1 / 27

Thomas R. Wentworth, Michael T. Lee, Mac Haupt, M. Forbes Boyle, Robert K. Peet 17 November 2010

Use of survival data for planted woody stems to refine a vegetation monitoring protocol for restoration sites. Thomas R. Wentworth, Michael T. Lee, Mac Haupt, M. Forbes Boyle, Robert K. Peet 17 November 2010. CVS-EEP Sampling Protocol. Optimized for field efficiency and repeatability.

jadyn
Download Presentation

Thomas R. Wentworth, Michael T. Lee, Mac Haupt, M. Forbes Boyle, Robert K. Peet 17 November 2010

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Use of survival data for planted woody stems to refine a vegetation monitoring protocol for restoration sites Thomas R. Wentworth, Michael T. Lee, Mac Haupt, M. Forbes Boyle, Robert K. Peet 17 November 2010

  2. CVS-EEP Sampling Protocol • Optimized for field efficiency and repeatability. • Resources include manuals, datasheets, and a data entry and reporting tool. • Scalable to meet future requirements. • Complies with US-FGDC National Vegetation Classification Standard.

  3. Current Monitoring Requirements

  4. Utility of the Collected Data? Stakeholder feedback: What is gained from measurements collected using the CVS-EEP Protocol? • Variables measured are mandated by EEP, not CVS. • EEP initially required multiple types of measurements because it was unclear which ones would be most useful in assessing stem success. • Available data from EEP Monitoring Firms will now allow CVS to assess the utility of each field measurement (e.g., ddh, height, DBH). • Which plant attributes should continue to be measured in the field? • Particular concerns were raised about ddh measurement.

  5. Current Status of CVS-EEP Inventory • Monitoring conducted for 5 years (2006-2010)

  6. Current Status of CVS-EEP Inventory • 785 unique plots monitored 2006-2010 • Range is 3-28 plots/project/year (median = 8)

  7. Overview of Woody Stem Database • As of October 2010, we have 30,544 individual records for planted woody stems. • 166 taxa, 127 species (18 oaks, 6 maples, etc.) • Median is 141 stems/project-year: • height data: 121 stems/project-year • ddh data: 98 stems/project-year • DBH data: 38 stems/project-year • three largest tallies for a project in a given year are 800, 617, and 460 planted stems.

  8. Modeling Rationale • Goal: take a stem and characterize its likelihood of surviving to the next year, • then compare model prediction with reality • among predictive variables available, which are essential and which are extraneous (particular focus on utility of ddh)? • independent variables allow model evaluation with and without ddh-related variables • benefits of such a modeling effort: • evaluating restoration plans, planting lists (including species, source, size, etc.) • being better able to identify projects on good or bad trajectories

  9. Modeling Approach • General approach was logistic regression using GLM, using survival to next year as dependent variable (1=survived, 0=died). • Independent variables incorporated into models: • ddh (ln transformed), RGR of ddh • height (ln transformed), RGR of height • year since planting (1-6) • vigor (1-4) • 1 = not expected to survive • 4 = excellent • source, for example: • ball and burlap (B) • potted (P) • bare root (R) • tubling (T)

  10. Subsetting Database • only planted woody stems • only those with ddh and height • minimum 3 years data (two years for RGR, third year to determine survival from year two) • no pseudoreplication (random selection of one three-year sequence) • withhold 25% of observations for validation (also random, for further work) • 2120 stems, of which 429 (20.2%) died in year three

  11. Raw Data

  12. Discussion • Models tested thus far are in the “fair” range, based on AUC criterion (0.69-0.79). • Height-only (AUC=0.69) and ddh-only (AUC=0.71) models perform similarly. • Combining height and ddh does not much improve model performance (AUC=0.71). • Complex (“everything”) model shows enhanced performance (AUC=0.79) over simple models. • Removing ddh from complex model results in little change in model performance (AUC=0.78). • Categorical-variables-only model performs reasonably well (AUC=0.76).

  13. Conclusions • Given our perspective (predicting stem survival to next year), height and ddh are comparable in utility. • Little benefit to including both variables. • Omitting ddh from complex model has relatively little impact. • In these models, it appears that ddh contributes little to prediction of stem survival, as long as we retain height measurement. However...

  14. Possible revision of measurements for planted woody stems

  15. Other Considerations • We should exercise caution in discarding variable for which we have such a solid existing data base. • ddh may yet prove to have benefits: • diameter (combined with height) allows for volume computation (d2h) • are there particular subsets of stems where ddh is a critical predictor of success (further work)? • What is the cost in our cost:benefit analysis for this particular variable?

  16. Thank You! http://cvs.bio.unc.edu/

More Related