RPANDA (version 2.3)

apply_prob_dtt: Calculates paleodiversity dynamics with the probabilistic approach.

Description

Applies prob_dtt() to outputs from shift.estimates().

Usage

apply_prob_dtt(phylo, data, sampling.fractions, shift.res,
               combi = 1, backbone.option = "crown.shift",
               m = NULL)

Value

A list of results from prob_dtt() for subclades and backbone(s).

Arguments

phylo

an object of type 'phylo' (see ape documentation)

data

a data.frame containing a database of monophyletic groups for which potential shifts can be investigated. This database should be based on taxonomy, ecology or traits and contain a column named "Species" with species names as in phylo.

sampling.fractions

the output resulting from get.sampling.fractions.

shift.res

the output resulting from shift.estimates.

backbone.option

type of the backbone analysis:

• "stem.shift": for every shift, the probability of the speciation event at the stem age of the subclade is included in the likelihood of the backbone thanks to the argument spec_times.

• "crown.shift": for every shift, both the probability of the speciation event at the stem age of the subclade and the probability that the stem of the subclade survives to the crown age are included in the likelihood of the backbone thanks to the argument branch_times.

combi

numeric. The combination of shifts defined by its rank in the global comparison.

m

NULL or numeric. The set of maximum values for m ranges. Should be as long as the number of parts in the combinaison. Default is NULL (see details).

Author

Nathan Mazet

Details

This funcion calls the function prob_dtt() to calculate paleodiversity dynamics with the probabilistic approach for the different parts of a combination of diversification shifts.

As explained in Billaud et al. (2020), all the sum of probabilities per million year must be equal to 1. However, it can be difficult to reach 1 for groups showing a paleodiversity decline because the range of paleodiversity over which we need to calculate the probabilities can be very large. To circumvent this issue, apply_prob_dtt() set the range of the paleodiversity to the maximum of the deterministic estimate from the function paleodiv() and successively multiplies this maximum by 2, 3, 5, 7 and 10 until the sums of probabilities for each million year reach a minimum of 95%. In few cases, this value of 95% is not reached for few million years. In this case, it might come from an extremely high range of m and maximum values can be manually set up with the argument m.

References

Morlon, H., Parsons, T.L. and Plotkin, J.B. (2011) Reconciling molecular phylogenies with the fossil record. Proc. Nat. Acad. Sci. 108: 16327-16332.

Billaud, O., Moen, D.S., Parsons, T.L., Morlon, H., (2020). Estimating Diversity Through Time Using Molecular Phylogenies: Old and Species-Poor Frog Families are the Remnants of a Diverse Past. Systematic Biology 69, 363–383.

Mazet, N., Morlon, H., Fabre, P., Condamine, F.L., (2023). Estimating clade‐specific diversification rates and palaeodiversity dynamics from reconstructed phylogenies. Methods in Ecology and in Evolution 14, 2575–2591. https://doi.org/10.1111/2041-210X.14195

See Also

fit_bd, plot_prob_dtt, prob_dtt

Examples

# \donttest{
# loading data
data("Cetacea")
data("taxo_cetacea")
data("shifts_cetacea")

taxo_cetacea_no_genus <- taxo_cetacea[names(taxo_cetacea) != "Genus"]

# apply_prob_dtt() needs the sampling fractions
f_df_cetacea <- get.sampling.fractions(phylo = Cetacea,
                                       data = taxo_cetacea_no_genus,
                                       plot = TRUE, cex = 0.3, lad = FALSE)

# use of apply_prob_dtt()
prob_dtt_cetacea <- apply_prob_dtt(phylo = Cetacea,
                                   data = taxo_cetacea_no_genus,
                                   shift.res = shifts_cetacea,
                                   sampling.fractions = f_df_cetacea,
                                   combi = 1)
# }