International Journal of Biometeorology

, Volume 60, Issue 3, pp 391–400 | Cite as

Estimating the onset of spring from a complex phenology database: trade-offs across geographic scales

  • Katharine L. GerstEmail author
  • Jherime L. Kellermann
  • Carolyn A. F. Enquist
  • Alyssa H. Rosemartin
  • Ellen G. Denny
Original Paper


Phenology is an important indicator of ecological response to climate change. Yet, phenological responses are highly variable among species and biogeographic regions. Recent monitoring initiatives have generated large phenological datasets comprised of observations from both professionals and volunteers. Because the observation frequency is often variable, there is uncertainty associated with estimating the timing of phenological activity. “Status monitoring” is an approach that focuses on recording observations throughout the full development of life cycle stages rather than only first dates in order to quantify uncertainty in generating phenological metrics, such as onset dates or duration. However, methods for using status data and calculating phenological metrics are not standardized. To understand how data selection criteria affect onset estimates of springtime leaf-out, we used status-based monitoring data curated by the USA National Phenology Network for 11 deciduous tree species in the eastern USA between 2009 and 2013. We asked, (1) How are estimates of the date of leaf-out onset, at the site and regional levels, influenced by different data selection criteria and methods for calculating onset, and (2) at the regional level, how does the timing of leaf-out relate to springtime minimum temperatures across latitudes and species? Results indicate that, to answer research questions at site to landscape levels, data users may need to apply more restrictive data selection criteria to increase confidence in calculating phenological metrics. However, when answering questions at the regional level, such as when investigating spatiotemporal patterns across a latitudinal gradient, there is low risk of acquiring erroneous results by maximizing sample size when using status-derived phenological data.


Phenology Onset Leaf-out Phenological metrics Data selection Sampling frequency 



Data were provided by the USA National Phenology Network and the many participants who contribute to its Nature’s Notebook program. Special thanks to Theresa Crimmins and Jake Weltzin for discussions and comments on earlier drafts. The project described in this publication was supported by Grant/Cooperative Agreement Number G14AC00405 from the United States Geological Survey.

Supplementary material

484_2015_1036_Fig6_ESM.gif (67 kb)
Supplementary Fig. 1a–k

Box plots and histograms showing the distribution of differences (number of days) in estimated onset dates of breaking leaf buds when comparing mean values at each site between each of the three datasets for each species. Box plots display the median and quantiles of the distribution; however boxes are not visually evident in cases when the majority of the distribution is clustered around the mean; in such cases the remaining visible points are considered outliers (GIF 67 kb)

484_2015_1036_Fig7_ESM.gif (66 kb)
Supplementary Fig. 1a–k

Box plots and histograms showing the distribution of differences (number of days) in estimated onset dates of breaking leaf buds when comparing mean values at each site between each of the three datasets for each species. Box plots display the median and quantiles of the distribution; however boxes are not visually evident in cases when the majority of the distribution is clustered around the mean; in such cases the remaining visible points are considered outliers (GIF 67 kb)

484_2015_1036_Fig8_ESM.gif (49 kb)
Supplementary Fig. 1a–k

Box plots and histograms showing the distribution of differences (number of days) in estimated onset dates of breaking leaf buds when comparing mean values at each site between each of the three datasets for each species. Box plots display the median and quantiles of the distribution; however boxes are not visually evident in cases when the majority of the distribution is clustered around the mean; in such cases the remaining visible points are considered outliers (GIF 67 kb)

484_2015_1036_MOESM1_ESM.eps (2.4 mb)
High Resolution Image (EPS 2,472 kb)
484_2015_1036_MOESM2_ESM.eps (2.4 mb)
High Resolution Image (EPS 2,482 kb)
484_2015_1036_MOESM3_ESM.eps (1.9 mb)
High Resolution Image (EPS 1,914 kb)


