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bioRxiv2015

Quantitative Genetics Meets Integral Projection Models: Unification of Widely Used Methods from Ecology and Evolution

O_LIMicro-evolutionary predictions are complicated by ecological feedbacks like density dependence, while ecological predictions can be complicated by evolutionary change. A widely used approach in micro-evolution, quantitative genetics, struggles to incorporate ecological processes into predictive models, while structured population modelling, a tool widely used in ecology, rarely incorporates evolution explicitly.\nC_LIO_LIIn this paper we develop a flexible, general framework that links quantitative genetics and structured population models. We use the quantitative genetic approach to write down the phenotype as an additive map. We then construct integral projection models for each component of the phenotype. The dynamics of the distribution of the phenotype are generated by combining distributions of each of its components. Population projection models can be formulated on per generation or on shorter time steps.\nC_LIO_LIWe introduce the framework before developing example models with parameters chosen to exhibit specific dynamics. These models reveal (i) how evolution of a phenotype can cause populations to move from one dynamical regime to another (e.g. from stationarity to cycles), (ii) how additive genetic variances and covariances (the G matrix) are expected to evolve over multiple generations, (iii) how changing heritability with age can maintain additive genetic variation in the face of selection and (iii) life history, population dynamics, phenotypic characters and parameters in ecological models will change as adaptation occurs.\nC_LIO_LIOur approach unifies population ecology and evolutionary biology providing a framework allowing a very wide range of questions to be addressed. The next step is to apply the approach to a variety of laboratory and field systems. Once this is done we will have a much deeper understanding of eco-evolutionary dynamics and feedbacks.\nC_LI

Evolutionary Biology↗

bioRxiv2022

Speciation on a complex landscape; phylogeny and demography of Mexican Goodeid fish.

Understanding the phylogeographic history of a group and identifying the factors contributing to speciation is an important challenge in evolutionary biology. Vicariance has clearly played a major role in diversification across diverse groups of organisms, but how this interacts with intrinsic biological features such as changes in reproductive system or sexual selection is less well understood. The Goodeinae are a Mexican endemic group of live-bearing fish. Here, we develop genomic resources for species within the Goodeinae and use phylogenomic approaches to characterise their evolutionary history. We sequenced, assembled and annotated the genomes of four new Goodeinae genomes, including A. toweri, the only matrotrophic live-bearing fish without a trophotaenia in the group. We produced a phylogeny of the Goodeinae and estimate timings of species divergence. We determined the extent and timing of introgression between the species to assess if this may have occurred during an early radiation, or in more recent episodes of secondary contact. We also analyse patterns in the changes of effective population size for the species and examine the time course of expansion and decline. We used branch-site models to detect genome-wide positive selection across Goodeinae, and we specifically ask if this differs in A. toweri, where reversal of placental viviparity has recently occurred. We find that Goodeinae diverged rapidly, with reductions in effective population size after each split. Contrary to expectations, we find evidence of gene flow between geographically isolated species, suggesting vicariant speciation was supplemented by limited post-speciation gene flow, potentially as a result of river piracy. Genes under positive selection in the group are likely to be associated with the switch to live-bearing. Overall, our studies suggest that both volcanism driven vicariance and changes in reproductive mode influenced radiation in the Goodeinae.

evolutionary biology↗

bioRxiv2025

Leaving Academia: Insights from Evolutionary Biologists on Their Career Transitions and Job Satisfaction

Many who have obtained PhDs in evolutionary biology will ultimately pursue careers that fall outside a narrow definition of an academic career. At the same time, PhD students and supervisors of PhD students are often ill-informed about career options outside of academia. Here, we report on a survey of evolutionary biologists who have pursued non-academic careers, to understand what careers they pursue, how they transitioned into those careers, how well prepared they were, and how satisfied they are with their current work. Overall, the message from this survey is positive- evolutionary biologists are readily employable outside of academia, generally well-prepared for those jobs, and report high levels of satisfaction in their non-academic careers. We also highlight areas where preparation for non-academic careers could be improved, which might be addressed by individual mentors or PhD training programmes.

evolutionary biology↗

bioRxiv2025

Stepwise emergence of recombination suppression precedes fissiparous asexuality in the planarian Schmidtea mediterranea

