diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000..7361c52 --- /dev/null +++ b/.nojekyll @@ -0,0 +1 @@ +d3b00800 \ No newline at end of file diff --git a/DAISIE.html b/DAISIE.html new file mode 100644 index 0000000..394c43d --- /dev/null +++ b/DAISIE.html @@ -0,0 +1,788 @@ + + + + + + + + + + +LearnDAISIEprep - 5  DAISIE + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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5  DAISIE

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+ + + +
+ +
+
Author
+
+

Luis Valente

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+ + + +
+ + +
+ +
+

6 Course Island Biology

+
+

6.1 Part 3 - Fitting DAISIE models using the Galápagos birds as example

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+
+

+
Phylogenies of Galápagos birds
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+
+
+
6.1.0.0.1 In this practical we are going to fit the DAISIE model to phylogenetic data for species of native land birds of the Galápagos islands (data from Valente, Phillimore and Etienne 2015 Ecology Letters; and Valente et al 2020 Nature). We will use software DAISIE to estimate rates of speciation, colonisation and extinction in the Galápagos. We will simulate islands with these estimated rates to see how species diversity has varied through time in the Galápagos.
+

Prepare the R environment to run the practical. Empty workspace of existing previous objects.

+
+
rm(list=ls())
+
+

Load required packages DAISIE and ape

+
+
library(DAISIE)
+library(ape)
+
+
+
+

6.1.1 3.1 - Load and visualise Galapágos bird data

+

The dataset of Galápagos birds includes colonisation and branching times for a total of 27 species of terrestrial birds of the Galápagos islands, distributed across 8 lineages. Some of the lineages have only 1 species, e.g. the Galápagos dove Zenaida. Others have radiated, including the Darwin’s finch radiation (16 species) and the mockingbirds Mimus radiation (4 species). Note: the dataset we will use is slightly different from the example dataset that is included as data in the DAISIE R package.

+
+

6.1.1.1 Load Galápagos DAISIE datalist

+
+
load(file="data/galapagos_datalist.Rdata")
+
+
+
+

6.1.1.2 View Galápagos datalist

+

Just type:

+
+
galapagos_datalist
+
+

This datalist is of the same format that DAISIEprep produces (e.g., same format as the data_list object that you just produced in the DAISIEprep tutorial).

+

View your Galápagos datalist by scrolling up and down in R. Or use function View()

+
+
View(galapagos_datalist)
+
+

You can find detailed information using the R documentation file:

+
+
? Galapagos_datalist 
+
+

To view the top of the list

+
+
galapagos_datalist[[1]]
+
+
$island_age
+[1] 4
+
+$not_present
+[1] 992
+
+
+

This element contains the island age as well as all the mainland species that do not have extant descendants on the island. These are important pieces of information for DAISIE.

+

To view just the Mimus colonisation:

+
+
galapagos_datalist[[4]]
+
+
$colonist_name
+[1] "Mimus"
+
+$branching_times
+[1] 4.00000 3.99999 3.68000 2.93000 0.29000
+
+$stac
+[1] 6
+
+$missing_species
+[1] 0
+
+$type1or2
+[1] 1
+
+
+

The first element of the $branching_times item is the island age, followed by the colonisation time and the branching times (if any). The $stac item is a code that tells DAISIE whether the lineage is endemic, non-endemic, and whether or not a precise age or a maximum-age should be used.

+

Or to view just the Darwin’s finches lineage:

+
+
galapagos_datalist[[3]]
+
+
$colonist_name
+[1] "Finches"
+
+$branching_times
+ [1] 4.0000 3.0280 1.3227 0.8223 0.4286 0.3462 0.2450 0.1180 0.0808 0.0756
+[11] 0.0527 0.0525 0.0327 0.0322 0.0221 0.0118
+
+$stac
+[1] 2
+
+$missing_species
+[1] 1
+
+$type1or2
+[1] 1
+
+
+

As you can see, there is 1 missing species in the Darwin’s finch lineage - this species was not sampled in the phylogeny, so does not have a corresponding branching time in the $branching_times item. However, $missing_species item tells DAISIE that it needs to consider one extra species in its computations.

+
+
+

6.1.1.3 Visualise Galápagos data

+

You can visualize the data in a different way using:

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+
DAISIE_plot_island(galapagos_datalist)
+
+

+
+
+

The above plot shows the 8 colonisation events (independent lineages), the time of colonisation, number of species sampled in each lineage (n), number of missing species (m) and the times of cladogenetic speciation events (horizontal lines). The dashed horizontal line is the island age. Unfilled symbols indicate that the time of colonisation was set as “max-age” (not precise), that is, the colonisation time could have happened any time between the time plotted and the present. This applies for instance to lineages with stem ages older than the island age, whose age is then set to be the age of the island (as was the cases of genus Mimus, whose stem age is older than the island age of 4 million years).

+
+
+

6.1.1.4 Plot age versus diversity

+

To visualise the diversity of each lineage according to their age:

+
+
DAISIE_plot_age_diversity(galapagos_datalist)
+
+

+
+
+

The above plot shows the number of species per lineage plotted against their colonisation time. You can see that one lineage - Darwin’s finches - stands out, it is not the oldest lineage, but it has 16 species, many more than the other lineages. One of the lineages, Mimus, is not plotted, because it’s age is set as max-age, that is why the number of species is 23 (27-4 species=23).
+

+


+

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+
+

6.1.2 3.2 – Fitting DAISIE models

+

We will now fit three different DAISIE models using maximum likelihood, using the function DAISIE_ML. The DAISIE function searches for the parameters that maximise the likelihood of the data given the specified model. We will then compare the likelihoods of the 3 different models to see which one is preferred

+

We will fit the 3 different DAISIE models to the phylogenetic data contained in the object galapagos_datalist.

+


+

+
+

6.1.2.1 Fit a full DAISIE model (M1)

+

The first model (M1) we will fit is a full DAISIE model with 5 parameters. The parameters in DAISIE are always placed in this order:

+
    +
  1. Cladogenesis rate (lambda_c) (unit: cladogenesis events per island lineage per time unit)
  2. +
  3. Extinction rate (mu) (unit: extinction events per island lineage per time unit)
  4. +
  5. Carrying capacity (K) (unit: number of species)
  6. +
  7. Colonisation rate (gamma) (unit: colonisation events per mainland lineage per time unit)
  8. +
  9. Anagenesis rate (lambda_a) (unit: anagenesis events per island lineage per time unit)
  10. +
+

Important settings of the DAISIE_ML function:

+
    +
  • initparsopt refers to the starting parameters of the maximum likelihood optimisation (the search for the optimal parameters). 1.5 is the starting cladogenesis rate, 1.1 is the starting extinction rate, 20 is the initial carrying capacity (20 species), 0.009 is the colonisation rate; 1.1 is the initial anagenesis rate.

  • +
  • idparsopt shows the parameters to optimise, in this case all 5, so 1:5

  • +
  • parsfix and idparsfix are set to NULL because we are not fixing any parameters this time.

  • +
+

The code below takes a while to run.

+
+
DAISIE_ML(
+  datalist = galapagos_datalist,
+  initparsopt = c(1.5,1.1,20,0.009,1.1),
+  ddmodel = 11,
+  idparsopt = 1:5,
+  parsfix = NULL,
+  idparsfix = NULL
+)
+
+
  lambda_c       mu       K       gamma     lambda_a    loglik df conv
+1 1.258226 1.136924 9968506 0.004957378 1.251793e-06 -84.78145  5    0
+
+
+

The output gives the ML parameters values, the loglikelihood, and the number of parameters (df).
+

+
+
+

6.1.2.2 Fit model with no carrying-capacity (M2)

+

As you can see above, the value of K in M1 is very high (hundreds of thousands of species). This suggest that there may be no upper bound to diversity (so K is in fact infinite). So let’s try a model without K as a free parameter.

+

The second model (M2) is just like M1, but we will fix K to be infinite. In comparison to M1, we now remove the 3rd parameter (carrying capacity) from the initparsopt and idparsopt settings. So we now have only parameters 1,2,4 and 5. We fix the parameter number 3 to Inf (carrying capacity K fixed to infinite):

+
+
DAISIE_ML(
+  datalist = galapagos_datalist,
+  initparsopt = c(1.5,1.1,0.009,1.1),
+  idparsopt = c(1,2,4,5),
+  parsfix = Inf,
+  idparsfix = 3,
+  ddmodel=0
+)
+
+
  lambda_c       mu   K      gamma     lambda_a    loglik df conv
+1 1.264389 1.149378 Inf 0.00505558 1.662578e-05 -84.78088  4    0
+
+
+


+

+
+
+

6.1.2.3 Fit model with no carrying capacity AND no anagenesis (M3)

+

Now, it appears that the parameter of anagenesis is close to 0 in M2 (see above). Perhaps a model without anagenesis will perform well.

+

The third model (M3) is like M2, but with no carrying-capacity and no anagenesis. This time we switch off the 3rd and 5th parameters (carrying-capacity and anagenesis). We fix the 3rd parameter (K) to infinite; and the 5th parameter (anagenesis) to zero:

+
+
DAISIE_ML(
+  datalist = galapagos_datalist,
+  initparsopt = c(1.5,1.1,0.009),
+  idparsopt = c(1,2,4),
+  parsfix = c(Inf,0),
+  idparsfix = c(3,5),
+  ddmodel=0
+)
+
+
  lambda_c       mu   K       gamma lambda_a    loglik df conv
+1 1.263034 1.146225 Inf 0.005040353        0 -84.78082  3    0
+
+
+


+

+
+
+

6.1.2.4 Select the best model using AIC

+

Now compare the log-likelihoods of the 3 models. They all have similar likelihoods, but one has fewer parameters, so it is preferred. Which model is it?

+

A more proper way to compare the models is using AIC (or other information criteria), which takes into account the number of parameters and the loglikelihood.

+

Create new function to compute AIC values base on the likelihood values and number of parameters for each model.

+
+
AIC_compare<- function(LogLik,k){
+  aic <- (2*k)-(2*LogLik)
+  return(aic)
+}
+
+

Fill in the values from your data in this format:

+

AIC_compare(c(likelihood_M1,likelihood_M2,likelihood_M3), c(n_parameters_M1,n_parameters_M2,n_parameters_M3)).

+
+
AICs<-AIC_compare(c(-84.78145 ,-84.78088, -84.78082),c(5,4,3))
+
+names(AICs)<-c('M1','M2','M3')
+AICs
+
+
      M1       M2       M3 
+179.5629 177.5618 175.5616 
+
+
+

The model with the lowest AIC value is the preferred model. (In this case, M3)

+


+

+
+
+
+

6.1.3 3.3 - Simulate islands

+

Now let’s use the ML parameters we estimated from the Galápagos bird data to simulate islands. Choose the parameters of the best model. We will simulate for 4 million years (time = 4). M is the mainland pool (keep it at 1000).

+
+

6.1.3.1 Simulate islands with the parameters estimated from the best model for the Galápagos bird data (takes a while to run, you can reduce number of replicates if you want)

+
+
Galapagos_sims<-DAISIE_sim(
+  time=4,
+  M=1000,
+  pars=c(1.26, 1.146, Inf, 0.005,0),
+  replicates= 100,
+  plot_sims = FALSE)
+
+
+
+

6.1.3.2 Plot the species-through-time plots resulting from the simulations

+
+
DAISIE_plot_sims(Galapagos_sims)
+
+

+
+
+
+
+

6.1.3.3 Play with different simulations settings

+

Play with different parameter settings in DAISIE_sim (e.g. changing extinction rate, colonisation rate, island age, mainland pool size, replicates). Is equilibrium reached?

+


+

+
+
+

6.1.3.4 Comparison with bird data from the Azores islands

+

Now let’s brielfy compare with simulatiosn for a completely different dataset, of birds from the Azores archipelago (Atlantic Ocean). Load datatable containing Azores bird data from Valente et al 2017 (Current Biology)

+
+
data(Macaronesia_datalist)
+Azores<-Macaronesia_datalist[[1]]
+
+
+
6.1.3.4.1 Visualise Azores data
+
+
DAISIE_plot_island(Azores)
+
+

+
+
+

Three of the species are extinct and only known from fossils.

+
+
+
6.1.3.4.2 Simulate Azores with pre-identified ML parameters
+
+
Azores_sims<-DAISIE_sim(
+  time=6.3,
+  M=300,
+  pars=c(0,1.053151832,Inf,0.052148979,0.512939011),
+  replicates= 100,
+  plot_sims=FALSE)
+
+


+

+
+
+
6.1.3.4.3 Plot the species-through-time plot for the Azores from the resulting from the simulations
+
+
DAISIE_plot_sims(Azores_sims)
+
+

+
+
+

How does the Azores plot differ from that of the Galápagos?

+


+The end of the DAISIE practical. Well done! Now you are ready to work on the Insula assignment.

