Predictive modeling of biological soil crusts as a tool for better - - PDF document
Predictive modeling of biological soil crusts as a tool for better range management Matthew A. Bowker, Dept. of Biological Sciences. NAU Jayne Belnap & Mark Miller USGS Geological Survey The charismatic microflora Mosses, lichens and
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Matthew A. Bowker, Dept. of Biological
Jayne Belnap & Mark Miller USGS Geological Survey
microfungi, liverworts, archaea, bacteria, chlorophytes, flagellates, diatoms, and a dependent food web of soil invertebrates
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Organisms that control the availability of resources and living space for other organisms by causing physical state changes in biotic or abiotic materials
(Jones et al. 1994, 1998) Microcoleus
Image: J. Johansen
1) Surface stabilization (increased living space) 2) Hydrology effects (increased water availability) 3) Dust trapping (increased nutrient availability)
Chemically Physically
Sand grains cyanobacteria cyanobacteria sheath w/clay
Hu et. al 2002 Mazor et. al 1996 Belnap & Gardner 1993
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0.000 0.002 0.004 0.006 0.008 0.010 0.012 0.014 0.016 0.018 0.020 5 10 15 20 25 30 35 40
Rsqr = 0.90
Cyanobacterial biomass (chl a mg gsoil-1) Soil stability (slake test2)
Bowker, Miller & Belnap unpublished
smooth crusts (hot) bumpy crusts (cold)
both types retain water longer- surface sealing
(e.g. Alexander & Calvo 1992, George et al. 2003)
Runoff Infiltration Infiltration Runoff
Issa et al. 1999
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Microcoleus
Image: J. Johansen, JCU
Syntrichia
Image: B. Mishler, Jepson Herbarium
Reynolds et al. 2001 Surfaces with BSCs enriched in eolian dust compared to bedrock P: 2 X K: 1.2 X Mg: 4.4 X Fe: 1.6 X Cu: 1.4 X Mn: 2.1 X Mo: 5 X
Collema spp. Nostoc & Scytonema spp.
Photosynthesizers: mosses, lichens, cyanobacteria
~ equal to a continuous leaf covering the ground surface- Otto Lange
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0.4 1.5 5.2 2.1 2.8 3.5 Kg ha-1 yr-1 130 km
Sonoran desert: 7 - 18 kg ha-1 y-1 Great Basin: 10 – 100 kg ha-1 y-1 Colorado Plateau: 1 – 4 kg ha-1 y-1 Australia: 1 kg ha-1 y-1 Nigeria: 3 – 9 kg ha-1 y-1 High Arctic: up to 10 kg ha-1 PER DAY!
How much N?
(reviewed in Evans & Lange 2001)
Bowker, Miller & Belnap unpublished
Ecosystem engineering Ecosystem functioning & services
And…management seeks to maintain, preserve and restore ecosystem processes Management must incorporate BSCs in decision making
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(Dregne & Chou 1992)
Livestock Agriculture Urbanization Climate change
Site/soil stability Hydrologic Function Biotic integrity
Degree of departure from reference
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130 km
Statistical model: potential soil crust
Crust data
Potential crust condition (base)
cover, biomass, diversity elevation, precipitation, soils
Crust function & properties (interpretive)
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Statistical model: potential soil crust
Crust data
Potential crust condition (base) Crust function & properties (interpretive)
(including rock-dominated)
NRCS soil map 111 sites Field surveys
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clay silty to sandy silty sandy +CaCO3
bentonitic gypsiferous limestone silty shale siliceous non-calc. calcareous kaiparowits Precipitation gradient
non-bent.
