Experience shapes wild boar spatial response to drive hunts

Human-induced disturbances of the environment are rapid and often unpredictable in space and time, exposing wildlife to strong selection pressure favouring plasticity in specific traits. Measuring wildlife behavioural plasticity in response to human-induced disturbances such as hunting pressures is crucial in understanding population expansion in the highly plastic wild boar species. We collected GPS-based movement data from 55 wild boars during drive hunts over three hunting seasons (2019-2022) in the Czech Republic and Sweden to identify behavioural plasticity in space use and movement strategies over a range of experienced hunting disturbances. Daily distance, daily range, and daily range overlap with hunting area were not affected by hunting intensity but were clearly related to wild boar hunting experience. On average, the post-hunt flight distance was 1.80 km, and the flight duration lasted 25.8 h until they returned to their previous ranging area. We detected no relationship in flight behaviour to hunting intensity or wild boar experience. Wild boar monitored in our study showed two behavioural responses to drive hunts, "remain" or "leave". Wild boars tended to "leave" more often with increasing hunting experience. Overall, this study highlights the behavioural plasticity of wild boar in response to drive hunts.

Genetic differentiation and phenotypic plasticity drive troglomorphic character development in European cavefish

The Aach cave loach (Barbatula barbatula), a recently discovered member of the Nemacheilidae family, offers a unique opportunity to understand the mechanisms underlying evolutionary change. In a common garden experiment, we reared groups of laboratory-bred cave, surface, and hybrid loach under different light conditions. Troglomorphic characters varied significantly among the fish, influenced to a different extent by parental origin and light conditions. Cavefish progeny consistently exhibited smaller eyes, lighter pigmentation, longer barbels, and larger olfactory epithelia than surface fish, while hybrids displayed intermediate characteristics. Surface and hybrid fish raised in complete darkness resembled the cavefish phenotype, while cavefish raised under a natural photoperiod approached the surface form. Characters associated with eye degeneration were found to be primarily heritable. Conversely, traits related to chemo- and mechano-reception were enhanced in the surface and hybrid groups reared in complete darkness, suggesting phenotypic plasticity. Our findings offer valuable insights into the interplay between genetic differentiation and phenotypic plasticity to troglomorphic adaption. This contributes to the broader understanding of the early stages of adaptation, where phenotypic plasticity, drift, and selection shape phenotypes. Relatively recently established cavefish, such as the Aach cave loach, are promising candidates for comparative research investigating evolutionary mechanisms.

Exploring the costs of phenotypic plasticity for evolvable digital organisms

Phenotypic plasticity is usually defined as a property of individual genotypes to produce different phenotypes when exposed to different environmental conditions. While the benefits of plasticity for adaptation are well established, the costs associated with plasticity remain somewhat obscure. Understanding both why and how these costs arise could help us explain and predict the behavior of living creatures as well as allow the design of more adaptable robotic systems. One of the challenges of conducting such investigations concerns the difficulty of isolating the effects of different types of costs and the lack of control over environmental conditions. The present study addresses these challenges by using virtual worlds (software) to investigate the environmentally regulated phenotypic plasticity of digital organisms. The experimental setup guarantees that potential genetic costs of plasticity are isolated from other plasticity-related costs. Multiple populations of organisms endowed with and without phenotypic plasticity in either the body or the brain are evolved in simulation, and organisms must cope with different environmental conditions. The traits and fitness of the emergent organisms are compared, demonstrating cases in which plasticity is beneficial and cases in which it is neutral. The hypothesis put forward here is that the potential benefits of plasticity might be undermined by the genetic costs related to plasticity itself. The results suggest that this hypothesis is true, while further research is needed to guarantee that the observed effects unequivocally derive from genetic costs and not from some other (unforeseen) mechanism related to plasticity.

avidaR: an R library to perform complex queries on an ontology-based database of digital organisms

Digital evolution is a branch of artificial life in which self-replicating computer programs-digital organisms-mutate and evolve within a user-defined computational environment. In spite of its value in biology, we still lack an up-to-date and comprehensive database on digital organisms resulting from evolution experiments. Therefore, we have developed an ontology-based semantic database-avidaDB-and an R package-avidaR-that provides users of the R programming language with an easy-to-use tool for performing complex queries without specific knowledge of SPARQL or RDF. avidaR can be used to do research on robustness, evolvability, complexity, phenotypic plasticity, gene regulatory networks, and genomic architecture by retrieving the genomes, phenotypes, and transcriptomes of more than a million digital organisms available on avidaDB. avidaR is already accepted on CRAN (i.e., a comprehensive collection of R packages contributed by the R community) and will make biologists better equipped to embrace the field of digital evolution.

