Fitness Landscape Research Articles

Deciphering GB1's Single Mutational Landscape: Insights from MuMi Analysis.

Mutational changes that affect the binding of the C2 fragment of Streptococcal protein G (GB1) to the Fc domain of human IgG (IgG-Fc) have been extensively studied using deep mutational scanning (DMS), and the binding affinity of all single mutations has been measured experimentally in the literature. To investigate the underlying molecular basis, we perform in silico mutational scanning for all possible single mutations, along with 2 μs-long molecular dynamics (WT-MD) of the wild-type (WT) GB1 in both unbound and IgG-Fc bound forms. We compute the hydrogen bonds between GB1 and IgG-Fc in WT-MD to identify the dominant hydrogen bonds for binding, which we then assess in conformations produced by Mutation and Minimization (MuMi) to explain the fitness landscape of GB1 and IgG-Fc binding. Furthermore, we analyze MuMi and WT-MD to investigate the dynamics of binding, focusing on the relative solvent accessibility of residues and the probability of residues being located at the binding interface. With these analyses, we explain the interactions between GB1 and IgG-Fc and display the structural features of binding. In sum, our findings highlight the potential of MuMi as a reliable and computationally efficient tool for predicting protein fitness landscapes, offering significant advantages over traditional methods. The methodologies and results presented in this study pave the way for improved predictive accuracy in protein stability and interaction studies, which are crucial for advancements in drug design and synthetic biology.

Learning from Protein Engineering by Deconvolution of Multi-Mutational Variants.

This review analyzes a development in biochemistry, enzymology and biotechnology that originally came as a surprise. Following the establishment of directed evolution of stereoselective enzymes in organic chemistry, the concept of partial or complete deconvolution of selective multi-mutational variants was introduced. Early deconvolution experiments of stereoselective variants led to the finding that mutations can interact cooperatively or antagonistically with one another, not just additively. During the past decade, this phenomenon was shown to be general. In some studies, molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) computations were performed in order to shed light on the origin of non-additivity at all stages of an evolutionary upward climb. Data of complete deconvolution can be used to construct unique multi-dimensional rugged fitness pathway landscapes, which provide mechanistic insights different from traditional fitness landscapes. Along a related line, biochemists have long tested the result of introducing two point mutations in an enzyme for mechanistic reasons, followed by a comparison of the respective double mutant in so-called double mutant cycles, which originally showed only additive effects, but more recently also uncovered cooperative and antagonistic non-additive effects. We conclude with suggestions for future work, and call for a unified overall picture of non-additivity and epistasis.

Lernen aus Protein‐Engineering durch Dekonvolution von Multi‐Mutationalen Enzymvarianten**

AbstractDiese Übersicht analysiert eine Entwicklung in der Biochemie, Enzymologie und Biotechnologie, die ursprünglich als Überraschung betrachtet wurde. Nach der Etablierung der gerichteten Evolution stereoselektiver Enzyme in der organischen Chemie wurde das Konzept der partiellen oder vollständigen Dekonvolution selektiver multi‐mutationaler Varianten eingeführt. Frühe Dekonvolutionsexperimente von stereoselektiven Enzymvarianten führten zu der Erkenntnis, dass Mutationen kooperativ oder antagonistisch miteinander interagieren können, nicht nur additiv. Im letzten Jahrzehnt wurde dieses Phänomen als allgemeingültig nachgewiesen. In einigen Studien wurden molekulardynamische (MD) und quantenmechanische/ molekülmechanische (QM/MM) Berechnungen durchgeführt, um Licht auf den Ursprung der Nicht‐Additivität in allen Phasen eines evolutionären Aufstiegs zu werfen. Daten der vollständigen Dekonvolution können verwendet werden, um einzigartige mehrdimensionale, zerklüftete („rugged“) Fitness‐Pfad‐Landschaften experimentell zu konstruieren. Sie bieten mechanistische Einblicke, die sich von traditionellen Fitnesslandschaften erheblich unterscheiden. In einem verwandten Bereich haben Biochemiker lange das Ergebnis der Einführung von zwei Punktmutationen in einem Enzym aus mechanistischen Gründen getestet, gefolgt von einem Vergleich mit der entsprechenden Doppelmutante in sogenannten Doppelmutanten‐Zyklen, die ursprünglich nur additive Effekte zeigten. In jüngerer Zeit wurden auch hier kooperative und antagonistische nicht‐additive Effekte aufgedeckt. Wir schließen mit Vorschlägen für zukünftige Arbeiten und fordern ein einheitliches Gesamtbild von Nicht‐Additivität und Epistasis.