  1. Anderson JT, Inouye DW, McKinney AM, Colautti RI, Mitchell-Olds T (2012) Phenotypic plasticity and adaptive evolution contribute to advancing flowering phenology in response to climate change. Proc R Soc B-Bioll Sci 279(1743):3843–3852. doi: 10.1098/rspb.2012.1051 CrossRefGoogle Scholar
  2. Ault TR, Henebry GM, de Beurs KM, Schwartz MD, Betancourt JL, Moore D (2013) The false spring of 2012, earliest in North American record. Eos, Trans AGU 94(20):181–182. doi: 10.1002/2013eo200001 CrossRefGoogle Scholar
  3. Badeck FW, Bondeau A, Bottcher K, Doktor D, Lucht W, Schaber J, Sitch S (2004) Responses of spring phenology to climate change. New Phytol 162(2):295–309. doi: 10.1111/j.1469-8137.2004.01059.x CrossRefGoogle Scholar
  4. Bird TJ, Bates AE, Lefcheck JS, Hill N, Thomson R, Edgar GJ, Stuart-Smith RD, Wotherspoon SJ, Krkosek M, Stuart-Smith JF, Pecl GT, Barrett NS, Frusher SD (2014) Statistical solutions for error and bias in global citizen science datasets. Biological Conservation 173 doi: doi:10.1016/j.biocon.2013.07.037Google Scholar
  5. Chapman DS, Haynes T, Beal S, Essl F, Bullock JM (2014) Phenology predicts the native and invasive range limits of common ragweed. Glob Chang Biol 20(1):192–202. doi: 10.1111/gcb.12380 CrossRefGoogle Scholar
  6. Cleland EE, Allen JM, Crimmins TM, Dunne JA, Pau S, Travers SE, Zavaleta ES, Wolkovich EM (2012) Phenological tracking enables positive species responses to climate change. Ecology 93(8):1765–1771CrossRefGoogle Scholar
  7. Cole H, Henson S, Martin A, Yool A (2012) Mind the gap: the impact of missing data on the calculation of phytoplankton phenology metrics. J Geophys Res Oceans 117(C8), C08030. doi: 10.1029/2012jc008249 CrossRefGoogle Scholar
  8. Cook BI, Wolkovich EM, Parmesan C (2012) Divergent responses to spring and winter warming drive community level flowering trends. Proc Natl Acad Sci U S A 109(23):9000–9005. doi: 10.1073/pnas.1118364109 CrossRefGoogle Scholar
  9. Cornelius C, Petermeier H, Estrella N, Menzel A (2011) A comparison of methods to estimate seasonal phenological development from BBCH scale recording. Int J Biometeorol 55(6):867–877. doi: 10.1007/s00484-011-0421-x CrossRefGoogle Scholar
  10. Courter JR, Johnson RJ, Bridges WC, Hubbard KG (2013) Assessing migration of ruby-throated hummingbirds (Archilochus colubris) at broad spatial and temporal scales. Auk 130(1):107–117. doi: 10.1525/auk.2012.12058 CrossRefGoogle Scholar
  11. Crimmins TM, Crimmins MA, Bertelsen CD (2010) Complex responses to climate drivers in onset of spring flowering across a semi-arid elevation gradient. J Ecol 98(5):1042–1051. doi: 10.1111/j.1365-2745.2010.01696.x CrossRefGoogle Scholar
  12. Denny EG, Gerst KL, Miller-Rushing AJ, Tierney GL, Crimmins TM, Enquist CAF, Guertin P, Rosemartin AH, Schwartz MD, Thomas KA, Weltzin JF (2014) Standardized phenology monitoring methods to track plants and animal activity for science and resource management applications. Int J Biometeorol. doi: 10.1007/s00484-014-0789-5 Google Scholar
  13. Diez JM, Ibáñez I, Silander JA, Primack R, Higuchi H, Kobori H, Sen A, James TY (2014) Beyond seasonal climate: statistical estimation of phenological responses to weather. Ecol Appl 24(7):1793–1802. doi: 10.1890/13-1533.1 CrossRefGoogle Scholar
