Describing long-term trends in precipitation using generalized additive models

With the current concern over climate change, descriptions of how rainfall patterns are changing over time can be useful. Observations of daily rainfall data over the last few decades provide information on these trends. Generalized linear models are typically used to model patterns in the occurrence and intensity of rainfall. These models describe rainfall patterns for an average year but are more limited when describing long-term trends, particularly when these are potentially non-linear. Generalized additive models (GAMS) provide a framework for modelling non-linear relationships by fitting smooth functions to the data. This paper describes how GAMS can extend the flexibility of models to describe seasonal patterns and long-term trends in the occurrence and intensity of daily rainfall using data from Mauritius from 1962 to 2001. Smoothed estimates from the models provide useful graphical descriptions of changing rainfall patterns over the last 40 years at this location. GAMS are particularly helpful when exploring non-linear relationships in the data. Care is needed to ensure the choice of smooth functions is appropriate for the data and modelling objectives. (c) 2008 Elsevier B.V. All rights reserved.

The socioeconomics of food crop production and climate change vulnerability: a global scale quantitative analysis of how grain crops are sensitive to drought

Many studies warn that climate change may undermine global food security. Much work on this topic focuses on modelling crop-weather interactions but these models do not generally account for the ways in which socio-economic factors influence how harvests are affected by weather. To address this gap, this paper uses a quantitative harvest vulnerability index based on annual soil moisture and grain production data as the dependent variables in a Linear Mixed Effects model with national scale socio-economic data as independent variables for the period 1990-2005. Results show that rice, wheat and maize production in middle income countries were especially vulnerable to droughts. By contrast, harvests in countries with higher investments in agriculture (e.g higher amounts of fertilizer use) were less vulnerable to drought. In terms of differences between the world's major grain crops, factors that made rice and wheat crops vulnerable to drought were quite consistent, whilst those of maize crops varied considerably depending on the type of region. This is likely due to the fact that maize is produced under very different conditions worldwide. One recommendation for reducing drought vulnerability risks is coordinated development and adaptation policies, including institutional support that enables farmers to take proactive action.

Linear vs. log-linear unit-root specification: an application of mis-specification encompassing

The objective of this paper is to apply the mis-specification (M-S) encompassing perspective to the problem of choosing between linear and log-linear unit-root models. A simple M-S encompassing test, based on an auxiliary regression stemming from the conditional second moment, is proposed and its empirical size and power are investigated using Monte Carlo simulations. It is shown that by focusing on the conditional process the sampling distributions of the relevant statistics are well behaved under both the null and alternative hypotheses. The proposed M-S encompassing test is illustrated using US total disposable income quarterly data.

A comparison of tests of non-linear cointegration with an application to the predictability of US interest rates using the term structure

We test whether there are nonlinearities in the response of short- and long-term interest rates to the spread in interest rates, and assess the out-of-sample predictability of interest rates using linear and nonlinear models. We find strong evidence of nonlinearities in the response of interest rates to the spread. Nonlinearities are shown to result in more accurate short-horizon forecasts, especially of the spread.

Application of constrained predictive control on a 3D crane system

This paper describes the SIMULINK implementation of a constrained predictive control algorithm based on quadratic programming and linear state space models, and its application to a laboratory-scale 3D crane system. The algorithm is compatible with Real Time. Windows Target and, in the case of the crane system, it can be executed with a sampling period of 0.01 s and a prediction horizon of up to 300 samples, using a linear state space model with 3 inputs, 5 outputs and 13 states.

Novel developments in process optimisation using predictive control

In industrial practice, constrained steady state optimisation and predictive control are separate, albeit closely related functions within the control hierarchy. This paper presents a method which integrates predictive control with on-line optimisation with economic objectives. A receding horizon optimal control problem is formulated using linear state space models. This optimal control problem is very similar to the one presented in many predictive control formulations, but the main difference is that it includes in its formulation a general steady state objective depending on the magnitudes of manipulated and measured output variables. This steady state objective may include the standard quadratic regulatory objective, together with economic objectives which are often linear. Assuming that the system settles to a steady state operating point under receding horizon control, conditions are given for the satisfaction of the necessary optimality conditions of the steady-state optimisation problem. The method is based on adaptive linear state space models, which are obtained by using on-line identification techniques. The use of model adaptation is justified from a theoretical standpoint and its beneficial effects are shown in simulations. The method is tested with simulations of an industrial distillation column and a system of chemical reactors.

Property company performance and real interest rates: a regime-switching approach

Linear models of property market performance may be misspecified if there exist distinct states where the market drivers behave in different ways. This paper examines the applicability of non-linear regime-based models. A Self Exciting Threshold Autoregressive (SETAR) model is applied to property company share data, using the real rate of interest to define regimes. Distinct regimes appear exhibiting markedly different market behaviour. The model both casts doubt on the specification of conventional linear models and offers the possibility of developing effective trading rules for real estate equities.