Structure-informed protein language models are robust predictors for variant effects.

Emerging variant effect predictors, protein language models (pLMs) learn evolutionary distribution of functional sequences to capture fitness landscape. Considering that variant effects are manifested through biological contexts beyond sequence (such as structure), we first assess how much structure context is learned in sequence-only pLMs and affecting variant effect prediction. And we establish a need to inject into pLMs protein structural context purposely and controllably. We thus introduce a framework of structure-informed pLMs (SI-pLMs), by extending masked sequence denoising to cross-modality denoising for both sequence and structure. Numerical results over deep mutagenesis scanning benchmarks show that our SI-pLMs, even when using smaller models and less data, are robustly top performers against competing methods including other pLMs, which shows that introducing biological context can be more effective at capturing fitness landscape than simply using larger models or bigger data. Case studies reveal that, compared to sequence-only pLMs, SI-pLMs can be better at capturing fitness landscape because (a) learned embeddings of low/high-fitness sequences can be more separable and (b) learned amino-acid distributions of functionally and evolutionarily conserved residues can be of much lower entropy, thus much more conserved, than other residues. Our SI-pLMs are applicable to revising any sequence-only pLMs through model architecture and training objectives. They do not require structure data as model inputs for variant effect prediction and only use structures as context provider and model regularizer during training.

Characterization and engineering of the type 3 secretion system needle monomer from Salmonella through the construction and screening of a comprehensive mutagenesis library.

Protein production strategies in bacteria are often limited due to the need for cell lysis and complicated purification schemes. To avoid these challenges, researchers have developed bacterial strains capable of secreting heterologous protein products outside the cell, but secretion titers often remain too low for commercial applicability. Improved understanding of the link between secretion system structure and its secretory abilities can help overcome the barrier to engineering higher secretion titers. Here, we investigated this link with the PrgI protein, the monomer of the secretory channel of the type 3 secretion system (T3SS) of Salmonella enterica. Despite detailed knowledge of the PrgI needle's assembly and structure, little is known about how its structure influences its secretory capabilities. To study this, we recently constructed a comprehensive codon mutagenesis library of the PrgI protein utilizing a novel one-pot recombineering approach. We then screened this library for functional T3SS assembly and secretion titer by measuring the secretion of alkaline phosphatase using a high-throughput activity assay. This allowed us to construct a first-of-its-kind secretion fitness landscape to characterize the PrgI needle's mutability at each position as well as the mutations which lead to enhanced T3SS secretion. We discovered new design rules for building a functional T3SS as well as identified hypersecreting mutants. This work can be used to increase understanding of the T3SS's assembly and identify further targets for engineering. This work also provides a blueprint for future efforts to engineer other complex protein assemblies through the construction of fitness landscapes.IMPORTANCEProtein secretion offers a simplified alternative method for protein purification from bacterial hosts. However, the current state-of-the-art methods for protein secretion in bacteria are still hindered by low yields relative to traditional protein purification strategies. Engineers are now seeking strategies to enhance protein secretion titers from bacterial hosts, often through genetic manipulations. In this study, we demonstrate that protein engineering strategies focused on altering the secretion apparatus can be a fruitful avenue toward this goal. Specifically, this study focuses on how changes to the PrgI needle protein from the type 3 secretion system from Salmonella enterica can impact secretion titer. We demonstrate that this complex is amenable to comprehensive mutagenesis studies and that this can yield both PrgI variants with increased secretory capabilities and insight into the normal functioning of the type 3 secretion system.

