sim_radiation: Main Niche Filling Simulation Function
In rdinnager/nichefillr: Niche Filling Simulations in R

Description Usage Arguments Value Parameter List K_parms a_parms macro_parms

Function to setup and run the niche filling simulation

1 2	sim_radiation(parms, save_tree = TRUE, progress = TRUE, trait_hist = TRUE, trait_hist_prop = 0.01)

`parms`	A named list containing three named list elements: "K_parms": Named list of parameters relating to the carrying capacity landscape function "a_parms": Named list of parameters relating to the competition function "macro_parms": Named list of parameters relating to the macroevolutionary simulation See Parameter List section for details on what parameters are in which lists.
`save_tree`	A logical determining whether the simulation should save intermediate states of the phylogeny during the simulation run
`progress`	Print progress bar if TRUE.
`trait_hist`	A logical determining whether the simulation should save all trait evolution histories. Set to FALSE if you are only interested in the end state of the simulation in order to save memory.
`trait_hist_prop`	If `trait_hist` is TRUE, what proportion of data should be kept? For example setting this parameter to 0.5 would tell the simulation to store 50% of the generations in the full simulation. I recommend setting this low to save storage space, and because it is often possible to get a very good animation with only a small proportion of the full simulation (default is 0.01 and this is usually plenty).

A niche_fill_sim object containing the final simulation object, a set of intermediate phylogenies, and the parameters used to run the simulation

There are three named lists in the parms parameter, relating to the carrying capacity, the competition, and the macroevolutionary model. The parameters found in each one are as follows:

`K_parms`

h0: Total maximum height of the carrying capacity landscape
hz: Maximum height of each of u peaks in the landscape; vector of length u
biz: Centre of each of u peaks in the landscape for each of d dimensions; matrix of dimension d by u
sigiz: Width of each of u peaks for each of d dimensions; matrix of length d by u
Piz: Super-gaussian parameter for each of u peaks for each of d dimensions; matrix of dimension d by u
sig0i: Total width of landscape in all dimensions; determines how far from zero the carrying capacity drops off to nearly zero; vector of length d
P0i: Total super-gaussian parameter; determines how quickly or gradually the carrying capacity drops off near the landscape borders in each dimension. Higher values give more extreme drop-offs; vector of length d
a: Minimum values of carrying capacity within landscape limits
c_var: Variance of the deviations added to each species carrying capacity. If this is zero, no deviations are added. The purpose of this is to ensure no two species occupying the same niche position can have exact fitness equivalence, which can lead to infinite coexistence. New deviates are drawn after every simulation event, to simulate demographic stochasticity.

`a_parms`

gamma_i: Strength of competition - influence; determines how quickly the competition strength between two species drops off with increasing distance between their niche trait values; vector of length d, where d is the number of niche dimensions
D_i: Super-gaussiain parameter for competition; this parameter controls how the precipitously the competition strength drops off with increasing niche distance. Higher values lead to a more cliff-like competition kernel, with strong competition between highly similar species, then very little competition beyond some threshold #'
C: Strength of competition - max competition; determines the maximum competition between two species as a proportion of competition within species (which is always 1)

`macro_parms`

b_rate: Pure birth rate for phylogeny simulation; the rate at which new species form by splitting
init_traits: Initial trait values for ancestral species; vector of length n_traits, where n_traits is the number of traits being simulated
e_var: Evolutionary rates for all traits; vector of length n_traits
init_Ns: Starting population sizes for two initial species in simulation, after first split with ancestral species; vector of length 2
init_br: Time to ancestral species for initial speciation split
check_extinct: Rate at which to check for species whose population size has dropped below a threshold (currently hard-coded), which are then set as extinct
tot_time: The total amount of time to run the simulation in simulation time steps
V_gi: Mutation rate for niche trait evolution; determines how quickly traits can evolve in the trait evolution simulation; typically this is set very low so that evolution proceeds much slower than population dynamics; vector of length n_trait #'
mult: Multiplier that determines how much traits 'jump' in trait space after a speciation event. This is a multiple of e_var, and is the standard deviation of a normal deviate that is added to all traits.