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Model comparison for a complex ecological system

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  • C. A. Ferguson
  • A. W. Bowman
  • E. M. Scott
  • L. Carvalho

Abstract

Summary. The ecological consequences of climate change and its interaction with other environmental pressures, such as nutrient pollution, are little understood. For freshwater ecosystems, knowledge of these combined effects is required for water resource management and in particular for successful implementation of the European Community Water Framework Directive 2000, which requires that all surface‐waters should be at, or above, ‘good status’ by 2016. Statistical analysis of detailed long‐term environmental data sets can be used to explore these combined effects. Loch Leven (Scotland) has been routinely monitored since 1967, providing one of the largest and most extensive of such data sets. Over this period there has been evidence of climate change and a period of eutrophication and recovery at the loch. Transfer functions, additive models and varying‐coefficient models are used to explore the complex ecological system at Loch Leven with the aim of obtaining insight into the combined effects of climate change and eutrophication on water quality.

Suggested Citation

  • C. A. Ferguson & A. W. Bowman & E. M. Scott & L. Carvalho, 2007. "Model comparison for a complex ecological system," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(3), pages 691-711, July.
    • Handle: RePEc:bla:jorssa:v:170:y:2007:i:3:p:691-711
    DOI: 10.1111/j.1467-985X.2006.00462.x
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    References listed on IDEAS

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    1. Yingcun Xia, 2004. "Efficient estimation for semivarying-coefficient models," Biometrika, Biometrika Trust, vol. 91(3), pages 661-681, September.
    2. Cai, Zongwu, 2007. "Trending time-varying coefficient time series models with serially correlated errors," Journal of Econometrics, Elsevier, vol. 136(1), pages 163-188, January.
    3. Jianqing Fan & Wenyang Zhang, 2000. "Simultaneous Confidence Bands and Hypothesis Testing in Varying‐coefficient Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(4), pages 715-731, December.
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    3. Yongsheng Jiang & Dong Zhao & Andrew Sanderford & Jing Du, 2018. "Effects of Bank Lending on Urban Housing Prices for Sustainable Development: A Panel Analysis of Chinese Cities," Sustainability, MDPI, vol. 10(3), pages 1-16, February.
    4. Jun Jin & Tiefeng Ma & Jiajia Dai, 2021. "New efficient spline estimation for varying-coefficient models with two-step knot number selection," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 693-712, July.

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