Integrative and Comparative Biology最新文献

Understanding how the structure of biological systems impacts their resilience (broadly defined) is a recurring question across multiple levels of biological organization. In ecology, considerable effort has been devoted to understanding how the structure of interactions between species in ecological networks is linked to different broad resilience outcomes, especially local stability. Still, nearly all of that work has focused on interaction structure in presence-absence terms and has not investigated quantitative structure, i.e., the arrangement of interaction strengths in ecological networks. We investigated how the interplay between binary and quantitative structure impacts stability in mutualistic interaction networks (those in which species interactions are mutually beneficial), using community matrix approaches. We additionally examined the effects of network complexity and within-guild competition for context. In terms of structure, we focused on understanding the stability impacts of nestedness, a structure in which more-specialized species interact with smaller subsets of the same species that more-generalized species interact with. Most mutualistic networks in nature display binary nestedness, which is puzzling because both binary and quantitative nestedness are known to be destabilizing on their own. We found that quantitative network structure has important consequences for local stability. In more-complex networks, binary-nested structures were the most stable configurations, depending on the quantitative structures, but which quantitative structure was stabilizing depended on network complexity and competitive context. As complexity increases and in the absence of within-guild competition, the most stable configurations have a nested binary structure with a complementary (i.e., anti-nested) quantitative structure. In the presence of within-guild competition, however, the most stable networks are those with a nested binary structure and a nested quantitative structure. In other words, the impact of interaction overlap on community persistence is dependent on the competitive context. These results help to explain the prevalence of binary-nested structures in nature and underscore the need for future empirical work on quantitative structure.

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."

Immunopathology, or the harm caused to an organism's own tissues during the activation of its immune system, carries substantial costs. Moreover, avoiding this self-harm may be an important mechanism underlying tolerance of infection, helping to reducing fitness costs without necessarily clearing parasites. Despite the apparent benefits of minimizing immunopathology, such damage persists across a range of host species. Prior work has explored a trade-off with resistance during a single infection as a potential driver of this persistence, with some collateral damage being unavoidable when killing parasites. Here, we present an additional trade-off that could favor the continued presence of immunopathology: robust immune responses during initial infection (e.g., innate immunity in vertebrates) can induce stronger memory (adaptive immunity), offering protection from future infections. We explore this possibility in an adaptive dynamics framework, using theoretical models parameterized from an ecologically relevant host-parasite system, house finches (Haemorhous mexicanus) infected with the bacterial pathogen, Mycoplasma gallisepticum. We find that some degree of immunopathology is often favored when immunopathology during first infection either reduces susceptibility to or enhances recovery from second infection. Further, interactions among factors like transmission rate, recovery rate, background mortality, and pathogen virulence also shape these evolutionary dynamics. Most notably, the evolutionary stability of investment in immunopathology is highly dependent upon the mechanism by which hosts achieve secondary protection (susceptibility vs. recovery), with the potential for abrupt evolutionary shifts between high and low investment under certain conditions. These results highlight the potential for immune memory to play an important role in the evolutionary persistence of immunopathology and the need for future empirical research to reveal the links between immunopathology during initial infections and longer-term immune protection.

Examples of resilience in nature give us hope amid a growing biodiversity crisis. While resilience has many definitions across disciplines, here I discuss resilience as the ability to continue to adapt and persist. Naturally, as biologists, we seek to uncover the underlying mechanisms that can help us explain the secrets of resilience across scales, from individuals to species to ecosystems and beyond. Perhaps we also ponder what the secrets to resilience are in our own lives, in our own research practices, and academic communities. In this paper, I highlight insights gained through studies of amphibian resilience following a global disease outbreak to uncover shared patterns and processes linked to resilience across amphibian communities. I also reflect on how classical resilience heuristics could be more broadly applied to these processes and to our own academic communities. Focusing on the amphibian systems that I have worked in-the Golden Frogs of Panama (Atelopus zeteki/varius) and the Mountain Yellow-Legged Frogs of California (Rana muscosa/sierrae)-I highlight shared and unique characteristics of resilience across scales and systems and discuss how these relate to adaptive renewal cycles. Reflecting on this work and previous resilience scholarship, I also offer my own thoughts about academia and consider what lessons we could take from mapping our own adaptive trajectories and addressing threats to our own community resilience.

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.

The Deepwater Horizon (DWH) oil spill in the northern Gulf of Mexico, occurred in 2010 at 1525 meters depth, releasing approximately 507 M liters of oil. Research cruises in 2010 and 2011 were conducted to assess the initial and subsequent effects of the oil spill on deep-sea infauna. The spatial-temporal response of the deep-sea meiofaunal harpacticoid community composition to the DWH oil spill was investigated at 34 stations ranging from < 1 km to nearly 200 km from the wellhead in 2010 and 2011. The pattern of reduced harpacticoid diversity in impacted zones compared to non-impacted zones in 2010 persisted in 2011. However, an increase in Hill's diversity index (N1) and the family richness across the two years in some of the impacted stations could suggest a first signal of a tentative recovery and an improvement of environmental conditions. The multivariate analysis of harpacticoid family composition revealed the persistence of an impact in 2011 with moderately high values of turnover diversity in the harpacticoid communities through time (37%) and space (38-39%). The consistent presence in all years and stations of long-term tolerant families (e.g., Ameiridae), the sharp decrease of fast-responding opportunistic families (e.g., Tisbidae), and the increase of more sensitive ones (e.g., Ectinosomatidae, Canthocamptidae, Cletopsyllidae, and Laophontidae) lead to the preliminary conclusion that some initial signals of recovery are evident. However, as impacts were still evident in 2011, and because recruitment and succession rates can be extremely slow in the deep sea, full community recovery had not yet occurred one year after the DWH disaster. This study confirmed that harpacticoid copepod family diversity can offer an accurate assessment of oil-spill impacts on deep-sea benthic communities over space and time as well as a better understanding of the recovery mode of the system after an oil spill event.

