Simultaneous integration and modularity underlie the exceptional body shape diversification of characiform fishes.

Evolutionary biology has long striven to understand why some lineages diversify exceptionally while others do not. Most studies have focused on how extrinsic factors can promote differences in diversification dynamics, but a clade's intrinsic modularity and integration can also catalyze or restrict its evolution. Here, we integrate geometric morphometrics, phylogenetic comparative methods and visualizations of covariance to infer the presence of distinct modules in the body plan of Characiformes, an ecomorphologically diverse fish radiation. Strong covariances reveal a cranial module, and more subtle patterns support a statistically significant subdivision of the postcranium into anterior (precaudal) and posterior (caudal) modules. We uncover substantial covariation among cranial and postcranial landmarks, indicating body-wide evolutionary integration as lineages transition between compressiform and fusiform body shapes. A novel method of matrix subdivision reveals that within- and among-module covariation contributes substantially to the overall eigenstructure of characiform morphospace, and that both phenomena led to biologically important divergence among characiform lineages. Functional integration between the cranium and post-cranial skeleton appears to have allowed lineages to optimize the aspect ratio of their bodies for locomotion, while the capacity for independent change in the head, body and tail likely eased adaptation to diverse dietary and hydrological regimes. These results reinforce a growing consensus that modularity and integration synergize to promote diversification.

Consilience of methods for phylogenetic analysis of variance.

Simulation-based and permutation-based inferential methods are commonplace in phylogenetic comparative methods, especially as evolutionary data have become more complex and parametric methods more limited for their analysis. Both approaches simulate many random outcomes from a null model to empirically generate sampling distributions of statistics. Although simulation-based and permutation-based methods seem commensurate in purpose, results from analysis of variance (ANOVA) based on the distributions of random F-statistics produced by these methods can be quite different in practice. Differences could be from either the null-model process that generates variation across many simulations or random permutations of the data, or different estimation methods for linear model coefficients and statistics. Unfortunately, because the null-model process and coefficient estimation are intrinsically linked in phylogenetic ANOVA methods, the precise reason for methodological differences has not been fully considered. Here we show that the null-model processes of phylogenetic simulation and randomizing residuals in a permutation procedure are indeed commensurate, and that both also produce results consistent with parametric ANOVA, for cases where parametric ANOVA is possible. We also provide results that caution against using ordinary least-squares estimation along with phylogenetic simulation; a typical phylogenetic ANOVA implementation.

Decreased Mitochondrial Dynamics Is Associated with Insulin Resistance, Metabolic Rate, and Fitness in African Americans.

CONTEXT: African American women (AAW) have a higher incidence of insulin resistance and are at a greater risk for the development of obesity and type 2 diabetes than Caucasian women (CW). Although several factors have been proposed to mediate these racial disparities, the mechanisms remain poorly defined. We previously demonstrated that sedentary lean AAW have lower peripheral insulin sensitivity, reduced maximal aerobic fitness (VO2max), and lower resting metabolic rate (RMR) than CW. We have also demonstrated that skeletal muscle mitochondrial respiration is lower in AAW and appears to play a role in these racial differences. OBJECTIVE: The goal of this study was to assess mitochondrial pathways and dynamics to examine the potential mechanisms of lower insulin sensitivity, RMR, VO2max, and mitochondrial capacity in AAW. DESIGN: To achieve this goal, we assessed several mitochondrial pathways in skeletal muscle using gene array technology and semiquantitative protein analysis. RESULTS: We report alterations in mitochondrial pathways associated with inner membrane small molecule transport genes, fusion-fission, and autophagy in lean AAW. These differences were associated with lower insulin sensitivity, RMR, and VO2max. CONCLUSIONS: Together these data suggest that the metabolic racial disparity of insulin resistance, RMR, VO2max, and mitochondrial capacity may be mediated by perturbations in mitochondrial pathways associated with membrane transport, fission-fusion, and autophagy. The mechanisms contributing to these differences remain unknown.

Comparing the strength of modular signal, and evaluating alternative modular hypotheses, using covariance ratio effect sizes with morphometric data.

