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MOFEP Data to Adds to Other Studies -- Coarse Woody Debris Estimation -- Landscape-scale Forest Planning -- Cavity Tree Estimation orth entral Research.

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Presentation on theme: "MOFEP Data to Adds to Other Studies -- Coarse Woody Debris Estimation -- Landscape-scale Forest Planning -- Cavity Tree Estimation orth entral Research."— Presentation transcript:

1 MOFEP Data to Adds to Other Studies -- Coarse Woody Debris Estimation -- Landscape-scale Forest Planning -- Cavity Tree Estimation orth entral Research Station Stephen R. Shifley Zaofei Fan Frank R. Thompson III William Dijak David R. Larsen Josh Millspaugh Michael Larson Martin Spetich John Kabrick Randy Jensen Brian Brookshire, Laura Brookshire

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5 Harvest Patterns Year 10 Even-aged harvesting MOFEP sites 7 and 8

6 Harvest Patterns Year 20 Even-aged harvesting MOFEP sites 7 and 8

7 Harvest Patterns Year 30 Even-aged harvesting MOFEP sites 7 and 8

8 Harvest Patterns Year 40 Even-aged harvesting MOFEP sites 7 and 8

9 Harvest Patterns Year 50 Even-aged harvesting MOFEP sites 7 and 8

10 Harvest Patterns Year 60 Even-aged harvesting MOFEP sites 7 and 8

11 Harvest Patterns Year 70 Even-aged harvesting MOFEP sites 7 and 8

12 Harvest Patterns Year 80 Even-aged harvesting MOFEP sites 7 and 8

13 Harvest Patterns Year 90 Even-aged harvesting MOFEP sites 7 and 8

14 Harvest Patterns Year 100 Even-aged harvesting MOFEP sites 7 and 8

15 Wind and Weather Disturbance Wind/weather disturbances creating crown openings affecting 0.1 to 2.5 ha per event have a return interval of approximately 670 years Tornados are a factor, but one we could not simulate spatially with LANDIS

16 Fires once were common -- Every 5-10 years in 1800’s With active suppression the mean fire return interval is now about 300 years. -- Crown fires are rare -- Prescribed fires can be simulated

17 Initial Age Classes MOFEP sites 7 and yrs yrs yrs yrs yrs yrs > 180 yrs

18 Age Classes after 10-year simulation MOFEP sites 7 and 8 Even-aged harvesting yrs yrs yrs yrs yrs yrs > 180 yrs

19 Age Classes after 20-year simulation MOFEP sites 7 and 8 Even-aged harvesting yrs yrs yrs yrs yrs yrs > 180 yrs

20 Age Classes after 40-year simulation MOFEP sites 7 and 8 Even-aged harvesting yrs yrs yrs yrs yrs yrs > 180 yrs

21 Age Classes after 60-year simulation MOFEP sites 7 and 8 Even-aged harvesting yrs yrs yrs yrs yrs yrs > 180 yrs

22 Age Classes after 80-year simulation MOFEP sites 7 and 8 Even-aged harvesting yrs yrs yrs yrs yrs yrs > 180 yrs

23 Age Classes after 100-year simulation MOFEP sites 7 and 8 Even-aged harvesting yrs yrs yrs yrs yrs yrs > 180 yrs

24 Initial Age Classes MOFEP sites 7 and yrs yrs yrs yrs yrs yrs > 180 yrs

25 Initial Age Classes MOFEP sites 7 and yrs yrs yrs yrs yrs yrs > 180 yrs

26 Age Classes after 10-year simulation MOFEP sites 7 and 8 Uneven-aged harvesting yrs yrs yrs yrs yrs yrs > 180 yrs

27 Age Classes after 20-year simulation MOFEP sites 7 and 8 Uneven-aged harvesting yrs yrs yrs yrs yrs yrs > 180 yrs

28 Age Classes after 40-year simulation MOFEP sites 7 and 8 Uneven-aged harvesting yrs yrs yrs yrs yrs yrs > 180 yrs

29 Age Classes after 60-year simulation MOFEP sites 7 and 8 Uneven-aged harvesting yrs yrs yrs yrs yrs yrs > 180 yrs

30 Age Classes after 80-year simulation MOFEP sites 7 and 8 Uneven-aged harvesting yrs yrs yrs yrs yrs yrs > 180 yrs

31 Age Classes after 100-year simulation MOFEP sites 7 and 8 Uneven-aged harvesting yrs yrs yrs yrs yrs yrs > 180 yrs

32 Age Classes after 20-year simulation MOFEP sites 7 and year simulation No harvest yrs yrs yrs yrs yrs yrs > 180 yrs

33 Age Classes after 40-year simulation MOFEP sites 7 and year simulation No harvest yrs yrs yrs yrs yrs yrs > 180 yrs

34 Age Classes after 60-year simulation MOFEP sites 7 and year simulation No harvest yrs yrs yrs yrs yrs yrs > 180 yrs

35 Age Classes after 80-year simulation MOFEP sites 7 and year simulation No harvest yrs yrs yrs yrs yrs yrs > 180 yrs

36 Age Classes after 100-year simulation MOFEP sites 7 and year simulation No harvest yrs yrs yrs yrs yrs yrs > 180 yrs

37 Age Classes after 100-year simulation MOFEP sites 7 and yrs yrs yrs yrs yrs yrs > 180 yrs EAM UAM No Harvest

38 Tree size classes - year 100 No Harv. Even 10% Uneven 5% 5 km

39 Ovenbird Late successional Edge sensitive

40 Tree age & Landtype Pine Edge Ovenbird Habitat Model 0.25 km

41 Ovenbird Habitat Suitability No harvest Even-age 10% Year 50 Year 200

42 Black bear habitat Fall food –Hard mast Summer food –Soft mast (tree age & land type) Interspersion of food types –Circular moving window Road density –Auxiliary map photo courtesy of Elaine Bindler

