity = “FD” and
FDtype=“AUC”), a numeric number to cut [0, 1] interval into
equal-spaced sub-intervals to obtain the AUC value by integrating the
tau-profile. Equivalently, the number of tau values that will be
considered to compute the integrated AUC value. Default is
FDcut_number = 30. A larger value can be set to obtain more
accurate AUC value.
|
by_pair
|
a logical variable specifying whether to perform diversity decomposition
for all pairs of assemblages or not. If by_pair = TRUE,
alpha/beta/gamma diversity will be computed for all pairs of assemblages
in the input data; if by_pair = FALSE, alpha/beta/gamma
diversity will be computed for multiple assemblages (i.e, more than two
assemblages) in the input data. Default is FALSE.
|
This function returns an "iNEXTbeta3D" object which can
be further used to make plots using the function
ggiNEXTbeta3D() to be described below. (only accept the
outcome from iNEXTbeta3D under
by_pair = FALSE)
Output of the main function iNEXTbeta3D()
By default (with base = 'coverage'), the
iNEXTbeta3D() function for each of the three dimensions
(TD, PD, and FD) returns the "iNEXTbeta3D" object including
seven data frames for each dataset:
- gamma (standardized gamma diversity)
- alpha (standardized alpha diversity)
- beta (standardized beta diversity)
- 1-C (standardized Sorensen-type non-overlap index)
- 1-U (standardized Jaccard-type non-overlap index)
- 1-V (standardized Sorensen-type turnover index)
- 1-S (standardized Jaccard-type turnover index)
When users set base = 'size', the
iNEXTbeta3D() function for each of the three dimensions
(TD, PD, and FD) returns the "iNEXTbeta3D" object including
two data frames for each dataset:
- gamma (size-based standardized gamma diversity)
- alpha (size-based standardized alpha diversity)
Size-based beta diversity and dissimilarity indices are not
statistically valid measures and thus are not provided.
GRAPHIC DISPLAYS: FUNCTION
ggiNEXTbeta3D()
The function ggiNEXTbeta3D() with default arguments is
described as follows: (only accept the outcome from
iNEXTbeta3D under by_pair = FALSE)
ggiNEXTbeta3D(output, type = "B")
output
|
output from the function iNEXTbeta3D.
|
type
|
(argument only for base = "coverage"),
type = ‘B’ for plotting the rarefaction and
extrapolation sampling curves for gamma, alpha, and beta diversity;
type = ‘D’ for plotting the rarefaction and
extrapolation sampling curves for four dissimilarity indices.
Skip the argument for plotting size-based rarefaction and extrapolation
sampling curves for gamma and alpha diversity.
|
The ggiNEXTbeta3D() function is a wrapper around the
ggplot2 package to create a R/E curve using a single line
of code. The resulting object is of class "ggplot", so it
can be manipulated using the ggplot2 tools. Users can
visualize the displays of coverage-based R/E sampling curves of gamma,
alpha and beta diversity as well as four classes of dissimilarity
indices by setting the parameter type.
DATA INFORMATION: FUNCTION
DataInfobeta3D()
The function DataInfobeta3D() provides basic data
information for (1) the reference sample in each individual assemblage,
(2) the gamma reference sample in the pooled assemblage, and (3) the
alpha reference sample in the joint assemblage. The function
DataInfobeta3D() with default arguments is shown below:
DataInfobeta3D(data, diversity = "TD", datatype = "abundance",
PDtree = NULL, PDreftime = NULL, FDdistM = NULL, FDtype = "AUC", FDtau = NULL,
by_pair = FALSE)
All arguments in the above function are the same as those for the
main function iNEXTbeta3D. Running the
DataInfobeta3D() function returns basic data information
including sample size, observed species richness, two sample coverage
estimates (SC(n) and SC(2n)) as well as other
relevant information in each of the three dimensions of diversity. We
use Brazil_rainforests data to demo the function for each
dimension.
