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Recent progress in observing sun-induced Chl fluorescence (SIF) provides an unprecedented opportunity to advance photosynthesis research in natural environments. However, we still lack an analytical framework to guide SIF studies and integration with the well-developed active fluorescence approaches. Here, we derive a set of coupled fundamental equations to describe the dynamics of SIF and its relationship with C₃ and C₄ photosynthesis. These equations show that, although SIF is dynamically as complex as photosynthesis, the measured SIF simplifies photosynthetic modeling from the perspective of light reactions by integrating over the dynamic complexities of photosynthesis. Specifically, the measured SIF contains direct information about the actual electron transport from photosystem II to photosystem I, giving a quantifiable link between light and dark reactions. With much-reduced requirements on inputs and parameters, the light-reactions-centric, SIF-based biophysical model complements the traditional, dark-reactions-centric biochemical model of photosynthesis. The SIF–photosynthesis relationship, however, is nonlinear. This is because photosynthesis saturates at high light whereas SIF has a stronger tendency to keep increasing, as fluorescence quantum yield has a relatively muted sensitivity to light levels. Successful applications of the SIF-based model of photosynthesis will depend on a predictive understanding of several previously underexplored physiological and biophysical processes. Advances can be facilitated by coordinated efforts in plant physiology, remote sensing, and eddy covariance flux observations.

Accurate forecasts of biological invasions are crucial for managing invasion risk but are hampered by niche shifts resulting from evolved environmental tolerances (fundamental niche shifts) or the presence of novel biotic and abiotic conditions in the invaded range (realized niche shifts). Distinguishing between these kinds of niche shifts is impossible with traditional, correlative approaches to invasion forecasts, which exclusively consider the realized niche. Here we overcome this challenge by combining a physiologically mechanistic model of the fundamental niche with correlative models based on the realized niche to study the global invasion of the cane toad Rhinella marina . We find strong evidence that the success of R . marina in Australia reflects a shift in the species’ realized niche, as opposed to evolutionary shifts in range-limiting traits. Our results demonstrate that R. marina does not fill its fundamental niche in its native South American range and that areas of niche unfilling coincide with the presence of a closely related species with which R. marina hybridizes. Conversely, in Australia, where coevolved taxa are absent, R. marina largely fills its fundamental niche in areas behind the invasion front. The general approach taken here of contrasting fundamental and realized niche models provides key insights into the role of biotic interactions in shaping range limits and can inform effective management strategies not only for invasive species but also for assisted colonization under climate change.

Mechanistic forecasts of how species will respond to climate change are highly desired but difficult to achieve. Because processes at different scales are explicit in such models, careful assessments of their predictive abilities can provide valuable insights that will be relevant to functionally similar species. However, there are surprisingly few comprehensive field tests of mechanistic niche models in the literature. We applied a general, thermodynamically grounded modeling framework to determine the fundamental niche of an extremely well-studied herbivorous ectotherm, the sleepy lizard Tiliqua rugosa. We then compared the model predictions with detailed long-term field observations that included sub-hourly data on microclimate, activity levels, home ranges, and body temperatures as well as annual to decadal patterns of body condition and growth. Body temperature predictions inferred from gridded climatic data were within 10% of empirically observed values and explained >70% of observed daytime activity patterns across all lizards. However, some periods of activity restriction were explained by predicted desiccation level rather than by temperature, and metabolically driven activity requirements were much lower than potential activity time. Decadal trajectories of field growth and body condition could also be explained to within 10% of observed values, with the variance in trajectories being attributable to whether individuals had access to permanent water. Continent-wide applications of the model partly captured the inland distribution limit, but only after accounting for water limitations. Predicted changes in habitat suitability under six climate change scenarios were generally positive within the species' current range, but varied strongly with predicted rainfall. Temperature is regarded as the major factor that will restrict the distribution and abundance of lizards and other terrestrial ectotherms under climate change. Yet our findings show how water can be more important than temperature in constraining the activity, habitat requirements, and distribution limits of terrestrial ectotherms. Our results demonstrate the feasibility of first-principles computation of the climatic limits on terrestrial animals from gridded environmental data, providing a coherent picture for how species will respond to climate change at different scales of space and time.