+ + +
+
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+ +
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+ + + + \ No newline at end of file diff --git a/DAISIE_files/figure-html/unnamed-chunk-10-1.png b/DAISIE_files/figure-html/unnamed-chunk-10-1.png new file mode 100644 index 0000000..0bce586 Binary files /dev/null and b/DAISIE_files/figure-html/unnamed-chunk-10-1.png differ diff --git a/DAISIE_files/figure-html/unnamed-chunk-11-1.png b/DAISIE_files/figure-html/unnamed-chunk-11-1.png new file mode 100644 index 0000000..6c25c57 Binary files /dev/null and b/DAISIE_files/figure-html/unnamed-chunk-11-1.png differ diff --git a/DAISIE_files/figure-html/unnamed-chunk-6-1.png b/DAISIE_files/figure-html/unnamed-chunk-6-1.png new file mode 100644 index 0000000..732a586 Binary files /dev/null and b/DAISIE_files/figure-html/unnamed-chunk-6-1.png differ diff --git a/DAISIE_files/figure-html/unnamed-chunk-7-1.png b/DAISIE_files/figure-html/unnamed-chunk-7-1.png new file mode 100644 index 0000000..7ad55b4 Binary files /dev/null and b/DAISIE_files/figure-html/unnamed-chunk-7-1.png differ diff --git a/DAISIE_files/figure-html/unnamed-chunk-8-1.png b/DAISIE_files/figure-html/unnamed-chunk-8-1.png new file mode 100644 index 0000000..e693615 Binary files /dev/null and b/DAISIE_files/figure-html/unnamed-chunk-8-1.png differ diff --git a/DAISIEprep.html b/DAISIEprep.html new file mode 100644 index 0000000..5057797 --- /dev/null +++ b/DAISIEprep.html @@ -0,0 +1,809 @@ + + + + + + + + + + +LearnDAISIEprep - 4  DAISIEprep + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

4  DAISIEprep

+
+ + + +
+ +
+
Author
+
+

Luis Valente

+
+
+ + + +
+ + +
+ +
+

5 Course Island Biology

+
+

5.1 Part 2 - DAISIEprep

+
+

5.1.1 In this tutorial, the main features of DAISIEprep are explained. DAISIEprep is an R package that facilitates formatting of data for subsequent use in DAISIE.

+
+
+

+
DAISIEprep R package logo
+
+
+
+

5.1.1.1 A typical DAISIEprep pipeline is as follows: 1) identify island colonisation events for the taxonomic group of interest from time-calibrated phylogenetic trees, 2) assign an island endemicity status (endemic, non-endemic, not present) to each of the species, 3) automatically extract times of colonisation of the island and diversification within the island from the phylogenies; 4) add any missing species, 5) format data for DAISIE.

+
+
+

5.1.1.2 The DAISIEprep tutorial is divided into 2 sections:

+
    +
  1. Phylogeny example - Using a simulated phylogeny including island and non-island species, learn how to extract and format island data for running DAISIE.

  2. +
  3. Adding missing species - Learn how to add missing species, lineages, etc, to your DAISIE data list.

  4. +
+
+
+

5.1.1.3 First load the required packages:

+
+
library(DAISIEprep)
+library(ape)
+library(phylobase)
+library(ggtree)
+library(ggimage)
+library(castor)
+
+
+
+
+
+

5.2 2.1. Single Phylogeny Example

+
+

5.2.0.1 In this section we demonstrate a simple example of extracting and formatting data from a single phylogeny. We use a simulated phylogeny, which facilitates explaining how the data is structured.

+

First we simulate a phylogeny using the package ape.

+
+
set.seed(
+  4,
+  kind = "Mersenne-Twister",
+  normal.kind = "Inversion",
+  sample.kind = "Rejection"
+)
+phylo <- ape::rcoal(10)
+
+

Important: DAISIEprep requires the tip labels (taxon names) in the phylogeny to be formatted as genus name and species name separated by an underscore (e.g. “Canis_lupus”, “Species_1”).

+

Here we add tip labels (representing our species) to the simulated phylogeny. In this case, all taxa sampled are different plant species from the same genus.

+
+
phylo$tip.label <- c("Plant_a", "Plant_b", "Plant_c", "Plant_d", "Plant_e",
+                     "Plant_f", "Plant_g", "Plant_h", "Plant_i", "Plant_j")
+
+

Then we convert the phylogeny to a phylo4 class, defined in the package phylobase. This allows users to easily work with data for each tip in the phylogeny, for example whether they are endemic to the island or not.

+
+
phylo <- phylobase::phylo4(phylo)
+phylobase::plot(phylo)
+
+
+
+

+
+
+
+
+

Now we have a phylogeny in the phylo4 format to which we can easily append data.

+

Specify tips corresponding to species from your focal island by specifying that they are endemic and/or non-endemic to the island. In the example below, species Plant_h is found on the island but it not endemic, and species Plant_f and Plant_e are found on the island and are endemic to the island. All other species on the phylogeny are NOT present on the island.

+
+
island_species <- data.frame(
+  tip_labels = c("Plant_h",
+                 "Plant_f",
+                 "Plant_e")
+  ,
+  tip_endemicity_status = c("nonendemic","endemic","endemic"))
+
+

Now that we know which species are present on the island, we can assign an endemicity status to all the species in the phylogeny.

+
+
endemicity_status <- create_endemicity_status(
+  phylo = phylo,
+  island_species = island_species
+)
+
+

And append this information to the phylogeny object:

+
+
phylod <- phylobase::phylo4d(phylo, endemicity_status)
+
+

We can now visualise our phylogeny with the island endemicity statuses plotted at the tips. This uses the ggtree and ggplot2 packages.

+
+
plot_phylod(phylod = phylod)
+
+
+
+

+
+
+
+
+

As you can see, in the example phylogeny, there appear to be 2 separate colonisation events of the island - one which has led to species Plant_f and Plant_e and other that has led to the non-endemic species Plant_h.

+

Now that we can see the tips that are present on the island, we can extract them to form our island community data set that can be used in the DAISIE R package to fit likelihood models of island colonisation and diversification.

+

Before we extract species, we first create an object to store all of the island colonists’ information. This uses the island_tbl class introduced in this package (DAISIEprep). This island_tbl object can then easily be converted to a DAISIE data list using the function create_daisie_data (more information on this below).

+
+
island_tbl <- island_tbl()
+island_tbl
+#> Class:  Island_tbl 
+#> [1] clade_name      status          missing_species col_time       
+#> [5] col_max_age     branching_times min_age         species        
+#> [9] clade_type     
+#> <0 rows> (or 0-length row.names)
+
+

We can see that this is an object containing an empty data frame. In order to fill this data frame with information on the island colonisation and diversification events we can run:

+
+
island_tbl <- extract_island_species(
+  phylod = phylod,
+  extraction_method = "min"
+)
+island_tbl
+#> Class:  Island_tbl 
+#>   clade_name     status missing_species  col_time col_max_age branching_times
+#> 1    Plant_e    endemic               0 0.1925924       FALSE    0.154371....
+#> 2    Plant_h nonendemic               0 0.1233674       FALSE              NA
+#>   min_age      species clade_type
+#> 1      NA Plant_e,....          1
+#> 2      NA      Plant_h          1
+island_tbl_min<-island_tbl
+
+

The function extract_island_species() is the main function in DAISIEprep to extract data from the phylogeny. In the example above, we used the “min” extraction algorithm. The “min” algorithm extracts island community data assuming no back-colonisation from the island to the mainland. Each row in the island_tbl corresponds to a separate colonisation of the island. In this case, two colonist lineages were correctly identified using the ‘min’ extraction algorithm, one endemic and another non-endemic. Each row in the table corresponds to one colonisation event.

+

However, if back-colonisation is frequent (for example, one species within a large endemic island radiation colonised another island or mainland), we recommend using the “asr” algorithm, which extracts the most likely colonisations inferred in an ancestral area reconstruction. To use the “asr” algorithm, we first need to know the probability of the ancestors of the island species being present on the island to determine the time of colonisation. To do this, we can fit one of many ancestral state reconstruction methods. Here we use the “mk” model (which uses a continuous-time markov model to reconstruct ancestral states), as it is a simple method that should prove reliable for reconstructing the ancestral species areas (i.e. on the island or not on the island) for most cases. First, we translate our extant species endemicity status to a numeric representation of whether that species is on the island.

+
+
phylod <- add_asr_node_states(phylod = phylod, asr_method = "mk")
+
+

Now we can plot the phylogeny, which this time includes the node labels for the presence/absence on the island in ancestral nodes.

+
+
plot_phylod(phylod = phylod)
+
+
+
+

+
+
+
+
+


+Sidenote: if you are wondering what the probabilities are at each node and whether this should influence your decision to pick a preference for island or mainland when the likelihoods for each state are equal, we can plot the probabilities at the nodes to visualise the ancestral state reconstruction using plot_phylod(phylod = phylod, node_pies = TRUE).

+

Now we can extract island colonisation and diversification times from the phylogeny with asr using the reconstructed ancestral states of island presence/absence.

+
+
island_tbl_asr <- extract_island_species(
+  phylod = phylod,
+  extraction_method = "asr"
+)
+
+
+
island_tbl_asr
+#> Class:  Island_tbl 
+#>   clade_name     status missing_species  col_time col_max_age branching_times
+#> 1    Plant_e    endemic               0 0.1925924       FALSE    0.154371....
+#> 2    Plant_h nonendemic               0 0.1233674       FALSE              NA
+#>   min_age      species clade_type
+#> 1      NA Plant_e,....          1
+#> 2      NA      Plant_h          1
+
+

By comparing island_tbl_min and island_tbl_asr you can see the differences in the 2 extraction methods.
+

+
+
all.equal(island_tbl_min,island_tbl_asr)
+#> [1] TRUE
+
+

In this case, the results using “asr” and “min” are exactly the same. For the purposes of this practical, we will proceed with the results from “asr” (island_tbl_asr). For convenience, let’s rename the object to island_tbl.

+
+
island_tbl<-island_tbl_asr
+
+

You can visualize the contents of the island_tbl object to try and figure out what they mean.

+
+
View(island_tbl)
+
+

For example, the code below shows you the names of the species included in each lineage:

+
+
island_tbl@island_tbl$species
+#> [[1]]
+#> [1] "Plant_e" "Plant_f"
+#> 
+#> [[2]]
+#> [1] "Plant_h"
+
+

Now that we have the island_tbl we can convert this to the DAISIE data list to be used by the DAISIE inference model. To convert to the DAISIE data list (i.e. the input data of the DAISIE inference model) we use create_daisie_data(), providing the island_tbl as input. We also need to specify:

+
    +
  • The age of the island or archipelago. Here we use an island age of twelve million years (island_age = 12).
  • +
  • Whether the colonisation times extracted from the phylogenetic data should be considered precise (precise_col_time = TRUE). We will not discuss the details of this here, but briefly by setting this to TRUE the data will tell the DAISIE model that the colonisation times are known without error. Setting precise_col_time = FALSE will change tell the DAISIE model that the colonisation time is uncertain and should interpret this as the upper limit to the time of colonisation and integrate over the uncertainty between this point and either the present time or to the first branching point (either speciation or divergence into subspecies).
  • +
  • The number of species in the mainland source pool. Here we set it to 100 (num_mainland_species = 100). This will be used to calculate the number of species that could have potentially colonised the island but have not. When we refer to the mainland pool, this does not necessarily have to be a continent, it could be a different island if the source of species immigrating to an island is largely from another nearby island (a possible example of this could be Madagascar being the source of species colonising Comoros). This information is used by the DAISIE model to calculate the colonisation rate of the island.
  • +
+
+
data_list <- create_daisie_data(
+  data = island_tbl,
+  island_age = 12,
+  num_mainland_species = 100,
+  precise_col_time = TRUE
+)
+
+

Below we show two elements of the DAISIE data list produced. The first element data_list[[1]] in every DAISIE data list is the island community metadata, containing the island age and the number of species in the mainland pool that did not leave descendants on the island at the present day. This is important information for DAISIE inference, as it is possible some mainland species colonised the island but went extinct leaving no trace of their island presence.

+
+
data_list[[1]]
+#> $island_age
+#> [1] 12
+#> 
+#> $not_present
+#> [1] 98
+
+

Next is the first element containing information on island colonists (every element data_list[[x]] in the list after the metadata contains information on individual island colonists). This contains the name of the colonist, the number of missing species, and the branching times, which is a vector containing the age of the island, the colonisation time and the times of any cladogenesis events. Confusingly, it may be that the branching times vector contains no branching times: when there are only two numbers in the vector these are the island age followed by the colonisation time. Then there is the stac, which stands for status of colonist. This is a number which tells the DAISIE model how to identify the endemicity and colonisation uncertainty of the island colonist (these are explained here if you are interested). Lastly, the type1or2 defines which macroevolutionary regime an island colonist is in. By macroevolutionary regime we mean the set of rates of colonisation, speciation and extinction for that colonist. Most applications will assume all island clades have the same regime and thus all are assigned type 1. However, if there is a priori expectation that one clade significantly different from the rest, e.g. the Galápagos finches amongst the other terrestrial birds of the Galápagos archipelago this clade can be set to type 2.

+
+
data_list[[2]]
+#> $colonist_name
+#> [1] "Plant_e"
+#> 
+#> $branching_times
+#> [1] 12.0000000  0.1925924  0.1543711
+#> 
+#> $stac
+#> [1] 2
+#> 
+#> $missing_species
+#> [1] 0
+#> 
+#> $type1or2
+#> [1] 1
+
+

This data list is now ready to be used in the DAISIE maximum likelihood inference model from the R package DAISIE. But let’s learn how to add missing species (species not sampled in the phylogeny) to this datalist.

