EEP wants to do a better job creating natural ecosystems. CVS provides improved reference data, target design, monitoring, and data management and analysis

CVS is developing tools and data sources to facilitate planning of restoration activities and to evaluate their success. New data sources and tools are being designed Suggestions for new tools and increased functionality are.

Document reference conditions Derive restoration targets Design site-specific restoration plan Implement the plan Monitor change and assess success Employ adaptive management

Simple goal – Deliver composition target based on the vegetation type most appropriate for the site and region. Sophisticated goal – Automated system that uses site information and reference plot data to predict vegetation composition.

1. Crude species lists 2. Plot database linked to the National Vegetation Classification and the Natural Heritage Program documentation 3. High-resolution community classification with quantitative descriptions. 4. Dynamically generated targets

Mountain Vegetation Montane upland forests Montane open upland vegetation Montane alluvial wetland vegetation Montane nonalluvial wetland vegetation Piedmont Vegetation Piedmont upland forests Piedmont open upland vegetation Piedmont alluvial wetland vegetation Piedmont nonalluvial wetland vegetation Coastal Plain Vegetation Coastal Plain upland forests Coastal Plain upland open & woodland vegetation Coastal Plain alluvial wetland vegetation Coastal Plain nonalluvial wetland vegetation Coastal Fringe Vegetation Maritime upland forests & shrublands Maritime open upland vegetation Maritime nontidal wetland vegetation Tidal wetland vegetation

Targeted communities  Mountain bogs  Piedmont alluvial forests Pulse events with regional focus

Xeric barrens & Subxeric uplands: Longleaf – turkey oak woodlands on entisols 9 Types 13 Types

Flatwoods: Longleaf woodlands of spodosols 5 types

Silty uplands: Longleaf woodlands on well-drained ultisols 12 types

Savannas and seeps: Longleaf woodlands on moist alfisols 13 types

Longleaf pine – feasibility study Few longleaf pine sites remain in “original” condition. Restoration targets must be extrapolated from a limited number of reference stands.

Dataset: -188 plots across fall-line sandhills of NC, SC, & GA - All sites contained near-natural, fire- maintained groundlayer vegetation - Soil attributes included for both the A and B horizon: sand, silt, clay, Ca, Mg, K, P, S, Mn, Na, Cu, Zn, Fe, BD, pH, organic content, CEC, BS.

Step 1. Classification. Developed a classification of the major vegetation types of the ecoregion. Used cluster analysis with a matrix of 188 plots x 619 species. Vegetation types were seen to be differentiated with respect to soil texture, moisture, nutrient status, & geography.

Step 2. Build model. - Forward selection with linear discriminant analysis identified predictor variables. - Critical variables were Latitude, Manganese, Phosphorus, Clay, Longitude. - 75% of plots correctly identified to vegetation series. Typically 75% of plots within a series were correctly classified to community type.

Step 3. Select species. 1.Generate a list of all species in type (species pool) with frequency, mean cover values, and mean richness. 2.Randomly order the list 3.Compare species frequency to random number between 0 & 1, and if the random number is less than the proportion of plots the species is selected. Continue until the number in list of selected species equals the number predicted.

Summary of overall strategy: Identify biogeographic region and obtain predictive models. Select pool of candidate species for a specific site based on range information. Divide restoration site into environmentally homogenous areas, stratifying by topography and soil. Use models to select species number and composition.