Comparative analyses of phenotypic sequences using phylogenetic trees

Abstract

Phenotypic sequences are a type of multivariate trait organized structurally, such as teeth distributed along the dental arch, or temporally, such as the stages of an ontogenetic series. However, unlike other multivariate traits, the elements of a phenotypic sequence are arranged along a vector, which allows for distinct evolutionary patterns between neighboring and distant positions. In fact, sequence traits share many characteristics with molecular sequences. We implement an approach to estimate rates of trait evolution that explicitly incorporates the sequence organization of traits. We apply models to study the temporal pattern evolution of cricket calling songs. We test whether songs show autocorrelation of rates (i.e., neighboring positions along a phenotypic sequence have correlated rates of evolution), or if they are best described by rate variation independent of sequence position. Our results show that models perform well when used with sequence phenotypes even under small sample sizes. We also show that silent regions of the songs evolve faster than chirp regions, which suggests that macroevolutionary changes are faster when associated with axes of variation less constrained by multiple sources of selection. Our approach is flexible and can be applied to any multivariate trait with units organized in a sequence-like structure.

RAD-Behavior (Recombining Atomized, Discretized, Behavior): A new framework for the quantitative analysis of behavioral execution

“…Given the vast community of researchers interested in behavioral execution, there are undoubtedly a multitude of creative, innovative approaches to leverage the behavioral strings created by RAD-behavior to answer longstanding questions about behavior, as well as diverse perspectives on the challenge of identifying relevant 485 behavioral atoms and weights. Future developments and implementations of RAD-behavior include, but are not limited to, fine-scale studies of within-individual variation in behavioral execution depending on context and learning, population-level studies focused on the role of drift and/or culture on variability in execution of shared behaviors, and interspecific comparisons of behaviors focused on homologous behavioral atoms and incorporating cutting-edge phylogenetic comparative analyses of sequence data 26 . It is our hope that RAD-behavior can serve as the conceptual 490 framework uniting studies focused in their attempts to uncover and compare variation in behavioral evolution across scales.…”

“…are represented by longer character strings within the composite string describing an organism's instantaneous behavioral state) than others based on simulation studies and comparisons of evolutionary rates (e.g. using recently developed methodologies 26 ). The flexibility of the overarching RAD-behavior analytical framework makes it well-suited to these case-by-case designations that will depend on the questions being asked by researchers.…”

“…which movement sequences are shared across individuals/contexts/etc) as well as "regions" that are unique. This approach has recently been used in an ground-breaking study examining the evolution of acoustic sequences in crickets 26 , and future investigations incorporating sequence-based analyses into phylogenetic comparative frameworks 290 are an exciting development in the field of evolutionary biology.…”

Comparative bioacoustics: a roadmap for quantifying and comparing animal sounds across diverse taxa

“…Evolutionary rate analysis allows researchers to examine the extent to which traits change over evolutionary time (Gingerich, 2009), to compare evolutionary rates among traits (Adams, 2014 b ) and to evaluate variation in evolutionary rates between phenotypic sequence subunits, which can be applied to multi‐sylable vocalizations (Caetano & Beaulieu, 2020). Additional methods have been developed to evaluate whether the evolutionary rate of a continuous trait is affected by another trait, either discrete (O'Meara et al ., 2006) or continuous (Weir & Lawson, 2015), and the following analyses have been extended to allow for or to test correlation between speciation and discrete traits (binary traits biSSE; Maddison, Midford & Otto, 2007), multi‐state traits (muSSE; FitzJohn, 2012), continuous traits (quaSSE; FitzJohn, 2010) and trait evolutionary rates (Adams et al ., 2009; Rabosky et al ., 2014).…”