Skip to main content

Advertisement

SpringerLink
Log in

Interdisciplinary approaches: towards new statistical methods for phenological studies

  • Published:
Climatic Change Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Alexander LV, Arblaster JM (2009) Assessing trends in observed and modelled climate extremes over Australia in relation to future projections. Int J Climatol 29:417–435

    Article  Google Scholar 

  • Alfo M, Farcomeni A, Tardella L (2007) Robust semiparametric mixing for detecting differentially expressed genes in microarray experiments. Comput Stat Data Anal 51:5253–5265

    Article  Google Scholar 

  • Aono Y, Kazui K (2008) Phenological data series of cherry tree flowering in Kyoto, Japan, and its application to reconstruction of springtime temperatures since the 9th century. Int J Climatol 28:905–914

    Article  Google Scholar 

  • Aono Y, Saito S (2010) Clarifying springtime temperature reconstructions of the medieval period by gap-filling the cherry blossom phenological data series at Kyoto, Japan. Int J Biometeorol 54:211–219

    Article  Google Scholar 

  • Baccini M, Biggeri A, Accetta G et al (2008) Heat effects on mortality in 15 European cities. Epidemiology 19:711–719

    Article  Google Scholar 

  • Bagnardi V, Zambon A, Quatto P et al (2004) Flexible meta-regression functions for modelling aggregate dose-response data, with an application to alcohol and mortality. Am J Epidemiol 159:1077–1086

    Article  Google Scholar 

  • Ballester J, Giorgi F, Rodó X (2010) Changes in European temperature extremes can be predicted from changes in PDF central statistics. Clim Change 98:277–284

    Article  Google Scholar 

  • Baltzer H, Gerard F, George C et al (2007) Coupling of vegetation growing season anomalies and fire activity with hemispheric and regional-scale climate patterns in Central and East Siberia. J Clim 20:3713–3729

    Article  Google Scholar 

  • Batschelet E (1981) Circular statistics in biology. Academic, London

    Google Scholar 

  • Bencke CSC, Morellato LPC (2002) Comparação de dois métodos de avaliação da fenologia de plantas, sua interpretação e representação. Rev Bras Bot 25:269–276

    Google Scholar 

  • Berchtold A (2006) March v.3.00 Markovian models computation and analysis users guide. Copyright A Bertchtold: online support. http://www.andreberchtold.com/march.html. Accessed 11 March 2010

  • Berchtold A, Raftery AE (2002) The mixture transition distribution model for high-order Markov chains and non-Gaussian time series. Stat Sci 17:328–356

    Article  Google Scholar 

  • Bertin RI (2008) Phenology and distribution in relation to recent climate change. J Torrey Bot Soc 135:126–146

    Article  Google Scholar 

  • Boehning D (2007) Editorial board. Comput Stat Data Anal 51(11):iii–v

    Article  Google Scholar 

  • Boehning D, Seidel W (2003) Recent developments in mixture models. Comput Stat Data Anal 41:349–357 (editorial)

    Article  Google Scholar 

  • Bolmgren K, Lonnberg K (2005) Herbarium data reveal an association between fleshy fruit type and earlier flowering time. Int J Plant Sci 166:663–670

    Article  Google Scholar 

  • Borchert R (1996) Phenology and flowering periodicity of Neotropical dry forest species: evidence from herbarium collections. J Trop Ecol 12:65–80

    Article  Google Scholar 

  • Borenstein M, Hedges LV, Higgins JPT et al (2009) Introduction to meta-analysis. Wiley, West Sussex

    Book  Google Scholar 

  • Bruns E, van Vliet AJH (2003) Standardisation and observation methodologies of phenological networks in Europe. Wageningen University, German Weather Service, Wageningen, Offenbach

  • Bucher F, Jeanneret F (1994) Phenology as a tool in topclimatology. A cross-section through the Swiss Jura Mountains. In: Beniston M (ed) Mountain environments in changing climates. Routledge, London

    Google Scholar 

  • Chambers LE (2006) Associations between climate change and natural systems in Australia. B Am Meteorol Soc 87:201–206

    Article  Google Scholar 

  • Chambers LE, Hughes L, Weston MA (2005) Climate change and its impact on Australia’s avifauna. EMU 105:1–20

    Article  Google Scholar 

  • Chapman CA, Chapman LJ, Wangham R et al (1992) Estimators of fruit abundance of tropical trees. Biotropica 24:527–531

    Article  Google Scholar 

  • Chapman CA, Wrangham R, Chapman L (1994) Indices of habitat-wide fruit abundance in tropical forests. Biotropica 26:160–171

    Article  Google Scholar 

  • Chuine I, Yiou P, Viovy N et al (2004) Historical phenology: grape ripening as a past climate indicator. Nature 432:289–290

    Article  Google Scholar 

  • Clark RM, Thompson R (2009) Predicting the impact of global warming on the timing of spring flowering. Int J Climatol. doi:10.1002/joc.2004

    Google Scholar 

  • Cleland EE, Chuine I, Menzel A et al (2007) Shifting plant phenology in response to global change. Trends Ecol Evol 22:357–365

    Article  Google Scholar 

  • Dalrymple M (2004) Poisson mixture methods and change point analyses to study the relationship between temporal profiles of sudden infant death syndrome and climate. Dissertation, University of Canterbury, Christchurch, New Zealand

  • Dalrymple M, Hudson I, Ford R (2003) Finite mixture, zero-inflated Poisson and hurdle models with application to SIDS. Comput Stat Data Anal 41:491–504

    Article  Google Scholar 

  • de Beurs KM, Henebry GM (2004a) Land surface phenology, climatic variation, and institutional change: analyzing agricultural land cover change in Kazakhstan. Remote Sens Environ 89:497–509

    Article  Google Scholar 

  • de Beurs KM, Henebry GM (2004b) Trend analysis of the Pathfinder AVHRR Land (PAL) NDVI data for the deserts of Central Asia. IEEE Trans Geosci Remote Sens 1:282–286. doi:10.1109/LGRS.2004.834805

