Yanai hb 2013 bartlett budgets
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This document summarizes research on quantifying and accounting for uncertainty in ecosystem budget studies at Bartlett Experimental Forest. It discusses various sources of uncertainty including natural variability, spatial variability, temporal variability, measurement error, and model error. It provides examples of the coefficient of variation between samples for factors like calcium concentration, soil mass, and root mass. The research aims to answer questions about calcium accumulation as forests regrow after harvesting and how this compares to changes in soil calcium pools over time. The document emphasizes that quantifying uncertainty is important and allows for more confident interpretation of results.
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This document summarizes a study examining how soil respiration and litterfall in northern hardwood forests are controlled by nutrient availability. The study measured soil CO2 fluxes and collected fine litterfall from treatment plots with different nitrogen and phosphorus additions across 13 forest stands from 2008-2019. Preliminary results found that nitrogen addition lowered soil respiration in nitrogen-poor sites from 2011-2013. While apparent trends were observed, the authors note that statistical analysis is still needed and fertilization effects on litterfall may be more complicated. Next steps include further statistical analysis and exploring why nutrient additions did not significantly impact soil respiration rates.
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1) The document discusses nitrogen, phosphorus, and fine roots in young northern hardwood stands.
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(1) The MELNHE study examines nutrient co-limitation of forest productivity across 13 forest stands of different ages located in three sites. Fertilization experiments add nitrogen, phosphorus, or both to test for co-limitation.
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In a 3-year pilot study, researchers placed sugar maple wood stakes in forest stands to examine the effects of adding nitrogen, phosphorus, both nutrients, and a control on wood decay. Over 273 stakes were distributed across plots with different nutrient additions or a control at 3 sites. The stakes will be collected and analyzed to determine percent mass loss over the study period, with future analysis of nutrients, soil microbes, and their role in wood decay.
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Collecting fungal sporocarps allows researchers to survey the effects of nutrient additions on mycorrhizal fungi fitness over a larger area than soil samples alone. The study aims to determine how ectomycorrhizal sporocarp abundance and fungal community composition vary with nitrogen, phosphorus, and combined nutrient additions and between seasons. Over 4,000 sporocarps from 35 genera and 160 morphospecies were collected and will be identified using genetic techniques to analyze impacts of nutrient manipulations.
Does the fate of n depend on p availability in soil - samuel butler
This document discusses using an N15 experiment to determine if the fate of nitrogen (N) in soil depends on the availability of phosphorus (P). Previous research found that adding phosphorus suppresses resin-available nitrogen in soils. Additional studies showed that trees grew more in response to added phosphorus in mid-aged and old growth forests, but responded more to added nitrogen in younger forests. The proposed experiment would use the N15 isotope to trace nitrogen pathways through soil organic matter, plant roots, the microbial biomass, and mineral soil.
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This document summarizes a study estimating belowground carbon allocation in forests by measuring soil respiration and aboveground litter inputs over multiple years. The researchers found that nitrogen decreases soil respiration and likely belowground carbon allocation, while phosphorus decreases leaf litter production. Preliminary results suggest phosphorus may matter more for aboveground growth while nitrogen affects belowground processes to a greater degree.
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The document discusses a study examining how nutrient treatments affect germination rates and seedling survival of American beech and sugar maple. The study had three hypotheses: 1) germination and survival of maple would be highest with calcium addition; 2) germination and survival of beech would reflect limitation of nitrogen or phosphorus; and 3) survival may reflect nutritional value of seedlings. Methods involved counting germinants and seedlings in nutrient treatment plots over two years. Results showed beech germination increased with nitrogen and phosphorus addition, while maple decreased. Seedling survival decreased with nitrogen and phosphorus addition but increased with calcium addition, with no difference between nitrogen and phosphorus effects.
And you thought there were no earthworms in our forests
There were few earthworms found at Bartlett but many earthworms were found at Jeffers Brook. Plots at Jeffers Brook with added nitrogen had fewer earthworms, while plots with added calcium had more earthworms.
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Forest inventory crews were analyzed to determine measurement errors in tree diameter. The study found bias may exist due to penalties for observations more than one class off. While measurement error magnitude was identified, the most uncertain measurements may not be the most important to improve. Propagating the errors into forest inventory data product calculations like timber volume and carbon storage will show which measurements are most important to improve.
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The document discusses Pecan Project's uncertainty analysis for LTER ASM 2018. It analyzes parameter uncertainty and sensitivity, and outputs uncertainty through trait meta-analysis, iterative Bayesian calibration, and variance decomposition. Key findings include growth respiration and root water uptake being highly sensitive, and stomatal conductance variability differing between conifer and hardwood species and latitudes. The analysis aims to characterize, propagate, analyze, and reduce uncertainties.
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Distinguishing N and P addition from the air using imaging spectroscopy. Alex...
