Estimating Relative Changes of Metabolic Fluxes

Fluxes are the central trait of metabolism and Kinetic Flux Profiling (KFP) is an effective method of measuring them. To generalize its applicability, we present an extension of the method that estimates the relative changes of fluxes using only relative quantitation of ¹³C-labeled metabolites. Such features are directly tailored to the more common experiment that performs only relative quantitation and compares fluxes between two conditions. We call our extension rKFP. Moreover, we examine the effects of common missing data and common modeling assumptions on (r)KFP, and provide practical suggestions. We also investigate the selection of measuring times for (r)KFP and provide a simple recipe. We then apply rKFP to ¹³C-labeled glucose time series data collected from cells under normal and glucose-deprived conditions, estimating the relative flux changes of glycolysis and its branching pathways. We identify an adaptive response in which de novo serine biosynthesis is compromised to maintain the glycolytic flux backbone. Together, these results greatly expand the capabilities of KFP and are suitable for broad biological applications.     

A Maximum Likelihood Method for Estimating Genome Length Using Genetic Linkage Data

The genetic length of a genome, in units of Morgans or centimorgans, is a fundamental characteristic of an organism. We propose a maximum likelihood method for estimating this quantity from counts of recombinants and nonrecombinants between marker locus pairs studied from a backcross linkage experiment, assuming no interference and equal chromosome lengths. This method allows the calculation of the standard deviation of the estimate and a confidence interval containing the estimate. Computer simulations have been performed to evaluate and compare the accuracy of the maximum likelihood method and a previously suggested method-of-moments estimator. Specifically, we have investigated the effects of the number of meioses, the number of marker loci, and variation in the genetic lengths of individual chromosomes on the estimate. The effect of missing data, obtained when the results of two separate linkage studies with a fraction of marker loci in common are pooled, is also investigated. The maximum likelihood estimator, in contrast to the method-of-moments estimator, is relatively insensitive to violation of the assumptions made during analysis and is the method of choice. The various methods are compared by application to partial linkage data from Xiphophorus.     

Metabolic Fluxes in an Illuminated Arabidopsis Rosette

Photosynthesis is the basis for life, and its optimization is a key biotechnological aim given the problems of population explosion and environmental deterioration. We describe a method to resolve intracellular fluxes in intact Arabidopsis thaliana rosettes based on time-dependent labeling patterns in the metabolome. Plants photosynthesizing under limiting irradiance and ambient CO₂ in a custom-built chamber were transferred into a ¹³CO₂-enriched environment. The isotope labeling patterns of 40 metabolites were obtained using liquid or gas chromatography coupled to mass spectrometry. Labeling kinetics revealed striking differences between metabolites. At a qualitative level, they matched expectations in terms of pathway topology and stoichiometry, but some unexpected features point to the complexity of subcellular and cellular compartmentation. To achieve quantitative insights, the data set was used for estimating fluxes in the framework of kinetic flux profiling. We benchmarked flux estimates to four classically determined flux signatures of photosynthesis and assessed the robustness of the estimates with respect to different features of the underlying metabolic model and the time-resolved data set.     

Strategies for Manipulating Metabolic Fluxes in Biotechnology

Strategies for biotechnologically manipulating metabolic fluxes are critically examined in relation to a model system. The common idea of first identifying the rate-limiting enzyme in the biosynthetic pathway to a desired end-product, and then increasing its activity, is shown to be completely ineffective; such manipulation typically produces only trivial changes in flux. Manipulating the activities of all of the enzymes in a biosynthetic pathway by amounts calculated to increase a desired flux while leaving all other fluxes and all concentrations unchanged is potentially effective, and can be applied to any system without regard to its regulatory design. however, it requires accurate knowledge of the initial state of the system and the ability to make precise changes to numerous activities. The classical information about the regulatory mechanisms that exist in living organisms suggests that one can make much simpler manipulations, involving only the steps that remove the desired end-product, with almost equally satisfactory results.     

