Phenological Research (eBook)

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