Through the looking glass: attempting to predict future opportunities and challenges in experimental biology

Over the 100 years that Journal of Experimental Biology (JEB) has been in existence, much has changed – from the ways in which data are collected and analyzed (e.g. McHenry and Hedrick, 2023; Pan and Perry, 2023; Provini et al., 2023; Tresguerres et al., 2023), to the people doing the science (Franklin and Wright, 2023; Hankins and Rutledge, 2023; Tomsic and Silva, 2023) and the disciplines they represent, to manuscript handling and the production of the journal itself (Hoppeler and Franklin, 2020; Somero, 2023). Throughout the years, however, JEB's focus has remained firmly on the question of ‘how animals work’ (Schmidt-Nielsen, 1972), publishing leading research on the form and function of animals at all levels of biological organization.

To celebrate its milestone year, JEB commissioned a collection of articles examining the past, present and future of ‘experimental biology’ (Franklin, 2023). Even a quick look to the past reveals myriad examples of how technological innovation advanced the field (e.g. McHenry and Hedrick, 2023; Pan and Perry, 2023; Provini et al., 2023; Tresguerres et al., 2023), and this effect is likely to continue. For example, biologgers (see Glossary) are becoming both more sophisticated and smaller, allowing more data of different types to be collected from a greater range of animals (Hawkes et al., 2021a,b). Similarly, improvements in high-speed, high-resolution imaging techniques have fueled progress in biomechanics (McHenry and Hedrick, 2023; Provini et al., 2023). The increasing availability of sequenced genomes from large numbers of species and the advent of relatively affordable techniques to probe the genome, epigenome, transcriptome or proteome are providing insight into the molecular mechanisms underlying physiological responses (e.g. in the context of resilience and resistance; see Glossary). Building on the availability of sequence data, genome-editing techniques are becoming available for a broadening array of species, allowing hypotheses to be tested experimentally (Dickinson et al., 2020). Although the power of these approaches is undeniable, management of the massive databases that they generate is also, in itself, driving research innovation. Cutting-edge computational tools including artificial intelligence (AI) and machine learning, as well as modeling and simulations are expanding the ways in which researchers can mine such databases and make sense of the data (e.g. McHenry and Hedrick, 2023; Yovel and Rechavi, 2023). Accompanying the technological advances are changes in policies and practices, such as ‘open science’ (Bertram et al., 2023), which are taking hold as the reliance on large databases increases.

These practices, together with open-source data and code, are facilitating interdisciplinary collaborations, for example between experimentalists and mathematicians or data scientists. The increasing difficulty of securing funding for fundamental research in experimental biology and the resultant need to diversify funding sources is also influencing the questions, species and systems on which experimental biologists are working. Our discipline is evolving and must continue to do so to remain relevant.

In this Commentary, we close the Centenary Article series by looking forward. Each section discusses a different example of what we consider to be the important research opportunities and challenges that await, including ways in which technological advances are enabling researchers to tackle the ‘big’ issues in the field. While recognizing that ‘it is very difficult to predict – especially the future’ (a Danish saying sometimes attributed to the physicist Niels Bohr; Mencher, 1971), we aim to identify areas where significant knowledge gaps remain and new areas into which the field can expand. We conclude with perspectives on the potential of experimental biology to have impacts beyond knowledge creation.

One of the greatest challenges for experimental biology in the 21st century is to improve our ability to predict the resilience and resistance of diverse organisms (Thorogood et al., 2023) to environmental change. Anthropogenic activities are already causing changes in temperature and rainfall patterns, exacerbating extreme events such as heatwaves, wildfires and episodes of aquatic hypoxia, and causing the accumulation of toxic chemical pollution, with potentially devastating effects (Trenberth, 2011; Deutsch et al., 2015; Frölicher et al., 2018; Jeppesen et al., 2020; Earhart et al., 2022; Persson et al., 2022). In turn, these changes may restructure communities and alter biotic interactions, posing further important challenges. How does environmental degradation affect the physiology and behavior of diverse animals? Do organisms have the capacity to recruit existing biochemical and physiological mechanisms to cope with these changes? Will adaptation (see Glossary) through natural selection be fast enough to help mitigate the effects of anthropogenic change (Somero, 2010; Radchuk et al., 2019; Hoffmann et al., 2023; Thurman et al., 2023)? What are the costs and limits of responding to environmental change? Importantly, can we use our knowledge of ‘how animals work’ to help predict and mitigate effects of anthropogenic environmental change?

