Data quality and analysis strategy for monitoring post fire rehabilitation treatments
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DATA QUALITY and ANALYSIS Strategy for Monitoring Post-fire Rehabilitation Treatments. Troy Wirth and David Pyke USGS – Biological Resources Division Forest and Rangeland Ecosystem Science Center Corvallis, Oregon. U.S. Department of Interior U.S. Geological Survey.

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Data quality and analysis strategy for monitoring post fire rehabilitation treatments

DATA QUALITY and ANALYSISStrategy for Monitoring Post-fire Rehabilitation Treatments

Troy Wirth and David Pyke

USGS – Biological Resources Division

Forest and Rangeland Ecosystem Science Center

Corvallis, Oregon

U.S. Department of Interior

U.S. Geological Survey

Supported by USGS - BLM Interagency

Agreement #HAI040045


Data quality
Data Quality

  • Assess the ability of the data to determine treatment success

  • Ability to achieve high data quality will depend on variability

  • Calculate data quality variables

    • Confidence intervals (precision)

    • Alpha (p-value) & beta levels

    • Sample size estimation


Confidence intervals
Confidence Intervals

  • Construct a simple confidence interval around data to determine precision of estimate

  • The narrower the confidence interval, the more precise the estimate

  • Must specify the alpha level

  • Alpha level, or type I error is the probability of declaring there is no difference when there is.

  • Specifies the width of the confidence interval (1 – alpha)




Sample size estimation
Sample Size Estimation

  • Equations which estimate the number of samples required to meet your sampling objective

    • For single populations (quantitative objective)

      • Confidence level (Type I error rate)

      • Confidence interval width

    • For detecting difference between two populations

      • Confidence levels (Type I and II error rate)

      • Minimum detectable change

  • Iterative process


Single population sample size

= sample size required

= alpha level for specified

level of confidence

= standard deviation

= desired precision level

(absolute term)

Single Population Sample Size

  • Calculate sample size estimate for each parameter of interest

  • Result will depend on variability of data

  • For example, using equation for single population:


Single population sample size estimation example
Single Population Sample Size Estimation Example

  • Alpha = 0.1

  • X = 18.5 % cover of perennial grass

  • S = 4.9 % cover

  • d = (18.5*0.2) = 3.7

  • Initial sample = 5

Need 3 more samples (precision achieved 4.7)


Improving data quality
Improving Data Quality

  • In order to increase data quality (achieve sample size) you need to:

    • Reduce standard deviation (variability)

    • Increase the number of samples

  • In order to reduce sample size estimates without more samples you can:

    • Increase alpha (less confidence)

    • Increase precision or MDC (detect a larger difference)


Graphical analysis using confidence intervals
Graphical Analysis Using Confidence Intervals

  • Compare treatment results to quantitative objectives or control areas

  • Several types of analysis to fit your situation

  • Types of graphical analysis:

    • Comparison of treatment to quantitative standard

      • Seeded plants vs. quantitative standard

      • All plants at treatment plots change from time 1 to time 2

    • Comparison of two populations (seeded/unseeded)

      • Treatment vs. control

      • Treatment vs. control (change from time 1 to time 2)



Graphical analysis comparison to a standard
Graphical Analysis(comparison to a standard)

  • Specify quantitative objective

  • Determine desired alpha level and precision

  • Collect data at treatment plots

  • Graph mean with confidence interval of desired width (typically 80 or 90%)

  • Graphically compare to quantitative standard to determine which situation exists

  • Need mean, standard deviation, and n

  • Use ES&R Equation spreadsheet to help


Graphical analysis comparison to a quantitative objective
Graphical Analysis(comparison to a quantitative objective)

  • Quantitative objective: 5 plants/m2

  • Alpha level: 0.1 (90% Confidence Interval)

  • X = 7 plants/m2

  • S = 1.3 plants/m2

  • N = 5






Graphical analysis treatment v control
Graphical Analysis(Treatment v. Control)

  • Confidence interval of the difference between the treatment and control

  • Uses the difference between the means of two treatments and constructs a single CI using the variance from both estimates (SE)

  • A mean of 0 represents no difference between the two treatments

  • Express the quantitative objective as an absolute value or as a multiple of the control.

  • Use the mean and CI to make a determination of treatment effect


Graphical analysis treatment v control1
Graphical Analysis(Treatment v. Control)

  • Using the ESR monitoring spreadsheet

    • Specify the desired alpha level

    • Enter the mean, standard deviation, and N from the data collected at the treatment and control plots

    • Specify the level of quantitative objective (multiple of control or absolute difference)

  • Make interpretation based on graphical analysis of the CI of the difference between the two treatments


Graphical analysis treatment v control2
Graphical Analysis(Treatment v. Control)

  • Quantitative objective: twice that of control plot (2) – note that because it is a CI of difference the original amount is subtracted from quantitative objective

  • Alpha level: 0.1 (90% Confidence Interval)

  • Control

  • X = 2.0

  • S = 0.5

  • N = 7

  • Treatment

  • X = 4.9

  • S = 0.9

  • N = 7



Treatment vs control1
Treatment vs. Control

B: The difference of the mean between is above the level of ecological significance, but the lower confidence limit for the difference is below the level of ecological significance.



Treatment vs. Control

D: The mean and confidence interval of the difference is below the level of ecological significance. Conclude that there is no ecologically significant difference between the control and treatment plots.



Treatment vs. Control

F: The mean of the difference is above 0, but the lower confidence limit is below 0 and the upper confidence limit is below the level of ecological significance.



Graphical analysis treatment vs control t2 t1
Graphical Analysis(Treatment vs. Control T2-T1)

  • Confidence interval of the difference in change between two time periods between treatment and control

  • Uses the difference between the change in the means of treatment and control and constructs a single CI using the variance from both estimates (SE)

  • A mean of 0 represents no difference in change between the two treatments

  • Express the quantitative objective as an absolute value or as a multiple of the control.

  • Use the mean and CI to make a determination of treatment effect


Appears that there is a difference in year three when there actually was not.

Accounting for initial difference in the degree of change


Change in treatment vs control
Change in Treatment vs. Control actually was not.

B: The difference of the mean between is above the level of ecological significance, but the lower confidence limit for the difference is below the level of ecological significance.


Graphical analysis treatment at two time periods t2 t1
Graphical Analysis actually was not.(Treatment at two time periods, T2-T1)

  • Confidence interval of the change between the two time periods

  • Treats the two time periods as paired, reducing variability

  • A mean of 0 represents no change between the two time periods

  • Express the quantitative objective as the desired change between the two different time periods

  • Use the mean and CI compared to the quantitative objective to make a determination of success


Reporting actually was not.

Paste graphs directly into reports and describe quantitative results e.g.

Perennial Grass Density

The density of perennial grasses is significantly greater in the treatment plots as compared to the control plots. We are 90% confident that the difference is between 1.06 to 4.54 plants/m2 greater than the control plots with a mean of 2.8 plants/m2)


Reporting
Reporting actually was not.

  • Link reports back to quantitative objectives

  • Re-assess whether objectives were reasonable and possible reasons for success and failure.

  • Make recommendations for future improvements to implementation and monitoring


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