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Mechanistic Models (mechanistic + models)
Selected AbstractsIntegrating physiology, population dynamics and climate to make multi-scale predictions for the spread of an invasive insect: the Argentine ant at Haleakala National Park, HawaiiECOGRAPHY, Issue 1 2010Stephen Hartley Mechanistic models for predicting species' distribution patterns present particular advantages and challenges relative to models developed from statistical correlations between distribution and climate. They can be especially useful for predicting the range of invasive species whose distribution has not yet reached equilibrium. Here, we illustrate how a physiological model of development for the invasive Argentine ant can be connected to differences in micro-site suitability, population dynamics and climatic gradients; processes operating at quite different spatial scales. Our study is located in the subalpine shrubland of Haleakala National Park, Hawaii, where the spread of Argentine ants Linepithema humile has been documented for the past twenty-five years. We report four main results. First, at a microsite level, the accumulation of degree-days recorded in potential ant nest sites under bare ground or rocks was significantly greater than under a groundcover of grassy vegetation. Second, annual degree-days measured where population boundaries have not expanded (456,521,degree-days), were just above the developmental requirements identified from earlier laboratory studies (445,degree-days above 15.9°C). Third, rates of population expansion showed a strong linear relationship with annual degree-days. Finally, an empirical relationship between soil degree-days and climate variables mapped at a broader scale predicts the potential for future range expansion of Argentine ants at Haleakala, particularly to the west of the lower colony and the east of the upper colony. Variation in the availability of suitable microsites, driven by changes in vegetation cover and ultimately climate, provide a hierarchical understanding of the distribution of Argentine ants close to their cold-wet limit of climatic tolerances. We conclude that the integration of physiology, population dynamics and climate mapping holds much promise for making more robust predictions about the potential spread of invasive species. [source] Trend estimation in extremes of synthetic North Sea surgesJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 4 2007Adam Butler Summary., Mechanistic models for complex atmospheric and hydrological processes are often used to simulate extreme natural events, usually to quantify the risks that are associated with these events. We use novel extreme value methods to analyse the statistical properties of output from a numerical storm surge model for the North Sea. The ,model data' constitute a reconstruction of the storm surge climate for the period 1955,2000 based on a high quality meteorological data set and constitute the only available source of information on surge elevations at offshore and unmonitored coastal locations over this period. Previous studies have used extreme value methods to analyse storm surge characteristics, but we can extend and improve on these analyses by using a local likelihood approach to provide a non-parametric description of temporal and spatial variations in the magnitude and frequency of storm surge events. [source] Correlative and mechanistic models of species distribution provide congruent forecasts under climate changeCONSERVATION LETTERS, Issue 3 2010Michael R. Kearney Abstract Good forecasts of climate change impacts on extinction risks are critical for effective conservation management responses. Species distribution models (SDMs) are central to extinction risk analyses. The reliability of predictions of SDMs has been questioned because models often lack a mechanistic underpinning and rely on assumptions that are untenable under climate change. We show how integrating predictions from fundamentally different modeling strategies produces robust forecasts of climate change impacts on habitat and population parameters. We illustrate the principle by applying mechanistic (Niche Mapper) and correlative (Maxent, Bioclim) SDMs to predict current and future distributions and fertility of an Australian gliding possum. The two approaches make congruent, accurate predictions of current distribution and similar, dire predictions about the impact of a warming scenario, supporting previous correlative-only predictions for similar species. We argue that convergent lines of independent evidence provide a robust basis for predicting and managing extinctions risks under climate change. [source] Long-distance biological transport processes through the air: can nature's complexity be unfolded in silico?DIVERSITY AND DISTRIBUTIONS, Issue 2 2005Ran Nathan ABSTRACT Understanding and predicting complex biological systems are best accomplished through the synthesis and integration of information across relevant spatial, temporal and thematic scales. We propose that mechanistic transport models, which integrate atmospheric turbulence with information on relevant biological attributes, can effectively incorporate key elements of aerial transport processes at scales ranging from a few centimetres and fractions of seconds, to hundreds of kilometres and decades. This capability of mechanistic models is critically