1. When is the onset of a phenophase? Calculating phenological metrics from status monitoring data in the National Phenology Database Jherime L. Kellermann 1, Katharine L. Gerst 1, Carolyn A.F. Enquist 1,2 Ellen Denny 1, Alyssa Rosemartin 1, Jake Weltzin 1 1 USA National Phenology Network, Tucson, AZ 2 The Wildlife Society

2. OUTLINE 1.Why phenology? 2.USA National Phenology Network 3.How does USA-NPN deliver complex data sets useful for science and management? What filters or uncertainty parameters should be specified for measuring the onset of a phenophase? 4.Methods & Results 5.Conclusion and Next Steps © J.L.Kellermann

3. Why Phenology? Highly sensitive to climate Excellent indicator of ecological change © J.L.Kellermann

4. USA National Phenology Network National Phenology Database (NPDb) Nature’s Notebook: Web- based full-service phenology monitoring program Multiple taxa, multi- phenophase (e.g. life history stage) Vetted methods & protocols Data visualization & download tools © J.L.Kellermann

5. Phenology data available >2.5 million records in National Phenology Database (NPDb)

6. Phenology data available >9500 sites across 50 states, PR, and US VI

7. Application of USA-NPN data © J.L.Kellermann Broader Question: How does the USA-NPN deliver complex data sets useful for science and management? Specific Question: What filters or uncertainty parameters should be specified for measuring the onset of a phenophase?

8. Application of USA-NPN data 1. FOR SCIENCE: Detection of trends in phenological response to changes in climate 2.FOR MANAGEMENT: Make recommendations for planning and management by estimating onset dates Two contexts: © J.L.Kellermann

9. Event Day of year Status & Abundance Status “Status (vs. event) monitoring” methods The Data Status – Sampling frequency – Error around date estimate – Absence – Unusual events – Multiple occurrences of a phenophase in a yr – Phenophase duration Event –First instance of phenological event –Phenology of species with predictable series of events

10. NPDb Case Study 1: Science Context How does temperature affect the onset of spring leaf- out in deciduous trees in the eastern U.S.? Variables: USA-NPN sites: 17 species of deciduous trees Latitude & Elevation Geographical region Mean maximum temperature: March 2009-2013

11. Data Selection & Evaluation The Criteria for onset of leaf-out: 1. F1: First “yes” 2.F01: First “yes” preceded by a “no” 3. Mid: Mid-date of F01 & <7 days b/w last “no” & 1st “yes” Criteria10-May11-May12-May13-May14-May15-May16-May17-May18-May19-May20-May F1----------1 F010---------1 Mid--0---0---1 % 50 reduction in data amount

12. Methods: Climate data (z = 12.1 P < 0.0001) Warm “early” springs “Normal” springs rspb.org Climate ‘type’ variable

13. 1. Onset~TMAX+Elevation+Latitude+Region+(State/Station/Individual) 2. Onset~TMAX+Latitude+Region+(State/Station/Individual) 3. Onset~TMAX+Elevation+Latitude+(State/Station/Individual) 4. Onset~TMAX+Latitude+(State/Station/Individual) 5. Onset~TMAX+Region+(State/Station/Individual) 6. Onset~TMAX+(State/Station/Individual) 7. Onset~TMAX+Latitude*Type+(State/Station/Individual) 8. Onset~TMAX*Type+Latitude+(State/Station/Individual) 9. Onset~TMAX*Latitude+Type+(State/Station/Individual) 10. Onset~Type*Latitude+(State/Station/Individual) Methods: Models Linear mixed-effect models (lme in nlme package) Hierarchically nested random effects Model selection: BIC 10 a priori models selected © J.L.Kellermann

14. Results: Top Model Onset ~ TMAX + Type*Latitude + (State/Station/Individual) (>5 BIC points over all other models) (F = 428, P < 0.0001) F1 criterion Maximum temperature, C Onset day of the year (DOY) (F = 27, P < 0.0001) F1 criterion Climate type Normal Warm Latitude

15. Results: Model coefficients for each criterion © J.L.Kellermann Maximum temperature, C Onset day of the year (DOY) Each criterion: P < 0.0001 But NOT significantly different from one another

16. NPDb Case Study 2: Management Context Can we estimate the onset of leaf-out or flowering to inform management planning & practices?

17. NPDb Case Study 2: Management Context Leaf-out in coastal Maine

18. NPDb Case Study 2: Management Context Flowering in Chesapeake Bay region

19. Conclusions—Take home messages YES, we can use NPDb data to investigate & detect trends in phenophase onset relative to climate variables: SCIENCE CONTEXT: No big trade- offs when investigating broad biogeographic patterns (e.g. minimal impact of data of uncertainty on model uncertainty). MANAGEMENT CONTEXT: More trade-offs when investigating at level of site or landscape level where data can be limited © J.L.Kellermann

20. Next steps Investigate data criteria in other & less temperate biogeographic regions (e.g., CA) Develop data products for science & management applications (e.g., predictive models, phenology calendars, decision support tools) Continue to expand spatial & temporal coverage of phenology monitoring through recruitment & retention of participants Apply rigorous QA/QC methods © J.L.Kellermann

21. Thank You!!! For more information: carolyn@usanpn.org jlkellermann@gmail.com katgerst@email.arizona.edu