Abstract1. Biological conclusions drawn from phylogenetic comparative methods can be sensitive to uncertainty in species sampling, phylogeny and data. To be confident about our conclusions, we need to quantify their robustness to such uncertainty.2. We present sensiPhy, an r-package, to easily and rapidly perform sensitivity analysis for phylogenetic comparative methods. sensiPhy allows researchers to evaluate the sampling effort, detect influential species and clades, assess phylogenetic uncertainty and quantify the effects of intraspecific variation, for phylogenetic regression and for metrics of phylogenetic signal, diversification and trait evolution.3. Uniquely, sensiPhy allows users to simultaneously quantify the effects of different types of uncertainty and potential interactions among them.4. Using real data, we show how conclusions from comparative methods can be affected by uncertainty and how sensiPhy can help determine if a conclusion is robust.5. By providing a single, intuitive and user-friendly resource that can evaluate various sources of uncertainty, sensiPhy aims to encourage researchers, and particularly less-experienced users, to incorporate sensitivity analyses in their phylogenetic comparative analyses.
K E Y W O R D Sbias, diversification, PGLS, phylogenetic regression, robustness, trait evolution
| Methods in Ecology and EvoluঞonPATERNO ET Al. biology (Cooper et al., 2016;Cornwell & Nakagawa, 2017;Donoghue & Ackerly, 1996). Here, we present sensiPhy, an r-package, to perform sensitivity analysis for the most frequently used phylogenetic comparative methods. Our main goal is to make it easier for lessexperienced users to implement the best practices when running comparative analyses. To our knowledge, this is the first effort to combine in a single resource functions to account for three types of uncertainty in commonly used comparative methods.
| THE s e n s i P h y PACK AG EsensiPhy is written in the r-language (R Core Team, 2017) and is available on the CRAN repository. The package provides an umbrella of statistical and graphical methods to estimate and report sensitivity to uncertainty in phylogenetic comparative analysis (PGLS, phylogenetic signal, diversification and trait evolution). We leverage methods implemented in the r-packages Phylolm, Phytools and geiger (Harmon, Weir, Brock, Glor, & Challenger, 2008;Ho & Ané, 2014;Revell, 2012) and implement functions to perform sensitivity analysis for phylogenetic generalized least squares models (PGLS; both using linear and logistic regression models), for estimates of phylogenetic signal in trait data (Blomberg, Garland, & Ives, 2003;Pagel, 1999), for macroevolutionary models (both continuous and discrete, binary, traits) and estimates of diversification rates (Harmon et al., 2008;Magallon & Sanderson, 2001). For each type of sensitivity analysis, a specific set of diagnostics graphics and summary statistics are provided ( Figure 1). In all PGLS functions, the evolutionary model to use can be specified (e.g. Brownian Motion and Ornst...