Suchergebnisse

This study aims to establish a quantitative relationship between lifestyle and happiness in the UK based on over 10,000 surveyed samples with 63 lifestyle variables from the UK Understanding Society Data. Transparent parametric models are built and a number of significant explanatory variables (lifestyle indicators) have been identified using a systems engineering modelling approach. Specifically; based on the traditional orthogonal forward regression (OFR) algorithm; the study introduces a new metrics; with which the impacts of lifestyle variables (and/or their interactions) can be quantitatively measured and identified one by one. These identified significant indicators provide a meaningful parsimonious representation of the relationship between happiness and lifestyle; revealing how happiness quantitatively depends on lifestyle; and how the lifestyle variables interactively affect happiness. For example; the quantitative results of a linear model indicate that lifestyle variables such as 'health'; 'income'; and 'retirement'; impacts happiness significantly. Furthermore; the results of a bilinear model show that some interaction variables such as 'retired' together with 'elder'; 'fair health' together with 'low-income' and so on; are significantly related to happiness.

To understand changes in ecosystems, the appropriate scale at which to study them must be determined. Large marine ecosystems (LMEs) cover thousands of square kilometres and are a useful classification scheme for ecosystem monitoring and assessment. However, averaging across LMEs may obscure intricate dynamics within. The purpose of this study is to mathematically determine local and regional patterns of ecological change within an LME using empirical orthogonal functions (EOFs). After using EOFs to define regions with distinct patterns of change, a statistical model originating from control theory is applied (Nonlinear AutoRegressive Moving Average with eXogenous input – NARMAX) to assess potential drivers of change within these regions. We have selected spatial data sets (0.5° latitude × 1°longitude) of fish abundance from North Sea fisheries research surveys (spanning 1980–2008) as well as of temperature, oxygen, net primary production and a fishing pressure proxy, to which we apply the EOF and NARMAX methods. Two regions showed significant changes since 1980: the central North Sea displayed a decrease in community size structure which the NARMAX model suggested was linked to changes in fishing; and the Norwegian trench region displayed an increase in community size structure which, as indicated by NARMAX results, was primarily linked to changes in sea‐bottom temperature. These regions were compared to an area of no change along the eastern Scottish coast where the model determined the community size structure was most strongly associated to net primary production. This study highlights the multifaceted effects of environmental change and fishing pressures in different regions of the North Sea. Furthermore, by highlighting this spatial heterogeneity in community size structure change, important local spatial dynamics are often overlooked when the North Sea is considered as a broad‐scale, homogeneous ecosystem (as normally is the case within the political Marine Strategy Framework Directive).