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Synopsis Throughout their lives, organisms must integrate and maintain stability across complex developmental, morphological, and physiological systems, all while responding to changing internal and external environments. Determining the mechanisms underlying organismal responses to environmental change and development is a major challenge for biology. This is particularly important in the face of the rapidly changing global climate, increasing human populations, and habitat destruction. In January 2024, we organized a symposium to highlight some current efforts to use modeling to understand organismal responses to short- and long-term changes in their internal and external environments. Our goal was to facilitate collaboration and communication between modelers and organismal biologists, which is one of the major aims of the Organismal Systems-type Modeling Research Coordination Network, OSyM. Accompanying this introduction are a series of papers that are aimed to enhance research and education in linking organismal biology and modeling and contribute to building a new community of scientists to tackle important questions using this approach. 

Synopsis Pursuing cutting edge questions in organismal biology in the future will require novel approaches for training the next generation of organismal biologists, including knowledge and use of systems-type modeling combined with integrative organismal biology. We link agendas recommending changes in science education and practice across three levels: Broadening the concept of organismal biology to promote modeling organisms as systems interacting with higher and lower organizational levels; enhancing undergraduate science education to improve applications of quantitative reasoning and modeling in the scientific process; and K-12 curricula based on Next Generation Science Standards emphasizing development and use of models in the context of explanatory science, solution design, and evaluating and communicating information. Out of each of these initiatives emerges an emphasis on routine use of models as tools for hypothesis testing and prediction. The question remains, however, what is the best approach for training the next generation of organismal biology students to facilitate their understanding and use of models? We address this question by proposing new ways of teaching and learning, including the development of interactive web-based modeling modules that lower barriers for scientists approaching this new way of imagining and conducting integrative organismal biology. 

Synopsis Antimicrobial peptides (AMPs) play a fundamental role in the innate defense against microbial pathogens, as well as other immune and non-immune functions. Their role in amphibian skin defense against the pathogenic fungus Batrachochytrium dendrobatidis (Bd) is exemplified by experiments in which depletion of host’s stored AMPs increases mortality from infection. Yet, the question remains whether there are generalizable patterns of negative or positive correlations between stored AMP defenses and the probability of infection or infection intensity across populations and species. This study aims to expand on prior field studies of AMP quantities and compositions by correlating stored defenses with an estimated risk of Bd exposure (prevalence and mean infection intensity in each survey) in five locations across the United States and a total of three species. In all locations, known AMPs correlated with the ability of recovered secretions to inhibit Bd in vitro. We found that stored AMP defenses were generally unrelated to Bd infection except in one location where the relative intensity of known AMPs was lower in secretions from infected frogs. In all other locations, known AMP relative intensities were higher in infected frogs. Stored peptide quantity was either positively or negatively correlated with Bd exposure risk. Thus, future experiments coupled with organismal modeling can elucidate whether Bd infection affects secretion/synthesis and will provide insight into how to interpret amphibian ecoimmunology studies of AMPs. We also demonstrate that future AMP isolating and sequencing studies can focus efforts by correlating mass spectrometry peaks to inhibitory capacity using linear decomposition modeling. 

Synopsis In the era of big data, ecological research is experiencing a transformative shift, yet big-data advancements in thermal ecology and the study of animal responses to climate conditions remain limited. This review discusses how big data analytics and artificial intelligence (AI) can significantly enhance our understanding of microclimates and animal behaviors under changing climatic conditions. We explore AI’s potential to refine microclimate models and analyze data from advanced sensors and camera technologies, which capture detailed, high-resolution information. This integration can allow researchers to dissect complex ecological and physiological processes with unprecedented precision. We describe how AI can enhance microclimate modeling through improved bias correction and downscaling techniques, providing more accurate estimates of the conditions that animals face under various climate scenarios. Additionally, we explore AI’s capabilities in tracking animal responses to these conditions, particularly through innovative classification models that utilize sensors such as accelerometers and acoustic loggers. For example, the widespread usage of camera traps can benefit from AI-driven image classification models to accurately identify thermoregulatory responses, such as shade usage and panting. AI is therefore instrumental in monitoring how animals interact with their environments, offering vital insights into their adaptive behaviors. Finally, we discuss how these advanced data-driven approaches can inform and enhance conservation strategies. In particular, detailed mapping of microhabitats essential for species survival under adverse conditions can guide the design of climate-resilient conservation and restoration programs that prioritize habitat features crucial for biodiversity resilience. In conclusion, the convergence of AI, big data, and ecological science heralds a new era of precision conservation, essential for addressing the global environmental challenges of the 21st century. 