    Article  Google Scholar 

  • de Beurs KM, Henebry GM (2008) Northern annular mode effects on the land surface phenologies of Northern Eurasia. J Clim 21:4257–4279

    Article  Google Scholar 

  • de Beurs KM, Henebry GM (2010) Spatio-temporal statistical methods for modelling land surface phenology. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 177–208

    Google Scholar 

  • Delbart N, Kergoat L, Le Toan T et al (2005) Determination of phenological dates in boreal regions using normalized difference water index. Remote Sens Environ 97:26–38

    Article  Google Scholar 

  • Diggle PJ, Heagerty P, Liang KY et al (2002) Analysis of longitudinal data, 2nd edn. Oxford University Press, Oxford

    Google Scholar 

  • Do KA, Mueller P, Tang F (2005) A Bayesian mixture model for differential gene expression. J R Stat Soc Ser C 54:627–644

    Article  Google Scholar 

  • D’Odorico PD, Yoo J, Jaeger S (2002) Changing seasons: an effect of the North Atlantic oscillation. J Clim 15:435–445

    Article  Google Scholar 

  • Doktor D, Badeck F-W, Hattermann F et al (2005) Analysis and modelling of spatially and temporally varying phenological phases. In: Renard P, Demougeot-Renard H, Froidevaux R (eds) Geostatistics for environmental applications. Proceedings of the 5th European conference on geostatistics for environmental applications. Springer, Berlin, pp 137–148

    Google Scholar 

  • Doi H (2007) Winter flowering phenology of Japanese apricot Prunus mume reflects climate change across Japan. Clim Res 34:99–104

    Article  Google Scholar 

  • Donnelly A, Jones MB, Sweeney J (2004) A review of indicators of climate change for use in Ireland. Int J Biometeorol 49:1–12

    Article  Google Scholar 

  • Dose V, Menzel A (2004) Bayesian analysis of climate change impacts in phenology. Glob Chang Biol 10:259–272

    Article  Google Scholar 

  • Draper NR, Smith H (1981) Applied regression analysis. Wiley, New York

    Google Scholar 

  • Duchemin B, Goubier J, Courrier G (1999) Monitoring phenological key stages and cycle duration of temperate deciduous forest ecosystems with NOAA/AVHRR data. Remote Sens Environ 67:68–82

    Article  Google Scholar 

  • Eastman JR, Fulk M (1993) Long sequence time series evaluation using standardized principal components. Photogramm Eng Remote Sensing 59:1307–1312

    Google Scholar 

  • Ebi K, Semenza JC (2008) Community-based adaptation to the health impacts of climate change. Am J Prev Med 35:501–507

    Article  Google Scholar 

  • Ebi KL, Kovats RS, Menne B (2006) An approach for assessing human health vulnerability and public health interventions to adapt to climate change. Environ Health Perspect 114:1930–1934

    Article  Google Scholar 

  • Elsner JB, Tsonis AA (1996) Singular spectrum analysis. A new tool in time series analysis. Plenum, New York

    Google Scholar 

  • Estrella N, Menzel A (2006) Responses of leaf colouring in four deciduous tree species to climate and weather in Germany. Clim Res 32:253–267

    Article  Google Scholar 

  • Fisher NI (1993) Statistical analysis of circular data. Cambridge University Press, Cambridge

    Google Scholar 

  • Fitter AH, Fitter RSR (2002) Rapid changes in flowering time in British plants. Science 296:1689–1691

    Article  Google Scholar 

  • Fitter AH, Fitter RSR, Harris ITB et al (1995) Relationship between first flowering date and temperature in the flora of a locality in central England. Funct Ecol 9:55–60

    Article  Google Scholar 

  • Fournier LA (1974) Un método cuantitativo para la medición de características fenológicas en árboles. Turrialba 24:54–59

    Google Scholar 

  • Frühwirth-Schnatter S (2006) Finite mixture and Markov switching models. Springer series in statistics. Springer, New York

    Google Scholar 

  • Frühwirth-Schnatter S, Pyne S (2010) Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions. Biostatistics 11:317–336

    Article  Google Scholar 

  • Fukuda K, Hudson IL (2005) Global and local climatic factors on sulfur dioxide levels: comparison of residential and industrial sites. In: Francis AR, Matawie KM, Oshlack A, Smyth GK (eds) Statistical solutions to modern problems, 20th international workshop on statistical modelling, 10–15 July, Sydney, University of Western Sydney (Penrith), pp 187–194, ISBN 1 74108 101 7

  • Gallagher RV, Hughes L, Leishman MR (2009) Phenological trends among Australian alpine species: using herbarium records to identify climate-change indicators. Aust J Bot 57:1–9

    Article  Google Scholar 

  • Gamborg M, Byberg L, Rasmussen F et al (2007) Weight and systolic blood pressure in adolescence and adulthood: meta-regression analysis of sex- and age-specific results from 20 Nordic studies. Am J Epidemiol 166:634–645

    Article  Google Scholar 

  • Golyandina N, Osipov E (2007) The “Caterpillar”—SSA method for analysis of time series with missing values. J Stat Plan Inference 137:2642–2653

    Article  Google Scholar 

  • Golyandina N, Nekrutkin V, Zhigljavsky A (2001) Analysis of time series structure: SSA and related techniques. Chapman & Hall/CRC, Boca Raton

    Google Scholar 

  • Gordon C, Cooper C, Senior CA, Banks H, Gregory JM, Johns TC, Mitchell JFB, Wood RA (2000) The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 16:147–168

    Article  Google Scholar 

  • Graham EA, Riordan EC, Yuen EM, Estrin D, Rundel PW (2010) Public internet-connected cameras used as a cross-continental ground-based plant phenology monitoring system. Glob Chang Biol. doi:10.1111/j.1365-2486.2010.02164.x

    Google Scholar 

  • Häkkinen R, Linkosalo T, Hari P (1995) Methods for combining phenological time series: application to budburst in birch (B. pendula) in Central Finland for the period 1896–1955. Tree Physiol 15:721–726