Increasing concern about the impacts of global warming on biodiversity has stimulated extensive discussion, but methods to translate broad-scale shifts in climate into direct impacts on living animals remain simplistic. A key missing element from models of climatic change impacts on animals is the buffering influence of behavioral thermoregulation. Here, we show how behavioral and mass/energy balance models can be combined with spatial data on climate, topography, and vegetation to predict impacts of increased air temperature on thermoregulating ectotherms such as reptiles and insects (a large portion of global biodiversity). We show that for most \"cold-blooded\" terrestrial animals, the primary thermal challenge is not to attain high body temperatures (although this is important in temperate environments) but to stay cool (particularly in tropical and desert areas, where ectotherm biodiversity is greatest). The impact of climate warming on thermoregulating ectotherms will depend critically on how changes in vegetation cover alter the availability of shade as well as the animals' capacities to alter their seasonal timing of activity and reproduction. Warmer environments also may increase maintenance energy costs while simultaneously constraining activity time, putting pressure on mass and energy budgets. Energy-and mass-balance models provide a general method to integrate the complexity of these direct interactions between organisms and climate into spatial predictions of the impact of climate change on biodiversity. This methodology allows quantitative organism-and habitat-specific assessments of climate change impacts.

This article presents an overview of Helios, a new three-dimensional (3D) plant and environmental modeling framework. Helios is a model coupling framework designed to provide maximum flexibility in integrating and running arbitrary 3D environmental system models. Users interact with Helios through a well-documented open-source C++ API. Version 1.0 comes with model plug-ins for radiation transport, the surface energy balance, stomatal conductance, photosynthesis, solar position, and procedural tree generation. Additional plug-ins are also available for visualizing model geometry and data and for processing and integrating LiDAR scanning data. Many of the plug-ins perform calculations on the graphics processing unit, which allows for efficient simulation of very large domains with high detail. An example modeling study is presented in which leaf-level heterogeneity in water usage and photosynthesis of an orchard is examined to understand how this leaf-scale variability contributes to whole-tree and -canopy fluxes.

Cognitive function relies on a dynamic, context-sensitive balance between functional integration and segregation in the brain. Previous work has proposed that this balance is mediated by global fluctuations in neural gain by projections from ascending neuromodulatory nuclei. To test this hypothesis in silico, we studied the effects of neural gain on network dynamics in a model of large-scale neuronal dynamics. We found that increases in neural gain directed the network through an abrupt dynamical transition, leading to an integrated network topology that was maximal in frontoparietal ‘rich club’ regions. This gain-mediated transition was also associated with increased topological complexity, as well as increased variability in time-resolved topological structure, further highlighting the potential computational benefits of the gain-mediated network transition. These results support the hypothesis that neural gain modulation has the computational capacity to mediate the balance between integration and segregation in the brain.

Body size and shape fundamentally determine organismal energy requirements by modulating heat and mass exchange with the environment and the costs of locomotion, thermoregulation, and maintenance. Ecologists have long used the physical linkage between morphology and energy balance to explain why the body size and shape of many organisms vary across climatic gradients, e.g., why larger endotherms are more common in colder regions. However, few modeling exercises have aimed at investigating this link from first principles. Body size evolution in bats contrasts with the patterns observed in other endotherms, probably because physical constraints on flight limit morphological adaptations. Here, we develop a biophysical model based on heat transfer and aerodynamic principles to investigate energy constraints on morphological evolution in bats. Our biophysical model predicts that the energy costs of thermoregulation and flight, respectively, impose upper and lower limits on the relationship of wing surface area to body mass (S-MR), giving rise to an optimal S-MR at which both energy costs are minimized. A comparative analysis of 278 species of bats supports the model’s prediction that S-MR evolves toward an optimal shape and that the strength of selection is higher among species experiencing greater energy demands for thermoregulation in cold climates. Our study suggests that energy costs modulate the mode of morphological evolution in bats—hence shedding light on a long-standing debate over bats’ conformity to ecogeographical patterns observed in other mammals—and offers a procedure for investigating complex macroecological patterns from first principles.