The study of modularity is paramount for understanding trends of phenotypic evolution, and for determining the extent to which covariation patterns are conserved across taxa and levels of biological organization. However, biologists currently lack quantitative methods for statistically comparing the strength of modular signal across datasets, and a robust approach for evaluating alternative modular hypotheses for the same dataset. As a solution to these challenges, we propose an effect size measure ( ZCR ) derived from the covariance ratio, and develop hypothesis-testing procedures for their comparison. Computer simulations demonstrate that ZCR displays appropriate statistical properties and low levels of mis-specification, implying that it correctly identifies modular signal, when present. By contrast, alternative methods based on likelihood (EMMLi) and goodness of fit (MINT) suffer from high false positive rates and high model mis-specification rates. An empirical example in sigmodontine rodent mandibles is provided to illustrate the utility of ZCR for comparing modular hypotheses. Overall, we find that covariance ratio effect sizes are useful for comparing patterns of modular signal across datasets or for evaluating alternative modular hypotheses for the same dataset. Finally, the statistical philosophy for pairwise model comparisons using effect sizes should accommodate any future analytical developments for characterizing modular signal.

Drought-induced Suppression of Female Fecundity in a Capital Breeder.

Human-induced global climate change is exerting increasingly strong selective pressures on a myriad of fitness traits that affect organisms. These traits, in turn, are influenced by a variety of environmental parameters such as temperature and precipitation, particularly in ectothermic taxa such as amphibians and reptiles. Over the past several decades, severe and prolonged episodes of drought are becoming commonplace throughout North America. Documentation of responses to this environmental crisis, however, is often incomplete, particularly in cryptic species. Here, we investigated reproduction in a population of pitviper snakes (copperhead, Agkistrodon contortrix), a live-bearing capital breeder. This population experienced a severe drought from 2012 through 2016. We tested whether declines in number of progeny were linked to this drought. Decline in total number offspring was significant, but offspring length and mass were unaffected. Reproductive output was positively impacted by precipitation and negatively impacted by high temperatures. We hypothesized that severe declines of prey species (e.g., cicada, amphibians, and small mammals) reduced energy acquisition during drought, negatively impacting reproductive output of the snakes. Support for this view was found using the periodical cicada (Magicicada spp.) as a proxy for prey availability. Various climate simulations, including our own qualitative analysis, predict that drought events will continue unabated throughout the geographic distribution of copperheads which suggests that long-term monitoring of populations are needed to better understand geographic variation in drought resilience and cascading impacts of drought phenomena on ecosystem function.

Correction: Deconstructing a Species-Complex: Geometric Morphometric and Molecular Analyses Define Species in the Western Rattlesnake (Crotalus viridis).

[This corrects the article DOI: 10.1371/journal.pone.0146166.].

Phylogenetic ANOVA: Group-clade aggregation, biological challenges, and a refined permutation procedure.

Phylogenetic regression is frequently used in macroevolutionary studies, and its statistical properties have been thoroughly investigated. By contrast, phylogenetic ANOVA has received relatively less attention, and the conditions leading to incorrect statistical and biological inferences when comparing multivariate phenotypes among groups remain underexplored. Here, we propose a refined method of randomizing residuals in a permutation procedure (RRPP) for evaluating phenotypic differences among groups while conditioning the data on the phylogeny. We show that RRPP displays appropriate statistical properties for both phylogenetic ANOVA and regression models, and for univariate and multivariate datasets. For ANOVA, we find that RRPP exhibits higher statistical power than methods utilizing phylogenetic simulation. Additionally, we investigate how group dispersion across the phylogeny affects inferences, and reveal that highly aggregated groups generate strong and significant correlations with the phylogeny, which reduce statistical power and subsequently affect biological interpretations. We discuss the broader implications of this phylogenetic group aggregation, and its relation to challenges encountered with other comparative methods where one or a few transitions in discrete traits are observed on the phylogeny. Finally, we recommend that phylogenetic comparative studies of continuous trait data use RRPP for assessing the significance of indicator variables as sources of trait variation.

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons.

On the comparison of the strength of morphological integration across morphometric datasets.

