The adaptive landscape has been suggested as a potential conceptual bridge between phenotypic evolution on generational and macroevolutionary timescales. However, this potential remains largely untapped due to a limited understanding of how it changes through time. Here, we assessed the dynamics of the adaptive landscape across different time intervals by analyzing phenotypic time series using multivariate models of adaptation. First, we examined whether a decrease in river water flow affected the optimal body mass of a salmon population over a few decades. Second, we explored whether temperature changes affected the optimal size of coccoliths in a species of coccolithophore across about a hundred thousand years in the Cretaceous. Finally, we analyzed the extent to which temperature changes affected the optimal shell size in a lineage of planktic foraminifera over a few million years during the Miocene. We find evidence that evolution induced by changes in the adaptive landscape, driven by environmental change, occurred in the salmon and foraminifer datasets, but we do not detect a causal link between the investigated paleo-proxies and size changes in the coccolithophore lineage. We argue that applying the same modeling framework to datasets covering different time spans not only offers a promising way to better connect evolutionary processes across timescales but also enables tests of adaptive hypotheses about within-lineage evolution.

Phylogenetic comparative methods are a major tool for evaluating macroevolutionary hypotheses. Methods based on the mean-reverting stochastic Ornstein-Uhlenbeck process allow for modelling adaptation on a phenotypic adaptive landscape that itself evolves and where fitness peaks depend on measured characteristics of the external environment and/or other organismal traits. Here, we give an overview of the conceptual framework for the many implementations of these methods and discuss how we might interpret estimated parameters. We emphasize that the ability to model a changing adaptive landscape sets these methods apart from other approaches and discuss why this aspect captures long-term trait evolution more realistically. Recent multivariate extensions of these methods provide a powerful framework for testing evolutionary hypotheses but are also more complicated to use and interpret. We provide some guidance on their usage and put recent literature on the topic in biological rather than mathematical terms. We further show how these methods provide a starting point for modelling reciprocal selection (i.e., coevolution) between interacting lineages. We then briefly review some critiques of the methodologies. Finally, we provide some ideas for future developments that we think will be useful to evolutionary biologists.

Evolutionary rates correlate negatively with time, which makes it complicated to compare rates across lineages that have diversified on different time intervals. The causes of this correlation are debated. Using simulations, we first show that rates of evolution estimated as a parameter in the unbiased random walk model lack a rate-time scaling when data has been generated using this model, even when time series are made incomplete and biased. This indicates that it is theoretically possible to estimate rates that are not time correlated from empirical data. We then analyze 643 empirical time series to assess whether accounting for model misspecification, sampling error, and model identifiability reduces the negative scaling, but none appear to have a significant impact. This suggests that the rate-time correlation requires an explanation grounded in evolutionary biology and that common models used in phylogenetic comparative studies and phenotypic time series analyses often fail to accurately describe trait evolution in empirical data. Making meaningful comparisons of estimated rates between clades and lineages covering different time intervals remains a challenge.

Despite its many extensions and implications, we argue that punctuated equilibrium itself has two core, empirical claims: (1) stasis dominates within fossil species; and (2) morphological change is concentrated in pulses that occur associated with speciation. Here we assess the state of the evidence for these two claims, 50 years after punctuated equilibrium’s foundational paper. Spurred by controversy, paleontologists have amassed a large number of case studies in which morphology in species-level lineages is tracked over time. Modern, likelihood-based methods have been used to fit to these data models of stasis, random walks, and directional trends, as well as more complex dynamics. Compilations reveal that the directional trends predicted by gradualist expectations are infrequent. Although stasis is commonly observed, it is favored in less than half of cases, and meandering random walks or more complex models generally account for the majority of cases. The second claim of punctuated equilibrium has received much less empirical scrutiny than the first. Although speciational pulses are plausible in theory, only a few paleontological studies integrate ancestor–descendant time series into a phylogenetic framework as is needed to estimate cladogenetic change and compare it with anagenesis. These studies, as well as more indirect analyses of extant clades, suggest that speciational change can occur, but we cannot yet assess with confidence its frequency or importance compared with anagenetic changes.

Heritable variation is a prerequisite for evolutionary change, but the relevance of genetic constraints on macroevolutionary timescales is debated. By using two datasets on fossil and contemporary taxa, we show that evolutionary divergence among populations, and to a lesser extent among species, increases with microevolutionary evolvability. We evaluate and reject several hypotheses to explain this relationship and propose that an effect of evolvability on population and species divergence can be explained by the influence of genetic constraints on the ability of populations to track rapid, stationary environmental fluctuations.

Sperm morphology varies considerably among species. Sperm traits may contribute to speciation if they diverge fast in allopatry and cause conspecific sperm precedence upon secondary contact. However, their role in driving prezygotic isolation has been poorly investigated. Here we test the hypothesis that, early in the speciation process, female promiscuity promotes a reduction in overlap in sperm length distributions among songbird populations. We assembled a data set of 20 pairs of populations with known sperm length distributions, a published estimate of divergence time, and an index of female promiscuity derived from extrapair paternity rates or relative testis size. We found that sperm length distributions diverged more rapidly in more promiscuous species. Faster divergence between sperm length distributions was caused by the lower variance in the trait in more promiscuous species, and not by faster divergence of the mean sperm lengths. The reduced variance is presumably due to stronger stabilizing selection on sperm length mediated by sperm competition. If divergent sperm length optima in allopatry causes conspecific sperm precedence in sympatry, which remains to be shown empirically, female promiscuity may promote prezygotic isolation, and rapid speciation in songbirds.