important for modelling the flow of organisms through the atmosphere because diverse aerial transport processes , such as pathogen spread, seed dispersal, spider ballooning and bird migration , are sensitive to the details of small-scale short-term turbulent deviations from the mean airflow. At the same time, all these processes are strongly influenced by the typical larger-scale variation in landscape structure, through its effects on wind flow patterns. We therefore highlight the useful coupling of detailed atmospheric models such as large eddy simulations (LES), which can provide a high-resolution description of turbulent airflow, with regional atmospheric models, which can capture the effects of landscape heterogeneity at various scales. Further progress in computational fluid dynamics (CFD) will enable rigorous exploration of transport processes in heterogeneous landscapes. [source] Can mechanism inform species' distribution models?ECOLOGY LETTERS, Issue 8 2010Lauren B. Buckley Ecology Letters (2010) 13: 1041,1054 Abstract Two major approaches address the need to predict species distributions in response to environmental changes. Correlative models estimate parameters phenomenologically by relating current distributions to environmental conditions. By contrast, mechanistic models incorporate explicit relationships between environmental conditions and organismal performance, estimated independently of current distributions. Mechanistic approaches include models that translate environmental conditions into biologically relevant metrics (e.g. potential duration of activity), models that capture environmental sensitivities of survivorship and fecundity, and models that use energetics to link environmental conditions and demography. We compared how two correlative and three mechanistic models predicted the ranges of two species: a skipper butterfly (Atalopedes campestris) and a fence lizard (Sceloporus undulatus). Correlative and mechanistic models performed similarly in predicting current distributions, but mechanistic models predicted larger range shifts in response to climate change. Although mechanistic models theoretically should provide more accurate distribution predictions, there is much potential for improving their flexibility and performance. [source] Comparison of synthetic surfactants and biosurfactants in enhancing biodegradation of polycyclic aromatic hydrocarbonsENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 10 2003Randhir S. Makkar Abstract Polycyclic aromatic hydrocarbon (PAH) contamination of the environment represents a serious threat to the health of humans and ecosystems. Given the human health effects of PAHs, effective and cost-competitive remediation technologies are required. Bioremediation has shown promise as a potentially effective and low-cost treatment option, but concerns about the slow process rate and bioavailability limitations have hampered more widespread use of this technology. An option to enhance the bioavailability of PAHs is to add surfactants directly to soil in situ or ex situ in bioreactors. Surfactants increase the apparent solubility and desorption rate of the PAH to the aqueous phase. However, the results with some synthetic surfactants have shown that surfactant addition can actually inhibit PAH biodegradation via toxic interactions, stimulation of surfactant degraders, or sequestration of PAHs into surfactant micelles. Biosurfactants have been shown to have many of the positive effects of synthetic surfactants but without the drawbacks. They are biodegradable and nontoxic, and many biosurfactants do not produce true micelles, thus facilitating direct transfer of the surfactant-associated PAH to bacteria. The results with biosurfactants to date are promising, but further research to elucidate surfactant,PAH interactions in aqueous environments is needed to lead to predictive, mechanistic models of biosurfactant-enhanced PAH bioavailability and thus better bioremediation design. [source] A CENTENNIAL CELEBRATION FOR QUANTITATIVE GENETICSEVOLUTION, Issue 5 2007Derek A. Roff Quantitative genetics is at or is fast approaching its centennial. In this perspective I consider five current issues pertinent to the application of quantitative genetics to evolutionary theory. First, I discuss the utility of a quantitative genetic perspective in describing genetic variation at two very different levels of resolution, (1) in natural, free-ranging populations and (2) to describe variation at the level of DNA transcription. Whereas quantitative genetics can serve as a very useful descriptor of genetic variation, its greater usefulness is in predicting evolutionary change, particularly when used in the first instance (wild populations). Second, I review the contributions of Quantitative trait loci (QLT) analysis in determining the number of loci and distribution of their genetic effects, the possible importance of identifying specific genes, and the ability of the multivariate breeder's equation to predict the results of bivariate selection experiments. QLT analyses appear to indicate that genetic effects are skewed, that at least 20 loci are generally involved, with an unknown number of alleles, and that a few loci have major effects. However, epistatic effects are common, which means that such loci might not have population-wide major effects: this question waits upon (QTL) analyses conducted