With improvements in genome sequencing and other ‘-omics’ technologies, it is now possible to address questions about the mechanisms of resilience and resistance to environmental change at the level of the whole genome, epigenome, transcriptome, proteome or metabolome across diverse taxa. This point is important, because it allows the field to expand the range of animals studied beyond those selected using the Krogh principle [i.e. ‘for such a large number of problems there will be some animal of choice, or a few such animals, on which it can be most conveniently studied’ (Krogh, 1929)]. It is also a significant change in worldview because not every question can be addressed by focusing on a limited number of ‘model’ species (e.g. Jackson et al., 2023). Similarly, it is now possible to assess the evidence for selection on specific loci at a whole-genome scale in almost any taxon of interest, as well as the potential for adaptive evolution from standing genetic variation (Dillon and Lozier, 2019; Hoffmann et al., 2023). However, these new opportunities also highlight the fundamental challenge of linking changes at the level of the genome to the impacts of these changes on the phenotype of the organism and, in turn, to the effects on fitness in natural environments. These integrative questions are key to making a convincing case for the adaptive benefit of a particular genomics change (Nielsen, 2009).

Advances in -omics technologies allow experimental biologists to probe biological processes at the cellular level in diverse organisms. Studies of the molecular mechanisms underlying responses to environmental change at a whole-genome scale typically involve analyzing either messenger RNA or protein abundance, but rarely both. This is problematic because the relationships among transcription, translation and cellular functioning are likely to be dynamic and context dependent (Buccitelli and Selbach, 2020), and levels of RNA transcription do not necessarily predict protein translation or lead to functional changes at higher levels of organization. The methodological approaches needed to address these issues are now accessible. However, we must pay careful attention to experimental design to take into account the dynamic nature of the processes and the complex impacts of interacting stressors (Rodgers and Gomez Isaza, 2023). An additional advance is the recent development of technologies for single-cell -omics and spatially resolved transcriptomics (Choi et al., 2020; Walker et al., 2022), which are allowing us to move beyond homogenizing whole tissues to probe function at the level of different cell types within a tissue.

Although many studies are now leveraging -omics approaches to probe the mechanisms underlying acute acclimation/acclimatization (see Glossary) responses to environmental change, less is known about the longer-lasting phenotypic changes associated with phenomena such as developmental plasticity or transgenerational plasticity (see Glossary). Such epigenetic effects are likely to be extremely important in determining resilience and resistance to environmental change, and recent studies in ecologically and environmentally relevant (non-model) organisms have begun to realize the potential of epigenomic approaches in addressing these questions (Bonasio, 2015; Fanter et al., 2022).

Similarly, it is now possible to obtain evidence for selection across species and populations (Dillon and Lozier, 2019; Jackson et al., 2020; Hoffmann et al., 2023). The relative ease and reduced cost of genome sequencing, as well as the wealth of genomic data already available, have opened the door to assessing whether there are broad-scale, taxon-spanning signals of selection in response to environmental factors such as temperature. Indeed, we can now move beyond single nucleotide polymorphisms (SNPs) to other features of the genome, such as copy number variants, repetitive elements, transposable elements and genome rearrangements. It is becoming apparent that these types of genomic features may be important targets of natural selection (Wellenreuther et al., 2019). The comparative approaches that are widely used in ecological physiology are also well suited for application to genome-level data, although it will require considerable investment in bioinformatics skills to identify conserved genomic elements and regulatory systems – as well as those that have diverged across species – that may play a role in environmental adaptation.

Identifying the processes responsible for plastic responses such as acclimation or acclimatization, as well as those involved in phenotypic differentiation among populations or species, can be facilitated through quantitative genetic approaches applicable to a wide range of taxa, such as genome-wide association studies (GWASs; see Glossary) and genome–environment association (GEA) studies (see Glossary). However, many of the physiological processes involved in environmental acclimation and adaptation are likely to be polygenic, with small contributions from multiple genes. Furthermore, our understanding of the genes that govern important physiological traits and their plasticity is still limited, and whole-genome approaches such as GWASs are notorious for uncovering regions of the genome with unknown functions. Although frustrating in the short-term, this reveals the great potential for novel discoveries in comparative physiology and biochemistry to contribute to our understanding of processes at the whole-genome scale, and to make the link between gene sequence and gene function.