+
+
+
+

5.3 2.2. Adding missing species

+

It is often the case that phylogenetic data is not available for some island species or even for entire lineages present in the island community. But we can still include these species in our DAISIE analyses using DAISIEprep. This section is about the tools that DAISIEprep provides in order to handle missing data, and generally to handle species that are missing and need to be input into the data manually.

+

For this section, as with the previous section, the core data structure we are going to work with is the island_tbl. We will use the island_tbl for the example plant dataset produced in the last section.

+
+

5.3.1 2.2.1 Adding missing species to an island clade that has been sampled in the phylogeny

+

This option is for cases in which a clade has been sampled in the phylogeny, and at least 1 colonisation or 1 branching time is available in the datalist, but 1 or more species from that clade were not sampled. For this example, we imagine that 2 species have not been sampled, and that we want to add them as missing species to the lineage containing species “Plant_e” that is sampled in the phylogeny. To assign two missing species to this clade we use the function add_missing_species() The argument species_to_add_to uses a representative sampled species from that island clade to work out which colonist in the island_tbl to assign the specified number of missing species (num_missing_species) to. You can find out which species are already sampled in each lineage using:

+
+
island_tbl@island_tbl$species
+#> [[1]]
+#> [1] "Plant_e" "Plant_f"
+#> 
+#> [[2]]
+#> [1] "Plant_h"
+
+

Now run the code:

+
+
island_tbl <- add_missing_species(
+  island_tbl = island_tbl,
+  # num_missing_species equals total species missing
+  num_missing_species = 2,
+  # name of a sampled species you want to "add" the missing to
+  # it can be any in the clade
+  species_to_add_to = "Plant_e"
+)
+
+

The new island table now has missing species added to the lineage we wanted to:

+
+
island_tbl@island_tbl$missing_species
+#> [1] 2 0
+
+

With the new missing species added to the island_tbl we can repeat the conversion steps above using create_daisie_data() to produce data accepted by the DAISIE model.

+
+
data_list <- create_daisie_data(
+  data = island_tbl,
+  island_age = 12,
+  num_mainland_species = 100,
+  precise_col_time = TRUE
+)
+
+
+
+

5.3.2 2.2.2 Adding an entire lineage when a phylogeny is not available for the lineage.

+

For cases when the missing species belong to a lineage that has not been sampled in the phylogeny. Assuming we did not have any phylogenetic data or colonisation time estimate for the island clade, we can insert species as missing and specify them as a separate lineage. Because not colonisation and branching times are known for this lineage, when this lineage later gets processed by the DAISIE inference model it will be assumed it colonised the island any time between the island’s formation and the present.

+

The input needed are:

+
    +
  • island_tbl to add to an existing island_tbl

  • +
  • clade_name a name to represent the new clade addeed, can either be a specific species from the clade or a genus name, or another name that represent those species

  • +
  • status either “endemic” or “nonendemic”

  • +
  • species a vector of species names contained within colonist (if applicable)

  • +
  • missing_species In the case of a lineage with just 1 species (i.e. not an island radiation) the number of missing species is zero, as by adding the colonist it already counts as one automatically. In the case of an island clade of more than one species, the number of missing species in this case should be n-1.

  • +
+

Example for adding lineage with 1 species:

+
+
island_tbl <- add_island_colonist(
+  island_tbl = island_tbl,
+  clade_name = "Plant_y",
+  status = "endemic",
+  # clade with just 1 species, missing_species = 0
+  # because adding the lineage already counts as 1
+  missing_species = 0,
+  col_time = NA_real_,
+  col_max_age = FALSE,
+  branching_times = NA_real_,
+  min_age = NA_real_,
+  clade_type = 1,
+  species = "Plant_a"
+)
+
+

Example for adding lineage with 5 species (“Plant_a”, “Plant_b”, “Plant_c”, “Plant_d”, “Plant_e”) to a lineage called “Plant_radiation”

+
+
island_tbl <- add_island_colonist(
+  island_tbl = island_tbl,
+  clade_name = "Plant_radiation",
+  status = "endemic",
+  # the total species is 5 and all are missing
+  # but we add missing_species = 4 because
+  # adding the lineage already counts as 1
+  missing_species = 4,
+  col_time = NA_real_,
+  col_max_age = FALSE,
+  branching_times = NA_real_,
+  min_age = NA_real_,
+  clade_type = 1,
+  species = c("Plant_a", "Plant_b", "Plant_c",
+              "Plant_d", "Plant_e")
+)
+
+

With the new missing species added to the island_tbl we can repeat the conversion steps above using create_daisie_data() to produce data accepted by the DAISIE model.

+
+
data_list <- create_daisie_data(
+  data = island_tbl,
+  island_age = 12,
+  num_mainland_species = 100,
+  precise_col_time = TRUE
+)
+
+

End of the DAISIEprep tutorial!

+ + +
+
+
+ +
+ + +
+ + + + \ No newline at end of file diff --git a/DAISIEprep_files/figure-html/convert phylo to phylo4-1.png b/DAISIEprep_files/figure-html/convert phylo to phylo4-1.png new file mode 100644 index 0000000..a26a3fd Binary files /dev/null and b/DAISIEprep_files/figure-html/convert phylo to phylo4-1.png differ diff --git a/DAISIEprep_files/figure-html/plot phylogeny with tip and node data-1.png b/DAISIEprep_files/figure-html/plot phylogeny with tip and node data-1.png new file mode 100644 index 0000000..bd96c7e Binary files /dev/null and b/DAISIEprep_files/figure-html/plot phylogeny with tip and node data-1.png differ diff --git a/DAISIEprep_files/figure-html/plot phylogeny with tip data-1.png b/DAISIEprep_files/figure-html/plot phylogeny with tip data-1.png new file mode 100644 index 0000000..59c0e2a Binary files /dev/null and b/DAISIEprep_files/figure-html/plot phylogeny with tip data-1.png differ diff --git a/data/Figure_Insula_Tip_states.pdf b/data/Figure_Insula_Tip_states.pdf new file mode 100644 index 0000000..bcfde5d Binary files /dev/null and b/data/Figure_Insula_Tip_states.pdf differ diff --git a/data/Figure_Insula_ancestral_state_reconstruction.pdf b/data/Figure_Insula_ancestral_state_reconstruction.pdf new file mode 100644 index 0000000..b853f72 Binary files /dev/null and b/data/Figure_Insula_ancestral_state_reconstruction.pdf differ diff --git a/data/Insula.tre b/data/Insula.tre new file mode 100644 index 0000000..c8ccfe6 --- /dev/null +++ b/data/Insula.tre @@ -0,0 +1,113 @@ +#NEXUS +[R-package APE, Fri Jan 19 17:24:12 2024] + +BEGIN TAXA; + DIMENSIONS NTAX = 50; + TAXLABELS + Spec_1 + Spec_2 + Spec_3 + Spec_4 + Spec_5 + Spec_6 + Spec_7 + Spec_8 + Spec_9 + Spec_10 + Spec_11 + Spec_12 + Spec_13 + Spec_14 + Spec_15 + Spec_16 + Spec_17 + Spec_18 + Spec_19 + Spec_20 + Spec_21 + Spec_22 + Spec_23 + Spec_24 + Spec_25 + Spec_26 + Spec_27 + Spec_28 + Spec_29 + Spec_30 + Spec_31 + Spec_32 + Spec_33 + Spec_34 + Spec_35 + Spec_36 + Spec_37 + Spec_38 + Spec_39 + Spec_40 + Spec_41 + Spec_42 + Spec_43 + Spec_44 + Spec_45 + Spec_46 + Spec_47 + Spec_48 + Spec_49 + Spec_50 + ; +END; +BEGIN TREES; + TRANSLATE + 1 Spec_1, + 2 Spec_2, + 3 Spec_3, + 4 Spec_4, + 5 Spec_5, + 6 Spec_6, + 7 Spec_7, + 8 Spec_8, + 9 Spec_9, + 10 Spec_10, + 11 Spec_11, + 12 Spec_12, + 13 Spec_13, + 14 Spec_14, + 15 Spec_15, + 16 Spec_16, + 17 Spec_17, + 18 Spec_18, + 19 Spec_19, + 20 Spec_20, + 21 Spec_21, + 22 Spec_22, + 23 Spec_23, + 24 Spec_24, + 25 Spec_25, + 26 Spec_26, + 27 Spec_27, + 28 Spec_28, + 29 Spec_29, + 30 Spec_30, + 31 Spec_31, + 32 Spec_32, + 33 Spec_33, + 34 Spec_34, + 35 Spec_35, + 36 Spec_36, + 37 Spec_37, + 38 Spec_38, + 39 Spec_39, + 40 Spec_40, + 41 Spec_41, + 42 Spec_42, + 43 Spec_43, + 44 Spec_44, + 45 Spec_45, + 46 Spec_46, + 47 Spec_47, + 48 Spec_48, + 49 Spec_49, + 50 Spec_50 + ; + TREE * UNTITLED = [&R] (((((((((((((42:0.05571727541,43:0.05571727541):0.004870062363,39:0.06058733777):0.04256341776,33:0.1031507555):0.03099285877,26:0.1341436143):0.05582430337,19:0.1899679177):0.01803055954,((47:0.02255066796,48:0.02255066796):0.05346701138,38:0.07601767934):0.1319807979):0.289245236,((((27:0.1195649616,28:0.1195649616):0.1133742873,11:0.2329392489):0.0767507219,((36:0.09445444181,37:0.09445444181):0.2076598178,7:0.3021142596):0.00757571112):0.03622926224,2:0.345919233):0.1513244802):0.07165283633,(3:0.3431694698,4:0.3431694698):0.2257270798):0.2341773391,((((34:0.0949459804,35:0.0949459804):0.09230507551,20:0.1872510559):0.03367478957,14:0.2209258455):0.2826695608,((40:0.05610008764,41:0.05610008764):0.1096897204,25:0.165789808):0.3378055983):0.2994784824):0.098010401,(((12:0.2294912807,13:0.2294912807):0.08287528163,(8:0.2892540979,9:0.2892540979):0.02311246449):0.3685314232,(31:0.1036446717,32:0.1036446717):0.5772533139):0.2201863041):0.09383530582,(((((49:0.007372492687,50:0.007372492687):0.03126202655,44:0.03863451924):0.1308346477,24:0.169469167):0.1467994181,6:0.3162685851):0.1793500324,((17:0.1970570628,18:0.1970570628):0.1259735983,5:0.323030661):0.1725879565):0.499300978):0.3694949509,((((45:0.03428446368,46:0.03428446368):0.3483089053,(29:0.1166230684,30:0.1166230684):0.2659703006):0.3347932892,1:0.7173866582):0.2714416272,((15:0.2145754979,16:0.2145754979):0.03219779196,10:0.2467732898):0.7420549955):0.375586261):0.5985589695,((22:0.1725127002,23:0.1725127002):0.01119431284,21:0.1837070131):1.779266503):0.0370264841; +END; diff --git a/data/Insula_checklist.xlsx b/data/Insula_checklist.xlsx new file mode 100644 index 0000000..9880ddc Binary files /dev/null and b/data/Insula_checklist.xlsx differ diff --git a/images/DAISIEprep_logo.png b/images/DAISIEprep_logo.png new file mode 100644 index 0000000..2baf387 Binary files /dev/null and b/images/DAISIEprep_logo.png differ diff --git a/images/Figure_Insula_ancestral_state_reconstruction.png b/images/Figure_Insula_ancestral_state_reconstruction.png new file mode 100644 index 0000000..c0c468f Binary files /dev/null and b/images/Figure_Insula_ancestral_state_reconstruction.png differ diff --git a/images/Figure_Insula_tip_states.png b/images/Figure_Insula_tip_states.png new file mode 100644 index 0000000..74a922b Binary files /dev/null and b/images/Figure_Insula_tip_states.png differ diff --git a/images/Galapagos_picture.png b/images/Galapagos_picture.png new file mode 100644 index 0000000..de754b6 Binary files /dev/null and b/images/Galapagos_picture.png differ diff --git a/index.html b/index.html new file mode 100644 index 0000000..3436abb --- /dev/null +++ b/index.html @@ -0,0 +1,424 @@ + + + + + + + + + + +LearnDAISIEprep + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

LearnDAISIEprep

+
+ + + +
+ +
+
Author
+
+

Luis Valente, Joshua W. Lambert

+
+
+ + + +
+ + +
+ +

Welcome to the LearnDAISIEprep website. Here you will find material to learn how to use the DAISIE ecosystem of R package to analyse the ecology and evolution of island communities. These tools have been developed to answer research questions in island biogeography and macroevolutionary science and can be powerful tools when applied appropriately.

+ + + +
+ + +
+ + + + \ No newline at end of file diff --git a/install_packages.html b/install_packages.html new file mode 100644 index 0000000..cf5e5b2 --- /dev/null +++ b/install_packages.html @@ -0,0 +1,487 @@ + + + + + + + + + + +LearnDAISIEprep - 2  Install packages + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

2  Install packages

+
+ + + +
+ +
+
Author
+
+

Luis Valente

+
+
+ + + +
+ + +
+ +
+

2.1 Open and run this script in Rstudio

+
+

2.1.1 Install required packages

+
+
install.packages('DAISIE',dependencies=T)
+install.packages('ape',dependencies=T)
+install.packages('DAISIEprep',dependencies=T)
+
+

Check if packages load correctly

+
+
library(DAISIE)
+library(ape)
+library(DAISIEprep)
+
+ + +
+
+ +
+ + +
+ + + + \ No newline at end of file diff --git a/insula_DAISIE.html b/insula_DAISIE.html new file mode 100644 index 0000000..65480fc --- /dev/null +++ b/insula_DAISIE.html @@ -0,0 +1,736 @@ + + + + + + + + + + +LearnDAISIEprep - 6  Insula DAISIE + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

6  Insula DAISIE

+
+ + + +
+ +
+
Author
+
+

Luis Valente

+
+
+ + + +
+ + +
+ +
+

6.1 Example of how to extract data for Jamaica species from the Insula tree, add missing species, fit DAISIE models and run simulations.

