Use of survival data for planted woody stems to refine a vegetation monitoring protocol for restoration sitesThomas R. Wentworth, Michael T. Lee, Mac Haupt, M. Forbes Boyle, Robert K. Peet17 November 2010

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.

CVS-EEP Sampling Protocol

Current Monitoring Requirements

Current Requirements

Height or Type ddh(mm units)

height(cm units)

DBH(cm units)

< 137 cm tall mm precision cm precision no

≥ 137 cm and < 250 cm tall mm precision cm precision cm precision

≥ 250 cm and < 400 cm tall no 10 cm precision cm precision

≥ 400 cm tall no 50 cm precision cm precision

Live stake no cm precision if ≥ 137 cm tall, cm precision

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.

Current Status of CVS-EEP Inventory

• Monitoring conducted for 5 years (2006-2010)

Number of years Number of projects Project-years

5 4 20

4 20 80

3 16 48

2 26 52

1 17 17

Totals: 83 217

Current Status of CVS-EEP Inventory

Level Stems monitored Plot-years

Level 1 Planted 725

Level 2 Planted, Natural 1259

Level 3 Planted, Natural 4

Total 1988

• 785 unique plots monitored 2006-2010• Range is 3-28 plots/project/year (median = 8)

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.

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

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)

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

Raw Data

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).

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...

Possible revision of measurements for planted woody stems

Current Requirements

Height or Type ddh(mm units)

height(cm units)

DBH(cm units)

< 137 cm tall mm precision cm precision no

≥ 137 cm and < 250 cm tall mm precision cm precision cm precision

≥ 250 cm and < 400 cm tall no 10 cm precision cm precision

≥ 400 cm tall no 50 cm precision cm precision

Live stake no cm precision if ≥ 137 cm tall, cm precision

Possible Revised Requirements

Height or Type height(cm units)

DBH(cm units)

< 137 cm tall cm precision no

≥ 137 cm and < 250 cm tall cm precision cm precision

≥ 250 cm varied precision cm precision

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?

Thank You!Thank You!

http://cvs.bio.unc.edu/http://cvs.bio.unc.edu/