Synopsis Marine organisms have complex life histories. For broadcast spawners, successful continuation of the population requires their small gametes to make contact in the water column for sufficiently long periods for fertilization to occur. Anthropogenic climate change has been shown to impact fertilization success in various marine invertebrates, including sea urchins, which are key grazers in their habitats. Gamete performance of both sexes declined when exposed to elevated temperatures and/or pCO2 levels. Examples of reduced performance included slower sperm swimming speed and thinning egg jelly coat. However, such responses to climate change stress were not uniform between individuals. Such variations could serve as the basis for selection. Fertilization kinetics have long been modeled as a particle collision process. Here, we present a modified fertilization kinetics model that incorporates individual variations in performance in a more environmentally relevant regime, and which the performance of groups with different traits can be separately tracked in a mixture. Numerical simulations highlight that fertilization outcomes are influenced by changes in gamete traits as they age in sea water and the presence of competition groups (multiple dams or sires). These results highlight the importance of considering multiple individuals and at multiple time points during in vivo assays. We also applied our model to show that interspecific variation in climate stress vulnerabilities elevates the risk of hybridization. By making a numerical model open-source, we aim to help us better understand the fate of organisms in the face of climate change by enabling the community to consider the mean and variance of the response to capture adaptive potential. 

Synopsis Climate resilience, a focus of many recent studies, has been examined from ecological, physiological, and evolutionary perspectives. However, sampling biases toward adults, males, and certain species have made establishing the link between environmental change and population-level change problematic. Here, we used data from four laboratory studies, in which we administered pre- and postnatal stressors, such as suboptimal incubation temperature, heat stress, and food restriction, to zebra finches. We then quantified hatching success, posthatch survival, and reproductive success, to parameterize age-structured population dynamics models with the goal of estimating the effect of the stressors on relative population growth rates. Using the same model structure, we tested the hypothesis that early life stages influence population growth rate more than later life stages. Our models suggested that stressful events during embryonic development, such as suboptimal incubation temperatures and reduced gas exchange for the embryos, have a greater total impact on population growth than posthatch stressors, such as heat stress and food restriction. However, among life history traits, differences in hatching success and sex ratio of offspring in response to stressors changed population growth rates more than differences in any other demographic rate estimates. These results suggest that when predicting population resilience against climate change, it is critical to account for effects of climate change on all life stages, including early stages of life, and to incorporate individuals’ physiology and stress tolerance that likely influence future stress responses, reproduction, and survival. 

Synopsis Predicting performance responses of insects to climate change is crucial for biodiversity conservation and pest management. While most projections on insects’ performance under climate change have used macro-scale weather station data, few incorporated the microclimates within vegetation that insects inhabit and their feeding behaviors (e.g., leaf-nesting: building leaf nests or feeding inside). Here, taking advantage of relatively homogenous vegetation structures in agricultural fields, we built microclimate models to examine fine-scale air temperatures within two important crop systems (maize and rice) and compared microclimate air temperatures to temperatures from weather stations. We deployed physical models of caterpillars and quantified effects of leaf-nesting behavior on operative temperatures of two Lepidoptera pests: Ostrinia furnacalis (Pyralidae) and Cnaphalocrocis medinalis (Crambidae). We built temperature-growth rate curves and predicted the growth rate of caterpillars with and without leaf-nesting behavior based on downscaled microclimate changes under different climate change scenarios. We identified widespread differences between microclimates in our crop systems and air temperatures reported by local weather stations. Leaf-nesting individuals in general had much lower body temperatures compared to non-leaf-nesting individuals. When considering microclimates, we predicted leaf-nesting individuals grow slower compared to non-leaf-nesting individuals with rising temperature. Our findings highlight the importance of considering microclimate and habitat-modifying behavior in predicting performance responses to climate change. Understanding the thermal biology of pests and other insects would allow us to make more accurate projections on crop yields and biodiversity responses to environmental changes. 