Multivariate analysis of the dielectric response of materials modeled using networks of resistors and capacitors

We discuss the modeling of dielectric responses of electromagnetically excited networks which are composed of a mixture of capacitors and resistors. Such networks can be employed as lumped-parameter circuits to model the response of composite materials containing conductive and insulating grains. The dynamics of the excited network systems are studied using a state space model derived from a randomized incidence matrix. Time and frequency domain responses from synthetic data sets generated from state space models are analyzed for the purpose of estimating the fraction of capacitors in the network. Good results were obtained by using either the time-domain response to a pulse excitation or impedance data at selected frequencies. A chemometric framework based on a Successive Projections Algorithm (SPA) enables the construction of multiple linear regression (MLR) models which can efficiently determine the ratio of conductive to insulating components in composite material samples. The proposed method avoids restrictions commonly associated with Archie’s law, the application of percolation theory or Kohlrausch-Williams-Watts models and is applicable to experimental results generated by either time domain transient spectrometers or continuous-wave instruments. Furthermore, it is quite generic and applicable to tomography, acoustics as well as other spectroscopies such as nuclear magnetic resonance, electron paramagnetic resonance and, therefore, should be of general interest across the dielectrics community.

PPARGC1A sequence variation and cardiovascular risk-factor levels: a study of the main genetic effects and gene x environment interactions in children from the European Youth Heart Study.

AIMS/HYPOTHESIS: The PPARGC1A gene coactivates multiple nuclear transcription factors involved in cellular energy metabolism and vascular stasis. In the present study, we genotyped 35 tagging polymorphisms to capture all common PPARGC1A nucleotide sequence variations and tested for association with metabolic and cardiovascular traits in 2,101 Danish and Estonian boys and girls from the European Youth Heart Study, a multicentre school-based cross-sectional cohort study. METHODS: Fasting plasma glucose concentrations, anthropometric variables and blood pressure were measured. Habitual physical activity and aerobic fitness were objectively assessed using uniaxial accelerometry and a maximal aerobic exercise stress test on a bicycle ergometer, respectively. RESULTS: In adjusted models, nominally significant associations were observed for BMI (rs10018239, p = 0.039), waist circumference (rs7656250, p = 0.012; rs8192678 [Gly482Ser], p = 0.015; rs3755863, p = 0.02; rs10018239, beta = -0.01 cm per minor allele copy, p = 0.043), systolic blood pressure (rs2970869, p = 0.018) and fasting glucose concentrations (rs11724368, p = 0.045). Stronger associations were observed for aerobic fitness (rs7656250, p = 0.005; rs13117172, p = 0.008) and fasting glucose concentrations (rs7657071, p = 0.002). None remained significant after correcting for the number of statistical comparisons. We proceeded by testing for gene x physical activity interactions for the polymorphisms that showed nominal evidence of association in the main effect models. None of these tests was statistically significant. CONCLUSIONS/INTERPRETATION: Variants at PPARGC1A may influence several metabolic traits in this European paediatric cohort. However, variation at PPARGC1A is unlikely to have a major impact on cardiovascular or metabolic health in these children.

On SETAR non-linearity and forecasting

We compare linear autoregressive (AR) models and self-exciting threshold autoregressive (SETAR) models in terms of their point forecast performance, and their ability to characterize the uncertainty surrounding those forecasts, i.e. interval or density forecasts. A two-regime SETAR process is used as the data-generating process in an extensive set of Monte Carlo simulations, and we consider the discriminatory power of recently developed methods of forecast evaluation for different degrees of non-linearity. We find that the interval and density evaluation methods are unlikely to show the linear model to be deficient on samples of the size typical for macroeconomic data

The outsourcing strategy of local and multinational firms: A supply base perspective

Firms outsource through connecting to local and global supply bases and making such connections produces costs of search and evaluation, which are a function of transaction characteristics and firm capabilities. We argue that firms outsource more when those costs are low. Hence, domestic subsidiaries of multinational firms, with low cost access to both local and global supply bases, outsource more than either domestic firms or foreign subsidiaries, as confirmed by evidence from a large data panel. We also propose that among foreign subsidiaries, distance from the home country co-determines search and evaluation costs such that subsidiaries from more distant countries outsource less. This is confirmed for geographic distance, but a positive effect is found for political distance and a mixed effect for cultural distance.

Can portmanteau nonlinearity tests serve as general mis-specification tests?

A number of recent papers have employed the BDS test as a general test for mis-specification for linear and nonlinear models. We show that for a particular class of conditionally heteroscedastic models, the BDS test is unable to detect a common mis-specification. Our results also demonstrate that specific rather than portmanteau diagnostics are required to detect neglected asymmetry in volatility. However for both classes of tests reasonable power is only obtained using very large sample sizes.