  14. Donnelly A, Caffarra A, O'Neill BF (2011) A review of climate-driven mismatches between interdependent phenophases in terrestrial and aquatic ecosystems. Int J Biometeorol 55(6):805–817. doi: 10.1007/s00484-011-0426-5 CrossRefGoogle Scholar
  15. Dunne JA, Saleska SR, Fischer ML, Harte J (2004) Integrating experimental and gradient methods in ecological climate change research. Ecology 85(4):904–916. doi: 10.1890/03-8003 CrossRefGoogle Scholar
  16. Ellwood ER, Temple SA, Primack RB, Bradley NL, Davis CC (2013) Record-breaking early flowering in the eastern United States. Plos One 8 (1). doi:10.1371/journal.pone.0053788Google Scholar
  17. Enquist CAF, Kellermann JL, Gerst KL, Miller-Rushing AJ (2014) Phenology research for natural resource management in the United States. Int J Biometeorol. doi: 10.1007/s00484-013-0772-6 Google Scholar
  18. EPA US (2014) Climate change indicators in the United States. Environmental Protection Agency
  19. Euskirchen ES, Carman TB, McGuire AD (2014) Changes in the structure and function of northern Alaskan ecosystems when considering variable leaf-out times across groupings of species in a dynamic vegetation model. Glob Chang Biol 20:963–978. doi: 10.1111/gcb.1239 CrossRefGoogle Scholar
  20. Ferreira AS, Visser AW, MacKenzie BR, Payne MR (2014) Accuracy and precision in the calculation of phenology metrics. J Geophys Res Oceans:n/a-n/a. doi: 10.1002/2014jc010323
  21. Hurlbert AH, Liang Z (2012) Spatiotemporal variation in avian migration phenology: citizen science reveals effects of climate change. Plos One 7 (2). doi: 10.1371/journal.pone.0031662
  22. Iler AM, Hoye TT, Inouye DW, Schmidt NM (2013) Long-term trends mask variation in the direction and magnitude of short-term phenological shifts. Am J Bot 100(7):1398–1406. doi: 10.3732/ajb.1200490 CrossRefGoogle Scholar
  23. IPCC (2014) Climate change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, p 151.Google Scholar
  24. Jeong S-J, Ho C-H, Gim H-J, Brown ME (2011) Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008. Glob Chang Biol 17(7):2385–2399. doi: 10.1111/j.1365-2486.2011.02397.x CrossRefGoogle Scholar
  25. Jeong S-J, Medvigy D, Shevliakova E, Malyshev S (2013) Predicting changes in temperate forest budburst using continental-scale observations and models. Geophys Res Lett 40(2):359–364. doi: 10.1029/2012gl054431 CrossRefGoogle Scholar
  26. Jochner S, Caffarra A, Menzel A (2013) Can spatial data substitute temporal data in phenological modelling? A survey using birch flowering. Tree Physiol 33(12):1256–1268. doi: 10.1093/treephys/tpt079 CrossRefGoogle Scholar
  27. Keatley MR, Hudson IL (2010) Phenological research methods for environmental and climate change analysis: introduction and overview. Phenol Res Methods Environ Climate Change Analysis. doi: 10.1007/978-90-481-3335-2_1
  28. Lechowicz MJ (1984) Why do temperate deciduous trees leaf out at different times—adaptation and ecology of forest communities. Am Nat 124(6):821–842. doi: 10.1086/284319 CrossRefGoogle Scholar
  29. Marra PP, Francis CM, Mulvihill RS, Moore FR (2005) The influence of climate on the timing and rate of spring bird migration. Oecologia 142(2):307–315. doi: 10.1007/s00442-004-1725-x CrossRefGoogle Scholar
