Evolution of degrees of carnivory and dietary specialization across Mammalia and their effects on speciation

ABSTRACTConflation between omnivory and dietary generalism limits ecological and evolutionary analyses of diet, including estimating contributions to speciation and diversification. Additionally, categorizing species into qualitative dietary classes leads to information loss in these analyses. Here, we constructed two continuous variables – degree of carnivory (i.e., the position along the continuum from complete herbivory to complete carnivory) and degree of dietary specialization (i.e., the number and variety of food resources utilized) – to elucidate their histories across Mammalia, and to tease out their independent contributions to mammalian speciation. We observed that degree of carnivory significantly affected speciation rate across Mammalia, whereas dietary specialization did not. We further considered phylogenetic scale in diet-dependent speciation and saw that degree of carnivory significantly affected speciation in ungulates, carnivorans, bats, eulipotyphlans, and marsupials, while the effect of dietary specialization was only significant in carnivorans. Across Mammalia, omnivores had the lowest speciation rates. Our analyses using two different categorical diet variables led to contrasting signals of diet-dependent diversification, and subsequently different conclusions regarding diet’s macroevolutionary role. We argue that treating variables such as diet as continuous instead of categorical reduces information loss and avoids the problem of contrasting macroevolutionary signals caused by differential discretization of biologically continuous traits.

Constraining PERMANOVA and LDM to within-set comparisons by projection improves the efficiency of analyses of matched sets of microbiome data

Background Matched-set data arise frequently in microbiome studies. For example, we may collect pre- and post-treatment samples from a set of individuals, or use important confounding variables to match data from case participants to one or more control participants. Thus, there is a need for statistical methods for data comprised of matched sets, to test hypotheses against traits of interest (e.g., clinical outcomes or environmental factors) at the community level and/or the operational taxonomic unit (OTU) level. Optimally, these methods should accommodate complex data such as those with unequal sample sizes across sets, confounders varying within sets, and continuous traits of interest. Methods PERMANOVA is a commonly used distance-based method for testing hypotheses at the community level. We have also developed the linear decomposition model (LDM) that unifies the community-level and OTU-level tests into one framework. Here we present a new strategy that can be used with both PERMANOVA and the LDM for analyzing matched-set data. We propose to include an indicator variable for each set as covariates, so as to constrain comparisons between samples within a set, and also permute traits within each set, which can account for exchangeable sample correlations. The flexible nature of PERMANOVA and the LDM allows discrete or continuous traits or interactions to be tested, within-set confounders to be adjusted, and unbalanced data to be fully exploited. Results Our simulations indicate that our proposed strategy outperformed alternative strategies, including the commonly used one that utilizes restricted permutation only, in a wide range of scenarios. Using simulation, we also explored optimal designs for matched-set studies. The flexibility of PERMANOVA and the LDM for a variety of matched-set microbiome data is illustrated by the analysis of data from two real studies. Conclusions Including set indicator variables and permuting within sets when analyzing matched-set data with PERMANOVA or the LDM is a strategy that performs well and is capable of handling the complex data structures that frequently occur in microbiome studies.

Modeling pulsed evolution and time-independent variation improves the confidence level of ancestral and hidden state predictions in continuous traits

Ancestral state reconstruction is a fundamental tool for studying trait evolution. It is also very useful for predicting the unknown trait values (hidden states) of extant species. A well-known problem in ancestral and hidden state predictions is that the uncertainty associated with predictions can be so large that predictions themselves are of little use. Therefore, for meaningful interpretation of predicted traits and hypothesis testing, it is prudent to accurately assess the uncertainty of the predictions. Commonly used Brownian motion (BM) model fails to capture the complexity of tempo and mode of trait evolution in nature, making predictions under the BM model vulnerable to lack-of-fit errors from model misspecification. Using simulations and empirical data (bacterial genomic traits and vertebrate body size), we show that the presence of pulsed evolution and time-independent variation significantly undermines the confidence level of continuous traits predicted under the BM model. The residual Z-scores are neither homoscedastic nor normally distributed. Consequently, the 95% confidence intervals of predicted traits are so unreliable that the actual coverage probability ranges from 29% (strongly permissive) to 100% (strongly conservative). To remedy the model misspecification problem, we develop RasperGade that accounts for both pulsed evolution and time-independent variation. When applied to simulated and empirical data, RasperGade outperforms commonly used tools such as ape. It restores the normality and homoscedasticity of the Z-score distributions. Accordingly, RasperGade greatly improves the reliability of confidence intervals of predictions and reduces the deviation of their actual coverage probabilities from the 95% expectation by as much as 99%.

Deconstructing and reconstructing joint hypermobility on an evo-devo perspective

Joint hypermobility is a common characteristic in humans. Its non-casual association with various musculoskeletal complaints is known and currently defined “the spectrum”. It includes hypermobile Ehlers–Danlos syndrome (hEDS) and hypermobility spectrum disorders (HSD). hEDS is recognized by a set of descriptive criteria, while HSD is the background diagnosis for individuals not fulfilling these criteria. Little is known about the aetiopathogenesis of the spectrum. It may be interpreted as a complex trait according to the integration model. Particularly, the spectrum is common in the general population, affects morphology, presents extreme clinical variability and is characterized by marked sex bias without a clear Mendelian or hormonal explanation. Joint hypermobility and the other hEDS systemic criteria are intended as qualitative derivatives of continuous traits of normal morphological variability. The need for a minimum set of criteria for hEDS diagnosis implies a tendency to co-vary of these underlying continuous traits. In evolutionary biology, such a co-variation (i.e. integration) is driven by multiple forces, including genetic, developmental, functional and environmental/acquired interactors. The aetiopathogenesis of the spectrum may be resolved by a deeper understanding of phenotypic variability, which superimposes on normal morphological variability.

