Introduction
Biodiversity in any sampled area depends on three components
- The total number of individuals
- The relative abundances of species
- The spatial distribution of species
Changes of each component result in specific signatures with respect
to the change of non-spatial and spatial biodiversity patterns (see
vignette Analysis of biodiversity patterns). Therefore,
biodiversity patterns can be assessed to investigate the scale-dependent
effects of biodiversity drivers, e.g. habitat loss, climate change etc.
on the three biodiversity components.
In this vignette we simulate independent variations of each of the
three components and demonstrate how rarefaction and
species-accumulation curves can be applied to diagnose changes in
biodiversity components.
For this purpose we first simulate a reference community. We assume a
random distribution of species in sapce, so we can use the function
sim_poisson_community:
library(mobsim)
S0 <- 100
N0 <- 1000
cv0 <- 0.5
sim_ref <- sim_poisson_community(s_pool = S0, n_sim = N0, sad_type = "lnorm",
sad_coef = list("cv_abund" = cv0), fix_s_sim = TRUE)
plot(sim_ref)
Change in total number
of individuals
First we vary the total number of individuals. For this purpose we
simulate a second community with just half of the individuals.
sim_lower_N <- sim_poisson_community(s_pool = S0, n_sim = N0/2, sad_type = "lnorm",
sad_coef = list("cv_abund" = cv0))
Then we evaluate the rarefaction and species-accumulation curve for
both communities
curve_ref <- spec_sample_curve(sim_ref)
curve_lower_N <- spec_sample_curve(sim_lower_N)
oldpar <- par(mfrow = c(1,2))
plot(spec_rarefied ~ n, data = curve_ref, type = "l", main = "Rarefaction curve",
xlab = "No. of individuals", ylab = "No. of species" )
lines(spec_rarefied ~ n, data = curve_lower_N, col = "red")
plot(spec_accum ~ n, data = curve_ref, type = "l", main = "Species accumulation curve",
xlab = "No. of individuals", ylab = "No. of species" )
lines(spec_accum ~ n, data = curve_lower_N, col = "red")
Due to the stochasticity of simulations the curves of the reference
and the changed community slightly differ, but on average random losses
of individuals are indicated by overlapping curves, which end at
different numbers of individuals.
Change in the abundance
distribution
The second simulated change represents a reduction of the evenness of
the species abundance distribution, i.e. there are more very rare and
more very common species than in the reference community. In
mobsim there is the argument cv_abund in the
simulation functions, which is the coefficient of variation (cv) of
species abundances. The higher the cv the lower the evenness and vice
versa. We also use the argument fix_s_local= TRUE to
simulate a community with exactly S0 species.
sim_uneven <- sim_poisson_community(s_pool = S0, n_sim = N0, sad_type = "lnorm",
sad_coef = list("cv_abund" = cv0*4),
fix_s_sim = TRUE)
curve_uneven <- spec_sample_curve(sim_uneven)
oldpar <- par(mfrow = c(1,2))
plot(spec_rarefied ~ n, data = curve_ref, type = "l", main = "Rarefaction curve",
xlab = "No. of individuals", ylab = "No. of species" )
lines(spec_rarefied ~ n, data = curve_uneven, col = "red")
plot(spec_accum ~ n, data = curve_ref, type = "l", main = "Species accumulation curve",
xlab = "No. of individuals", ylab = "No. of species" )
lines(spec_accum ~ n, data = curve_uneven, col = "red")
As we see changes in the abundance distributions without changes in
total species richness are indicated by changing slopes and shapes of
the curves.
Change in spatial
distributions
Finally we change the spatial patterns of species, by simulating
clumping of conspecific individuals using a Thomas process model instead
of a Poisson
sim_clumped <- sim_thomas_community(s_pool = S0, n_sim = N0, sad_type = "lnorm",
sad_coef = list("cv_abund" = cv0),
fix_s_sim = TRUE,
sigma = 0.05)
oldpar <- par(mfrow = c(1,2))
plot(sim_ref)
plot(sim_clumped)
As before we compare the rarefaction and accumulation curves:
curve_clumped<- spec_sample_curve(sim_clumped)
oldpar <- par(mfrow = c(1,2))
plot(spec_rarefied ~ n, data = curve_ref, type = "l", main = "Rarefaction curve",
xlab = "No. of individuals", ylab = "No. of species" )
lines(spec_rarefied ~ n, data = curve_clumped, col = "red")
plot(spec_accum ~ n, data = curve_ref, type = "l", main = "Species accumulation curve",
xlab = "No. of individuals", ylab = "No. of species" )
lines(spec_accum ~ n, data = curve_clumped, col = "red")
When there is only a change in spatial distribution the rarefaction
curve does not change, because it only depends on species abundances,
but not on spatial distributions (see vignette Analysis of
biodiversity patterns). In contrast, the species-accumulation
curves indicates higher intraspecific clumping (also called aggregation)
by a lower slope and lower expected species richness for the same number
of sampled individuals.