    Google Scholar 

  • Hall-Beyer M (2003) Comparison of single-year and multiyear NDVI time series principal components in cold temperate biomes. IEEE Geosci Remote Sens 41:2568–2574

    Article  Google Scholar 

  • Hamer KC, Hill JK, Mustaffa N et al (2005) Temporal variation in abundance and diversity of butterflies in Bornean rain forests: opposite impacts of logging recorded in different seasons. J Trop Ecol 21:417–425

    Article  Google Scholar 

  • Hemingway CA, Overdorff DJ (1999) Sampling effects on food availability estimates: phenological method, sample size, and species composition. Biotropica 31:354–364

    Article  Google Scholar 

  • Hudson IL (2010a) Meta-analysis and its application in phenological research: a review and new statistical approaches. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 463–509

    Google Scholar 

  • Hudson IL (2010b) Meta-analysis, 2nd edn. In: Schneider S (ed) The Oxford encyclopedia of climate and weather. Oxford University Press, Oxford (in press)

    Google Scholar 

  • Hudson IL, Keatley MR (eds) (2010a) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht

    Google Scholar 

  • Hudson IL, Keatley MR (2010b) Singular spectrum analysis: climatic niche identification. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 393–424

    Google Scholar 

  • Hudson IL, Fukuda K, Keatley MR (2004) Detecting underlying time series structures and change points within a phenological dataset using SSA. In: Proceedings 22nd international biometric conference, 11–16 July, Cairns Convention Centre, Queensland Australia

  • Hudson IL, Keatley MR, Roberts AMI (2005) Statistical methods in phenological research. In: Francis AR, Matawie KM, Oshlack A, Smyth GK (eds) Statistical solutions to modern problems, 20th international workshop on statistical modelling, 10–15 July, Sydney, University of Western Sydney (Penrith), pp 259–270, ISBN 1 74108 101 7

  • Hudson IL, Keatley MR, Kim SW, Kang I (2006) Synchronicity in phenology: from PAP Moran to now. In 18th biennial Australian Statistical Conference (ASC2008)/New Zealand Statistical Association (NZSA) conference, 3–6 July, Auckland, New Zealand

  • Hudson IL, Dalrymple M, Faddy MJ (2007) New mixture models for discrete counts time series: with an application to modelling mortality and climate in NZ. In: Oxley L, Kulasiri D (eds) MODSIM 2007 international congress on modelling and simulation land, water and environmental management: integrated systems for sustainability. Modelling and simulation society of Australia and New Zealand Christchurch, New Zealand, pp 3024–3030, ISBN 978-0-9758400-4-7. http://www.mssanz.au/modsim07/Papers/NewMixtures16_Hudson_.pdf

  • Hudson IL, Rea A, Dalrymple M (2008) Climate impacts on sudden infant death syndrome: a GAMLSS approach. In: Eilers PHC (ed) 23rd international workshop on statistical modelling, 7–11 July, Utrecht, The Netherlands, Ipskamp Partners, Enschede, pp 277–280

  • Hudson IL, Kim SW, Keatley MR (2009) Climatic influences on the flowering phenology of four Eucalypts: a GAMLSS approach. In: Anderssen RS, Braddock RD, Newham LTH (eds) 18th World IMACS Congress and MODSIM09 international congress on modelling and simulation. Modelling and simulation society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, July 2009, pp 2611–2617. ISBN: 978-0-9758400-7-8. http://www.mssanz.org.au/modsim09/G2/hudson_il.pdf

  • Hudson IL, Kang I, Keatley MR (2010a) Wavelet analysis of flowering and climatic niche identification. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 361–391

    Google Scholar 

  • Hudson IL, Kim SW, Keatley MR (2010b) Climatic influences on the flowering phenology of four eucalypts: a GAMLSS approach. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 209–228

    Google Scholar 

  • Hudson IL, Kim SW, Keatley MR (2010c) Modelling the flowering of four eucalypt species using new mixture transition distribution models. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 299–320

    Google Scholar 

  • Hudson IL, Keatley MR, Kang I (2010d) Wavelet characterisation of eucalypt flowering and the influence of climate. Environ Ecol Stat (in press)

  • Hudson IL, Lee SL, Keatley MR (2010e) SOM clustering of phenological records. In: 25th International Workshop on Statistical Modelling (IWSM 2010), Glasgow, Scotland (accepted)

  • Hughes L (2000) Biological consequences of global warming: is the signal already apparent? Trends Ecol Evol 15:56–61

    Article  Google Scholar 

  • Hughes L (2003) Climate change and Australia: trends, projections and impacts. Austral Ecol 28:423–443

    Article  Google Scholar 

  • Inouye DW, Saavedra F, Lee-Yang W (2003) Environmental influences on the phenology and abundance of flowering by Androsace septentrionalis (Primulaceae). Am J Bot 90:905–910

    Article  Google Scholar 

  • Intergovernmental Panel on Climate Change (IPCC) (2007a) Climate change 2007: the physical science basis, contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, New York

    Google Scholar 

  • Intergovernmental Panel on Climate Change (IPCC) (2007b) Climate change 2007: impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, New York

    Google Scholar 

  • Jeanneret F (1972) Methods and problems of mesoclimatic surveys in a mountainous country. A research programme in the Canton of Berne, Switzerland. In: Stokes E (ed) Proceedings 7th geography conference, New Zealand Geographical Society. Hamilton, New Zealand

  • Jeanneret F (1997) From spatial sensing to environmental monitoring: a topoclimatic and phenological survey through Switzerland. In: Hočevar A, Črepinšek Z, Bogataj-Kajfez L (eds) Biometeorology. In: Proceedings of the 14th international congress on biometeorology, Ljubljana, pp 201–207

  • Jeanneret J (2010) The rhythm of seasonality: a phenological season diagram, submitted to Analele Universiatii de Vest din Timisoara, Seria Geograpfie (in press)

  • Jeanneret F, Brügger R (2005) Plant phenology, fog and snow cover duration—a topoclimatic survey of seasonality. Ann Meteorol 41:528–531