Conditioning of the weak-constraint variational data assimilation problem for numerical weather prediction

4-Dimensional Variational Data Assimilation (4DVAR) assimilates observations through the minimisation of a least-squares objective function, which is constrained by the model flow. We refer to 4DVAR as strong-constraint 4DVAR (sc4DVAR) in this thesis as it assumes the model is perfect. Relaxing this assumption gives rise to weak-constraint 4DVAR (wc4DVAR), leading to a different minimisation problem with more degrees of freedom. We consider two wc4DVAR formulations in this thesis, the model error formulation and state estimation formulation. The 4DVAR objective function is traditionally solved using gradient-based iterative methods. The principle method used in Numerical Weather Prediction today is the Gauss-Newton approach. This method introduces a linearised `inner-loop' objective function, which upon convergence, updates the solution of the non-linear `outer-loop' objective function. This requires many evaluations of the objective function and its gradient, which emphasises the importance of the Hessian. The eigenvalues and eigenvectors of the Hessian provide insight into the degree of convexity of the objective function, while also indicating the difficulty one may encounter while iterative solving 4DVAR. The condition number of the Hessian is an appropriate measure for the sensitivity of the problem to input data. The condition number can also indicate the rate of convergence and solution accuracy of the minimisation algorithm. This thesis investigates the sensitivity of the solution process minimising both wc4DVAR objective functions to the internal assimilation parameters composing the problem. We gain insight into these sensitivities by bounding the condition number of the Hessians of both objective functions. We also precondition the model error objective function and show improved convergence. We show that both formulations' sensitivities are related to error variance balance, assimilation window length and correlation length-scales using the bounds. We further demonstrate this through numerical experiments on the condition number and data assimilation experiments using linear and non-linear chaotic toy models.

EDITOR'S CHOICE: REVIEW: Trait matching of flower visitors and crops predicts fruit set better than trait diversity

Understanding the relationships between trait diversity, species diversity and ecosystem functioning is essential for sustainable management. For functions comprising two trophic levels, trait matching between interacting partners should also drive functioning. However, the predictive ability of trait diversity and matching is unclear for most functions, particularly for crop pollination, where interacting partners did not necessarily co-evolve. World-wide, we collected data on traits of flower visitors and crops, visitation rates to crop flowers per insect species and fruit set in 469 fields of 33 crop systems. Through hierarchical mixed-effects models, we tested whether flower visitor trait diversity and/or trait matching between flower visitors and crops improve the prediction of crop fruit set (functioning) beyond flower visitor species diversity and abundance. Flower visitor trait diversity was positively related to fruit set, but surprisingly did not explain more variation than flower visitor species diversity. The best prediction of fruit set was obtained by matching traits of flower visitors (body size and mouthpart length) and crops (nectar accessibility of flowers) in addition to flower visitor abundance, species richness and species evenness. Fruit set increased with species richness, and more so in assemblages with high evenness, indicating that additional species of flower visitors contribute more to crop pollination when species abundances are similar. Synthesis and applications. Despite contrasting floral traits for crops world-wide, only the abundance of a few pollinator species is commonly managed for greater yield. Our results suggest that the identification and enhancement of pollinator species with traits matching those of the focal crop, as well as the enhancement of pollinator richness and evenness, will increase crop yield beyond current practices. Furthermore, we show that field practitioners can predict and manage agroecosystems for pollination services based on knowledge of just a few traits that are known for a wide range of flower visitor species.

Ecological traits affect the sensitivity of bees to land-use pressures in European agricultural landscapes

1. Bees are a functionally important and economically valuable group, but are threatened byland-use conversion and intensification. Such pressures are not expected to affect all species identically; rather, they are likely to be mediated by the species’ ecological traits. 2. Understanding which types of species are most vulnerable under which land uses is an important step towards effective conservation planning.3. We collated occurrence and abundance data for 257 bee species at 1584 European sites from surveys reported in 30 published papers (70 056 records) and combined them with species-level ecological trait data. We used mixed-effects models to assess the importance of land use (land-use class, agricultural use-intensity and a remotely-sensed measure of vegetation),traits and trait 9 land-use interactions, in explaining species occurrence and abundance.4. Species’ sensitivity to land use was most strongly influenced by flight season duration and foraging range, but also by niche breadth, reproductive strategy and phenology, with effects that differed among cropland, pastoral and urban habitats.5. Synthesis and applications. Rather than targeting particular species or settings, conservation action s may be more effective if focused on mitigating situations where species’ traits strongly and negatively interact with land-use pressures. We find evidence that low-intensity agriculture can maintain relatively diverse bee communities; in more intensive settings, added floral resources may be beneficial, but will require careful placement with respect to foraging ranges of smaller bee species. Protection of semi-natural habitats is essential, however; in particular, conversion to urban environments could have severe effects on bee diversity and pollination services. Our results highlight the importance of exploring how ecological traits mediate species responses to human impacts, but further research is needed to enhance the predictive ability of such analyses.