OneMax is not the Easiest Function for Fitness Improvements.

We study the (1:s+1) success rule for controlling the population size of the (1,λ)- EA. It was shown by Hevia Fajardo and Sudholt that this parameter control mechanism can run into problems for large s if the fitness landscape is too easy. They conjectured that this problem is worst for the ONEMAX benchmark, since in some well-established sense ONEMAX is known to be the easiest fitness landscape. In this paper we disprove this conjecture. We show that there exist s and ɛ such that the self-adjusting (1,λ)-EA with the (1:s+1)-rule optimizes ONEMAX efficiently when started with ɛn zero-bits, but does not find the optimum in polynomial time on DYNAMIC BINVAL. Hence, we show that there are landscapes where the problem of the (1:s+1)-rule for controlling the population size of the (1,λ)-EA is more severe than for ONEMAX. The key insight is that, while ONEMAX is the easiest function for decreasing the distance to the optimum, it is not the easiest fitness landscape with respect to finding fitness-improving steps.

Drift Analysis with Fitness Levels for Elitist Evolutionary Algorithms.

The fitness level method is a popular tool for analyzing the hitting time of elitist evolutionary algorithms. Its idea is to divide the search space into multiple fitness levels and estimate lower and upper bounds on the hitting time using transition probabilities between fitness levels. However, the lower bound generated by this method is often loose. An open question regarding the fitness level method is what are the tightest lower and upper time bounds that can be constructed based on transition probabilities between fitness levels. To answer this question, we combine drift analysis with fitness levels and define the tightest bound problem as a constrained multi-objective optimization problem subject to fitness levels. The tightest metric bounds by fitness levels are constructed and proven for the first time. Then linear bounds are derived from metric bounds and a framework is established that can be used to develop different fitness level methods for different types of linear bounds. The framework is generic and promising, as it can be used to draw tight time bounds on both fitness landscapes with and without shortcuts. This is demonstrated in the example of the (1+1) EA maximizing the TwoMax1 function.

ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering

Structural graph clustering is a data analysis technique that groups nodes within a graph based on their connectivity and structural similarity. The Structural graph clustering SCAN algorithm, a density-based clustering method, effectively identifies core points and their neighbors within areas of high density to form well-defined clusters. However, the clustering quality of SCAN heavily depends on the input parameters, ϵ and μ, making the clustering results highly sensitive to parameter selection. Different parameter settings can lead to significant differences in clustering results, potentially compromising the accuracy of the clusters. To address this issue, a novel structural graph clustering algorithm based on the adaptive selection of density peaks is proposed in this paper. Unlike traditional methods, our algorithm does not rely on external parameters and eliminates the need for manual selection of density peaks or cluster centers by users. Density peaks are adaptively identified using the generalized extreme value distribution, with consideration of the structural similarities and interdependencies among nodes, and clusters are expanded by incorporating neighboring nodes, enhancing the robustness of the clustering process. Additionally, a distance-based structural similarity method is proposed to re-cluster noise nodes to the correct clusters. Extensive experiments on real and synthetic graph datasets validate the effectiveness of our algorithm. The experiment results show that the ADPSCAN has a superior performance compared with several state-of-the-art (SOTA) graph clustering methods.

Neural network extrapolation to distant regions of the protein fitness landscape

Machine learning (ML) has transformed protein engineering by constructing models of the underlying sequence-function landscape to accelerate the discovery of new biomolecules. ML-guided protein design requires models, trained on local sequence-function information, to accurately predict distant fitness peaks. In this work, we evaluate neural networks’ capacity to extrapolate beyond their training data. We perform model-guided design using a panel of neural network architectures trained on protein G (GB1)-Immunoglobulin G (IgG) binding data and experimentally test thousands of GB1 designs to systematically evaluate the models’ extrapolation. We find each model architecture infers markedly different landscapes from the same data, which give rise to unique design preferences. We find simpler models excel in local extrapolation to design high fitness proteins, while more sophisticated convolutional models can venture deep into sequence space to design proteins that fold but are no longer functional. We also find that implementing a simple ensemble of convolutional neural networks enables robust design of high-performing variants in the local landscape. Our findings highlight how each architecture’s inductive biases prime them to learn different aspects of the protein fitness landscape and how a simple ensembling approach makes protein engineering more robust.

