Sequestration of carbon by soil

Soil carbon is a major component of the terrestrial carbon cycle. The soils of the world contain more carbon than the combined total amounts occurring in vegetation and the atmosphere. Consequently, soils are a major reservoir of carbon and an important sink. Because of the relatively long period of time that carbon spends within the soil and is thereby withheld from the atmosphere, it is often referred to as being sequestered. Increasing the capacity of soils to sequester C provides a partial, medium-term countermeasure to help ameliorate the increasing CO2 levels in the atmosphere arising from fossil fuel burning and land clearing. Such action will also help to alleviate the environmental impacts arising from increasing levels of atmospheric CO2. The C sequestration potential of any soil depends on its capacity to store resistant plant components in the medium term and to protect and accumulate the humic substances (HS) formed from the transformations or organic materials in the soil environment. The sequestration potential of a soil depends on the vegetation it supports, its mineralogical composition, the depth of the solum, soil drainage, the availability of water and air, and the temperature of the soil environment. The sequestration potential also depends on the chemical characteristics of the soil organic matter and its ability to resist microbial decomposition. When accurate information for these features is incorporated in model systems, the potentials of different soils to sequester C can be reliably predicted. It is encouraging to know that improved soil and crop management systems now allow field yields to be maintained and soil C reserves to be increased, even for soils with depleted levels of soil C. Estimates of the soil C sequestration potential are discussed. Inevitably HS are the major components of the additionally sequestered C. It will be important to know more about the compositions and associations of these substances in the soil if we are able to predict reasonably accurately the ability of any soil type to sequester C in different cropping and soil management systems.

Patchy populations in stochastic environments: Critical number of patches for persistence

We introduce a model for the dynamics of a patchy population in a stochastic environment and derive a criterion for its persistence. This criterion is based on the geometric mean (GM) through time of the spatial-arithmetic mean of growth rates. For the population to persist, the GM has to be greater than or equal to1. The GM increases with the number of patches (because the sampling error is reduced) and decreases with both the variance and the spatial covariance of growth rates. We derive analytical expressions for the minimum number of patches (and the maximum harvesting rate) required for the persistence of the population. As the magnitude of environmental fluctuations increases, the number of patches required for persistence increases, and the fraction of individuals that can be harvested decreases. The novelty of our approach is that we focus on Malthusian local population dynamics with high dispersal and strong environmental variability from year to year. Unlike previous models of patchy populations that assume an infinite number of patches, we focus specifically on the effect that the number of patches has on population persistence. Our work is therefore directly relevant to patchily distributed organisms that are restricted to a small number of habitat patches.

A zero-augmented gamma mixed model for longitudinal data with many zeros

In many occupational safety interventions, the objective is to reduce the injury incidence as well as the mean claims cost once injury has occurred. The claims cost data within a period typically contain a large proportion of zero observations (no claim). The distribution thus comprises a point mass at 0 mixed with a non-degenerate parametric component. Essentially, the likelihood function can be factorized into two orthogonal components. These two components relate respectively to the effect of covariates on the incidence of claims and the magnitude of claims, given that claims are made. Furthermore, the longitudinal nature of the intervention inherently imposes some correlation among the observations. This paper introduces a zero-augmented gamma random effects model for analysing longitudinal data with many zeros. Adopting the generalized linear mixed model (GLMM) approach reduces the original problem to the fitting of two independent GLMMs. The method is applied to evaluate the effectiveness of a workplace risk assessment teams program, trialled within the cleaning services of a Western Australian public hospital.

Reply to comment on `Integrable Kondo impurity in one-dimensional q-deformed t - J models

This is a reply to the comment by P Schlottmann and A A Zvyagin.

