1. Using Geographic Information Systems in Predicting Reference Communities for Landscape Scale Restoration by ESRA OZDENEROL, PhD University of Memphis Department of Earth Sciences

2. A nonprofit, community-based organization that exists to help communities restore, manage and learn about their natural environment through volunteer involvement.

4. Oak Savanna

7. The Vision …

13. BIG RIVERS PARTNERSHIP PROJECT AREA

14. Collaborators: Cynthia Lane, Ph.D. Greg Noe, Ph.D. Bart Richardson

15. Methods: 1. Land Cover Classification data 2. Environmental data 3. Data categorized 4. Statistical Analyses 5. Predictive Model 6. Filters

16. 1. LAND COVER CLASSIFICATION DATA MLCCS Hierarchical Classification System:  Cultural or Natural/Semi-natural  Five level system beginning with vegetation type or dominant cover type  % impervious estimated for cultural cover types  Modifiers for adding information for specific polygons

18. 2. ENVIRONMENTAL DATA Data obtained for each HQN and Restorable site (polygon):  Soil Texture and Drainage  Slope and Aspect  Shade

19.  Soil Drainage and Texture  USDA-NRCS, Official Soil Series description  Soil characteristics commonly affecting the establishment and persistence of perennial native vegetation  Predominant drainage and texture in upper horizon Soil

20. Soils Drainage: Drainage class: 1. = Excessively drained, Somewhat Excessively drained 2. = Well drained, Moderately well drained 3. = Somewhat Poorly Drained, Poorly drained, Very poorly drained Drainage class diversity

21. Soil Texture: S = Sand L = Loam O = Organic Texture class diversity

22.  U.S.G.S. 30 meter digital elevation model  Converted to grid format using ArcView Spatial Analyst  Slope - mean and standard deviation for each site Slope and Aspect:

23. Aspect:  Mean aspect & angular dispersion (aspect variability)  Mean aspect converted to sine and cosine using circular statistics

24.  Shade layer generated using DEM and ArcView Spatial Analyst  Hottest day and time of day modeled Shade:

25.  High Quality Native Community  Disturbed  Unsuitable  Unknown  Restorable 3. SITES CATEGORIZED

26. Disturbed = Disturbed = soils classified as “urban lands”, “udorthents”, and “gravel pits”; >75% impervious cover Unknown = Unknown = no soils data or aspect Unsuitable = Unsuitable = wetlands; >90% impervious

27. High Quality Native Community# sites  Oak Forest (mesic, dry) 228  Maple Basswood Forest 44  Aspen Forest (temporarily flooded) 14  Floodplain Forest (silver maple) 212  Lowland Hardwood Forest 48  White Pine Hardwood Forest 2  Oak Woodland Brushland 120  Mesic Prairie 8  Dry Prairie (barrens, bedrock bluff, sand gravel) 58  Wet Meadow (shrub) 11  Dry Oak Savanna (sand gravel) 12  Mesic Oak Savanna 23

28. Restorable Cover Type # polys #ha  Sparse trees + turf/grassland 647 3563  Agricultural crops 142 1835  Turf/grassland 481 1556  Deciduous trees 278 1437  Boxelder/Green ash forest 263 690  Mixed woodland, disturbed 187 397  Mixed coniferous & deciduous 28 147  Coniferous trees 55 144

30. 3. STATISTICAL ANALYSES 1. Tested relationship between High Quality Native Communities and environmental characteristics 2. Applied results of analysis to Restorable polygons to predict target community

31. 3. STATISTICAL ANALYSES  Factor analysis  Linear discriminant function analysis

32. High Quality Native Community  Oak Forest (mesic, dry)  Maple Basswood Forest  Aspen Forest (temporarily flooded)  Floodplain Forest (silver maple)  Lowland Hardwood Forest  White Pine Hardwood Forest  Oak Woodland Brushland  Mesic Prairie  Dry Prairie (barrens, bedrock bluff, sand gravel)  Wet Meadow (shrub)  Dry Oak Savanna (sand gravel)  Mesic Oak Savanna 21 full, 12 aggregated

33. RESULTS – Full Analysis  All environmental variables significantly different (Wilks’ Lamda, P<.00001)  94.8% of variation explained  6 discriminant functions statistically significant  9 communities reliably predicted >50%

34. RESULTS – Aggregated Analysis  All environmental variables significantly different (Wilks’ Lamda, P<.00001), except shade  98.3% of variation explained  6 discriminant functions statistically significant  6 communities reliably predicted >50%

35. Predicted Native Communities

36. Undifferentiable communities:  Oak forest  Maple basswood forest  Oak woodland brushland  Mesic prairie  Aspen forest  Lowland hardwood forest

37. 3. FILTERS  Cost, Ease of restoration  Rare native community  Landscape – Patch size & Connectivity

38. Cost Filter:  Ease of Conversion  Patch size (polygon size)  % impervious surface  Access (slope)

39. Conversion matrix:

41. Patch Size & Connectivity Filter:  Straight line allocation  Existing native communities used as targets  Undifferentiable sites converted to nearest native community

44. Prioritize restoration sites:  Target rare community for restoration  Communities reliably predicted from full analysis

46. SUMMARY:  9 full, 6 aggregated communities reliably predicted  Target refined using cost and landscape filters  Method can be used to prioritize sites based on project goals

47. Acknowledgements:  Legislative Commission on Minnesota Resources  Mississippi National River and Recreation Area  Minnesota Department of Natural Resources, Conservation Partners Grant