Venoms are biochemical arsenals that have emerged in numerous animal lineages, where they have co-evolved with morphological and behavioural traits for venom production and delivery. In centipedes, venom evolution is thought to be constrained by the morphological complexity of their venom glands due to physiological limitations on the number of toxins produced by their secretory cells. Here we show that the uneven toxin expression that results from these limitations have enabled Scolopendra morsitans to regulate the composition of their secreted venom despite the lack of gross morphologically complex venom glands. We show that this control is probably achieved by a combination of this heterogenous toxin distribution with a dual mechanism of venom secretion that involves neuromuscular innervation as well as stimulation via neurotransmitters. Our results suggest that behavioural control over venom composition may be an overlooked aspect of venom biology and provide an example of how exaptation can facilitate evolutionary innovation and novelty. Uneven toxin production in giant centipedes due to morphological and physiological constraints has been evolutionarily exapted through neuromuscular innervation and neurotransmitter stimulation to provide behavioural control over venom composition.

Abstract The PCMBase R package is a powerful computational tool that enables efficient calculations of likelihoods for a wide range of phylogenetic Gaussian models. Taking advantage of it, we redesigned the R package mvSLOUCH. Here, we demonstrate how the new version of the package can be used to thoroughly examine the evolution and adaptation of traits in a large dataset of 1252 vascular plants through the use of multivariate Ornstein–Uhlenbeck processes. The results of our analysis demonstrate the ability of the modelling framework to distinguish between various alternative hypotheses regarding the evolution of functional traits in angiosperms.

Adams and Collyer argue that contemporary multivariate (Gaussian) phylogenetic comparative methods are prone to favouring more complex models of evolution and sometimes rotation invariance can be an issue. Here we dissect the concept of rotation invariance and point out that, depending on the understanding, this can be an issue with any method that relies on numerical instead of analytical estimation approaches. We relate this to the ongoing discussion concerning phylogenetic principal component analysis. Contrary to what Adams and Collyer found, we do not observe a bias against the simpler Brownian motion process in simulations when we use the new, improved, likelihood evaluation algorithm employed by mvSLOUCH, which allows for studying much larger phylogenies and more complex model setups.

Extrapolating microevolutionary models does not always provide satisfactory explanations for phenotypic diversification on million-year time scales. For example, short-term evolutionary change is often modelled assuming a fixed adaptive landscape, but macroevolutionary changes are likely to involve changes in the adaptive landscape itself. A better understanding of how the adaptive landscape changes across different time intervals and how these changes cause populations to evolve has the potential to narrow the gap between micro- and macroevolution. Here, we analyze two fossil diatom time series of exceptional quality and resolution covering time intervals of a few hundred thousand years using models that account for different behaviours of the adaptive landscape. We find that one of the lineages evolves on a randomly and continuously changing landscape, whereas the other lineage evolves on a landscape that shows a rapid shift in the position of the adaptive peak of a magnitude that is typically associated with species-level differentiation. This suggests phenotypic evolution beyond generational timescales may be a consequence of both gradual and sudden repositioning of adaptive peaks. Both lineages show rapid and erratic evolutionary change and are constantly readapting towards the optimal trait state, observations that align with evolutionary dynamics commonly observed in contemporary populations. The inferred trait evolution over a span of a few hundred thousand years in these two lineages is, therefore, chimeric in the sense that it combines components of trait evolution typically observed on both short and long timescales.

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 is 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 of how evolution works within and across timescales.

Measurement theory, a branch of applied mathematics, offers guiding principles for extracting meaning from empirical observations and is applicable to any science involving measurements. Measurement theory is highly relevant in paleobiology because statistical approaches assuming ratio-scaled variables are commonly used on data belonging to nominal and ordinal scale types. We provide an informal introduction to representational measurement theory and argue for its importance in robust scientific inquiry. Although measurement theory is widely applicable in paleobiology research, we use the study of disparity to illustrate measurement theoretical challenges in the quantitative study of the fossil record. Respecting the inherent properties of different measurements enables meaningful inferences about evolutionary and ecological processes from paleontological data.

Adams and Collyer (2018) argue that contemporary multivariate (Gaussian) phylogenetic comparative methods are prone to favouring more complex models of evolution and sometimes rotation invariance can be an issue. Here we dissect the concept of rotation invariance and point out that, depending on the understanding, this can be an issue with any method that relies on numerical instead of analytical estimation approaches. We relate this to the ongoing discussion concerning phylogenetic principal components analysis. Contrary to what Adams and Collyer (2018) found, we do not observe a bias against the simpler Brownian motion process in simulations when we use the new, improved, likelihood evaluation algorithm employed by mvSLOUCH, which allows for studying much larger phylogenies and more complex model setups.

