10.5061/DRYAD.TG37F
Etienne, Rampal S.
University of Groningen
Pigot, Alex L.
University of Groningen
Phillimore, Albert
University of Edinburgh
Phillimore, Albert B.
University of Edinburgh
Data from: How reliably can we infer diversity-dependent diversification
from phylogenies?
Dryad
dataset
2017
Conditioning
Diversity-dependence
Parametric bootstrap
Birth-death model
simulations
2017-03-18T00:00:00Z
en
https://doi.org/10.1111/2041-210X.12565
2052884617 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Slowdowns in lineage accumulation in phylogenies suggest that speciation
rates decline as diversity increases. Likelihood methods have been
developed to detect such diversity dependence. However, a thorough test of
whether such approaches correctly infer diversity dependence is lacking.
Here, we simulate phylogenetic branching under linear negative
diversity-dependent and diversity-independent models and estimate from the
simulated phylogenies the maximum-likelihood parameters for three
different conditionings – on survival of the birth–death process given the
crown age, on tree size (N) and on tree size given the crown age. We
report the accuracy of recovering the simulation parameters and the
reliability of the model selection based on the χ2 likelihood ratio test.
Parameter estimate accuracy: Conditioning on survival given the crown age
yields a severe bias of the carrying capacity K towards N and an upward
bias of the speciation rate, particularly in clades where
diversity-dependent feedbacks are still weak (N « K). Conditioning on N
yields an overestimate of K and an underestimate of speciation rate,
particularly when saturation has been reached. Dual conditioning yields
relatively unbiased parameter estimates on average, but the deviation from
the true value for any single estimate may be large. Model selection
reliability: The frequency of incorrectly rejecting a
diversity-independent model when the simulation was diversity-independent
(type I error) differs substantially from the significance level α used in
the likelihood ratio test, rendering the likelihood ratio test
inappropriate. The frequency of correctly rejecting the
diversity-independent model when the simulation was diversity-dependent
(power) is larger when the clade is closer to equilibrium and for
conditioning on crown age. We conclude that conditioning on crown age has
the best statistical properties overall, but caution that parameter
estimates may be biased. To assess parameter uncertainty in future studies
of diversity dependence on real data, we recommend parametric
bootstrapping, examination of the likelihood surface and comparison of
estimates across the types of conditioning. To assess model selection
reliability, we discourage the use of the χ2 likelihood ratio test or AIC
(which are equivalent in this case), but recommend a likelihood ratio test
based on parametric bootstrap. We illustrate this method for the
diversification of Dendroica warblers.
Simulations under CR and DD models - part 2Second batch of .RData files of
the tree simulations under the CR and DD
models.biastestCRDD_sims2.zipSimulations under CR and DD models - part
1First of two batches of .RData files of the trees simulation under the CR
and DD models and the R-script that generated both
batches.biastestCRDD_sims1.zipMaximum likelihood results under CR
modelRData files with maximum likelihoods and corresponding parameter
estimates under the constant-rate (CR) model, and the R-script that
generated them.biastestCR_LL.zipMaximum likelihood results under DD model
- 1RData files with maximum likelihoods and corresponding parameter
estimates under the diversity-dependent model, and the R-script that
generated them. The starting values of the optimization are the simulation
values.biastestDD_LL1.zipMaximum likelihood results under DD model -
2RData files with maximum likelihoods and corresponding parameter
estimates under the diversity-dependent model, and the R-script that
generated them. The starting values of the optimization are the results
from the CR models fits with a large but finite carrying
capacity..biastestDD_LL2.zipR-script to make plots from resultsR-script to
make plots from simulation resultsplot_params.RBootstrap likelihood ratio
test results of the Dendroica case studyRData file of the bootstrap
likelihood ratio test results of the Dendroica case
study.DendroicaLRtestresults.RData