A Mathematical Model of the Human Metabolic System and Metabolic Flexibility

In healthy subjects some tissues in the human body display metabolic flexibility, by this we mean the ability for the tissue to switch its fuel source between predominantly carbohydrates in the postprandial state and predominantly fats in the fasted state. Many of the pathways involved with human metabolism are controlled by insulin and insulin-resistant states such as obesity and type-2 diabetes are characterised by a loss or impairment of metabolic flexibility. In this paper we derive a system of 12 first-order coupled differential equations that describe the transport between and storage in different tissues of the human body. We find steady state solutions to these equations and use these results to nondimensionalise the model. We then solve the model numerically to simulate a healthy balanced meal and a high fat meal and we discuss and compare these results. Our numerical results show good agreement with experimental data where we have data available to us and the results show behaviour that agrees with intuition where we currently have no data with which to compare.     

Metabolic Effects on Ion Fluxes in Tolypella intricata

The uptake of C1 ions by cells of Tolypella intricata is greatlyincreased by light, and must be an active process. K ions inthe cells (at 90–110 mM concentration) are in approximateelectrochemical equilibrium with the external solution, butthe K influx is affected (directly or indirectly) by cellularmetabolism. The K influx is increased by light, and the increaseis greater in the presence of C1 than when C1 is removed fromthe solution. K uptake is inhibited by chemicals which alsoinhibit the C1 pump.It is suggested that light increases thepermeability of the plasmalemma to K, but there must also belinks between K and C1 uptake. The possible nature of theselinks is discussed.The internal Na concentration (3–10mM) is considerably below the expected equilibrium concentration,but the Na influx is also very low (and is not increased bylight). The permeability of the plasmalemma to Na is thus verylow, and there can be little active extrusion of Na under normalconditions.     

Inter-Vertebral Flexibility of the Ostrich Neck: Implications for Estimating Sauropod Neck Flexibility

The flexibility and posture of the neck in sauropod dinosaurs has long been contentious. Improved constraints on sauropod neck function will have major implications for what we know of their foraging strategies, ecology and overall biology. Several hypotheses have been proposed, based primarily on osteological data, suggesting different degrees of neck flexibility. This study attempts to assess the effects of reconstructed soft tissues on sauropod neck flexibility through systematic removal of muscle groups and measures of flexibility of the neck in a living analogue, the ostrich (Struthio camelus). The possible effect of cartilage on flexibility is also examined, as this was previously overlooked in osteological estimates of sauropod neck function. These comparisons show that soft tissues are likely to have limited the flexibility of the neck beyond the limits suggested by osteology alone. In addition, the inferred presence of cartilage, and varying the inter-vertebral spacing within the synovial capsule, also affect neck flexibility. One hypothesis proposed that flexibility is constrained by requiring a minimum overlap between successive zygapophyses equivalent to 50% of zygapophyseal articular surface length (ONP50). This assumption is tested by comparing the maximum flexibility of the articulated cervical column in ONP50 and the flexibility of the complete neck with all tissues intact. It is found that this model does not adequately convey the pattern of flexibility in the ostrich neck, suggesting that the ONP50 model may not be useful in determining neck function if considered in isolation from myological and other soft tissue data.     

Metabolic Flexibility as a Major Predictor of Spatial Distribution in Microbial Communities