on more than a few inbred lines. Third, I examine the importance of research into the action of specific genes on traits. Although great progress has been made in identifying specific genes contributing to trait variation, the high level of gene interactions underlying quantitative traits makes it unlikely that in the near future we will have mechanistic models for such traits, or that these would have greater predictive power than quantitative genetic models. In the fourth section I present evidence that the results of bivariate selection experiments when selection is antagonistic to the genetic covariance are frequently not well predicted by the multivariate breeder's equation. Bivariate experiments that combine both selection and functional analyses are urgently needed. Finally, I discuss the importance of gaining more insight, both theoretical and empirical, on the evolution of the G and P matrices. [source] Linking movement behaviour, dispersal and population processes: is individual variation a key?JOURNAL OF ANIMAL ECOLOGY, Issue 5 2009Colin Hawkes Summary 1Movement behaviour has become increasingly important in dispersal ecology and dispersal is central to the development of spatially explicit population ecology. The ways in which the elements have been brought together are reviewed with particular emphasis on dispersal distance distributions and the value of mechanistic models. 2There is a continuous range of movement behaviours and in some species, dispersal is a clearly delineated event but not in others. The biological complexities restrict conclusions to high-level generalizations but there may be principles that are common to dispersal and other movements. 3Random walk and diffusion models when appropriately elaborated can provide an understanding of dispersal distance relationships on spatial and temporal scales relevant to dispersal. Leptokurtosis in the relationships may be the result of a combination of factors including population heterogeneity, correlation, landscape features, time integration and density dependence. The inclusion in diffusion models of individual variation appears to be a useful elaboration. The limitations of the negative exponential and other phenomenological models are discussed. 4The dynamics of metapopulation models are sensitive to what appears to be small differences in the assumptions about dispersal. In order to represent dispersal realistically in population models, it is suggested that phenomenological models should be replaced by those based on movement behaviour incorporating individual variation. 5The conclusions are presented as a set of candidate principles for evaluation. The main features of the principles are that uncorrelated or correlated random walk, not linear movement, is expected where the directions of habitat patches are unpredictable and more complex behaviour when organisms have the ability to orientate or navigate. Individuals within populations vary in their movement behaviour and dispersal; part of this variation is a product of random elements in movement behaviour and some of it is heritable. Local and metapopulation dynamics are influenced by population heterogeneity in dispersal characteristics and heritable changes in dispersal propensity occur on time-scales short enough to impact population dynamics. [source] Horwitz's rule, transforming both sides and the design of experiments for mechanistic modelsJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 3 2003Anthony C. Atkinson Summary. The paper develops methods for the design of experiments for mechanistic models when the response must be transformed to achieve symmetry and constant variance. The power transformation that is used is partially justified by a rule in analytical chemistry. Because of the nature of the relationship between the response and the mechanistic model, it is necessary to transform both sides of the model. Expressions are given for the parameter sensitivities in the transformed model and examples are given of optimum designs, not only for single-response models, but also for experiments in which multivariate responses are measured and for experiments in which the model is defined by a set of differential equations which cannot be solved analytically. The extension to designs for checking models is discussed. [source] Set theoretic formulation of performance reliability of multiple response time-variant systems due to degradations in system componentsQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 2 2007Young Kap Son Abstract This paper presents a design stage method for assessing performance reliability of systems with multiple time-variant responses due to component degradation. Herein the system component degradation profiles over time are assumed to be known and the degradation of the system is related to component degradation using mechanistic models. Selected performance measures (e.g. responses) are related to their critical levels by time-dependent limit-state functions. System failure is defined as the non-conformance of any response and unions of the multiple failure regions are required. For discrete time, set theory establishes the minimum union size needed to identify a true incremental failure region. A cumulative failure distribution function is built by summing incremental failure probabilities. A practical implementation of the theory can be manifest by approximating