Community data

Community data is:

• either a numeric vector containing abundances of species (the number of individual of each species) or their probabilities (the proportion of individuals of each species, summing to 1). This format is convenient when a single community is considered.
• or a dataframe whose lines are communities and column are species. Its values are either abundances or probabilities. Special columns contain the site names, and their weights (e.g. their area or number of individuals).

Example data is provided in the dataset paracou_6_abd. Let’s get the abundances of tree species in the 6.25-ha tropical forest plot #6 from Paracou forest station in French Guiana. It is divided into 4 equally-sized subplots:

library("divent")
paracou_6_abd

## # A tibble: 4 × 337
##   site      weight Abarema_jupunba Abarema_mataybifolia Amaioua_guianensis
##   <chr>      <dbl>           <int>                <int>              <int>
## 1 subplot_1   1.56               2                    2                  1
## 2 subplot_2   1.56               2                    0                  1
## 3 subplot_3   1.56               2                    2                  0
## 4 subplot_4   1.56               4                    0                  0
## # ℹ 332 more variables: Amanoa_congesta <int>, Amanoa_guianensis <int>,
## #   Ambelania_acida <int>, Amphirrhox_longifolia <int>, Andira_coriacea <int>,
## #   Apeiba_glabra <int>, Aspidosperma_album <int>, Aspidosperma_cruentum <int>,
## #   Aspidosperma_excelsum <int>, Bocoa_prouacensis <int>,
## #   Brosimum_guianense <int>, Brosimum_rubescens <int>, Brosimum_utile <int>,
## #   Carapa_surinamensis <int>, Caryocar_glabrum <int>, Casearia_decandra <int>,
## #   Casearia_javitensis <int>, Catostemma_fragrans <int>, …

# Number of individuals in each community
abd_sum(paracou_6_abd)

## # A tibble: 4 × 3
##   site      weight abundance
##   <chr>      <dbl>     <dbl>
## 1 subplot_1   1.56       942
## 2 subplot_2   1.56       872
## 3 subplot_3   1.56       929
## 4 subplot_4   1.56       798

The data in paracou_6_abd is an object of class abundances, i.e. a tibble with species as columns and sites as rows. It can be manipulated as any dataframe and plotted as a rank-abundance curve:

autoplot(paracou_6_abd[1, ])

The rcommunity function allows drawing random communities, e.g. a log-normal one (Preston 1948):

rc <- rcommunity(1, size = 10000, distribution = "lnorm")
autoplot(rc, fit_rac = TRUE, distribution = "lnorm")

The Whittaker plot (rank-abundance curve) of a random log-normal distribution of 10000 individuals simulated with default parameter (σ = 1) is produced.

Diversity estimation

The classical indices of diversity are richness (the number of species), Shannon’s and Simpson’s entropies:

div_richness(paracou_6_abd)

## # A tibble: 4 × 5
##   site      weight estimator   order diversity
##   <chr>      <dbl> <chr>       <dbl>     <dbl>
## 1 subplot_1   1.56 Jackknife 3     0       355
## 2 subplot_2   1.56 Jackknife 2     0       348
## 3 subplot_3   1.56 Jackknife 2     0       315
## 4 subplot_4   1.56 Jackknife 2     0       296

ent_shannon(paracou_6_abd)

## # A tibble: 4 × 5
##   site      weight estimator order entropy
##   <chr>      <dbl> <chr>     <dbl>   <dbl>
## 1 subplot_1   1.56 UnveilJ       1    4.57
## 2 subplot_2   1.56 UnveilJ       1    4.73
## 3 subplot_3   1.56 UnveilJ       1    4.65
## 4 subplot_4   1.56 UnveilJ       1    4.55

ent_simpson(paracou_6_abd)

## # A tibble: 4 × 5
##   site      weight estimator order entropy
##   <chr>      <dbl> <chr>     <dbl>   <dbl>
## 1 subplot_1   1.56 Lande         2   0.976
## 2 subplot_2   1.56 Lande         2   0.978
## 3 subplot_3   1.56 Lande         2   0.980
## 4 subplot_4   1.56 Lande         2   0.972

When applied to probabilities (created with as_probaVector in the following example), no estimation-bias correction is applied: this means that indices are just calculated by applying their definition function to the probabilities (that is the naive, or plugin estimator).

library("dplyr")

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

paracou_6_abd %>% 
  as_probabilities() %>% 
  ent_shannon()

## # A tibble: 4 × 5
##   site      weight estimator order entropy
##   <chr>      <dbl> <chr>     <dbl>   <dbl>
## 1 subplot_1   1.56 naive         1    4.34
## 2 subplot_2   1.56 naive         1    4.48
## 3 subplot_3   1.56 naive         1    4.45
## 4 subplot_4   1.56 naive         1    4.32

When abundances are available, many estimators can be used (Marcon 2015) to address unobserved species and the non-linearity of the indices:

ent_shannon(paracou_6_abd)