The availability of genetic engineering approaches such as CRISPR–Cas9 provides a potential solution to the challenge of linking DNA-level changes to effects at the level of the cell, tissue or organism (Zimmer et al., 2019). However, CRISPR–Cas9 approaches are most readily applied in species with relatively short generation times that are easy to rear in a laboratory environment, which limits their applicability to many endangered species of intense conservation interest. Application of these approaches to non-model organisms requires time, effort and funding but is both worthwhile and necessary to drive new discovery (Dickinson et al., 2020).

Another intriguing advance that is relevant to the challenge of linking gene function to gene sequence is the development of machine learning and AI technologies for predicting protein structure and function from primary sequence data (e.g. Jumper et al., 2021). At present, these approaches remain limited by their reliance on a relatively static view of protein structure and function, whereas the functional properties of proteins are often determined by their ability to undergo dynamic shape changes.

Perhaps the largest limitation facing the application of -omics technologies to critical questions about the mechanisms underlying responses to environmental change is our ignorance of how processes acting at the molecular and cellular level scale up to affect integrative physiological function, and how processes at each level of biological organization interact to influence emergent properties at higher levels. It is here that the greatest contribution of experimental biology is likely to be made, as the tradition in our field is to consider the organism holistically. Indeed, we are even beginning to integrate concepts of the ‘extended phenotype’, acknowledging the important role of the microbiome in determining organismal resilience in a changing world (Fontaine and Kohl, 2023).

The increasing resolution, precision and statistical power of -omics technologies is allowing these approaches to be applied to different levels of organization within an individual (e.g. different cell types, tissues or organs), generating rich datasets in remarkably short times. Historically, much of the experimental and statistical focus was on treatment-, population- or species-level variation. However, there has been renewed attention given to ecophysiological trait variation among individuals, to better understand temporal responses such as development and senescence, and how these feed into population-, species- and community-level responses to perturbations (Fig. 1). Studying phenotypic variation across diverse communities living within the same environment or within a taxon that inhabits different environments is useful because the phenotype expressed is a product of recent experience, developmental conditions and evolutionary history (Dillon and Lozier, 2019; Jackson et al., 2020). In other words, powerful insights should be generated by employing meta-omics on classic experimental biology questions at different spatial and temporal scales; such approaches have been used to investigate whether there are common solutions to different stressors across species (e.g. Jackson et al., 2020), the adaptability and tuneability of pre-existing biochemical and molecular stress pathways (Barkai and Leibler, 1997; Kim et al., 2019; Manicka et al., 2022), and as discussed above, whether species will be robust and resilient in the face of rapid environmental change (e.g. Dillon and Lozier, 2019; Hoffmann et al., 2023).

Although much is known at the population (treatment) or species levels in these kinds of comparisons, phenotypic plasticity occurs at the level of individuals and trait variation can be generated through processes that are not well described by a gene-centric view of the organism's response. A case in point is the neural system (dys)function and loss of ion homeostasis involved in the onset of, and recovery from, chill coma in insects; a process for which little insight into physiological dynamics is yielded by a gene expression approach (Overgaard and MacMillan, 2017). Indeed, there are renewed arguments about focusing attention on individual-level ecophysiological responses, the time-course of such responses and their reversibility (or lack thereof) to understand phenotypic plasticity of traits that may give adaptive advantages for coping with rapid anthropogenic change (e.g. Terblanche and Hoffmann, 2020; Hoffmann and Bridle, 2022; Moubarak et al., 2023). To date, researchers have often been forced to pool samples from individuals to try to partition out unwanted sources of ‘noise’ in comparative data. As costs decline and techniques improve, it is becoming feasible to probe more deeply an individual's responses over time, and to obtain large(r) samples of -omics data to address questions of adaptability of organisms and systems. Thus, we can envision flourishing stress time-course data coupled with different types of behavioral and ecophysiological responses. Concurrently, the ability to integrate across hierarchical levels will bring new challenges. For example, high-dimensionality datasets produced at ever finer resolution are likely to need new analytical tools. However, such datasets will provide much-needed insight into how individual animals experience and respond to multisensory information and diverse stressors, in turn allowing more robust links to be made between laboratory and field settings.

Most mechanistic experimental biology studies over the past 100 years have taken place in a laboratory setting where it is possible to have tight control over the environmental conditions while modifying one or a few parameters. Important fundamental principles were discovered using this approach, but there is a growing realization that the multifactorial interactions within natural environments are missed in controlled laboratory experiments. There is an urgent need to understand how animals function in complex environments (Fontaine and Kohl, 2023) because anthropogenic pressures are changing climates and habitats; researchers interested in mechanistic physiology and biomechanics need diverse approaches to broaden our understanding in this context.