+
+

6.1.1 Load required packages

+
+
rm(list=ls())
+library(ape)
+library(DAISIEprep)
+library(DAISIE)
+
+
+
+

6.1.2 Load tree

+
+
Insula_tree<-read.nexus("data/Insula.tre")
+
+

Visualise tree (easier to use Figtree!)

+
+
plot(Insula_tree)
+
+

+
+
+
+
+
+

6.2 Prepare data using DAISIEprep

+

Look in the checklist to see which species occur on Jamaica. Specify tips corresponding to Jamaica species by specifying that they are endemic and/or non-endemic to the island:

+
+
island_species <- data.frame(
+  tip_labels = c("Spec_29",
+                 "Spec_48",
+                 "Spec_47",
+                 "Spec_38",
+                 "Spec_43",
+                 "Spec_42",
+                 "Spec_39",
+                 "Spec_33",
+                 "Spec_26",
+                 "Spec_19",
+                 "Spec_41",
+                 "Spec_40",
+                 "Spec_25",
+                 "Spec_9",
+                 "Spec_24")
+  ,
+  tip_endemicity_status = c(rep("endemic",14),"nonendemic"))
+
+

Assign island endemicity status to all species in the dataset (including the non-Jamaican species)

+
+
endemicity_status <- create_endemicity_status(
+  phylo = Insula_tree,
+  island_species = island_species
+)
+
+

Add endemicity status to the phylogeny

+
+
phylod <- phylobase::phylo4d(Insula_tree, endemicity_status)
+
+

Visualize this on the tree

+
+
plot_phylod(phylod = phylod)
+
+

+
+
+

Extract data from the phylogeny using the min algorithm

+
+
island_tbl_min <- extract_island_species(
+  phylod = phylod,
+  extraction_method = "min"
+)
+island_tbl_min
+
+
Class:  Island_tbl 
+  clade_name     status missing_species  col_time col_max_age branching_times
+1     Spec_9    endemic               0 0.2892541       FALSE              NA
+2    Spec_19    endemic               0 0.4972437       FALSE    0.207998....
+3    Spec_24 nonendemic               0 0.1694692       FALSE              NA
+4    Spec_25    endemic               0 0.5035954       FALSE    0.165789....
+5    Spec_29    endemic               0 0.1166231       FALSE              NA
+  min_age      species clade_type
+1      NA       Spec_9          1
+2      NA Spec_19,....          1
+3      NA      Spec_24          1
+4      NA Spec_25,....          1
+5      NA      Spec_29          1
+
+
+

Extract data from the phylogeny using the ancestral state algorithm

+
+
phylod <- add_asr_node_states(phylod = phylod, asr_method = "mk")
+plot_phylod(phylod = phylod)
+
+

+
+
island_tbl_asr <- extract_island_species(
+  phylod = phylod,
+  extraction_method = "asr"
+)
+island_tbl_asr
+
+
Class:  Island_tbl 
+  clade_name     status missing_species  col_time col_max_age branching_times
+1     Spec_9    endemic               0 0.2892541       FALSE              NA
+2    Spec_19    endemic               0 0.4972437       FALSE    0.207998....
+3    Spec_24 nonendemic               0 0.1694692       FALSE              NA
+4    Spec_25    endemic               0 0.5035954       FALSE    0.165789....
+5    Spec_29    endemic               0 0.1166231       FALSE              NA
+  min_age      species clade_type
+1      NA       Spec_9          1
+2      NA Spec_19,....          1
+3      NA      Spec_24          1
+4      NA Spec_25,....          1
+5      NA      Spec_29          1
+
+
+

Compare 2 options:

+
+
all.equal(island_tbl_min,island_tbl_asr)
+
+
[1] TRUE
+
+
+

As you can see, the results of the 2 extractions (min and asr) are exactly the same in this case, so we can use either for the subsequent analyses.

+
+

6.2.1 Add missing species

+

Add missing species “Spec_51”, which is not closely related to any species

+
+
island_tbl<-island_tbl_min
+island_tbl <- add_island_colonist(
+  island_tbl = island_tbl,
+  clade_name = "Spec_51",
+  status = "endemic",
+  missing_species = 0,
+  col_time = NA_real_,
+  col_max_age = FALSE,
+  branching_times = NA_real_,
+  min_age = NA_real_,
+  clade_type = 1,
+  species = "Spec_51"
+)
+
+

Add missing species Spec_52, closely related to Spec_42

+
+
island_tbl <- add_missing_species(
+  island_tbl = island_tbl,
+  num_missing_species = 1, 
+  species_to_add_to = "Spec_42"
+)
+
+

Create DAISIE datalist

+
+
insula_data_list <- create_daisie_data(
+  data = island_tbl,
+  island_age = 4,
+  num_mainland_species = 1000,
+  precise_col_time = TRUE
+)
+
+
+
+
+

6.3 Fit DAISIE models to data

+

Fit model with 5 parameters

+
+
M1<-DAISIE_ML(
+  datalist = insula_data_list,
+  initparsopt = c(1.5,1.1,20,0.009,1.1),
+  ddmodel = 11,
+  idparsopt = 1:5,
+  parsfix = NULL,
+  idparsfix = NULL
+)
+M1
+
+
  lambda_c       mu       K      gamma lambda_a    loglik df conv
+1 5.892194 7.805793 1356008 0.02516541 3.244882 -37.10636  5    0
+
+
+

Fit model with no carrying capacity

+
+
M2<-DAISIE_ML(
+  datalist = insula_data_list,
+  initparsopt = c(1.5,1.1,0.009,1.1),
+  idparsopt = c(1,2,4,5),
+  parsfix = Inf,
+  idparsfix = 3,
+  ddmodel=0
+)
+M2
+
+
  lambda_c       mu   K      gamma lambda_a    loglik df conv
+1 5.896242 7.810848 Inf 0.02519944 3.206366 -37.10633  4    0
+
+
+

Fit model with no anagenesis

+
+
M3<-DAISIE_ML(
+  datalist = insula_data_list,
+  initparsopt = c(1.5,1.1,0.009),
+  idparsopt = c(1,2,4),
+  parsfix = c(Inf,0),
+  idparsfix = c(3,5),
+  ddmodel=0
+)
+M3
+
+
  lambda_c       mu   K      gamma lambda_a    loglik df conv
+1 6.729751 8.726048 Inf 0.02742398        0 -37.29935  3    0
+
+
+

Save model results in a table

+
+
model_results<-rbind(M1,M2,M3)
+model_results
+
+
  lambda_c       mu       K      gamma lambda_a    loglik df conv
+1 5.892194 7.805793 1356008 0.02516541 3.244882 -37.10636  5    0
+2 5.896242 7.810848     Inf 0.02519944 3.206366 -37.10633  4    0
+3 6.729751 8.726048     Inf 0.02742398 0.000000 -37.29935  3    0
+
+
+

Create AIC function for model comparison

+
+
AIC_compare<- function(LogLik,k){
+  aic <- (2*k)-(2*LogLik)
+  return(aic)
+}
+
+

Compute AIC for all the models

+
+
AICs<-AIC_compare(c(M1$loglik ,M2$loglik, M3$loglik),c(M1$df,M2$df,M3$df))
+names(AICs)<-c('M1','M2','M3')
+AICs
+
+
      M1       M2       M3 
+84.21273 82.21265 80.59871 
+
+
+

In this case, the preferred model is M3.

+
+
+

6.4 Simulate islands based on parameters from preferred model

+

Run simulations

+
+
Insula_sims<-DAISIE_sim(
+  time=4,
+  M=1000,
+  pars=as.numeric(M3[1:5]),
+  replicates= 100,
+  verbose = 1,
+  plot_sims = FALSE)
+
+

Plot simulations

+
+
DAISIE_plot_sims(Insula_sims)
+
+

+
+
+

It looks like Insula diversity in the island of Jamaica is at a dynamic equilibrium.

+ + +
+ +
+ + +
+ + + + \ No newline at end of file diff --git a/insula_DAISIE_files/figure-html/unnamed-chunk-17-1.png b/insula_DAISIE_files/figure-html/unnamed-chunk-17-1.png new file mode 100644 index 0000000..4a1043d Binary files /dev/null and b/insula_DAISIE_files/figure-html/unnamed-chunk-17-1.png differ diff --git a/insula_DAISIE_files/figure-html/unnamed-chunk-3-1.png b/insula_DAISIE_files/figure-html/unnamed-chunk-3-1.png new file mode 100644 index 0000000..ad2695d Binary files /dev/null and b/insula_DAISIE_files/figure-html/unnamed-chunk-3-1.png differ diff --git a/insula_DAISIE_files/figure-html/unnamed-chunk-7-1.png b/insula_DAISIE_files/figure-html/unnamed-chunk-7-1.png new file mode 100644 index 0000000..6cf9eaa Binary files /dev/null and b/insula_DAISIE_files/figure-html/unnamed-chunk-7-1.png differ diff --git a/insula_DAISIE_files/figure-html/unnamed-chunk-9-1.png b/insula_DAISIE_files/figure-html/unnamed-chunk-9-1.png new file mode 100644 index 0000000..2af890b Binary files /dev/null and b/insula_DAISIE_files/figure-html/unnamed-chunk-9-1.png differ diff --git a/insula_tutorial_intro.html b/insula_tutorial_intro.html new file mode 100644 index 0000000..d26d1e2 --- /dev/null +++ b/insula_tutorial_intro.html @@ -0,0 +1,593 @@ + + + + + + + + + + +LearnDAISIEprep - 3  Insula practical tutorial + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

3  Insula practical tutorial

+
+ + + +
+ +
+
Author
+
+

Luis Valente

+
+
+ + + +
+ + +
+ +
+

4 Course Island Biology

+
+

4.1 Evolutionary island biogeography using DAISIE

+
+
+

+
+
+

In this practical we will estimate rates of colonisation, speciation and extinction for a (hypothetical) genus of beetles found in the Caribbean. We will then use these estimated rates to simulate how the number of species has changed through time in this taxonomic group. The practical will use mainly the R packages DAISIE and DAISIEprep. We will learn how to interpret and prepare data for analyses using DAISIE, how to fit DAISIE models to phylogenetic data, and how to simulate phylogenies of island communities.

+

The practical is divided into 3 main parts:

+
    +
  1. Learn how to use DAISIEprep to prepare and extract data (1-2 hours). DAISIEprep is an R package that allows users to format phylogenetic data for subsequent analyses in DAISIE.

  2. +
  3. Learn how to use DAISIE to estimate parameters and simulate islands (1-2 hours). DAISIE is an R package that allows fitting island biogeography models and simulating islands.

  4. +
  5. Apply the above approaches to a dataset of beetles from the Caribbean. Writing up this part is the graded assignment.

  6. +
+

Before you start with the DAISIEprep and DAISIE tutorials, please read the introduction to the graded assignment below.

+
+
+
+

5 Before you begin:

+
+

5.1 Install Software required:

+
    +
  • Install: FigTree

  • +
  • Install R

  • +
  • Install Rstudio

  • +
  • Open the program RStudio. Open the file 0_Install_packages and run that code in Rstudio. This should install all required R packages (may take a while!).

  • +
+
+
+

5.2 Tutorial files

+

You will need to follow the instructions of the tutorials, available in HTML format, which you can open on a browser

+
+
+

5.3 1. DAISIEprep

+

DAISIEprep tutorial

+
+
+

5.4 2. DAISIE

+

DAISIE tutorial

+
+

5.4.0.1 After you have finished the DAISIEprep and DAISIE practicals, you can begin applying these techniques to the Insula dataset.

+
+
+
+

5.5 3. Insula assignment

+

The beetle genus Insula (imaginary genus, does not exist in reality), is a large genus of beetles of approximately 50 species, distributed in Central America and the Caribbean islands. The majority of the species in the genus are endemic to islands, and it is thought to be a great example of colonisation and diversification on islands. The species of the genus occur in forests, shrublands or freshwater habitats. They are good swimmers but cannot survive in sea water.

+

One of the most diverse islands for the genus Insula is Jamaica, a large Caribbean island with a complex geological history. It is thought that Jamaica has a continental origin, but has been isolated from other landmasses for approximately 40 million years. The mainland pool of closely related species of beetles in the continent (i.e. species that could have colonised the islands) has been estimated to be approximately 100 species. More than 15 species of Insula are thought to be endemic to Jamaica, with a few species being only recently described or discovered. However, the evolutionary island biogeography of Insula on Jamaica remains unknown.

+

A very well-sampled time-calibrated phylogeny for the genus has recently been published, covering all the island where Insula occurs. A few species have been recently discovered and were not sampled in the phylogeny. There is no evidence of recently extinct species. The authors of the study performed an ancestral range reconstruction, so it is possible to infer the ancestral areas and number of colonisation events.

