Date post: | 20-Jun-2015 |
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Education |
Upload: | utah-section-society-for-range-management |
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Using geospatial environmental characteristics to determine
plant community resilience to fire and fire surrogate
treatments
Nathan Cline, Bruce Roundy, William Christensen, and Chris Balzotti
The Big Question: Will cheatgrass dominate if we treat sagebrush or
woodlands?
Cheatgrass Cover
Before Treatment
After Treatment
High High
Low ?
Perennial Grass Cover
Cheatgrass cover
• Identify the site and climate characteristics that influence cover
Project objective and proposed products
• Create models of cheatgrass and perennial grass cover using site and climate characteristics.
• Develop tools for land managers to use in predicting the probability of cheatgrass at other sites.• A field guide
• A geospatial map
Sites: Trees Mechanically Shredded
Data
Vegetation cover
• Cheatgrass
• Perennial grass
• Sagebrush
• Perennial Forbs
Site Characteristics• Bioclim and
ClimateWNA
• Aspect, slope, elevation, geospatial coordinates, solar radiation
• Treatment and woodland encroachment phase
Climate VariablesAnnual Mean Temperature
Mean Diurnal Range (Mean of monthly (max temp - min temp))
Isothermality
Temperature Seasonality (standard deviation *100)
Max Temperature of Warmest Month
Min Temperature of Coldest Month
Temperature Annual Range
Mean Temperature of Wettest Quarter
Mean Temperature of Driest Quarter
Mean Temperature of Warmest Quarter
Mean Temperature of Coldest Quarter
Annual Precipitation
Precipitation of Wettest Month
Precipitation of Driest Month
Precipitation Seasonality (Coefficient of Variation)
Precipitation of Wettest Quarter
Precipitation of Driest Quarter
Precipitation of Warmest Quarter
Precipitation of Coldest Quarter
Continentality (°C)
Mean annual precipitation (mm)
Mean summer (May to Sep) precipitation (mm)
Annual heat moisture index
Summer heat moisture index
Degree-days below 0°C (chilling degree days)
Degree-days above 5°C (growing degree days)
The number of frost-free days
The julian date on which the frost-free period begins
The julian date on which the frost-free period ends
Precipitation as snow (mm)
Extreme minimum temperature over 30 years (°C)
Hargreave's reference evaporation
Hargreave's climatic moisture index
Hogg's climate moisture index
Hogg's summer (Jun to Aug) climate moisture index
Winter (Dec to Feb) mean temperature (°C)
Summer (Jun to Aug) mean temperature (°C)
Winter (Dec to Feb) precipitation (mm)
Summer (Jun to Aug) precipitation (mm)
Analysis
• Spatial Regression Analysis – space is only important within sites
• Canonical correlation and step-wise regression analyses
• Canonical correspondence analysis (CCA)
• Random forest analysis
Analysis was done on the subplot scale (6 to 24 subplots per site) – 450 subplots
Canonical Correlation and Step-wise regression
Run Cheat Sage P Grass P Forbs avgR2 numterms0 0.555 0.684 0.67 0.526 0.608751 0.555 0.684 0.67 0.526 0.608756 0.552 0.682 0.667 0.521 0.6055
11 0.535 0.669 0.644 0.508 0.589 2416 0.47 0.65 0.564 0.39 0.5185 1921 0.363 0.609 0.551 0.377 0.475 1425 0.336 0.54 0.4879 0.2651 0.40725 1026 0.328 0.54 0.4727 0.262 0.400675 927 0.318 0.54 0.469 0.202 0.38225 828 0.2758 0.5198 0.466 0.169 0.35765 729 0.272 0.475 0.4661 0.136 0.337275 630 0.269 0.474 0.462 0.095 0.325 5 Elevation MeanAnnTem MTWM mTCM PWQ31 0.1985 0.4288 0.4387 0.0945 0.290125 4 Elevation MeanAnnTem MTWM PWQ
R2 VALUES
• Need at last 24 characteristics to achieve > 50%• P. grass and sagebrush need fewer site characterizes than
cheatgrass and forbs
Perennial grass vs. cheatgrass cover
Perennial grass Cheatgrass
Axis 1 Axis 2 Axis 3Variance in cover data % of variance explained 54.8 11.9 2.5 Cumulative % explained 54.8 66.7 69.2Pearson Correlation 0.890 0.724 0.562
P = 0.01
Canonical correspondence analysis (CCA)• Cover data included: Perennial grass, cheatgrass,
perennial forbs, & sagebrush• Site characteristics: aspect, slope, elevation, and 30
climate variables
Sagebrush and Forbs
Sagebrush cover is similar to perennial grass
Random Forest Analysis
Random Forest Analysis
Probability of cheatgrass
8 predictor variables included
Whiter shades = higher probability
Darker shades = lower probability
Future Analysis: Structural Equation Modelling
Conclusions
• Our analyses explained between 50-70% of variation among the four cover classes.
• Cheatgrass requires up to 24 variables to explain > 50% of variability.
• Isothermality, temperature and precipitation during warm and dry periods, elevation, and solar radiation may all be important predictors.
• Geospatial maps are coming…