Metabolic setpoint control mechanisms in different physiological systems at rest and during exercise

Using a number of different homeostatic control mechanisms in the brain and peripheral physiological systems, metabolic activity is continuously regulated at rest and during exercise to prevent catastrophic system failure. Essential for the function of these regulatory processes are baseline “setpoint” levels of metabolic function, which can be used to calculate the level of response required for the maintenance of system homeostasis after system perturbation, and to which the perturbed metabolic activity levels are returned to at the end of the regulatory process. How these setpoint levels of all the different metabolic variables in the different peripheral physiological systems are created and maintained, and why they are similar in different individuals, has not been well explained. In this article, putative system regulators of metabolic setpoint levels are described. These include that: (i) innate setpoint values are stored in a certain region of the central nervous system, such as the hypothalamus; (ii) setpoint values are created and maintained as a response to continuous external perturbations, such as gravity or “zeitgebers”, (iii) setpoint values are created and maintained by complex system dynamical activity in the different peripheral systems, where setpoint levels are regulated by the ongoing feedback control activity between different peripheral variables; (iv) human anatomical and biomechanical constraints contribute to the creation and maintenance of metabolic setpoints values; or (v) a combination of all these four different mechanisms occurs. Exercise training and disease processes can affect these metabolic setpoint values, but the setpoint values are returned to pre-training or pre-disease levels if the training stimulus is removed or if the disease process is cured. Further work is required to determine what the ultimate system regulator of metabolic setpoint values is, why some setpoint values are more stringently protected by homeostatic regulatory mechanisms than others, and the role of conscious decision making processes in determining the regulation of metabolic setpoint values.     

Uncertainties in the response of a forest landscape to global climatic change

Many studies have been conducted to quantify the possible ecosystem/landscape response to the anticipated global warming. However, there is a large amount of uncertainty in the future climate predictions used for these studies. Specifically, the climate predictions can be very different based on a variety of global climate models and alternative greenhouse emission scenarios. In this study, we coupled a forest landscape model, LANDIS-II, and a forest process model, PnET-II, to examine the uncertainty (that results from the uncertainty in the future climate predictions) in the forest-type composition prediction for a transitional forest landscape [the Boundary Water Canoe Area]. Using an improved global-sensitivity analysis technique [Fourier amplitude sensitivity test], we also quantified the amount of uncertainty in the forest-type composition prediction contributed by different climate variables including temperature, CO₂, precipitation and photosynthetic active radiation (PAR). The forest landscape response was simulated for the period 2000–2400 ad based on the differential responses of 13 tree species under an ensemble of 27 possible climate prediction profiles (monthly time series of climate variables). Our simulations indicate that the uncertainty in the forest-type composition becomes very high after 2200 ad , which is close to the time when the current forests are largely removed by windthrow disturbances and natural mortality. The most important source of uncertainty in the forest-type composition prediction is from the uncertainty in temperature predictions. The second most important source is PAR, the third is CO₂ and the least important is precipitation. Our results also show that if the optimum photosynthetic temperature rises due to CO₂ enrichment, the forest landscape response to climatic change measured by forest-type composition may be substantially reduced.     

What Can We Learn from Global Sensitivity Analysis of Biochemical Systems?

Most biological models of intermediate size, and probably all large models, need to cope with the fact that many of their parameter values are unknown. In addition, it may not be possible to identify these values unambiguously on the basis of experimental data. This poses the question how reliable predictions made using such models are. Sensitivity analysis is commonly used to measure the impact of each model parameter on its variables. However, the results of such analyses can be dependent on an exact set of parameter values due to nonlinearity. To mitigate this problem, global sensitivity analysis techniques are used to calculate parameter sensitivities in a wider parameter space. We applied global sensitivity analysis to a selection of five signalling and metabolic models, several of which incorporate experimentally well-determined parameters. Assuming these models represent physiological reality, we explored how the results could change under increasing amounts of parameter uncertainty. Our results show that parameter sensitivities calculated with the physiological parameter values are not necessarily the most frequently observed under random sampling, even in a small interval around the physiological values. Often multimodal distributions were observed. Unsurprisingly, the range of possible sensitivity coefficient values increased with the level of parameter uncertainty, though the amount of parameter uncertainty at which the pattern of control was able to change differed among the models analysed. We suggest that this level of uncertainty can be used as a global measure of model robustness. Finally a comparison of different global sensitivity analysis techniques shows that, if high-throughput computing resources are available, then random sampling may actually be the most suitable technique.     