+

Making use of the knowledge you gathered from DAISIEprep and DAISIE, in combination with the phylogenetic tree and the species checklist provided, you can extract data for the island of Jamaica and fit DAISIE to the data.

+
+

5.5.0.1 The aim is to be able to answer the following questions:

+

A. How many times has Insula colonised the Caribbean islands?

+

B. How many times has Insula colonised Jamaica?

+

C. How many radiations have occured on Jamaica?

+

D. What are the rates of colonisation, speciation and extinction for Insula in Jamaica?

+

E. Is there evidence for diversity-dependence in the Insula species of Jamaica?

+

F. Is there evidence for equilibrium dynamics on the island of Jamaica?

+

G. How has the diversity of Insula on Jamaica changed through time (according to DAISIE simulations)?

+

Written assignment:

+
    +
  • Follow the self-guided tutorials of the computer practical and run analyses using DAISIE to answer all of the above questions in an individually written report.

  • +
  • In your report, briefly describe the methodology used for the analyses of Insula, your main results, and limitations of the analyses.

  • +
  • Format: .doc and .pdf, 2-3 pages, Calibri font (or similar), minimum font size 10.

  • +
  • Include any tables and figures that you think could be relevant (be creative!), as long as they are well explained (including legends) and keep within the 2-3 page limit.

  • +
  • Do not include code in the assignment (unless you want to point out something specific with the script).

  • +
  • Focus on the interpretation of the results from a biological perspective.

  • +
+

In sum, you need to load the phylogeny into R, use DAISIEprep to extract the DAISIE datalist. Then, fit DAISIE to that datalist to estimate parameters (extinction, colonisation, speciation, carrying capacity, and simulate islands), and to simulate islands. Make use of the DAISIEprep and DAISIE tutorials as well as the Insula tree, tables and figures provided. With these analyses you will be able to answer all the questions.

+
+
+

5.5.1 In the practical folder you will find all the relevant data:

+
    +
  • Insula.tre the phylogenetic tree of Insula (to be loaded into R for analyses, but also visualized using Figtree): note that the species have numbers instead of latin names (Spec 1, Spec 2, etc).
  • +
+
+
library(ape)
+Insula_tree<-read.nexus("data/Insula.tre")
+plot(Insula_tree)
+
+

+
+
+
    +
  • Insula_checklist.xlsx: A species checklist including the distribution, endemicity status as well as the species that are missing from the phylogeny but which should be considered in the DAISIE analyses.

  • +
  • Figure_Insula_Tip_states.pdf: A figure of the phylogeny with the tips coloured by the island where the species occurs.

  • +
+
+
+

+
Phylogeny showing the geographical distribution of the extant species
+
+
+ +
+
+

+
Ancestral area reconstruction
+
+
+

Tips:

+

For the DAISIE analyses use:

+

Island age: 40 million years

+

Mainland pool size: 100 species

+

Don’t forget to add the missing species.

+

After the practical, a file with an example of how to run the Insula analyses will be provided, which can be used as a guideline in case you had difficulties completing the practical.