  30. McCormack ML, Gaines K, Pastore M, Eissenstat D (2014) Early season root production in relation to leaf production among six diverse temperate tree species. Plant Soil:1–9. doi: 10.1007/s11104-014-2347-7
  31. 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(6):1289–1296. doi: 10.1111/j.1365-2745.2008.01436.x CrossRefGoogle Scholar
  32. Morellato LPC, Camargo MGG, Neves FFD, Luize BG, Mantovani A, Hudson IL (2010) The influence of sampling method, sample size, and frequency of observations on plant phenological patterns and interpretation in tropical forest trees. Phenol Res Methods Environ Climate Change Analysis. doi: 10.1007/978-90-481-3335-2_5
  33. Moussus JP, Julliard R, Jiguet F (2010) Featuring 10 phenological estimators using simulated data. Methods Ecol Evol 1(2):140–150. doi: 10.1111/j.2041-210X.2010.00020.x CrossRefGoogle Scholar
  34. Parmesan C (2006) Ecological and evolutionary responses to recent climate change. In: Annual Review of Ecology Evolution and Systematics, vol 37. Annual Rev Ecol Evol Syst. pp 637–669. doi: 10.1146/annurev.ecolsys.37.091305.110100
  35. Parmesan C (2007) Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Glob Chang Biol 13(9):1860–1872. doi: 10.1111/j.1365-2486.2007.01404.x CrossRefGoogle Scholar
  36. Peeters F, Straile D, Lorke A, Livingstone DM (2007) Earlier onset of the spring phytoplankton bloom in lakes of the temperate zone in a warmer climate. Glob Chang Biol 13(9):1898–1909. doi: 10.1111/j.1365-2486.2007.01412.x CrossRefGoogle Scholar
  37. Phillimore AB, Proios K, O'Mahony N, Bernard R, Lord AM, Atkinson S, Smithers RJ (2013) Inferring local processes from macro-scale phenological pattern: a comparison of two methods. J Ecol 101(3):774–783. doi: 10.1111/1365-2745.12067 CrossRefGoogle Scholar
  38. Polgar CA, Primack RB (2011) Leaf-out phenology of temperate woody plants: from trees to ecosystems. New Phytol 191(4):926–941. doi: 10.1111/j.1469-8137.2011.03803.x CrossRefGoogle Scholar
  39. Rafferty NE, CaraDonna PJ, Burkle LA, Iler AM, Bronstein JL (2013) Phenological overlap of interacting species in a changing climate: an assessment of available approaches. Ecol Evol 3(9):3183–3193. doi: 10.1002/ece3.668 CrossRefGoogle Scholar
  40. Richardson AD, Keenan TF, Migliavacca M, Ryu Y, Sonnentag O, Toomey M (2013) Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric For Meteorol 169:156–173. doi: 10.1016/j.agrformet.2012.09.012 CrossRefGoogle Scholar
  41. Rollinson CR, Kaye MW (2012) Experimental warming alters spring phenology of certain plant functional groups in an early successional forest community. Glob Chang Biol 18(3):1108–1116. doi: 10.1111/j.1365-2486.2011.02612.x CrossRefGoogle Scholar
  42. Rosemartin AH, Crimmins TM, Enquist CAF, Gerst KL, Kellermann JL, Posthumus EE, Weltzin JF, Denny EG, Guertin P, Marsh LR (2013) Organizing phenological data resources to inform natural resource conservation. biological conservation. doi: 10.1016/j.biocon.2013.07.003
  43. Schwartz MD, Ahas R, Aasa A (2006) Onset of spring starting earlier across the northern hemisphere. Glob Chang Biol 12(2):343–351. doi: 10.1111/j.1365-2486.2005.01097.x CrossRefGoogle Scholar
