1. Nature’s Notebook year-end summary
Your 2014 results

2. Outline Continued growth and activity – thank you!
Focus on fall phenology
What’s coming next spring

5. Thank you!
1,310,882 records…and counting!

7. Focus on fall phenology
In our end-of-spring webinar, we talked about how many studies suggest that spring is occurring earlier
There’s generally less emphasis placed on autumn, and how the timing of fall events might be changing
Poll Q

8. Why do we care about fall phenology?
Tourism/leaf-peeping
Predicting carbon cycling
Albedo
NPP

9. NN data applications

10. Gaps in our understanding…
Recent studies have suggested that warming temperatures can delay leaf color change
But – unclear what drives leaf color change and abscission?
Photoperiod?
Temperature?
Does this vary by species?
What drives leaf color change?
Does this vary by species?
Will the timing change in the future?
Jeong and Medvigy, 2014

11. What they did… paper birch Betula papyrifera red maple Acer rubrum
American beech
Fagus grandifolia
Northern red oak
Quercus rubra
Data from Harvard Forest and USA-NPN
Six species of deciduous trees
sugar maple
Acer saccharum
aspen
Populus tremuloides
Jeong and Medvigy, 2014

12. Jeong and Medvigy, 2014

13. What they found… Harvard Forest USA-NPN observations
Delay in leaf color change
Temperature-only model worked best
Photoperiod + temperature model worked best
Species showed different sensitivities
Warmer regions: greater sensitivity to temperature
Leaf color change has gotten later at Harvard Forest since 1993 (~0.24 day/year)
Explained best by a temperature-only model
Applied this model to observations collected by Nature’s Notebook participants
Found that photoperiod + temperature best explained timing of leaf color change
Jeong and Medvigy, 2014

14. What they found… Jeong and Medvigy, 2014
RCP 2.6 – increase of 2.6 C by 2100 – delay of nearly 7 days
RCP 8.5 – increase of 10.8 C – delayed by more than 20 days
Jeong and Medvigy, 2014

15. What they found… RCP 2.6: delay of ~7 days RCP 2.6: delay of ~2 days
Jeong and Medvigy, 2014

16. Take-home message…
Timing of leaf coloration can be predicted using photoperiod + temperature
- Cold temps matter – but have to be “cold enough” to count!
*definitely could have implications for tourism, nutrient cycling, species interactions…
Will species change at the same rates?
Jeong and Medvigy, 2014

18. What do we see in 2014?
#October was 4th warmest on record for contig USA #StateOfClimate

19. Green Wave 2014 First reported “Yes” for Colored leaves
Acer spp. (maples)
Quercus spp. (oaks)
Popululs spp. (poplars)

20. Green Wave 2014 First reported “Yes” for Breaking leaf buds
Documenting fundamental patterns and relationships
Documenting changes in the timing of life cycle events

21. Green Wave 2014 Maple (Acer spp.)
First reported “Yes” to Colored leaves

22. Green Wave 2014 Oaks (Quercus spp.)
First reported “Yes” to Colored leaves

23. Green Wave 2014 Poplars (Populus spp.)
First reported “Yes” to Colored leaves

24. NN observations improve predictions of the future
**The finding from the Jeong & Medvigy paper that responses vary by species is one that is found commonly, and is important. One application for this is in vegetation models that predict what our landscape might look like under future climate change scenarios. Frequently, these models are parameterized – or “programmed” – with the phenology of only a single species. But we know that’s far from an accurate depiction of what “real life” looks like. Some researchers recently

25. Dynamic Vegetation Model:
NN data applications
Improve predictions of vegetation composition under future climate scenarios
Dynamic Vegetation Model:
**The finding from the Jeong & Medvigy paper that responses vary by species is one that is found commonly, and is important. One application for this is in vegetation models that predict what our landscape might look like under future climate change scenarios. Frequently, these models are parameterized – or “programmed” – with the phenology of only a single species. But we know that’s far from an accurate depiction of what “real life” looks like. Some researchers recently

26. NN data applications

27. NN data applications
Improve predictions of vegetation composition under future climate scenarios
Used Nature’s Notebook observations to determine leaf-out date of several plant species
Demonstrate future changes in plant composition
Ecosystem composition (%)
Euskirchen et al., Global Change Biology, 2014

29. Available now! Summarized data
Poll Q

30. Example information available using “summarized data”
Documenting fundamental patterns and relationships
Documenting changes in the timing of life cycle events

31. Downloading Summarized data

32. Downloading Summarized data

33. http://www. star. nesdis. noaa. gov/star/news2014_201410_FallFoliage

34. Volunteers submit high-quality observations!
Fuccillo et al., Int’l J of Biometeorology, 2014

35. Volunteers submit high-quality observations!
Fuccillo et al., Int’l J of Biometeorology, 2014

36. Coming next spring…

37. Coming next spring…

38. Coming next spring…

39. Coming next spring…

40. Coming next spring… Especially for Green Wave Campaign participants!
“At your location, you have reported Breaking leaf buds as early as March 15 and as late as April 22. This spring is expected to be exceptionally warm where you live, so we expect Breaking leaf buds to occur early. Begin to watch for buds starting in early March!”
Documenting fundamental patterns and relationships
Documenting changes in the timing of life cycle events

41. Happy fall…and happy holidays!
Documenting fundamental patterns and relationships
Documenting changes in the timing of life cycle events

42. Thank you! theresa@usanpn.org @TheresaCrimmins www.usanpn.org