43 Black Bear Habitat Suitability 4 km wide

44 Habitat model links Ovenbird Prairie warbler Hooded warbler Pine warbler Wild turkey Ruffed grouse Gray squirrel Black bear Bobcat Red bat Northern bat Redback salamander

45 Could we create a working hypothesis of MOFEP change over the life of the experiment? Vegetation pattern Woody species composition Volume CWD Cavities Wildlife habitat …

46 Cavity tree estimation at multiple spatial scales Tree level Stand level Landscape level

47 Probability of cavity trees Tree level –Live, dead –Dbh –Species group –Decay class if dead Stand level Landscape level

48 Probability of cavity trees Tree level Stand level –Stand age class –Dbh class probability distribution Landscape level

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51 Probability of cavity trees Tree level Stand level Landscape level –Acres by age class Seed/sap, pole, sawtimber, old-growth

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53 Initial efforts were in the SE Missouri Ozarks Developed Agricultural Deciduous Coniferous Mixed Forested Wetland Water Barren N ^ Land use classification, southeast Missouri Ellington Bunker Eminence Clearwater Lake 5,040 sq.. km 80 km 63 km

54 Goal: Develop A Landscape Model Simulates the impact of various disturbances on forests. Predicts the composite impacts (in aggregate) on a landscape composed of numerous forest stands. Predicts/contrasts changes in ecosystem attributes that result from alternative disturbance regimes

55 Our Basic Modeling Assumptions Vegetation change is relentless. Vegetation is constantly responding to (recovering from) disturbance. To some degree (and to a greater degree than most other ecosystem components), patterns of vegetation change are predictable. The landscape can be divided into ecologically similar units (ECS). If we know (or can predict) the vegetation conditions across a landscape at some future point in time, we can say significant things about other ecosystem components. Requires a team effort.

56 This work utilizes the LANDIS model Generic framework for simulating landscape change in response to disturbance Handles all the basic bookkeeping and mapping Scaleable pixel size (0.1 ha) Tracks presence/absence of tree species by age and location High degree of stochastic variation Simulates stochastic fire events Simulates stochastic wind events Newly completed harvest simulator Can be calibrated for different forest conditions

57 Calibration Process for LANDIS Identify Land Units Calibrate species reproduction and survival dynamics based on life history characteristics –Longevity, shade tolerance, fire tolerance, dominance –Sprouting, age to sexual maturity, seed dispersal Calibrate wind and fire disturbance –Simulates stochastic fire events that differ by ELT –Simulates stochastic wind events that differ by ELT

58 Required Input Maps (raster) Land units Initial vegetation cover and age class Additional maps required to simulate harvest –Management areas –Stand boundaries

59 Harvest Scenarios Even-aged management –Clearcut 10% of stands each decade –Oldest first –No adjacency constraints –Fire and wind disturbance turned on Uneven-aged management –Group openings averaging 0.2 ha (2 pixels) –Harvest 8% of area in each stand each decade –No adjacency constraints –Fire and wind disturbance turned on No Harvest –Fire and wind disturbance turned on

60 Wind and Fire Disturbance

61 WindFire Mean return interval 800 yrs300 yrs Mean Size 1 ha 8 ha Minimum size 0.1 ha 0.1 ha Maximum Size 20 ha 600 ha Severity N/ALow-Med

62 Output Maps for Each Decade of Simulation Vegetation cover Vegetation age class Fire damage Wind damage Type and location of harvest

63 Strengths of This Approach Provides the big picture. Great tool to view large scale forest change Compare management alternatives visually Analyze projected landscape characteristics Compare landscape statistics among alternatives Assess change over time Make linkages to other resources

64 Limitations Not suitable for site-specific planning Probabilistic model (+/-) Requires GIS capability Big effort to learn to use it Requires maps of land units and stands for most harvest simulations Needs lots of computing horsepower for big landscapes

65 LANDIS Representation of a Site (pixel) Species 10 year age classes 1 = present, 0 = absent maple shortleaf black oak white oak

66 Number of Snags by Dbh Class

67 Down Wood Volume Volume (cu.m/ha)

68 Down Wood Size Distribution

69 Dbh Distribution by Species Dbh (inches) Shortleaf Pine Red Oak White Oak Big Spring MOFEP %

70 Harvest Patterns Year 10 Uneven-aged harvesting MOFEP sites 7 and 8

71 Harvest Patterns Year 20 Uneven-aged harvesting MOFEP sites 7 and 8

72 Harvest Patterns Year 30 Uneven-aged harvesting MOFEP sites 7 and 8

73 Harvest Patterns Year 40 Uneven-aged harvesting MOFEP sites 7 and 8

74 Harvest Patterns Year 50 Uneven-aged harvesting MOFEP sites 7 and 8

75 Harvest Patterns Year 60 Uneven-aged harvesting MOFEP sites 7 and 8

76 Harvest Patterns Year 70 Uneven-aged harvesting MOFEP sites 7 and 8

77 Harvest Patterns Year 80 Uneven-aged harvesting MOFEP sites 7 and 8

78 Harvest Patterns Year 90 Uneven-aged harvesting MOFEP sites 7 and 8

79 Harvest Patterns Year 100 Uneven-aged harvesting MOFEP sites 7 and 8


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