Evolutionary morphologists frequently wish to understand the extent to which organisms are integrated, and whether the strength of morphological integration among subsets of phenotypic variables differ among taxa or other groups. However, comparisons of the strength of integration across datasets are difficult, in part because the summary measures that characterize these patterns (RV coefficient and rPLS ) are dependent both on sample size and on the number of variables. As a solution to this issue, we propose a standardized test statistic (a z-score) for measuring the degree of morphological integration between sets of variables. The approach is based on a partial least squares analysis of trait covariation, and its permutation-based sampling distribution. Under the null hypothesis of a random association of variables, the method displays a constant expected value and confidence intervals for datasets of differing sample sizes and variable number, thereby providing a consistent measure of integration suitable for comparisons across datasets. A two-sample test is also proposed to statistically determine whether levels of integration differ between datasets, and an empirical example examining cranial shape integration in Mediterranean wall lizards illustrates its use. Some extensions of the procedure are also discussed.

Deconstructing a Species-Complex: Geometric Morphometric and Molecular Analyses Define Species in the Western Rattlesnake (Crotalus viridis).

Morphological data are a conduit for the recognition and description of species, and their acquisition has recently been broadened by geometric morphometric (GM) approaches that co-join the collection of digital data with exploratory 'big data' analytics. We employed this approach to dissect the Western Rattlesnake (Crotalus viridis) species-complex in North America, currently partitioned by mitochondrial (mt)DNA analyses into eastern and western lineages (two and seven subspecies, respectively). The GM data (i.e., 33 dorsal and 50 lateral head landmarks) were gleaned from 2,824 individuals located in 10 museum collections. We also downloaded and concatenated sequences for six mtDNA genes from the NCBI GenBank database. GM analyses revealed significant head shape differences attributable to size and subspecies-designation (but not their interactions). Pairwise shape distances among subspecies were significantly greater than those derived from ancestral character states via squared-change parsimony, with the greatest differences separating those most closely related. This, in turn, suggests the potential for historic character displacement as a diversifying force in the complex. All subspecies, save one, were significantly differentiated in a Bayesian discriminant function analysis (DFA), regardless of whether our priors were uniform or informative (i.e., mtDNA data). Finally, shape differences among sister-clades were significantly greater than expected by chance alone under a Brownian model of evolution, promoting the hypothesis that selection rather than drift was the driving force in the evolution of the complex. Lastly, we combine head shape and mtDNA data so as to derived an integrative taxonomy that produced robust boundaries for six OTUs (operational taxonomic units) of the C. viridis complex. We suggest these boundaries are concomitant with species-status and subsequently provide a relevant nomenclature for its recognition and representation.

Nowhere to Go but Up: Impacts of Climate Change on Demographics of a Short-Range Endemic (Crotalus willardi obscurus) in the Sky-Islands of Southwestern North America.

Biodiversity elements with narrow niches and restricted distributions (i.e., 'short range endemics,' SREs) are particularly vulnerable to climate change. The New Mexico Ridge-nosed Rattlesnake (Crotalus willardi obscurus, CWO), an SRE listed under the U.S. Endangered Species Act within three sky islands of southwestern North America, is constrained at low elevation by drought and at high elevation by wildfire. We combined long-term recapture and molecular data with demographic and niche modeling to gauge its climate-driven status, distribution, and projected longevity. The largest population (Animas) is numerically constricted (N = 151), with few breeding adults (Nb = 24) and an elevated inbreeding coefficient (ΔF = 0.77; 100 years). Mean home range (0.07 km2) is significantly smaller compared to other North American rattlesnakes, and movements are within, not among sky islands. Demographic values, when gauged against those displayed by other endangered/Red-Listed reptiles [e.g., Loggerhead Sea Turtle (Caretta caretta)], are either comparable or markedly lower. Survival rate differs significantly between genders (female

Permutation tests for phylogenetic comparative analyses of high-dimensional shape data: what you shuffle matters.

Evaluating statistical trends in high-dimensional phenotypes poses challenges for comparative biologists, because the high-dimensionality of the trait data relative to the number of species can prohibit parametric tests from being computed. Recently, two comparative methods were proposed to circumvent this difficulty. One obtains phylogenetic independent contrasts for all variables, and statistically evaluates the linear model by permuting the phylogenetically independent contrasts (PICs) of the response data. The other uses a distance-based approach to obtain coefficients for generalized least squares models (D-PGLS), and subsequently permutes the original data to evaluate the model effects. Here, we show that permuting PICs is not equivalent to permuting the data prior to the analyses as in D-PGLS. We further explain why PICs are not the correct exchangeable units under the null hypothesis, and demonstrate that this misspecification of permutable units leads to inflated type I error rates of statistical tests. We then show that simply shuffling the original data and recalculating the independent contrasts with each iteration yields significance levels that correspond to those found using D-PGLS. Thus, while summary statistics from methods based on PICs and PGLS are the same, permuting PICs can lead to strikingly different inferential outcomes with respect to statistical and biological inferences.