A combinatorially complete epistatic fitness landscape in an enzyme active site

Protein engineering often targets binding pockets or active sites which are enriched in epistasis-nonadditive interactions between amino acid substitutions-and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence-fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model-guided enzyme engineering approaches. We present here a combinatorially complete, 160,000-variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable β-subunit of tryptophan synthase (TrpB) in a nonnative environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most-fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences-a result that conservation-based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more-active variants, these approaches struggle to identify and differentiate among the most-active variants, even for this near-native function. Overall, this work presents a large-scale testing ground for model-guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling.

Evolution across the adaptive landscape in a hyperdiverse beetle radiation

The extraordinary diversification of beetles on Earth is a textbook example of adaptive evolution. Yet, the tempo and drivers of this super-radiation remain largely unclear. Here, we address this problem by investigating macroevolutionary dynamics in darkling beetles (Coleoptera: Tenebrionidae), one of the most ecomorphologically diverse beetle families (with over 30,000 species). Using multiple genomic datasets and analytical approaches, we resolve the long-standing inconsistency over deep relationships in the family. In conjunction with a landmark-based dataset of body shape morphology, we show that the evolutionary history of darkling beetles is marked by ancient rapid radiations, frequent ecological transitions, and rapid bursts of morphological diversification. On a global scale, our analyses uncovered a significant pulse of phenotypic diversification proximal to the Cretaceous-Palaeogene (K/Pg) mass extinction and convergence of body shape associated with recurrent ecological specializations. On a regional scale, two major Australasian radiations, the Adeliini and the Heleine clade, exhibited contrasting patterns of ecomorphological diversification, representing phylogenetic niche conservatism versus adaptive radiation. Our findings align with the Simpsonian model of adaptive evolution across the macroevolutionary landscape and highlight a significant role of ecological opportunity in driving the immense ecomorphological diversity in a hyperdiverse beetle group.

Influence of encoding and neighborhood in landscape analysis and tabu search performance for job shop scheduling problem

Fitness landscape analysis is used to understand search spaces of combinatorial problems that can hardly be solved exactly, such as the job shop scheduling problem. We analyze the influence of the solution encodings and the neighborhood operators on usual metrics for landscape analysis, and try to correlate the results to the performance of a local search method such as tabu search using these encodings and operators for a wide range of instances of the job shop scheduling problem.

Gliding toward an understanding of the origin of flight in bats.

Bats are the only mammals capable of powered flight and have correspondingly specialized body plans, particularly in their limb morphology. The origin of bat flight is still not fully understood due to an uninformative fossil record but, from the perspective of a functional transition, it is widely hypothesized that bats evolved from gliding ancestors. Here, we test predictions of the gliding-to-flying hypothesis of the origin of bat flight by using phylogenetic comparative methods to model the evolution of forelimb and hindlimb traits on a dataset spanning four extinct bats and 231 extant mammals with diverse locomotor modes. Our results reveal that gliders exhibit adaptive trait optima (1) toward relatively elongate forelimbs that are intermediate between those of bats and non-gliding arborealists, and (2) toward relatively narrower but not longer hindlimbs that are intermediate between those of non-gliders and bats. We propose an adaptive landscape based on limb length and width optimal trends derived from our modeling analyses. Our results support a hypothetical evolutionary pathway wherein glider-like postcranial morphology precedes a bat-like morphology adapted to powered-flight, setting a foundation for future developmental, biomechanical, and evolutionary research to test this idea.