Digital elevation models Average annual precipitation USGS PRISM
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Statistical model: potential soil crust
Crust data
Potential crust condition (base) Crust function & properties (interpretive)
soil soil soil elevation precip.
limestone calcareous non-bentonitic gypsiferous non-calcareous bentonitic siliceous kaiparowits limestone non-bent non-calc siliceous low calc, gyps, bent or kaip high bent or kaip high & dry calc or gyps high & wet calc or gyps 15.0 ± 9.2 25.4 ± 11.1 1.8 ± 2.4 3.5 ± 2.4 12.2 ± 2.7 7.1 ± 4.2
in
Response: moss cover Predictors: soil precipitation elevation
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1) Randomly withheld 12%
2) Generate model 3) Test model against withheld data using linear regression 4) Repeat 5 times (bootstrapping)
0.1 0.2 0.3 0.4 0.1 0.2 0.3 0.4 0.5 0.6
Predicted Observed
Total Lichen Cover
R2 = 0.69
model performance 0.60
0.09
0.22 light cyano 0.49 dark cyano 0.55 moss 0.69 lichen R2 Model
Statistical model: potential soil crust
Crust data
Potential crust condition (base) Crust function & properties (interpretive)
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Total moss cover
Dark cyanobacterial crust Total lichen
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Moss + lichen + dark cyano cover
1) Collect crust cover and ground cover data for sites to be evaluated e.g. point intercept transect crust functional groups or total crust cover 2) Compute cover data as percent of available habitat 3) Determine potential by locating your site on my maps
Pics here 80% cover 40% cover 95% available habitat 50% available habitat 80/95 = 84% cover of avail. habitat 40/50 = 80% cover of avail. habitat
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Site ID litter rock plant bare crust all crust % avail. habitat E0002 12 38 36 14 20 21 E0091 4 33 63 E0134 26 4 28 36 2 2 3 E1502 14 24 50 10 2 5 8
We selected 4 sites because they had varying potential crust cover formatting data for comparison with mine:
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4% 58% 21% 0%
Percent cover
E0134 E0091 E0002 E1502
100 100
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Crust cover changes, even in the absence of disturbance My maps are a conservative estimate of potential crust cover because they are based on drought years
I primarily sampled flat sites
N less cover more cover model OK model OK
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NRCS maps “map unit complexes”
5001- Mido fine sand 2-15% slopes Mido fine sand 85% Dune land 5% Mido family & similar 5% Earlweed & similar 5% = Calcareous sand
If you think your site deviates from my maps, you can classify it yourself* and use the trees
soil soil elevation elevation elevation ppt.
L,G,NC,S,B C,NB,K # 1668 # 1505 >1668 >1505 # 1632 >1632 L,G,NC S,B # 20 >20 36.1% 35.9% 68.1% 48.2% 11.6% 44.6% 51.2%
d) Total light cyanobacterial crust R2 = 0.80 * I owe you decision rules to classify soils
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Statistical model: potential soil crust
Crust data
Potential crust condition (base) Crust function & properties (interpretive)
Gypsiferous takes the prize
0.3 5.6 20.3 9.2 15.1 18.4
# species
12.3
130 km
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base models + published rates (Belnap 2001)
0.4 1.5 5.2 2.1 2.8 3.5 Kg ha-1 yr-1 130 km
Other input options: C-fixation Eolian dust retention Surface roughness Endemic/rare species Surface stabilization Weighting is user-defined
= highest priorities
130 km
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ID of conservation priorities Setting appropriate restoration goals Rangeland health assessment
Estimating ecosystem services
GSENM crust models
Better informed land management
staff, Roger Rosentreter, Thom O’Dell, Nancy Johnson, Johnson & Sisk labs (NAU)
Graham, Harry Barber, Sean Stewart, Joel Tuhey, Angie Evenden, Paul Evangelista, Paul Chapman, and the GSENM staff
Sasha Reed, Bernadette Graham, Moab lab folks, Jenn Brundage, Laura Pfenninger, Sara Bartlett, Laura & Walt Fertig, Elaine Kneller Kneller
Environmental Research And soil crusts everywhere!!!!
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Percent cover
E0134 E0091 E0002 E1502
100 100
B=3 S=3 B=4 S=5 B=3 S=3 B=5 S=4 active passive
removal of stressor inoculation fertilization & microsite engineering artificial soil stabilization Dry crumbled crust (Belnap 1993) Wet slurry (St. Clair et al.1986) Ex situ mass culturing (Buttars et al. 1998)
potential actions difficulty +
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Mean = 0.0042 mg g-1 Mean = 0.0013 mg g-1 Spring 2001 Summer 2001, Summer 2002
Days since start of sampling
Bowker et. al 2002