The Paradox of Predictability Provides a Bridge Between Micro- and Macroevolution.

The relationship between the evolutionary dynamics observed in contemporary populations (microevolution) and evolution on timescales of millions of years (macroevolution) has been a topic of considerable debate. Historically, this debate centers on inconsistencies between microevolutionary processes and macroevolutionary patterns. Here, we characterize a striking exception: emerging evidence indicates that standing variation in contemporary populations and macroevolutionary rates of phenotypic divergence are often positively correlated. This apparent consistency between micro- and macroevolution is paradoxical because it contradicts our previous understanding of phenotypic evolution and is so far unexplained. Here, we explore the prospects for bridging evolutionary timescales through an examination of this "paradox of predictability." We begin by explaining why the divergence-variance correlation is a paradox, followed by data analysis to show that the correlation is a general phenomenon across a broad range of temporal scales, from a few generations to tens of millions of years. Then we review complementary approaches from quantitative-genetics, comparative morphology, evo-devo, and paleontology to argue that they can help to address the paradox from the shared vantage point of recent work on evolvability. In conclusion, we recommend a methodological orientation that combines different kinds of short-term and long-term data using multiple analytical frameworks in an interdisciplinary research program. Such a program will increase our general understanding about how evolution works within and across timescales.

Trait-Fitness Associations via Fecundity and Competition in a Two-Million-Year-Long Fossil Record.

AbstractThe evolution of phenotypic traits is usually studied on generational timescales or across species on million-year timescales. We bridge this conceptual gap by using high-density sampling of a species lineage, Microporella agonistes (Bryozoa, Cheilostomatida), over 2 million years of its evolutionary history, to ask whether trait-fitness associations are consistent with evolutionary trait models often applied to phenotypic time series. We use average fecundity and competitive outcome as two different fitness components, where competitive outcome is a proxy for partial survival. Examining three quantitative traits in multivariate analyses, we present evidence that some traits experienced substantial selective pressures, in part controlled by past environments. A complex interplay of resource competition with an altering set of competitors and past temperatures has contributed to the changing patterns of phenotypes within the focal species. A comparison with congeneric species living in the same regional community suggests that size traits are more temporally variable and less constrained than shape traits. Our analyses also show that while controls on phenotypes are complex and varied in time, ecological and evolutionary processes that unfold on shorter timescales are not inconsistent with macroevolutionary patterns observed on longer timescales.

BioEncoder: A metric learning toolkit for comparative organismal biology.

In the realm of biological image analysis, deep learning (DL) has become a core toolkit, for example for segmentation and classification. However, conventional DL methods are challenged by large biodiversity datasets characterized by unbalanced classes and hard-to-distinguish phenotypic differences between them. Here we present BioEncoder, a user-friendly toolkit for metric learning, which overcomes these challenges by focussing on learning relationships between individual data points rather than on the separability of classes. BioEncoder is released as a Python package, created for ease of use and flexibility across diverse datasets. It features taxon-agnostic data loaders, custom augmentation options, and simple hyperparameter adjustments through text-based configuration files. The toolkit's significance lies in its potential to unlock new research avenues in biological image analysis while democratizing access to advanced deep metric learning techniques. BioEncoder focuses on the urgent need for toolkits bridging the gap between complex DL pipelines and practical applications in biological research.

Morphological integration during postnatal ontogeny: implications for evolutionary biology.

How covariance patterns of phenotypes change during development is fundamental for a broader understanding of evolution. There is compelling evidence that mammalian cranium covariance patterns change during ontogeny. However, it is unclear to what extent variation in covariance patterns during ontogeny can impact the response to selection. To tackle this question, we explored: (a) the extent to which covariance patterns change during postnatal ontogeny; (b) in which ontogenetic stages covariance patterns differ the most; and (c) the extent to which the phenotypic covariance pattern at different ontogenetic stages can be explained by the same processes determining additive genetic covariance. We sampled the postnatal ontogenetic series for both marsupials and placentals. Within each ontogenetic series, we compared covariance matrices (P-matrices) at different ontogenetic stages. Furthermore, we compared these P-matrices to two target matrices [adult P-matrix and an additive genetic covariance matrix (G-matrix)]. Our results show that for all ontogenetic series, covariance patterns from weaning onward are conserved and probably shaped by the same processes determining the G-matrix. We conclude that irrespective of eventual differences in how selection operates during most of the postnatal ontogeny, the net response to such pressures will probably not be affected by ontogenetic differences in the covariance pattern.

Automated landmarking via multiple templates.

