Climatic Change

, Volume 100, Issue 1, pp 143–171

Interdisciplinary approaches: towards new statistical methods for phenological studies


DOI: 10.1007/s10584-010-9859-9

Cite this article as:
Hudson, I.L. Climatic Change (2010) 100: 143. doi:10.1007/s10584-010-9859-9


The importance of global environmental questions has significantly advanced the impact of climate change phenology. Whilst spatial applications continue to be a core application of phenology; in recent years the temporal dimension has also been revisited, with studies showing that temporal changes, either with a natural or an anthropogenic origin, have significantly altered phenological rhythms and seasonal development—changes attributed now to an anthropogenically induced temperature increase. This paper explores and introduces recent and newly developing analytic methods in phenology; with a view to increasing an interdisciplinary perspective and dialogue. Of particular focus is how we can and best deal with nonlinearity of phenological change in time and with multiple location studies; rigorously model the inherent multivariate time series structures in climate-phenology data; further Bayesian and non-Bayesian methods, detect multiple change-points; map seasonality calendars; model de-synchronisation of species globally; invoke old fashioned, yet rarely used circular statistical methods; adapt new transitional state modelling of phenophases with respect to climate and progress a unified paradigm for meta analytic studies in phenology. The provision of uncertainty analysis is also still much needed in climate-related phenological research. Reaching consensus on design, method of data collection and comparable analytic methods is integral to advancing the generalisability of phenological results; as is a consensus on inclusion criterion for studies selected for phenological meta-analytic studies. A coherent nomenclature is critically required, but it is currently lacking in many areas of phenology.

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.School of Mathematics and StatisticsUniversity of South Australia (UniSA)AdelaideSouth Australia
  2. 2.Institute for Sustainable Systems and TechnologiesUniSAMawson LakesSouth Australia

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