A central paradox in evolutionary biology is the rarity of asexual reproduction, which is often attributed to developmental constraints and long-term costs. Yet, fissiparous asexuality--where animals split and regenerate--is widespread among planarians, hinting at genomic features predisposing them to asexuality. We investigated the genomic underpinnings and evolutionary consequences of asexuality in the planarian Schmidtea mediterranea, which exists as both obligately fissiparous and sexual strains. We generated a haplotype-phased genome assembly of the asexual strain and collected population genomic data from laboratory and wild populations to uncover extensive heterozygous chromosomal rearrangements affecting all chromosomes. We show that these rearrangements arose in a sexually reproducing ancestor without directly disrupting reproductive genes but instead progressively suppressing recombination across the genome. The asexual genome exhibits minimal deleterious mutation accumulation, indicating a low cost of asexuality. Population-genomic data show this strain lacks any detectable sexual reproduction and originated recently (0.17-0.4 Ma), but the young age is insufficient to explain the low mutational burden. Instead, planarians may be able to exploit the lack of a single-cell bottleneck in fissiparous reproduction to mitigate the costs of asexuality. Altogether, our results support a model in which stepwise recombination suppression due to structural rearrangements eroded the benefits of sex and enabled the emergence of fissiparous asexuality in S. mediterranea.

evolutionary biology↗

bioRxiv2018

Leaps and bounds: geographical and ecological distance constrained the colonisation of the Afrotemperate by Erica

AO_SCPCAPBSTRACTC_SCPCAPThe coincidence of long distance dispersal and biome shift is assumed to be the result of a multifaceted interplay between geographical distance and ecological suitability of source and sink areas. Here, we test the influence of these factors on the dispersal history of the flowering plant genus Erica (Ericaceae) across the Afrotemperate. We quantify similarity of Erica climate niches per biogeographic area using direct observations of species, and test various colonisation scenarios while estimating ancestral areas for the Erica clade using parametric biogeographic model testing. We infer that the overall dispersal history of Erica across the Afrotemperate is the result of infrequent colonisation limited by geographic proximity and niche similarity. However, the Drakensberg Mountains represent a colonisation sink, rather than acting as a \"stepping stone\" between more distant and ecologically dissimilar Cape and Tropical African regions. Strikingly, the most dramatic examples of species radiations in Erica were the result of single unique dispersals over longer distances between ecologically dissimilar areas, contradicting the rule of phylogenetic biome conservatism. These results highlight the importance of rare biome shifts, in which a unique dispersal event fuels evolutionary radiation.\n\nThis article has been peer-reviewed and recommended by: Peer Community in Evolutionary Biology (DOI: 10.24072/pci.evolbiol.100065)

evolutionary biology↗

bioRxiv2023

Interaction between mutation type and gene pleiotropy drives parallel evolution in the laboratory

What causes evolution to be repeatable is a fundamental question in evolutionary biology. Pleiotropy, i.e. the effect of an allele on multiple traits, is thought to enhance repeatability by constraining the number of available beneficial mutations. Additionally, pleiotropy may promote repeatability by allowing large fitness benefits of single mutations via adaptive combinations of phenotypic effects. Yet, this latter evolutionary potential may be reaped solely by specific types of mutations able to realize optimal combinations of phenotypic effects while avoiding the costs of pleiotropy. Here, we address the interaction of gene pleiotropy and mutation type on evolutionary repeatability in a meta-analysis of experimental evolution studies with Escherichia coli. We hypothesize that single-nucleotide polymorphisms are principally able to yield large fitness benefits by targeting highly pleiotropic genes, whereas indels and structural variants provide smaller benefits and are restricted to genes with lower pleiotropy. By using gene connectivity as proxy for pleiotropy, we show that nondisruptive single-nucleotide polymorphisms (SNPs) in highly pleiotropic genes yield the largest fitness benefits, since they contribute more to parallel evolution, especially in large populations, than inactivating SNPs, indels and structural variants. Our findings underscore the importance of considering genetic architecture together with mutation type for understanding evolutionary repeatability.

evolutionary biology↗

bioRxiv2025

Ecological stoichiometry and life history theory, not the identity of genomic variants, predict rapid adaptation

