Presented by: Eric Behrens Research and Creative Activity Fair Small Scale Heterogeneity in Vegetation Structure in a Patch-Burn-Grazing Landscape Presented by: Eric Behrens Research and Creative Activity Fair

The Spatial Ecology of Disturbance in Tallgrass Prairies Large Herbivore Grazing and the Spatial Structure of Plant Communities Directly Intensive grazing and selectivity of forage (Hickman et al. 2004) Indirectly Nitrogen inputs from feces and urine (Steinauer and Collins 1995, Anderson et al. 2006) Wildfires and the Spatial Structure of Plant Communities Seasonal variation favors the abundance of different functional groups (Fall Burns: C3 Forbs and Spring Burns: C4 Grasses) (Dickson et al. in preparation).

Disturbance and the Plant Community: Scale Dependent Interactions Prescribed or Wild Fire Homogenous plant community structure (Low Variability) measured at all scales. = Large Herbivore Grazing = Heterogeneous plant community structure (High Variability) measured at a small scale Interactive disturbances Scale dependency may confound how interactive disturbances affect the plant community spatial structure (Collins and Smith 2006) + =

The Fire-Grazing Interaction Recent Fire Better Forage Focused Grazing on Recently Burned Areas (Vermeire et al. 2004, Allred et al. 2011)

Homogenous and Heterogeneous Rangeland Management 3-4 years Uniform Method Patch-Burn-Grazing (PBG) Method 2-3years

Research Question Is small scale heterogeneity higher within patches in PBG managed tallgrass prairie compared to uniformly managed prairie? (Fuhlendorf and Engle. 2004) PBG Management Small Scale Small Scale Uniform Management

Objectives A.) Measure small scale heterogeneity and differences in vegetation characteristics using remote sensing and biomass sampling in both uniform and patch-burn-grazing treatment units. B.) Compare small scale heterogeneity measurements of the remote sensing data and the biomass data. C.) Identify ecosystem and management factors that may affect small scale heterogeneity within patches. H0: There is no difference between small scale heterogeneity between patch-burn-grazing and uniformly managed tallgrass prairie HA: Patch-burn-grazing management has significantly higher small scale heterogeneity then uniform management.

Methods 1. Site Description 2. Data Collection 3. Statistical Design

Site Characteristics Oklahoma State Universities (OSU) Range Research Station. PBG and paired uniform treatments were established in 1999 (17 years of management) Tallgrass prairie with interspersed with the Cross Timbers deciduous forest in lowland coarse soils.

Experimental unit organization Experimental unit organization. Each black square/polygon represents one 64.8 hectare area experimental unit. Units labelled PBG (Patch-Burn-Grazing) and Check is (Uniformly Managed).

Burned 1 year Ago Burned Current Year Burned 2 years Ago Patch organization within a 64.8 hectare patch-burn-grazing unit. Numbers 1-6 indicate the seasonal prescribed burning treatment to the associated patch and the time since burn in relation to the year the patch was sampled. Even patches 2, 4, and 6 burned in the spring. Uniform units designated with “patches” but the entire unit is burned every three years in the spring. Uniform units were last burned one year prior to sampling. Odd numbered patches were not sampled.

Methods 1. Site Description 2. Data Collection 3. Statistical Design

An ADC multispectral camera elevated above the ground was used to collect spectral information from the vegetation canopy at a spatial resolution of 0.80mm and an instantaneous field of view (IFOV) of 1.6 X 1.2 meters. (Supplied by Dr. Jim Hayes in the UNO Geography Department) Biomass clippings were sorted into graminoid, forb, and litter to assess the mean differences and variation between PBG and Uniform treatments. Quadrat biomass sampling (green squares) and multispectral image (blue squares) transect sampling method in each “patch” for each treatment.

NDVI values range between -1 and +1 The normalized difference vegetation index (NDVI) was calculated in ERDAS Imagine and PixelWrench2 for the images taken with the multispectral camera. NDVI values range between -1 and +1 NDVI measures the health and greenness of vegetation, and has been known to be sensitive to changes in grassland biomass density (Kanemasu et al. 1990). Color Processed NDVI

Methods 1. Site Description 2. Data Collection 3. Statistical Design

Small Scale Heterogeneity Parameters The Coefficient of variation (CV) (Standard deviation / Mean) was used as the measure of small scale heterogeneity. Biomass: (CV) of total live biomass, graminoid, forb, and litter between the four samples from each “patch”. NDVI: Average CV of pixel values from four randomly sampled images from a pool of 12 per patch (Not including biomass correlation images).