## # A tibble: 4 × 5
##   site      weight estimator order entropy
##   <chr>      <dbl> <chr>     <dbl>   <dbl>
## 1 subplot_1   1.56 UnveilJ       1    4.57
## 2 subplot_2   1.56 UnveilJ       1    4.73
## 3 subplot_3   1.56 UnveilJ       1    4.65
## 4 subplot_4   1.56 UnveilJ       1    4.55

ent_shannon(paracou_6_abd, estimator = "ChaoJost")

## # A tibble: 4 × 5
##   site      weight estimator order entropy
##   <chr>      <dbl> <chr>     <dbl>   <dbl>
## 1 subplot_1   1.56 ChaoJost      1    4.51
## 2 subplot_2   1.56 ChaoJost      1    4.68
## 3 subplot_3   1.56 ChaoJost      1    4.62
## 4 subplot_4   1.56 ChaoJost      1    4.50

The best available estimator is chosen by default: its name is returned.

Those indices are special cases of the Tsallis entropy (1988) or order q (respectively q = 0, 1, 2 for richness, Shannon, Simpson):

ent_tsallis(paracou_6_abd, q = 1)

## # A tibble: 4 × 5
##   site      weight estimator order entropy
##   <chr>      <dbl> <chr>     <dbl>   <dbl>
## 1 subplot_1   1.56 UnveilJ       1    4.57
## 2 subplot_2   1.56 UnveilJ       1    4.73
## 3 subplot_3   1.56 UnveilJ       1    4.65
## 4 subplot_4   1.56 UnveilJ       1    4.55

Entropy should be converted to its effective number of species, i.e. the number of species with equal probabilities that would yield the observed entropy, called Hill (1973) numbers or simply diversity (Jost 2006).

div_hill(paracou_6_abd, q = 1)

## # A tibble: 4 × 5
##   site      weight estimator order diversity
##   <chr>      <dbl> <chr>     <dbl>     <dbl>
## 1 subplot_1   1.56 UnveilJ       1      96.3
## 2 subplot_2   1.56 UnveilJ       1     113. 
## 3 subplot_3   1.56 UnveilJ       1     105. 
## 4 subplot_4   1.56 UnveilJ       1      94.6

Diversity is the deformed exponential of order q of entropy, and entropy is the deformed logarithm of of order q of diversity:

(d2 <- div_hill(paracou_6_abd, q = 2)$diversity)

## [1] 42.28417 44.58777 48.83999 36.01687

ln_q(d2, q = 2)

## [1] 0.9763505 0.9775723 0.9795250 0.9722352

(e2 <-ent_tsallis(paracou_6_abd, q = 2)$entropy)

## [1] 0.9763505 0.9775723 0.9795250 0.9722352

exp_q(e2, q = 2)

## [1] 42.28417 44.58777 48.83999 36.01687

If an ultrametric dendrogram describing species phylogeny (here, a mere taxonomy with family, genus and species) is available, phylogenetic entropy and diversity (Marcon and Hérault 2015) can be calculated:

div_phylo(paracou_6_abd, tree = paracou_6_taxo, q = 1)

## # A tibble: 4 × 4
##   site      weight estimator diversity
##   <chr>      <dbl> <chr>         <dbl>
## 1 subplot_1   1.56 UnveilJ        41.0
## 2 subplot_2   1.56 UnveilJ        52.9
## 3 subplot_3   1.56 UnveilJ        46.1
## 4 subplot_4   1.56 UnveilJ        43.2

Recall that all those functions can be applied to a numeric vector containing abundances, without having to build an object of class abundances.

# Richness of a community of 100 species, each of them with 10 individuals
div_richness(rep(10, 100))

## # A tibble: 1 × 3
##   estimator   order diversity
##   <chr>       <dbl>     <int>
## 1 Jackknife 0     0       100

With a Euclidian distance matrix between species, similarity-based diversity (Leinster and Cobbold 2012; Marcon, Zhang, and Hérault 2014) is available:

# Similarity is computed from the functional distance matrix of Paracou species
Z <- fun_similarity(paracou_6_fundist)
# Calculate diversity of order 2
div_similarity(paracou_6_abd, similarities = Z, q = 2)

## # A tibble: 4 × 5
##   site      weight estimator order diversity
##   <chr>      <dbl> <chr>     <dbl>     <dbl>
## 1 subplot_1   1.56 UnveilJ       2      1.31
## 2 subplot_2   1.56 UnveilJ       2      1.33
## 3 subplot_3   1.56 UnveilJ       2      1.32
## 4 subplot_4   1.56 UnveilJ       2      1.30

Diversity profiles

Diversity can be plotted against its order to provide a diversity profile. Order 0 corresponds to richness, 1 to Shannon’s and 2 to Simpson’s diversities:

profile_hill(paracou_6_abd) %>% autoplot

Profiles of phylogenetic diversity and similarity-based diversity are obtained the same way.

profile_phylo(paracou_6_abd, tree = paracou_6_taxo) %>% autoplot

# Similarity matrix
Z <- fun_similarity(paracou_6_fundist)
profile_similarity(paracou_6_abd, similarities = Z) %>% autoplot

Diversity accumulation

Diversity can be interpolated or extrapolated to arbitrary sampling levels.

