Fitness Landscape Research Articles

Improved ACOM pattern matching in 4D-STEM through adaptive sub-pixel peak detection and image reconstruction

The technique known as 4D-STEM has recently emerged as a powerful tool for the local characterization of crystalline structures in materials, such as cathode materials for Li-ion batteries or perovskite materials for photovoltaics. However, the use of new detectors optimized for electron diffraction patterns and other advanced techniques requires constant adaptation of methodologies to address the challenges associated with crystalline materials. In this study, we present a novel image-processing method to improve pattern matching in the determination of crystalline orientations and phases. Our approach uses sub-pixel adaptive image processing to register and reconstruct electron diffraction signals in large 4D-STEM datasets. By using adaptive prominence and linear filters, we can improve the quality of the diffraction pattern registration. The resulting data compression rate of 103 is well-suited for the era of big data and provides a significant enhancement in the performance of the entire ACOM data processing method. Our approach is evaluated using dedicated metrics, which demonstrate a high improvement in phase recognition. Several features are extracted from the registered data to map properties such as the spot count, and various virtual dark fields, which are used to enhance the handling of the results maps. Our results demonstrate that this data preparation method not only enhances the quality of the resulting image but also boosts the confidence level in the analysis of the outcomes related to determining crystal orientation and phase. Additionally, it mitigates the impact of user bias that may occur during the application of the method through the manipulation of parameters.

From heterosis to outbreeding depression: genotype-by-environment interaction shifts hybrid fitness in opposite directions.

In F1 hybrids, phenotypic values are expected to be near the parental means under additive effects or close to one parent under dominance. However, F1 traits can fall outside the parental range, and outbreeding depression occurs when inferior fitness is observed in hybrids. Another possible outcome is heterosis, a phenomenon that interspecific hybrids or intraspecific crossbred F1s exhibit improved fitness compared to both parental species or strains. As an application of heterosis, hybrids between channel catfish females and blue catfish males are superior in feed conversion efficiency, carcass yield, and harvestability. Over 20 years of hybrid catfish production in experimental settings and farming practices generated abundant phenotypic data, making it an ideal system to investigate heterosis. In this study, we characterized fitness in terms of growth and survival longitudinally, revealing environment-dependent heterosis. In ponds, hybrids outgrow both parents due to an extra rapid growth phase of 2-4 months in year 2. This bimodal growth pattern is unique to F1 hybrids in pond culture environments only. In sharp contrast, the same genetic types cultured in tanks display outbreeding depression, where hybrids perform poorly, while channel catfish demonstrate superiority in growth throughout development. Our findings represent the first example, known to the authors, of opposite fitness shifts in response to environmental changes in interspecific vertebrate hybrids, suggesting a broader fitness landscape for F1 hybrids. Future genomic studies based on this experiment will help understand genome-environment interaction in shaping the F1 progeny fitness in the scenario of environment-dependent heterosis and outbreeding depression.

An extension of the Walsh-Hadamard transform to calculate and model epistasis in genetic landscapes of arbitrary shape and complexity.

Accurate models describing the relationship between genotype and phenotype are necessary in order to understand and predict how mutations to biological sequences affect the fitness and evolution of living organisms. The apparent abundance of epistasis (genetic interactions), both between and within genes, complicates this task and how to build mechanistic models that incorporate epistatic coefficients (genetic interaction terms) is an open question. The Walsh-Hadamard transform represents a rigorous computational framework for calculating and modeling epistatic interactions at the level of individual genotypic values (known as genetical, biological or physiological epistasis), and can therefore be used to address fundamental questions related to sequence-to-function encodings. However, one of its main limitations is that it can only accommodate two alleles (amino acid or nucleotide states) per sequence position. In this paper we provide an extension of the Walsh-Hadamard transform that allows the calculation and modeling of background-averaged epistasis (also known as ensemble epistasis) in genetic landscapes with an arbitrary number of states per position (20 for amino acids, 4 for nucleotides, etc.). We also provide a recursive formula for the inverse matrix and then derive formulae to directly extract any element of either matrix without having to rely on the computationally intensive task of constructing or inverting large matrices. Finally, we demonstrate the utility of our theory by using it to model epistasis within both simulated and empirical multiallelic fitness landscapes, revealing that both pairwise and higher-order genetic interactions are enriched between physically interacting positions.

