Maternal investment evolves with larger body size and higher diversification rate in sharks and rays.

Across vertebrates, live bearing evolved at least 150 times from ancestral egg laying into diverse forms and degrees of prepartum maternal investment.1,2 A key question is how reproductive diversity arose and whether reproductive diversification underlies species diversification.3,4,5,6,7,8,9,10,11 To test this, we evaluate the most basal jawed vertebrates: the sharks, rays, and chimaeras, which have one of the greatest ranges of reproductive and ecological diversity among vertebrates.2,12 We reconstruct the sequence of reproductive mode evolution across a phylogeny of 610 chondrichthyans.13 We reveal egg laying as ancestral, with live bearing evolving at least seven times. Matrotrophy evolved at least 15 times, with evidence of one reversal. In sharks, transitions to live bearing and matrotrophy are more prevalent in larger-bodied tropical species. Further, the evolution of live bearing is associated with a near doubling of the diversification rate, but there is only a small increase associated with the appearance of matrotrophy. Although pre-copulatory sexual selection is associated with increased rates of speciation in teleosts,3 sexual size dimorphism in chondrichthyans does not appear to be related to sexual selection,14,15 and instead we find increased rates of speciation associated with the colonization of novel habitats. This highlights a potential key difference between chondrichthyans and other fishes, specifically a slower rate of evolution of reproductive isolation following speciation, suggesting different rate-limiting mechanisms for diversification between these clades.16 The chondrichthyan diversification and radiation, particularly throughout shallow tropical shelf seas and oceanic pelagic habitats, appear to be associated with the evolution of live bearing and proliferation of a wide range of maternal investment in developing offspring.

Unifying Phylogenetic Birth-Death Models in Epidemiology and Macroevolution.

Birth-death stochastic processes are the foundations of many phylogenetic models and are widely used to make inferences about epidemiological and macroevolutionary dynamics. There are a large number of birth-death model variants that have been developed; these impose different assumptions about the temporal dynamics of the parameters and about the sampling process. As each of these variants was individually derived, it has been difficult to understand the relationships between them as well as their precise biological and mathematical assumptions. Without a common mathematical foundation, deriving new models is nontrivial. Here, we unify these models into a single framework, prove that many previously developed epidemiological and macroevolutionary models are all special cases of a more general model, and illustrate the connections between these variants. This unification includes both models where the process is the same for all lineages and those in which it varies across types. We also outline a straightforward procedure for deriving likelihood functions for arbitrarily complex birth-death(-sampling) models that will hopefully allow researchers to explore a wider array of scenarios than was previously possible. By rederiving existing single-type birth-death sampling models, we clarify and synthesize the range of explicit and implicit assumptions made by these models. [Birth-death processes; epidemiology; macroevolution; phylogenetics; statistical inference.].

Phylogenetic history of vascular plant metabolism revealed using a macroevolutionary common garden.

While the fundamental biophysics of C3 photosynthesis is highly conserved across plants, substantial leaf structural and enzymatic variation translates into variability in rates of carbon assimilation. Although this variation is well documented, it remains poorly understood how photosynthetic rates evolve, and whether macroevolutionary changes are related to the evolution of leaf morphology and biochemistry. A substantial challenge in large-scale comparative studies is disentangling evolutionary adaptation from environmental acclimation. We overcome this by using a 'macroevolutionary common garden' approach in which we measured metabolic traits (Jmax and Vcmax) from 111 phylogenetically diverse species in a shared environment. We find substantial phylogenetic signal in these traits at moderate phylogenetic timescales, but this signal dissipates quickly at deeper scales. Morphological traits exhibit phylogenetic signal over much deeper timescales, suggesting that these are less evolutionarily constrained than metabolic traits. Furthermore, while morphological and biochemical traits (LMA, Narea and Carea) are weakly predictive of Jmax and Vcmax, evolutionary changes in these traits are mostly decoupled from changes in metabolic traits. This lack of tight evolutionary coupling implies that it may be incorrect to use changes in these functional traits in response to global change to infer that photosynthetic strategy is also evolving.

The latitudinal taxonomy gradient.

