Calculate biodiversity statistics from sites by species table.
calc_comm_div(
abund_mat,
index,
effort = NA,
extrapolate = TRUE,
return_NA = FALSE,
rare_thres = 0.05,
scales = c("alpha", "gamma", "beta"),
replace = FALSE,
C_target_gamma = NA,
...
)A data.frame with four columns:
scale ... Group label for sites
index ... Name of the biodiversity index
sample_size ... The number of samples used to compute the
statistic, helpful for interpreting beta and gamma metrics.
effort ... Sampling effort for rarefied richness
(NA for the other indices)
gamma_coverage ... The coverage value for that particular
effort value on the gamma scale rarefaction curve. Will be NA unless
coverage based richness (S_C) and/or beta diversity is computed.
value ... Value of the biodiversity index
Abundance based site-by-species table. Species as columns
The calculated biodiversity indices. The options are
N ... Number of individuals (total abundance)
S ... Number of species
S_n ... Rarefied or extrapolated number of species for n individuals
S_C ... Estimate species richness of a given level of coverage by C_target_gamma
S_asymp ... Estimated asymptotic species richness
f_0 ... Estimated number of undetected species
pct_rare ... The percent of rare species as defined by rare_thres
PIE ... Hurlbert's PIE (Probability of Interspecific Encounter)
S_PIE ... Effective number of species based on PIE
See Details for additional information on the biodiversity statistics.
The standardized number of individuals used for the calculation of rarefied species richness. This can a be single integer or a vector of integers.
Boolean which specifies if richness should be extrapolated when effort is larger than the number of individuals using the chao1 method.
Boolean in which the rarefaction function
returns the observed S when effort is larger than the number of
individuals. If set to TRUE then NA is returned. Note that this argument
is only relevant when extrapolate = FALSE.
The threshold that determines how pct_rare is computed. It can range from (0, 1] and defaults to 0.05 which specifies that any species with less than or equal to 5 considered rare. It can also be specified as "N/S" which results in using average abundance as the threshold which McGill (2011) found to have the best small sample behavior.
The scales to compute the diversity indices for:
alpha ... for each row of the site x species community matrix
gamma ... for the entire site x species community matrix
beta ... the ratio of diversity at the gamma and
alpha scales.
Defaults to all three scales: c('alpha', 'gamma', 'beta')
Used for PIE and SPIE. If TRUE, sampling with
replacement is used. Otherwise, sampling without replacement (default).
When computing coverage based richness (S_C) then
this argument can be used to specify the coverage to be used for the gamma scale
richness estimate. This defaults to NA in which case the target cover
is computed by calc_C_target (i.e., the largest allowable sample
size).
additional arguments that can be passed to calc_div
Felix May and Dan McGlinn
BIODIVERSITY INDICES
N: total community abundance is the total number of individuals observed across all species in the sample
S: species richness is the observed number of species that occurs at least once in a sample
S_n: Rarefied species richness is the expected number of species, given a
defined number of sampled individuals (n) (Gotelli & Colwell 2001). Rarefied
richness at the alpha-scale is calculated for the values provided in
effort_samples as long as these values are not smaller than the
user-defined minimum value effort_min. In this case the minimum value
is used and samples with less individuals are discarded. When no values for
effort_samples are provided the observed minimum number of individuals
of the samples is used, which is the standard in rarefaction analysis
(Gotelli & Colwell 2001). Because the number of individuals is expected to
scale linearly with sample area or effort, at the gamma-scale the number of
individuals for rarefaction is calculated as the minimum number of samples
within groups multiplied by effort_samples. For example, when there are 10
samples within each group, effort_groups equals 10 *
effort_samples. If n is larger than the number of individuals in sample and
extrapolate = TRUE then the Chao1 (Chao 1984, Chao 1987) method is
used to extrapolate the rarefaction curve.
pct_rare: Percent of rare species Is the ratio of the number of rare
species to the number of observed species x 100 (McGill 2011). Species are
considered rare in a particular sample if they have fewer individuals than
rare_thres * N where rare_thres can be set by the user and
N is the total number of individuals in the sample. The default value
of rare_thres of 0.05 is arbitrary and was chosen because McGill
(2011) found this metric of rarity performed well and was generally less
correlated with other common metrics of biodiversity. Essentially this metric
attempt to estimate what proportion of the species in the same occur in the
tail of the species abundance distribution and is therefore sensitive to
presence of rare species.
S_asymp: Asymptotic species richness is the expected number of
species given complete sampling and here it is calculated using the Chao1
estimator (Chao 1984, Chao 1987) see calc_chao1. Note: this metric
is typically highly correlated with S (McGill 2011).
f_0: Undetected species richness is the number of undetected species
or the number of species observed 0 times which is an indicator of the degree
of rarity in the community. If there is a greater rarity then f_0 is expected
to increase. This metric is calculated as S_asymp - S. This metric is less
correlated with S than the raw S_asymp metric.
PIE: Probability of intraspecific encounter represents the probability that two randomly drawn individuals belong to the same species. Here we use the definition of Hurlbert (1971), which considers sampling without replacement. PIE is closely related to the well-known Simpson diversity index, but the latter assumes sampling with replacement.
S_PIE: Effective number of species for PIE represents the effective number of species derived from the PIE. It is calculated using the asymptotic estimator for Hill numbers of diversity order 2 (Chao et al, 2014). S_PIE represents the species richness of a hypothetical community with equally-abundant species and infinitely many individuals corresponding to the same value of PIE as the real community. An intuitive interpretation of S_PIE is that it corresponds to the number of dominant (highly abundant) species in the species pool.
For species richness S, rarefied richness S_n, undetected
richness f_0, and the Effective Number of Species S_PIE we also
calculate beta-diversity using multiplicative partitioning (Whittaker 1972,
Jost 2007). That means for these indices we estimate beta-diversity as the
ratio of gamma-diversity (total diversity across all plots) divided by
alpha-diversity (i.e., average plot diversity).
McGill, B. J. 2011. Species abundance distributions. Pages 105-122 Biological Diversity: Frontiers in Measurement and Assessment, eds. A.E. Magurran and B.J. McGill.
data(tank_comm)
div_metrics <- calc_comm_div(tank_comm, 'S_n', effort = c(5, 10))
div_metrics
div_metrics <- calc_comm_div(tank_comm, 'S_C', C_target_gamma = 0.75)
div_metrics
Run the code above in your browser using DataLab