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We don’t want to sample too little and not detect an
important effect.
But collecting and analyzing samples is expensive,
so we don’t want to sample more intensively than
necessary.
Quantifying the relationship between sampling
intensity and minimum detectable differences can help
Better Monitoring through Uncertainty Analysis:
Optimize allocation of effort,
save time and money

Plan for the Workshop
Introductions: name, where you are from, what you
monitor, what are your concerns (too much, too little, how
can we help you today)
Presentations (5 minutes each, followed by discussion, no
more than 10 minutes total for each)
• Craig See: Taxonomy of Uncertainty, Results of the
QUEST Survey (we may not know what uncertainties are
important)
• Christine Laney: NEON examples
• Mark Green: rain gauge reduction example (HBR)
• Yang Yang: Hg monitoring, loons and fish (detectable
difference or rate of change)
• Ruth Yanai: NH roots, Calhoun soils (detectable
difference or rate of change)
• Alex Young: monitoring measurement uncertainty in the
FIA (confidence in inputs, waiting on sensitivity)
General Discussion: Did this help you? What do you still
need, and how can we help with that?

Current Practices in
Reporting Uncertainty
in Ecosystem Ecology
Ruth Yanai, State University of New York
Craig See, University of Minnesota
John Campbell, United States Forest Service

Taxonomy of Uncertainty

Survey Distribution
Listserves: Additional
Distribution:

Survey Demographics
135 respondents
90 sites
>13 Countries, 6 Continents
All Current LTER sites

Survey Methods
Respondents were asked:
• How they identify unusable values
• How they handle missing/unusable
values
• How they deal with values below
detection
• Whether these methods are
standardized at their site

Survey Methods
Identified major sources of uncertainty in:
For each source respondents were asked:
• If they report the source
• If they know how to report the source
• If they feel the source is important
Streams Precipitatio
n
Soils Biomass

(Campbell et al. 2016)

(Campbell et al. 2016)
Higher
confidence in
Ca losses
(streams) than
inputs
(rainfall) at
Hubbard
Brook

Importance to net Ca
flux at Hubbard Brook
Precip. chem. gaps
Precip. chem. analysis
Streamflow gaps
Stream chem. analysis
Watershed area
Stream flux calculation
Precip. catch
Precip interpolation model
Precip volume gaps
Gage/discharge model
Stream flux calculation
Gage/discharge model
Stream chem. analysis
Streamflow gaps
Precip. catch
Watershed area
Precip chem. analysis
Precip volume gaps
Precip interpolation model
Precip. chem. gaps
Survey importance
ranking

Summary
We did a survey
It can be hard to tell which sources of uncertainty
matter, so it’s important to formally check
This can save you time and money! You can do
more with less!

Thank You