Eco-Revelatory: The Missing Link in the Development of Adaptive Landscape to Industrial Heritage Sites

ABSTRACT Adaptive reuse of industrial heritage paves the way for revitalization, new use, and achievement of dynamism and sustainability. The aim of the present study is to access the missing link in developing a landscape model adaptable to industrial heritage sites. The present study was conducted within a qualitative approach framework and used the grounded theory method. Qualitative data analysis of the research was performed by MAXQDA2020 software, and based on the results, a paradigm model of adaptive industrial heritage landscape was explained. The research results include causal conditions (physical), core category (environmental), contextual conditions (historical, infrastructural), intervening conditions (economic, technology), strategies (policies and laws), and consequences (social, cultural). According to the identified components of the adaptability of industrial heritage to the landscape, the concept of Eco-Revelatory was extracted. It also states that the missing link is the adaptability of landscape and Eco-Revelatory industrial heritage, the use of which leads to the dynamism and stability of industrial heritage.

Challenges and advances in measuring phenotypic convergence.

Tests of phenotypic convergence can provide evidence of adaptive evolution, and the popularity of such studies has grown in recent years due to the development of novel, quantitative methods for identifying and measuring convergence. These methods include the commonly applied C1-C4 measures of Stayton (2015a), which measure morphological distances between lineages, and Ornstein-Uhlenbeck (OU) model-fitting analyses, which test whether lineages converged on shared adaptive peaks. We test the performance of C-measures and other convergence measures under various evolutionary scenarios and reveal a critical issue with C-measures: they often misidentify divergent lineages as convergent. We address this issue by developing novel convergence measures-Ct1-Ct4-measures-that calculate distances between lineages at specific points in time, minimizing the possibility of misidentifying divergent taxa as convergent. Ct-measures are most appropriate when focal lineages are of the same or similar geologic ages (e.g., extant taxa), meaning that the lineages' evolutionary histories include considerable overlap in time. Beyond C-measures, we find that all convergence measures are influenced by the position of focal taxa in phenotypic space, with morphological outliers often statistically more likely to be measured as strongly convergent. Further, we mimic scenarios in which researchers assess convergence using OU models with a priori regime assignments (e.g., classifying taxa by ecological traits) and find that multiple-regime OU models with phenotypically divergent lineages assigned to a shared selective regime often outperform simpler models. This highlights that model support for these multiple-regime OU models should not be assumed to always reflect convergence among focal lineages of a shared regime. Our new Ct1-Ct4-measures provide researchers with an improved comparative tool, but we emphasize that all available convergence measures are imperfect, and researchers should recognize the limitations of these methods and use multiple lines of evidence to test convergence hypotheses.

An intensification approach based on fitness landscape characteristics for job shop scheduling problem

An intensification approach based on fitness landscape characteristics for job shop scheduling problem

ST-ADPTC: a method for clustering spatiotemporal raster data based on improved density peak detection

Spatiotemporal raster (STR) data employ an array of grids to represent temporally varying and spatially distributed information, commonly utilized for recording environmental variables and socioeconomic indices. To reveal the geographic patterns embedded in STR data, the clustering by fast search and finding of density peaks (CFSFDP) algorithm is considered effective and suitable. However, this algorithm encounters limitations in identifying cluster centers, handling large data volumes, and measuring the coupled spatial-temporal-attribute distance when applied to STR data. To overcome these challenges, we propose an improved method named spatial temporal-adaptive density peak tree clustering (ST-ADPTC). This method leverages adaptive density peak tree segmentation to identify cluster centers and optimizes memory usage through the k-nearest neighbors (kNN) technique. By constructing a neighborhood that incorporates both spatiotemporal and thematic attribute similarities, ST-ADPTC computes the local density of STR data, facilitating the discovery of time-varying clusters. Based on the proposed method, we develop an open-source Python package (Geo_ADPTC). Experiments conducted using benchmarking datasets illustrate improvements in cluster identification and memory reduction. Additionally, a case study of sea surface temperature data demonstrates the feasibility and effectiveness of exploring spatial and temporal distribution patterns using the proposed method.

Is innovation failure just a dead end?

