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DATA QUALITY and ANALYSIS Strategy for Monitoring Post-fire Rehabilitation Treatments

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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

- 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

- 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)

From Elzinga et al. 1998

- 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

- For single populations (quantitative objective)
- Iterative process

= sample size required

= alpha level for specified

level of confidence

= standard deviation

= desired precision level

(absolute term)

- Calculate sample size estimate for each parameter of interest
- Result will depend on variability of data
- For example, using equation for single population:

- 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)

- 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)

- 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)

- Comparison of treatment to quantitative 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

- Quantitative objective: 5 plants/m2
- Alpha level: 0.1 (90% Confidence Interval)
- X = 7 plants/m2
- S = 1.3 plants/m2
- N = 5

Comparison to a Quantitative Objective

Comparison to a Quantitative Objective

Comparison to a Quantitative Objective

Comparison to a Quantitative Objective

- 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

- 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

- 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

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

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

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.

- 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

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.

- 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

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)

- 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