Yanai hb 2013 bartlett budgets
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Transcript of Yanai hb 2013 bartlett budgets
Bartlett Budgets with Uncertainty
Ruth D. Yanai Steven P. Hamburg, Joel D. Blum, Mary A. Arthur,
Matthew A. Vadeboncoeur, Carrie R. Levine, Kikang Bae, Paul J. Lilly, Farrah R. Fatemi
Ecosystem Budgets Had No Error
Yanai (1992) Biogeochemis
try
Hamburg et al. (2003) Ecosystems
How much Ca is accumulating as forests develop after harvesting?How does this compare to changes in soil Ca pools over time?
The 9 MELNHE stands at Bartlett were established in 2004 to answer questions about sources of Ca to regrowing forests.
UNCERTAINTY
Natural Variability
Spatial Variability
Temporal Variability
Knowledge Uncertainty
Measurement Error
Model Error
Types of uncertainty commonly encountered in ecosystem studies
Adapted from Harmon et al. (2007)
Yanai et al. (2012) Journal of Forestry
Monte Carlo
Simulation
Yanai, Battles, Richardson, Rastetter, Wood, and Blodgett (2010) Ecosystems
Monte Carlo simulations use random sampling of the distribution of the inputs to a calculation. After many iterations, the distribution of the output is analyzed.
Uncertainty in Tissue Concentrationof Tree Tissues
CVs of Ca concentrations in tree tissues average 23% (bark), 19% (foliage), 18% (branches) and 22% (wood).
Uncertainty in Calcium Contentof Aboveground Biomass
From young to mid: 1238 ± 396 kg Ca/ha From mid to old: 3909 ± 892 kg Ca/ha
Omitting C3, which was not as young as we thought!
Measurement Uncertainty Sampling UncertaintySpatial Variability
Model Uncertainty y Error within models Error between models
Excludes areas not sampled: rock area 5%, stem area: 1%
Measurement uncertainty and spatial variation make it difficult to estimate soil carbon and nutrient contents precisely
Quantitative Soil Pits
0.5 m2 frame
Excavate Forest Floor by horizon
Mineral Soil by depth increment
Sieve and weigh in the field
Subsample for laboratory analysis
In some studies, we excavate in the C horizon!
C1 C2 C4 C6 C8 C9
So
il Ma
ss (ton/ha
)
-150
-100
-50
0
OrganicMineral
CV:30% 36% 5% 40% 20% 37%CV of Age: 9% 25% 9%
CV of Site: 14%
Soil Mass in Six StandsThree Pits per Stand
Variation is high within stand, averaging 28% CV.
Variation among pits in soil concentrations was no better than soil mass
CVs for soil Ca concentrations across pitswithin depth incrementsaveraged 52% (exchangeable)51% (apatite)41% (total)
Calcium Concentrations in Six StandsThree Pits per Stand
How much Ca is accumulating as forests develop after harvesting?How does this compare to changes in soil Ca pools over time?
How much Ca is accumulating as forests develop after harvesting?How does this compare to changes in soil Ca pools over time?
How much Ca is accumulating as forests develop after harvesting?How does this compare to changes in soil Ca pools over time?
Regrowing forests can weather apatite!
How much Ca is accumulating as forests develop after harvesting?How does this compare to changes in soil Ca pools over time?
Conclusions
Quantifying uncertainty is not hard
Conclusions
Quantifying uncertainty is possibleand allows confidence to be reported.It can also guide improvements.
Measurement errors are small for above-ground biomass. Spatial variation is high.
Interpretation is also prone to error!
Quantifying uncertainty is possible
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QUANTIFYING UNCERTAINTY IN ECOSYSTEM STUDIES
Levine et al. (2012) SSSAJ
Because soils are so variable spatially, collecting more samples may be more important than maximizing the accuracy of each sample.
C1 C2 C4 C6 C8 C9
Ro
ot M
ass (kg/ha
)
-400
-200
0
200OrganicMineral
CV:54% 33% 29% 19% 12% 25%CV of Age: 9% 29% 25%
CV of Site: 20%
Root Mass in Six StandsThree Pits per Stand