Working together to restore North Carolina’s natural communities March 3, 2010 We shall: Explain our vision, Summarize our accomplishments, Describe feedback we have received, Present opportunities we.
Download ReportTranscript Working together to restore North Carolina’s natural communities March 3, 2010 We shall: Explain our vision, Summarize our accomplishments, Describe feedback we have received, Present opportunities we.
Working together to restore North Carolina’s natural communities March 3, 2010 We shall: Explain our vision, Summarize our accomplishments, Describe feedback we have received, Present opportunities we have built, Solicit guidance on future directions. Multi-institutional collaborative program. Established in 1988 to document the composition and status of natural vegetation of the Carolinas. Provides data, data services, software development and analysis to EEP and monitoring firms. * Document natural conditions with high-quality reference plots. 2. * Derive site-specific restoration targets. 3. * Design site-specific restoration plan. 4. Implement the plan. 5. * Monitor change and trajectory toward success. 6. * Employ adaptive management as needed. 7. * Document the results. 1. (* = Major CVS role) Detailed, justifiable, & efficient generation of restoration targets. State-of-the-art predictions that satisfy the most stringent current and future restoration guidelines. Tracking of individual trees demonstrates compliance with US-ACE requirements. Greater plant success through selection based on past species performance and site characteristics. Early detection of likely failure so that corrective action can be taken. Robust and documented planning that should be resistant to future litigation by diverse interest groups. Optimized data collection procedures. Consistency between years & monitoring firms. Automated analysis, QA/QC, report generation, & evaluation of plans. Improved ease & efficacy of plant selection. Early detection of project problems or success. A methodology that is scalable to more robust and challenging regulation. 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. Then print datasheets… Quickly find stems with the printed map. Baseline data preprinted Efficient format, pre-populated fields, flagged errors, picklists of valid options, etc. Table 7 Report: A plot-by-plot summary of the most recent data with a summary for each year Highlights plot or year failing to meet requirements! Stem Disturbance LS=Live Stake P =Planted T =Total Vegetation This page shows 2 of 13 available reports Field and database training for practitioners. Feedback leads to improvement in sampling protocol efficiency as well as database usability and functionality. > 6000 High-quality reference sites 280 Natural community types with >= 4 plots 495 Natural community types with >= 1 plot Available data include - Species frequency - Species importance - Woody stem diameters - Site data - Soil data - Maps of occurrences - Descriptions You asked -- 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). Opp 1: Better, cheaper, more defendable restoration targets Phase 1 – Web tool for documenting reference conditions by NVC types (partially implemented). Phase 2 – Constrain NVC types and plots by geographic region (in development). Phase 3 – Web tool for predicting a target from site conditions (prototype complete -- future development). http://cvs.bio.unc.edu Physiognomic Group http://cvs.bio.unc.edu/vegetation.htm Vegetation types classified Critical environmental fields defined Restoration sites chosen and environmental data collected Restoration sites matched to vegetation type Planting list generated from vegetation type species list Data flow for identifying target community and planting list Internal decision tree showing how site data predict community Prototype tool predicts target vegetation type based on site data. Planting lists could be automatically generated from community data. Alternative to searching out reference areas – just look them up in minutes in your office. Greater likelihood of selecting species that will grow well at your site. More effective restoration – which is better for our state and better for you. Better assessment & prediction of change, success, and failure over time. Automatic generation of reports for US-ACE. How is my project doing? What is my risk of failure? How did my project work out? What am I getting into? CVS will develop a tool that draws on multiple datasets to aid in selection and evaluation of species for planting designs. This will help: Design firms in selecting planting materials, EEP in evaluating proposed planting materials, Growers to better predict demand. Dataset 1: Community composition, as documented in the Vegetation of the Carolinas database, Dataset 2: Geographic distribution, as documented in the SE Floristic Atlas, Database 3: Species environmental tolerance, as documented in the CVS reference plot database. Examine the success of material (species, source, size) used in earlier EEP projects on similar sites. Past success can be deduced from CVSmanaged data from monitoring studies. How many monitoring plots are needed? Which plant attributes should continue to be measured in the field? How often should plots be monitored? Should there be a mixed monitoring strategy for tracking stems and observing site-wide variation? Larger ddh and taller height both resulted in higher survival of stems. We built a model to predict survival based on ddh and height. The model did little better than a model based on height or ddh alone. DBH does not predict stem survival until stems reach 5 cm. Height or Type Current Requirements DDH Height (mm units) (cm units) DBH (cm units) 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 < 137 cm tall Possible Revised Requirements Height DBH Height or Type (cm units) (cm units) < 137 cm tall cm precision no ≥ 137 cm and < 250 cm tall cm precision cm precision ≥ 250 cm maybe?? cm precision Data being processed by CVS could be used in various ways to make restoration and monitoring more efficient and effective. We could facilitate and enhance this process with regular meetings of CVS with EEP, US-ACE and ACEC firms. CVS could reserve a portion of analysis time for responding to issues raised at those meetings. Potential return on investment: Cost savings > $200K/yr … if continued. CVS is now prepared to develop state-of-theart tools that address key components of the CVS-EEP vision. Tools currently available and those under development would take advantage of the results of our past CVS-EEP collaboration and allow EEP and its monitoring firms to do a significantly better job more quickly with less risk and at substantially less cost. If EEP does not pursue these opportunities at this time, key CVS staff will not be retained and the described opportunities will likely vanish. Loss of the CVS-EEP partnership would result in loss of data management & report generation. Moreover, it would significantly increase costs for both EEP and ACEC firms. Continuation of the CVS-EEP collaboration would ensure ongoing maintenance of the EEP-CVS databases for monitoring and reference data and tools for their effective use.