Tails play essential roles in functions related to locomotor stability and maneuverability among terrestrial and arboreal animals. In kangaroo rats, bipedal hopping rodents, tails are used as effective inertial appendages for stability in hopping, but also facilitate stability and maneuverability during predator escape leaps. The complexity of tail functionality shows great potential for bio-inspiration and robotic device design, as maneuvering is accomplished by a long and light-weight inertial appendage. To (1) further understand the mechanics of how kangaroo rats use their tails during aerial maneuvers and (2) explore if we can achieve this behavior with a simplified tail-like appendage (i.e., template), we combined quantified animal observations, computational simulations, and experiments with a two degrees of freedom (2-DoF) tailed robot. We used video data from free-ranging kangaroo rats escaping from a simulated predator and analyzed body and tail motion for the airborne phase. To explain tail contributions to body orientation (i.e., spatial reorientation), we built a mid-air kangaroo rat computational model and demonstrated that the three-dimensional body orientation of the model can be controlled by a simplified 2-DoF tail with a nonlinear control strategy. Resulting simulated trajectories show movement patterns similar to those observed in kangaroo rats. Our robot experiments show that a lightweight tail can generate a large yaw displacement and stabilize pitch and roll angles to zero simultaneously. Our work contributes to better understanding of the form-function relationship of the kangaroo rat tail and lays out an important foundation for bio-inspiration in robotic devices that have lightweight tail-like appendages for mid-air maneuvering.

Understanding the flow physics behind fish schooling poses significant challenges due to the difficulties in directly measuring hydrodynamic performance and the three-dimensional, chaotic, and complex flow structures generated by collective moving organisms. Numerous previous simulations and experiments have utilized computational, mechanical, or robotic models to represent live fish. And existing studies of live fish schools have contributed significantly to dissecting the complexities of fish schooling. But the scarcity of combined approaches that include both computational and experimental studies, ideally of the same fish schools, has limited our ability to understand the physical factors that are involved in fish collective behavior. This underscores the necessity of developing new approaches to working directly with live fish schools. An integrated method that combines experiments on live fish schools with computational fluid dynamics (CFD) simulations represents an innovative method of studying the hydrodynamics of fish schooling. CFD techniques can deliver accurate performance measurements and high-fidelity flow characteristics for comprehensive analysis. Concurrently, experimental approaches can capture the precise locomotor kinematics of fish and offer additional flow information through particle image velocimetry (PIV) measurements, potentially enhancing the accuracy and efficiency of CFD studies via advanced data assimilation techniques. The flow patterns observed in PIV experiments with fish schools and the complex hydrodynamic interactions revealed by integrated analyses highlight the complexity of fish schooling, prompting a reevaluation of the classic Weihs model of school dynamics. The synergy between CFD models and experimental data grants us comprehensive insights into the flow dynamics of fish schools, facilitating the evaluation of their functional significance and enabling comparative studies of schooling behavior. In addition, we consider the challenges in developing integrated analytical methods and suggest promising directions for future research.

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.

Animals and robots must self-right on the ground after overturning. Biology research has described various strategies and motor patterns in many species. Robotics research has devised many strategies. However, we do not well understand the physical principles of how the need to generate mechanical energy to overcome the potential energy barrier governs behavioral strategies and 3D body rotations given the morphology. Here, I review progress on this which I led studying cockroaches self-righting on level, flat, solid, low-friction ground, by integrating biology experiments, robotic modeling, and physics modeling. Animal experiments using three species (Madagascar hissing, American, and discoid cockroaches) found that ground self-righting is strenuous and often requires multiple attempts to succeed. Two species (American and discoid cockroaches) often self-right dynamically, using kinetic energy to overcome the barrier. All three species use and often stochastically transition across diverse strategies. In these strategies, propelling motions are often accompanied by perturbing motions. All three species often display complex yet stereotyped body rotation. They all roll more in successful attempts than in failed ones, which lowers the barrier, as revealed by a simplistic 3D potential energy landscape of a rigid body self-righting. Experiments of an initial robot self-righting via rotation about a fixed axis revealed that the longer and faster appendages push, the more mechanical energy can be gained to overcome the barrier. However, the cockroaches rarely achieve this. To further understand the physical principles of strenuous ground self-righting, we focused on the discoid cockroach's leg-assisted winged self-righting. In this strategy, wings propel against the ground to pitch the body up but are unable to overcome the highest pitch barrier. Meanwhile, legs flail in the air to perturb the body sideways to self-right via rolling. Experiments using a refined robot and an evolving 3D potential energy landscape revealed that, although wing propelling cannot generate sufficient kinetic energy to overcome the highest pitch barrier, it reduces the barrier to allow small kinetic energy from the perturbing legs to probabilistically overcome the barrier to self-right via rolling. Thus, only by combining propelling and perturbing can self-righting be achieved when it is so strenuous; this physical constraint leads to the stereotyped body rotation. Finally, multi-body dynamics simulation and template modeling revealed that the animal's substantial randomness in wing and leg motions helps it, by chance, to find good coordination, which accumulates more mechanical energy to overcome the barrier, thus increasing the likelihood of self-righting.