A better understand the ecology of microbes and their role in the global ecosystem could be achieved if traditional ecological theories can be applied to microbes. In ecology organisms are defined as specialists or generalists according to the breadth of their niche. Spatial distribution is often used as a proxy measure of niche breadth; generalists have broad niches and a wide spatial distribution and specialists a narrow niche and spatial distribution. Previous studies suggest that microbial distribution patterns are contrary to this idea; a microbial generalist genus (Desulfobulbus) has a limited spatial distribution while a specialist genus (Methanosaeta) has a cosmopolitan distribution. Therefore, we hypothesise that this counter-intuitive distribution within generalist and specialist microbial genera is a common microbial characteristic. Using molecular fingerprinting the distribution of four microbial genera, two generalists, Desulfobulbus and the methanogenic archaea Methanosarcina, and two specialists, Methanosaeta and the sulfate-reducing bacteria Desulfobacter were analysed in sediment samples from along a UK estuary. Detected genotypes of both generalist genera showed a distinct spatial distribution, significantly correlated with geographic distance between sites. Genotypes of both specialist genera showed no significant differential spatial distribution. These data support the hypothesis that the spatial distribution of specialist and generalist microbes does not match that seen with specialist and generalist large organisms. It may be that generalist microbes, while having a wider potential niche, are constrained, possibly by intrageneric competition, to exploit only a small part of that potential niche while specialists, with far fewer constraints to their niche, are more capable of filling their potential niche more effectively, perhaps by avoiding intrageneric competition. We suggest that these counter-intuitive distribution patterns may be a common feature of microbes in general and represent a distinct microbial principle in ecology, which is a real challenge if we are to develop a truly inclusive ecology.     

The Carbon Assimilation Network in Escherichia coli Is Densely Connected and Largely Sign-Determined by Directions of Metabolic Fluxes

Gene regulatory networks consist of direct interactions but also include indirect interactions mediated by metabolites and signaling molecules. We describe how these indirect interactions can be derived from a model of the underlying biochemical reaction network, using weak time-scale assumptions in combination with sensitivity criteria from metabolic control analysis. We apply this approach to a model of the carbon assimilation network in Escherichia coli. Our results show that the derived gene regulatory network is densely connected, contrary to what is usually assumed. Moreover, the network is largely sign-determined, meaning that the signs of the indirect interactions are fixed by the flux directions of biochemical reactions, independently of specific parameter values and rate laws. An inversion of the fluxes following a change in growth conditions may affect the signs of the indirect interactions though. This leads to a feedback structure that is at the same time robust to changes in the kinetic properties of enzymes and that has the flexibility to accommodate radical changes in the environment.     

Reduced Flexibility Associated with Metabolic Syndrome in Community-Dwelling Elders

Background

The ageing process may lead to reductions in physical fitness, a known risk factor in the development of metabolic syndrome. The purpose of the current study was to evaluate cross-sectional and combined associations of metabolic syndrome with body composition and physical fitness in a community based geriatric population.

Methods

A total of 628 community-dwelling elders attending a geriatric health examination were enrolled in the study. The diagnosis of metabolic syndrome was based on the modified National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criterion with Asian cutoff of waist girth was adopted in this study. Body composition was obtained using bioimpedance analysis, and physical fitness was evaluated through the measurement of muscle strength (handgrip force), lower extremity muscle endurance (sit-to-stand test), flexibility (sit-and-reach test), and cardiorespiratory endurance (2-minute step test). Multivariable logistic regression and correlation analysis were performed to determine the association of metabolic syndrome with body composition and functionality variables.

Results

Metabolic syndrome was associated with increased skeletal muscle index (SMI) (odds ratio (OR), 1.61, 95% confidence interval (CI), 1.25–2.07) and decreased flexibility (OR, 0.97, 95% CI, 0.95–0.99) compared with those without metabolic syndrome. When body mass index was accounted for in the analysis, the association of SMI with metabolic syndrome was reduced. Waist circumference was positively correlated with SMI but negatively correlated with flexibility, whereas high density lipoprotein was positively correlated with flexibility but negatively correlated with SMI.

Conclusion

Reduced flexibility was positively associated with metabolic syndrome independent of age, gender, body composition, and functionality measurements in a community based geriatric population. Significant associations between metabolic syndrome with muscle strength and cardiorespiratory fitness in the elderly were not observed. Furthermore, flexibility should be included in the complete evaluation for metabolic syndrome.     