This paper explores whether failures in innovation projects at the firm level contribute to strengthening firms’ innovative activities and the odds of future innovation. Building on the literature on NK fitness landscapes, our paper focuses on technological complexity as a key factor that influences the response to innovation failures, guiding whether to discontinue the current path or, alternatively, to leverage the failure as a foundation for future innovation success. We then test the hypothesis derived from our model using a panel data set constituted by ten waves of the Community Innovation Survey held in the Netherlands from 1996 to 2014. Our findings show the fundamental relevance of the different forms of learning that are available to the firm. In particular, we highlight the positive role of learning after a previous innovation project has been abandoned. Previous failure in innovating produces some forms of learning inside the firms, and this learning positively contributes to subsequent successful innovation. Moreover, by differentiating between radical and incremental innovation, and between complex and less-complex innovation landscapes, we highlight the role of incremental innovation over the whole spectrum of landscape complexity, while the role of radical innovation appears to be more limited to less turbulent technological landscapes.

Dine and dash: how trophic ecology and migration shape functional locomotory traits in clupeiform fishes

Abstract Understanding how interactions between multiple selective forces influence traits at the macroevolutionary scale is key to understanding adaptive landscapes. Diadromy, an extreme form of migration between marine and freshwater environments, is thought to require locomotory traits conducive to long-distance migration. Yet, other selective forces, such as predator avoidance, habitat use, and prey acquisition, are also likely to shape locomotory adaptation in fishes. We examined how diadromy and trophic ecology together influenced locomotory trait diversity across Clupeiformes, a clade of fishes containing high trophic diversity and numerous transitions to diadromy. We found that both diadromy and trophic ecology influenced the pattern and pace of trait evolution. Diadromous taxa rapidly evolved traits characterized by high cruising efficiency, but the extent to which diadromous and non-diadromous taxa differed depended on their trophic ecology. Macropredators showed greater differences in locomotory traits between diadromous and non-diadromous taxa than phytodetritivores and micropredators, suggesting that traits conducive to migration might be most costly to consumers of evasive prey. This work shows that simultaneously characterizing the roles of multiple ecological or life-history factors in phenotypic evolution can bring the topography of adaptive landscapes into sharper focus and provide a more holistic view of the forces driving patterns of trait evolution.

The Untapped Potential of Comparative Biology in Aging Research: Insights From the Extraordinary-Long-Lived Naked Mole-Rat.

The search for solutions to the vagaries of aging has, historically, been akin to searching at night in the bright light under street lamps by utilizing the few preexisting and well-established animal model systems. Throughout my career as a comparative biologist, I have ventured into the darkness across 4 continents and studied over 150 different animal species, many of which have evolved remarkable adaptations to survive on the harsh and rugged fitness landscape that exists outside of the laboratory setting. In this Fellows Forum, I will discuss the main focus of my research for the last 25 years and dig deeply into the biology of the preternaturally long-lived naked mole-rat that makes it an ideal model system for the characterization of successful strategies to combat aging.

Unpredictability of the Fitness Effects of Antimicrobial Resistance Mutations Across Environments in Escherichia coli.

The evolution of antimicrobial resistance (AMR) in bacteria is a major public health concern, and antibiotic restriction is often implemented to reduce the spread of resistance. These measures rely on the existence of deleterious fitness effects (i.e. costs) imposed by AMR mutations during growth in the absence of antibiotics. According to this assumption, resistant strains will be outcompeted by susceptible strains that do not pay the cost during the period of restriction. The fitness effects of AMR mutations are generally studied in laboratory reference strains grown in standard growth environments; however, the genetic and environmental context can influence the magnitude and direction of a mutation's fitness effects. In this study, we measure how three sources of variation impact the fitness effects of Escherichia coli AMR mutations: the type of resistance mutation, the genetic background of the host, and the growth environment. We demonstrate that while AMR mutations are generally costly in antibiotic-free environments, their fitness effects vary widely and depend on complex interactions between the mutation, genetic background, and environment. We test the ability of the Rough Mount Fuji fitness landscape model to reproduce the empirical data in simulation. We identify model parameters that reasonably capture the variation in fitness effects due to genetic variation. However, the model fails to accommodate the observed variation when considering multiple growth environments. Overall, this study reveals a wealth of variation in the fitness effects of resistance mutations owing to genetic background and environmental conditions, which will ultimately impact their persistence in natural populations.