    Google Scholar 

  • Jeanneret F, Rutishauser T (2010a) Seasonality as a core business of phenology. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 63–74

    Google Scholar 

  • Jeanneret F, Rutishauser T (2010b) Phenology for topoclimatological surveys and large-scale mapping. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 159–175

    Google Scholar 

  • Jennions MD, Møller AP, Curie PM et al (2002) Meta-analysis can “fail”: reply to Kotiaho and Tomkins. Oikos 104:191–193

    Article  Google Scholar 

  • Jönsson P, Eklundh L (2004) TIMESAT—a program for analyzing time-series of satellite sensor data. Comput Geosci 30:833–845

    Article  Google Scholar 

  • Kang I, Hudson IL, Rudge AD, Chase JG (2005) Wavelet signatures of agitation and sedation profiles of ICU patients. In: Francis AR, Matawie KM, Oshlack A, Smyth GK (eds) Statistical solutions to modern problems. 20th international workshop on statistical modelling, Sydney, July 10–15, University of Western Sydney (Penrith), pp 293–296, ISBN 1 74108 101 7

  • Karlsen SR, Elvebakk A, Hogda KA et al (2006) Satellite-based mapping of the growing season and bioclimatic zones in Fennoscandia. Glob Ecol Biogeogr 15:416–430

    Article  Google Scholar 

  • Karlsen SR, Solheim I, Beck PSA et al (2007) Variability of the start of the growing season in Fennoscandia, 1982–2002. Int J Biometeorol 51:513–524

    Article  Google Scholar 

  • Katul GG, Lai CT, Schafer K et al (2001) Multiscale analysis of vegetation surface fluxes: from seconds to years. Adv Water Resour 24:1119–1132

    Article  Google Scholar 

  • Keatley MR, Hudson IL (2000) Influences on the flowering phenology of three eucalypts. In: de Dear RJ, Kalma JD, Oke TR, Aucliems A (eds) Selected papers from the conference ICB-ICUC’ 99. Biometeorology and urban climatology at the turn of the century. World Meteorological Organisation, Geneva, Switzerland, pp 191–196

  • Keatley MR, Hudson IL (2005) Singular spectrum analysis: an additional tool for examining phenological time series? In: Annalen der Meteorologie, vol 2. Proceedings of the 17th international congress of biometeorology Garmisch-Partenkirchen, 5–9 September, Deutscher Wetterdienst: Offenbach am Main, Germany, pp 516–519

  • Keatley MR, Hudson IL (2007) Shift in flowering dates of Australian plants related to climate: 1983–2006. In: Oxley L, Kulasiri D (eds) MODSIM 2007 international congress on modelling and simulation land, water and environmental management: integrated systems for sustainability modelling and simulation society of Australia and New Zealand christchurch, New Zealand, pp 504–510, ISBN : 978-0-9758400-4-7. http://www.mssanz.au/modsim07/Papers/ShiftInFloweringDates_s54_Keatley.pdf

  • Keatley MR, Hudson IL (2008) Shifts and changes in a 24 year Australian flowering record: 1983–2006. In: 18th international congress of biometeorology conference. Theme: harmony with nature, vol ECO-O05. International Society of Biometeorology, 26–27 Sept, Tokyo, Japan, pp 1–4. http://www.icb2008.com/ScientificP.html

  • Keatley MR, Hudson IL (2010) Introduction and overview. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 1–22

    Google Scholar 

  • Keatley MR, Fletcher TD, Hudson IL, Ades PK (2002) Phenological studies in Australia: potential application in historical and future climate analysis. Int J Climatol 22:1769–1780

    Article  Google Scholar 

  • Keatley MR, Hudson IL and Fletcher TD (2004) Long-term flowering synchrony of box-ironbark eucalypts. Aust J Bot 52:47–54

    Article  Google Scholar 

  • Kelly N (2010) Accounting for correlated error structure within phenological data: a case study of trend analysis of snowdrop flowering. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 271–298

    Google Scholar 

  • Kelly AE, Goulden ML (2008) Rapid shifts in plant distribution with recent climate change. Proc Natl Acad Sci 105:11823–11826

    Article  Google Scholar 

  • Kim SW, Hudson IL, Keatley MR (2005) Mixture transition distribution analysis of flowering and climatic states. In: Francis AR, Matawie KM, Oshlack A, Smyth GK (eds) Statistical solutions to modern problems, 20th international workshop on statistical modelling, 10–15 July, Sydney, University of Western Sydney(Penrith), pp 305–312, ISBN 1 74108 101 7

  • Kim SW, Hudson IL, Neffe A, Abell AD (2006) Identification of important docking parameters, exemplified for calpain inhibitors, with mixture cluster analysis. BioinfoSummer 2006. ICE-EM Summer symposium in bioinformatics, Centre for Bioinformation Science, The Australian National University, 4–8 December. http://wwwmaths.anu.edu.au/events/BioInfoSummer06/

  • Kim SW, Hudson IL, Keatley MR (2008a) Multivariate synchronization statistics: assessing groups of a/synchronizing Eucalypt species. In: 19th biennial Australian Statistical Conference (ASC2008). Theme: celebrating diversity, 30 Jun–3 July, Melbourne, Australia

  • Kim SW, Hudson IL, Agrawal M, Keatley MR (2008b) Modelling and synchronization of four Eucalypt species via MTD and EKF. In: Eilers PHC (ed) 23rd international workshop on statistical modelling, Utrecht, The Netherlands, 7–11 July, Ipskamp Partners, Enschede, pp 287–292

  • Kim SW, Hudson IL, Neffe A, Abell A (2008c) Bayesian multivariate mixture (BMM) model: an upgraded classification method. In: The International Society for Bayesian Analysis, 9th world meeting, Hamilton Island, Australia

  • Kim SW, Hudson IL, Keatley MR (2009) Modelling the flowering of four eucalypts species via MTDg with interactions. In: Anderssen RS, Braddock RD, Newham LTH (eds) 18th World IMACS Congress and MODSIM09 international congress on modelling and simulation. Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, 13–17 July, pp 2625–2631, ISBN: 978-0-9758400-7-8. http://www.mssanz.org.au/modsim09/G2/kim_sw.pdf

