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Life Sciences - Ecology | Phenological Research - Methods for Environmental and Climate Change Analysis

Phenological Research

Methods for Environmental and Climate Change Analysis

Hudson, Irene L., Keatley, Marie R. (Eds.)

2010, XIV, 521 p.

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  • Features a unique scope, pinpointing an untapped market
  • Adds and defines certainty of the results of any analysis of phenological data
  • Offers rigorous statistical treatment of available data
  • Covers a wide range of different datasets as well as possible approaches for the analysis
  • Presents up to date and state of the art approaches

As climate change continues to dominate the international environmental agenda, phenology – the study of the timing of recurring biological events – has received increasing research attention, leading to an emerging consensus that phenology can be viewed as an ‘early warning system’ for climate change impact.

A multidisciplinary science involving many branches of ecology, geography and remote sensing, phenology to date has lacked a coherent methodological text. This new synthesis, including contributions from many of the world’s leading phenologists, therefore fills a critical gap in the current biological literature. Providing critiques of current methods, as well as detailing novel and emerging methodologies, the book, with its extensive suite of references, provides readers with an understanding of both the theoretical basis and the potential applications required to adopt and adapt new analytical and design methods.

An invaluable source book for researchers and students in ecology and climate change science, the book also provides a useful reference for practitioners in a range of sectors, including human health, fisheries, forestry, agriculture and natural resource management.

Content Level » Research

Keywords » Ecology - Fitting - Time series - climate change - data acquisition - databases - environment - linear regression - principal component analysis - vegetation

Related subjects » Ecology - Environmental Sciences - Global Change - Climate Change - Life Sciences, Medicine & Health - Probability Theory and Stochastic Processes

Table of contents 

Dedication.- Contributing Authors.- Forward.- 1. Introduction and overview. 1.1 History. 1.2 Current issues in phenology. 1.3 Aims of this book; MR Keatley, IL Hudson.- 2. Global Framework for data collection – data bases, data availability, future networks, online databases; E Koch.- 3. Seasonality as a core business of phenology. 3.1 Seasons as genuine phenological units. 3.2 Seasonal patterns describe the annual rhythm. 3.3 The phenological season diagram. 3.4 Seasons at a glance; F Jeanneret, T Rutishauser.- 4. Societal adaptation options to changes in phenology. 4.1 Introduction. 4.2 Phenological changes: impact and required adaptation. 4.2.1 Primary Production Sectors. 4.2.2 Public Health. 4.3 Successful adaptation requires answers to four questions. 4.4 Contribution of phenological networks to the adaptation process. 4.4.1 Phenological monitoring. 4.4.2 Phenological analysis. 4.4 Conclusions; AJH van Vliet.- 5. The influence of sampling method, sample size, and frequency of observations on plant phenological patterns and interpretation in tropical forest trees. 5.1. Introduction. 5.2. Methods. 5.2.1. Frequency of observations. 5.2.2 Sample size. 5.2.3. Comparison of sampling and estimation methods. 5.2.4. Phenological observations. 5.2.5. Data analyses. 5.3. Results. 5.3.1. Frequency of observations. 5.3.2 Sample Size. 5.3.3. Comparison of sampling methods. 5.4. Discussion and concluding remarks; LPC Morellato et al.- 6. Regression and causality. 6.1 Introduction. 6.2 An example dataset. 6.3 Linear regression. 6.4 Polynomial regression. 6.5 Some alternative ways of identifying trends. 6.6 Effects of starting year, end year and duration. 6.7 Multiple regression: Relations with temperature. 6.8 Comparing slopes. 6.9 Final thoughts; T Sparks, P Tryjanowski.- 7. Combining messyphenological time series. 7.1 Introduction. 7.2 Linear Models of phenological time series. 7.2.1 Linear Models. 7.2.2 Fixed and mixed effects models. 7.2.3 Practical issues and the R pheno-package. 7.2.4 Outlier detection. 7.2.5 Gaussian normals. 7.3 Applications. 7.3.1 Gaussian Normals. 7.3.2 Station effects. 7.4 Summary; J Schaber et al.- 8. Phenology for topoclimatological surveys and large-scale mapping. 8.1 Phenology in space and time. 8.1.1 At the crossroad of interdisciplinarity. 8.1.2 Sources of data acquisition. 8.1.3 Available phenology data for survey. 8.2 Network and survey data for mapping. 8.2.1 The space: phenology for survey. 8.2.2 Classical phenological maps at medium and large scale. 8.3 Mapping in detail: Topoclimatic scale. 8.3.1 A special network in mountainous areas. 8.3.2 Topo scale maps – a genuine and unique product of phenology. 8.4 Interpolation, extrapolation and spatial modeling. 8.4.1 Phenology mapped at mesoscale. 8.4.2 Modelled phenological maps in GIS. 8.4.3 Toposcale maps in Switzerland. 8.4.4 Urban phenology – surveys in a special type of space. 8.5 Future phenological mapping; F Jeanneret, T Rutishauser.- 9. Spatio-temporal statistical methods for modelling land surface phenology. 9.1 Introduction. 9.2 Thresholds. 9.2.1 Vegetation Indices (VI) thresholds. 9.3 Derivatives. 9.3.1 Greatest increase/decrease in VI. 9.3.3 Camelback phenology algorithm. 9.4 Smoothing function and model fits. 9.4.1 Autoregressive moving average. 9.4.2 Fourier analysis. 9.4.3 Principal component analysis. 9.5 Model fit. 9.5.1 Logistic Models. 9.5.2 Gaussian Local Functions. 9.5.3 Models based on growing degree-days. 9.6 Case study. 9.6.1 Thresholds. 9.6.2 Moving Average. 9.6.3 Derivatives. 9.6.4 Model fitting. 9.6 Synopsis. 9.6.1 A nomenclature is needed. 9.6.2 Uncertain error structures. 9.6.3 Limits of fitted models. 9.6.4 Parochial perspectives. 9.6.5 The challenge of water-limited systems. 9.6.6

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