Error estimates for biometric measurements of NEP Mike Ryan and Rudy King USDA Forest Service Motivation: Understand how age, fertility, species change.

Slides:



Advertisements
Similar presentations
Multistage Sampling.
Advertisements

Estimating Uncertainty in Ecosystem Budgets Ruth Yanai, SUNY-ESF, Syracuse Ed Rastetter, Ecosystems Center, MBL Dusty Wood, SUNY-ESF, Syracuse.
Properties of Least Squares Regression Coefficients
USAID-CIFOR-ICRAF Project Assessing the Implications of Climate Change for USAID Forestry Programs (2009) 1 Carbon accounting: Field measurements Topic.
CHAPTER 14: Confidence Intervals: The Basics
The C budget of Japan: Ecosystem Model (TsuBiMo) Y. YAMAGATA and G. ALEXANDROV Climate Change Research Project, National Institute for Environmental Studies,
A MONASH UNIVERSITY PERSPECTIVE Musa Kilinc and Danielle Martin School of Geography and Environmental Science.
Carbon flux at the scale up field of GLBRC. The Eddy Covariance cluster towers Terenzio Zenone 1 Jiquan Chen 1 Burkhard Wilske 1 and Mike Deal 1 Kevin.
Objectives Look at Central Limit Theorem Sampling distribution of the mean.
Jun-Aug/annual mean T precip.sum (degC) (mm) / / / / / / / /810 Jun-Aug/annual mean.
Confidence Intervals Underlying model: Unknown parameter We know how to calculate point estimates E.g. regression analysis But different data would change.
Managing for Forest Carbon Storage. USDA Forest Service GTR NE-343.
Carbon Allocation in Forest Ecosystems Mike Ryan USDA Forest Service Rocky Mountain Research Station Creighton Litton California State University, Fullerton.
Chapter 4 Multiple Regression.
What Do You See? Message of the Day: Informed forest management decisions need information about current and projected conditions.
Timed. Transects Statistics indicate that overall species Richness varies only as a function of method and that there is no difference between sites.
Managing for Forest Carbon Storage. Inter-governmental Panel on Climate Change.
Using ranking and DCE data to value health states on the QALY scale using conventional and Bayesian methods Theresa Cain.
Sampling Distributions & Point Estimation. Questions What is a sampling distribution? What is the standard error? What is the principle of maximum likelihood?
Survey Methodology Sampling error and sample size EPID 626 Lecture 4.
Statistical Methods For Engineers ChE 477 (UO Lab) Larry Baxter & Stan Harding Brigham Young University.
Linear Regression Inference
Effects of Forest Management on Carbon Flux and Storage Jiquan Chen, Randy Jensen, Qinglin Li, Rachel Henderson & Jianye Xu University of Toledo & Missouri.
Fixed vs. Random Effects
Portfolio Management-Learning Objective
Is It True? At What Scale? What Is The Mechanism? Can It Be Managed? 150 Is The New 80: Continuing Carbon Storage In Aging Great Lakes Forests UMBS Forest.
Estimation of Statistical Parameters
Non-pollutant ecosystem stress impacts on defining a critical load Or why long-term critical loads estimates are likely too high Steven McNulty USDA Forest.
FORESTRY AND FOREST PRODUCTS Project Level Carbon Accounting Toolkit CSIRO Forestry and Forest Products Department of Forestry, Australian National University.
University of Ottawa - Bio 4118 – Applied Biostatistics © Antoine Morin and Scott Findlay 08/10/ :23 PM 1 Some basic statistical concepts, statistics.
A Statistical Analysis of Seedlings Planted in the Encampment Forest Association By: Tony Nixon.
Carbon Sequestration in Farm and Forest Ecosystems Sarah Hines April 2009
Methods Model. The TECOS model is used as forward model to simulate carbon transfer among the carbon pools (Fig.1). In the model, ecosystem is simplified.
Introduction: Globally, atmospheric concentrations of CO 2 are rising, and are expected to increase forest productivity and carbon storage. However, forest.
Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.
A Grand Plan for FIA’s role in a FS National Carbon Accounting System Linda S. Heath USDA Forest Service Northern Research Station, FIA Forest Carbon Accounting.
Primary Production in Terrestrial Systems Fundamentals of Ecosystem Ecology Class Cary Institute January 2013 Gary Lovett.
Variability observed in C flux. (Pine plantation, SW Fance) (Rimu Forest SW NZ) A foresters paradise Environmentalist paradise.
BPS - 3rd Ed. Chapter 131 Confidence Intervals: The Basics.
Understanding Your Data Set Statistics are used to describe data sets Gives us a metric in place of a graph What are some types of statistics used to describe.
Lecture PowerPoint Slides Basic Practice of Statistics 7 th Edition.
Estimates of Carbon Transfer coefficients Using Probabilistic Inversion for Three Forest Ecosystems in East China Li Zhang 1, Yiqi Luo 2, Guirui Yu 1,
What have we learned from forest tower flux data following disturbance? Brian Amiro, A. Barr, J. Barr, T.A. Black, R. Bracho, M. Brown, J. Chen, K. Clark,
WP coordinator meeting June 17/ WP3 progress report.
Statistics Who Spilled Math All Over My Biology?!.
Chronosequence of soil respiration in ChEAS sites (sub-topic of spatial upscaling of carbon measurement) Jim Tang Department of Forest Resources University.
1 Mean Analysis. 2 Introduction l If we use sample mean (the mean of the sample) to approximate the population mean (the mean of the population), errors.
Ch 8 Estimating with Confidence 8.1: Confidence Intervals.
1 Carbon Dioxide in the Atmosphere: CO 2 - Parametric Analysis - Differential Equations Chris P. Tsokos Department of Mathematics and Statistics University.
Ex St 801 Statistical Methods Inference about a Single Population Mean (CI)
Scott Saleska (U. of Arizona) Lucy Hutyra, Elizabeth Hammond-Pyle, Dan Curran, Bill Munger, Greg Santoni, Steve Wofsy (Harvard University) Kadson Oliveira.
Daily net carbon exchange as a mediator of heterotrophic soil respiration across two forest chronosequences Jared L. DeForest, Asko Noormets, and Jiquan.
Variability. The differences between individuals in a population Measured by calculations such as Standard Error, Confidence Interval and Sampling Error.
Figure 10. Improvement in landscape resolution that the new 250-meter MODIS (Moderate Resolution Imaging Spectroradiometer) measurement of gross primary.
Yueyang Jiang1, John B. Kim2, Christopher J
Variability.
a tree level perspective
More on Inference.
Measurement, Quantification and Analysis
Chapter 6 Inferences Based on a Single Sample: Estimation with Confidence Intervals Slides for Optional Sections Section 7.5 Finite Population Correction.
Conghe Song Department of Geography University of North Carolina
Ecosystem Respiration
Meta-analysis statistical models: Fixed-effect vs. random-effects
Statistics in Applied Science and Technology
More on Inference.
Bootstrap Confidence Intervals using Percentiles
Dennis Baldocchi & James Dorsey
CHAPTER 14: Confidence Intervals The Basics
What Do You See? Message of the Day: Informed forest management decisions need information about current and projected conditions.
ESTIMATION
Presentation transcript:

Error estimates for biometric measurements of NEP Mike Ryan and Rudy King USDA Forest Service Motivation: Understand how age, fertility, species change NEP; compare with Eddy Flux NEE

One Method of Estimating NEP from Biometric Measurements NEP = GPP – R H - R A = Annual change in ecosystem C storage =  [C DW (dead wood) + C W (live biomass)+ C S (soil) + C R (Roots) + C L (fine litter)]

Myths Variability is too large to do this accurately Compounding error will give very uncertain estimates of NEP However, most variability is local (scale of m 2 ) and not important for our questions (scale of ha or larger)

Two Cases Replicate Plots: Inference at a landscape or experiment scale Single site: Inference at a site scale (for example Ameriflux tower footprint)

Replicate Plots Sub-sample variance not included in replicate plot is negligible and does not have to be propagated (n/N ~ 0) : Poor sub-sampling will give larger variance among replicate plots, and good sub- sampling will give smaller variance among replicate plots, but you do not need to propagate any additional sub-sample variance.

Replicate Plots Cumulative Error: NEP calculated using plot-level estimates of all components contains cumulative (  variance + covariance) and sub-sample variance. Allometric equations? Working on it.

Results For Eucalypt plantation in Hawaii: CV for NEP ~40%. 95% CI = 20 – 30% of mean for n=6. Larger plots for biomass sampling would probably lower CV. 50% higher NEP in high fertility plots

Results CV for lodgepole pine chronosequence suggests similar variance for NEP: 5-25%Soil C 8-12%Forest Floor C 17-45%Dead Wood C 12-20%Biomass C

Single Site CI and SE will decrease with sqrt(sample size). Plots (with sub-sampling) can be used to minimize small-scale variability. Cumulative Error: NEP calculated using plot-level estimates of all components contains cumulative (  variance + covariance) and sub-sample variance.

What can improve precision? Replicate sample units Time (Precision of D increases linearly with number of years) Increased subsamples/replicate plot will decrease overall variance-- especially important with small n.

Needs for Ameriflux Commitment to measuring C stocks at all sites Commitment to measuring NEP based on changes in C stocks over some standard interval (5-10 years). Development of standard methodology for measuring C stocks and changes at sites. Research into efficient sampling methods.