Constraining PERMANOVA and LDM to Within-Set Comparisons by Projection Improves the Efficiency of Analyses of Matched sets of Microbiome Data

Background: Matched-set data arise frequently in microbiome studies. For example, we may collect pre- and post-treatment samples from a set of individuals, or use important confounding variables to match data from case participants to one or more control participants. Thus, there is a need for statistical methods for data comprised of matched sets, to test hypotheses against traits of interest (e.g., clinical outcomes or environmental factors) at the community level and/or the OTU (operational taxonomic unit) level. Optimally, these methods should accommodate complex data such as those with unequal sample sizes cross sets, confounders varying within sets, as well as continuous traits of interest. Methods: PERMANOVA is a commonly used distance-based method for testing hypotheses at the community level. We have also developed the linear decomposition model (LDM) that unifies the community-level and OTU-level tests into one framework. Here we present a new strategy that can be used with both PERMANOVA and the LDM for analyzing matched-set data. We propose to include an indicator variable for each set as covariates, so as to constrain comparisons between samples within a set, and also permute traits within each set, which can account for exchangeable sample correlations. The flexible nature of PERMANOVA and the LDM allows discrete or continuous traits or interactions to be tested, within-set confounders to be adjusted, and unbalanced data to be fully exploited. Results: Our simulations indicate that our proposed strategy outperformed alternative strategies, including the commonly-used one that utilizes restricted permutation only, in a wide range of scenarios. Using simulation, we also explored optimal designs for matched-set studies. The flexibility of PERMANOVA and the LDM for a variety of matched-set microbiome data is illustrated by the analysis of data from two real studies. Conclusions: Including set indicator variables and permuting within sets when analyzing matched-set data with PERMANOVA or the LDM is a strategy that performs well and is capable of handling the complex data structures that frequently occur in microbiome studies.

Analyzing matched sets of microbiome data using the LDM and PERMANOVA

Background: Matched-set data arise frequently in microbiome studies. For example, we may collect pre- and post-treatment samples from a set of individuals, or use important confounding variables to match data from case participants to one or more control participants. Thus, there is a need for statistical methods for data comprised of matched sets, to test hypotheses against traits of interest (e.g., clinical outcomes or environmental factors) at the community level and/or the OTU (operational taxonomic unit) level. Optimally, these methods should accommodate complex data such as those with unequal sample sizes cross sets, confounders varying within sets, as well as continuous traits of interest. Methods: PERMANOVA is a commonly used distance-based method for testing hypotheses at the community level. We have also developed the linear decomposition model (LDM) that unifies the community-level and OTU-level tests into one framework. Here we present a strategy that can be used with both PERMANOVA and the LDM for analyzing matched-set data. We propose to include an indicator variable for each set as covariates, so as to constrain comparisons between samples within a set, and also permute traits within each set, which can account for exchangeable sample correlations. The flexible nature of PERMANOVA and the LDM allows discrete or continuous traits or interactions to be tested, within-set confounders to be adjusted, and unbalanced data to be fully exploited. Results: Our simulations indicate that our proposed strategy outperformed alternative strategies in a wide range of scenarios. Using simulation, we also explored optimal designs for matched-set studies. The flexibility of PERMANOVA and the LDM for a variety of matched-set microbiome data is illustrated by the analysis of data from two real studies. Conclusions: Including set indicator variables and permuting within sets when analyzing matched-set data with PERMANOVA or the LDM is a strategy that performs well and is capable of handling the complex data structures that frequently occur in microbiome studies.

Analyzing matched sets of microbiome data using the LDM and PERMANOVA

BackgroundMatched-set data arise frequently in microbiome studies. For example, we may collect pre- and post-treatment samples from a set of individuals, or use important confounding variables to match data from case participants to one or more control participants. Thus, there is a need for statistical methods for data comprised of matched sets, to test hypotheses against traits of interest (e.g., clinical outcomes or environmental factors) at the community level and/or the OTU (operational taxonomic unit) level. Optimally, these methods should accommodate complex data such as those with unequal sample sizes cross sets, confounders varying within sets, as well as continuous traits of interest.MethodsPERMANOVA is a commonly used distance-based method for testing hypotheses at the community level. We have also developed the linear decomposition model (LDM) that unifies the community-level and OTU-level tests into one framework. Here we present a strategy that can be used with both PERMANOVA and the LDM for analyzing matched-set data. We propose to include an indicator variable for each set as covariates, so as to constrain comparisons between samples within a set, and also permute traits within each set, which can account for exchangeable sample correlations. The flexible nature of PERMANOVA and the LDM allows discrete or continuous traits or interactions to be tested, within-set confounders to be adjusted, and unbalanced data to be fully exploited.ResultsOur simulations indicate that our proposed strategy outperformed alternative strategies in a wide range of scenarios. Using simulation, we also explored optimal designs for matched-set studies. The flexibility of PERMANOVA and the LDM for a variety of matched-set microbiome data is illustrated by the analysis of data from two real studies.ConclusionsIncluding set indicator variables and permuting within sets when analyzing matched-set data with PERMANOVA or the LDM is a strategy that performs well and is capable of handling the complex data structures that frequently occur in microbiome studies.