Transient-State Analysis of Metabolic Fluxes in Crabtree-Positive and Crabtree-Negative Yeasts

In bakers' yeast, an immediate alcoholic fermentation begins when a glucose pulse is added to glucose-limited, aerobically grown cells. The mechanism of this short-term Crabtree effect was investigated via a comparative enzymic analysis of eight yeast species. It was established that the fermentation rate of the organisms upon transition from glucose limitation to glucose excess is positively correlated with the level of pyruvate decarboxylase (EC 4.1.1.1). In the Crabtree-negative yeasts, the pyruvate decarboxylase activity was low and did not increase when excess glucose was added. In contrast, in the Crabtree-positive yeasts, the activity of this enzyme was on the average sixfold higher and increased after exposure to glucose excess. In Crabtree-negative species, relatively high activities of acetaldehyde dehydrogenases (EC 1.2.1.4 and EC 1.2.1.5) and acetyl coenzyme A synthetase (EC 6.2.1.1), in addition to low pyruvate decarboxylase activities, were present. Thus, in these yeasts, acetaldehyde can be effectively oxidized via a bypass that circumvents the reduction of acetaldehyde to ethanol. Growth rates of most Crabtree-positive yeasts did not increase upon transition from glucose limitation to glucose excess. In contrast, the Crabtree-negative yeasts exhibited enhanced rates of biomass production which in most cases could be ascribed to the intracellular accumulation of reserve carbohydrates. Generally, the glucose consumption rate after a glucose pulse was higher in the Crabtree-positive yeasts than in the Crabtree-negative yeasts. However, the respiratory capacities of steady-state cultures of Crabtree-positive yeasts were not significantly different from those of Crabtree-negative yeasts. Thus, a limited respiratory capacity is not the primary cause of the Crabtree effect in yeasts. Instead, the difference between Crabtree-positive and Crabtree-negative yeasts is attributed to differences in the kinetics of glucose uptake, synthesis of reserve carbohydrates, and pyruvate metabolism.     

一种中间代谢途径代谢通量的定量分析方法

以¹³C标记的碳源,用二维核磁共振技术(¹H-¹³C,HMQC)测定代谢中产生的氨基酸标记模式,研究对中间代谢途径胞内代谢通量进行定量分析的方法.通过开发软件包,改进同位素分布的数学模型,提出了反应映射矩阵(RMM)等概念.由简化算法,提高程序的执行效率,建立了定量分析胞内代谢通量的平台.代谢模型涉及了糖酵解途径、磷酸戊糖途径、三羧酸循环、几种回补反应、发酵途径和氨基酸合成途径.     

Detecting Concealed Knowledge Using a Novel Attentional Blink Paradigm

Traditional concealed information paradigms rely on the idea that stimuli that are meaningful to a person (critical items) will draw attentional resources disproportionately, relative to stimuli that are not (irrelevant items), generating detectable differences on a suitable dependent variable (behavioral, psychophysiological, or neural). Here, we introduce a behavioral paradigm that could be used to reveal concealed information by exploiting the link between concealed information and attentional processes more directly. This novel paradigm is based on the attentional blink phenomenon in which detection of a stimulus reduces detection accuracy rates of subsequent target stimuli within a 200–500 ms time window. We hypothesized that a well-known face used as a critical item could capture attentional resources automatically, making it harder to detect the occurrence of a subsequently presented target face. The results confirmed this hypothesis, and showed that concealed knowledge of a famous face could be detected in 9 out of 12 individuals by looking for a relative dip in target detection accuracy after the presentation of a critical item.     

Genome-Scale Metabolic Network Validation of Shewanella oneidensis Using Transposon Insertion Frequency Analysis

