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