One approach is to use ecologically relevant stimuli/conditions/contexts in more realistic laboratory experiments (Morash et al., 2018; Moss et al., 2023). For example, environmental biologgers are being used to monitor abiotic fluctuations in nature (e.g. tidal pools; Sun et al., 2023) so as to create and ‘play back’ realistic conditions in laboratory experiments. Tracking animal movement within habitats and monitoring the physical environmental variables (e.g. temperature, salinity, water depth, oxygen levels) concurrently through animal-borne environmental sensors can provide more depth to our understanding of the environmental conditions animals are exposed to, and this can, in turn, aid our design of laboratory-based experiments (Grosell et al., 2020; Turko et al., 2023).

An increasingly popular approach is the use of terrestrial or aquatic mesocosms (see Glossary) to run experiments where a variable (e.g. diet, temperature, pH, UV and visible light levels) can be controlled and where multiple cues or stressors, that can interact synergistically or antagonistically, are evaluated under semi-natural conditions (Ohmer et al., 2023; Wild et al., 2023). Recently, mesocosms have been used to study the impact of climate change stressors on fish embryonic development (Wild et al., 2023), artificial light at night on stress responses in corals (Mardones et al., 2023) and limited food resources on energy budgets in marsupials (Nespolo et al., 2022). Mesocosms are a valuable tool for understanding physiological responses to semi-natural conditions, but this approach is not always suitable (e.g. when studying large or migratory animals).

The development of sophisticated technologies to quantify physiological variables in free-ranging animals in the natural environment is crucial to enriching our understanding of the full range of physiological and biomechanical responses to environmental perturbations (Watanabe and Goldbogen, 2021; Williams et al., 2021; Costa and Favilla, 2023). For example, long-term studies of crocodiles (e.g. Barham et al., 2023) using implanted tracking devices have provided extensive datasets of habitat use, movement patterns and behavior. Recent advances in AI technologies are enabling detailed movement analyses of cetaceans using satellite images without the potential complications of attaching invasive biologgers (Khan et al., 2023). With engineering and computer science expertise, new advances have also been made to understand the ecological biomechanics of cheetahs, the fastest-moving land mammals (Wilson et al., 2013, 2018; Shield et al., 2023).

Traditionally used to track animal movement, external and implanted devices can now provide detailed information on the physiology of animals, including body temperature, heart rate, blood oxygen and CO₂ levels, blood pressure, and blood concentrations of metabolites such as glucose or lactate (Hawkes et al., 2021a,b; Khan et al., 2022; Watanabe and Papastamatiou, 2023). Implantable electronic technology also allows for experiments where physiological function can be controlled; for example, using pacemakers to regulate heart rate and remote drug-delivery systems to transfer pharmaceutical agents to targeted locations. Such technology provides exceptional opportunities for comparative physiologists interested in elucidating mechanism to do so in animals living in their natural environments.

Researchers interested in flight have also gained from emerging technologies. For example, novel insights have been discovered using custom-built loggers on bar-headed geese migrating at extreme attitudes (e.g. Hawkes et al., 2011; Parr et al., 2019) or on ibis flying in formation (Portugal et al., 2014), although limitations are imposed by recovery of instrumented birds, data retrieval and the mass of the device. Biologgers have been miniaturised to accommodate smaller birds (English et al., 2023 preprint), but biologger shape and attachment position may negatively impact flight (Mizrahy-Rewald et al., 2023). Similarly, miniaturized radio transmitters implanted on dragonflies have revealed migratory patterns, although the experimenters had to chase the dragonflies in Cessna aircraft equipped with sensitive receivers, which probably precludes this approach from being widely used (Wikelski et al., 2006). Indeed, this example illustrates an important point, namely that the cost of sophisticated, miniature devices, particularly those that can capture and transmit data in real time, is beyond the budget of many experimental biologists. An exciting possibility (that may help to address budget issues) is to adapt wearable technology created for human use to animals in the field. For example, one group is proposing the application of wearable sensors for use in marine environments to monitor physiological changes in marine animals as well as changes in the surrounding environment (Kaidarova et al., 2023). Indeed, it may soon become feasible to collect large-scale datasets and use AI methodologies to create an ‘internet’ of ocean life.

