Loading data table

To load the package:

library(DAISIE)

The raw dataset is inputted as a table. The Galápagos dataset table can be visualized with:

data(Galapagos_datatable)
knitr::kable(Galapagos_datatable)

Each row in the table represents an independent colonisation event. The table has four columns:

• Clade_name: name of the independent colonization event.
• Status: One of the following categories:
  • Endemic: applicable for both anagenetic species and radiations.
  • Non_endemic: If the taxon is not endemic to the island, and the age of colonisation is based on a phylogeny where both island and non-island populations of the species have been sampled.
  • Non_endemic_MaxAge: If the taxon is not endemic to the island, and only an upper bound to the time of colonisation of the island is known. This applies if individuals from the island population of the species have not been sampled, but an age of the species is known.
  • Endemic&Non_Endemic: When an endemic clade is present and the mainland ancestor has re-colonized. For remote islands this is expected to be very rare.
• Missing_species: Number of island species that were not sampled for a particular clade (only applicable for radiations).
• Branching_times – This should be the stem age of the population/species in the case of Non-endemic, Non-endemic_MaxAge and Endemic anagenetic species. For cladogenetic species these should be branching times of the radiation including the stem age of the radiation. Note – if there are species within the radiation that are not found on the island (e.g. back-colonisation) the branching times of these species should be excluded, as the mainland species pool is treated as static.

The same data can also be visualized:

DAISIE::DAISIE_plot_island(Galapagos_datatable, island_age = 4)
#> Colonisation time of 7.456 for Coccyzus is older than island age
#> Colonisation time of 10.285 for Pyrocephalus is older than island age

Formatting table to run in DAISIE

Before running analyses, the datatable needs to be converted to a DAISIE datalist format using the function DAISIE_dataprep.

We will prepare two different datalists based on the Galápagos datatable. In the 1st datalist we will treat all taxa as equivalent. We will specify an island age of four million years (island_age=4) and a mainland pool size of 1000 (M=1000).

data(Galapagos_datatable) 
      
Galapagos_datalist <- DAISIE_dataprep( 
  datatable = Galapagos_datatable, 
  island_age = 4, 
  M = 1000
)
#> Colonisation time of 7.456 for Coccyzus is older than island age
#> Colonisation time of 10.285 for Pyrocephalus is older than island age

In the 2nd datalist we will allow for the Darwin’s finches to form a separate group for which rates can be decoupled from those governing the macroevolutionary process in all other clades (number_clade_types=2 and list_type2_clades = “Finches”). We will set the proportion of Darwin’s finch type species in the mainland pool to be 0.163. (prop_type2_pool=0.163). If prop_type2_pool is not specified then by default it is given the value of the proportion of the Galapagos lineages that Darwin’s finches represent (1/8=0.125 in this case).

data(Galapagos_datatable) 

Galapagos_datalist_2types <- DAISIE_dataprep( 
  datatable = Galapagos_datatable, 
  island_age = 4, 
  M = 1000, 
  number_clade_types = 2, 
  list_type2_clades = "Finches", 
  prop_type2_pool = 0.163
)
#> Colonisation time of 7.456 for Coccyzus is older than island age
#> Colonisation time of 10.285 for Pyrocephalus is older than island age

The objects Galapagos_datalist and Galapagos_datalist_2types can now be run directly in DAISIE functions.

Optimizing parameters using maximum likelihood

The function that conducts maximum likelihood optimization of DAISIE model parameters is called DAISIE_ML.

Different models can be specified using ddmodel option in DAISIE_ML:

• ddmodel = 0 : no diversity-dependence
• ddmodel = 1 : linear diversity-dependence in speciation rate
• ddmodel = 11: linear diversity-dependence in speciation and immigration rate
• ddmodel = 2 : exponential diversity-dependence in speciation rate
• ddmodel = 21: exponential diversity-dependence in speciation and immigration rate

Different types of parameters can be optimized or fixed. The parameters are given in the following order: (1) cladogenesis rate, (2) extinction rate, (3) K’ or carrying capacity (maximum number of species that a clade can attain within the island), (4) colonisation rate, and (5) anagenesis rate.

The identities of the parameters to be optimized or fixed are specified with idparsopt and idparsfix within the DAISIE_ML function. For example, to optimize all parameters we set idparsopt=1:5 and idparsfix=NULL. To optimize all parameters but fix the rate of extinction, we set idparsopt=c(1,3,4,5) and idparsfix=2. To optimize all parameters except cladogenesis and anagenesis we set idparsopt=c(2,3,4) and idparsfix=c(1,5).

The values of the parameters to be used as initial values for the optimization are specified with initparsopt, and the values to be fixed are specified with parsfix. For example, if we want to optimize all parameters with a starting value of 2 we set initparsopt=c(2,2,2,2,2) and parsfix=NULL. If we want all starting rates to be 0.1, but K’ to be fixed at 20, we use initparsopt=c(0.1,0.1,0.1,0.1) and parsfix=20.

When running your own data, we strongly recommend that you test multiple initial starting parameters for each model, particularly when optimizing models with multiple free parameters, as there is a high risk of being trapped in local likelihood sub-optima. We also suggest running two rounds of optimization using the optimized parameter set of the 1st round as the initial starting values for the 2nd round. Also note that the initial starting values in the examples of this tutorial may not be appropriate for your data.

data(Galapagos_datalist) 

DAISIE_ML( 
  datalist = Galapagos_datalist, 
  initparsopt = c(2.5,2.7,20,0.009,1.01), 
  ddmodel = 11, 
  idparsopt = 1:5, 
  parsfix = NULL, 
  idparsfix = NULL
) 

data(Galapagos_datalist) 

DAISIE_ML( 
  datalist = Galapagos_datalist, 
  initparsopt = c(2.5,2.7,0.009,1.01), 
  idparsopt = c(1,2,4,5), 
  parsfix = Inf, 
  idparsfix = 3
) 

data(Galapagos_datalist) 
DAISIE_ML( 
  datalist=Galapagos_datalist, 
  initparsopt = c(2.5,2.7,0.009), 
  idparsopt = c(1,2,4), 
  parsfix = c(Inf,0), 
  idparsfix = c(3,5)
) 

data(Galapagos_datalist_2types) 

DAISIE_ML( 
  ddmodel=11, 
  datalist=Galapagos_datalist_2types, 
  initparsopt= c(0.38,0.55,20,0.004,1.1,2.28), 
  idparsopt = c(1,2,3,4,5,6), 
  parsfix = 0.163, 
  idparsfix = c(11), 
  idparsnoshift = c(7,8,9,10)
) 

data(Galapagos_datalist_2types) 
 
DAISIE_ML( 
  ddmodel=0, 
  datalist=Galapagos_datalist_2types, 
  initparsopt = c(0.38,0.55,0.004,1.1,2.28,2), 
  idparsopt = c(1,2,4,5,6,7), 
  parsfix = c(Inf,0.163), 
  idparsfix = c(3,11), 
  idparsnoshift = c(8,9,10)
)