SignificanceForecasting how populations adapt to changing environments is a central challenge in evolutionary biology. We tested the predictability of rapid adaptation by tracking both phenotypic and genomic trajectories of evolution in replicate outdoor populations of Drosophila melanogaster and comparing these data to predictions derived from life history theory, ecological stoichiometry, and genomic data. We found that life history theory and ecological stoichiometry forecasted the direction of phenotypic evolution, even with temporal changes in the direction of evolution. In contrast, genomic predictors were largely unable to forecast the SNPs or genes underlying adaptation. These results demonstrate the utility of ecological frameworks to provide general, but robust, forecasts of adaptation in complex environments. Determining the scale and scope of the predictability of evolution is fundamental to understanding biological processes and to managing biodiversity. Ecological frameworks, including life history theory and ecological stoichiometry, offer testable predictions about the direction of adaptation and trade-offs between traits in response to environmental change. Similarly, well-characterized molecular pathways, genotype-phenotype linkages, and prior evolutionary genomic studies could be predictive of the genomic architecture underlying adaptation. We tested whether ecological frameworks and evolutionary genomic data can be used to forecast rapid adaptation in replicated outdoor populations of Drosophila melanogaster evolving in response to natural seasonal fluctuations from summer to late fall. Life history theory predicted the observed pattern of adaptive tracking: in summer, reproductive output increased and stress tolerance decreased, while in fall, this direction reversed, with evolution of increased stress tolerance at the expense of reproduction. Stoichiometric phenotypic evolution was also predictable, with phosphorus and magnesium content, both linked to growth rate, and alkali metal, associated with maintaining homeostasis in response to thermal stress, showing rapid and parallel evolution indicative of adaptation. Temporal genomic data revealed a complex genomic architecture of temporal adaptation and the SNPs and genes involved in adaptation were largely unpredictable. These results demonstrate that ecological frameworks, more than genomic data, have utility in forecasting adaptation in complex and variable environments.

evolutionary biology↗

bioRxiv2021

Target enrichment and extensive population sampling help untangle the recent, rapid radiation of Oenothera sect. Calylophus

Oenothera sect. Calylophus is a North American group of 13 recognized taxa in the evening primrose family (Onagraceae) with an evolutionary history that may include independent origins of bee pollination, edaphic endemism, and permanent translocation heterozygosity. Like other groups that radiated relatively recently and rapidly, taxon boundaries within Oenothera sect. Calylophus have remained challenging to circumscribe. In this study, we used target enrichment, flanking non-coding regions, summary coalescent methods, tests for gene flow modified for target-enrichment data, and morphometric analysis to reconstruct phylogenetic hypotheses, evaluate current taxon circumscriptions, and examine character evolution in Oenothera sect. Calylophus. Because sect. Calylophus comprises a clade with a relatively restricted geographic range, we were able to extensively sample across the range of geographic and morphological diversity in the group. We found that the combination of exons and flanking non-coding regions led to improved support for species relationships. We reconstructed potential hybrid origins of some accessions and note that if processes such as hybridization are not taken into account, the number of inferred evolutionary transitions may be artificially inflated. We recovered strong evidence for multiple origins of the evolution of bee pollination from ancestral hawkmoth pollination, the evolution of edaphic specialization on gypsum, and permanent translocation heterozygosity. This study applies newly emerging techniques alongside dense infraspecific sampling and morphological analyses to effectively address a relatively common but recalcitrant problem in evolutionary biology.

evolutionary biology↗

bioRxiv2024

Highly recurrent multi-nucleotide mutations in SARS-CoV-2

Multi-nucleotide mutations (MNMs) simultaneously replace multiple nu-cleotides. They are a significant contributor to evolution and disease, as well as to misdiagnosis, misannotation and other biases in genome data analysis. MNMs are generally thought to be rare and random events. However, by processing millions of publicly shared genomes, we show that certain MNMs are highly recurrent in SARS-CoV-2: they repeatedly and consistently modify the same multiple nucleotides at the same genome position in the same way. The most frequent of these MNMs have independently occurred hundreds of times across all SARS-CoV-2 lineages. The vast majority of these recurrent MNMs are linked to transcription regulatory sequences. We propose a mechanism that explains them through template switching as part of the natural transcription process of the virus. This previously unknown mutational pattern increases our understanding of the evolution of SARS-CoV-2 and potentially many other nidoviruses. It also has important consequences for computational evolutionary biology: we show that for example recurrent MNMs cause approximately 14% of false positives during inference of recombination in SARS-CoV-2.