  • Koch E (2010) Global framework for data collection—data bases, data availability, future networks, online databases. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 23–61

    Google Scholar 

  • Koch M, Marković D (2007) Evidences for climate change in Germany over the 20th century from the stochastic analysis of hydro-meteorological time-series. In: Oxley L, Kulasiri D (eds) MODSIM 2007 international congress on modelling and simulation land, water and environmental management: integrated systems for sustainability modelling and simulation society of Australia and New Zealand Christchurch, New Zealand, pp 596–602, ISBN 978-0-9758400-4-7. http://www.mssanz.au/modsim07/Papers/EvidenceForClimates61_Koch_.pdf

  • Kotiaho JS, Tomkins JL (2002) Meta-analysis: can it ever fail? Oikos 96:551–553

    Article  Google Scholar 

  • Kurz WA, Dymond CC, Stenson G, Rampley GJ, Carroll AL, Ebata T, Safranyik L (2008) Mountain pine beetle and forest carbon feedback to climate change. Nature 452:987–990

    Article  Google Scholar 

  • Last FT, Roberts A, Patterson D (2003) Climate change? A statistical account of flowering in East Lothian: 1978–2001. In: Baker S (ed) East Lothian fourth statistical account 1945–2000. Volume one: the county. East Lothian council library service for the East Lothian statistical account society, East Lothian, pp 22–29

  • Loarie SR, Duffy PB, Hamilton H, Asner GP, Field CB, Ackerly DD (2009) The velocity of climate change. Nature 462:1052–1055

    Article  Google Scholar 

  • Lavoie C, Lachance D (2006) A new herbarium-based method for reconstructing the phenology of plant species across large areas. Am J Bot 93(4):512–516

    Article  Google Scholar 

  • Lehikoinen E, Sparks TH, Zalakevicius M (2004) Arrival and departure dates. Adv Ecol Res 35:1–31

    Article  Google Scholar 

  • Liang KY, Zeger SL (1993) Regression analysis for correlated data. Annu Rev Public Health 14:43–68

    Article  Google Scholar 

  • Linkosalo T (1999) Regularities and patterns in the spring phenology of some boreal trees. Silva Fenn 33:237–245

    Google Scholar 

  • Linkosalo T, Häkkinen R, Hari P (1996) Improving the reliability of a combined phenological time series by analyzing observation quality. Tree Physiol 16:661–664

    Google Scholar 

  • Linkosalo T, Häkkinen R, Terhivuo J et al (2009) The time series of flowering and leaf bud burst of boreal trees (1846–2005) support the direct temperature observations of climatic warming. Agric For Meteorol 149:453–461

    Article  Google Scholar 

  • Loiselle BA, Jørgensen PM, Consiglio T et al (2008) Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes? J Biogeogr 35:105–116

    Google Scholar 

  • Lu PL, Yu Q, Liu JD et al (2006) Effects of changes in spring temperature on flowering dates of woody plants across China. Bot Stud 47:153–181

    Google Scholar 

  • Lu X, Liu R, Liu J et al (2007) Removal of noise by wavelet method to generate high quality temporal data of terrestrial MODIS products. Photogramm Eng Remote Sensing 73:1129–1140

    Google Scholar 

  • Luterbacher J, Liniger MA, Menzel A et al (2007) The exceptional European warmth of autumn 2006 and winter 2007: historical context, the underlying dynamics and its phenological impacts. Geophys Res Lett 34:L12704

    Article  Google Scholar 

  • MacGillivray F, Hudson IL, Lowe AJ (2010) Herbarium collections and photographic images: alternative data sources for phenological research. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 425–461

    Google Scholar 

  • Mardia KV, Jupp PE (2000) Directional statistics. Wiley, Chichester

    Google Scholar 

  • Marx BD, Eilers PHC (1999) Generalized linear regression on sampled signals and curves: a P-spline approach. Technometrics 41:1–13

    Article  Google Scholar 

  • Marx BD, Eilers PHC (2005) Multidimensional penalized signal regression. Technometrics 47:13–22

    Article  Google Scholar 

  • McGrath LJ, van Riper C, Fontaine JJ (2009) Flower power: tree flowering phenology as a settlement cue for migrating birds. J Anim Ecol 78:22–30

    Article  Google Scholar 

  • McLachlan G, Peel D (2000) Finite mixture models. Wiley series in probability and statistics, applied probability and statistics section. Wiley, Canada

    Google Scholar 

  • McLachlan GJ, Peel D, Bean RW (2003) Modelling high-dimensional data by mixtures of factor analyzers. Comput Stat Data Anal 41:379–388

    Article  Google Scholar 

  • McLachlan GJ, Bean RW, Ben-Tovim Jones L (2007) Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution. Comput Stat Data Anal 51:5327–5338

    Article  Google Scholar 

  • Meier N, Rutishauser T, Pfister C et al (2007) Grape harvest dates as a proxy for Swiss April to August temperature reconstructions back to AD 1480. Geophys Res Lett 34:L20705

    Article  Google Scholar 

  • McMichael J, Woodruff RE, Hales S (2006) Climate change and human health: present and future risks. Lancet 367:859–869

    Article  Google Scholar 

  • McMichael AJ, Friel S, Nyong T, Corvalan C (2008a) Global environmental change and health: impacts, inequalities, and the health sector. Br Med J 336:191–194

    Article  Google Scholar 

  • McMichael AJ, Neira M, Heymann DL (2008b) Commentary: world health assembly 2008: climate change and health. Lancet 371:1895–1896

    Article  Google Scholar 

  • McMichael AJ, Wilkinson P, Kovats SR, Pattenden S, Shakoor H, Armstrong B et al (2008c) International study of temperature, heat and urban mortality: the ‘ISOTHURM’ project. Int J Epidemiol 37:121–132

    Article  Google Scholar 

  • Meligkotsidou L (2007) Bayesian multivariate Poisson mixtures with an unknown number of components. Stat Comput 17:93–107