Manually collecting landmarks for quantifying complex morphological phenotypes can be laborious and subject to intra and interobserver errors. However, most automated landmarking methods for efficiency and consistency fall short of landmarking highly variable samples due to the bias introduced by the use of a single template. We introduce a fast and open source automated landmarking pipeline (MALPACA) that utilizes multiple templates for accommodating large-scale variations. We also introduce a K-means method of choosing the templates that can be used in conjunction with MALPACA, when no prior information for selecting templates is available. Our results confirm that MALPACA significantly outperforms single-template methods in landmarking both single and multi-species samples. K-means based template selection can also avoid choosing the worst set of templates when compared to random template selection. We further offer an example of post-hoc quality check for each individual template for further refinement. In summary, MALPACA is an efficient and reproducible method that can accommodate large morphological variability, such as those commonly found in evolutionary studies. To support the research community, we have developed open-source and user-friendly software tools for performing K-means multi-templates selection and MALPACA.

ALPACA: A fast and accurate computer vision approach for automated landmarking of three-dimensional biological structures.

Landmark-based geometric morphometrics has emerged as an essential discipline for the quantitative analysis of size and shape in ecology and evolution. With the ever-increasing density of digitized landmarks, the possible development of a fully automated method of landmark placement has attracted considerable attention. Despite the recent progress in image registration techniques, which could provide a pathway to automation, three-dimensional (3D) morphometric data are still mainly gathered by trained experts. For the most part, the large infrastructure requirements necessary to perform image-based registration, together with its system specificity and its overall speed, have prevented its wide dissemination.Here, we propose and implement a general and lightweight point cloud-based approach to automatically collect high-dimensional landmark data in 3D surfaces (Automated Landmarking through Point cloud Alignment and Correspondence Analysis). Our framework possesses several advantages compared with image-based approaches. First, it presents comparable landmarking accuracy, despite relying on a single, random reference specimen and much sparser sampling of the structure's surface. Second, it can be efficiently run on consumer-grade personal computers. Finally, it is general and can be applied at the intraspecific level to any biological structure of interest, regardless of whether anatomical atlases are available.Our validation procedures indicate that the method can recover intraspecific patterns of morphological variation that are largely comparable to those obtained by manual digitization, indicating that the use of an automated landmarking approach should not result in different conclusions regarding the nature of multivariate patterns of morphological variation.The proposed point cloud-based approach has the potential to increase the scale and reproducibility of morphometrics research. To allow ALPACA to be used out-of-the-box by users with no prior programming experience, we implemented it as a SlicerMorph module. SlicerMorph is an extension that enables geometric morphometrics data collection and 3D specimen analysis within the open-source 3D Slicer biomedical visualization ecosystem. We expect that convenient access to this platform will make ALPACA broadly applicable within ecology and evolution.

Revisiting a Landmark Study System: No Evidence for a Punctuated Mode of Evolution in Metrarabdotos.

Is speciation generally a "special time" in morphological evolution, or are lineage-splitting events just "more of the same" where the end product happens to be two separate lineages? Data on evolutionary dynamics during anagenetic and cladogenetic events among closely related lineages within a clade are rare, but the fossil record of the bryozoan genus Metrarabdotos is considered a textbook example of a clade where speciation causes rapid evolutionary change against a backdrop of morphological stasis within lineages. Here, we point to some methodological and measurement theoretical issues in the original work on Metrarabdotos. We then reanalyze a subset of the original data that can be meaningfully investigated using quantitative statistical approaches similar to those used in the original studies. We consistently fail to find variation in the evolutionary process during within-lineage evolution compared with cladogenetic events: the rates of evolution, the strength of selection, and the directions traveled in multivariate morphospace are not different when comparing evolution within lineages and at speciation events in Metrarabdotos, and genetic drift cannot be excluded as a sufficient explanation for the morphological differentiation within lineages and during speciation. Although widely considered the best example of a punctuated mode of evolution, morphological divergence and speciation are not linked in Metrarabdotos.

Measuring the magnitude of morphological integration: The effect of differences in morphometric representations and the inclusion of size.