Ontology for the Avida digital evolution platform

The Ontology for Avida (OntoAvida) aims to develop an integrated vocabulary for the description of Avida, the most widely used computational approach for performing experimental evolution using digital organisms-self-replicating computer programs that evolve within a user-defined computational environment. The lack of a clearly defined vocabulary makes some biologists feel reluctant to embrace the field of digital evolution. This integrated framework empowers biologists by equipping them with the necessary tools to explore and analyze the field of digital evolution more effectively. By leveraging the vocabulary of Avida, researchers can gain deeper insights into the evolutionary processes and dynamics of digital organisms. In addition, OntoAvida allows researchers to make inference based on certain rules and constraints, facilitate the reproducibility of in silico evolution experiments and trace the provenance of the data stored in avidaDB-an RDF database containing the genomes, transcriptomes, and phenotypes of more than a million digital organisms. OntoAvida is part of the Open Biological and Biomedical Ontologies (OBO Foundry) and is available at http://www.obofoundry.org/ontology/ontoavida.html .

Predicting the Responses of Functional Leaf Traits to Global Warming: An In Situ Temperature Manipulation Design Using Iris pumila L

Phenotypic plasticity is widely acknowledged as one of the most common solutions for coping with novel environmental conditions following climate change. However, it is less known whether the current amounts of trait plasticity, which is sufficient for matching with the contemporary climate, will be adequate when global temperatures exceed historical levels. We addressed this issue by exploring the responses of functional and structural leaf traits in Iris pumila clonal individuals to experimentally increased temperatures (~1.5 °C) using an open top chamber (OTC) design. We determined the phenotypic values of the specific leaf area, leaf dry matter content, specific leaf water content, and leaf thickness in the leaves sampled from the same clone inside and outside of the OTC deployed on it, over seasons and years within two natural populations. We analyzed the data using a repeated multivariate analysis of variance, which primarily focusses on the profiles (reaction norms (RNs)) of a variable gathered from the same individual at several different time points. We found that the mean RNs of all analyzed traits were parallel regardless of experienced temperatures, but differed in the level and the shape. The populations RNs were similar as well. As the amount of plasticity in the analyzed leaf trait was adequate for coping with elevated temperatures inside the OTCs, we predict that it will be also sufficient for responding to increased temperatures if they exceed the 1.5 °C target.

Intrinsically disordered proteins and conformational noise: The hypothesis a decade later

Phenotypic plasticity is the ability of individual genotypes to produce different phenotypes in response to environmental perturbations. We previously postulated how conformational noise emanating from conformational dynamics of intrinsically disordered proteins (IDPs) which is distinct from transcriptional noise, can contribute to phenotypic switching by rewiring the cellular protein interaction network. Since most transcription factors are IDPs, we posited that conformational noise is an integral component of transcriptional noise implying that IDPs may amplify total noise in the system either stochastically or in response to environmental changes. Here, we review progress in elucidating the details of the hypothesis. We highlight empirical evidence supporting the hypothesis, discuss conceptual advances that underscore its fundamental importance and implications, and identify areas for future investigations.

Integrating neuroplasticity and evolution

Neuroplasticity and evolutionary biology have been prominent fields of study for well over a century. However, they have advanced largely independently, without consideration of the benefits of integration. We propose a new framework by which researchers can begin to examine the evolutionary causes and consequences of neuroplasticity. Neuroplasticity can be defined as changes to the structure, function or connections of the nervous system in response to individual experience. Evolution can alter levels of neuroplasticity if there is variation in neuroplasticity traits within and between populations. Neuroplasticity may be favored or disfavored by natural selection depending on the variability of the environment and the costs of neuroplasticity. Additionally, neuroplasticity may affect rates of genetic evolution in many ways: for example, decreasing rates of evolution by buffering against selection or increasing them via the Baldwin effect, by increasing genetic variation or by incorporating evolved peripheral changes to the nervous system. These mechanisms can be tested using comparative and experimental approaches and by examining patterns and consequences of variation in neuroplasticity among species, populations and individuals.

Transgenerational effects of benzotriazole on the gene expression, growth, and reproduction of Daphnia magna

Due to its widespread and intensive use as a corrosion inhibitor, benzotriazole is ubiquitously detected from a few parts per billion to several hundred parts per million in aquatic environments. The long-term toxicity of benzotriazole is unclear despite its low acute toxicity. Therefore, we investigated the transgenerational effects of benzotriazole at the genomic and individual levels using the freshwater zooplankton Daphnia magna. Maternal exposure to sublethal concentrations (15 and 30 mg/L) of benzotriazole exerted transgenerational effects on D. magna at the genomic and individual levels even in descendants that have never been exposed to benzotriazole. Significant alterations in the expression of Cyp, GST, Vtg1, and Hb and in neonate size were observed in the unexposed F3 generation, confirming the transgenerational effect of benzotriazole. Interestingly, detoxification related genes Cyp and GST were unaffected or downregulated in the exposed generation but upregulated in the following unexposed generations. Furthermore, continuous multigenerational exposure to an environmental concentration (4.3 μg/L) of benzotriazole also upregulated detoxification genes in decent generations but exerted no individual-level effects in subsequent generations.