evolutionary biology↗

bioRxiv2014

How the tortoise beats the hare: Slow and steady adaptation in structured populations suggests a rugged fitness landscape in bacteria

AbstractIn the context of Wrights adaptive landscape, genetic epistasis can yield a multipeaked or \"rugged\" topography. In an unstructured population, a lineage with selective access to multiple peaks is expected to rapidly fix on one, which may not be the highest peak. Contrarily, beneficial mutations in a population with spatially restricted migration take longer to fix, allowing distant parts of the population to explore the landscape semi-independently. Such a population can simultaneous discover multiple peaks and the genotype at the highest discovered peak is expected to fix eventually. Thus, structured populations sacrifice initial speed of adaptation for breadth of search. As in the Tortoise-Hare fable, the structured population (Tortoise) starts relatively slow, but eventually surpasses the unstructured population (Hare) in average fitness. In contrast, on single-peak landscapes (e.g., systems lacking epistasis), all uphill paths converge. Given such \"smooth\" topography, breadth of search is devalued, and a structured population only lags behind an unstructured population in average fitness (ultimately converging). Thus, the Tortoise-Hare pattern is an indicator of ruggedness. After verifying these predictions in simulated populations where ruggedness is manipulable, we then explore average fitness in metapopulations of Escherichia coli. Consistent with a rugged landscape topography, we find a Tortoise-Hare pattern. Further, we find that structured populations accumulate more mutations, suggesting that distant peaks are higher. This approach can be used to unveil landscape topography in other systems, and we discuss its application for antibiotic resistance, engineering problems, and elements of Wrights Shifting Balance Process.\n\nSignificance StatementAdaptive landscapes are a way of describing how mutations interact with each other to produce fitness. If an adaptive landscape is rugged, organisms achieve higher fitness with more difficulty because the mutations to reach high fitness genotypes may not be always beneficial. By evolving populations of Escherichia coli with different degrees of spatial structure, we identified a Tortoise-Hare pattern, where structured populations were initially slower, but overtook less structured populations in mean fitness. These results, combined with genetic sequencing and computational simulation, indicate this bacterial adaptive landscape is rugged. Our findings address one of the most enduring questions in evolutionary biology, in addition to, providing insight into how evolution may influence medicine and engineering.

Evolutionary Biology↗

bioRxiv2018

An Empirical Demonstration of Unsupervised Machine Learning in Species Delimitation

One major challenge to delimiting species with genetic data is successfully differentiating species divergences from population structure, with some current methods biased towards overestimating species numbers. Many fields of science are now utilizing machine learning (ML) approaches, and in systematics and evolutionary biology, supervised ML algorithms have recently been incorporated to infer species boundaries. However, these methods require the creation of training data with associated labels. Unsupervised ML, on the other hand, uses the inherent structure in data and hence does not require any user-specified training labels, thus providing a more objective approach to species delimitation. In the context of integrative taxonomy, we demonstrate the utility of three unsupervised ML approaches, specifically random forests, variational autoencoders, and t-distributed stochastic neighbor embedding, for species delimitation utilizing a short-range endemic harvestman taxon (Laniatores, Metanonychus). First, we combine mitochondrial data with examination of male genitalic morphology to identify a priori species hypotheses. Then we use single nucleotide polymorphism data derived from sequence capture of ultraconserved elements (UCEs) to test the efficacy of unsupervised ML algorithms in successfully identifying a priori species, comparing results to commonly used genetic approaches. Finally, we use two validation methods to assess a priori species hypotheses using UCE data. We find that unsupervised ML approaches successfully cluster samples according to species level divergences and not to high levels of population structure, while standard model-based validation methods over-split species, in some instances suggesting that all sampled individuals are distinct species. Moreover, unsupervised ML approaches offer the benefits of better data visualization in two-dimensional space and the ability to accommodate various data types. We argue that ML methods may be better suited for species delimitation relative to currently used model-based validation methods, and that species delimitation in a truly integrative framework provides more robust final species hypotheses relative to separating delimitation into distinct \"discovery\" and \"validation\" phases. Unsupervised ML is a powerful analytical approach that can be incorporated into many aspects of systematic biology, including species delimitation. Based on results of our empirical dataset, we make several taxonomic changes including description of a new species.