## Data information for taxonomic diversity (not by pairs)
data(Brazil_rainforests)
DataInfobeta3D(data = Brazil_rainforests[1:2], diversity = 'TD', datatype = 'abundance')
#> Dataset Assemblage n S.obs SC(n) SC(2n) f1 f2 f3 f4 f5
#> 1 Marim Edge 158 84 0.691 0.852 49 18 8 4 1
#> 2 Marim Interior 144 80 0.704 0.899 43 23 7 5 0
#> 3 Marim Pooled assemblage 302 119 0.855 0.969 44 34 17 9 7
#> 4 Marim Joint assemblage 302 164 0.696 0.876 92 41 15 9 1
#> 5 Rebio2 Edge 162 70 0.754 0.895 40 17 4 2 0
#> 6 Rebio2 Interior 168 74 0.763 0.877 40 13 8 4 4
#> 7 Rebio2 Pooled assemblage 330 118 0.819 0.901 60 18 15 5 3
#> 8 Rebio2 Joint assemblage 330 144 0.758 0.886 80 30 12 6 4
Output description:
Dataset = the input datasets.
Pair = combinations of assemblage pairs (if
calculating not by pairs, then there is no such column).
Assemblage = Individual assemblages,
'Pooled assemblage' (for gamma) or
'Joint assemblage' (for alpha).
n = number of observed individuals in the reference
sample (sample size).
S.obs = number of observed species in the reference
sample.
SC(n) = sample coverage estimate of the reference
sample.
SC(2n) = sample coverage estimate of twice the
reference sample size.
f1-f5 = the first five species
abundance frequency counts in the reference sample.
## Data information for taxonomic diversity for all pairs of assemblages
data(Brazil_rainforests)
data = list("Edge" = sapply(Brazil_rainforests, function(x) x[,1]),
"Interior" = sapply(Brazil_rainforests, function(x) x[,2]))
DataInfobeta3D(data = data, diversity = 'TD', datatype = 'abundance', by_pair = TRUE)
#> Dataset Pair Assemblage n S.obs SC(n) SC(2n) f1 f2 f3 f4
#> 1 Edge Marim 158 84 0.691 0.852 49 18 8 4
#> 2 Edge Rebio2 162 70 0.754 0.895 40 17 4 2
#> 3 Edge Rochedo 179 82 0.733 0.889 48 21 3 3
#> 4 Edge Marim vs. Rebio2 Pooled assemblage 320 123 0.791 0.889 67 21 8 11
#> 5 Edge Marim vs. Rebio2 Joint assemblage 320 154 0.723 0.874 89 35 12 6
#> 6 Edge Marim vs. Rochedo Pooled assemblage 337 132 0.799 0.909 68 27 13 9
#> 7 Edge Marim vs. Rochedo Joint assemblage 337 166 0.713 0.872 97 39 11 7
#> 8 Edge Rebio2 vs. Rochedo Pooled assemblage 341 122 0.830 0.942 58 31 10 9
#> 9 Edge Rebio2 vs. Rochedo Joint assemblage 341 152 0.743 0.892 88 38 7 5
#> 10 Interior Marim 144 80 0.704 0.899 43 23 7 5
#> 11 Interior Rebio2 168 74 0.763 0.877 40 13 8 4
#> 12 Interior Rochedo 195 80 0.781 0.928 43 24 6 3
#> 13 Interior Marim vs. Rebio2 Pooled assemblage 312 126 0.815 0.932 58 29 14 9
#> 14 Interior Marim vs. Rebio2 Joint assemblage 312 154 0.735 0.889 83 36 15 9
#> 15 Interior Marim vs. Rochedo Pooled assemblage 339 142 0.791 0.929 71 38 17 7
#> 16 Interior Marim vs. Rochedo Joint assemblage 339 160 0.747 0.915 86 47 13 8
#> 17 Interior Rebio2 vs. Rochedo Pooled assemblage 363 139 0.805 0.921 71 32 11 8
#> 18 Interior Rebio2 vs. Rochedo Joint assemblage 363 154 0.772 0.907 83 37 14 7
Output description: definitions are the same as before and thus are
omitted.