Emerging large-scale datasets coupled with statistical advances have provided new insights into the processes that generate the latitudinal diversity gradient (LDG). But many of these studies run into an old, if often underappreciated, problem: The interpretation of the data critically depends on the consistent application of criteria to define what constitutes a species. This is particularly pernicious for the LDG because good species have been easier to recognize in temperate than in tropical regions. We provide evidence that this latitudinal taxonomy gradient exists, discuss how this potentially impacts inferences about latitudinal variation in ecoevolutionary processes such as population differentiation and speciation, and provide a roadmap for how to mitigate taxonomic biases in the study of biodiversity patterns.

Macroevolutionary history predicts flowering time but not phenological sensitivity to temperature in grasses.

PREMISE: Long-term observations show that flowering phenology has shifted in many lineages in response to climate change. However, it remains unclear whether these results can be generalized to predict the presence, direction, or magnitude of responses in lineages for which we lack long time-series data. If phenological responses are phylogenetically conserved, we can extrapolate from species for which we have data to predict the responses of close relatives. While several studies have found that closely related species flower at similar times, fewer have evaluated whether phylogenetically proximal species respond to environmental change similarly. METHODS: We paired flowering time data from 3161 manually scored herbarium specimens of 72 species of grasses (Poaceae) with historical climate data and analyzed the phylogenetic signal and phylogenetic half-life of phenological sensitivity. We also ran these analyses on a subset of species showing statistically significant sensitivities, in order to assess the role of sampling bias on phylogenetic signal. RESULTS: Closely related grass species tend to flower at similar times, but flowering times respond to temperature changes in species-specific ways. We also show that only including species for which there is strong evidence of phenological shifts results in overestimating phylogenetic signal. CONCLUSIONS: In agreement with other recent studies, our results suggest caution in extrapolating from evidence of phylogenetic similarity to predicting shared responses in this ecologically relevant trait. Future work is needed to better understand the discrepancy between the phylogenetic signal in observed phenological shifts and absence of such signal in sensitivity.

Why extinction estimates from extant phylogenies are so often zero.

Time-calibrated phylogenies of extant species ("extant timetrees") are widely used to estimate historical speciation and extinction rates by fitting stochastic birth-death models.1 These approaches have long been controversial, as many phylogenetic studies report zero extinction in many taxa, contradicting the high extinction rates seen in the fossil record and the fact that the majority of species ever to have existed are now extinct.2-9 To date, the causes of this discrepancy remain unresolved. Here, we provide a novel and simple explanation for these "zero-inflated" extinction estimates, based on the recent discovery that there exist many alternative "congruent" diversification scenarios that cannot be distinguished based solely on extant timetrees.10 Due to such congruencies, estimation methods tend to converge to some scenario congruent to (i.e., statistically indistinguishable from) the true diversification scenario, but not necessarily to the true diversification scenario itself. This congruent scenario may exhibit negative extinction rates, a biologically meaningless but mathematically feasible situation, in which case estimators will tend to stick to the boundary of zero extinction. Based on this explanation, we make multiple testable predictions, which we confirm using analyses of simulated trees and 121 empirical trees. In contrast to other proposed mechanisms for erroneous extinction rate estimates,5,11-14 our proposed mechanism specifically explains the zero inflation of previous extinction rate estimates in the absence of detectable model violations, even for large trees. Not only do our results likely resolve a long-standing mystery in phylogenetics, they demonstrate that model congruencies can have severe consequences in practice.

Fundamental Identifiability Limits in Molecular Epidemiology.

Viral phylogenies provide crucial information on the spread of infectious diseases, and many studies fit mathematical models to phylogenetic data to estimate epidemiological parameters such as the effective reproduction ratio (Re) over time. Such phylodynamic inferences often complement or even substitute for conventional surveillance data, particularly when sampling is poor or delayed. It remains generally unknown, however, how robust phylodynamic epidemiological inferences are, especially when there is uncertainty regarding pathogen prevalence and sampling intensity. Here, we use recently developed mathematical techniques to fully characterize the information that can possibly be extracted from serially collected viral phylogenetic data, in the context of the commonly used birth-death-sampling model. We show that for any candidate epidemiological scenario, there exists a myriad of alternative, markedly different, and yet plausible "congruent" scenarios that cannot be distinguished using phylogenetic data alone, no matter how large the data set. In the absence of strong constraints or rate priors across the entire study period, neither maximum-likelihood fitting nor Bayesian inference can reliably reconstruct the true epidemiological dynamics from phylogenetic data alone; rather, estimators can only converge to the "congruence class" of the true dynamics. We propose concrete and feasible strategies for making more robust epidemiological inferences from viral phylogenetic data.

