Factors influencing the planning undertaken by women during pregnancy for their return to work after maternity leave

The planning women (N = 199) do during pregnancy for their return to work post birth and the factors that influence employment planning during late pregnancy were investigated in this study. The findings revealed three components of planning: Planning for Childcare, Planning with Partner, and Planning with Employer. Several factors emerged as consistent cross-sectional predictors of these components (work satisfaction, hours worked before commencing maternity leave, anticipated weeks of maternity leave and anticipated hours per week on the return to work). Anticipated support from family and friends, and from the workplace also predicted Planning with Partner and Planning with Employer, respectively. The theoretical and practical implications of these findings are discussed.

Load forecasting and neural networks : a prediction interval-based perspective

Successfully determining competitive optimal schedules for electricity generation intimately hinges on the forecasts of loads. The nonstationarity and high volatility of loads make their accurate prediction somewhat problematic. Presence of uncertainty in data also significantly degrades accuracy of point predictions produced by deterministic load forecasting models. Therefore, operation planning utilizing these predictions will be unreliable. This paper aims at developing prediction intervals rather than producing exact point prediction. Prediction intervals are theatrically more reliable and practical than predicted values. The delta and Bayesian techniques for constructing prediction intervals for forecasted loads are implemented here. To objectively and comprehensively assess quality of constructed prediction intervals, a new index based on length and coverage probability of prediction intervals is developed. In experiments with real data, and through calculation of global statistics, it is shown that neural network point prediction performance is unreliable. In contrast, prediction intervals developed using the delta and Bayesian techniques are satisfactorily narrow, with a high coverage probability.

Ageing mallee eucalypt vegetation after fire : insights for successional trajectories in semi-arid mallee ecosystems

A critical requirement in the ecological management of fire is knowledge of the age-class distribution of the vegetation. Such knowledge is important because it underpins the distribution of ecological features important to plants and animals including retreat sites, food sources and foraging microhabitats. However, in many regions, knowledge of the age-class distribution of vegetation is severely constrained by the limited data available on fire history. Much fire-history mapping is restricted to post-1972 fires, following satellite imagery becoming widely available. To investigate fire history in the semi-arid Murray Mallee region in southern Australia, we developed regression models for six species of mallee eucalypt (Eucalyptus oleosa F.Muell. ex. Miq. subsp. oleosa, E. leptophylla F.Muell. ex. Miq., E. dumosa J. Oxley, E. costata subsp. murrayana L. A. S. Johnson & K. D. Hill, E. gracilis F.Muell. and E. socialis F.Muell. ex. Miq.) to quantify the relationship between mean stem diameter and stem age (indicated by fire-year) at sites of known time since fire. We then used these models to predict mean stem age, and thus infer fire-year, for sites where the time since fire was not known. Validation of the models with independent data revealed a highly significant correlation between the actual and predicted time since fire (r = 0.71, P < 0.001, n = 88), confirming the utility of this method for ageing stands of mallee eucalypt vegetation. Validation data suggest the models provide a conservative estimate of the age of a site (i.e. they may under-estimate the minimum age of sites >35 years since fire). Nevertheless, this approach enables examination of post-fire chronosequences in semi-arid mallee ecosystems to be extended from 35 years post-fire to over 100 years. The predicted ages identified for mallee stands imply a need for redefining what is meant by ‘old-growth’ mallee, and challenges current perceptions of an over-abundance of ‘long-unburnt’ mallee vegetation. Given the strong influence of fire on semi-arid mallee vegetation, this approach offers the potential for a better understanding of long-term successional dynamics and the status of biota in an ecosystem that encompasses more than 250 000 km² of southern Australia.

Fire management for biodiversity conservation : key research questions and our capacity to answer them

Knowing how species respond to fire regimes is essential for ecologically sustainable management. This axiom raises two important questions: (1) what knowledge is the most important to develop and (2) to what extent can current research methods deliver that knowledge? We identify three areas of required knowledge: (i) a mechanistic understanding of species’ responses to fire regimes; (ii) knowledge of how the spatial and temporal arrangement of fires influences the biota; and (iii) an understanding of interactions of fire regimes with other processes. We review the capacity of empirical research to address these knowledge gaps, and reveal many limitations. Manipulative experiments are limited by the number and scope of treatments that can be applied, natural experiments are limited by treatment availability and confounding factors, and longitudinal studies are difficult to maintain, particularly due to unplanned disturbance events. Simulation modelling is limited by the quality of the underlying empirical data and by uncertainty in how well model structure represents reality. Due to the constraints on large-scale, long-term research, the potential for management experiments to inform adaptive management is limited. Rather than simply recommending adaptive management, we define a research agenda to maximise the rate of learning in this difficult field. This includes measuring responses at a species level, building capacity to implement natural experiments, undertaking simulation modelling, and judicious application of experimental approaches. Developing ecologically sustainable fire management practices will require sustained research effort and a sophisticated research agenda based on carefully targeting appropriate methods to address critical management questions.

Resolving conflicts in fire management using decision theory: asset-protection versus biodiversity conservation

Agencies charged with nature conservation and protecting built-assets from fire face a policy dilemma because management that protects assets can have adverse impacts on biodiversity. Although conservation is often a policy goal, protecting built-assets usually takes precedence in fire management implementation. To make decisions that can better achieve both objectives, existing trade-offs must first be recognized, and then policies implemented to manage multiple objectives explicitly. We briefly review fire management actions that can conflict with biodiversity conservation. Through this review, we find that common management practices might not appreciably reduce the threat to built-assets but could have a large negative impact on biodiversity. We develop a framework based on decision theory that could be applied to minimize these conflicts. Critical to this approach is (1) the identification of the full range of management options and (2) obtaining data for evaluating the effectiveness of those options for achieving asset protection and conservation goals. This information can be used to compare explicitly the effectiveness of different management choices for conserving species and for protecting assets, given budget constraints. The challenge now is to gather data to quantify these trade-offs so that fire policy and practices can be better aligned with multiple objectives