  44. Schwartz MD, Ault TR, Betancourt JL (2013a) Spring onset variations and trends in the continental United States: past and regional assessment using temperature-based indices. Int J Climatol 33(13):2917–2922. doi: 10.1002/joc.3625 CrossRefGoogle Scholar
  45. Schwartz MD, Beaubien EG, Crimmins TM, Weltzin JF (2013b) North America. In: Schwartz MD (ed) Phenology: an integrative environmental science, 2nd edn. Springer, Netherlands, pp 67–89. doi: 10.1007/978-94-007-6925-0_5 CrossRefGoogle Scholar
  46. Schwartz MD, Betancourt JL, Weltzin JF (2012) From Caprio's lilacs to the USA National Phenology Network. Front Ecol Environ 10(6):324–327. doi: 10.1890/110281 CrossRefGoogle Scholar
  47. Thackeray SJ, Sparks TH, Frederiksen M, Burthe S, Bacon PJ, Bell JR, Botham MS, Brereton TM, Bright PW, Carvalho L, Clutton-Brock T, Dawson A, Edwards M, Elliott JM, Harrington R, Johns D, Jones ID, Jones JT, Leech DI, Roy DB, Scott WA, Smith M, Smithers RJ, Winfield IJ, Wanless S (2010) Trophic level asynchrony in rates of phenological change for marine, freshwater and terrestrial environments. Glob Chang Biol 16(12):3304–3313. doi: 10.1111/j.1365-2486.2010.02165.x CrossRefGoogle Scholar
  48. Tierney G, Mitchell B, Miller-Rushing A, Katz J, Denny E, Brauer C, Donovan T, Richardson AD, Toomey M, Kozlowski A, Weltzin J, Gerst K, Sharron E, Sonnentag O, Dieffenbach F (2013) Phenology monitoring protocol: Northeast Temperate Network. Natural Resource Report. NPS/NETN/NRR—2013/681. Fort Collins, COGoogle Scholar
  49. USA National Phenology Network (2013) Plant phenology data for the United States, 2009–2013. USA-NPN, Tucson, Arizona, USA. Data set accessed 15-08-2013 at
  50. Wolkovich EM, Cook BI, Allen JM, Crimmins TM, Betancourt JL, Travers SE, Pau S, Regetz J, Davies TJ, Kraft NJB, Ault TR, Bolmgren K, Mazer SJ, McCabe GJ, McGill BJ, Parmesan C, Salamin N, Schwartz MD, Cleland EE (2012) Warming experiments underpredict plant phenological responses to climate change. Nature 485(7399):494–497. doi: 10.1038/nature11014 Google Scholar
  51. Zhang X, Tarpley D, Sullivan JT (2007) Diverse responses of vegetation phenology to a warming climate. Geophys Res Lett 34 (19). doi: 10.1029/2007gl031447
  52. Zhang XY, Friedl MA, Schaaf CB, Strahler AH (2004) Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data. Glob Chang Biol 10(7):1133–1145. doi: 10.1111/j.1529-8817.2003.00784.x CrossRefGoogle Scholar
  53. Zhao TT, Schwartz MD (2003) Examining the onset of spring in Wisconsin. Clim Res 24(1):59–70. doi: 10.3354/cr024059 CrossRefGoogle Scholar

Copyright information

© ISB 2015

Authors and Affiliations

  • Katharine L. Gerst
    • 1
    • 2
    Email author
  • Jherime L. Kellermann
    • 1
    • 2
    • 3
  • Carolyn A. F. Enquist
    • 1
    • 2
    • 4
  • Alyssa H. Rosemartin
    • 1
    • 2
  • Ellen G. Denny
    • 1
    • 2
  1. 1.National Coordinating OfficeUSA National Phenology NetworkTucsonUSA
  2. 2.School of Natural Resources and the EnvironmentUniversity of ArizonaTucsonUSA
  3. 3.Natural Sciences DepartmentOregon Institute of TechnologyKlamath FallsUSA
  4. 4.Southwest Climate Science CenterU.S. Geological SurveyTucsonUSA

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