    Article  Google Scholar 

  • Menzel A (2003) Plant phenology “Fingerprints”. In: Schwartz MD (ed) Phenology: an integrative environmental science. Tasks for vegetation science, vol 39. Kluwer, The Netherlands

    Google Scholar 

  • Menzel A, Sparks TH, Estrella N et al (2006a) Altered geographic and temporal variability in phenology in response to climate change. Global Ecol Biogeogr 15:498–504

    Google Scholar 

  • Menzel A, Sparks TH, Estrella N et al (2006b) European phenological response to climate change matches the warming pattern. Glob Chang Biol 12:1969–1976

    Article  Google Scholar 

  • Menzel A, Estrella N, Heitland W et al (2008) Bayesian analysis of the species-specific lengthening of the growing season in two European countries and the influence of an insect pest. Int J Biometeorol 52:209–218

    Article  Google Scholar 

  • Messerli B, Volz R, Wanner H et al (1978) Beiträge zum Klima des Kantons Bern. Jahrb Geogr Ges Bern 52:1975–76

    Google Scholar 

  • Michelozzi P, Accetta G, De Sario M et al (2009) High temperature and hospitalizations for cardiovascular and respiratory causes in 12 European cities. Am J Respir Crit Care Med 179: 383–389

    Article  Google Scholar 

  • Miller-Rushing AJ, Primack RB (2008) Global warming and flowering times in Thoreau’s Concord: a community perspective. Ecology 89:332–341

    Article  Google Scholar 

  • Miller-Rushing AJ, Primack RB, Primack D et al (2006) Photographs and herbarium specimens as tools to document response to global warming. Am J Bot 93:667–1674

    Article  Google Scholar 

  • Miller-Rushing AJ, Inouye DW, Primack RB (2008) How well do first flowering dates measure plant responses to climate change? The effects of population size and sampling frequency. J Ecol 96:1289–1296

    Article  Google Scholar 

  • Møller AP, Jennions MD (2001) Testing and adjusting for publication bias. Trends Ecol Evol 16:580–586

    Article  Google Scholar 

  • Møller AP, Rubolini D, Lehikoinen E (2008) Populations of migratory bird species that did not show a phenological response to climate change are declining. Proc Natl Acad Sci 105:16195–16200

    Article  Google Scholar 

  • Moody A, Johnson DM (2001) Land-surface phenologies from AVHRR using the discrete Fourier transform. Remote Sens Environ 75:305–323

    Article  Google Scholar 

  • Moran P (1953a) The statistical analysis of the Canadian lynx cycle. I. Structure and prediction. Aust J Zool 1:163–173

    Article  Google Scholar 

  • Moran P (1953b) The statistical analysis of the Canadian lynx cycle. II. Synchronization and meteorology. Aust J Zool 1:291–298

    Article  Google Scholar 

  • Morellato LPC (2003) South America. In: Schwartz MD (ed) Phenology: an integrative environmental science, Tasks for vegetation science, vol 39. Kluwer, The Netherlands

    Google Scholar 

  • Morellato LPC, Alberti LF, Hudson IL (2010a) Applications of circular statistics in plant phenology: a case studies approach. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 339–359

    Google Scholar 

  • Morellato LPC, Camargo MGG, D’Eça Neves FF, Luize BG, Mantovani A, Hudson IL (2010b) The influence of sampling method, sample size, and frequency of observations on plant phenological patterns and interpretation in tropical forest trees. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 99–121

    Google Scholar 

  • Morisette JT, Richardson AD, Knapp AK, Fisher JI, Graham EA, Abatzoglou J, Wilson BE, Breshears DD, Henebry GH, Hanes JM, Liang L (2009) Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. Front Ecol Environ 7:253–260

    Article  Google Scholar 

  • Moskvina V, Zhigljavsky A (2003) Change-point detection algorithm based on the singular-spectrum analysis. Comm Stat Simul Comput 32:319–352

    Article  Google Scholar 

  • Newstrom LE, Frankie GW, Baker HG et al (1994) Diversity of long-term flowering patterns. In: McDade LA, Bawa KS, Hespenheide HA et al (eds) La Selva: ecology and natural history of a neotropical rain forest. The University of Chicago Press, Chicago, pp 142–160

    Google Scholar 

  • Paluš M, Novotná D, Tichavský P (2005) Shifts of seasons at the European mid-latitudes: natural fluctuations correlated with the North Atlantic Oscillation. Geophys Res Lett 32:L12805

    Article  Google Scholar 

  • Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Ann Rev Ecol Evol Syst 37:637–669

    Article  Google Scholar 

  • Parmesan C (2007) Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Glob Chang Biol 13:1860–1872

    Article  Google Scholar 

  • Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37–42

    Article  Google Scholar 

  • Peng RD, Dominici F, Welty LJ (2009) A Bayesian hierarchical distributed lag model for estimating the time course of risk of hospitalization associated with particulate matter air pollution. Appl Stat 58:3–24

    Google Scholar 

  • Percival D, Walden A (2000) Wavelet methods for time series analysis. Cambridge University Press, Cambridge

    Google Scholar 

  • Percival DB, Wang M, Overland JE (2004) An introduction to wavelet analysis with applications to vegetation monitoring. Community Ecol 5:19–30

    Article  Google Scholar 

  • Pounds JA, Bustamante MR, Coloma LA et al (2006) Widespread amphibian extinctions from epidemic disease driven by global warming. Nature 439:161–167

    Article  Google Scholar 

  • Primack RB, Miller-Rushing AJ (2009) The role of botanical gardens in climate change research. New Phytol 182:303–313

    Article  Google Scholar 

  • Primack D, Imbres C, Primack RB et al (2004) Herbarium specimens demonstrate earlier flowering times in response to warming in Boston. Am J Bot 91:1260–1264

    Article  Google Scholar 

  • Raftery AE (1985) A model for high-order Markov chains. J R Stat Soc Ser B 47:528–539

    Google Scholar 

  • Rammig A, Jonas T, Zimmermann NE, Rixen C (2009) Changes in alpine plant growth under future climate conditions. Biogeosci Discuss 6:1–31