Genotype sampling for deep-learning assisted experimental mapping of a combinatorially complete fitness landscape.

Experimental characterization of fitness landscapes, which map genotypes onto fitness, is important for both evolutionary biology and protein engineering. It faces a fundamental obstacle in the astronomical number of genotypes whose fitness needs to be measured for any one protein. Deep learning may help to predict the fitness of many genotypes from a smaller neural network training sample of genotypes with experimentally measured fitness. Here I use a recently published experimentally mapped fitness landscape of more than 260 000 protein genotypes to ask how such sampling is best performed. I show that multilayer perceptrons, recurrent neural networks, convolutional networks, and transformers, can explain more than 90% of fitness variance in the data. In addition, 90% of this performance is reached with a training sample comprising merely ≈103 sequences. Generalization to unseen test data is best when training data is sampled randomly and uniformly, or sampled to minimize the number of synonymous sequences. In contrast, sampling to maximize sequence diversity or codon usage bias reduces performance substantially. These observations hold for more than one network architecture. Simple sampling strategies may perform best when training deep learning neural networks to map fitness landscapes from experimental data. The fitness landscape data analyzed here is publicly available as described previously (Papkou et al. 2023). All code used to analyze this landscape is publicly available at https://github.com/andreas-wagner-uzh/fitness_landscape_sampling.

Robust genetic codes enhance protein evolvability.

The standard genetic code defines the rules of translation for nearly every life form on Earth. It also determines the amino acid changes accessible via single-nucleotide mutations, thus influencing protein evolvability-the ability of mutation to bring forth adaptive variation in protein function. One of the most striking features of the standard genetic code is its robustness to mutation, yet it remains an open question whether such robustness facilitates or frustrates protein evolvability. To answer this question, we use data from massively parallel sequence-to-function assays to construct and analyze 6 empirical adaptive landscapes under hundreds of thousands of rewired genetic codes, including those of codon compression schemes relevant to protein engineering and synthetic biology. We find that robust genetic codes tend to enhance protein evolvability by rendering smooth adaptive landscapes with few peaks, which are readily accessible from throughout sequence space. However, the standard genetic code is rarely exceptional in this regard, because many alternative codes render smoother landscapes than the standard code. By constructing low-dimensional visualizations of these landscapes, which each comprise more than 16 million mRNA sequences, we show that such alternative codes radically alter the topological features of the network of high-fitness genotypes. Whereas the genetic codes that optimize evolvability depend to some extent on the detailed relationship between amino acid sequence and protein function, we also uncover general design principles for engineering nonstandard genetic codes for enhanced and diminished evolvability, which may facilitate directed protein evolution experiments and the bio-containment of synthetic organisms, respectively.

Bioinformatics investigation of adaptive immune-related genes in peri-implantitis and periodontitis: Characteristics and diagnostic values.

Peri-implantitis and periodontitis have similar immunological bioprocesses and inflammatory phenotypes. In the inflammatory process, the adaptive immune cells can drive the development of disease. This research investigated the differences and diagnostic significance of peri-implantitis and periodontitis in adaptive immune responses. We acquired four GEO datasets of gene expressions in surrounding tissues in healthy person, healthy implant, periodontitis, and peri-implantitis patients. The structural characteristics and enrichment analyses of differential expression genes were examined. The adaptive immune landscapes in peri-implantitis and periodontitis were then evaluated using single sample gene set enrichment analysis. The STRING database and Cytoscape were used to identify adaptive hub genes, and the ROC curve was used to verify them. Finally, qRT-PCR method was used to verify the expression level of Hub gene in activated T cells on the titanium-containing or titanium-free culture plates. At the transcriptome level, the data of healthy implant, peri-implantitis and periodontitis were highly dissimilar. The peri-implantitis and periodontitis both exhibited adaptive immune response. Except for the activated CD4+T cells, there was no significant difference in other adaptive immune cells between peri-implantitis and periodontitis. In addition, correlation analysis showed that CD53, CYBB, and PLEK were significantly positively linked with activated CD4+T cells in the immune microenvironment of peri-implantitis, making them effective biomarkers to differentiate it from periodontitis. Peri-implantitis has a uniquely immunogenomic landscape that differs from periodontitis. This study provides new insights and ideas into the activated CD4+T cells and hub genes that underpin the immunological bioprocess of peri-implantitis.