Estimating Two-Stage Models for Genetic Influences on Alcohol, Tobacco or Drug Use Initiation and Dependence Vulnerability in Twin and Family Data

Genetic research on risk of alcohol, tobacco or drug dependence must make allowance for the partial overlap of risk-factors for initiation of use, and risk-factors for dependence or other outcomes in users. Except in the extreme cases where genetic and environmental risk-factors for initiation and dependence overlap completely or are uncorrelated, there is no consensus about how best to estimate the magnitude of genetic or environmental correlations between Initiation and Dependence in twin and family data. We explore by computer simulation the biases to estimates of genetic and environmental parameters caused by model misspecification when Initiation can only be defined as a binary variable. For plausible simulated parameter values, the two-stage genetic models that we consider yield estimates of genetic and environmental variances for Dependence that, although biased, are not very discrepant from the true values. However, estimates of genetic (or environmental) correlations between Initiation and Dependence may be seriously biased, and may differ markedly under different two-stage models. Such estimates may have little credibility unless external data favor selection of one particular model. These problems can be avoided if Initiation can be assessed as a multiple-category variable (e.g. never versus early-onset versus later onset user), with at least two categories measurable in users at risk for dependence. Under these conditions, under certain distributional assumptions., recovery of simulated genetic and environmental correlations becomes possible, Illustrative application of the model to Australian twin data on smoking confirmed substantial heritability of smoking persistence (42%) with minimal overlap with genetic influences on initiation.

Patterns and levels of endemism in the Australian Wet Tropics rainforest: evidence from flightless insects

This study examines the level and pattern of endemism among 274 flightless rainforest insects found in the Wet Tropics region of Australia. Endemism is measured at two nested scales: (1) those confined to the Wet Tropics, termed 'regional endemics'; and (2) the subset of those species confined to a single subregion of the Wet Tropics, termed 'subregional endemics'. Fifty per cent of the regional endemic flightless insects are also subregional endemics compared with 15% of the known regional endemic vertebrates. The four subregions with the most endemic flightless insect species are the uplands of Mt Finnigan, Carbine, Bellenden-Ker/Bartle Frere and Atherton. Multiple regression suggests that the combination of rainforest area and shape explain the most variance (R-2 = 0.603) in the numbers of species of regional endemic insects. However, subregional endemism is not closely correlated with the size or shape of the subregions in which they occur, or a combination of these factors. Candidate refugial and recolonised subregions are identified, and are consistent with data from palaeoclimatic models and refugia identified using other taxa. We group upland subregions into larger areas of endemism using parsimony analysis of endemism. These groupings are consistent with our understanding of the history of the Wet Tropics rainforests.

Dynamics of a strongly driven two-component Bose-Einstein condensate

We consider a two-component Bose-Einstein condensate in two spatially localized modes of a double-well potential, with periodic modulation of the tunnel coupling between the two modes. We treat the driven quantum field using a two-mode expansion and define the quantum dynamics in terms of the Floquet Operator for the time periodic Hamiltonian of the system. It has been shown that the corresponding semiclassical mean-field dynamics can exhibit regions of regular and chaotic motion. We show here that the quantum dynamics can exhibit dynamical tunneling between regions of regular motion, centered on fixed points (resonances) of the semiclassical dynamics.

Evolutionary experiments on mate recognition in the Drosophila serrata species complex

It is becoming increasingly apparent that at least some aspects of the evolution of mate recognition may be amenable to manipulation in evolutionary experiments. Quantitative genetic analyses that focus on the genetic consequences of evolutionary processes that result in mate recognition evolution may eventually provide an understanding of the genetic basis of the process of speciation. We review a series of experiments that have attempted to determine the genetic basis of the response to natural and sexual selection on mate recognition in the Drosophila serrata species complex. The genetic basis of mate recognition has been investigated at three levels: (1) between the species of D. serrata and D. birchii using interspecific hybrids, (2) between populations of D. serrata that are sympatric and allopatric with respect to D. birchii, and (3) within populations of D. serrata. These experiments suggest that it may be possible to use evolutionary experiments to observe important events such as the reinforcement of mate recognition, or the generation of the genetic associations that are central to many sexual selection models.