Transposon mutagenesis, in combination with parallel sequencing, is becoming a powerful tool for en-masse mutant analysis. A probability generating function was used to explain observed miniHimar transposon insertion patterns, and gene essentiality calls were made by transposon insertion frequency analysis (TIFA). TIFA incorporated the observed genome and sequence motif bias of the miniHimar transposon. The gene essentiality calls were compared to: 1) previous genome-wide direct gene-essentiality assignments; and, 2) flux balance analysis (FBA) predictions from an existing genome-scale metabolic model of Shewanella oneidensis MR-1. A three-way comparison between FBA, TIFA, and the direct essentiality calls was made to validate the TIFA approach. The refinement in the interpretation of observed transposon insertions demonstrated that genes without insertions are not necessarily essential, and that genes that contain insertions are not always nonessential. The TIFA calls were in reasonable agreement with direct essentiality calls for S. oneidensis, but agreed more closely with E. coli essentiality calls for orthologs. The TIFA gene essentiality calls were in good agreement with the MR-1 FBA essentiality predictions, and the agreement between TIFA and FBA predictions was substantially better than between the FBA and the direct gene essentiality predictions.     

Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.Rational and quantitative assessment of metabolic changes in response to genetic modification (GM) is an open question and in need of innovative solutions. Nontargeted metabolite profiling can detect thousands of compounds, but it is not easy to understand the significance of the changed metabolites in the biochemical and biological context of the organism. To better assess the changes in metabolites from nontargeted metabolomics studies, it is important to examine the changed metabolites in the context of the genome-scale metabolic network of the organism.Metabolomics is a technique that aims to quantify all the metabolites in a biological system (Nikolau and Wurtele, 2007; Nicholson and Lindon, 2008; Roessner and Bowne, 2009). It has been used widely in studies ranging from disease diagnosis (Holmes et al., 2008; DeBerardinis and Thompson, 2012) and drug discovery (Cascante et al., 2002; Kell, 2006) to metabolic reconstruction (Feist et al., 2009; Kim et al., 2012) and metabolic engineering (Keasling, 2010; Lee et al., 2011). Metabolomic studies have demonstrated the possibility of identifying gene functions from changes in the relative concentrations of metabolites (metabotypes or metabolic signatures; Ebbels et al., 2004) in various species including yeast (Saccharomyces cerevisiae; Raamsdonk et al., 2001; Allen et al., 2003), Arabidopsis (Arabidopsis thaliana; Brotman et al., 2011), tomato (Solanum lycopersicum; Schauer et al., 2006), and maize (Zea mays; Riedelsheimer et al., 2012). Metabolomics has also been used to better understand how plants interact with their environments (Field and Lake, 2011), including their responses to biotic and abiotic stresses (Dixon et al., 2006; Arbona et al., 2013), and to predict important agronomic traits (Riedelsheimer et al., 2012). Metabolite profiling has been performed on many plant species, including angiosperms such as Arabidopsis, poplar (Populus trichocarpa), and Catharanthus roseus (Sumner et al., 2003; Rischer et al., 2006), basal land plants such as Selaginella moellendorffii and Physcomitrella patens (Erxleben et al., 2012; Yobi et al., 2012), and Chlamydomonas reinhardtii (Fernie et al., 2012; Davis et al., 2013). With the availability of whole genome sequences of various species, metabolomics has the potential to become a useful tool for elucidating the functions of genes using large-scale systematic analyses (Fiehn et al., 2000; Saito and Matsuda, 2010; Hur et al., 2013).Although metabolomics data have the potential for identifying the roles of genes that are associated with metabolic phenotypes, the biochemical mechanisms that link functions of genes with metabolic phenotypes