Ecological tradeoffs lead to complex evolutionary trajectories and sustained diversity on dynamic fitness landscapes

The course and outcome of evolution are critically determined by the fitness landscape, which maps genotype to fitness. Most theory has considered static fitness landscapes or fitness landscapes that fluctuate according to abiotic environmental changes. In the presence of biotic interactions between coexisting genotypes, the fitness landscape becomes dynamic and frequency-dependent.Here, we introduce a fitness landscape model that incorporates ecological interactions between individuals in a population. In the model, fitness is determined by individuals competing for resources according to a set of traits they possess. An individual’s genotype determines the trait values through a Rough Mount Fuji fitness landscape model, allowing for tunable epistasis (i.e. non-additive gene interaction) and trait correlations (i.e. whether there are tradeoffs or synergies in the ability to use resources). Focusing on the effects of epistasis and trait correlations, we quantify the resulting eco-evolutionary dynamics under simulated Wright–Fisher dynamics (i.e. including genetic drift, mutation, and selection under the assumption of a constant population size) on the dynamics fitness landscape in comparison with a similar, static, fitness landscape model without ecological interactions.Whereas the non-ecological model ultimately leads to the maintenance of one main genotype in the population, evolution in the ecological model can lead to the long-term coexistence of several genotypes at intermediate frequencies across much of the parameter range. Including ecological interactions increases steady-state diversity whenever the trait correlations are not too strong. However, strong epistasis can hinder coexistence, and additive genotype–phenotype maps yield the highest haplotype diversity at the steady state. Interestingly, we frequently observe long-term coexistence also in the absence of induced trade-offs in the ability to consume resources.In summary, our simulation study presents a new dynamic fitness landscape model that highlights the complex eco-evolutionary consequences of a (finite) genotype–phenotype-fitness map in the presence of biotic interactions.

Genotype × Environment interaction and the evolution of sexual dimorphism: adult nutritional environment mediates selection and expression of sex-specific genetic variance in Drosophila melanogaster.

Sexual conflict plays a key role in the dynamics of adaptive evolution in sexually reproducing populations, and theory suggests an important role for variance in resource acquisition in generating or masking sexual conflict over fitness and life history traits. Here, I used a quantitative genetic genotype × environment experiment in Drosophila melanogaster to test the theoretical prediction that variance in resource acquisition mediates variation in sex-specific component fitness. Holding larval conditions constant, I found that adult nutritional environments characterized by high protein content resulted in reduced survival of both sexes and lower male reproductive success compared to an environment of lower protein content. Despite reduced mean fitness of both sexes in high protein environments, I found a sex*treatment interaction for the relationship between resource acquisition and fitness; estimates of the adaptive landscape indicate males were furthest from their optimum resource acquisition level in high protein environments, and females were furthest in low protein environments. Expression of genetic variance in resource acquisition and survival was highest for each sex in the environment it was best adapted to, although the treatment effects on expression of genetic variance eroded in the path from resource acquisition to total fitness. Cross-sex genetic correlations were strongly positive for resource acquisition, survival, and total fitness and negative for mating success, although estimation error was high for all. These results demonstrate that environmental effects on resource acquisition can have predictable consequences for the expression of sex-specific genetic variance but also that these effects of resource acquisition can erode through life history.

Elevated [CO2] and temperature augment gas exchange and shift the fitness landscape in a montane forb.

Climate change is simultaneously increasing carbon dioxide concentrations ([CO2]) and temperature. These factors could interact to influence plant physiology and performance. Alternatively, increased [CO2] may offset costs associated with elevated temperatures. Furthermore, the interaction between elevated temperature and [CO2] may differentially affect populations from along an elevational gradient and disrupt local adaptation. We conducted a multifactorial growth chamber experiment to examine the interactive effects of temperature and [CO2] on fitness and ecophysiology of diverse accessions of Boechera stricta (Brassicaceae) sourced from a broad elevational gradient in Colorado. We tested whether increased [CO2] would enhance photosynthesis across accessions, andwhether warmer conditions would depress the fitness of high-elevation accessions owing to steep reductions in temperature with increasing elevation in this system. Elevational clines in [CO2] are not as evident, making it challenging to predict how locally adapted ecotypes will respond to elevated [CO2]. This experiment revealed that elevated [CO2] increased photosynthesis and intrinsic water use efficiency across all accessions. However, these instantaneous responses to treatments did not translate to changes in fitness. Instead, increased temperatures reduced the probability of reproduction for all accessions. Elevated [CO2] and increased temperatures interacted to shift the adaptive landscape, favoring lower elevation accessions for the probability of survival and fecundity. Our results suggest that elevated temperatures and [CO2] associated with climate change could have severe negative consequences, especially for high-elevation populations.

