Presentation Abstract
Many regional monitoring programs are designed to answer questions about the effectiveness of restoration or management actions. How do we evaluate regional effectiveness of restoration efforts from project scale studies? Regional decision-making depends on results from local-scale projects. Statistical meta-analysis provides a method for determining which restoration actions are the most effective. Meta-analysis is widely applied in other fields to evaluate the effectiveness of medical treatments and educational programs. We define an effectiveness study as one in which monitoring data are collected before and after a restoration action. Many examples of effectiveness monitoring studies exist in Puget Sound, including projects to reduce pollutants or contaminants in rivers, nearshore areas, and sediment. Other examples include projects designed to restore habitat such as riparian forest or estuarine areas. Project success may be measured in terms of improved water quality, reduced toxics, or increased fish use. Meta-analysis provides a framework for comparing across studies and across restoration endpoints that use different response variables to measure change over time. To make these comparisons, meta-analysis standardizes the response variable by calculating a unitless statistic from each study, called Cohen’s d. The change statistic is calculated as the difference before and after the restoration action divided by the pooled variance. Cohen’s d can be used to identify which treatments are most effective and which variables most responsive. We compared a diverse set of projects to evaluate which types of projects are most successful in terms of measurable change over time. Because meta-analysis depends on the data available for the study, we vetted our results with regional experts who collect and work with these data.
Session Title
Session S-05G: Beyond the Numbers - How Science Informs Decisions to Catalyze Action
Conference Track
Planning Assessment & Communication
Conference Name
Salish Sea Ecosystem Conference (2014 : Seattle, Wash.)
Document Type
Event
Start Date
1-5-2014 10:30 AM
End Date
1-5-2014 12:00 PM
Location
Room 6E
Genre/Form
conference proceedings; presentations (communicative events)
Contributing Repository
Digital content made available by University Archives, Heritage Resources, Western Libraries, Western Washington University.
Subjects – Topical (LCSH)
Restoration monitoring (Ecology)--Washington (State)--Puget Sound
Geographic Coverage
Salish Sea (B.C. and Wash.); Puget Sound (Wash.)
Rights
This resource is displayed for educational purposes only and may be subject to U.S. and international copyright laws. For more information about rights or obtaining copies of this resource, please contact University Archives, Heritage Resources, Western Libraries, Western Washington University, Bellingham, WA 98225-9103, USA (360-650-7534; heritage.resources@wwu.edu) and refer to the collection name and identifier. Any materials cited must be attributed to the Salish Sea Ecosystem Conference Records, University Archives, Heritage Resources, Western Libraries, Western Washington University.
Type
Text
Language
English
Format
application/pdf
Included in
Meta-Analysis of Project Effectiveness: Learning at the Regional Scale
Room 6E
Many regional monitoring programs are designed to answer questions about the effectiveness of restoration or management actions. How do we evaluate regional effectiveness of restoration efforts from project scale studies? Regional decision-making depends on results from local-scale projects. Statistical meta-analysis provides a method for determining which restoration actions are the most effective. Meta-analysis is widely applied in other fields to evaluate the effectiveness of medical treatments and educational programs. We define an effectiveness study as one in which monitoring data are collected before and after a restoration action. Many examples of effectiveness monitoring studies exist in Puget Sound, including projects to reduce pollutants or contaminants in rivers, nearshore areas, and sediment. Other examples include projects designed to restore habitat such as riparian forest or estuarine areas. Project success may be measured in terms of improved water quality, reduced toxics, or increased fish use. Meta-analysis provides a framework for comparing across studies and across restoration endpoints that use different response variables to measure change over time. To make these comparisons, meta-analysis standardizes the response variable by calculating a unitless statistic from each study, called Cohen’s d. The change statistic is calculated as the difference before and after the restoration action divided by the pooled variance. Cohen’s d can be used to identify which treatments are most effective and which variables most responsive. We compared a diverse set of projects to evaluate which types of projects are most successful in terms of measurable change over time. Because meta-analysis depends on the data available for the study, we vetted our results with regional experts who collect and work with these data.