+ + +
+
+
+ +
+ + +
+ + + + \ No newline at end of file diff --git a/insula_tutorial_intro_files/figure-html/unnamed-chunk-1-1.png b/insula_tutorial_intro_files/figure-html/unnamed-chunk-1-1.png new file mode 100644 index 0000000..ad2695d Binary files /dev/null and b/insula_tutorial_intro_files/figure-html/unnamed-chunk-1-1.png differ diff --git a/search.json b/search.json new file mode 100644 index 0000000..ffc8117 --- /dev/null +++ b/search.json @@ -0,0 +1,135 @@ +[ + { + "objectID": "index.html", + "href": "index.html", + "title": "LearnDAISIEprep", + "section": "", + "text": "Welcome to the LearnDAISIEprep website. Here you will find material to learn how to use the DAISIE ecosystem of R package to analyse the ecology and evolution of island communities. These tools have been developed to answer research questions in island biogeography and macroevolutionary science and can be powerful tools when applied appropriately." + }, + { + "objectID": "install_packages.html#open-and-run-this-script-in-rstudio", + "href": "install_packages.html#open-and-run-this-script-in-rstudio", + "title": "2  Install packages", + "section": "2.1 Open and run this script in Rstudio", + "text": "2.1 Open and run this script in Rstudio\n\n2.1.1 Install required packages\n\ninstall.packages('DAISIE',dependencies=T)\ninstall.packages('ape',dependencies=T)\ninstall.packages('DAISIEprep',dependencies=T)\n\nCheck if packages load correctly\n\nlibrary(DAISIE)\nlibrary(ape)\nlibrary(DAISIEprep)" + }, + { + "objectID": "insula_tutorial_intro.html", + "href": "insula_tutorial_intro.html", + "title": "3  Insula practical tutorial", + "section": "", + "text": "4 Course Island Biology" + }, + { + "objectID": "insula_tutorial_intro.html#evolutionary-island-biogeography-using-daisie", + "href": "insula_tutorial_intro.html#evolutionary-island-biogeography-using-daisie", + "title": "3  Insula practical tutorial", + "section": "4.1 Evolutionary island biogeography using DAISIE", + "text": "4.1 Evolutionary island biogeography using DAISIE\n\n\n\n\n\nIn this practical we will estimate rates of colonisation, speciation and extinction for a (hypothetical) genus of beetles found in the Caribbean. We will then use these estimated rates to simulate how the number of species has changed through time in this taxonomic group. The practical will use mainly the R packages DAISIE and DAISIEprep. We will learn how to interpret and prepare data for analyses using DAISIE, how to fit DAISIE models to phylogenetic data, and how to simulate phylogenies of island communities.\nThe practical is divided into 3 main parts:\n\nLearn how to use DAISIEprep to prepare and extract data (1-2 hours). DAISIEprep is an R package that allows users to format phylogenetic data for subsequent analyses in DAISIE.\nLearn how to use DAISIE to estimate parameters and simulate islands (1-2 hours). DAISIE is an R package that allows fitting island biogeography models and simulating islands.\nApply the above approaches to a dataset of beetles from the Caribbean. Writing up this part is the graded assignment.\n\nBefore you start with the DAISIEprep and DAISIE tutorials, please read the introduction to the graded assignment below." + }, + { + "objectID": "insula_tutorial_intro.html#install-software-required", + "href": "insula_tutorial_intro.html#install-software-required", + "title": "3  Insula practical tutorial", + "section": "5.1 Install Software required:", + "text": "5.1 Install Software required:\n\nInstall: FigTree\nInstall R\nInstall Rstudio\nOpen the program RStudio. Open the file 0_Install_packages and run that code in Rstudio. This should install all required R packages (may take a while!)." + }, + { + "objectID": "insula_tutorial_intro.html#tutorial-files", + "href": "insula_tutorial_intro.html#tutorial-files", + "title": "3  Insula practical tutorial", + "section": "5.2 Tutorial files", + "text": "5.2 Tutorial files\nYou will need to follow the instructions of the tutorials, available in HTML format, which you can open on a browser" + }, + { + "objectID": "insula_tutorial_intro.html#sec-1.-daisieprep", + "href": "insula_tutorial_intro.html#sec-1.-daisieprep", + "title": "3  Insula practical tutorial", + "section": "5.3 1. DAISIEprep", + "text": "5.3 1. DAISIEprep\nDAISIEprep tutorial" + }, + { + "objectID": "insula_tutorial_intro.html#sec-2.-daisie", + "href": "insula_tutorial_intro.html#sec-2.-daisie", + "title": "3  Insula practical tutorial", + "section": "5.4 2. DAISIE", + "text": "5.4 2. DAISIE\nDAISIE tutorial\n\n5.4.0.1 After you have finished the DAISIEprep and DAISIE practicals, you can begin applying these techniques to the Insula dataset." + }, + { + "objectID": "insula_tutorial_intro.html#sec-insula", + "href": "insula_tutorial_intro.html#sec-insula", + "title": "3  Insula practical tutorial", + "section": "5.5 3. Insula assignment", + "text": "5.5 3. Insula assignment\nThe beetle genus Insula (imaginary genus, does not exist in reality), is a large genus of beetles of approximately 50 species, distributed in Central America and the Caribbean islands. The majority of the species in the genus are endemic to islands, and it is thought to be a great example of colonisation and diversification on islands. The species of the genus occur in forests, shrublands or freshwater habitats. They are good swimmers but cannot survive in sea water.\nOne of the most diverse islands for the genus Insula is Jamaica, a large Caribbean island with a complex geological history. It is thought that Jamaica has a continental origin, but has been isolated from other landmasses for approximately 40 million years. The mainland pool of closely related species of beetles in the continent (i.e. species that could have colonised the islands) has been estimated to be approximately 100 species. More than 15 species of Insula are thought to be endemic to Jamaica, with a few species being only recently described or discovered. However, the evolutionary island biogeography of Insula on Jamaica remains unknown.\nA very well-sampled time-calibrated phylogeny for the genus has recently been published, covering all the island where Insula occurs. A few species have been recently discovered and were not sampled in the phylogeny. There is no evidence of recently extinct species. The authors of the study performed an ancestral range reconstruction, so it is possible to infer the ancestral areas and number of colonisation events.\nMaking use of the knowledge you gathered from DAISIEprep and DAISIE, in combination with the phylogenetic tree and the species checklist provided, you can extract data for the island of Jamaica and fit DAISIE to the data.\n\n5.5.0.1 The aim is to be able to answer the following questions:\nA. How many times has Insula colonised the Caribbean islands?\nB. How many times has Insula colonised Jamaica?\nC. How many radiations have occured on Jamaica?\nD. What are the rates of colonisation, speciation and extinction for Insula in Jamaica?\nE. Is there evidence for diversity-dependence in the Insula species of Jamaica?\nF. Is there evidence for equilibrium dynamics on the island of Jamaica?\nG. How has the diversity of Insula on Jamaica changed through time (according to DAISIE simulations)?\nWritten assignment:\n\nFollow the self-guided tutorials of the computer practical and run analyses using DAISIE to answer all of the above questions in an individually written report.\nIn your report, briefly describe the methodology used for the analyses of Insula, your main results, and limitations of the analyses.\nFormat: .doc and .pdf, 2-3 pages, Calibri font (or similar), minimum font size 10.\nInclude any tables and figures that you think could be relevant (be creative!), as long as they are well explained (including legends) and keep within the 2-3 page limit.\nDo not include code in the assignment (unless you want to point out something specific with the script).\nFocus on the interpretation of the results from a biological perspective.\n\nIn sum, you need to load the phylogeny into R, use DAISIEprep to extract the DAISIE datalist. Then, fit DAISIE to that datalist to estimate parameters (extinction, colonisation, speciation, carrying capacity, and simulate islands), and to simulate islands. Make use of the DAISIEprep and DAISIE tutorials as well as the Insula tree, tables and figures provided. With these analyses you will be able to answer all the questions.\n\n\n5.5.1 In the practical folder you will find all the relevant data:\n\nInsula.tre the phylogenetic tree of Insula (to be loaded into R for analyses, but also visualized using Figtree): note that the species have numbers instead of latin names (Spec 1, Spec 2, etc).\n\n\nlibrary(ape)\nInsula_tree<-read.nexus(\"data/Insula.tre\")\nplot(Insula_tree)\n\n\n\n\n\nInsula_checklist.xlsx: A species checklist including the distribution, endemicity status as well as the species that are missing from the phylogeny but which should be considered in the DAISIE analyses.\nFigure_Insula_Tip_states.pdf: A figure of the phylogeny with the tips coloured by the island where the species occurs.\n\n\n\n\nPhylogeny showing the geographical distribution of the extant species\n\n\n\nFigure_Insula_ancestral_state_reconstruction.pdf: A figure of an ancestral range reconstruction for the genus.\n\n\n\n\nAncestral area reconstruction\n\n\nTips:\nFor the DAISIE analyses use:\nIsland age: 40 million years\nMainland pool size: 100 species\nDon’t forget to add the missing species.\nAfter the practical, a file with an example of how to run the Insula analyses will be provided, which can be used as a guideline in case you had difficulties completing the practical." + }, + { + "objectID": "DAISIEprep.html", + "href": "DAISIEprep.html", + "title": "4  DAISIEprep", + "section": "", + "text": "5 Course Island Biology" + }, + { + "objectID": "DAISIEprep.html#part-2---daisieprep", + "href": "DAISIEprep.html#part-2---daisieprep", + "title": "4  DAISIEprep", + "section": "5.1 Part 2 - DAISIEprep", + "text": "5.1 Part 2 - DAISIEprep\n\n5.1.1 In this tutorial, the main features of DAISIEprep are explained. DAISIEprep is an R package that facilitates formatting of data for subsequent use in DAISIE.\n\n\n\nDAISIEprep R package logo\n\n\n\n5.1.1.1 A typical DAISIEprep pipeline is as follows: 1) identify island colonisation events for the taxonomic group of interest from time-calibrated phylogenetic trees, 2) assign an island endemicity status (endemic, non-endemic, not present) to each of the species, 3) automatically extract times of colonisation of the island and diversification within the island from the phylogenies; 4) add any missing species, 5) format data for DAISIE.\n\n\n5.1.1.2 The DAISIEprep tutorial is divided into 2 sections:\n\nPhylogeny example - Using a simulated phylogeny including island and non-island species, learn how to extract and format island data for running DAISIE.\nAdding missing species - Learn how to add missing species, lineages, etc, to your DAISIE data list.\n\n\n\n5.1.1.3 First load the required packages:\n\nlibrary(DAISIEprep)\nlibrary(ape)\nlibrary(phylobase)\nlibrary(ggtree)\nlibrary(ggimage)\nlibrary(castor)" + }, + { + "objectID": "DAISIEprep.html#single-phylogeny-example", + "href": "DAISIEprep.html#single-phylogeny-example", + "title": "4  DAISIEprep", + "section": "5.2 2.1. Single Phylogeny Example", + "text": "5.2 2.1. Single Phylogeny Example\n\n5.2.0.1 In this section we demonstrate a simple example of extracting and formatting data from a single phylogeny. We use a simulated phylogeny, which facilitates explaining how the data is structured.\nFirst we simulate a phylogeny using the package ape.\n\nset.seed(\n 4,\n kind = \"Mersenne-Twister\",\n normal.kind = \"Inversion\",\n sample.kind = \"Rejection\"\n)\nphylo <- ape::rcoal(10)\n\nImportant: DAISIEprep requires the tip labels (taxon names) in the phylogeny to be formatted as genus name and species name separated by an underscore (e.g. “Canis_lupus”, “Species_1”).\nHere we add tip labels (representing our species) to the simulated phylogeny. In this case, all taxa sampled are different plant species from the same genus.\n\nphylo$tip.label <- c(\"Plant_a\", \"Plant_b\", \"Plant_c\", \"Plant_d\", \"Plant_e\",\n \"Plant_f\", \"Plant_g\", \"Plant_h\", \"Plant_i\", \"Plant_j\")\n\nThen we convert the phylogeny to a phylo4 class, defined in the package phylobase. This allows users to easily work with data for each tip in the phylogeny, for example whether they are endemic to the island or not.\n\nphylo <- phylobase::phylo4(phylo)\nphylobase::plot(phylo)\n\n\n\n\n\n\n\n\nNow we have a phylogeny in the phylo4 format to which we can easily append data.\nSpecify tips corresponding to species from your focal island by specifying that they are endemic and/or non-endemic to the island. In the example below, species Plant_h is found on the island but it not endemic, and species Plant_f and Plant_e are found on the island and are endemic to the island. All other species on the phylogeny are NOT present on the island.\n\nisland_species <- data.frame(\n tip_labels = c(\"Plant_h\",\n \"Plant_f\",\n \"Plant_e\")\n ,\n tip_endemicity_status = c(\"nonendemic\",\"endemic\",\"endemic\"))\n\nNow that we know which species are present on the island, we can assign an endemicity status to all the species in the phylogeny.\n\nendemicity_status <- create_endemicity_status(\n phylo = phylo,\n island_species = island_species\n)\n\nAnd append this information to the phylogeny object:\n\nphylod <- phylobase::phylo4d(phylo, endemicity_status)\n\nWe can now visualise our phylogeny with the island endemicity statuses plotted at the tips. This uses the ggtree and ggplot2 packages.\n\nplot_phylod(phylod = phylod)\n\n\n\n\n\n\n\n\nAs you can see, in the example phylogeny, there appear to be 2 separate colonisation events of the island - one which has led to species Plant_f and Plant_e and other that has led to the non-endemic species Plant_h.\nNow that we can see the tips that are present on the island, we can extract them to form our island community data set that can be used in the DAISIE R package to fit likelihood models of island colonisation and diversification.\nBefore we extract species, we first create an object to store all of the island colonists’ information. This uses the island_tbl class introduced in this package (DAISIEprep). This island_tbl object can then easily be converted to a DAISIE data list using the function create_daisie_data (more information on this below).\n\nisland_tbl <- island_tbl()\nisland_tbl\n#> Class: Island_tbl \n#> [1] clade_name status missing_species col_time \n#> [5] col_max_age branching_times min_age species \n#> [9] clade_type \n#> <0 rows> (or 0-length row.names)\n\nWe can see that this is an object containing an empty data frame. In order to fill this data frame with information on the island colonisation and diversification events we can run:\n\nisland_tbl <- extract_island_species(\n phylod = phylod,\n extraction_method = \"min\"\n)\nisland_tbl\n#> Class: Island_tbl \n#> clade_name status missing_species col_time col_max_age branching_times\n#> 1 Plant_e endemic 0 0.1925924 FALSE 0.154371....\n#> 2 Plant_h nonendemic 0 0.1233674 FALSE NA\n#> min_age species clade_type\n#> 1 NA Plant_e,.... 1\n#> 2 NA Plant_h 1\nisland_tbl_min<-island_tbl\n\nThe function extract_island_species() is the main function in DAISIEprep to extract data from the phylogeny. In the example above, we used the “min” extraction algorithm. The “min” algorithm extracts island community data assuming no back-colonisation from the island to the mainland. Each row in the island_tbl corresponds to a separate colonisation of the island. In this case, two colonist lineages were correctly identified using the ‘min’ extraction algorithm, one endemic and another non-endemic. Each row in the table corresponds to one colonisation event.\nHowever, if back-colonisation is frequent (for example, one species within a large endemic island radiation colonised another island or mainland), we recommend using the “asr” algorithm, which extracts the most likely colonisations inferred in an ancestral area reconstruction. To use the “asr” algorithm, we first need to know the probability of the ancestors of the island species being present on the island to determine the time of colonisation. To do this, we can fit one of many ancestral state reconstruction methods. Here we use the “mk” model (which uses a continuous-time markov model to reconstruct ancestral states), as it is a simple method that should prove reliable for reconstructing the ancestral species areas (i.e. on the island or not on the island) for most cases. First, we translate our extant species endemicity status to a numeric representation of whether that species is on the island.