Methodology for forming a digital farming management system

The paper presents methodological approaches for the creation of a digital farming management system (DFMS). A convergent approach, based on cognitive (conceptual) analysis methods, is employed in the research and applied to the formation of adaptive landscape farming systems. The fundamental principles of organizing DFMS include crop and environmental monitoring (in situ, remote sensing); the formation of farming system archetypes based on the analysis of long-term field experiments; spatial object modeling and land typology using GIS; planning and support for agrotechnologies to adapt to natural and economic conditions; modeling ecosystem services and biodiversity; assessing the impact on the sustainability and economics of crop production. The system is implemented using geoinformation models in a specific geographic coordinate. DFMS involves conducting a "inventory" of natural and production resources, as well as identifying limits of climatic, soil, and agrolandscape parameters at different levels of land use intensity. At each stage of organizing system blocks, methods of intelligent data analysis and machine learning are used, with the core of the system relying on the use of knowledge bases and logical rules of the subject area. A key element of the system is the scaling of the results of long-term field experiments and accumulated knowledge in different management areas based on the parameterization of the multi-level variability of farming systems and the formation of their archetypes. The practical implementation of the main provisions of DFMS allows approaching the solution of key issues related to reducing the level of uncertainty and associated risks in agriculture. This is achieved through scientifically justified organization of rational land use, increasing the resilience of crop production in different land use conditions, and providing information support to rural producers.

Evolutionary graph theory beyond single mutation dynamics: on how network-structured populations cross fitness landscapes.

Spatially resolved datasets are revolutionizing knowledge in molecular biology, yet are under-utilized for questions in evolutionary biology. To gain insight from these large-scale datasets of spatial organization, we need mathematical representations and modeling techniques that can both capture their complexity, but also allow for mathematical tractability. Evolutionary graph theory utilizes the mathematical representation of networks as a proxy for heterogeneous population structure and has started to reshape our understanding of how spatial structure can direct evolutionary dynamics. However, previous results are derived for the case of a single new mutation appearing in the population and the role of network structure in shaping fitness landscape crossing is still poorly understood. Here we study how network-structured populations cross fitness landscapes and show that even a simple extension to a two-mutational landscape can exhibit complex evolutionary dynamics that cannot be predicted using previous single-mutation results. We show how our results can be intuitively understood through the lens of how the two main evolutionary properties of a network, the amplification and acceleration factors, change the expected fate of the intermediate mutant in the population and further discuss how to link these models to spatially resolved datasets of cellular organization.

Hybrid speciation driven by multilocus introgression of ecological traits

Hybridization allows adaptations to be shared among lineages and may trigger the evolution of new species1,2. However, convincing examples of homoploid hybrid speciation remain rare because it is challenging to demonstrate that hybridization was crucial in generating reproductive isolation3. Here we combine population genomic analysis with quantitative trait locus mapping of species-specific traits to examine a case of hybrid speciation in Heliconius butterflies. We show that Heliconius elevatus is a hybrid species that is sympatric with both parents and has persisted as an independently evolving lineage for at least 180,000 years. This is despite pervasive and ongoing gene flow with one parent, Heliconius pardalinus, which homogenizes 99% of their genomes. The remaining 1% introgressed from the other parent, Heliconius melpomene, and is scattered widely across the H. elevatus genome in islands of divergence from H. pardalinus. These islands contain multiple traits that are under disruptive selection, including colour pattern, wing shape, host plant preference, sex pheromones and mate choice. Collectively, these traits place H. elevatus on its own adaptive peak and permit coexistence with both parents. Our results show that speciation was driven by introgression of ecological traits, and that speciation with gene flow is possible with a multilocus genetic architecture.