Phylogenetic signatures reveal multilevel selection and fitness costs in SARS-CoV-2.

Large-scale sequencing of SARS-CoV-2 has enabled the study of viral evolution during the COVID-19 pandemic. Some viral mutations may be advantageous to viral replication within hosts but detrimental to transmission, thus carrying a transient fitness advantage. By affecting the number of descendants, persistence times and growth rates of associated clades, these mutations generate localised imbalance in phylogenies. Quantifying these features in closely-related clades with and without recurring mutations can elucidate the tradeoffs between within-host replication and between-host transmission. We implemented a novel phylogenetic clustering algorithm ( mlscluster, https://github.com/mrc-ide/mlscluster) to systematically explore time-scaled phylogenies for mutations under transient/multilevel selection. We applied this method to a SARS-CoV-2 time-calibrated phylogeny with >1.2 million sequences from England, and characterised these recurrent mutations that may influence transmission fitness across PANGO-lineages and genomic regions using Poisson regressions and summary statistics. We found no major differences across two epidemic stages (before and after Omicron), PANGO-lineages, and genomic regions. However, spike, nucleocapsid, and ORF3a were proportionally more enriched for transmission fitness polymorphisms (TFP)-homoplasies than other proteins. We provide a catalog of SARS-CoV-2 sites under multilevel selection, which can guide experimental investigations within and beyond the spike protein. This study provides empirical evidence for the existence of important tradeoffs between within-host replication and between-host transmission shaping the fitness landscape of SARS-CoV-2. This method may be used as a fast and scalable means to shortlist large sequence databases for sites under putative multilevel selection which may warrant subsequent confirmatory analyses and experimental confirmation.

Integrating Genomic, Climatic, and Immunological Factors to Analyze Seasonal Patterns of Influenza Variants

Influenza, often referred to as the flu, is an extremely contagious respiratory illness caused by influenza viruses, impacting populations globally with significant health consequences annually. A hallmark of influenza is its seasonal patterns, influenced by a mix of geographic, evolutionary, immunological, and environmental factors. Understanding these seasonal trends is crucial for informing public health decisions, including the planning of vaccination campaigns and their formulation. In our study, we introduce a genotype-structured infectious disease model for influenza transmission, immunity, and evolution. In this model, the population of infected individuals is structured according to the virus they harbor. It considers a symmetrical fitness landscape where the influenza A and B variants are considered. The model incorporates the effects of population immunity, climate, and epidemic heterogeneity, which makes it suitable for investigating influenza seasonal dynamics. We parameterize the model to the genomic surveillance data of flu in the US and use numerical simulations to elucidate the scenarios that result in the alternating or consecutive prevalence of flu variants. We show that the speed of virus evolution determines the alternation and co-circulation patterns of seasonal influenza. Our simulations indicate that slow immune waning reduces how often variants change, while cross-immunity regulates the co-circulation of variants. The framework can be used to predict the composition of future influenza outbreaks and guide the development of cocktail vaccines and antivirals that mitigate influenza in both the short and long term.

A Q-learning driven competitive surrogate assisted evolutionary optimizer with multiple oriented mutation operators for expensive problems

Surrogate-assisted evolutionary algorithms (SAEAs) prevail in the solution of computationally expensive optimization problems. However, with the growth of problem scale and complexity, the high-dimensional problem space greatly restricts their effectiveness and applicability. This paper proposes a Q-learning driven competitive surrogate-assisted evolutionary optimizer with multiple oriented mutation operators (CSEO-MOMO) to improve the prediction effectiveness and accuracy of SAEAs in different scenarios of optimization problems. CSEO-MOMO constructs isomorphic models with different complexities during iteration. The Q-learning method is employed to pick the proper model for prediction based on the state feedback of the iterative population. To explore the search space more comprehensively and increase the probability of finding a better solution, two competitive sub-populations coevolve with personalized mutation operators, of which different oriented optimal tractive base vectors are nominated to guide the mutation direction. In addition, a tailored infilling strategy concerning both the performance of the iterative individuals and their spatial distribution is used to winnow out the individuals with great potential for real evaluation. A suit of high-dimensional CEC benchmarks of different properties and an openly shared topology optimization problem are used for the effectiveness and efficiency verification of CSEO-MOMO. The experimental results reveal that CSEO-MOMO trumps seven excellent SAEAs in solving high-dimensional complex problems and possesses better accuracy and robustness under various fitness landscape scenarios. (The relevant MATLAB code of CSEO-MOMO is publicly available in the first author’s GitHub repository: https://github.com/qinna-zhu/CSEO-MOMO.)

