Integrative and Comparative Biology最新文献

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.

Intraspecific variation can be as great as variation across species, but the role of intraspecific variation in driving local and large-scale patterns is often overlooked, particularly in the field of thermal biology. In amphibians, which depend on environmental conditions and behavior to regulate body temperature, recognizing intraspecific thermal trait variation is essential to comprehensively understanding how global change impacts populations. Here, we examine the drivers of micro- and macrogeographical intraspecific thermal trait variation in amphibians. At the local scale, intraspecific variation can arise via changes in ontogeny, body size, and between the sexes, and developmental plasticity, acclimation, and maternal effects may modulate predictions of amphibian performance under future climate scenarios. At the macrogeographic scale, local adaptation in thermal traits may occur along latitudinal and elevational gradients, with seasonality and range-edge dynamics likely playing important roles in patterns that may impact future persistence. We also discuss the importance of considering disease as a factor affecting intraspecific variation in thermal traits and population resilience to climate change, given the impact of pathogens on thermal preferences and critical thermal limits of hosts. Finally, we make recommendations for future work in this area. Ultimately, our goal is to demonstrate why it is important for researchers to consider intraspecific variation to determine the resilience of amphibians to global change.

Foundational habitats such as seagrasses and coral reefs are at severe risk globally from climate warming. Infectious disease associated with warming events is both a cause of decline and an indicator of stress in both habitats. Since new approaches are needed to detect refugia and design climate-smart networks of marine protected areas, we test the hypothesis that the health of eelgrass (Zostera marina) in temperate ecosystems can serve as a proxy indicative of higher resilience and help pinpoint refugia. Eelgrass meadows worldwide are at risk from environmental stressors, including climate warming and disease. Disease outbreaks of Labyrinthula zosterae are associated with recent, widespread declines in eelgrass meadows throughout the San Juan Islands, Washington, USA. Machine language learning, drone surveys, and molecular diagnostics reveal climate impacts on seagrass wasting disease prevalence (proportion of infected individuals) and severity (proportion of infected leaf area) from San Diego, California, to Alaska. Given that warmer temperatures favor many pathogens such as L. zosterae, we hypothesize that absent or low disease severity in meadows could indicate eelgrass resilience to climate and pathogenic stressors. Regional surveys showed the San Juan Islands as a hotspot for both high disease prevalence and severity, and surveys throughout the Northeast Pacific indicated higher prevalence and severity in intertidal, rather than subtidal, meadows. Further, among sites with eelgrass declines, losses were more pronounced at sites with shallower eelgrass meadows. We suggest that deeper meadows with the lowest disease severity will be refuges from future warming and pathogenic stressors in the Northeast Pacific. Disease monitoring may be a useful conservation approach for marine foundation species, as low or absent disease severity can pinpoint resilient refugia that should be prioritized for future conservation efforts. Even in declining or at-risk habitats, disease surveys can help identify meadows that may contain especially resilient individuals for future restoration efforts. Our approach of using disease as a pulse point for eelgrass resilience to multiple stressors could be applied to other habitats such as coral reefs to inform conservation and management decisions.

Science is becoming increasingly interdisciplinary; the widespread emergence of dedicated interdisciplinary journals, conferences, and graduate programs reflects this trend. Interdisciplinary scientific events are extremely valuable in that they offer opportunities for career advancement, especially among early career researchers, for collaboration beyond traditional disciplinary echo chambers, and for the creative generation of innovative solutions to longstanding scientific problems. However, organizing such events can pose unique challenges due to the intentionality required to meaningfully break down the barriers that separate long-independent disciplines. In this paper, we propose five key strategies for organizing and hosting interdisciplinary scientific events. The recommendations offered here apply both to small symposia aiming to contribute an interdisciplinary component to a larger event and to broad interdisciplinary conferences hosting hundreds or thousands of attendees.

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.

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.

Shark skin is composed of denticles, or complex scale-like features, which have been shown to reduce drag in turbulent and laminar flow. The denticle crown features undulating structures called riblets that interact with the turbulent boundary layer to reduce drag. Two mechanisms thought to contribute to the drag-reducing properties of riblets include the lifting of streamwise vortices and the hampering of spanwise vortex interactions to reduce crossflow, which could translate to similar flow mechanisms for denticles. Because of the varied morphologies of dermal denticles on different shark species, which also depend on body location, the impact of these denticle geometries on flow is of interest to the biology community, including related fields such as fluid mechanics and oceanography. This review highlights the past 15 years of manufacturing techniques and experimental measurements of drag over denticle-inspired surface structures, including real shark skin samples and engineered denticles and riblets. State-of-the-art additive manufacturing and other techniques are primarily limited to mm-length denticle scales, which have demonstrated drag reduction in lower flow speeds, under 1 m s-1. New manufacturing approaches can create sub-mm length denticles and nanotextured surface structures, which have achieved reported drag reductions of up to 31%. We synthesize results from the literature to illustrate the drag reduction properties of bioinspired denticles and riblets according to their geometry and flow conditions. Using these trends, we suggest design features and focus areas for future research, such as increasing studies of different denticle morphologies, hydrophobicity, antifouling properties, and acoustic noise reduction. Continued work on bioinspired denticles for drag reduction has wider implications in comparative biology and applications to design more energy-efficient, persistent vehicles for environmental monitoring.

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.

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.

Insects exhibit remarkable adaptability in their locomotive strategies in diverse environments, a crucial trait for foraging, survival, and predator avoidance. Microvelia americana, tiny 2-3 mm insects that adeptly walk on water surfaces, exemplify this adaptability by using the alternating tripod gait in both aquatic and terrestrial terrains. These insects commonly inhabit low-flow ponds and streams cluttered with natural debris like leaves, twigs, and duckweed. Using high-speed imaging and pose-estimation software, we analyze M. americana movement on water, sandpaper (simulating land), and varying duckweed densities (10%, 25%, and 50% coverage). Our results reveal M. americana maintain consistent joint angles and strides of their upper and hind legs across all duckweed coverages, mirroring those seen on sandpaper. Microvelia americana adjust the stride length of their middle legs based on the amount of duckweed present, decreasing with increased duckweed coverage and at 50% duckweed coverage, their middle legs' strides closely mimic their strides on sandpaper. Notably, M. americana achieve speeds up to 56 body lengths per second on the deformable surface of water, nearly double those observed on sandpaper and duckweed, which are rough, heterogeneous surfaces. This study highlights M. americana's ecological adaptability, setting the stage for advancements in amphibious robotics that emulate their unique tripod gait for navigating complex terrains.