## Data information for phylogenetic diversity (not by pairs)
data(Brazil_rainforests)
data(Brazil_tree)
DataInfobeta3D(data = Brazil_rainforests[1:2], diversity = 'PD', datatype = 'abundance',
PDtree = Brazil_tree, PDreftime = NULL)
#> Dataset Assemblage n S.obs SC(n) SC(2n) PD.obs f1* f2* g1 g2 Reftime
#> 1 Marim Edge 158 84 0.691 0.852 8805 49 26 3278 2188 400
#> 2 Marim Interior 144 80 0.704 0.899 8436 43 28 2974 1935 400
#> 3 Marim Pooled assemblage 302 119 0.855 0.969 11842 44 39 3172 2995 400
#> 4 Marim Joint assemblage 302 164 0.696 0.876 17241 92 54 6252 4123 400
#> 5 Rebio2 Edge 162 70 0.754 0.895 7874 40 23 3648 1717 400
#> 6 Rebio2 Interior 168 74 0.763 0.877 8360 40 17 3365 1954 400
#> 7 Rebio2 Pooled assemblage 330 118 0.819 0.901 11979 60 23 5063 1637 400
#> 8 Rebio2 Joint assemblage 330 144 0.758 0.886 16234 80 40 7013 3671 400
Information description:
Dataset, Pair, Assemblage,
n, S.obs, SC(n) and
SC(2n): definitions are the same as in the TD
output.
PD.obs = the observed total branch length in the
phylogenetic tree spanned by all observed species.
f1*,f2* = the number of singletons and
doubletons in the node/branch abundance set.
g1,g2 = the total branch length of
those singletons/doubletons in the node/branch abundance set.
Reftime = reference time for phylogenetic diversity
(the age of the root of phylogenetic tree).
## Data information for functional diversity (under a specified threshold level, FDtype = 'tau_value',
## and not by pairs)
data(Brazil_rainforests)
data(Brazil_distM)
DataInfobeta3D(data = Brazil_rainforests[1:2], diversity = 'FD', datatype = 'abundance',
FDdistM = Brazil_distM, FDtype = 'tau_value', FDtau = NULL)
#> Dataset Assemblage n S.obs SC(n) SC(2n) a1* a2* h1 h2 Tau
#> 1 Marim Edge 158 84 0.691 0.852 0 0 0 0 0.343
#> 2 Marim Interior 144 80 0.704 0.899 0 0 0 0 0.343
#> 3 Marim Pooled assemblage 302 119 0.855 0.969 0 0 0 0 0.343
#> 4 Marim Joint assemblage 302 164 0.696 0.876 0 0 0 0 0.343
#> 5 Rebio2 Edge 162 70 0.754 0.895 0 0 0 0 0.343
#> 6 Rebio2 Interior 168 74 0.763 0.877 0 0 0 0 0.343
#> 7 Rebio2 Pooled assemblage 330 118 0.819 0.901 0 0 0 0 0.343
#> 8 Rebio2 Joint assemblage 330 144 0.758 0.886 0 0 0 0 0.343
Information description:
Dataset, Pair, Assemblage,
n, S.obs, SC(n) and
SC(2n): definitions are the same as in the TD
output.
a1*,a2* = the number of singletons
(a1*) and of doubletons (a2*) among the
functionally indistinct set at the specified threshold level
'Tau'.
h1,h2 = the total contribution of
singletons (h1) and of doubletons (h2) at the
specified threshold level 'Tau'.
Tau = the specified threshold level of
distinctiveness. Default is dmean (the mean distance between any two
individuals randomly selected from the pooled data over all
datasets).