Choice of predictor variables as a source of uncertainty in continental‐scale species distribution modelling under climate change

Aim Species distribution modelling is commonly used to guide future conservation policies in the light of potential climate change. However, arbitrary decisions during the model‐building process can affect predictions and contribute to uncertainty about where suitable climate space will exist. For many species, the key climatic factors limiting distributions are unknown. This paper assesses the uncertainty generated by using different climate predictor variable sets for modelling the impacts of climate change. Location Europe, 10° W to 50° E and 30° N to 60° N. Methods Using 1453 presence pixels at 30 arcsec resolution for the great bustard (Otis tarda), predictions of future distribution were made based on two emissions scenarios, three general climate models and 26 sets of predictor variables. Twenty‐six current models were created, and 156 for both 2050 and 2080. Map comparison techniques were used to compare predictions in terms of the quantity and the location of presences (map comparison kappa, MCK) and using a range change index (RCI). Generalized linear models (GLMs) were used to partition explained deviance in MCK and RCI among sources of uncertainty. Results The 26 different variable sets achieved high values of AUC (area under the receiver operating characteristic curve) and yet introduced substantial variation into maps of current distribution. Differences between maps were even greater when distributions were projected into the future. Some 64–78% of the variation between future maps was attributable to choice of predictor variable set alone. Choice of general climate model and emissions scenario contributed a maximum of 15% variation and their order of importance differed for MCK and RCI. Main conclusions Generalized variable sets produce an unmanageable level of uncertainty in species distribution models which cannot be ignored. The use of sound ecological theory and statistical methods to check predictor variables can reduce this uncertainty, but our knowledge of species may be too limited to make more than arbitrary choices. When all sources of modelling uncertainty are considered together, it is doubtful whether ensemble methods offer an adequate solution. Future studies should explicitly acknowledge uncertainty due to arbitrary choices in the model‐building process and develop ways to convey the results to decision‐makers.     

Genetic and environmental factors in the resistance of Drosophila subobscura adults to high temperature shock

Summary Survival time following a high temperature shock of Drosophila subobscura adults in dry air has shown great variability. This experimental condition involved desiccation as the first cause of death. Here survival is studied under saturation humidity, so that the mortality may be imputed only to thermic stress. We analyze the influence of culture temperature and crowding on resistance for different sex and age of the adults. The results show strong influences of these environmental factors on heat shock resistance and show interactions with the age and sex of the adults. We suggest that these facts could be due to acclimatization and/or to adaptation. The acclimatization would occur during development and would affect physiological processes related to aging of the flies. The adaptation would take place for selection, acting through differential mortality before the heat shock. Of course, other processes could be significant. Whatever the causal explanation, it will be necessary in any future research related with heat shock resistance to take these factors into account.     

Population dynamics of the narwhal Monodon monoceros: an initial assessement (Odontoceti: Monodontidae)

A simplified population equation for Monodon Pnonoceros shows that current estimates of the values of the life history variables are inconsistent with the hypothesis of a stationary population. Instantaneous adult mortality must be less than 0,10/yr, not the published estimate of 12–13%/yr, for accepted values of the other variables to be consistent with stationarity. For sustainable harvest, permissible exploitation rates are no larger than 3–4%/yr, and instantaneous natural mortality must then be well below 10%/yr. Present uncertainty in the values of survival rates, both of adults and young, contributes twice as much to uncertainty in population growth rate as does uncertainty in reproductive rates.     

