Using climate and site characteristics to predict plant communities after fire treatments
•Télécharger en tant que PPTX, PDF•
0 j'aime•329 vues
Using Geospatial Environmental Characteristics to Determine Plant Community Resilience to Fire and Fire Surrogate Treatments
Recommandé
Recommandé
Contenu connexe
Similaire à Using climate and site characteristics to predict plant communities after fire treatments
Similaire à Using climate and site characteristics to predict plant communities after fire treatments (20)
Plus de Utah Section Society for Range Management
Plus de Utah Section Society for Range Management (20)
Dernier
Dernier (20)
Using climate and site characteristics to predict plant communities after fire treatments
- 1. Using geospatial environmental characteristics to determine plant community resilience to fire and fire surrogate treatments Nathan Cline, Bruce Roundy, William Christensen, and Chris Balzotti
- 2. 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
- 3. 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
- 4. Sites: Trees Mechanically Shredded
- 5. 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
- 6. Climate Variables Annual 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)
- 7. 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
- 8. Canonical Correlation and Step-wise regression R2 VALUES Run Cheat Sage P Grass P Forbs avgR2 numterms 0 0.555 0.684 0.67 0.526 0.60875 1 0.555 0.684 0.67 0.526 0.60875 6 0.552 0.682 0.667 0.521 0.6055 11 0.535 0.669 0.644 0.508 0.589 24 16 0.47 0.65 0.564 0.39 0.5185 19 21 0.363 0.609 0.551 0.377 0.475 14 25 0.336 0.54 0.4879 0.2651 0.40725 10 26 0.328 0.54 0.4727 0.262 0.400675 9 27 0.318 0.54 0.469 0.202 0.38225 8 28 0.2758 0.5198 0.466 0.169 0.35765 7 29 0.272 0.475 0.4661 0.136 0.337275 6 30 0.269 0.474 0.462 0.095 0.325 5 Elevation MeanAnnTem MTWM mTCM PWQ 31 0.1985 0.4288 0.4387 0.0945 0.290125 4 Elevation MeanAnnTem MTWM PWQ • Need at last 24 characteristics to achieve > 50% • P. grass and sagebrush need fewer site characterizes than cheatgrass and forbs
- 9. Perennial grass vs. cheatgrass cover Perennial grass Cheatgrass Axis 1 Axis 2 Axis 3 P = 0.01 Variance in cover data % of variance explained 54.8 11.9 2.5 Cumulative % explained 54.8 66.7 69.2 Pearson Correlation 0.890 0.724 0.562 Canonical correspondence analysis (CCA) • Cover data included: Perennial grass, cheatgrass, perennial forbs, & sagebrush • Site characteristics: aspect, slope, elevation, and 30 climate variables
- 10. Sagebrush and Forbs Sagebrush cover is similar to perennial grass
- 13. Probability of cheatgrass 8 predictor variables included Whiter shades = higher probability Darker shades = lower probability
- 14. Future Analysis: Structural Equation Modelling
- 15. 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…
Notes de l'éditeur
- When treating vegetation in sagebrush step communities, the question is always “ will I get cheatgrass
- Talk about the range of variation in these sites
- Canonical correspondence analysis (CCA) The ordination was significant P=0.01 locations: Utah shred data at 470 subplots (42 sites). Both diagrams are illustrations of the same ordination. Perennial grass, cheatgrass, perennial forb, and sagebrush cover data where included to create the ordination. Triangle size: The larger the triangle the higher relative perennial grass and cheatgrass cover. Most influential characteristics (correlation values > 0.5): Elevation, solar radiation, precipitation of the wetter quarter of the year, precipitation of the driest month, mean temperature of the driest quarter of the year. Isothermal: (mean monthly diurnal temperature range/annual temperature range)*100 = an indicator of temperature fluctuation Cheatgrass cover increases with increasing solar radiation and Isothermal percentage. Cheatgrass also appears to increase with decreasing elevation and precipitation during the wettest quarter of the year. Cheatgrass decreases with increasing precipitation during the driest month. The point on solar radiation fits Condon et al. 2011’s finding that increasing cheatgrass cover is associated with increasing solar radiation. Isothermal percentage - If we consider temperature a resource, this finding fits Davis 2001’s fluctuating resource hypothesis in that cheatgrass increases with increasing temperature fluctuation. Elevation and cheatgrass…. I’ll let you discuss chamber’s findings on the subject and how you feel about that. Perennial grass cover increases with increasing mean temperature during the driest quarter of the year and precipitation during driest month (extension of the growing season?). It decreases with increasing solar radiation and isothermal percentage.
- Although RF, a form of machine learning, has been around for some time, its use in ecological studies is relatively new (Cutler et al., 2007; Prasad et al., 2006). RF uses bootstrap samples and a randomized subset of the predictor variables to create a series of classification trees (a forest) that predict species presence. These trees (typically over 500) are then combined for the final model prediction. R with the ModelMap package was used for this analysis