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OBJECTIVES 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. Supported by USGS - BLM Interagency

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OBJECTIVES Strategy for Monitoring Post-fire Rehabilitation Treatments


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    1. OBJECTIVESStrategy 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

    2. Monitoring Approach • Objectives • Stratification • Control Plots • Random Sampling • Data Quality • Statistical Analysis • Repeatable, objective field methods using Rangeland Monitoring Database

    3. Objectives • Objectives are statements of conditions that result in a successful treatment • Objectives should be clearly defined prior to ESR treatment implementation • Quantitative • Measurable • Attain, increase, decrease a level of a certain parameter • Compare objective to monitoring data to make a determination of success

    4. Objectives • Two types of Objectives: Management and Sampling • Management Objectives - set specific goals for a parameter or ecological condition • Sampling Objectives - set specific goals for the measurement of the parameter or ecological condition

    5. Management Objectives • Components of a management objective (From Elzinga et al. 1998) • Action – attain, decrease, increase • Attribute – cover, density, etc. • Species or Habitat Indicator – ARTR2 or shrub height • Location – where the objective applies • Status – numerical goal • Time Frame – when the objective will be met

    6. Example Management Objective • Management Objective: Attain (action) a density (attribute) of perennial native seeded grasses (species or indicator) in monitoring unit 1 (location) of at least 2.5 plants/m2(status) by the end of the third growing season (time frame) following treatment. • Management Objective: Attain 35% cover of native perennial grasses in monitoring unit 2 by the end of the third growing season.

    7. Sampling Objectives • Companion sampling objectives are written for each management objective • Two types: Target and change objectives depending on the “action” of the management objective • Target: set quantitative objective • Change: change from one time period to another or difference rather than a quantified target, expressed as an absolute value or a percentage of a measured value (control)

    8. Sampling Objectives • Target objectives consist of: • Confidence Level: how confident are you that the true mean lies within the confidence interval • Confidence Interval: interval around the estimated mean where you believe the true mean lies • Precision = ½ confidence interval width

    9. Example Target Sampling Objectives • Obtain estimates of mean number of plants/m2 with 90% confidence (confidence level) that are within ± 20% (confidence interval width or precision) of the estimated density (α = 0.1, d = 0.2) • Estimate the percent cover of bluebunch wheatgrass with 95% confidence that is within ± 25% of the estimated cover (α = 0.05, d = 0.25)

    10. Using Target Sampling Objectives

    11. Sampling Change Objectives • Change objectives consist of: • Acceptable Type I and Type II error • Type I (false-change error) • Type II (missed-change error) • Minimum detectable change • The change that can be detected by the monitoring effort. Depends on variability in the monitoring data.

    12. Example Sampling Change Objective • Change Sampling Objective: Detect a change (positive or negative) of 20% (MDC) in the mean number of plants/m2 with 90% confidence (Type I). We are also willing to take a 20% chance (Type II) that we will conclude there is no change when there was a change.

    13. Using Change Objectives