\n\nphylod <- add_asr_node_states(phylod = phylod, asr_method = \"mk\")\n\nNow we can plot the phylogeny, which this time includes the node labels for the presence/absence on the island in ancestral nodes.\n\nplot_phylod(phylod = phylod)\n\n\n\n\n\n\n\n\n\nSidenote: if you are wondering what the probabilities are at each node and whether this should influence your decision to pick a preference for island or mainland when the likelihoods for each state are equal, we can plot the probabilities at the nodes to visualise the ancestral state reconstruction using plot_phylod(phylod = phylod, node_pies = TRUE).\nNow we can extract island colonisation and diversification times from the phylogeny with asr using the reconstructed ancestral states of island presence/absence.\n\nisland_tbl_asr <- extract_island_species(\n phylod = phylod,\n extraction_method = \"asr\"\n)\n\n\nisland_tbl_asr\n#> Class: Island_tbl \n#> clade_name status missing_species col_time col_max_age branching_times\n#> 1 Plant_e endemic 0 0.1925924 FALSE 0.154371....\n#> 2 Plant_h nonendemic 0 0.1233674 FALSE NA\n#> min_age species clade_type\n#> 1 NA Plant_e,.... 1\n#> 2 NA Plant_h 1\n\nBy comparing island_tbl_min and island_tbl_asr you can see the differences in the 2 extraction methods.\n\n\nall.equal(island_tbl_min,island_tbl_asr)\n#> [1] TRUE\n\nIn this case, the results using “asr” and “min” are exactly the same. For the purposes of this practical, we will proceed with the results from “asr” (island_tbl_asr). For convenience, let’s rename the object to island_tbl.\n\nisland_tbl<-island_tbl_asr\n\nYou can visualize the contents of the island_tbl object to try and figure out what they mean.\n\nView(island_tbl)\n\nFor example, the code below shows you the names of the species included in each lineage:\n\nisland_tbl@island_tbl$species\n#> [[1]]\n#> [1] \"Plant_e\" \"Plant_f\"\n#> \n#> [[2]]\n#> [1] \"Plant_h\"\n\nNow that we have the island_tbl we can convert this to the DAISIE data list to be used by the DAISIE inference model. To convert to the DAISIE data list (i.e. the input data of the DAISIE inference model) we use create_daisie_data(), providing the island_tbl as input. We also need to specify:\n\nThe age of the island or archipelago. Here we use an island age of twelve million years (island_age = 12).\nWhether the colonisation times extracted from the phylogenetic data should be considered precise (precise_col_time = TRUE). We will not discuss the details of this here, but briefly by setting this to TRUE the data will tell the DAISIE model that the colonisation times are known without error. Setting precise_col_time = FALSE will change tell the DAISIE model that the colonisation time is uncertain and should interpret this as the upper limit to the time of colonisation and integrate over the uncertainty between this point and either the present time or to the first branching point (either speciation or divergence into subspecies).\nThe number of species in the mainland source pool. Here we set it to 100 (num_mainland_species = 100). This will be used to calculate the number of species that could have potentially colonised the island but have not. When we refer to the mainland pool, this does not necessarily have to be a continent, it could be a different island if the source of species immigrating to an island is largely from another nearby island (a possible example of this could be Madagascar being the source of species colonising Comoros). This information is used by the DAISIE model to calculate the colonisation rate of the island.\n\n\ndata_list <- create_daisie_data(\n data = island_tbl,\n island_age = 12,\n num_mainland_species = 100,\n precise_col_time = TRUE\n)\n\nBelow we show two elements of the DAISIE data list produced. The first element data_list[[1]] in every DAISIE data list is the island community metadata, containing the island age and the number of species in the mainland pool that did not leave descendants on the island at the present day. This is important information for DAISIE inference, as it is possible some mainland species colonised the island but went extinct leaving no trace of their island presence.\n\ndata_list[[1]]\n#> $island_age\n#> [1] 12\n#> \n#> $not_present\n#> [1] 98\n\nNext is the first element containing information on island colonists (every element data_list[[x]] in the list after the metadata contains information on individual island colonists). This contains the name of the colonist, the number of missing species, and the branching times, which is a vector containing the age of the island, the colonisation time and the times of any cladogenesis events. Confusingly, it may be that the branching times vector contains no branching times: when there are only two numbers in the vector these are the island age followed by the colonisation time. Then there is the stac, which stands for status of colonist. This is a number which tells the DAISIE model how to identify the endemicity and colonisation uncertainty of the island colonist (these are explained here if you are interested). Lastly, the type1or2 defines which macroevolutionary regime an island colonist is in. By macroevolutionary regime we mean the set of rates of colonisation, speciation and extinction for that colonist. Most applications will assume all island clades have the same regime and thus all are assigned type 1. However, if there is a priori expectation that one clade significantly different from the rest, e.g. the Galápagos finches amongst the other terrestrial birds of the Galápagos archipelago this clade can be set to type 2.\n\ndata_list[[2]]\n#> $colonist_name\n#> [1] \"Plant_e\"\n#> \n#> $branching_times\n#> [1] 12.0000000 0.1925924 0.1543711\n#> \n#> $stac\n#> [1] 2\n#> \n#> $missing_species\n#> [1] 0\n#> \n#> $type1or2\n#> [1] 1\n\nThis data list is now ready to be used in the DAISIE maximum likelihood inference model from the R package DAISIE. But let’s learn how to add missing species (species not sampled in the phylogeny) to this datalist." + }, + { + "objectID": "DAISIEprep.html#adding-missing-species", + "href": "DAISIEprep.html#adding-missing-species", + "title": "4  DAISIEprep", + "section": "5.3 2.2. Adding missing species", + "text": "5.3 2.2. Adding missing species\nIt is often the case that phylogenetic data is not available for some island species or even for entire lineages present in the island community. But we can still include these species in our DAISIE analyses using DAISIEprep. This section is about the tools that DAISIEprep provides in order to handle missing data, and generally to handle species that are missing and need to be input into the data manually.\nFor this section, as with the previous section, the core data structure we are going to work with is the island_tbl. We will use the island_tbl for the example plant dataset produced in the last section.\n\n5.3.1 2.2.1 Adding missing species to an island clade that has been sampled in the phylogeny\nThis option is for cases in which a clade has been sampled in the phylogeny, and at least 1 colonisation or 1 branching time is available in the datalist, but 1 or more species from that clade were not sampled. For this example, we imagine that 2 species have not been sampled, and that we want to add them as missing species to the lineage containing species “Plant_e” that is sampled in the phylogeny. To assign two missing species to this clade we use the function add_missing_species() The argument species_to_add_to uses a representative sampled species from that island clade to work out which colonist in the island_tbl to assign the specified number of missing species (num_missing_species) to. You can find out which species are already sampled in each lineage using:\n\nisland_tbl@island_tbl$species\n#> [[1]]\n#> [1] \"Plant_e\" \"Plant_f\"\n#> \n#> [[2]]\n#> [1] \"Plant_h\"\n\nNow run the code:\n\nisland_tbl <- add_missing_species(\n island_tbl = island_tbl,\n # num_missing_species equals total species missing\n num_missing_species = 2,\n # name of a sampled species you want to \"add\" the missing to\n # it can be any in the clade\n species_to_add_to = \"Plant_e\"\n)\n\nThe new island table now has missing species added to the lineage we wanted to:\n\nisland_tbl@island_tbl$missing_species\n#> [1] 2 0\n\nWith the new missing species added to the island_tbl we can repeat the conversion steps above using create_daisie_data() to produce data accepted by the DAISIE model.\n\ndata_list <- create_daisie_data(\n data = island_tbl,\n island_age = 12,\n num_mainland_species = 100,\n precise_col_time = TRUE\n)\n\n\n\n5.3.2 2.2.2 Adding an entire lineage when a phylogeny is not available for the lineage.\nFor cases when the missing species belong to a lineage that has not been sampled in the phylogeny. Assuming we did not have any phylogenetic data or colonisation time estimate for the island clade, we can insert species as missing and specify them as a separate lineage. Because not colonisation and branching times are known for this lineage, when this lineage later gets processed by the DAISIE inference model it will be assumed it colonised the island any time between the island’s formation and the present.\nThe input needed are:\n\nisland_tbl to add to an existing island_tbl\nclade_name a name to represent the new clade addeed, can either be a specific species from the clade or a genus name, or another name that represent those species\nstatus either “endemic” or “nonendemic”\nspecies a vector of species names contained within colonist (if applicable)\nmissing_species In the case of a lineage with just 1 species (i.e. not an island radiation) the number of missing species is zero, as by adding the colonist it already counts as one automatically. In the case of an island clade of more than one species, the number of missing species in this case should be n-1.\n\nExample for adding lineage with 1 species:\n\nisland_tbl <- add_island_colonist(\n island_tbl = island_tbl,\n clade_name = \"Plant_y\",\n status = \"endemic\",\n # clade with just 1 species, missing_species = 0\n # because adding the lineage already counts as 1\n missing_species = 0,\n col_time = NA_real_,\n col_max_age = FALSE,\n branching_times = NA_real_,\n min_age = NA_real_,\n clade_type = 1,\n species = \"Plant_a\"\n)\n\nExample for adding lineage with 5 species (“Plant_a”, “Plant_b”, “Plant_c”, “Plant_d”, “Plant_e”) to a lineage called “Plant_radiation”\n\nisland_tbl <- add_island_colonist(\n island_tbl = island_tbl,\n clade_name = \"Plant_radiation\",\n status = \"endemic\",\n # the total species is 5 and all are missing\n # but we add missing_species = 4 because\n # adding the lineage already counts as 1\n missing_species = 4,\n col_time = NA_real_,\n col_max_age = FALSE,\n branching_times = NA_real_,\n min_age = NA_real_,\n clade_type = 1,\n species = c(\"Plant_a\", \"Plant_b\", \"Plant_c\",\n \"Plant_d\", \"Plant_e\")\n)\n\nWith the new missing species added to the island_tbl we can repeat the conversion steps above using create_daisie_data() to produce data accepted by the DAISIE model.\n\ndata_list <- create_daisie_data(\n data = island_tbl,\n island_age = 12,\n num_mainland_species = 100,\n precise_col_time = TRUE\n)\n\nEnd of the DAISIEprep tutorial!" + }, + { + "objectID": "DAISIE.html", + "href": "DAISIE.html", + "title": "5  DAISIE", + "section": "", + "text": "6 Course Island Biology" + }, + { + "objectID": "DAISIE.html#part-3---fitting-daisie-models-using-the-galápagos-birds-as-example", + "href": "DAISIE.html#part-3---fitting-daisie-models-using-the-galápagos-birds-as-example", + "title": "5  DAISIE", + "section": "6.1 Part 3 - Fitting DAISIE models using the Galápagos birds as example", + "text": "6.1 Part 3 - Fitting DAISIE models using the Galápagos birds as example\n\n\n\nPhylogenies of Galápagos birds\n\n\n\n6.1.0.0.1 In this practical we are going to fit the DAISIE model to phylogenetic data for species of native land birds of the Galápagos islands (data from Valente, Phillimore and Etienne 2015 Ecology Letters; and Valente et al 2020 Nature). We will use software DAISIE to estimate rates of speciation, colonisation and extinction in the Galápagos. We will simulate islands with these estimated rates to see how species diversity has varied through time in the Galápagos.\nPrepare the R environment to run the practical. Empty workspace of existing previous objects.\n\nrm(list=ls())\n\nLoad required packages DAISIE and ape\n\nlibrary(DAISIE)\nlibrary(ape)\n\n\n\n6.1.1 3.1 - Load and visualise Galapágos bird data\nThe dataset of Galápagos birds includes colonisation and branching times for a total of 27 species of terrestrial birds of the Galápagos islands, distributed across 8 lineages. Some of the lineages have only 1 species, e.g. the Galápagos dove Zenaida. Others have radiated, including the Darwin’s finch radiation (16 species) and the mockingbirds Mimus radiation (4 species). Note: the dataset we will use is slightly different from the example dataset that is included as data in the DAISIE R package.\n\n6.1.1.1 Load Galápagos DAISIE datalist\n\nload(file=\"data/galapagos_datalist.Rdata\")\n\n\n\n6.1.1.2 View Galápagos datalist\nJust type:\n\ngalapagos_datalist\n\nThis datalist is of the same format that DAISIEprep produces (e.g., same format as the data_list object that you just produced in the DAISIEprep tutorial).\nView your Galápagos datalist by scrolling up and down in R. Or use function View()\n\nView(galapagos_datalist)\n\nYou can find detailed information using the R documentation file:\n\n? Galapagos_datalist \n\nTo view the top of the list\n\ngalapagos_datalist[[1]]\n\n$island_age\n[1] 4\n\n$not_present\n[1] 992\n\n\nThis element contains the island age as well as all the mainland species that do not have extant descendants on the island. These are important pieces of information for DAISIE.\nTo view just the Mimus colonisation:\n\ngalapagos_datalist[[4]]\n\n$colonist_name\n[1] \"Mimus\"\n\n$branching_times\n[1] 4.00000 3.99999 3.68000 2.93000 0.29000\n\n$stac\n[1] 6\n\n$missing_species\n[1] 0\n\n$type1or2\n[1] 1\n\n\nThe first element of the $branching_times item is the island age, followed by the colonisation time and the branching times (if any). The $stac item is a code that tells DAISIE whether the lineage is endemic, non-endemic, and whether or not a precise age or a maximum-age should be used.\nOr to view just the Darwin’s finches lineage:\n\ngalapagos_datalist[[3]]\n\n$colonist_name\n[1] \"Finches\"\n\n$branching_times\n [1] 4.0000 3.0280 1.3227 0.8223 0.4286 0.3462 0.2450 0.1180 0.0808 0.0756\n[11] 0.0527 0.0525 0.0327 0.0322 0.0221 0.0118\n\n$stac\n[1] 2\n\n$missing_species\n[1] 1\n\n$type1or2\n[1] 1\n\n\nAs you can see, there is 1 missing species in the Darwin’s finch lineage - this species was not sampled in the phylogeny, so does not have a corresponding branching time in the $branching_times item. However, $missing_species item tells DAISIE that it needs to consider one extra species in its computations.\n\n\n6.1.1.3 Visualise Galápagos data\nYou can visualize the data in a different way using:\n\nDAISIE_plot_island(galapagos_datalist)\n\n\n\n\nThe above plot shows the 8 colonisation events (independent lineages), the time of colonisation, number of species sampled in each lineage (n), number of missing species (m) and the times of cladogenetic speciation events (horizontal lines). The dashed horizontal line is the island age. Unfilled symbols indicate that the time of colonisation was set as “max-age” (not precise), that is, the colonisation time could have happened any time between the time plotted and the present. This applies for instance to lineages with stem ages older than the island age, whose age is then set to be the age of the island (as was the cases of genus Mimus, whose stem age is older than the island age of 4 million years).\n\n\n6.1.1.4 Plot age versus diversity\nTo visualise the diversity of each lineage according to their age:\n\nDAISIE_plot_age_diversity(galapagos_datalist)\n\n\n\n\nThe above plot shows the number of species per lineage plotted against their colonisation time. You can see that one lineage - Darwin’s finches - stands out, it is not the oldest lineage, but it has 16 species, many more than the other lineages. One of the lineages, Mimus, is not plotted, because it’s age is set as max-age, that is why the number of species is 23 (27-4 species=23).\n\n\n\n\n\n\n6.1.2 3.2 – Fitting DAISIE models\nWe will now fit three different DAISIE models using maximum likelihood, using the function DAISIE_ML. The DAISIE function searches for the parameters that maximise the likelihood of the data given the specified model. We will then compare the likelihoods of the 3 different models to see which one is preferred\nWe will fit the 3 different DAISIE models to the phylogenetic data contained in the object galapagos_datalist.