How should functional relationships be evaluated using phylogenetic comparative methods? A case study using metabolic rate and body temperature.

Phylogenetic comparative methods are often used to test functional relationships between traits. However, million-year macroevolutionary observational datasets cannot definitively prove causal links between traits-correlation does not equal causation and experimental manipulation over such timescales is impossible. Although this caveat is widely understood, it is less appreciated that different phylogenetic approaches imply different causal assumptions about the functional relationships of traits. To make meaningful inferences, it is critical that our statistical methods make biologically reasonable assumptions. Here we illustrate the importance of causal reasoning in comparative biology by examining a recent study by Avaria-Llautureo et al (2019). that tested for the evolutionary coupling of metabolic rate and body temperature across endotherms and found that these traits were unlinked through evolutionary time and that body temperatures were, on average, higher in the early Cenozoic than they are today. We argue that the causal assumptions embedded into their models made it impossible for them to test the relevant functional and evolutionary hypotheses. We reanalyze their data using more biologically appropriate models and find support for the exact opposite conclusions, corroborating previous evidence from physiology and paleontology. We highlight the vital need for causal thinking, even when experiments are impossible.

Investigating Morphological Complexes Using Informational Dissonance and Bayes Factors: A Case Study in Corbiculate Bees.

It is widely recognized that different regions of a genome often have different evolutionary histories and that ignoring this variation when estimating phylogenies can be misleading. However, the extent to which this is also true for morphological data is still largely unknown. Discordance among morphological traits might plausibly arise due to either variable convergent selection pressures or else phenomena such as hemiplasy. Here, we investigate patterns of discordance among 282 morphological characters, which we scored for 50 bee species particularly targeting corbiculate bees, a group that includes the well-known eusocial honeybees and bumblebees. As a starting point for selecting the most meaningful partitions in the data, we grouped characters as morphological modules, highly integrated trait complexes that as a result of developmental constraints or coordinated selection we expect to share an evolutionary history and trajectory. In order to assess conflict and coherence across and within these morphological modules, we used recently developed approaches for computing Bayesian phylogenetic information allied with model comparisons using Bayes factors. We found that despite considerable conflict among morphological complexes, accounting for among-character and among-partition rate variation with individual gamma distributions, rate multipliers, and linked branch lengths can lead to coherent phylogenetic inference using morphological data. We suggest that evaluating information content and dissonance among partitions is a useful step in estimating phylogenies from morphological data, just as it is with molecular data. Furthermore, we argue that adopting emerging approaches for investigating dissonance in genomic datasets may provide new insights into the integration and evolution of anatomical complexes. [Apidae; entropy; morphological modules; phenotypic integration; phylogenetic information.].

Phylogenetics is the New Genetics (for Most of Biodiversity).

Despite substantial progress in understanding the genetic basis for differences in morphology, physiology, and behavior, many phenotypes of interest are difficult to study with traditional genetic approaches because their origin traces to deep nodes in the tree of life. Moreover, many species are not amenable to either large-scale sampling or laboratory crosses. We argue that phylogenetic methods and theory provide tremendous power to identify the functional genetic variation underlying trait evolution. We anticipate that existing statistical comparative approaches will be more commonly applied to studying the genetic basis for phenotypic evolution as whole genomes continue to populate the tree of life. Nevertheless, new methods and approaches will be needed to fully capitalize on the power of clade-scale genomic datasets.

Extant timetrees are consistent with a myriad of diversification histories.

Time-calibrated phylogenies of extant species (referred to here as 'extant timetrees') are widely used for estimating diversification dynamics1. However, there has been considerable debate surrounding the reliability of these inferences2-5 and, to date, this critical question remains unresolved. Here we clarify the precise information that can be extracted from extant timetrees under the generalized birth-death model, which underlies most existing methods of estimation. We prove that, for any diversification scenario, there exists an infinite number of alternative diversification scenarios that are equally likely to have generated any given extant timetree. These 'congruent' scenarios cannot possibly be distinguished using extant timetrees alone, even in the presence of infinite data. Importantly, congruent diversification scenarios can exhibit markedly different and yet similarly plausible dynamics, which suggests that many previous studies may have over-interpreted phylogenetic evidence. We introduce identifiable and easily interpretable variables that contain all available information about past diversification dynamics, and demonstrate that these can be estimated from extant timetrees. We suggest that measuring and modelling these identifiable variables offers a more robust way to study historical diversification dynamics. Our findings also make it clear that palaeontological data will continue to be crucial for answering some macroevolutionary questions.