    Article  Google Scholar 

  • Rammig A, Jönsson AM, Hickler T, Smith B, Bärring L, Sykes MT (2010) Impacts of changing frost regimes on Swedish forests: incorporating cold hardiness in a regional ecosystem model. Ecol Modell 221:303–313

    Article  Google Scholar 

  • Reed BC, Brown JF, VanderZee D et al (1994) Measuring phenological variability from satellite imagery. J Veg Sci 5:703–714

    Article  Google Scholar 

  • Rencher AC (2000) Linear models in statistics. Wiley, New York

    Google Scholar 

  • Rigby RA, Stasinopoulos DM (2005) Generalized additive models for location, scale and shape. Appl Stat 54:507–554

    Google Scholar 

  • Roberts AMI (2008) Exploring relationships between phenological and weather data using smoothing. Int J Biometeorol 52:463–470

    Article  Google Scholar 

  • Roberts AMI (2010) Smoothing methods. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 255–269

    Google Scholar 

  • Roberts AMI, Last FT, Kempton E (2004) Preliminary analyses of changes in the first flowering dates of a range of plants between 1978 and 2001. Scottish Natural Heritage Commissioned Report No. 035, Edinburgh

  • Root TL, Schneider SH (1995) Ecology and climate: research strategies and implications. Science 269:334–341

    Article  Google Scholar 

  • Root TL, Schneider SH (2006) Conservation and climate change: the challenges ahead. Conserv Biol 20:706–708

    Article  Google Scholar 

  • Root TL, Price JT, Hall KR et al (2003) Fingerprints of global warming on wild animals and plants. Nature 421:57-60

    Article  Google Scholar 

  • Rosenzweig C, Karoly D, Vicarelli M et al (2008) Attributing physical and biological impacts to anthropogenic climate change. Nature 453:353–358

    Article  Google Scholar 

  • Rötzer T, Wittenzeller M, Haeckel H et al (2000) Phenology in central Europe—differences and trends of spring phenophases in urban and rural areas. Int J Biometeorol 44:60–66

    Article  Google Scholar 

  • Roy DB, Sparks T (2000) Phenology of British butterflies and climate change. Glob Chang Biol 6:407–416

    Article  Google Scholar 

  • Rumpff L, Coates F, Messina A (2008) Potential biological indicators of climate change: evidence from phenology of plants along the Victorian coast. Arthur Rylah Institute for Environmental Research, Technical Report No. 175. Department of Sustainability and Environment: Heidelberg, Victoria

  • Ruppert D, Wand MP, Carroll RJ (2003) Semiparametric regression. Cambridge University Press, New York

    Google Scholar 

  • Ruppert D, Wand MP, Carroll RJ (2009) Semiparametric regression during 2003–2007. Electron J Stat 3:1193–1256

    Article  Google Scholar 

  • Rutishauser T, Luterbacher J, Jeanneret F et al (2007) A phenology-based reconstruction of interannual changes in past spring seasons. J Geophys Res 112:G04016

    Article  Google Scholar 

  • Rutishauser T, Luterbacher J, Defila C et al (2008) Swiss spring plant phenology 2007: extremes, a multi-century perspective and changes in temperature sensitivity. Geophys Res Lett 35:L05703

    Article  Google Scholar 

  • Sakai S (2001) Phenological diversity in tropical forests. Popul Ecol 43:77–86

    Article  Google Scholar 

  • Schaber J, Badeck FW (2002) Evaluation of methods for the combination of phenological time series and outlier detection. Tree Physiol 22:973–982

    Google Scholar 

  • Schaber J, Badeck F, Doktor D, von Bloh W (2010) Combining messy phenological time series. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 147–158

    Google Scholar 

  • Schleip C, Menzel A, Estrella N et al (2006) The use of Bayesian analysis to detect recent changes in phenological events throughout the year. Agric For Meteorol 141:179–191

    Article  Google Scholar 

  • Schleip C, Rutishauser T, Luterbacher J, Menzel A (2008a) Time series modeling and central European temperature impact assessment of phenological records over the last 250 years. J Geophys Res 113:G04026. doi:10.1029/2007JG000646

    Article  Google Scholar 

  • Schleip C, Menzel A, Dose V (2008b) Norway spruce Picea abies: Bayesian analysis of the relationship between temperature and bud burst. Agric For Meteorol 148:631–643

    Article  Google Scholar 

  • Schleip C, Menzel A, Dose V (2010) Bayesian methods in phenology. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 229–254

    Google Scholar 

  • Schwartz MD (2003) Phenology: an integrative environmental science. Tasks for vegetation science, vol 39. Kluwer, The Netherlands

    Google Scholar 

  • Siljamo P, Sofiev M, Ranta H et al (2008) Representativeness of point-wise phenological Betula data collected in different parts of Europe. Glob Ecol Biogeogr 17:489–502

    Article  Google Scholar 

  • Sleep JA, Hudson IL (2008) Comparison of self-organising maps, mixture, k-means and hybrid approaches to risk classification of passive railway crossings. In: Eilers PHC (ed) 23rd international workshop on statistical modelling, 7–11 July, Utrecht, Netherlands, Ipskamp Partners, Enschede, pp 396–401

  • Sparks TH (2007) Lateral thinking on data to identify climate impacts. Trends Ecol Evol 22:169–171

    Article  Google Scholar 

  • Sparks TH, Carey PD (1995) The responses of species to climate over two centuries: an analysis of the Marshman phenological record, 1736–1947. J Ecol 83:321–329

    Article  Google Scholar 

  • Sparks T, Tryjanowski P (2010) Regression and causality. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, Dordrecht, pp 123–145

    Google Scholar 

  • Sparks TH, Jeffree EP, Jeffree CE (2000) An examination of the relationship between flowering times and temperature at the national scale using long-term phenological records from the UK. Int J Biometeorol 44:82–87

    Article  Google Scholar 

  • Sparks TH, Huber K, Croxton PJ (2006) Plant development scores from fixed-date photographs: the influence of weather variables and recorder experience. Int J Biometeorol 50:275–279