\n\n\n\n6.1.2.1 Fit a full DAISIE model (M1)\nThe first model (M1) we will fit is a full DAISIE model with 5 parameters. The parameters in DAISIE are always placed in this order:\n\nCladogenesis rate (lambda_c) (unit: cladogenesis events per island lineage per time unit)\nExtinction rate (mu) (unit: extinction events per island lineage per time unit)\nCarrying capacity (K) (unit: number of species)\nColonisation rate (gamma) (unit: colonisation events per mainland lineage per time unit)\nAnagenesis rate (lambda_a) (unit: anagenesis events per island lineage per time unit)\n\nImportant settings of the DAISIE_ML function:\n\ninitparsopt refers to the starting parameters of the maximum likelihood optimisation (the search for the optimal parameters). 1.5 is the starting cladogenesis rate, 1.1 is the starting extinction rate, 20 is the initial carrying capacity (20 species), 0.009 is the colonisation rate; 1.1 is the initial anagenesis rate.\nidparsopt shows the parameters to optimise, in this case all 5, so 1:5\nparsfix and idparsfix are set to NULL because we are not fixing any parameters this time.\n\nThe code below takes a while to run.\n\nDAISIE_ML(\n datalist = galapagos_datalist,\n initparsopt = c(1.5,1.1,20,0.009,1.1),\n ddmodel = 11,\n idparsopt = 1:5,\n parsfix = NULL,\n idparsfix = NULL\n)\n\n lambda_c mu K gamma lambda_a loglik df conv\n1 1.258226 1.136924 9968506 0.004957378 1.251793e-06 -84.78145 5 0\n\n\nThe output gives the ML parameters values, the loglikelihood, and the number of parameters (df).\n\n\n\n6.1.2.2 Fit model with no carrying-capacity (M2)\nAs you can see above, the value of K in M1 is very high (hundreds of thousands of species). This suggest that there may be no upper bound to diversity (so K is in fact infinite). So let’s try a model without K as a free parameter.\nThe second model (M2) is just like M1, but we will fix K to be infinite. In comparison to M1, we now remove the 3rd parameter (carrying capacity) from the initparsopt and idparsopt settings. So we now have only parameters 1,2,4 and 5. We fix the parameter number 3 to Inf (carrying capacity K fixed to infinite):\n\nDAISIE_ML(\n datalist = galapagos_datalist,\n initparsopt = c(1.5,1.1,0.009,1.1),\n idparsopt = c(1,2,4,5),\n parsfix = Inf,\n idparsfix = 3,\n ddmodel=0\n)\n\n lambda_c mu K gamma lambda_a loglik df conv\n1 1.264389 1.149378 Inf 0.00505558 1.662578e-05 -84.78088 4 0\n\n\n\n\n\n\n6.1.2.3 Fit model with no carrying capacity AND no anagenesis (M3)\nNow, it appears that the parameter of anagenesis is close to 0 in M2 (see above). Perhaps a model without anagenesis will perform well.\nThe third model (M3) is like M2, but with no carrying-capacity and no anagenesis. This time we switch off the 3rd and 5th parameters (carrying-capacity and anagenesis). We fix the 3rd parameter (K) to infinite; and the 5th parameter (anagenesis) to zero:\n\nDAISIE_ML(\n datalist = galapagos_datalist,\n initparsopt = c(1.5,1.1,0.009),\n idparsopt = c(1,2,4),\n parsfix = c(Inf,0),\n idparsfix = c(3,5),\n ddmodel=0\n)\n\n lambda_c mu K gamma lambda_a loglik df conv\n1 1.263034 1.146225 Inf 0.005040353 0 -84.78082 3 0\n\n\n\n\n\n\n6.1.2.4 Select the best model using AIC\nNow compare the log-likelihoods of the 3 models. They all have similar likelihoods, but one has fewer parameters, so it is preferred. Which model is it?\nA more proper way to compare the models is using AIC (or other information criteria), which takes into account the number of parameters and the loglikelihood.\nCreate new function to compute AIC values base on the likelihood values and number of parameters for each model.\n\nAIC_compare<- function(LogLik,k){\n aic <- (2*k)-(2*LogLik)\n return(aic)\n}\n\nFill in the values from your data in this format:\nAIC_compare(c(likelihood_M1,likelihood_M2,likelihood_M3), c(n_parameters_M1,n_parameters_M2,n_parameters_M3)).\n\nAICs<-AIC_compare(c(-84.78145 ,-84.78088, -84.78082),c(5,4,3))\n\nnames(AICs)<-c('M1','M2','M3')\nAICs\n\n M1 M2 M3 \n179.5629 177.5618 175.5616 \n\n\nThe model with the lowest AIC value is the preferred model. (In this case, M3)\n\n\n\n\n\n6.1.3 3.3 - Simulate islands\nNow let’s use the ML parameters we estimated from the Galápagos bird data to simulate islands. Choose the parameters of the best model. We will simulate for 4 million years (time = 4). M is the mainland pool (keep it at 1000).\n\n6.1.3.1 Simulate islands with the parameters estimated from the best model for the Galápagos bird data (takes a while to run, you can reduce number of replicates if you want)\n\nGalapagos_sims<-DAISIE_sim(\n time=4,\n M=1000,\n pars=c(1.26, 1.146, Inf, 0.005,0),\n replicates= 100,\n plot_sims = FALSE)\n\n\n\n6.1.3.2 Plot the species-through-time plots resulting from the simulations\n\nDAISIE_plot_sims(Galapagos_sims)\n\n\n\n\n\n\n6.1.3.3 Play with different simulations settings\nPlay with different parameter settings in DAISIE_sim (e.g. changing extinction rate, colonisation rate, island age, mainland pool size, replicates). Is equilibrium reached?\n\n\n\n\n6.1.3.4 Comparison with bird data from the Azores islands\nNow let’s brielfy compare with simulatiosn for a completely different dataset, of birds from the Azores archipelago (Atlantic Ocean). Load datatable containing Azores bird data from Valente et al 2017 (Current Biology)\n\ndata(Macaronesia_datalist)\nAzores<-Macaronesia_datalist[[1]]\n\n\n6.1.3.4.1 Visualise Azores data\n\nDAISIE_plot_island(Azores)\n\n\n\n\nThree of the species are extinct and only known from fossils.\n\n\n6.1.3.4.2 Simulate Azores with pre-identified ML parameters\n\nAzores_sims<-DAISIE_sim(\n time=6.3,\n M=300,\n pars=c(0,1.053151832,Inf,0.052148979,0.512939011),\n replicates= 100,\n plot_sims=FALSE)\n\n\n\n\n\n6.1.3.4.3 Plot the species-through-time plot for the Azores from the resulting from the simulations\n\nDAISIE_plot_sims(Azores_sims)\n\n\n\n\nHow does the Azores plot differ from that of the Galápagos?\n\nThe end of the DAISIE practical. Well done! Now you are ready to work on the Insula assignment." + }, + { + "objectID": "insula_DAISIE.html#example-of-how-to-extract-data-for-jamaica-species-from-the-insula-tree-add-missing-species-fit-daisie-models-and-run-simulations.", + "href": "insula_DAISIE.html#example-of-how-to-extract-data-for-jamaica-species-from-the-insula-tree-add-missing-species-fit-daisie-models-and-run-simulations.", + "title": "6  Insula DAISIE", + "section": "6.1 Example of how to extract data for Jamaica species from the Insula tree, add missing species, fit DAISIE models and run simulations.", + "text": "6.1 Example of how to extract data for Jamaica species from the Insula tree, add missing species, fit DAISIE models and run simulations.\n\n6.1.1 Load required packages\n\nrm(list=ls())\nlibrary(ape)\nlibrary(DAISIEprep)\nlibrary(DAISIE)\n\n\n\n6.1.2 Load tree\n\nInsula_tree<-read.nexus(\"data/Insula.tre\")\n\nVisualise tree (easier to use Figtree!)\n\nplot(Insula_tree)" + }, + { + "objectID": "insula_DAISIE.html#prepare-data-using-daisieprep", + "href": "insula_DAISIE.html#prepare-data-using-daisieprep", + "title": "6  Insula DAISIE", + "section": "6.2 Prepare data using DAISIEprep", + "text": "6.2 Prepare data using DAISIEprep\nLook in the checklist to see which species occur on Jamaica. Specify tips corresponding to Jamaica species by specifying that they are endemic and/or non-endemic to the island:\n\nisland_species <- data.frame(\n tip_labels = c(\"Spec_29\",\n \"Spec_48\",\n \"Spec_47\",\n \"Spec_38\",\n \"Spec_43\",\n \"Spec_42\",\n \"Spec_39\",\n \"Spec_33\",\n \"Spec_26\",\n \"Spec_19\",\n \"Spec_41\",\n \"Spec_40\",\n \"Spec_25\",\n \"Spec_9\",\n \"Spec_24\")\n ,\n tip_endemicity_status = c(rep(\"endemic\",14),\"nonendemic\"))\n\nAssign island endemicity status to all species in the dataset (including the non-Jamaican species)\n\nendemicity_status <- create_endemicity_status(\n phylo = Insula_tree,\n island_species = island_species\n)\n\nAdd endemicity status to the phylogeny\n\nphylod <- phylobase::phylo4d(Insula_tree, endemicity_status)\n\nVisualize this on the tree\n\nplot_phylod(phylod = phylod)\n\n\n\n\nExtract data from the phylogeny using the min algorithm\n\nisland_tbl_min <- extract_island_species(\n phylod = phylod,\n extraction_method = \"min\"\n)\nisland_tbl_min\n\nClass: Island_tbl \n clade_name status missing_species col_time col_max_age branching_times\n1 Spec_9 endemic 0 0.2892541 FALSE NA\n2 Spec_19 endemic 0 0.4972437 FALSE 0.207998....\n3 Spec_24 nonendemic 0 0.1694692 FALSE NA\n4 Spec_25 endemic 0 0.5035954 FALSE 0.165789....\n5 Spec_29 endemic 0 0.1166231 FALSE NA\n min_age species clade_type\n1 NA Spec_9 1\n2 NA Spec_19,.... 1\n3 NA Spec_24 1\n4 NA Spec_25,.... 1\n5 NA Spec_29 1\n\n\nExtract data from the phylogeny using the ancestral state algorithm\n\nphylod <- add_asr_node_states(phylod = phylod, asr_method = \"mk\")\nplot_phylod(phylod = phylod)\n\n\n\nisland_tbl_asr <- extract_island_species(\n phylod = phylod,\n extraction_method = \"asr\"\n)\nisland_tbl_asr\n\nClass: Island_tbl \n clade_name status missing_species col_time col_max_age branching_times\n1 Spec_9 endemic 0 0.2892541 FALSE NA\n2 Spec_19 endemic 0 0.4972437 FALSE 0.207998....\n3 Spec_24 nonendemic 0 0.1694692 FALSE NA\n4 Spec_25 endemic 0 0.5035954 FALSE 0.165789....\n5 Spec_29 endemic 0 0.1166231 FALSE NA\n min_age species clade_type\n1 NA Spec_9 1\n2 NA Spec_19,.... 1\n3 NA Spec_24 1\n4 NA Spec_25,.... 1\n5 NA Spec_29 1\n\n\nCompare 2 options:\n\nall.equal(island_tbl_min,island_tbl_asr)\n\n[1] TRUE\n\n\nAs you can see, the results of the 2 extractions (min and asr) are exactly the same in this case, so we can use either for the subsequent analyses.\n\n6.2.1 Add missing species\nAdd missing species “Spec_51”, which is not closely related to any species\n\nisland_tbl<-island_tbl_min\nisland_tbl <- add_island_colonist(\n island_tbl = island_tbl,\n clade_name = \"Spec_51\",\n status = \"endemic\",\n missing_species = 0,\n col_time = NA_real_,\n col_max_age = FALSE,\n branching_times = NA_real_,\n min_age = NA_real_,\n clade_type = 1,\n species = \"Spec_51\"\n)\n\nAdd missing species Spec_52, closely related to Spec_42\n\nisland_tbl <- add_missing_species(\n island_tbl = island_tbl,\n num_missing_species = 1, \n species_to_add_to = \"Spec_42\"\n)\n\nCreate DAISIE datalist\n\ninsula_data_list <- create_daisie_data(\n data = island_tbl,\n island_age = 4,\n num_mainland_species = 1000,\n precise_col_time = TRUE\n)" + }, + { + "objectID": "insula_DAISIE.html#fit-daisie-models-to-data", + "href": "insula_DAISIE.html#fit-daisie-models-to-data", + "title": "6  Insula DAISIE", + "section": "6.3 Fit DAISIE models to data", + "text": "6.3 Fit DAISIE models to data\nFit model with 5 parameters\n\nM1<-DAISIE_ML(\n datalist = insula_data_list,\n initparsopt = c(1.5,1.1,20,0.009,1.1),\n ddmodel = 11,\n idparsopt = 1:5,\n parsfix = NULL,\n idparsfix = NULL\n)\nM1\n\n lambda_c mu K gamma lambda_a loglik df conv\n1 5.892194 7.805793 1356008 0.02516541 3.244882 -37.10636 5 0\n\n\nFit model with no carrying capacity\n\nM2<-DAISIE_ML(\n datalist = insula_data_list,\n initparsopt = c(1.5,1.1,0.009,1.1),\n idparsopt = c(1,2,4,5),\n parsfix = Inf,\n idparsfix = 3,\n ddmodel=0\n)\nM2\n\n lambda_c mu K gamma lambda_a loglik df conv\n1 5.896242 7.810848 Inf 0.02519944 3.206366 -37.10633 4 0\n\n\nFit model with no anagenesis\n\nM3<-DAISIE_ML(\n datalist = insula_data_list,\n initparsopt = c(1.5,1.1,0.009),\n idparsopt = c(1,2,4),\n parsfix = c(Inf,0),\n idparsfix = c(3,5),\n ddmodel=0\n)\nM3\n\n lambda_c mu K gamma lambda_a loglik df conv\n1 6.729751 8.726048 Inf 0.02742398 0 -37.29935 3 0\n\n\nSave model results in a table\n\nmodel_results<-rbind(M1,M2,M3)\nmodel_results\n\n lambda_c mu K gamma lambda_a loglik df conv\n1 5.892194 7.805793 1356008 0.02516541 3.244882 -37.10636 5 0\n2 5.896242 7.810848 Inf 0.02519944 3.206366 -37.10633 4 0\n3 6.729751 8.726048 Inf 0.02742398 0.000000 -37.29935 3 0\n\n\nCreate AIC function for model comparison\n\nAIC_compare<- function(LogLik,k){\n aic <- (2*k)-(2*LogLik)\n return(aic)\n}\n\nCompute AIC for all the models\n\nAICs<-AIC_compare(c(M1$loglik ,M2$loglik, M3$loglik),c(M1$df,M2$df,M3$df))\nnames(AICs)<-c('M1','M2','M3')\nAICs\n\n M1 M2 M3 \n84.21273 82.21265 80.59871 \n\n\nIn this case, the preferred model is M3." + }, + { + "objectID": "insula_DAISIE.html#simulate-islands-based-on-parameters-from-preferred-model", + "href": "insula_DAISIE.html#simulate-islands-based-on-parameters-from-preferred-model", + "title": "6  Insula DAISIE", + "section": "6.4 Simulate islands based on parameters from preferred model", + "text": "6.4 Simulate islands based on parameters from preferred model\nRun simulations\n\nInsula_sims<-DAISIE_sim(\n time=4,\n M=1000,\n pars=as.numeric(M3[1:5]),\n replicates= 100,\n verbose = 1,\n plot_sims = FALSE)\n\nPlot simulations\n\nDAISIE_plot_sims(Insula_sims)\n\n\n\n\nIt looks like Insula diversity in the island of Jamaica is at a dynamic equilibrium." + } +] \ No newline at end of file diff --git a/site_libs/bootstrap/bootstrap-icons.css b/site_libs/bootstrap/bootstrap-icons.css new file mode 100644 index 0000000..94f1940 --- /dev/null +++ b/site_libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,2018 @@ +@font-face { + 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document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" 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this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const 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b/site_libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + console.log({ + childtop: marginChild.getBoundingClientRect().top, + scroll: window.scrollY, + top, + lastBottom, + }); + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + + console.log({ + top, + height: marginChild.getBoundingClientRect().height, + marginTop, + total: top + marginChild.getBoundingClientRect().height + marginTop, + }); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(layoutMarginEls, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/site_libs/quarto-html/tippy.css b/site_libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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b/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,277 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver( + updateDocumentOffsetWithoutAnimation + ); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/site_libs/quarto-search/autocomplete.umd.js b/site_libs/quarto-search/autocomplete.umd.js new file mode 100644 index 0000000..619c57c --- /dev/null +++ b/site_libs/quarto-search/autocomplete.umd.js @@ -0,0 +1,3 @@ +/*! @algolia/autocomplete-js 1.7.3 | MIT License | © Algolia, Inc. and contributors | https://github.com/algolia/autocomplete */ +!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof 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Apache Software License 2.0 + * + * http://www.apache.org/licenses/LICENSE-2.0 + */ +var e,t;e=this,t=function(){"use strict";function e(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function t(t){for(var n=1;ne.length)&&(t=e.length);for(var n=0,r=new Array(t);n0&&void 0!==arguments[0]?arguments[0]:1,t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3,n=new Map,r=Math.pow(10,t);return{get:function(t){var i=t.match(C).length;if(n.has(i))return n.get(i);var o=1/Math.pow(i,.5*e),c=parseFloat(Math.round(o*r)/r);return n.set(i,c),c},clear:function(){n.clear()}}}var $=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},n=t.getFn,i=void 0===n?I.getFn:n,o=t.fieldNormWeight,c=void 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a/site_libs/quarto-search/quarto-search.js b/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..f5d852d --- /dev/null +++ b/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1140 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query !== null && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query !== null && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = () => { + setIsOpen(false); + }; + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.map((event) => { + const maxEvents = event.objectIDs.slice(0, 20); + return { + ...event, + objectIDs: maxEvents, + }; + }); + + insights.viewedObjectIDs(...events); + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + // create fuse index + const options = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, + }; + const fuse = new window.Fuse([], options); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard(createElement, icon, title, section, text, href) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + [iconEl, titleEl] + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text"].forEach((keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + }); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +function fuseSearch(query, fuse, fuseOptions) { + return fuse.search(query, fuseOptions).map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + }; + }); +}