Are the numbers adding up? Exploiting discrepancies among complementary population models

Large carnivores are difficult to monitor because they tend to be sparsely distributed, sensitive to human activity, and associated with complex life histories. Consequently, understanding population trend and viability requires conservationists to cope with uncertainty and bias in population data. Joint analysis of combined data sets using multiple models (i.e., integrated population model) can improve inference about mechanisms (e.g., habitat heterogeneity and food distribution) affecting population dynamics. However, unobserved or unobservable processes can also introduce bias and can be difficult to quantify. We developed a Bayesian hierarchical modeling approach for inference on an integrated population model that reconciles annual population counts with recruitment and survival data (i.e., demographic processes). Our modeling framework is flexible and enables a realistic form of population dynamics by fitting separate density-dependent responses for each demographic process. Discrepancies estimated from shared parameters among different model components represent unobserved additions (i.e., recruitment or immigration) or removals (i.e., death or emigration) when annual population counts are reliable. In a case study of gray wolves in Wisconsin (1980–2011), concordant with policy changes, we estimated that a discrepancy of 0% (1980–1995), −2% (1996–2002), and 4% (2003–2011) in the annual mortality rate was needed to explain annual growth rate. Additional mortality in 2003–2011 may reflect density-dependent mechanisms, changes in illegal killing with shifts in wolf management, and nonindependent censoring in survival data. Integrated population models provide insights into unobserved or unobservable processes by quantifying discrepancies among data sets. Our modeling approach is generalizable to many population analysis needs and allows for identifying dynamic differences due to external drivers, such as management or policy changes.     

Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling

Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here, we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species‐specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured interannual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including aboveground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model‐data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate.     

Mortality and aging in a heterogeneous population: a stochastic process model with observed and unobserved variables

Various multivariate stochastic process models have been developed to represent human physiological aging and mortality. These efforts are extended by considering the effects of observed and unobserved state variables on the age trajectory of physiological parameters. This is done by deriving the Kolmogorov-Fokker-Planck equations describing the distribution of the unobserved state variables conditional on the history of the observed state variables. Given some assumptions, it is proved that the distribution is Gaussian. Strategies for estimating the parameters of the distribution are suggested based on an extension of the theory of Kalman filters to include systematic mortality selection. Various empirical applications of the model to studies of human aging and mortality as well as to other types of "failure" processes in heterogeneous populations are discussed.     

Model parameterization to represent processes at unresolved scales and changing properties of evolving systems

Modeling has become an indispensable tool for scientific research. However, models generate great uncertainty when they are used to predict or forecast ecosystem responses to global change. This uncertainty is partly due to parameterization, which is an essential procedure for model specification via defining parameter values for a model. The classic doctrine of parameterization is that a parameter is constant. However, it is commonly known from modeling practice that a model that is well calibrated for its parameters at one site may not simulate well at another site unless its parameters are tuned again. This common practice implies that parameter values have to vary with sites. Indeed, parameter values that are estimated using a statistically rigorous approach, that is, data assimilation, vary with time, space, and treatments in global change experiments. This paper illustrates that varying parameters is to account for both processes at unresolved scales and changing properties of evolving systems. A model, no matter how complex it is, could not represent all the processes of one system at resolved scales. Interactions of processes at unresolved scales with those at resolved scales should be reflected in model parameters. Meanwhile, it is pervasively observed that properties of ecosystems change over time, space, and environmental conditions. Parameters, which represent properties of a system under study, should change as well. Tuning has been practiced for many decades to change parameter values. Yet this activity, unfortunately, did not contribute to our knowledge on model parameterization at all. Data assimilation makes it possible to rigorously estimate parameter values and, consequently, offers an approach to understand which, how, how much, and why parameters vary. To fully understand those issues, extensive research is required. Nonetheless, it is clear that changes in parameter values lead to different model predictions even if the model structure is the same.     