Mathematical modeling allows for a predictive understanding of physiology and biomechanics that spans levels of organization. Over the years, papers in JEB have frequently sought to develop conceptual or simulated models of the phenomena being studied. For example, models of material properties (e.g. Ker, 1999), structural mechanics (e.g. Jimenez et al., 2023), fluid dynamics (e.g. Costello et al., 2023) and muscle contraction dynamics (e.g. Sponberg et al., 2023) have played central roles in understanding animal form and function, as have models that address the feedback control mechanisms that ensure maintenance of a given state (e.g. homeostasis or a behavioral state). Sometimes the models are conceptual and non-formal, capturing the inferred phenomena in the form of a flow chart or a block diagram. Alternatively, models may employ engineering mathematical techniques, such as control theory (see Glossary). The approach has been used effectively to model gene networks (Smolen et al., 2000), metabolic systems (Song et al., 2013; Watson et al., 2015) and sensorimotor processes in animals (e.g. Natesan et al., 2019), as well as processes at the spatial and temporal scales of ecology and evolution (e.g. Cowan et al., 2014). A series of such input–output relationships can model a biochemical or behavioral pathway, and can predict the dynamics of the modeled phenomenon, thus providing clear and testable predictions. Often, such approaches make assumptions of linearity that may constrain their utility, but overall, this approach is quite amenable to generating computational simulations as well as robotic applications, which connects it to engineering applications (e.g. Kanzaki et al., 2013). Systems-level approaches are usually effective in a rapid exploration of the parameter space of input–output relationships, but typically reveal very little about what lies inside the ‘black box’. Understanding the mechanistic details generally requires a rigorous experimental approach that includes systematic manipulation of the system. Thus, the systems approach trades mechanistic insight for a detailed examination of input–output relationships.

The advent of powerful and economical computational algorithms has resulted in many papers in JEB adopting computational approaches to model biological questions, while incorporating experimental data to validate the models (Biewener et al., 2012). For instance, investigations of insect and bird flight or fish locomotion have used computational fluid dynamics (CFD) to model the flows and forces around animal limbs moving in a fluid medium (air or water). These approaches were adopted early on by the JEB community (e.g. Liu et al., 1998; Miller and Peskin, 2004) and have proven to be powerful in addressing diverse questions in animal locomotion. The finite element method provides another example (e.g. Herbert et al., 2000); it has been a useful computational tool in the hands of biomechanists who seek to model how solids with complex geometries behave in response to external forces. Similarly, recent advances in computing and simulation tools are allowing for the generation of increasingly complex models to test integrative biomechanical hypotheses, verging on ‘synthetic’ or virtual organisms in simulation (Ijspeert and Daley, 2023; Ramdya and Ijspeert, 2023). Simulations can be leveraged to make predictions for multiple competing hypotheses and to explore potential sources of variation and diversity in biomechanical function. With unconstrained computational power, the ability to collect large datasets and the availability of data for diverse animals, we are finally poised to rigorously test, through models, hypotheses at the intersection of mechanical function and evolutionary processes. Yet, even as we embrace these new modeling and simulation tools, we must ensure that we maintain a focus on rigorous experimental science and data collection. JEB has a longstanding tradition of testing hypothesis-driven models through carefully designed experiments, and this approach will continue to be essential as the models become more sophisticated. Maintaining the cycle from model predictions to experimental refutation of competing alternatives and back to prediction remains critical for ensuring that our simulations are relevant to the organisms we study. To continue this productive interaction and dialogue between modeling/simulations and experimental biology, JEB recently launched a new article type, ‘Theory & Modelling’ (https://journals.biologists.com/jeb/pages/article-types#theory) for studies that develop and analyze models in the context of addressing a fundamental biological question relevant to comparative animal biology.

As engineers advance in their ability to manufacture sensors, actuators and energy generation and storage solutions that approach the performance of animals, we also see growing interest in understanding animal function using robots as physical models to test hypotheses about the integration of sensorimotor and biomechanical function. We therefore anticipate continuing expansion and interest in biomimetics, bio-inspired robotics, sensors and control algorithms (Ijspeert and Daley, 2023).

