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Campbell, Quantifying uncertainty in ecology: Examples from small watershed studies.
1. Quantifying Uncertainty in Ecology:
Examples from Small Watershed Studies
John Campbell – US Forest Service
Ruth Yanai – SUNY-ESF
Mark Green – Plymouth State Univ.
Ecological Society of America Meeting
Minneapolis, MN - August 2013
3. QUEST is a NSF funded Research
Coordination Network (PI: Ruth Yanai)
The goal is to improve understanding and
facilitate use of uncertainty analyses in
ecosystem studies.
4. Become a part of QUEST!
•Visit our website
(www.quantifyinguncertainty.org)
•Download papers and
presentations
•Get sample code
•Stay updated with QUEST News
•Join our mailing list
(quantifyinguncertainty@gmail.com)
•Meet us for dinner tonight
(7pm - Hell’s Kitchen)
5. Paired watershed studies
• Watersheds are
unreplicated
• It’s difficult to find
suitable replicate
watersheds and
expensive to treat them
• Uncertainty analysis can
be used to report
statistical confidence
Andréassian 2004 Journal of
Hydrology 29:1-27
6. Easier said than done…
• Difficult to identify
sources of
uncertainty
• Difficult to quantify
sources
• Multiple approaches
to uncertainty
analysis
• No single answer
7. Uncertainty in the flux of Ca
W6
W5
• Net hydrologic flux = precipitation inputs minus stream outputs
• W5 - whole tree harvest during winter of 1983-1984
• All trees >5 cm dbh were removed (boles and branches)
• Purpose: evaluate impact of this more intensive management
practice on nutrient removals and site productivity
8. Net hydrologic flux (kg ha-1 yr-1)
Ca response to harvesting
0
Harvest
-3
-6
-9
-12
-15
-18
W6 (reference)
W5 (harvested)
-21
-24
1960
1970
1980
1990
2000
Water year (June 1)
Calcium data courtesy G.E. Likens
2010
9. Sources of uncertainty
Precipitation
Stream water
• Interpolation model
• Watershed area
• Collector undercatch
• Rating curve
• Chemical analysis
• Gaps in discharge
• Gaps in chemistry
• Chemical analysis
• Streamwater
interpolation model
12. Chemical analyses
• Precision describes
the variation in
replicate analysis of
the same sample
• At Hubbard Brook,
one sample of every
40 is analyzed four
times
Uncertainty = 1.0%
16. Gaps in streamflow
•
Randomly generate fake gaps
•
Fill the gaps based on regression from the
reference watershed
•
Calculate the different
between the predicted
and actual value
•
Repeat thousands of
times
•
More detail to follow
Uncertainty = 3.3%
17. What is Monte Carlo analysis?
Monte Carlo simulations use repeated, random
sampling to compute results.
1) Select a distribution to describe possible
values (not necessary to assume a
normal distribution)
2) Generate data from this distribution
3) Use the generated data as possible
values in the calculation to produce output
22. Conclusions
•
Uncertainty analysis can be used in
cases where replication is not possible
•
Monte Carlo is just one of many possible
approaches
•
There’s no such thing as a perfect
uncertainty analysis
•
It’s important to report how the
uncertainty was calculated
23. Acknowledgments
LTER Workshop Participants
Craig See
Brannon Barr
Gene Likens
Amey Bailey
Ian Halm
Nick Grant
Tammy Wooster
Brenda Minicucci
Funding was provided by the NSF and LTER Network
Office. Calcium data were obtained through funding
from the A.W. Mellon Foundation and the
NSF, including LTER and LTREB.
