Implications of current ecological thinking for biodiversity conservation: A review of the salient issues

Given escalating concern worldwide about the loss of biodiversity, and given biodiversity's centrality to quality of life, it is imperative that current ecological knowledge fully informs societal decision making. Over the past two decades, ecological science has undergone many significant shifts in emphasis and perspective, which have important implications for how we manage ecosystems and species. In particular, a shift has occurred from the equilibrium paradigm to one that recognizes the dynamic, non-equilibrium nature of ecosystems. Revised thinking about the spatial and temporal dynamics of ecological systems has important implications for management. Thus, it is of growing concern to ecologists and others that these recent developments have not been translated into information useful to managers and policy makers. Many conservation policies and plans are still based on equilibrium assumptions. A fundamental difficulty with integrating current ecological thinking into biodiversity policy and management planning is that field observations have yet to provide compelling evidence for many of the relationships suggested by non-equilibrium ecology. Yet despite this scientific uncertainty, management and policy decisions must still be made. This paper was motivated by the need for considered scientific debate on the significance of current ideas in theoretical ecology for biodiversity conservation. This paper aims to provide a platform for such discussion by presenting a critical synthesis of recent ecological literature that (1) identifies core issues in ecological theory, and (2) explores the implications of current ecological thinking for biodiversity conservation.

An adaptive neural-wavelet model for short term load forecasting

This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.

Nitrate in soil humic acids revealed by N-14 nuclear magnetic resonance spectroscopy

Ecosystem management such as plant residue retention and prescribed burning can significantly affect soil organic matter (SOM) composition and, thereby, the closely associated carbon (C) and nitrogen (N) cycling processes, which underpin terrestrial ecosystem productivity and sustainability. Humic acid (HA) is an important SOM component and its chemical composition has attracted much attention. Here we report the first application of N-14 nuclear magnetic resonance (NMR) spectroscopy to soil HA study, revealing the surprising existence of nitrate-N and ammonia-N in the HAs. This newly discovered HA nitrate-N, though in a relatively low concentrations, is closely related to soil N availability and responsive to plant residue management regimes in contrasting forest ecosystems. The HA nitrate-N may be a useful and sensitive biochemical indicator of SOM quality in response to different ecosystem management regimes.