## Data information for functional diversity (FDtype = 'AUC' and not by pairs)
data(Brazil_rainforests)
data(Brazil_distM)
DataInfobeta3D(data = Brazil_rainforests[1:2], diversity = 'FD', datatype = 'abundance',
FDdistM = Brazil_distM, FDtype = 'AUC')
#> Dataset Assemblage n S.obs SC(n) SC(2n) dmin dmean dmax
#> 1 Marim Edge 158 84 0.691 0.852 0 0.329 0.755
#> 2 Marim Interior 144 80 0.704 0.899 0 0.313 0.663
#> 3 Marim Pooled assemblage 302 119 0.855 0.969 0 0.323 0.776
#> 4 Marim Joint assemblage 302 164 0.696 0.876 0 0.323 0.776
#> 5 Rebio2 Edge 162 70 0.754 0.895 0 0.376 0.659
#> 6 Rebio2 Interior 168 74 0.763 0.877 0 0.310 0.660
#> 7 Rebio2 Pooled assemblage 330 118 0.819 0.901 0 0.355 0.776
#> 8 Rebio2 Joint assemblage 330 144 0.758 0.886 0 0.355 0.776
Information description:
Dataset, Pair, Assemblage,
n, S.obs, SC(n) and
SC(2n): definitions are the same as in TD and thus are
omitted.
dmin = the minimum distance among all non-diagonal
elements in the distance matrix.
dmean = the mean distance between any two
individuals randomly selected from each assemblage.
dmax = the maximum distance among all elements in
the distance matrix.
Below We use the demo dataset (Second-growth forests) to
show the output of the function DataInfobeta3D for
incidence data:
## Data information for taxonomic diversity with incidence data (not by pairs)
data(Second_growth_forests)
data = list("CR 2005 vs. 2017" = Second_growth_forests[[1]][c(1,3)],
"JE 2005 vs. 2017" = Second_growth_forests[[2]][c(1,3)])
DataInfobeta3D(data = data, diversity = 'TD', datatype = 'incidence_raw')
#> Dataset Assemblage T U S.obs SC(T) SC(2T) Q1 Q2 Q3 Q4 Q5
#> 1 CR 2005 vs. 2017 Year_2005 100 787 135 0.919 0.953 64 17 16 6 4
#> 2 CR 2005 vs. 2017 Year_2017 100 768 134 0.917 0.956 64 20 11 8 3
#> 3 CR 2005 vs. 2017 Pooled assemblage 100 923 151 0.925 0.959 70 21 14 6 6
#> 4 CR 2005 vs. 2017 Joint assemblage 100 1555 269 0.918 0.954 128 37 27 14 7
#> 5 JE 2005 vs. 2017 Year_2005 100 503 71 0.955 0.979 23 9 8 4 0
#> 6 JE 2005 vs. 2017 Year_2017 100 659 91 0.953 0.979 31 12 8 3 5
#> 7 JE 2005 vs. 2017 Pooled assemblage 100 864 107 0.963 0.987 32 17 9 4 8
#> 8 JE 2005 vs. 2017 Joint assemblage 100 1162 162 0.954 0.979 54 21 16 7 5
Information description:
Dataset = the input datasets.
Pair = combinations of assemblage pairs (if
calculating not by pairs, then there is no such column).
Assemblage = Individual assemblages,
'Pooled assemblage' (for gamma) or
'Joint assemblage' (for alpha).
T = number of sampling units in the reference sample
(sample size for incidence data).
U = total number of incidences in the reference
sample.
S.obs = number of observed species in the reference
sample.
SC(T) = sample coverage estimate of the reference
sample.
SC(2T) = sample coverage estimate of twice the
reference sample size.
Q1-Q5 = the first five species
incidence frequency counts in the reference sample.
