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Soil and environmental influences on post-fire recovery in the southern Nevada Mojave Desert Cayenne Engel and Scott R. Abella UNLV School of Environmental.

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Presentation on theme: "Soil and environmental influences on post-fire recovery in the southern Nevada Mojave Desert Cayenne Engel and Scott R. Abella UNLV School of Environmental."— Presentation transcript:

1 Soil and environmental influences on post-fire recovery in the southern Nevada Mojave Desert Cayenne Engel and Scott R. Abella UNLV School of Environmental and Public Affairs Photo credit: Troy Phelps, Las Vegas BLM

2 Background Large fires are becoming increasingly common and destructive across the Mojave Desert landscape. Little is documented about the natural progression of post-fire recovery in the Mojave that specifically focuses on soil and environmental factors that may influence natural successional processes Vegetative recovery may (in part) be affected by variation in soil, climate, etc. across the landscape

3 ElevationEcosystem classification (m) Low High

4 Objective Questions How are soil texture and nutrients affected by fire? Is post-fire recovery predicted by abiotic factors? Correlate abiotic factors with post-fire recovery at the landscape scale (look for explanatory variables)

5 Years of burns US Forest Service Bureau of Land Mgmt Fish and Wildlife Service National Park Service Las Vegas Nevada Fire chronosequence approach

6 Fire Chronosequence Study Methods 32 fires ranging from 2 – 30 years post-burn 2007 – 2009 we sampled perennial plant community composition (foliar cover) within burned and adjacent unburned Mojave Desert shrublands  Larrea tridentata (creosote) and Coleogyne ramosissima (blackbrush) dominated communities At each site we collected soil samples from interspaces and had them analyzed for texture and nutrients We related post-burn plant composition to abiotic site characteristics (elevation, aspect, slope gradient, and UTMs), soil texture and chemistry using ANOVA (perMANOVA), and multiple regressions

7 Fork (2005)

8 1980 Burn (RRCNCA) BurnedUnburned

9 How are soil texture and nutrients affected by fire? Soil variables measured Soil texture: % clay, % sand, % silt Soil nutrients: pH, electrical conductivity, bulk density Statistical Approach PerMANOVA (soil “community”), ANOVA (inivid vars) B Ca CaCO 3 Cd Cl Co Cu Fe K Mg Mn Mo Na Ni NO 3 P Pb SO 4 Zn Total C Inorganic C Total organic C Total N

10 How are soil texture and nutrients affected by fire? PerMANOVA: burn x comm type x decade Overall, no effects of fire on soils (only on two variables) Soils differ between plant communities Term num df den df pseudo -F P (perm) P (Monte Carlo) Prop. Var Plant Community Decade Plant Comm X Decade Burn Plant Comm. X Burn Decade X Burn Plant Comm. X Decade X Burn

11 Few overall responses to burns, some responses mediated by community type P = 0.04 * * Fire × community type: P = 0.03 P = Effects of fire:, some responses only in blackbrush communities

12 Is post-fire recovery predicted by abiotic factors? Approach 1: Examine whether soil and environmental variables predict similarity of vegetation between burned and unburned plots.  Multiple regression using the Sørensen similarity index with individual environmental and soil variables (from unburned plots) Approach 2: Looked for relationships between burned and unburned veg communities (Hellinger distance, used for perMANOVA) and the unburned – burn value for each soil variable  Multiple regression

13 Is post-fire recovery predicted by abiotic factors? Sørensen similarity approach Similarity in perennial vegetation between burned and unburned sites was largely attributed to elevation and year since fire VariableDirectionPartial r 2 Elevation YSF Model r 2 = 0.45 Soil texture and nutrient composition had little influence (adding less than 7% to the partial r 2 ). Some interesting associations emerged within different age groups (need more information to properly interpret) VariableDirectionPartial r 2 Zn NO UTMx Model r 2 = 0.93 Full model mult reg 1980s fires mult reg

14 r 2 = 0.31 r 2 = 0.12 r 2 = 0.30r 2 = 0.32

15 Is post-fire recovery predicted by abiotic factors? Hellingers distance = unburned – burned soils dfEstimateSEtP-valueVIF Intercept < BulkDensity pH Na K NO SO P InorganicC TotalorgC TotalN

16 Does vegetation track differences among soils? PerMANOVA indicates that vegetation responses do not track soil responses (sig. responses don’t match) Vegetation dataSoil data Term num df den df pseudo -F P (perm)P (M.C) pseudo -F P (perm)P (M.C) Plant Community Decade Plant Comm X Decade Burn Plant Comm. X Burn Decade X Burn Plant Comm. X Decade X Burn

17 Summary Soil – vegetation relationships are swamped when looking at the landscape scale by influences of site location and plant community identity. Elevation/precip/temp are the most consistent abiotic predictors of community composition and of amount of recovery (similarity) between burned and unburned plots. Overall, at the landscape scale, relatively little was predicted by specific nutrients or soil texture across the landscape, and long term effects of fire on the soil properties that we measured were few. Information about fire characteristics (intensity, severity, etc.) would likely correlate with plant recovery…

18 Acknowledgements Funding: This study was supported through cooperative agreements between the University of Nevada Las Vegas (UNLV) and the Bureau of Land Management (Southern Nevada District) and National Park Service (Lake Mead National Recreation Area)in part funded by the Joint Fire Science Program. We thank Christina Lund (formerly of SND), and Kevin Oliver and Nora Caplette of SND, and Alice Newton (LMNRA) for facilitating work under these agreements; Tim Rash (formerly of SND) for supplying fire records. Cheryl Vanier for statistical help (perMANOVA) Field Assistance: Nick Bechtold, Teague Embry, Adria DeCorte, Kate Prengaman, Chris Roberts, and Sarah Schmid Soil samples were analyzed by the Environmental Soil Analysis Laboratory at UNLV.

19 Photo credit: Troy Phelps, Las Vegas BLM

20 Are patterns of post-fire recovery within fires influenced by site-specific parameters more than time since burn, location, and initial community type? Similarity among plots is greater within burns than among burns Within burns Similarity (Sørensen) CV (%) Burned Unburned Among burns Burned Unburned0.2776

21 All sites, vegetation ordination

22 Does post-fire plant community composition correlate with certain soil or environmental variables? Strongest influences are elevation and community type Easting and Na each explain 20% of the variation in the distribution Silt (13%) and P (12%) follow

23 Comm. type Creosote Cover Blackbrush Cover Elev. (m) Annual Precip (mm) Tmin (C) Tmax (C) Creosote28%2% Blackbrush2%49% Associated Species Blackbrush: Nevada jointfir Banana Yucca Mohave Yucca Big galleta grass Spiny menodora Creosote: Bursage Nevada jointfir Rhatany Desert Almond Mohave Yucca Sites throughout Southern NV (Northern AZ) in Mojave Desert shrub communities including creosote and blackbrush dominated communities

24 Elevation predicts similarity only in creosote communities r 2 = 0.26


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