The magnitude of morphological integration is a major aspect of multivariate evolution, providing a simple measure of the intensity of association between morphological traits. Studies concerned with morphological integration usually translate phenotypes into morphometric representations to quantify how different morphological elements covary. Geometric and classic morphometric representations translate biological form in different ways, raising the question if magnitudes of morphological integration estimates obtained from different morphometric representations are compatible. Here we sought to answer this question using the relative eigenvalue variance of the covariance matrix obtained for both geometric and classical representations of empirical and simulated datasets. We quantified the magnitude of morphological integration for both shape and form and compared results between representations. Furthermore, we compared integration values between shape and form to evaluate the effect of the inclusion or not of size on the quantification of the magnitude of morphological integration. Results show that the choice of morphological representation has significant impact on the integration magnitude estimate, either for shape or form. Despite this, ordination of the integration values within representations is relatively the same, allowing for similar conclusions to be reached using different methods. However, the inclusion of size in the dataset significantly changes the estimates of magnitude of morphological integration, hindering the comparison of this statistic obtained from different spaces. Morphometricians should be aware of these differences and must consider how biological hypothesis translate into predictions about integration in each particular choice of representation.

Reliability of genomic predictions of complex human phenotypes.

Genome-wide association studies have helped us identify a wealth of genetic variants associated with complex human phenotypes. Because most variants explain a small portion of the total phenotypic variation, however, marker-based studies remain limited in their ability to predict such phenotypes. Here, we show how modern statistical genetic techniques borrowed from animal breeding can be employed to increase the accuracy of genomic prediction of complex phenotypes and the power of genetic mapping studies. Specifically, using the triglyceride data of the GAW20 data set, we apply genomic-best linear unbiased prediction (G-BLUP) methods to obtain empirical genetic values (EGVs) for each triglyceride phenotype and each individual. We then study 2 different factors that influence the prediction accuracy of G-BLUP for the analysis of human data: (a) the choice of kinship matrix, and (b) the overall level of relatedness. The resulting genetic values represent the total genetic component for the phenotype of interest and can be used to represent a trait without its environmental component. Finally, using empirical data, we demonstrate how this method can be used to increase the power of genetic mapping studies. In sum, our results show that dense genome-wide data can be used in a wider scope than previously anticipated.

Genome-wide linkage scan for loci influencing plasma triglyceride levels.

We conducted a genome-wide linkage scan to detect loci that influence the levels of fasting triglycerides in plasma. Fasting triglyceride levels were available at 4 time points (visits), 2 pre- and 2 post-fenofibrate intervention. Multipoint identity-by-descent (MIBD) matrices were derived from genotypes using IBDLD. Variance-component linkage analyses were then conducted using SOLAR (Sequential Oligogenic Linkage Analysis Routines). We found evidence of linkage (logarithm of odds [LOD] ≥3) at 5 chromosomal regions with triglyceride levels in plasma. The highest LOD scores were observed for linkage to the estimated genetic value (additive genetic component) of the log-normalized triglyceride levels in plasma. Our results suggest that a chromosome 10 locus at 37 cM (LODpre = 3.01, LODpost = 3.72) influences fasting triglyceride levels in plasma regardless of the fenofibrate intervention, and that loci in chromosomes 1 at 170 cM and 4 at 24 cM ceases to affect the triglyceride levels when fenofibrate is present, while the regions in chromosomes 6 at 136 to 162 cM and 11 at 39 to 40 cM appear to influence triglyceride levels in response to fenofibrate.

Heritability and genetic associations of triglyceride and HDL-C levels using pedigree-based and empirical kinships.

The heritability of a phenotype is an estimation of the percent of variance in that phenotype that is attributable to additive genetic factors. Heritability is optimally estimated in family-based sample populations. Traditionally, this involves use of a pedigree-based kinship coefficient generated from the collected genealogical relationships between family members. An alternative, when dense genotype data are available, is to directly measure the empirical kinship between samples. This study compares the use of pedigree and empirical kinships in the GAW20 data set. Two phenotypes were assessed: triglyceride levels and high-density lipoprotein cholesterol (HDL-C) levels pre- and postintervention with the cholesterol-reducing drug fenofibrate. Using SOLAR (Sequential Oligogenic Linkage Analysis Routines), pedigree-based kinships and empirically calculated kinships (using IBDLD and LDAK) were used to calculate phenotype heritability. In addition, a genome-wide association study was conducted using each kinship model for each phenotype to identify genetic variants significantly associated with phenotypic variation. The variant rs247617 was significantly associated with HDL-C levels both pre- and post-fenofibrate intervention. Overall, the phenotype heritabilities calculated using pedigree based kinships or either of the empirical kinships generated using IBDLD or LDAK were comparable. Phenotype heritabilities estimated from empirical kinships generated using IBDLD were closest to the pedigree-based estimations. Given that there was not an appreciable amount of unknown relatedness between the pedigrees in this data set, a large increase in heritability in using empirical kinship was not expected, and our calculations support this. Importantly, these results demonstrate that when sufficient genotypic data are available, empirical kinship estimation is a practical alternative to using pedigree-based kinships.

