ecospace (version 1.0.0)

neutral: Use Neutral Rule to Simulate Ecological Diversification of a Biota.

Description

Implement Monte Carlo simulation of a biota undergoing ecological diversification using the neutral rule. Can be used as a simple permutation test (draw species at random with replacement from provided species pool) if set Sseed equal to Smax.

Usage

neutral(nreps = 1, Sseed, Smax, ecospace)

Arguments

nreps

Vector of integers (such as a sequence) specifying sample number produced. Only used when function is applied within lapply or related function. Default nreps=1 or any other integer produces a single sample.

Sseed

Integer giving number of species (or other taxa) to use at start of simulation.

Smax

Maximum number of species (or other taxa) to include in simulation.

ecospace

An ecospace framework (functional trait space) of class ecospace.

Value

Returns a data frame with Smax rows (representing species) and as many columns as specified by number of characters/states (functional traits) in the ecospace framework. Columns will have the same data type (numeric, factor, ordered numeric, or ordered factor) as specified in the ecospace framework.

Details

Simulations are implemented as Monte Carlo processes in which species are added iteratively to assemblages, with all added species having their character states specified by the model rules, here the 'neutral' rule. Simulations begin with the seeding of Sseed number of species, chosen at random (with replacement) from either the species pool (if provided in the weight.file when building the ecospace framework using create_ecospace) or following the neutral-rule algorithm (if a pool is not provided). Once seeded, the simulations proceed iteratively (character-by-character, species-by-species) by following the appropriate algorithm, as explained below, until terminated at Smax.

Neutral rule algorithm: Choose remaining species (or seed species, if no pool) as random multinomial draws from theoretical ecospace framework (using whatever constraints and structure was provided by the ecospace framework in create_ecospace). Thus, if relative weighting was provided to character states (functional traits), simulated species will mimic these weights, on average. If state cominations were constrained (by setting constraint in create_ecospace), then unallowed state combinations will not be allowed in simulated species.

Note that this simulation is not a simple permutation test of a species pool (if provided). The life habit of each new species is built character-by-character from the realm of theoretically possible states allowed by the ecospace framework. Simulated species can occupy combinations of character states that did not occur in the species pool (if provided). This is an important feature of the simulations, allowing the entire theoretical ecospace to be explored by the neutral model. However, the simulation can be used as a simple permutation test (draw species at random with replacement from provided species pool) if set Sseed equal to Smax and a species pool is supplied when building the ecospace framework.

This rule has also been termed the diffusional, null, and passive model (Bush and Novack-Gottshall 2012). Additional details on the neutral simulation are provided in Novack-Gottshall (In pressA, B), including sensitivity to ecospace framework (functional trait space) structure, recommendations for model selection, and basis in ecological and evolutionary theory.

References

Bush, A. and P.M. Novack-Gottshall. 2012. Modelling the ecological-functional diversification of marine Metazoa on geological time scales. Biology Letters 8: 151-155.

Novack-Gottshall, P.M. In review at Paleobiology, submitted Oct. 5, 2015. General models of ecological diversification. I. Conceptual synthesis.

Novack-Gottshall, P.M. In review at Paleobiology, submitted Oct. 5, 2015. General models of ecological diversification. II. Simulations and empirical applications.

See Also

create_ecospace, redundancy, partitioning, expansion

Examples

# Create an ecospace framework with 15 3-state factor characters
# Can also accept following character types: "numeric", "ord.num", "ord.fac"
nchar <- 15
ecospace <- create_ecospace(nchar=nchar, char.state=rep(3, nchar),
  char.type=rep("factor", nchar))

# Single (default) sample produced by neutral function:
Sseed <- 5
Smax <- 50
x <- neutral(Sseed=Sseed, Smax=Smax, ecospace=ecospace)
head(x, 10)

# Plot results, showing order of assembly
# (Seed species in red, next 5 in black, remainder in gray)
# Notice the neutral model fills the entire ecospace with life habits
seq <- seq(nchar)
types <- sapply(seq, function(seq) ecospace[[seq]]$type)
if(any(types == "ord.fac" | types == "factor")) pc <- prcomp(FD::gowdis(x)) else
  pc <- prcomp(x)
plot(pc$x, type="n", main=paste("Neutral model,\n", Smax, "species"))
text(pc$x[,1], pc$x[,2], labels=seq(Smax), col=c(rep("red", Sseed), rep("black", 5),
  rep("slategray", (Smax - Sseed - 5))), pch=c(rep(19, Sseed), rep(21, (Smax - Sseed))),
  cex=.8)

# Create 5 samples using multiple nreps and lapply
nreps <- 1:5
samples <- lapply(X=nreps, FUN=neutral, Sseed=5, Smax=50, ecospace)
str(samples)

# Implement as simple permutation test by setting Sseed=Smax and providing species pool)
nchar <- 18
char.state <- c(2, 7, 3, 3, 2, 2, 5, 5, 2, 5, 2, 2, 5, 2, 5, 5, 3, 3)
char.type <- c("numeric", "ord.num", "numeric", "numeric", "numeric", "numeric",
  "ord.num", "ord.num", "numeric", "ord.num", "numeric", "numeric", "ord.num",
  "numeric", "ord.num", "numeric", "numeric", "numeric")
data(KWTraits)
ecospace <- create_ecospace(nchar, char.state, char.type, constraint=2,
  weight.file=KWTraits)

x <- neutral(Sseed=100, Smax=100, ecospace=ecospace)
mean(dist(x))

# Note ecological disparity (functional diversity) is less when perform permutation
x <- neutral(Sseed=5, Smax=100, ecospace=ecospace)
mean(dist(x))

# Simulated character states (functional traits) proportionally mimic those in species pool
x <- neutral(Sseed=5, Smax=234, ecospace=ecospace)
table(x[,1:2])
table(KWTraits$SEXL, KWTraits$ASEX)