A General and Efficient Algorithm for the Likelihood of Diversification and Discrete-Trait Evolutionary Models.

As the size of phylogenetic trees and comparative data continue to grow and more complex models are developed to investigate the processes that gave rise to them, macroevolutionary analyses are becoming increasingly limited by computational requirements. Here, we introduce a novel algorithm, based on the "flow" of the differential equations that describe likelihoods along tree edges in backward time, to reduce redundancy in calculations and efficiently compute the likelihood of various macroevolutionary models. Our algorithm applies to several diversification models, including birth-death models and models that account for state- or time-dependent rates, as well as many commonly used models of discrete-trait evolution, and provides an alternative way to describe macroevolutionary model likelihoods. As a demonstration of our algorithm's utility, we implemented it for a popular class of state-dependent diversification models-BiSSE, MuSSE, and their extensions to hidden-states. Our implementation is available through the R package $\texttt{castor}$. We show that, for these models, our algorithm is one or more orders of magnitude faster than existing implementations when applied to large phylogenies. Our algorithm thus enables the fitting of state-dependent diversification models to modern massive phylogenies with millions of tips and may lead to potentially similar computational improvements for many other macroevolutionary models.

Successful Integration of Data Science in Undergraduate Biostatistics Courses Using Cognitive Load Theory.

Biostatistics courses are integral to many undergraduate biology programs. Such courses have often been taught using point-and-click software, but these programs are now seldom used by researchers or professional biologists. Instead, biology professionals typically use programming languages, such as R, which are better suited to analyzing complex data sets. However, teaching biostatistics and programming simultaneously has the potential to overload the students and hinder their learning. We sought to mitigate this overload by using cognitive load theory (CLT) to develop assignments for two biostatistics courses. We evaluated the effectiveness of these assignments by comparing student cohorts who were taught R using these assignments (n = 146) with those who were taught R through example scripts or were instructed on a point-and-click software program (control, n = 181). We surveyed all cohorts and analyzed statistical and programming ability through students' lab reports or final exams. Students who learned R through our assignments rated their programming ability higher and were more likely to put the usage of R as a skill in their curricula vitae. We also found that the treatment students were more motivated, less frustrated, and less stressed when using R. These results suggest that we can use CLT to teach challenging material.

Digest: Process-based phylogenetic models provide unique insights into the evolution of mutualistic networks.

Figs and their pollinating fig wasps are a classic example of long-term obligate associations between different species. Satler et al. use a process-based model adopted from molecular evolution to identify the major processes that affect cophylogenetic matching between figs and fig wasps. They find that host-switching is one of the most important evolutionary processes contributing to current cophylogenetic patterns, illustrating the value of probabilistic approaches to studying the evolutionary history of mutualisms.

Assessing the utility of conserving evolutionary history.

It is often claimed that conserving evolutionary history is more efficient than species-based approaches for capturing the attributes of biodiversity that benefit people. This claim underpins academic analyses and recommendations about the distribution and prioritization of species and areas for conservation, but evolutionary history is rarely considered in practical conservation activities. One impediment to implementation is that arguments related to the human-centric benefits of evolutionary history are often vague and the underlying mechanisms poorly explored. Herein we identify the arguments linking the prioritization of evolutionary history with benefits to people, and for each we explicate the purported mechanism, and evaluate its theoretical and empirical support. We find that, even after 25 years of academic research, the strength of evidence linking evolutionary history to human benefits is still fragile. Most - but not all - arguments rely on the assumption that evolutionary history is a useful surrogate for phenotypic diversity. This surrogacy relationship in turn underlies additional arguments, particularly that, by capturing more phenotypic diversity, evolutionary history will preserve greater ecosystem functioning, capture more of the natural variety that humans prefer, and allow the maintenance of future benefits to humans. A surrogate relationship between evolutionary history and phenotypic diversity appears reasonable given theoretical and empirical results, but the strength of this relationship varies greatly. To the extent that evolutionary history captures unmeasured phenotypic diversity, maximizing the representation of evolutionary history should capture variation in species characteristics that are otherwise unknown, supporting some of the existing arguments. However, there is great variation in the strength and availability of evidence for benefits associated with protecting phenotypic diversity. There are many studies finding positive biodiversity-ecosystem functioning relationships, but little work exists on the maintenance of future benefits or the degree to which humans prefer sets of species with high phenotypic diversity or evolutionary history. Although several arguments link the protection of evolutionary history directly with the reduction of extinction rates, and with the production of relatively greater future biodiversity via increased adaptation or diversification, there are few direct tests. Several of these putative benefits have mismatches between the relevant spatial scales for conservation actions and the spatial scales at which benefits to humans are realized. It will be important for future work to fill in some of these gaps through direct tests of the arguments we define here.