Reconciling paleodistribution models and comparative phylogeography in the Wet Tropics rainforest land snail Gnarosophia bellendenkerensis (Brazier 1875)

Comparative phylogeography has proved useful for investigating biological responses to past climate change and is strongest when combined with extrinsic hypotheses derived from the fossil record or geology. However, the rarity of species with sufficient, spatially explicit fossil evidence restricts the application of this method. Here, we develop an alternative approach in which spatial models of predicted species distributions under serial paleoclimates are compared with a molecular phylogeography, in this case for a snail endemic to the rainforests of North Queensland, Australia. We also compare the phylogeography of the snail to those from several endemic vertebrates and use consilience across all of these approaches to enhance biogeographical inference for this rainforest fauna. The snail mtDNA phylogeography is consistent with predictions from paleoclimate modeling in relation to the location and size of climatic refugia through the late Pleistocene-Holocene and broad patterns of extinction and recolonization. There is general agreement between quantitative estimates of population expansion from sequence data (using likelihood and coalescent methods) vs. distributional modeling. The snail phylogeography represents a composite of both common and idiosyncratic patterns seen among vertebrates, reflecting the geographically finer scale of persistence and subdivision in the snail. In general, this multifaceted approach, combining spatially explicit paleoclimatological models and comparative phylogeography, provides a powerful approach to locating historical refugia and understanding species' responses to them.

Model specification tests in nonparametric stochastic regression models

In this paper, we consider testing for additivity in a class of nonparametric stochastic regression models. Two test statistics are constructed and their asymptotic distributions are established. We also conduct a small sample study for one of the test statistics through a simulated example. (C) 2002 Elsevier Science (USA).

Supersymmetric t-J Gaudin models and KZ equations

Supersymmetric t-J Gaudin models with open boundary conditions are investigated by means of the algebraic Bethe ansatz method. Off-shell Bethe ansatz equations of the boundary Gaudin systems are derived, and used to construct and solve the KZ equations associated with sl (2\1)((1)) superalgebra.

A review of animal models for the study of arsenic carcinogenesis

As inorganic arsenic is a proven human carcinogen, significant effort has been made in recent decades in an attempt to understand arsenic carcinogenesis using animal models, including rodents (rats and mice) and larger mammals such as beagles and monkeys. Transgenic animals were also used to test the carcinogenic effect of arsenicals, but until recently all models had failed to mimic satisfactorily the actual mechanism of arsenic carcinogenicity. However, within the past decade successful animal models have been developed using the most common strains of mice or rats. Thus dimethylarsinic acid (DMA), an organic arsenic compound which is the major metabolite of inorganic arsenicals in mammals, has been proven to be tumorigenic in such animals. Reports of successful cancer induction in animals by inorganic arsenic (arsenite and arsenate) have been rare, and most carcinogenetic studies have used organic arsenicals such as DMA combined with other tumor initiators. Although such experiments used high concentrations. of arsenicals for the promotion of tumors, animal models using doses of arsenicals species closed to the exposure level of humans in endemic areas are obviously the most significant. Almost all researchers have used drinking water or food as the pathway for the development of animal model test systems in order to mimic chronic arsenic poisoning in humans; such pathways seem more likely to achieve desirable results. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

Partial automation of database processing of simulation outputs from L-systems models of plant morphogenesis

Models of plant architecture allow us to explore how genotype environment interactions effect the development of plant phenotypes. Such models generate masses of data organised in complex hierarchies. This paper presents a generic system for creating and automatically populating a relational database from data generated by the widely used L-system approach to modelling plant morphogenesis. Techniques from compiler technology are applied to generate attributes (new fields) in the database, to simplify query development for the recursively-structured branching relationship. Use of biological terminology in an interactive query builder contributes towards making the system biologist-friendly. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.