bio-ecological-genomics-biodiversity-metrics

name	bio-ecological-genomics-biodiversity-metrics
description	Calculates species richness, diversity, and turnover using the Hill number framework with iNEXT coverage-based rarefaction/extrapolation, asymptotic diversity estimation, and beta diversity partitioning (betapart turnover vs nestedness). Compares assemblages using coverage-standardized rather than size-standardized rarefaction. Use when quantifying biodiversity from species abundance or incidence data, comparing diversity across sites, or constructing rarefaction curves. Not for clinical 16S microbiome alpha/beta diversity (see microbiome/diversity-analysis).
tool_type	r
primary_tool	iNEXT

Biodiversity Metrics

Calculates alpha diversity using Hill numbers, coverage-based rarefaction/extrapolation, and beta diversity partitioning into turnover and nestedness components.

Hill Numbers Framework

Hill numbers unify diversity indices into a single parametric family controlled by order q:

Order (q)	Name	Sensitivity	Equivalent Index
q = 0	Species richness	Rare species only	S (species count)
q = 1	Shannon diversity	Equal weight all	exp(H') Shannon exponential
q = 2	Simpson diversity	Dominant species	1/D Simpson inverse

Higher q values downweight rare species. q = 1 weights species by frequency and is the most balanced measure.

iNEXT Coverage-Based Rarefaction

Coverage-based rarefaction standardizes by completeness rather than sample size, enabling fair comparison of assemblages sampled with different effort (Chao & Jost 2012).

library(iNEXT)

# abundance data: named list of species abundance vectors per site
abundance_data <- list(
    site_A = c(100, 45, 23, 12, 8, 5, 3, 2, 1, 1),
    site_B = c(80, 60, 40, 30, 20, 15, 10, 8, 5, 3, 2, 1),
    site_C = c      





result  iNEXTabundance_data q     datatype   nboot  


ggiNEXTresult type    theme_bw


ggiNEXTresult type    theme_bw


ggiNEXTresult type    theme_bw

Point Estimates at Standardized Coverage

# Estimate diversity at a common coverage level
# coverage=0.95: typical target for adequate sampling
# 0.95 means 95% of individuals in the community belong to detected species
est <- estimateD(abundance_data, datatype = 'abundance',
                 base = 'coverage', level = 0.95)
est

Asymptotic Diversity Estimation

# Chao1 (q=0), Chao-Shannon (q=1), Chao-Simpson (q=2)
# Asymptotic = estimated true diversity if sampling were complete
asymptotic <- iNEXT(abundance_data, q = c(0, 1, 2), datatype = 'abundance')
asymptotic$AsyEst

iNEXT.3D: Taxonomic, Phylogenetic, and Functional Diversity

library(iNEXT.3D)

# Taxonomic diversity (TD) - standard Hill numbers
td <- iNEXT3D(abundance_data, diversity = 'TD', q = c(0, 1, 2), datatype = 'abundance')

# Phylogenetic diversity (PD) - requires ultrametric tree
# PD generalizes Faith's PD via Hill numbers on branch lengths
pd <- iNEXT3D(abundance_data, diversity = 'PD', q = c(0, 1, 2),
              datatype = 'abundance', PDtree = phylo_tree)

# Functional diversity (FD) - requires species distance matrix
# FD captures trait space coverage via Hill numbers
fd <- iNEXT3D(abundance_data, diversity = 'FD', q    
              datatype   FDdistM  trait_dist_matrix FDtype  

Classic Diversity with vegan

library(vegan)

# community_matrix: sites (rows) x species (columns)
richness <- specnumber(community_matrix)
shannon <- diversity(community_matrix, index = 'shannon')
simpson <- diversity(community_matrix, index = 'simpson')
invsimpson <- diversity(community_matrix, index = 'invsimpson')

# Classic rarefaction to minimum sample size
# Rarefies to the smallest sample, discarding extra reads
raremin <- min(rowSums(community_matrix))
rarecurve(community_matrix, step = 20, sample = raremin)
rare_richness <- rarefy(community_matrix, sample = raremin)

Beta Diversity Partitioning with betapart

Decomposes total beta diversity (Sorensen or Jaccard) into turnover (species replacement) and nestedness (richness difference) components:

library(betapart)

# Presence/absence matrix required
pa_matrix <- ifelse(community_matrix > 0, 1, 0)

# --- Pairwise decomposition ---
# Sorensen family: beta.sim (turnover) + beta.sne (nestedness) = beta.sor (total)
pair_sor <- beta.pair(pa_matrix, index.family = 'sorensen')
pair_sor$beta.sim   # turnover component
pair_sor$beta.sne   # nestedness component
pair_sor$beta.sor   # total beta diversity

# Jaccard family: beta.jtu (turnover) + beta.jne (nestedness) = beta.jac (total)
pair_jac <- beta.pair(pa_matrix, index.family = 'jaccard')

# --- Multi-site decomposition ---
multi <- beta.multi(pa_matrix, index.family = 'sorensen')
multi$beta.SIM   # multi-site turnover
multi$beta.SNE   # multi-site nestedness
multi$beta.SOR   # multi-site total

# --- Abundance-based beta diversity ---
pair_abund <- beta.pair.abundcommunity_matrix index.family  
pair_abundbeta.bray.bal   
pair_abundbeta.bray.gra   

Interpreting Beta Diversity Components

Dominance	Ecological Meaning
Turnover >> Nestedness	Species replacement along gradients; distinct communities
Nestedness >> Turnover	Poor sites are subsets of rich sites; nested pattern
Both similar	Mixed processes driving community differences

Visualization

library(ggplot2)

# Beta diversity triangle plot (turnover vs nestedness proportions)
beta_df <- data.frame(
    turnover = as.vector(pair_sor$beta.sim),
    nestedness = as.vector(pair_sor$beta.sne),
    total = as.vector(pair_sor$beta.sor)
)
beta_df$turn_prop <- beta_df$turnover / beta_df$total

ggplot(beta_df, aes(x = total, y = turn_prop)) +
    geom_point(alpha = 0.5) +
    geom_hline(yintercept = 0.5, linetype = 'dashed') +
    labs(x = 'Total beta diversity (Sorensen)',
         y = 'Turnover proportion' 
    theme_bw

Related Skills

• edna-metabarcoding - Generate species tables from eDNA data
• community-ecology - Constrained ordination of assemblages
• microbiome/diversity-analysis - 16S microbiome diversity metrics
• data-visualization/ggplot2-fundamentals - Customize diversity plots