Datastorr: a workflow and package for delivering successive versions of 'evolving data' directly into R.

The sharing and re-use of data has become a cornerstone of modern science. Multiple platforms now allow easy publication of datasets. So far, however, platforms for data sharing offer limited functions for distributing and interacting with evolving datasets- those that continue to grow with time as more records are added, errors fixed, and new data structures are created. In this article, we describe a workflow for maintaining and distributing successive versions of an evolving dataset, allowing users to retrieve and load different versions directly into the R platform. Our workflow utilizes tools and platforms used for development and distribution of successive versions of an open source software program, including version control, GitHub, and semantic versioning, and applies these to the analogous process of developing successive versions of an open source dataset. Moreover, we argue that this model allows for individual research groups to achieve a dynamic and versioned model of data delivery at no cost.

Detecting the macroevolutionary signal of species interactions.

Species interactions lie at the heart of many theories of macroevolution, from adaptive radiation to the Red Queen. Although some theories describe the imprint that interactions will have over long timescales, we are still missing a comprehensive understanding of the effects of interactions on macroevolution. Current research shows strong evidence for the impact of interactions on macroevolutionary patterns of trait evolution and diversification, yet many macroevolutionary studies have only a tenuous relationship to ecological studies of interactions over shorter timescales. We review current research in this area, highlighting approaches that explicitly model species interactions and connect them to broad-scale macroevolutionary patterns. We also suggest that progress has been made by taking an integrative interdisciplinary look at individual clades. We focus on African cichlids as a case study of how this approach can be fruitful. Overall, although the evidence for species interactions shaping macroevolution is strong, further work using integrative and model-based approaches is needed to spur progress towards understanding the complex dynamics that structure communities over time and space.

The choice of tree prior and molecular clock does not substantially affect phylogenetic inferences of diversification rates.

Comparative methods allow researchers to make inferences about evolutionary processes and patterns from phylogenetic trees. In Bayesian phylogenetics, estimating a phylogeny requires specifying priors on parameters characterizing the branching process and rates of substitution among lineages, in addition to others. Accordingly, characterizing the effect of prior selection on phylogenies is an active area of research. The choice of priors may systematically bias phylogenetic reconstruction and, subsequently, affect conclusions drawn from the resulting phylogeny. Here, we focus on the impact of priors in Bayesian phylogenetic inference and evaluate how they affect the estimation of parameters in macroevolutionary models of lineage diversification. Specifically, we simulate trees under combinations of tree priors and molecular clocks, simulate sequence data, estimate trees, and estimate diversification parameters (e.g., speciation and extinction rates) from these trees. When substitution rate heterogeneity is large, diversification rate estimates deviate substantially from those estimated under the simulation conditions when not captured by an appropriate choice of relaxed molecular clock. However, in general, we find that the choice of tree prior and molecular clock has relatively little impact on the estimation of diversification rates insofar as the sequence data are sufficiently informative and substitution rate heterogeneity among lineages is low-to-moderate.

Macroevolutionary diversification rates show time dependency.

For centuries, biologists have been captivated by the vast disparity in species richness between different groups of organisms. Variation in diversity is widely attributed to differences between groups in how fast they speciate or go extinct. Such macroevolutionary rates have been estimated for thousands of groups and have been correlated with an incredible variety of organismal traits. Here we analyze a large collection of phylogenetic trees and fossil time series and describe a hidden generality among these seemingly idiosyncratic results: speciation and extinction rates follow a scaling law in which both depend on the age of the group in which they are measured, with the fastest rates in the youngest clades. Using a series of simulations and sensitivity analyses, we demonstrate that the time dependency is unlikely to be a result of simple statistical artifacts. As such, this time scaling is likely a genuine feature of the tree of life, hinting that the dynamics of biodiversity over deep time may be driven in part by surprisingly simple and general principles.