A hierarchical reinforcement learning-aware hyper-heuristic algorithm with fitness landscape analysis

The automation of meta-heuristic algorithm configuration holds the utmost significance in evolutionary computation. A hierarchical reinforcement learning-aware hyper-heuristic algorithm with fitness landscape analysis (HRLHH) is proposed to flexibly configure the suitable algorithms under various optimization scenarios. Two kinds of fitness landscape analysis techniques improved based on specific problem characteristics construct the state spaces for hierarchical reinforcement learning. Among them, an adaptive classification based on dynamic ruggedness of information entropy is designed to discern the complexity of problems, which serves as the basis for decision-making actions in upper-layer space. Additionally, an online dispersion metric based on knowledge is further presented to distinguish the precise landscape features in lower-layer space. In light of the characteristics of the state spaces, the hierarchical action spaces composed of meta-heuristics with disparate exploration and exploitation are designed, and various action selection strategies are introduced. Taking into account the real-time environment and algorithm evolution behavior, dynamic reward mechanisms based on evolutionary success rate and population convergence rate are utilized to enhance search efficiency. The experimental results on the IEEE Congress on Evolutionary Computation (CEC) 2017, CEC 2014, and large-scale CEC 2013 test suites demonstrate that the proposed HRLHH exhibits superiority in terms of accuracy, stability, and convergence speed, and possesses strong generalization.

How evolution makes us age: Introducing the evolvable soma theory of ageing

At any moment in time, evolution is faced with a formidable challenge: refining the already highly optimised design of biological species, a feat accomplished through all preceding generations. In such a scenario, the impact of random changes (the method employed by evolution) is much more likely to be harmful than advantageous, potentially lowering the reproductive fitness of the affected individuals. Our hypothesis is that ageing is, at least in part, caused by the cumulative effect of all the experiments carried out by evolution to improve a species’ design. These experiments are almost always unsuccessful, as expected given their pseudorandom nature, cause harm to the body and ultimately lead to death. This hypothesis is consistent with the concept of “terminal addition”, by which nature is biased towards adding innovations at the end of development. From the perspective of evolution as an optimisation algorithm, ageing is advantageous as it allows to test innovations during a phase when their impact on fitness is present but less pronounced. Our inference suggests that ageing has a key biological role, as it contributes to the system’s evolvability by exerting a regularisation effect on the fitness landscape of evolution.

Fitness Landscapes and Evolution of Catalytic RNA.

The relationship between genotype and phenotype, or the fitness landscape, is the foundation of genetic engineering and evolution. However, mapping fitness landscapes poses a major technical challenge due to the amount of quantifiable data that is required. Catalytic RNA is a special topic in the study of fitness landscapes due to its relatively small sequence space combined with its importance in synthetic biology. The combination of in vitro selection and high-throughput sequencing has recently provided empirical maps of both complete and local RNA fitness landscapes, but the astronomical size of sequence space limits purely experimental investigations. Next steps are likely to involve data-driven interpolation and extrapolation over sequence space using various machine learning techniques. We discuss recent progress in understanding RNA fitness landscapes, particularly with respect to protocells and machine representations of RNA. The confluence of technical advances may significantly impact synthetic biology in the near future.

Evolution in fossil time series reconciles observations in micro- and macroevolution.

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 modeled 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 behaviors 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 are showing 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.