# Estimate the diversity of 1000 individuals
div_hill(paracou_6_abd, q = 1, level = 1000)

## # A tibble: 4 × 6
##   site      weight estimator order level diversity
##   <chr>      <dbl> <chr>     <dbl> <dbl>     <dbl>
## 1 subplot_1   1.56 Chao2015      1  1000      78.1
## 2 subplot_2   1.56 Chao2015      1  1000      91.1
## 3 subplot_3   1.56 Chao2015      1  1000      87.2
## 4 subplot_4   1.56 Chao2015      1  1000      78.8

The sampling level can be a sample coverage, that is converted to the equivalent number of individuals.

# Estimate the diversity at 80% coverage
div_hill(paracou_6_abd, q = 1, level = 0.8)

## # A tibble: 4 × 6
##   site      weight estimator     order level diversity
##   <chr>      <dbl> <chr>         <dbl> <dbl>     <dbl>
## 1 subplot_1   1.56 Interpolation     1   304      60.5
## 2 subplot_2   1.56 Interpolation     1   347      71.4
## 3 subplot_3   1.56 Interpolation     1   333      68.6
## 4 subplot_4   1.56 Interpolation     1   303      60.5

Diversity accumulation curves are available.

accum_hill(
  paracou_6_abd[1, ], 
  q = 1, 
  levels = 1:500,
  n_simulations = 100
) %>% 
  autoplot()

Phylogenetic diversity can be addressed the same way. Confidence intervals of the estimation can be computed, taking into account sampling variability.

accum_div_phylo(
  paracou_6_abd[1, ],
  tree = paracou_6_taxo,
  q = 1, 
  levels = 1:2000
) %>% 
  autoplot()

Meta-community data

A metacommunity is the assemblage several communities.

The set of communities is described by the abundances of their species and their weight.

Species probabilities in the meta-community are by definition the weighted average of their probabilities in the communities. Abundances are calculated so that the total abundance of the metacommunity is the sum of all abundances of communities. If weights are equal, then the abundances of the metacommunity are simply the sum of those of the communities. If they are not, the abundances of the metacommunity are generally not integer values, which complicates the estimation of diversity.

Example:

# Abundances of three communities with four species
(abd <- matrix(
  c(
    10,  0, 25, 10, 
    20, 15, 10, 35, 
     0, 10,  5,  2
  ),
  ncol = 4
))

##      [,1] [,2] [,3] [,4]
## [1,]   10   10   10   10
## [2,]    0   20   35    5
## [3,]   25   15    0    2

# Community weights
w <- c(1, 2, 1)

A set of communities is built.

(communities <- as_abundances(abd, weights = w))

## # A tibble: 3 × 6
##   site   weight  sp_1  sp_2  sp_3  sp_4
##   <chr>   <dbl> <int> <int> <int> <int>
## 1 site_1      1    10    10    10    10
## 2 site_2      2     0    20    35     5
## 3 site_3      1    25    15     0     2

The function metacommunity() creates a metacommunity. To plot it, use argument type = "Metacommunity when plotting the species_distribution.

(mc <- metacommunity(communities))

## # A tibble: 1 × 6
##   site          weight  sp_1  sp_2  sp_3  sp_4
##   <chr>          <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 metacommunity      4  24.6  45.7  56.2  15.5

plot(communities, type = "Metacommunity")

Each shade of grey represents a species. Heights correspond to the probability of species and the width of each community is its weight.

Diversity estimation

High level functions allow computing diversity of all communities (α diversity), of the meta-community (γ diversity), and β diversity, i.e. the number of effective communities (the number of communities with equal weights and no common species that would yield the observed β diversity).

The div_part function calculates everything at once, for a given order of diversity q:

div_part(paracou_6_abd, q = 1)

## # A tibble: 7 × 6
##   site          scale     estimator order diversity weight
##   <chr>         <chr>     <chr>     <dbl>     <dbl>  <dbl>
## 1 Metacommunity gamma     "UnveilJ"     1    111.     6.25
## 2 Metacommunity beta      ""            1      1.09  NA   
## 3 Metacommunity alpha     ""            1    102.    NA   
## 4 subplot_1     community "UnveilJ"     1     96.3    1.56
## 5 subplot_2     community "UnveilJ"     1    113.     1.56
## 6 subplot_3     community "UnveilJ"     1    105.     1.56
## 7 subplot_4     community "UnveilJ"     1     94.6    1.56

An alternative is the gamma argument of all diversity estimation function to obtain γ diversity instead of the diversity of each community.

div_hill(paracou_6_abd, q = 1, gamma = TRUE)

## # A tibble: 1 × 4
##   site          estimator order diversity
##   <chr>         <chr>     <dbl>     <dbl>
## 1 Metacommunity UnveilJ       1      111.