Retrospective stable isotope analysis reveals ecosystem responses to river regulation over the last century.

Disruption of natural flow regimes, nutrient pollution, and other consequences of human population growth and development have impacted most major rivers of the world. Alarming losses of aquatic biodiversity coincide with human-caused river alteration, but effects of biotic homogenization on aquatic ecosystem processes are not as well documented. This is because unaltered systems for comparison are scarce, and some ecosystem-wide effects may take decades to manifest. We evaluated aquatic ecosystem responses to extensive river- floodplain engineering and nutrient addition in the Rio Grande of southwestern North America as revealed by changes in trophic structure of, and resource availability to, the fish community. Stable Isotope Analysis (SIA) was conducted on museum-preserved fishes collected over a 70-year period of intensive river management and exponential human population growth. Trophic complexity and resource heterogeneity for fish consumers (measured as "isotopic niche breadth") decreased following sediment deprivation and channelization, and these effects persist into the present. Increased nutrient inputs led to δ15N enrichment in the entire fish community at all affected sites, and a shift to autochthonous sources of carbon at the most proximal site downstream of wastewater release, probably via bottom-up transfer. Overall, retrospective SIA of apex consumers suggests radical change and functional impairment of a floodplain river ecosystem already marked by significant biodiversity loss.

Contemporary evolutionary divergence for a protected species following assisted colonization.

BACKGROUND: Contemporary evolution following assisted colonization may increase the probability of persistence for refuge populations established as a bet-hedge for protected species. Such refuge populations are considered "genetic replicates" that might be used for future re-colonization in the event of a catastrophe in the native site. Although maladaptive evolutionary divergence of captive populations is well recognized, evolutionary divergence of wild refuge populations may also occur on contemporary time scales. Thus, refuge populations may lose their "value" as true genetic replicates of the native population. Here, we show contemporary evolutionary divergence in body shape in an approximately 30-year old refuge population of the protected White Sands pupfish (Cyprinodon tularosa) resulting in a body-shape mismatch with its native environment. METHODOLOGY/PRINCIPAL FINDINGS: Geometric morphometic data were collected from C. tularosa cultures raised in experimental mesocosms. Cultures were initiated with fish from the two native populations, plus hybrids, in high or low salinity treatments representing the salinities of the two native habitats. We found that body shape was heritable and that shape variation due to phenotypic plasticity was small compared to shape variation due to population source. C. tularosa from the high salinity population retained slender body shapes and fish from the low salinity population retained deep body shapes, irrespective of mesocosm salinity. These data suggest that the observed divergence of a recently established pupfish population was not explained by plasticity. An analysis of microsatellite variation indicated that no significant genetic drift occurred in the refuge population, further supporting the adaptive nature of changes in body shape. These lines of evidence suggest that body shape divergence of the refuge population reflects a case of contemporary evolution (over a 30-year period). CONCLUSIONS/SIGNIFICANCE: These results suggest assisted colonization can introduce novel, and/or relaxed selection, and lead to unintended evolutionary divergence.

A general hypothesis-testing framework for stable isotope ratios in ecological studies.

We propose a framework for hypothesis-testing of stable isotope ratios in ecological studies. Statistical procedures are based on analysis of nested linear models and a residual permutation procedure (RPP) that is employed to evaluate probabilities associated with test statistics. We used simulated examples and a real data set to illustrate the utility and generality of the method. First, we developed a test for differences in centroid location and dispersion of delta13C and delta15N values within and among groups of isotopic data. Second, we evaluated magnitude and direction of change in centroid position (termed "path") of a pair of isotopic samples separated in space/time relative to paths of other paired sample sets. Third, we compared attributes of path trajectories (size, direction, and shape) over sample sets containing more than two samples to provide a quantitative description of how patterns of isotopic ratios change in response to spatial and temporal gradients. Examples are limited to the bivariate case (delta13C-delta15N biplots), but the statistical method can readily be applied to univariate and multivariate cases.