evolutionary biology↗

bioRxiv2024

Local genetic adaptation to habitat in wild chimpanzees

How populations adapt to their environment is a fundamental question in biology. Yet we know surprisingly little about this process, especially for endangered species such as non-human great apes. Chimpanzees, our closest living relatives, are particularly interesting because they inhabit diverse habitats, from rainforest to woodland-savannah. Whether genetic adaptation facilitates such habitat diversity remains unknown, despite having wide implications for evolutionary biology and conservation. Using 828 newly generated exomes from wild chimpanzees, we find evidence of fine-scale genetic adaptation to habitat. Notably, adaptation to malaria in forest chimpanzees is mediated by the same genes underlying adaptation to malaria in humans. This work demonstrates the power of non-invasive samples to reveal genetic adaptations in endangered populations and highlights the importance of adaptive genetic diversity for chimpanzees. One-Sentence SummaryChimpanzees show evidence of local genetic adaptation to habitat, particularly to pathogens, such as malaria, in forests.

evolutionary biology↗

bioRxiv2019

Is evolution predictable? Quantitative genetics under complex genotype-phenotype maps

A fundamental aim of post-genomic 21st century biology is to understand the genotype-phenotype map (GPM) or how specific genetic variation relates to specific phenotypic variation (1). Quantitative genetics approximates such maps using linear models, and has developed methods to predict the response to selection in a population (2, 3). The other major field of research concerned with the GPM, developmental evolutionary biology or evo-devo (1, 4-6), has found the GPM to be highly nonlinear and complex (4, 7). Here we quantify how the predictions of quantitative genetics are affected by the complex, nonlinear maps found in developmental biology. We combine a realistic development-based GPM model and a population genetics model of recombination, mutation and natural selection. Each individual in the population consists of a genotype and a multi-trait phenotype that arises through the development model. We simulate evolution by applying natural selection on multiple traits per individual. In addition, we estimate the quantitative genetics parameters required to predict the response to selection. We found that the disagreements between predicted and observed responses to selection are common, roughly in a third of generations, and are highly dependent on the traits being selected. These disagreements are systematic and related to the nonlinear nature of the genotype-phenotype map. Our results are a step towards integrating the fields studying the GPM.

evolutionary biology↗

bioRxiv2020

Ecological memory preserves phage resistance mechanisms in bacteria

Defense mechanisms against pathogens are prevalent in nature, and their maintenance is critical for long-term survival of a species. Such mechanisms, which include CRISPR-mediated immunity in bacteria and the R genes in plants, carry substantial costs to organisms and can be rapidly lost when pathogens are eliminated. How a species preserves its molecular defenses despite their costs, in the face of variable pathogen levels, and across an ecology of localized patches remains a major unsolved problem in epidemiology and evolutionary biology. Using techniques of game theory and non-linear dynamical systems, we show that by maintaining a non-zero failure rate of immunity, hosts sustain sufficient levels of pathogen across an ecology to select against loss of the defense. This resistance switching strategy is evolutionarily stable, and provides a powerful evolutionary mechanism that maintains host-pathogen interactions and enables co-evolutionary dynamics in a wide range of systems.

evolutionary biology↗

bioRxiv2018

Robust methods for detecting convergent shifts in evolutionary rates

Identifying genomic elements underlying phenotypic adaptations is an important problem in evolutionary biology. Comparative analyses learning from convergent evolution of traits are gaining momentum in accurately detecting such elements. We previously developed a method for predicting phenotypic associations of genetic elements by contrasting patterns of sequence evolution in species showing a phenotype with those that do not. Using this method, we successfully demonstrated convergent evolutionary rate shifts in genetic elements associated with two phenotypic adaptations, namely the independent subterranean and marine transitions of terrestrial mammalian lineages. Our method calculates gene-specific rates of evolution on branches of phylogenetic trees using linear regression. These rates represent the extent of sequence divergence on a branch after removing the expected divergence on the branch due to background factors. The rates calculated using this regression analysis exhibit an important statistical limitation, namely heteroscedasticity. We observe that the rates on branches that are longer on average show higher variance, and describe how this problem adversely affects the confidence with which we can make inferences about rate shifts. Using a combination of data transformation and weighted regression, we have developed an updated method that corrects this heteroscedasticity in the rates. We additionally illustrate the improved performance offered by the updated method at robust detection of convergent rate shifts in phylogenetic trees of protein-coding genes across mammals, as well as using simulated tree datasets. Overall, we present an important extension to our evolutionary-rates-based method that performs more robustly and consistently at detecting convergent shifts in evolutionary rates.