The new technologies, immense datasets, tools for genetic manipulation and powerful computational methods (and supercomputers) that were developed initially for model organisms are now available for comparative studies. We anticipate that these tools, coupled with the capacity to take laboratory methods into the field, will advance the transition from observation and description to a deeper mechanistic understanding of how animals work in their natural (and changing) environments. New and broader perspectives will be achieved by applying these approaches to animals living outside the temperate zones that have historically been understudied (Denlinger, 2023; Tomsic and Silva, 2023). Perhaps most excitingly, studies of the behavior, physiology and survival skills of animals in inaccessible habitats – such as the forest canopy or the depths of the ocean – have potential to reveal completely new physiological mechanisms in the coming years (Knight, 2023a). For example, it may become possible to understand the energetics, information flow and mechanisms underlying the coordinated behavior of animal swarms or entire ecosystems. Non-invasive or minimally invasive methods to record physiological parameters in actively behaving animals will make it possible to study animals migrating through remote habitats, understand how a bee finds its nest after a foraging trip (Collett and Hempel de Ibarra, 2023) or elucidate the physical basis of memory (Rivi et al., 2023).

New tools and methods also provide opportunities to study animals previously seen as too small to be investigated (e.g. French et al., 2023; Ijspeert and Daley, 2023). Miniaturization of nervous systems and body morphology has happened in many animal groups, and understanding the kinematics, energetics, general physiology and neuronal control of very small animals will lead to important insights into general mechanisms. Synchrotron-based methods, computed tomography and tissue preparation techniques that allow high-resolution imaging of intact tissues (e.g. CLARITY; Chung et al., 2013) are allowing us to fully describe brain ‘connectomes’ (e.g. Chua et al., 2023), providing the information needed to compile a model of neuron activity as the animal navigates from its nest to a food source and back (e.g. Kelber et al., 2019). Miniature tags allow detailed data to be collected from small bats (Moss et al., 2023) or bumblebees (Crall et al., 2015) during their foraging flights. Methods to investigate single mitochondria (Nie et al., 2021) and sensors that can measure temperature with sub-cellular resolution (Sotoma et al., 2021) will inform our knowledge of energetics. Similarly, genetically encoded calcium indicators such as GCaMP are allowing calcium imaging to be carried out in the brains of live animals, providing extraordinary insight into patterns of neuron activation (e.g. Poulsen et al., 2021; Depetris-Chauvin et al., 2023). Importantly, all animals start small – and miniaturized tools and methods allow us to study the physiology and biomechanics of developing animals. Such studies may, for instance, inform our understanding of how innate behaviors in a metamorphosing or embryonic insect can be precise and well-coordinated despite the absence of feedback control. Along similar lines, we still have much to learn about the development of sensory organs or circulatory systems that change through ontogeny depending on changing needs and limitations.

Last but by no means least, it is becoming increasingly important to study animal physiology and biomechanics (Dellaert and Putnam, 2023; Fontaine and Kohl, 2023) in a world that is rapidly changing (Hoffmann et al., 2023; Rodgers and Gomez Isaza, 2023). We anticipate that comparative physiology will be particularly important in this regard, providing insight into species that, by virtue of living in unpredictable environments, hostile or changing habitats, or extreme situations, are more likely to have unexpected survival strategies. Here, as in other fields, we must look to broaden our horizons by increasing the diversity of voices, for example, by integrating the deep knowledge of indigenous peoples into work on animals and their environments (Franklin and Wright, 2023; Hird et al., 2023).

In this Commentary, we have peered into the potential future of comparative physiology, biomechanics and experimental biology (Fig. 1). Our shared perspective has been influenced by the thought-provoking and inspiring Centenary Articles that have been published this year in JEB. We believe that advances in technology, including rapidly evolving generative AI, computational modeling and machine learning, are poised to accelerate our understanding of how animals function and respond in a challenging world. These tools may be deployed most effectively in an interdisciplinary fashion, through creative collaborations among researchers having different backgrounds and using different tools. Highlighting the relevance of our discipline beyond knowledge creation is becoming ever more important, especially when changes in the funding landscape make it increasingly difficult to secure funds to conduct fundamental research, and even more so for long-term studies or studies that cross geopolitical boundaries. Our rich history (Knight, 2023b,c) demonstrates that our discipline can and will continue to have real-world impact. Biomechanics will inspire innovations in engineering, including the development of novel materials and bio-inspired robots. Experimental biology will play a pivotal role in finding solutions to complex biological and environmental challenges, none more so than anthropogenic-driven global change. As species face increasing threats from habitat loss and climate change, comparative physiology will provide tools for their conservation. We conclude our centenary year by noting that, what is, and always has been, at the core of JEB is the knowledge creation that comes from conducting high-quality, curiosity-driven science; we strongly believe that these contributions will continue to play a pivotal role in highlighting and unravelling the intricacies of life on Earth over the next 100 years and beyond.

We gratefully acknowledge the help of Charlotte Rutledge, Reviews Editor at JEB, in coordinating this group effort and editing the text.