International Journal of Biometeorology

, Volume 60, Issue 3, pp 391–400

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

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

DOI: 10.1007/s00484-015-1036-4

Cite this article as:
Gerst, K.L., Kellermann, J.L., Enquist, C.A.F. et al. Int J Biometeorol (2016) 60: 391. doi:10.1007/s00484-015-1036-4

Abstract

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.

Keywords

Phenology Onset Leaf-out Phenological metrics Data selection Sampling frequency 

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)

Copyright information

© ISB 2015

Authors and Affiliations

  • Katharine L. Gerst
    • 1
    • 2
  • 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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