Evaluation of profile and functionality of memory T cells in pulmonary tuberculosis.

The cells T CD4+ T and CD8+ can be subdivided into phenotypes naïve, T of central memory, T of effector memory and effector, according to the expression of surface molecules CD45RO and CD27. The T lymphocytes are cells of long life with capacity of rapid expansion and function, after a new antigenic exposure. In tuberculosis, it was found that specific memory T cells are present, however, gaps remain about the role of such cells in the disease immunology. In this study, the phenotypic profile was analyzed and characterized the functionality of CD4+ T lymphocytes and CD8+ T cells of memory and effector, in response to specific stimuli in vitro, in patients with active pulmonary TB, compared to individuals with latent infection with Mycobacterium tuberculosis the ones treated with pulmonary TB. It was observed that the group of patients with active pulmonary tuberculosis was the one which presented the highest proportion of cells T CD4+ of central memory IFN-É£+ e TNF-α+, suggesting that in TB, these T of central memory cells would have a profile of protective response, being an important target of study for the development of more effective vaccines; this group also developed lower proportion of CD8+ T effector lymphocytes than the others, a probable cause of specific and less effective response against the bacillus in these individuals; the ones treated for pulmonary tuberculosis were those who developed higher proportion of T CD4+ of memory central IL-17+ cells, indicating that the stimulation of long duration, with high antigenic load, followed by elimination of the pathogen, contribute to more significant generation of such cells; individuals with latent infection by M. tuberculosis and treated for pulmonary tuberculosis, showed greater response of CD8+ T effector lymphocytes IFN-É£+ than the controls, suggesting that these cells, as well as CD4+ T lymphocytes, have crucial role of protection against M. tuberculosis. These findings have contributed to a better understanding of the immunologic changes in M. tuberculosis infection and the development of new strategies for diagnosis and prevention of tuberculosis.

How many more? Sample size determination in studies of morphological integration and evolvability.

1. The variational properties of living organisms are an important component of current evolutionary theory. As a consequence, researchers working on the field of multivariate evolution have increasingly used integration and evolvability statistics as a way of capturing the potentially complex patterns of trait association and their effects over evolutionary trajectories. Little attention has been paid, however, to the cascading effects that inaccurate estimates of trait covariance have on these widely used evolutionary statistics. 2. Here, we analyze the relationship between sampling effort and inaccuracy in evolvability and integration statistics calculated from 10-trait matrices with varying patterns of covariation and magnitudes of integration. We then extrapolate our initial approach to different numbers of traits and different magnitudes of integration and estimate general equations relating the inaccuracy of the statistics of interest to sampling effort. We validate our equations using a dataset of cranial traits, and use them to make sample size recommendations. 3. Our results suggest that highly inaccurate estimates of evolvability and integration statistics resulting from small sample sizes are likely common in the literature, given the sampling effort necessary to properly estimate them. We also show that patterns of covariation have no effect on the sampling properties of these statistics, but overall magnitudes of integration interact with sample size and lead to varying degrees of bias, imprecision, and inaccuracy. 4. Finally, we provide R functions that can be used to calculate recommended sample sizes or to simply estimate the level of inaccuracy that should be expected in these statistics, given a sampling design.

Evolution of the Genotype-to-Phenotype Map and the Cost of Pleiotropy in Mammals.

Evolutionary studies have long emphasized that the genetic architecture of traits holds important microevolutionary consequences. Yet, studies comparing the genetic architecture of traits across species are rare, and discussions of the evolution of genetic systems are made on theoretical arguments rather than on empirical evidence. Here, we compared the genetic architecture of cranial traits in two different mammalian model organisms: the gray short-tailed opossum, Monodelphis domestica, and the laboratory mouse, Mus musculus We show that both organisms share a highly polygenic genetic architecture for craniofacial traits, with many loci of small effect. However, these two model species differ significantly in the overall degree of pleiotropy, N, of the genotype-to-phenotype map, with opossums presenting a higher average N They also diverge in their degree of genetic modularity, with opossums presenting less modular patterns of genetic association among traits. We argue that such differences highlight the context dependency of gene effects, with developmental systems shaping the variational properties of genetic systems. Finally, we also demonstrate based on the opossum data that current measurements for the relationship between the mutational effect size and N need to be re-evaluated in relation to the importance of the cost of pleiotropy for mammals.

Modularity: genes, development and evolution.