Blocked split-plot organization of the whole plot level (Uniform/PBG Treatment Units) and the patch split-plot level (2, 4, and 6). PROC MIXED was used in SAS software to run a two-way ANOVA on the whole plot and split plot level treatments of the CV values and mean differences between patches and treatments

Post hoc Analysis of Statistical Power and Ground-truthing Gpower was used to determine the statistical power of the mean differences between patches and the CV in the biomass data. Changes of 25, 50, and 75 percent from the original highest mean estimates. CV and Mean Difference Live CV Live 25% 50% 75% Ground-truthing : Quantitatively comparing how the multispectral imagery correlates with the biomass data. Multispectral images were taken of the biomass sampling quadrats. The area immediately above the quadrat was sampled for biomass. NDVI values were calculated for the area that was clipped after the image was taken.

Table 1: Results of the PROC MIXED analysis of mean biomass between treatments.   Live Biomass Litter Graminoids Forbs PBG/Uniform: Whole Plot F1,12 = 0.00 P = 0.9530 F1,12 =(3.95) P =0.1561 F1,12 =(3.41) P =0.1099 F1,12 =(4.83) P =0.0484 Year of Burn: Split-plot F2,10 = 1.27 P = 0.3237 F2,10 =(3.01) P =0.0947 F2,10 =(0.37) P =0.6986 F2,10 =(3.32) P =0.0784 Table 2: Results of the PROC MIXED analysis of the mean coefficients of variation (CV) between treatments.   CV Live CV Litter CV Graminoids CV Forbs PBG/Uniform: Whole Plot F1,12 = (0.98) P= 0.3776 F1,12 =(0.07) P =0.7969 F1,12 =(0.05) P =0.8327 F1,12 =(0.01) P =0.9214 Year of Burn: Split-plot F2,10 = 0.72 P = 0.5094 F2,10 =(0.78) P =0.4830 F2,10 =(0.54) P =0.5966 F2,10 =(0.07) P =0.9351

Table 3: Results of the PROC MIXED analysis of the mean NDVI and CV.   NDVI Mean NDVI CV PBG/Uniform: Whole Plot F1,12 = (0.46) P= 0.5210 F1,12 =(0.34) P=0.5734 Year of Burn: Split-plot F2,10 = (1.23) P = 0.3337 F2,10 =(2.56) P =0.1265

Figure 1:Ground-truthed NDVI positive linear correlation with live biomass samples.

Discussion Measures of biomass may not indicate significant changes in spatial structure of the plant community. Grazers may perceive heterogeneity in vegetation at different scales based on body size which may affect the scale that their grazing influences the plant community (Ritchie and Olff. 1999) NDVI values from multispectral imagery may be influenced by other non-photosynthetic material that could be used as a measure of heterogeneity (Bare soil cover, flower head density, senescent vegetation abundance).

Conclusions Results indicate that small scale heterogeneity measured at a 10 meter scale is not significantly different between uniform and PBG managed tallgrass prairie. Further study into spatial autocorrelations using remote sensing and ground-truthed biomass samples in PBG managed rangeland might be an area worth considering.

Acknowledgements People: Funding and Scholarships Advisor (Dr. Timothy Dickson) Committee Members (Dr. Thomas Bragg and Dr. James Hayes) OSU Range Research Station Staff (Chris Stansberry, Matt Grammer, and OSU faculty Dr. John Weir, Dr. Samuel Fuhlendorf) Funding and Scholarships UCRCA from the Office of Research and Creative Activity Dr. Murle E Brooks Scholarship

Citations Collins, S., and M. D. Smith. 2006. Scale-Dependent Interaction of Fire and Grazing on Community Heterogeneity in Tallgrass Prairie. Ecology 87:2058–2067. Vermeire, L. T., R. B. Mitchell, S. D. Fuhlendorf, and R. L. Gillan. 2004. Patch burning effects on grazing distribution. Journal of Range Management Archives 57:248–252. Allred, B. W., S. D. Fuhlendorf, D. M. Engle, and R. D. Elmore. 2011. Ungulate preference for burned patches reveals strength: Of fire-grazing interaction. Ecology and Evolution 1:132–144. Kanemasu, E. T., Demetriades-Shah, T. H., Su, H., & Lang, A. R. G. (1990). Estimating grassland biomass using remotely sensed data. Applications of remote sensing in agriculture, 185ą199. Hickman, K. R., D. C. Hartnett, R. C. Cochran, and C. E. Owensby. 2004. Grazing management effects on plant species diversity in tallgrass prairie. Rangeland Ecology & Management 57:58–65. Steinauer, E. M., and S. L. Collins. 1995. Effects of urine deposition on small-scale patch structure in prairie vegetation. Ecology 76:1195–1205. Anderson, R. H., S. D. Fuhlendorf, and D. M. Engle. 2006. Soil Nitrogen Availability in Tallgrass Prairie Under the Fire–Grazing Interaction. Rangeland Ecology & Management 59:625–631. Fuhlendorf, S. D., and D. M. Engle. 2004. Application of the fire-grazing interaction to restore a shifting mosaic on tallgrass prairie. Journal of Applied Ecology 41:604–614. Ritchie, M. E., and H. Olff. 1999. Spatial scaling laws yield a synthetic theory of biodiversity. Nature 400:557–60.