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Germinants: The next generation. Denise Rauschendorfer
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Mycorrhizal type and co-limitation of forest productivity. Noah Blumenthal
Mycorrhizal type and co-limitation of forest productivity. Noah Blumenthal
Grow with the changes: Forest succession and woody debris. Joe Nash
Grow with the changes: Forest succession and woody debris. Joe Nash
Litterfall and soil respiration fluxes in the MELNHE study. Thomas Mann
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N-P co-limitation in young northern hardwood forest. Tim Fahey
N-P co-limitation in young northern hardwood forest. Tim Fahey
Fine dining: root morphology, mycorrhizal colonization, and nutrient amendmen...
Fine dining: root morphology, mycorrhizal colonization, and nutrient amendmen...
Could co-limitation be caused by different nutrients limiting shoots and root...
Could co-limitation be caused by different nutrients limiting shoots and root...
Sugar maple leaf characteristics vary with depth in the crown and nutrient ad...
Sugar maple leaf characteristics vary with depth in the crown and nutrient ad...
The timing of senescence affects foliar p resorption dan hong
The timing of senescence affects foliar p resorption dan hong
Space wars the fight for light in the stands of bartlett sarah congress
Space wars the fight for light in the stands of bartlett sarah congress
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Does the fate of n depend on p availability in soil - samuel butler
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Belowground c allocation estimated as soil respiration minus aboveground litt...
Belowground c allocation estimated as soil respiration minus aboveground litt...
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Baby boomers are there any surviving germinants from the recent mast year -...
And you thought there were no earthworms in our forests
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Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
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Test Automation with generative AI and Open AI.
UiPath integration with generative AI
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Yanai hb 2013 bartlett budgets
- 1. Bartlett Budgets with Uncertainty Ruth D. Yanai Steven P. Hamburg, Joel D. Blum, Mary A. Arthur, Matthew A. Vadeboncoeur, Carrie R. Levine, KikangBae, Paul J. Lilly, Farrah R.
- 2. Ecosystem Budgets Had No Error Yanai (1992) Biogeochemistry
- 3. Hamburg et al. (2003) Ecosystems How much Ca is accumulating as forests develop after harvesting? The 9 MELNHE stands at Bartlett were established in 2004 to answer questions about sources of Ca to regrowing forests.
- 4. 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)
- 5. Yanai et al. (2012) Journal of Forestry
- 6. 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.
- 10. Uncertainty in Tissue Concentration of Tree Tissues CVs of Ca concentrations in tree tissues average 23% (bark), 19% (foliage), 18% (branches) and 22% (wood).
- 11. Uncertainty in Calcium Content of 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
- 12. Measurement Uncertainty Sampling Uncertainty Spatial 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
- 13. Quantitative Soil Pits 0.5 m2 frame
- 14. Excavate Forest Floor by horizon Mineral Soil by depth increment
- 15. Sieve and weigh in the field Subsample for laboratory analysis
- 16. In some studies, we excavate in the C horizon!
- 17. C1 C2 C4 C6 C8 C9 SoilMass(ton/ha) -150 -100 -50 0 Organic Mineral CV:30% 36% 5% 40% 20% 37% CV of Age: 9% 25% 9% CV of Site: 14% Soil Mass in Six Stands Three Pits per Stand Variation is high within stand, averaging 28% CV.
- 18. Variation among pits in soil concentrations was no better than soil mass CVs for soil Ca concentrations across pits within depth increments averaged 52% (exchangeable) 51% (apatite) 41% (total) Calcium Concentrations in Six Stands Three Pits per Stand
- 19. How much Ca is accumulating as forests develop after harvesting? How does this compare to changes in soil Ca pools over time?
- 20. How much Ca is accumulating as forests develop after harvesting? How does this compare to changes in soil Ca pools over time?
- 21. 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!
- 22. How much Ca is accumulating as forests develop after harvesting? How does this compare to changes in soil Ca pools over time?
- 24. Conclusions Quantifying uncertainty is not hard
- 25. Conclusions Quantifying uncertainty is possible and 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
- 26. Be a part of QUEST! •Find more information at:www.quantifyinguncertainty.org • Read papers, share sample code, stay updated with QUEST News • Email us at quantifyinguncertainty@gmail.com • Follow us on LinkedIn and Twitter: @QUEST_RCN QUANTIFYING UNCERTAINTY IN ECOSYSTEM STUDIES
- 27. 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.
- 28. C1 C2 C4 C6 C8 C9 RootMass(kg/ha) -400 -200 0 200 Organic Mineral CV:54% 33% 29% 19% 12% 25% CV of Age: 9% 29% 25% CV of Site: 20% Root Mass in Six Stands Three Pits per Stand
Editor's Notes
- For the last few years, I’ve been talking about uncertainty instead of talking in the Shoestring session. So, this year, I’m talking about uncertainty in Bartlett Budgets.I started at HB 30 years ago, in 1983. Where’s Waldo? Nora found me.
- Natural variability is what we most often report with sampling error. We can’t reduce natural variability, but we can describe it with more confidence.Model selection error, most often overlooked.
- Journal of Forestry, 2012.
- Rather than do anything analytical (like a Gaussian approach)
- RCN funded for 5 years starting this year. We just started Tweeting last week!