Modularity has emerged as a central concept for evolutionary biology, providing the field with a theory of organismal structure and variation. This theory has reframed long standing questions and serves as a unified conceptual framework for genetics, developmental biology and multivariate evolution. Research programs in systems biology and quantitative genetics are bridging the gap between these fields. While this synthesis is ongoing, some major themes have emerged and empirical evidence for modularity has become abundant. In this review, we look at modularity from an historical perspective, highlighting its meaning at different levels of biological organization and the different methods that can be used to detect it. We then explore the relationship between quantitative genetic approaches to modularity and developmental genetic studies. We conclude by investigating the dynamic relationship between modularity and the adaptive landscape and how this potentially shapes evolution and can help bridge the gap between micro- and macroevolution.

The benefits of soft sensor and multi-rate control for the implementation of Wireless Networked Control Systems.

Recent advances in wireless networking technology and the proliferation of industrial wireless sensors have led to an increasing interest in using wireless networks for closed loop control. The main advantages of Wireless Networked Control Systems (WNCSs) are the reconfigurability, easy commissioning and the possibility of installation in places where cabling is impossible. Despite these advantages, there are two main problems which must be considered for practical implementations of WNCSs. One problem is the sampling period constraint of industrial wireless sensors. This problem is related to the energy cost of the wireless transmission, since the power supply is limited, which precludes the use of these sensors in several closed-loop controls. The other technological concern in WNCS is the energy efficiency of the devices. As the sensors are powered by batteries, the lowest possible consumption is required to extend battery lifetime. As a result, there is a compromise between the sensor sampling period, the sensor battery lifetime and the required control performance for the WNCS. This paper develops a model-based soft sensor to overcome these problems and enable practical implementations of WNCSs. The goal of the soft sensor is generating virtual data allowing an actuation on the process faster than the maximum sampling period available for the wireless sensor. Experimental results have shown the soft sensor is a solution to the sampling period constraint problem of wireless sensors in control applications, enabling the application of industrial wireless sensors in WNCSs. Additionally, our results demonstrated the soft sensor potential for implementing energy efficient WNCS through the battery saving of industrial wireless sensors.

Size variation, growth strategies, and the evolution of modularity in the mammalian skull.

Allometry is a major determinant of within-population patterns of association among traits and, therefore, a major component of morphological integration studies. Even so, the influence of size variation over evolutionary change has been largely unappreciated. Here, we explore the interplay between allometric size variation, modularity, and life-history strategies in the skull from representatives of 35 mammalian families. We start by removing size variation from within-species data and analyzing its influence on integration magnitudes, modularity patterns, and responses to selection. We also carry out a simulation in which we artificially alter the influence of size variation in within-taxa matrices. Finally, we explore the relationship between size variation and different growth strategies. We demonstrate that a large portion of the evolution of modularity in the mammalian skull is associated to the evolution of growth strategies. Lineages with highly altricial neonates have adult variation patterns dominated by size variation, leading to high correlations among traits regardless of any underlying modular process and impacting directly their potential to respond to selection. Greater influence of size variation is associated to larger intermodule correlations, less individualized modules, and less flexible responses to natural selection.

Covariance structure in the skull of Catarrhini: a case of pattern stasis and magnitude evolution.

The study of the genetic variance/covariance matrix (G-matrix) is a recent and fruitful approach in evolutionary biology, providing a window of investigating for the evolution of complex characters. Although G-matrix studies were originally conducted for microevolutionary timescales, they could be extrapolated to macroevolution as long as the G-matrix remains relatively constant, or proportional, along the period of interest. A promising approach to investigating the constancy of G-matrices is to compare their phenotypic counterparts (P-matrices) in a large group of related species; if significant similarity is found among several taxa, it is very likely that the underlying G-matrices are also equivalent. Here we study the similarity of covariance and correlation structure in a broad sample of Old World monkeys and apes (Catarrhini). We made phylogenetically structured comparisons of correlation and covariance matrices derived from 39 skull traits, ranging from between species to the superfamily level. We also compared the overall magnitude of integration between skull traits (r2) for all Catarrhini genera. Our results show that P-matrices were not strictly constant among catarrhines, but the amount of divergence observed among taxa was generally low. There was significant and positive correlation between the amount of divergence in correlation and covariance patterns among the 30 genera and their phylogenetic distances derived from a recently proposed phylogenetic hypothesis. Our data demonstrate that the P-matrices remained relatively similar along the evolutionary history of catarrhines, and comparisons with the G-matrix available for a New World monkey genus (Saguinus) suggests that the same holds for all anthropoids. The magnitude of integration, in contrast, varied considerably among genera, indicating that evolution of the magnitude, rather than the pattern of inter-trait correlations, might have played an important role in the diversification of the catarrhine skull.