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Harvard Forest Data Archive

HF309

Assessing Plant Phenological Patterns in the Eastern United States Over the Last 120 Years

Related Publications

Data

Overview

  • Lead: Daniel Park, Alex Williams, Edith Law, Aaron Ellison, Charles Davis
  • Investigators:
  • Contact: Aaron Ellison
  • Start date: 1895
  • End date: 2016
  • Status: ongoing
  • Location: Eastern United States
  • Latitude: +25 to +48
  • Longitude: -88 to -65
  • Elevation: 1 to 1100 meter
  • Taxa: Anemone Canadensis, Anemone hepatica, Aquilegia Canadensis, Bidens vulgate, Celastrus orbiculatus, Centaurea stoebe, Cirsium arvense, Cirsium discolor, Geranium maculatum, Geranium robertianum, Hemerocallis fulva, Hibiscus moscheutos, Impatiens capensis, Iris pseudacorus, Iris versicolor, Lilium canadense, Lonicera bella, Lonicera canadensis, Lonicera japonica, Malus pumila, Malva neglecta, Oenothera perennis, Orobanche uniflora, Rosa gallica, Rubus odoratus, Sarracenia purpurea, Sisyrinchium mucronatum, Solanum rostratum, Trillium grandiflorum, Trillium undulatum
  • Release date: 2018
  • Revisions:
  • EML file: knb-lter-hfr.309.2
  • DOI: digital object identifier
  • Related links:
  • Study type: historical
  • Research topic: ecological informatics and modelling; historical and retrospective studies; regional studies
  • LTER core area: populations
  • Keywords: climate change, phenology, plant species, temperature
  • Abstract:

    Phenology is a key biological trait of an organism’s success and is one of the best indicators of its response to recent climate change. Plants are among the most well-studied organisms in this regard, but observational data bearing on this topic are largely restricted to woody species of the northern hemisphere, mostly from ca. the last three decades. Recent research has demonstrated that mobilized online herbarium specimens provide important, albeit mostly neglected, information on plant phenology. Here, we use the web tool CrowdCurio to crowdsource phenological data from more than 10,000 herbarium specimens representing 30 flowering plant species broadly distributed across the eastern United States. Our results, spanning 120 years and generated from over 2,000 crowdsourcers, clarify numerous aspects of plant phenology. First, they reveal that plant reproductive phenology is significantly advancing in response to warming, which is consistent with previous studies. Second, among those species with broad latitudinal ranges, populations from more southern latitudes are significantly more phenologically sensitive to temperature than those from northern populations. Last, contrary to some recent findings, plants in warmer, less variable climates may be much more dynamic, on average, in their phenological sensitivity. Our results are robust to a variety of confounding factors and span large phylogenetic distances and myriad life histories. These may represent more global trends in the latitudinal gradient of phenological response with myriad potential ecological and evolutionary consequences, and leads us to hypothesize that phenological sensitivity across species' ranges is driven by adaptation to local climates.

  • Methods:

    The web tool CrowdCurio is used to crowd source phenological data from more than 10,000 herbarium specimens representing 30 flowering plant species across the eastern United States.

  • Use:

    This dataset is released to the public under Creative Commons license CC BY (Attribution). Please keep the designated contact person informed of any plans to use the dataset. Consultation or collaboration with the original investigators is strongly encouraged. Publications and data products that make use of the dataset must include proper acknowledgement.

  • Citation:

    Park D, Williams A, Law E, Ellison A, Davis C. 2018. Assessing Plant Phenological Patterns in the Eastern United States Over the Last 120 Years. Harvard Forest Data Archive: HF309.

Detailed Metadata

hf309-01: crowdsourcing results

  1. state: state
  2. county: county
  3. user: crowdsourcer ID
  4. metric: climate metric
  5. date: collection date
  6. year: collection year
  7. month: collection month
  8. day: collection day
  9. name: species binomial
  10. county.lon: specimen longitude (unit: degree / missing value: NA)
  11. county.lat: specimen latitude (unit: degree / missing value: NA)
  12. link: link to specimen image
  13. doy: Julian day of year (unit: nominalDay / missing value: NA)
  14. jan: mean temperature of month in collection year (unit: celsius / missing value: NA)
  15. feb: mean temperature of month in collection year (unit: celsius / missing value: NA)
  16. mar: mean temperature of month in collection year (unit: celsius / missing value: NA)
  17. apr: mean temperature of month in collection year (unit: celsius / missing value: NA)
  18. may: mean temperature of month in collection year (unit: celsius / missing value: NA)
  19. jun: mean temperature of month in collection year (unit: celsius / missing value: NA)
  20. jul: mean temperature of month in collection year (unit: celsius / missing value: NA)
  21. aug: mean temperature of month in collection year (unit: celsius / missing value: NA)
  22. sep: mean temperature of month in collection year (unit: celsius / missing value: NA)
  23. oct: mean temperature of month in collection year (unit: celsius / missing value: NA)
  24. nov: mean temperature of month in collection year (unit: celsius / missing value: NA)
  25. dec: mean temperature of month in collection year (unit: celsius / missing value: NA)
  26. p.jan: mean temperature of month in previous year (unit: celsius / missing value: NA)
  27. p.feb: mean temperature of month in previous year (unit: celsius / missing value: NA)
  28. p.mar: mean temperature of month in previous year (unit: celsius / missing value: NA)
  29. p.apr: mean temperature of month in previous year (unit: celsius / missing value: NA)
  30. p.may: mean temperature of month in previous year (unit: celsius / missing value: NA)
  31. p.jun: mean temperature of month in previous year (unit: celsius / missing value: NA)
  32. p.jul: mean temperature of month in previous year (unit: celsius / missing value: NA)
  33. p.aug: mean temperature of month in previous year (unit: celsius / missing value: NA)
  34. p.sep: mean temperature of month in previous year (unit: celsius / missing value: NA)
  35. p.oct: mean temperature of month in previous year (unit: celsius / missing value: NA)
  36. p.nov: mean temperature of month in previous year (unit: celsius / missing value: NA)
  37. p.dec: mean temperature of month in previous year (unit: celsius / missing value: NA)
  38. pp.dec: mean temperature of month two years previous (unit: celsius / missing value: NA)
  39. variable: climate variable
  40. alt: elevation (unit: meter / missing value: NA)
  41. maxp: crowdsourcer consistency (unit: dimensionless / missing value: NA)
  42. phenophase: phenological phase
    • pfl: peak flower
    • ffr: first fruit
    • pfr: peak fruit
    • efl: first flower
  43. annual: annual mean temperature (unit: celsius / missing value: NA)
  44. spring: spring mean temperature (unit: celsius / missing value: NA)
  45. summer: summer mean temperature (unit: celsius / missing value: NA)
  46. fall: fall mean temperature (unit: celsius / missing value: NA)
  47. winter: winter mean temperature (unit: celsius / missing value: NA)
  48. domainid: NEON domain ID
  49. domainname: NEON domain name
  50. ba.climatezone2: climate zone

hf309-02: species traits

  1. name: species binomial
  2. habit: life span
  3. form: growth form
  4. status: native status
  5. self.compatible: self compatibility
  6. ref: citations for mating system

hf309-03: R code, associated data, and figures

  • Compression: zip
  • Format: multiple
  • Type: zip file