## Data information for taxonomic diversity for all pairs of assemblages (incidence data)
data(Second_growth_forests)
data = Second_growth_forests
names(data) = c("CR", "JE")
DataInfobeta3D(data = data, diversity = 'TD', datatype = 'incidence_raw',
by_pair = TRUE)
#> Dataset Pair Assemblage T U S.obs SC(T) SC(2T) Q1 Q2 Q3 Q4
#> 1 CR Year_2005 100 787 135 0.919 0.953 64 17 16 6
#> 2 CR Year_2011 100 768 135 0.916 0.952 65 18 12 7
#> 3 CR Year_2017 100 768 134 0.917 0.956 64 20 11 8
#> 4 CR Year_2005 vs. Year_2011 Pooled assemblage 100 860 145 0.920 0.954 69 19 13 5
#> 5 CR Year_2005 vs. Year_2011 Joint assemblage 100 1555 270 0.917 0.952 129 35 28 13
#> 6 CR Year_2005 vs. Year_2017 Pooled assemblage 100 923 151 0.925 0.959 70 21 14 6
#> 7 CR Year_2005 vs. Year_2017 Joint assemblage 100 1555 269 0.918 0.954 128 37 27 14
#> 8 CR Year_2011 vs. Year_2017 Pooled assemblage 100 837 142 0.923 0.958 65 20 15 7
#> 9 CR Year_2011 vs. Year_2017 Joint assemblage 100 1536 269 0.917 0.954 129 38 23 15
#> 10 JE Year_2005 100 503 71 0.955 0.979 23 9 8 4
#> 11 JE Year_2011 100 631 88 0.942 0.962 37 8 4 6
#> 12 JE Year_2017 100 659 91 0.953 0.979 31 12 8 3
#> 13 JE Year_2005 vs. Year_2011 Pooled assemblage 100 757 96 0.951 0.969 37 8 6 7
#> 14 JE Year_2005 vs. Year_2011 Joint assemblage 100 1134 159 0.947 0.970 60 17 12 10
#> 15 JE Year_2005 vs. Year_2017 Pooled assemblage 100 864 107 0.963 0.987 32 17 9 4
#> 16 JE Year_2005 vs. Year_2017 Joint assemblage 100 1162 162 0.954 0.979 54 21 16 7
#> 17 JE Year_2011 vs. Year_2017 Pooled assemblage 100 788 100 0.958 0.981 33 13 8 6
#> 18 JE Year_2011 vs. Year_2017 Joint assemblage 100 1290 179 0.948 0.971 68 20 12 9
Output description: definitions are the same as before and thus are
omitted.
License and feedback
The iNEXT.beta3D package is licensed under the GPLv3. To
help refine iNEXT.beta3D, users’ comments or feedback would
be welcome (please send them to Anne Chao or report an issue on the
iNEXT.beta3D github iNEXT.beta3D_github.
References
Chao, A., Chiu, C.-H., Hu, K.-H., and Zeleny, D. (2023a).
Revisiting Alwyn H. Gentry’s forest transect data: a statistical
sampling-model-based approach. Japanese Journal of Statistics and
Data Science, 6, 861-884. (https://doi.org/10.1007/s42081-023-00214-1)
Chao, A., Henderson, P. A., Chiu, C.-H., Moyes, F., Hu, K.-H.,
Dornelas, M. and Magurran, A. E. (2021). Measuring temporal change in
alpha diversity: a framework integrating taxonomic, phylogenetic and
functional diversity and the iNEXT.3D standardization. Methods in
Ecology and Evolution, 12, 1926-1940.
Chao, A., Thorn, S., Chiu, C.-H., Moyes, F., Hu, K.-H., Chazdon,
R. L., Wu, J., Magnago, L. F. S., Dornelas, M., Zeleny, D., Colwell, R.
K., and Magurran, A. E. (2023b). Rarefaction and extrapolation with beta
diversity under a framework of Hill numbers: the iNEXT.beta3D
standardization. Ecological Monographs e1588.