the probability of the unions by second-order bounds. Further, for numerical efficiency probabilities are evaluated by first-order reliability methods (FORM). The presented method is quite different from Monte Carlo sampling methods. The proposed method can be used to assess mean and tolerance design through simultaneous evaluation of quality and performance reliability. The work herein sets the foundation for an optimization method to control both quality and performance reliability and thus, for example, estimate warranty costs and product recall. An example from power engineering shows the details of the proposed method and the potential of the approach. Copyright © 2006 John Wiley & Sons, Ltd. [source] Modelling growth and body composition in fish nutrition: where have we been and where are we going?AQUACULTURE RESEARCH, Issue 2 2010André Dumas Abstract Mathematical models in fish nutrition have proven indispensable in estimating growth and feed requirements. Nowadays, reducing the environmental footprint and improving product quality of fish culture operations are of increasing interest. This review starts by examining simple models applied to describe/predict fish growth profiles and progresses towards more comprehensive concepts based on bioenergetics and nutrient metabolism. Simple growth models often lack biological interpretation and overlook fundamental properties of fish (e.g. ectothermy, indeterminate growth). In addition, these models disregard possible variations in growth trajectory across life stages. Bioenergetic models have served to predict not only fish growth but also feed requirements and waste outputs from fish culture operations. However, bioenergetics is a concept based on energy-yielding equivalence of chemicals and has significant limitations. Nutrient-based models have been introduced into the fish nutrition literature over the last two decades and stand as a more biologically sound alternative to bioenergetic models. More mechanistic models are required to expand current understanding about growth targets and nutrient utilization for biomass gain. Finally, existing models need to be adapted further to address effectively concerns regarding sustainability, product quality and body traits. [source] Good modeling practice for PAT applications: Propagation of input uncertainty and sensitivity analysisBIOTECHNOLOGY PROGRESS, Issue 4 2009Gürkan Sin Abstract The uncertainty and sensitivity analysis are evaluated for their usefulness as part of the model-building within Process Analytical Technology applications. A mechanistic model describing a batch cultivation of Streptomyces coelicolor for antibiotic production was used as case study. The input uncertainty resulting from assumptions of the model was propagated using the Monte Carlo procedure to estimate the output uncertainty. The results showed that significant uncertainty exists in the model outputs. Moreover the uncertainty in the biomass, glucose, ammonium and base-consumption were found low compared to the large uncertainty observed in the antibiotic and off-gas CO2 predictions. The output uncertainty was observed to be lower during the exponential growth phase, while higher in the stationary and death phases - meaning the model describes some periods better than others. To understand which input parameters are responsible for the output uncertainty, three sensitivity methods (Standardized Regression Coefficients, Morris and differential analysis) were evaluated and compared. The results from these methods were mostly in agreement with each other and revealed that only few parameters (about 10) out of a total 56 were mainly responsible for the output uncertainty. Among these significant parameters, one finds parameters related to fermentation characteristics such as biomass metabolism, chemical equilibria and mass-transfer. Overall the uncertainty and sensitivity analysis are found promising for helping to build reliable mechanistic models and to interpret the model outputs properly. These tools make part of good modeling practice, which can contribute to successful PAT applications for increased process understanding, operation and control purposes. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009 [source] Substrate recognition of type III secretion machines ,testing the RNA signal hypothesisCELLULAR MICROBIOLOGY, Issue 9 2005Joseph A. Sorg Summary Secretion by the type III pathway of Gram-negative microbes transports polypeptides into the extracellular medium or into the cytoplasm of host cells during infection. In pathogenic Yersinia spp., type III machines recognize 14 different Yop protein substrates via discrete signals genetically encoded in 7,15 codons at the 5, portion of yop genes. Although the signals necessary and sufficient for substrate recognition of Yop proteins have been mapped, a clear mechanism on how proteins are recognized by the machinery and then initiated into the transport pathway has not yet emerged. As synonymous substitutions, mutations that alter mRNA sequence but not codon specificity, affect the function of some secretion signals, recent work with several different microbes tested the hypothesis of an RNA-encoded secretion signal for polypeptides that travel the type III pathway. This review summarizes experimental observations and mechanistic models for substrate recognition in this field. [source] | |||||||||||||||||||