are still poorly characterized. For example, we do not yet know the principles behind how perturbing the expression of a single gene changes the metabolic system as a whole. Large-scale metabolomics data have provided useful resources for linking phenotypes to genotypes (Fiehn et al., 2000; Roessner et al., 2001; Tikunov et al., 2005; Schauer et al., 2006; Lu et al., 2011; Fukushima et al., 2014). For example, Lu et al. (2011) compared morphological and metabolic phenotypes from more than 5,000 Arabidopsis chloroplast mutants using gas chromatography (GC)- and liquid chromatography (LC)-mass spectrometry (MS). Fukushima et al. (2014) generated metabolite profiles from various characterized and uncharacterized mutant plants and clustered the mutants with similar metabolic phenotypes by conducting multidimensional scaling with quantified metabolic phenotypes. Nonetheless, representation and analysis of such a large amount of data remains a challenge for scientific discovery (Lu et al., 2011). In addition, these studies do not examine the topological and functional characteristics of metabolic changes in the context of a genome-scale metabolic network. To understand the relationship between genotype and metabolic phenotype, we need to investigate the metabolic changes caused by perturbing the expression of a gene in a genome-scale metabolic network perspective, because metabolic pathways are not independent biochemical factories but are components of a complex network (Berg et al., 2002; Merico et al., 2009).Much progress has been made in the last 2 decades to represent metabolism at a genome scale (Terzer et al., 2009). The advances in genome sequencing and emerging fields such as biocuration and bioinformatics enabled the representation of genome-scale metabolic network reconstructions for model organisms (Bassel et al., 2012). Genome-scale metabolic models have been built and applied broadly from microbes to plants. The first step toward modeling a genome-scale metabolism in a plant species started with developing a genome-scale metabolic pathway database for Arabidopsis (AraCyc; Mueller et al., 2003) from reference pathway databases (Kanehisa and Goto, 2000; Karp et al., 2002; Zhang et al., 2010). Genome-scale metabolic pathway databases have been built for several plant species (Mueller et al., 2005; Zhang et al., 2005, 2010; Urbanczyk-Wochniak and Sumner, 2007; May et al., 2009; Dharmawardhana et al., 2013; Monaco et al., 2013, 2014; Van Moerkercke et al., 2013; Chae et al., 2014; Jung et al., 2014). Efforts have been made to develop predictive genome-scale metabolic models using enzyme kinetics and stoichiometric flux-balance approaches (Sweetlove et al., 2008). de Oliveira Dal’Molin et al. (2010) developed a genome-scale metabolic model for Arabidopsis and successfully validated the model by predicting the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. Other genome-scale models have been developed for Arabidopsis (Poolman et al., 2009; Radrich et al., 2010; Mintz-Oron et al., 2012), C. reinhardtii (Chang et al., 2011; Dal’Molin et al., 2011), maize (Dal’Molin et al., 2010; Saha et al., 2011), sorghum (Sorghum bicolor; Dal’Molin et al., 2010), and sugarcane (Saccharum officinarum; Dal’Molin et al., 2010). These predictive models have the potential to be applied broadly in fields such as metabolic engineering, drug target discovery, identification of gene function, study of evolutionary processes, risk assessment of genetically modified crops, and interpretations of mutant phenotypes (Feist and Palsson, 2008; Ricroch et al., 2011).Here, we interrogate the metabotypes caused by 136 single gene perturbations of Arabidopsis by analyzing the relative concentration changes of 1,348 chemically identified metabolites using a reconstructed genome-scale metabolic network. We examine the characteristics of the changed metabolites (the metabolites whose relative concentrations were significantly different in mutants relative to the wild type) in the metabolic network to uncover biological and topological consequences of the perturbed genes.     