Modelling phosphorus fluxes in the hypertrophic Loosdrecht Lakes

A dynamic, deterministic model is presented to simulate the phosphorus cycle and plankton growth in the shallow, hypertrophic Loosdrecht Lakes (The Netherlands) before and after restoration measures. Besides inorganic phosphorus (SRP) in both the surface water and the interstitial water, the model comprises three algal groups, zooplankton, fish, detritus, zoobenthos and upper sediment (all modelled both in carbon and in phosphorus). Within the model system, the phosphorus cycle is completely closed. Carbon and phosphorus are described independently, so that the dynamics of the P/C ratios can be modelled. Sediment processes are described in a simplified form.Simulated values are largely within the range of observed ones. The detrital fraction of the seston (=phytoplankton+detritus) varies from 50–60% in summer to about 90% in winter. SRP in the surface water is very low during most of the year. Sensitivity for external phosphorus input is larger for algal and detrital P than for algal and detrital C and chlorophyll-a. So the P/C ratio of the seston decreases following restoration measures, as is observed in the lakes, while the much higher P/C ratios of zooplankton and fish remain constant. Phosphorus mobilisation from the sediment decreases with decreasing external input. Adaptation of the model system to the reduced loading takes place within about two years.Sources of uncertainty in the model include the limited knowledge on selective grazing as well as on mortality and mineralisation processes.     

Assessment of uncertainty in functional-structural plant models

Background and Aims

Constructing functional–structural plant models (FSPMs) is a valuable method for examining how physiology and morphology interact in determining plant processes. However, such models always have uncertainty concerned with whether model components have been selected and represented effectively, with the number of model outputs simulated and with the quality of data used in assessment. We provide a procedure for defining uncertainty of an FSPM and how this uncertainty can be reduced.

Methods

An important characteristic of FSPMs is that typically they calculate many variables. These can be variables that the model is designed to predict and also variables that give indications of how the model functions. Together these variables are used as criteria in a method of multi-criteria assessment. Expected ranges are defined and an evolutionary computation algorithm searches for model parameters that achieve criteria within these ranges. Typically, different combinations of model parameter values provide solutions achieving different combinations of variables within their specified ranges. We show how these solutions define a Pareto Frontier that can inform about the functioning of the model.

Key Results

The method of multi-criteria assessment is applied to development of BRANCHPRO, an FSPM for foliage reiteration on old-growth branches of Pseudotsuga menziesii. A geometric model utilizing probabilities for bud growth is developed into a causal explanation for the pattern of reiteration found on these branches and how this pattern may contribute to the longevity of this species.

Conclusions

FSPMs should be assessed by their ability to simulate multiple criteria simultaneously. When different combinations of parameter values achieve different groups of assessment criteria effectively a Pareto Frontier can be calculated and used to define the sources of model uncertainty.     

Bacterial strategies to inhabit acidic environments

Bacteria can inhabit a wide range of environmental conditions, including extremes in pH ranging from 1 to 11. The primary strategy employed by bacteria in acidic environments is to maintain a constant cytoplasmic pH value. However, many data demonstrate that bacteria can grow under conditions in which pH values are out of the range in which cytoplasmic pH is kept constant. Based on these observations, a novel notion was proposed that bacteria have strategies to survive even if the cytoplasm is acidified by low external pH. Under these conditions, bacteria are obliged to use acid-resistant systems, implying that multiple systems having the same physiological role are operating at different cytoplasmic pH values. If this is true, it is quite likely that bacteria have genes that are induced by environmental stimuli under different pH conditions. In fact, acid-inducible genes often respond to another factor(s) besides pH. Furthermore, distinct genes might be required for growth or survival at acid pH under different environmental conditions because functions of many systems are dependent on external conditions. Systems operating at acid pH have been described to date, but numerous genes remain to be identified that function to protect bacteria from an acid challenge. Identification and analysis of these genes is critical, not only to elucidate bacterial physiology, but also to increase the understanding of bacterial pathogenesis.     