A general framework for the analysis of phenotypic trajectories in evolutionary studies.

Many evolutionary studies require an understanding of phenotypic change. However, while analyses of phenotypic variation across pairs of evolutionary levels (populations or time steps) are well established, methods for testing hypotheses that compare evolutionary sequences across multiple levels are less developed. Here we describe a general analytical procedure for quantifying and comparing patterns of phenotypic evolution. The phenotypic evolution of a lineage is defined as a trajectory across a set of evolutionary levels in a multivariate phenotype space. Attributes of these trajectories (their size, direction, and shape), are quantified, and statistically compared across pairs of taxa, and a summary statistic is used to determine the extent to which patterns of phenotypic evolution are concordant across multiple taxa. This approach provides a direct quantitative description of how patterns of phenotypic evolution differ, as well as a statistical assessment of the degree of repeatability in the evolutionary responses to selection among taxa. We describe how this approach can quantify phenotypic trajectories from many ecological and evolutionary processes, whose data encode multivariate characterizations of the phenotype, including: phenotypic plasticity, ecological selection, ontogeny and growth, local adaptation, and biomechanics. We illustrate the approach by examining the phenotypic evolution of several fossil lineages of Globorotalia.

Phenotypic plasticity of native vs. invasive purple loosestrife: a two-state multivariate approach.

The differences in phenotypic plasticity between invasive (North American) and native (German) provenances of the invasive plant Lythrum salicaria (purple loosestrife) were examined using a multivariate reaction norm approach testing two important attributes of reaction norms described by multivariate vectors of phenotypic change: the magnitude and direction of mean trait differences between environments. Data were collected for six life history traits from native and invasive plants using a split-plot design with experimentally manipulated water and nutrient levels. We found significant differences between native and invasive plants in multivariate phenotypic plasticity for comparisons between low and high water treatments within low nutrient levels, between low and high nutrient levels within high water treatments, and for comparisons that included both a water and nutrient level change. The significant genotype x environment (G x E) effects support the argument that invasiveness of purple loosestrife is closely associated with the interaction of high levels of soil nutrient and flooding water regime. Our results indicate that native and invasive plants take different strategies for growth and reproduction; native plants flowered earlier and allocated more to flower production, while invasive plants exhibited an extended period of vegetative growth before flowering to increase height and allocation to clonal reproduction, which may contribute to increased fitness and invasiveness in subsequent years.

Analysis of two-state multivariate phenotypic change in ecological studies.

Analyses of two-state phenotypic change are common in ecological research. Some examples include phenotypic changes due to phenotypic plasticity between two environments, changes due to predator/non-predator character shifts, character displacement via competitive interactions, and patterns of sexual dimorphism. However, methods for analyzing phenotypic change for multivariate data have not been rigorously developed. Here we outline a method for testing vectors of phenotypic change in terms of two important attributes: the magnitude of change (vector length) and the direction of change described by trait covariation (angular difference between vectors). We describe a permutation procedure for testing these attributes, which allows non-targeted sources of variation to be held constant. We provide examples that illustrate the importance of considering vector attributes of phenotypic change in biological studies, and we demonstrate how greater inference can be made than by evaluating variance components with MANOVA alone. Finally, we consider how our method may be extended to more complex data.

Analysis of character divergence along environmental gradients and other covariates.

Character displacement is typically identified by comparing phenotypic differences in sympatry and allopatry. Recently, however, Goldberg and Lande (2006) pointed out that when phenotypic characters vary along an environmental gradient, the standard approach may fail to identify sympatric character divergence. Here we present a general analytical procedure for identifying sympatric character divergence while accounting for phenotypic changes that covary with environmental variables. Our approach uses residual randomization from a generalized linear model, and allows the statistical comparison of sympatric phenotypic divergence to allopatric phenotypic divergence while accounting for phenotypic variation along a gradient. Through simulation we demonstrate that our approach correctly identifies patterns of sympatric character divergence when they are present, and does not identify such patterns when they are not. Our analytical approach complements and extends the suggestions of Goldberg and Lande (2006), by allowing a full statistical assessment of the varied patterns of character displacement along environmental gradients, or while accounting for other covariates and sources of variation.