The advent of fast computational algorithms for phylogenetic comparative methods allows for considering multiple hypotheses concerning the co-adaptation of traits and also for studying if it is possible to distinguish between such models based on contemporary species measurements. Here we demonstrate how one can perform a study with multiple competing hypotheses using mvSLOUCH by analyzing two data sets, one concerning feeding styles and oral morphology in ungulates, and the other concerning fruit evolution in Ferula (Apiaceae). We also perform simulations to determine if it is possible to distinguish between various adaptive hypotheses. We find that Akaike's information criterion corrected for small sample size has the ability to distinguish between most pairs of considered models. However, in some cases there seems to be bias towards Brownian motion or simpler Ornstein-Uhlenbeck models. We also find that measurement error and forcing the sign of the diagonal of the drift matrix for an Ornstein-Uhlenbeck process influences identifiability capabilities. It is a cliché that some models, despite being imperfect, are more useful than others. Nonetheless, having a much larger repertoire of models will surely lead to a better understanding of the natural world, as it will allow for dissecting in what ways they are wrong. [Adaptation; AICc; model selection; multivariate Ornstein-Uhlenbeck process; multivariate phylogenetic comparative methods; mvSLOUCH.].

Bryozoans are becoming an increasingly popular study system in macroevolutionary, ecological, and paleobiological research. Members of this colonial invertebrate phylum are notable for displaying an exceptional degree of division of labor in the form of specialized modules (polymorphs), which allow for the inference of individual allocation of resources to reproduction, defense, and growth using simple morphometric tools. However, morphometric characterizations of bryozoans are notoriously labored, due to the high number of structures often captured per image, as well as the need for specialized knowledge necessary for classifying individual skeletal structures within those images. We here introduce DeepBryo, a web application for deep learning-based morphometric characterization of cheilostome bryozoans. DeepBryo requires a single image as input and performs measurements automatically using instance segmentation algorithms. DeepBryo is capable of detecting objects belonging to six classes and outputting fourteen morphological shape measurements for each object based on the inferred segmentation maps. The users can visualize the predictions, check for errors, and directly filter model outputs on the web browser. Measurements can then be downloaded as a comma-separated values file. DeepBryo has been trained and validated on a total of 72,412 structures, belonging to six different object classes in 935 SEM images of cheilostome bryozoans belonging to 109 different families. The model shows high (>0.8) recall and precision for zooid-level structures. Its misclassification rate is low (~4%) and largely concentrated in a single object class (opesia). The model’s estimated structure-level area, height, and width measurements are statistically indistinguishable from those obtained via manual annotation (r 2 varying from 0.89 to 0.98) and show no detectable bias. DeepBryo reduces the person-hours required for characterizing the zooids in individual colonies to less than 1% of the time required for manual annotation at no significant loss of measurement accuracy. Our results indicate that DeepBryo enables cost-, labor,- and time-efficient morphometric characterization of cheilostome bryozoans. DeepBryo can greatly increase the scale of macroevolutionary, ecological, taxonomic, and paleobiological analyses, as well as the accessibility of deep learning tools for this emerging model system. Finally, DeepBryo provides the building blocks necessary for adapting the current application to other study groups.

In the NHMO zoological collections, specimens from the Arctic include about 9,000 mammals and 7,100 birds, whereas the Insect Collection holds about 105,000 specimens plus more than hundred jars with unsorted material. The Fish Collection contains approximately 1,400 specimens, while the Herptile Collection (amphibians & reptiles) holds only thirty-one specimens of three taxa. Many of these specimens originate from expeditions to E Greenland, N Canada, Svalbard, Novaya Zemlya, Finnmark, and NE Siberia in the period 1898 to 1966. Furthermore, the DNA Bank has about 5,600 tissue and extracted DNA samples, mostly sampled from wild animals during the last decades but also from specimens in the voucher collections. Most of the Arctic specimens have been digitized and are available in online data portals like GBIF, except for the Insect Collection, where only the type material and about 30 percent of the total specimens are digitized.

Abstract Models based on the Ornstein–Uhlenbeck process have become standard for the comparative study of adaptation. Cooper et al. (2016) have cast doubt on this practice by claiming statistical problems with fitting Ornstein–Uhlenbeck models to comparative data. Specifically, they claim that statistical tests of Brownian motion may have too high Type I error rates and that such error rates are exacerbated by measurement error. In this note, we argue that these results have little relevance to the estimation of adaptation with Ornstein–Uhlenbeck models for three reasons. First, we point out that Cooper et al. (2016) did not consider the detection of distinct optima (e.g. for different environments), and therefore did not evaluate the standard test for adaptation. Second, we show that consideration of parameter estimates, and not just statistical significance, will usually lead to correct inferences about evolutionary dynamics. Third, we show that bias due to measurement error can be corrected for by standard methods. We conclude that Cooper et al. (2016) have not identified any statistical problems specific to Ornstein–Uhlenbeck models, and that their cautions against their use in comparative analyses are unfounded and misleading. [adaptation, Ornstein–Uhlenbeck model, phylogenetic comparative method.]