Synopsis Biology as a field has transformed since the time of its foundation from an organized enterprise cataloging the diversity of the natural world to a quantitatively rigorous science seeking to answer complex questions about the functions of organisms and their interactions with each other and their environments. As the mathematical rigor of biological analyses has improved, quantitative models have been developed to describe multi-mechanistic systems and to test complex hypotheses. However, applications of quantitative models have been uneven across fields, and many biologists lack the foundational training necessary to apply them in their research or to interpret their results to inform biological problem-solving efforts. This gap in scientific training has created a false dichotomy of “biologists” and “modelers” that only exacerbates the barriers to working biologists seeking additional training in quantitative modeling. Here, we make the argument that all biologists are modelers and are capable of using sophisticated quantitative modeling in their work. We highlight four benefits of conducting biological research within the framework of quantitative models, identify the potential producers and consumers of information produced by such models, and make recommendations for strategies to overcome barriers to their widespread implementation. Improved understanding of quantitative modeling could guide the producers of biological information to better apply biological measurements through analyses that evaluate mechanisms, and allow consumers of biological information to better judge the quality and applications of the information they receive. As our explanations of biological phenomena increase in complexity, so too must we embrace modeling as a foundational skill. 

Synopsis Movement is integral to animal life, and most animal movement is actuated by the same engine: striated muscle. Muscle input is typically mediated by skeletal elements, resulting in musculoskeletal systems that are geared: at any instant, the muscle force and velocity are related to the output force and velocity only via a proportionality constant G, the “mechanical advantage”. The functional analysis of such “simple machines” has traditionally centered around this instantaneous interpretation, such that a small vs large G is thought to reflect a fast vs forceful system, respectively. But evidence is mounting that a comprehensive analysis ought to also consider the mechanical energy output of a complete contraction. Here, we approach this task systematically, and deploy the theory of physiological similarity to study how gearing affects the flow of mechanical energy in a minimalist model of a musculoskeletal system. Gearing influences the flow of mechanical energy in two key ways: it can curtail muscle work output, because it determines the ratio between the characteristic muscle kinetic energy and work capacity; and it defines how each unit of muscle work is partitioned into different system energies, that is, into kinetic vs “parasitic” energy such as heat. As a consequence of both effects, delivering maximum work in minimum time and with maximum output speed generally requires a mechanical advantage of intermediate magnitude. This optimality condition can be expressed in terms of two dimensionless numbers that reflect the key geometric, physiological, and physical properties of the interrogated musculoskeletal system, and the environment in which the contraction takes place. Illustrative application to exemplar musculoskeletal systems predicts plausible mechanical advantages in disparate biomechanical scenarios, yields a speculative explanation for why gearing is typically used to attenuate the instantaneous force output ($$G_{\text{opt}} \lt 1)$$, and predicts how G needs to vary systematically with animal size to optimize the delivery of mechanical energy, in superficial agreement with empirical observations. A many-to-one mapping from musculoskeletal geometry to mechanical performance is identified, such that differences in G alone do not provide a reliable indicator for specialization for force vs speed—neither instantaneously, nor in terms of mechanical energy output. The energy framework presented here can be used to estimate an optimal mechanical advantage across variable muscle physiology, anatomy, mechanical environment, and animal size, and so facilitates investigation of the extent to which selection has made efficient use of gearing as a degree of freedom in musculoskeletal “design.”