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Abstract / Description of output
Slowdowns in lineage accumulation in phylogenies suggest that speciation rates decline as diversity increases. Likelihood methods have been developed to detect such diversitydependence. However, a thorough test of whether such approaches correctly infer diversitydependence is lacking.
Here we simulate phylogenetic branching under linear negative diversitydependent and diversityindependent models and estimate from the simulated phylogenies the maximum likelihood parameters for three different conditionings – on survival of the birthdeath 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 toward N, and an upward bias of the speciation rate, particularly in clades where diversitydependent 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 diversityindependent model when the simulation was diversityindependent (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 diversityindependent model when the simulation was diversitydependent (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 diversitydependence 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.
Here we simulate phylogenetic branching under linear negative diversitydependent and diversityindependent models and estimate from the simulated phylogenies the maximum likelihood parameters for three different conditionings – on survival of the birthdeath 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 toward N, and an upward bias of the speciation rate, particularly in clades where diversitydependent 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 diversityindependent model when the simulation was diversityindependent (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 diversityindependent model when the simulation was diversitydependent (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 diversitydependence 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.
Original language  English 

Journal  Methods in ecology and evolution 
Early online date  25 Mar 2016 
DOIs  
Publication status  Published  Sept 2016 
Keywords / Materials (for Nontextual outputs)
 Birthdeath model
 diversitydependence
 simulation
 extinction
 parametric bootstrap
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Dive into the research topics of 'How reliably can we infer diversitydependent diversification from phylogenies?'. Together they form a unique fingerprint.Projects
 1 Finished

Climate driven phenotypic change: macroecology meets quantitative genetics
1/02/12 → 31/01/17
Project: Research
Datasets

Data from: How reliably can we infer diversitydependent diversification from phylogenies?
Etienne, R. S. (Creator), Pigot, A. L. (Creator), Phillimore, A. (Creator) & Phillimore, A. (Creator), Dryad, 18 Mar 2017
DOI: 10.5061/dryad.tg37f, https://datadryad.org/stash/dataset/doi:10.5061/dryad.tg37f
Dataset