A computational investigation of cis-gene regulation in evolution

In biological processes involving gene networks, genes regulate other genes that determine the phenotypic traits. Gene regulation plays an important role in evolutionary dynamics. In a genetic algorithm, a trans-gene regulatory mechanism was shown to speed up adaptation and evolution. Here, we examine the effect of cis-gene regulation on an adaptive system. The model is haploid. A chromosome is partitioned into regulatory loci and structural loci. The regulatory genes regulate the expression and functioning of structural genes via the cis-elements in a probabilistic manner. In the simulation, the change in the allele frequency, the mean population fitness and the efficiency of phenotypic selection are monitored. Cis-gene regulation increases adaption and accelerates the evolutionary process in comparison with the case involving absence of gene regulation. Some special features of the simulation results are as follows. A low ratio of regulatory loci and structural loci gives higher adaptation for fixed total number of loci. Plasticity is advantageous beyond a threshold value. Adaptation is better for large number of total loci when the ratio of regulatory loci to structural loci is one. However, it reaches a saturation beyond which the increase in the total loci is not advantageous. Efficiency of the phenotypic selection is higher for larger value of the initial plasticity.

Plant Species' Capacity for Range Shifts at the Habitat and Geographic Scales: A Trade-Off-Based Framework

Climate change is causing rapid shifts in the abiotic and biotic environmental conditions experienced by plant populations, but we lack generalizable frameworks for predicting the consequences for species. These changes may cause individuals to become poorly matched to their environments, potentially inducing shifts in the distributions of populations and altering species' habitat and geographic ranges. We present a trade-off-based framework for understanding and predicting whether plant species may undergo range shifts, based on ecological strategies defined by functional trait variation. We define a species' capacity for undergoing range shifts as the product of its colonization ability and the ability to express a phenotype well-suited to the environment across life stages (phenotype-environment matching), which are both strongly influenced by a species' ecological strategy and unavoidable trade-offs in function. While numerous strategies may be successful in an environment, severe phenotype-environment mismatches result in habitat filtering: propagules reach a site but cannot establish there. Operating within individuals and populations, these processes will affect species' habitat ranges at small scales, and aggregated across populations, will determine whether species track climatic changes and undergo geographic range shifts. This trade-off-based framework can provide a conceptual basis for species distribution models that are generalizable across plant species, aiding in the prediction of shifts in plant species' ranges in response to climate change.

Artificial selection methods from evolutionary computing show promise for directed evolution of microbes

Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a general-purpose search engine for solutions to challenging computational problems. Despite their overlapping approaches, artificial selection methods from evolutionary computing are not commonly applied to living systems in the laboratory. In this work, we ask whether parent selection algorithms—procedures for choosing promising progenitors—from evolutionary computation might be useful for directing the evolution of microbial populations when selecting for multiple functional traits. To do so, we introduce an agent-based model of directed microbial evolution, which we used to evaluate how well three selection algorithms from evolutionary computing (tournament selection, lexicase selection, and non-dominated elite selection) performed relative to methods commonly used in the laboratory (elite and top 10% selection). We found that multiobjective selection techniques from evolutionary computing (lexicase and non-dominated elite) generally outperformed the commonly used directed evolution approaches when selecting for multiple traits of interest. Our results motivate ongoing work transferring these multiobjective selection procedures into the laboratory and a continued evaluation of more sophisticated artificial selection methods.

Humans have long known how to co-opt evolutionary processes for their own benefit. Carefully choosing which individuals to breed so that beneficial traits would take hold, they have domesticated dogs, wheat, cows and many other species to fulfil their needs.

Biologists have recently refined these ‘artificial selection’ approaches to focus on microorganisms. The hope is to obtain microbes equipped with desirable features, such as the ability to degrade plastic or to produce valuable molecules. However, existing ways of using artificial selection on microbes are limited and sometimes not effective.

Computer scientists have also harnessed evolutionary principles for their own purposes, developing highly effective artificial selection protocols that are used to find solutions to challenging computational problems. Yet because of limited communication between the two fields, sophisticated selection protocols honed over decades in evolutionary computing have yet to be evaluated for use in biological populations.

In their work, Lalejini et al. compared popular artificial selection protocols developed for either evolutionary computing or work with microorganisms. Two computing selection methods showed promise for improving directed evolution in the laboratory. Crucially, these selection protocols differed from conventionally used methods by selecting for both diversity and performance, rather than performance alone. These promising approaches are now being tested in the laboratory, with potentially far-reaching benefits for medical, biotech, and agricultural applications.

While evolutionary computing owes its origins to our understanding of biological processes, it has much to offer in return to help us harness those same mechanisms. The results by Lalejini et al. help to bridge the gap between computational and biological communities who could both benefit from increased collaboration.