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…