evolutionary biology↗

bioRxiv2020

A demogenetic agent based model for the evolution of traits and genome architecture under sexual selection

Sexual selection has long been known to favor the evolution of mating behaviors such as mate preference and competitiveness, and to affect their genetic architecture, for instance by favoring genetic correlation between some traits. Reciprocally, genetic architecture can affect the expression and the evolvability of traits and mating behaviors. But sexual selection is highly context-dependent, making interactions between individuals a central process in evolution, governing the transmission of genotypes to the next generation. This loop between the genetic structure conditioning the expression and evolution of traits and behaviour, and the feedback of this phenotypic evolution on the architecture of the genome in the dynamic context of sexual selection, has yet to be thoroughly investigated. We argue that demogenetic agent-based models (DG-ABM) are especially suited to tackle such a challenge because they allow explicit modelling of both the genetic architecture of traits and the behavioural interactions in a dynamic population context. We here present a DG-ABM able to simultaneously track individual variation in traits (such as gametic investment, preference, competitiveness), fitness and genetic architecture throughout evolution. Using two simulation experiments, we compare various mating systems and show that behavioral interactions during mating triggered some complex feedback in our model, between fitness, population demography, and genetic architecture, placing interactions between individuals at the core of evolution through sexual selection. DG-ABMs can, therefore, relate to theoretical patterns expected at the population level from simpler analytical models in evolutionary biology, and at the same time provide a more comprehensive framework regarding individual trait and behaviour variation, that is usually envisioned separately from genome architecture in behavioural ecology.

evolutionary biology↗

bioRxiv2020

Information Content of Trees: Three-taxon Statements Inference Rules and Dependency

AO_SCPLOWBSTRACTC_SCPLOWThe three-taxon statement is the fundamental unit of rooted trees in Cladistics, stating that for three terminal taxa, two are more related to each other than to a third. Because of their fundamental role in phylogenetics, three-taxon statements are present in methodological research of various disciplines in evolutionary biology, as in consensus methods, supertree methods, species-tree methods, distance metrics, and even phylogenetic reconstruction. However, three-taxon statements methods are subject to important flaws related to information redundancy. We aim to study the behavior of three-taxon statements and the interactions among them in order to enhance their performance in evolutionary studies. We show here how specific interactions between three-taxon statements are responsible of the emergence of redundancy and dependency within trees, and how they can be used for the improvement of weighting procedures. Our proposal is subsequently empirically tested in the supertree framework using simulations. We show that three-taxon statements using fractional weights perform drastically better than classical methods such as MRP or methods using unweighted statements. Our study shows that appropriate fractional weighting of three taxon statements is of critical importance for removing redundancy in any method using three-taxon statements, as in consensus, supertrees, distance metrics, and phylogenetic or biogeographic analyses.

evolutionary biology↗

bioRxiv2025

A century of theories of balancing selection

Traits that affect organismal fitness are often very genetically variable. This genetic variation is vital for populations to adapt to their environments, but it is also surprising given that nature (after all) "selects" the best genotypes at the expense of those that fall short. Explaining the extensive genetic variation of fitness-related traits is thus a longstanding puzzle in evolutionary biology, with cascading implications for ecology, conservation, and human health. Balancing selection--an umbrella term for scenarios of natural selection that maintain genetic variation-- is a century-old explanation to resolve this paradox that has gained recent momentum from genome-scale methods for detecting it. Yet evaluating whether balancing selection can, in fact, resolve the paradox is challenging, given the logistical constraints of distinguishing balancing selection from alternative hypotheses and the daunting collection of theoretical models that formally underpin this debate. Here, we track the development of balancing selection theory over the last century and provide an accessible review of this rich collection of models. We first outline the range of biological scenarios that can generate balancing selection. We then examine how fundamental features of genetic systems--including non-random mating between individuals, differences in ploidy, genetic drift, and different genetic architectures of traits-- have been progressively incorporated into the theory. We end by linking these theoretical predictions to ongoing empirical efforts to understand the evolutionary processes that explain genetic variation.

evolutionary biology↗