Despite decades of research, we lack a mechanistic framework capable of predicting how movement-related signals are transformed into the diversity of muscle spindle afferent firing patterns observed experimentally, particularly in naturalistic behaviors. Here, a biophysical model demonstrates that well-known firing characteristics of mammalian muscle spindle Ia afferents – including movement history dependence, and nonlinear scaling with muscle stretch velocity – emerge from first principles of muscle contractile mechanics. Further, mechanical interactions of the muscle spindle with muscle-tendon dynamics reveal how motor commands to the muscle (alpha drive) versus muscle spindle (gamma drive) can cause highly variable and complex activity during active muscle contraction and muscle stretch that defy simple explanation. Depending on the neuromechanical conditions, the muscle spindle model output appears to ‘encode’ aspects of muscle force, yank, length, stiffness, velocity, and/or acceleration, providing an extendable, multiscale, biophysical framework for understanding and predicting proprioceptive sensory signals in health and disease.

•Computational models are useful for investigating the effects of brain stimulation•There are common construction choices when building generative brain models•We propose three phases to generate testable, model-informed stimulation targets Brain stimulation is an increasingly popular neuromodulatory tool used in both clinical and research settings; however, the effects of brain stimulation, particularly those of non-invasive stimulation, are variable. This variability can be partially explained by an incomplete mechanistic understanding, coupled with a combinatorial explosion of possible stimulation parameters. Computational models constitute a useful tool to explore the vast sea of stimulation parameters and characterise their effects on brain activity. Yet the utility of modelling stimulation in-silico relies on its biophysical relevance, which needs to account for the dynamics of large and diverse neural populations and how underlying networks shape those collective dynamics. The large number of parameters to consider when constructing a model is no less than those needed to consider when planning empirical studies. This piece is centred on the application of phenomenological and biophysical models in non-invasive brain stimulation. We first introduce common forms of brain stimulation and computational models, and provide typical construction choices made when building phenomenological and biophysical models. Through the lens of four case studies, we provide an account of the questions these models can address, commonalities, and limitations across studies. We conclude by proposing future directions to fully realise the potential of computational models of brain stimulation for the design of personalized, efficient, and effective stimulation strategies.

Neurons rely on the interplay between two critical components, dendritic morphology and ion channels, to transform synaptic inputs into a sequence of somatic spikes. Detailed biophysical models with active dendrites have been instrumental in exploring this interaction. However, such models can be challenging to understand and validate due to the large number of parameters involved. In this work, we introduce , a toolbox designed to make detailed biophysical models with active dendrites more intuitive and more interactive. features a web-based graphical interface, where users can explore single-cell neuronal models and adjust their morphological and biophysical parameters with real-time visual feedback. In particular, focuses on subcellular properties, such as kinetics and distribution of ion channels, as well as the dynamics and placement of synaptic inputs. The toolbox supports various experimental protocols designed to illuminate how morpho-electric properties map to dendritic events and how these dendritic events shape neuronal output, thereby enhancing model validation. It helps users build high-level, modular model representations and includes a rich set of tools for parsing, generating, and standardizing commonly used neuronal data formats. Finally, it enables model simplification through a built-in morphology reduction algorithm, allowing users to export models for further use in faster, more interpretable networks. By combining extensive visualization capabilities and comprehensive data management functionality, introduces a novel interactive approach for unraveling dendritic dynamics. This approach will accelerate research on dendritic computations, their underlying mechanisms, and their fundamental role in brain function.