Using Topology of the Metabolic Network to Predict Viability of Mutant Strains

Background  

Understanding the relationships between the structure (topology) and function of biological networks is a central question of systems biology. The idea that topology is a major determinant of systems function has become an attractive and highly-disputed hypothesis. While the structural analysis of interaction networks demonstrates a correlation between the topological properties of a node (protein, gene) in the network and its functional essentiality, the analysis of metabolic networks fails to find such correlations. In contrast, approaches utilizing both the topology and biochemical parameters of metabolic networks, e.g. flux balance analysis (FBA), are more successful in predicting phenotypes of knock-out strains.     

Estimating Tropical Forest Structure Using a Terrestrial Lidar

Forest structure comprises numerous quantifiable biometric components and characteristics, which include tree geometry and stand architecture. These structural components are important in the understanding of the past and future trajectories of these biomes. Tropical forests are often considered the most structurally complex and yet least understood of forested ecosystems. New technologies have provided novel avenues for quantifying biometric properties of forested ecosystems, one of which is LIght Detection And Ranging (lidar). This sensor can be deployed on satellite, aircraft, unmanned aerial vehicles, and terrestrial platforms. In this study we examined the efficacy of a terrestrial lidar scanner (TLS) system in a tropical forest to estimate forest structure. Our study was conducted in January 2012 at La Selva, Costa Rica at twenty locations in a predominantly undisturbed forest. At these locations we collected field measured biometric attributes using a variable plot design. We also collected TLS data from the center of each plot. Using this data we developed relative vegetation profiles (RVPs) and calculated a series of parameters including entropy, Fast Fourier Transform (FFT), number of layers and plant area index to develop statistical relationships with field data. We developed statistical models using a series of multiple linear regressions, all of which converged on significant relationships with the strongest relationship being for mean crown depth (r² = 0.88, p < 0.001, RMSE = 1.04 m). Tree density was found to have the poorest significant relationship (r² = 0.50, p < 0.01, RMSE = 153.28 n ha^-1). We found a significant relationship between basal area and lidar metrics (r² = 0.75, p < 0.001, RMSE = 3.76 number ha^-1). Parameters selected in our models varied, thus indicating the potential relevance of multiple features in canopy profiles and geometry that are related to field-measured structure. Models for biomass estimation included structural canopy variables in addition to height metrics. Our work indicates that vegetation profiles from TLS data can provide useful information on forest structure.     

Detecting and Estimating Hormesis Using a Model-Based Approach

Hormesis is defined as a dose-response relationship that is stimulatory at low doses, but is inhibitory at higher doses. In a given experiment, it is not unusual to observe enhanced responses at low doses, however, such enhanced responses may not imply hormesis, but the random fluctuation of the data. Statistical tests can be developed to detect hormesis when enhanced responses at low concentrations are observed. We propose the use of a model-based approach to detect the presence of, and estimate the extent of, hormesis. This approach includes two steps: detection and estimation. In the detection step, we compare the full and the reduced models. The full model describes the dose-response relationship incorporating the hormetic effect; the reduced model describes the dose-response relationship without the hormetic effect. The full model is an extension of the reduced model and has an extra parameter that measures the amount of increase in response at low doses. A test of statistical significance of this extra parameter can essentially be a test for detecting hormesis. In the estimation step, we obtain the area under the best-fitted dose-response curve falling within the hormetic zone. Considering both the number of concentrations within the hormetic zone and the magnitude of the stimulatory response, we propose using the ratio of the area under the hormetic zone (AUC_H) and the area under the best-fitted curve from zero to zero equivalent point (AUC_ZEP) as an estimate of magnitude of the hormetic effect. Two numerical examples are used to illustrate the use of this model-based approach.     

Intra-Seasonal Flexibility in Avian Metabolic Performance Highlights the Uncoupling of Basal Metabolic Rate and Thermogenic Capacity

Stochastic winter weather events are predicted to increase in occurrence and amplitude at northern latitudes and organisms are expected to cope through phenotypic flexibility. Small avian species wintering in these environments show acclimatization where basal metabolic rate (BMR) and maximal thermogenic capacity (M_SUM) are typically elevated. However, little is known on intra-seasonal variation in metabolic performance and on how population trends truly reflect individual flexibility. Here we report intra-seasonal variation in metabolic parameters measured at the population and individual levels in black-capped chickadees ( Poecile atricapillus ). Results confirmed that population patterns indeed reflect flexibility at the individual level. They showed the expected increase in BMR (6%) and M_SUM (34%) in winter relative to summer but also, and most importantly, that these parameters changed differently through time. BMR began its seasonal increase in November, while M_SUM had already achieved more than 20% of its inter-seasonal increase by October, and declined to its starting level by March, while M_SUM remained high. Although both parameters co-vary on a yearly scale, this mismatch in the timing of variation in winter BMR and M_SUM likely reflects different constraints acting on different physiological components and therefore suggests a lack of functional link between these parameters.