when is the onset of a phenophase? calculating phenological metrics from status monitoring data in...
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When is the onset of a phenophase?
Calculating phenological metrics from status monitoring data in the National Phenology Database
Jherime L. Kellermann1, Katharine L. Gerst1, Carolyn A.F. Enquist1,2
Ellen Denny1, Alyssa Rosemartin1, Jake Weltzin1
1USA National Phenology Network, Tucson, AZ2The Wildlife Society
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 theonset of a phenophase?
4. Methods & Results5. Conclusion and Next Steps
© J.L.Kellermann
Why Phenology?
• Highly sensitive to climate
• Excellent indicator of ecological change
© J.L.Kellermann
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
Phenology data available
>2.5 million records in National Phenology Database (NPDb)
0500
1,0001,5002,0002,5003,0003,5004,0004,5005,000
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
Dai
ly R
ecor
ds
Cum
ulati
ve re
cord
s
Phenology data available
>9500 sites across 50 states, PR, and US VI
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 theonset of a phenophase?
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
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
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
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”
Criteria 10-May 11-May 12-May 13-May 14-May 15-May 16-May 17-May 18-May 19-May 20-MayF1 - - - - - - - - - - 1
F01 0 - - - - - - - - - 1
Mid - - 0 - - - 0 - - - 1
Records Stations IndividualsF1 1,608 480 1,224F01 1,111 348 807Mid 785 265 610
% 50 reduction in data amount
2009 2010 2011 2012 20138
9
10
11
12
13
14
15
16
Mea
n M
arch
tem
pera
ture
(°C)
Methods: Climate data
(z = 12.1 P < 0.0001)
Warm “early” springs
“Normal” springs
rspb.org
Climate ‘type’ variable
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
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
Ons
et d
ay o
f the
yea
r (D
OY)
(F = 27, P < 0.0001)
F1 criterion
Climate typeNormal Warm
Latitude
Results: Model coefficients for each criterion
TMAX
On
set d
ate
50
100
150
0 5 10 15 20 25
© J.L.Kellermann
Maximum temperature, C
Ons
et d
ay o
f the
yea
r (D
OY)
Each criterion: P < 0.0001But NOT significantly different from one another
Phenophase SpeciesLeaf out Acer rubrum red maple
Acer saccharum sugar maple Quercus rubra northern red oak Quercus alba white oakFagus grandiflora American beechjuglans negra black walnut
Flowering Cornus florida flowering dogwood Cercis Canadensis eastern redbud Liriodendron tulipifera tuliptreePrunus virginiana chokecherry Prunus serotina black cherry Magnolia graniflora southern Magnolia
NPDb Case Study 2: Management Context
Can we estimate the onset of leaf-out or flowering to inform management planning & practices?
F1 F01 Mid106107108109110111112113114
Deciduous (6 spp)
Ons
et d
ate
(DO
Y)
F1 F01 Mid106107108109110111112113114
Acer rubrum
Ons
et d
ate
(DO
Y)NPDb Case Study 2: Management Context
Leaf-out in coastal Maine
F1 F01 Mid9092949698
100102104106108 Flowering trees (6 spp)
Ons
et d
ate
(DO
Y)
F1 F01 Mid90
95
100
105
110
115Cornus florida
Ons
et d
ate
(DO
Y)
NPDb Case Study 2: Management Context
Flowering in Chesapeake Bay region
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
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