Species distribution models in climate change scenarios are still not useful for informing policy planning: an uncertainty assessment using fuzzy logic

We compared the effect of general circulation models and greenhouse gas emission scenarios on the uncertainty associated with models predicting changes in areas favourable to animal species. Given that mountain species are particularly at risk due to climate warming, we selected one amphibian (Baetic midwife toad), one reptile (Lataste's viper), one bird (Bonelli's eagle), and one mammal (Iberian wild goat) present in Spanish mountains to model their distributional response to climate change during this century. Climate forecasts for the whole century were provided by the Agencia Estatal de Meteorología (AEMET; National Meteorological Agency) of Spain, which adapted the general circulation models CGCM2 and ECHAM4 and produced expected temperature and precipitation values for Spain according to the A2 and B2 emission scenarios. We constructed separate models of the species response to spatial, topographic, human, and climate variables using current values of the corresponding variables. We predicted future areas favourable to the species by replacing the current climate values with those expected according to each climate change scenario, while keeping spatial, topographic and human variables constant. Fuzzy logic was used to compute the coincidence between predictions for different emission scenarios in the same global circulation model, and the consistency between predictions for the same emission scenario applying different general circulation models. In general, coincidences were higher than consistencies and, thus, discrepancies between predictions were more attributable to uncertainty in global circulation models, i.e. our insufficient knowledge concerning the effect of the oceans and atmosphere on climate, than to the putative effect of different emission scenarios on future climates. Our conclusion is that species distribution models in climate warming scenarios are still not useful for informing emission policy planning, although they have great potential as tools once consistencies become higher than coincidences.     

干旱胁迫植物个体生理响应及其生态模型预测研究进展

全球气候突变导致干旱事件频发,进而易引发严重的植物衰退甚至死亡,聚焦植物尤其是树木死亡的生理学机制并期望基于此评估及预测气候变化导致植物死亡风险已成为热点话题。植物通过调整内在生理代谢过程,例如通过调节渗透物质的含量,来平衡渗透势、维持细胞膨压、调节植物激素的信号水平,诱导植物气孔开放程度降低,有利于植物保存水分、调控植物水通道蛋白的表达,进而保持体内水分稳定并对干旱胁迫做出快速响应。这些生理过程中的每一环调节都为了确保水分运输的效率和安全性,增加植物抗旱性以及生态系统稳定性。植物的抗旱性不仅体现在生理代谢方面的调节,还表现在植物水力特性与解剖结构间相辅相成。当植物改变水力特性的同时,其茎叶会在解剖结构上做出调整以满足植物在干旱环境下水分供需平衡,从而降低植物蒸腾水分散失、增强细胞储水并提高生存能力。植物应对水分胁迫的策略通常与水分消耗和碳获取之间的平衡有关,明晰植物水分消耗与光合碳获取间存在平衡关系的性状特征便于更好地理解植物的水分利用策略。然而,植物表现出的任意单一性状特征的强弱都无法代表整个植物适应逆境的优劣,未来只有通过将植物更多性状特征进行相互关联,以具有代表植物水力功能、结...     

大尺度森林碳循环过程模拟模型综述

森林生态系统碳循环是全球陆地生态系统碳循环的重要组成部分,而碳循环模型已经成为研究森林碳循环的必要手段。森林碳循环模型可以分为统计模型和过程模型,其中过程模型以其完整的理论框架、严谨的结构分析和清晰的过程机理,逐渐占据了主导地位。从地球化学过程模型、陆面物理过程模型和生物过程模型等3个方面综述区域尺度到全球尺度(本文称为大尺度)森林碳循环过程模型研究进展,论述了各类模型的主要特征、优缺点以及应用现状,探讨了森林碳循环模拟研究中存在的问题,并讨论了森林碳循环过程模型的主流研究方向。可为不同空间尺度下森林生态系统碳循环模拟模型的选择提供参考,以及为森林碳循环研究提供借鉴。     

