ct_alpha_diversity: Alpha diversity index

In ct: Camera Trap Data Management and Multi-Method Ecological Analysis

Alpha diversity index

Description

Calculate index diversity within a particular area or ecosystem; usually expressed by the number of species (i.e., species richness) in that ecosystem.

Usage

ct_alpha_diversity(
  data,
  to_community = TRUE,
  index = "shannon",
  site_column,
  species_column,
  size_column = NULL,
  margin = 1
)

Arguments

data	A data frame containing species observation data.
to_community	Logical; if TRUE, the function first transforms data into a community matrix format where sites are rows and species are columns before computing indices. Default is TRUE.
index	A character vector specifying the diversity index to calculate. Accepted values are "shannon", "simpson", "invsimpson", "evenness", and "pielou". Multiple indices can be computed simultaneously by providing a vector.
site_column	The column name in data that represents the site or location where species were recorded.
species_column	The column(s) in data representing species or taxa. This can be a single column name, a range of column indices (e.g., 2:5), or a selection helper (e.g., dplyr::starts_with("sp_")).
size_column	(Optional) The column in data containing the count or abundance of individuals per species. If NULL, the function assumes each row represents one individual.
margin	An integer specifying whether diversity calculations should be performed by row (margin = 1) or by column (margin = 2). Default is 1 (row-wise).

Details

Simpson diversity index

Simpson (1949) introduced a diversity index that quantifies the likelihood of two randomly chosen individuals belonging to the same species. This probability increases as diversity decreases; in a scenario with no diversity (only one species), the probability reaches 1. Simpson's Index is computed using the following formula:

D = \sum_{i=1}^{S} \left( \frac{n_{i}}{N} \right)^2

where n_{i} is the number of individuals in species i, N = total number of individuals of all species, and \frac{n_{i}}{N} = pi (proportion of individuals of species i), and S = species richness. The value of Simpson's D ranges from 0 to 1, with 0 representing infinite diversity and 1 representing no diversity, so the larger the value of D, the lower the diversity. For this reason, Simpson's index is often as its complement (1-D). Simpson's Dominance Index is the inverse of the Simpson's Index (1/D).

Shannon-Weiner Diversity Index

Shannon-Weiner Diversity Index is a measure of diversity that takes into account both species richness and evenness, introduced by Claude Shannon in 1948. Commonly referred to as Shannon's Diversity Index, it is based on the concept of uncertainty. For instance, in a community with very low diversity, there is a high level of certainty (or low uncertainty) about the identity of a randomly selected organism. Conversely, in a highly diverse community, the uncertainty increases, making it harder to predict which species a randomly chosen organism will belong to (low certainty or high uncertainty).

H = -\sum_{i=1}^{S} p_{i} * \ln p_{i}

where p_{i} = proportion of individuals of species i, and ln is the natural logarithm, and S = species richness. The value of H ranges from 0 to Hmax. Hmax is different for each community and depends on species richness. (Note: Shannon-Weiner is often denoted H' ).

Pielou or Evenness diversity index

Species evenness refers to the relative abundance of each species within an environment. For example, if there are 40 foxes and 1000 dogs, the community is uneven because one species dominates. However, if there are 40 foxes and 42 dogs, the community is much more even, as the species are more balanced in number. The degree of evenness in a community can be quantified using Pielou's evenness index (Pielou, 1966):

J=\frac{H}{H_{\max }}

The value of J ranges from 0 to 1. Higher values indicate higher levels of evenness. At maximum evenness, J = 1. J and D can be used as measures of species dominance (the opposite of diversity) in a community. Low J indicates that 1 or few species dominate the community.

Value

A tibble with diversity index values for each site. The first column corresponds to site_column, followed by one or more columns containing the computed diversity indices, depending on the values specified in the index argument.

References

Pielou, E.C. (1966). The measurement of diversity in different types of biological collections. Journal of Theoretical Biology, 13, pp. 131-144. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/0022-5193(66)90013-0")}.

Simpson, E.H. (1949). Measurement of diversity. Nature, 163, pp. 688. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/163688a0")}

Shannon, C.E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, pp. 379-423.\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/j.1538-7305.1948.tb01338.x")}

Examples

data(penessoulou)
cam_data <- penessoulou %>%
  dplyr::filter(project == "Last")

# Transform data to community format and compute diversity indices
alpha1 <- cam_data %>%
  ct_alpha_diversity(
    to_community = TRUE,
    size_column = number,
    site_column = camera,
    species_column = species,
    index = c("shannon", "evenness", "invsimpson")
  )

# Alternative method using a manually transformed community matrix
alpha2 <- cam_data %>%
  ct_to_community(site_column = camera, species_column = species,
                  size_column = number, values_fill = 0) %>%
  ct_alpha_diversity(
    to_community = FALSE,
    site_column = camera,
    species_column = 2:11,
    index = c("shannon", "evenness", "invsimpson")
  )
alpha2
# Compare results
all(alpha1 == alpha2) # TRUE

ct documentation built on Oct. 22, 2026, 1:07 a.m.