    Article  Google Scholar 

  • Stasinopoulos DM, Rigby RA (2007) Generalized additive models for location, scale and shape (GAMLSS) in R. J Stat Softw 23:1–46

    Google Scholar 

  • Stenseth NC, Mysterud A, Ottersen G, Hurrell JW, Chan KS, Lima M (2002) Ecological effects of climate fluctuations. Science 297:1292–1296

    Article  Google Scholar 

  • Stöckli R, Rutishauser T, Dragoni D, O’Keefe J, Thornton PE, Jolly M, Lu L, Denning AS (2008) Remote sensing data assimilation for a prognostic phenology model. J Geophys Res 113:G04021

    Article  Google Scholar 

  • Studer S, Appenzeller C, Defila C (2005) Inter-annual variability and decadal trends in alpine spring phenology: a multivariate approach. Clim Change 73:395–414

    Article  Google Scholar 

  • Studer S, Stöckli R, Appenzeller C, Vidale P (2007) A comparative study of satellite and ground-based phenology. Int J Biometeorol 51:405–414

    Article  Google Scholar 

  • Sturm M, Racine C, Tape K (2001) Climate change: increasing shrub abundance in the Arctic. Nature 411:546–457

    Article  Google Scholar 

  • Tarpley JD, Schneider SR, Money RL (1984) Global vegetation indices from the NOAA-7 meteorological satellite. J Clim Appl Meteorol 23:491–494

    Article  Google Scholar 

  • Thackery S, Sparks T, Frederiksen M et al (2010) Trophic level asynchrony in rates of phenological change for marine, freshwater and terrestrial environments. Glob Chang Biol. doi:10.1111/j.1365-2486.2010.02165.x

    Google Scholar 

  • Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, et al (2004) Extinction risk from climate change. Nature 427:145–148

    Article  Google Scholar 

  • Thompson R, Clark RM (2006) Spatio-temporal modelling and assessment of within-species phenological variability using thermal time methods. Int J Biometeorol 50:312–322

    Article  Google Scholar 

  • Thompson R, Clark RM (2008) Is spring starting earlier? Holocene 18:95–104

    Article  Google Scholar 

  • Thuiller W, Albert C, Araújo MB, Berry PM, Guisan A, Hickler T, Midgley GF, Paterson J, Schurr FM, Sykes MT, Zimmermann NE (2008) Predicting global change impacts on plant species distributions: future challenges. Perspect Plant Ecol Evol Syst 9:137–152

    Article  Google Scholar 

  • Tøttrup AP, Thorup K, Rahbek C (2006) Patterns of change in timing of spring migration in North European songbird populations. J Avian Biol 37:84–92

    Google Scholar 

  • Tsung-I L, Hsiu JH, Shen PS (2009) Computationally efficient learning of multivariate t mixture models with missing information. Comput Stat 24:375–392

    Article  Google Scholar 

  • Tzy-Chy L, Tsung-I L (2010) Supervised learning of multivariate skew normal mixture models with missing information. Comput Stat. doi:10.1007/s00180-009-0169-5

    Google Scholar 

  • Verbeke G, Molenberghs G (2000) Linear mixed models for longitudinal data. Springer, Berlin

    Google Scholar 

  • Visser ME, Both C (2005) Shifts in phenology due to global climate change: the need for a yardstick. Proc R Soc Lond B 272:2561–2569

    Article  Google Scholar 

  • Walther GR, Hughes L, Vitousek P et al (2005) Consensus on climate change. Trends Ecol Evol 20:648–649

    Article  Google Scholar 

  • Whitcher BJ, Guttorp P, Percival DB (2000) Wavelet analysis of covariance with application to atmospheric time series. J Geophys Res 105:941–962

    Article  Google Scholar 

  • White MA, Brunsell N, Schwartz MD (2003) Vegetation phenology in global change studies. In: Schwartz MD (ed) Phenology: an integrative environmental science. Tasks for vegetation science, vol 39. Kluwer, The Netherlands, pp 453–466

    Google Scholar 

  • Williams SE, Bolitho EE, Fox S (2003) Climate change in Australian tropical rainforests: an impending environmental catastrophe. Proc R Soc Lond B 270:1887–1892

    Article  Google Scholar 

  • Williams SE, Shoo LP, Isaac JL, Hoffmann AA, Langham G (2008) Towards an integrated framework for assessing the vulnerability of species to climate change. PLoS Biol 6(12):e325

    Article  Google Scholar 

  • Yang Y, Kang J (2010) Joint analysis of mixed Poisson and continuous longitudinal data with nonignorable missing values. Comput Stat Data Anal 54:193–207

    Article  Google Scholar 

  • Zar JH (1999) Biostatistical analysis. Prentice Hall, New Jersey

    Google Scholar 

  • Zhang X, Friedl MA, Schaaf CB et al (2004) Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data. Glob Chang Biol 10:1133–1145

    Article  Google Scholar 

  • Zhou L, Kaufmann RK, Tian Y et al (2003) Relation between interannual variations in satellite measures of northern forest greenness and climate between 1982 and 1999. J Geophys Res. 108:4004

    Article  Google Scholar 

  • Zimmerman JK, Wright SJ, Calderón O et al (2007) Flowering and fruiting phenologies of seasonal and aseasonal neotropical forests: the role of annual changes in irradiance. J Trop Ecol 23:231–251

    Article  Google Scholar 

  • Zwiers FW, Hegerl G (2008) Climate change: attributing cause and effect. Nature 453:296–297

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, University of South Australia (UniSA), Adelaide, South Australia

    Irene Lena Hudson

  2. Institute for Sustainable Systems and Technologies, UniSA, Mawson Lakes, South Australia

    Irene Lena Hudson

Authors
  1. Irene Lena Hudson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Irene Lena Hudson.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hudson, I.L. Interdisciplinary approaches: towards new statistical methods for phenological studies. Climatic Change 100, 143–171 (2010). https://doi.org/10.1007/s10584-010-9859-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10584-010-9859-9

Keywords

Search

Navigation