Improvement of bioprocess monitoring: development of novel concepts

The advancement of bioprocess monitoring will play a crucial role to meet the future requirements of bioprocess technology. Major issues are the acceleration of process development to reduce the time to the market and to ensure optimal exploitation of the cell factory and further to cope with the requirements of the Process Analytical Technology initiative. Due to the enormous complexity of cellular systems and lack of appropriate sensor systems microbial production processes are still poorly understood. This holds generally true for the most microbial production processes, in particular for the recombinant protein production due to strong interaction between recombinant gene expression and host cell metabolism. Therefore, it is necessary to scrutinise the role of the different cellular compartments in the biosynthesis process in order to develop comprehensive process monitoring concepts by involving the most significant process variables and their interconnections. Although research for the development of novel sensor systems is progressing their applicability in bioprocessing is very limited with respect to on-line and in-situ measurement due to specific requirements of aseptic conditions, high number of analytes, drift, and often rather low physiological relevance. A comprehensive survey of the state of the art of bioprocess monitoring reveals that only a limited number of metabolic variables show a close correlation to the currently explored chemical/physical principles. In order to circumvent this unsatisfying situation mathematical methods are applied to uncover "hidden" information contained in the on-line data and thereby creating correlations to the multitude of highly specific biochemical off-line data. Modelling enables the continuous prediction of otherwise discrete off-line data whereby critical process states can be more easily detected. The challenging issue of this concept is to establish significant on-line and off-line data sets. In this context, online sensor systems are reviewed with respect to commercial availability in combination with the suitability of offline analytical measurement methods. In a case study, the aptitude of the concept to exploit easily available online data for prediction of complex process variables in a recombinant E. coli fed-batch cultivation aiming at the improvement of monitoring capabilities is demonstrated. In addition, the perspectives for model-based process supervision and process control are outlined.     

High-dimensional coexistence of temperate tree species: functional traits, demographic rates, life-history stages, and their physical context

Theoretical models indicate that trade-offs between growth and survival strategies of tree species can lead to coexistence across life history stages (ontogeny) and physical conditions experienced by individuals. There exist predicted physiological mechanisms regulating these trade-offs, such as an investment in leaf characters that may increase survival in stressful environments at the expense of investment in bole or root growth. Confirming these mechanisms, however, requires that potential environmental, ontogenetic, and trait influences are analyzed together. Here, we infer growth and mortality of tree species given size, site, and light characteristics from forest inventory data from Wisconsin to test hypotheses about growth-survival trade-offs given species functional trait values under different ontogenetic and environmental states. A series of regression analyses including traits and rates their interactions with environmental and ontogenetic stages supported the relationships between traits and vital rates expected from the expectations from tree physiology. A combined model including interactions between all variables indicated that relationships between demographic rates and functional traits supports growth-survival trade-offs and their differences across species in high-dimensional niche space. The combined model explained 65% of the variation in tree growth and supports a concept of community coexistence similar to Hutchinson's n-dimensional hypervolume and not a low-dimensional niche model or neutral model.     

Host-Defense Mechanism of the Ocular Surfaces

The defense of the ocular surfaces presents an unique challenge in that not only must integrity be maintained against microbial, inflammatory and physical assault, but it must be done while minimizing the risk of loss of corneal transparency. This puts severe limitations on the degree to which scarring or neovascularization can occur in the cornea secondary to any infectious, inflammatory, immunological or wound healing process. Moreover, this defense system must be equally effective under two extremes of conditions: those found in the open eye and the closed eye environments. It is our contention that these constraints have resulted in the evolution of a highly complex fail-safe defense system that utilizes distinctly different strategies in open and closed eye conditions. The extraordinary effectiveness of this system is evidenced by the fact that despite continued exposure to a microbe rich environment, the external ocular surfaces maintain a very low microbial titer and are highly resistant to breaching by all but a few pathogens. It is the intent of this review to provide a working model of this defense system as it operates under both open and closed eye conditions, to provide evidence in support of this model as well as highlight some of the many areas of uncertainty.