soil property recovery on a natural gas pipeline reclamation chronosequence
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Soil property recovery on a natural gas pipeline reclamation chronosequence. Caley Gasch, Snehalata Huzurbazar , Peter Stahl. Our goal: assist in the recovery of degraded ecosystems Our basis for assessment: reference sites. - PowerPoint PPT PresentationTRANSCRIPT
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Soil property recovery on a natural gas pipeline reclamation chronosequence
Caley Gasch, Snehalata Huzurbazar, Peter Stahl
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Our goal: assist in the recovery of degraded ecosystems
Our basis for assessment: reference sites
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Like an average parameter, the degree of variability of properties is of interest
Changes in variance indicate ecological consequences of disturbance, stability, and recovery
The variance may help in defining an acceptable range of values for indicating recovery
Investigate the recovery of soil properties on reclaimed soils by incorporating both the mean and variance in our
assessment of similarity with regard to reference soils
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Like an average parameter, the degree of variability of properties is of interest
Changes in variance indicate ecological consequences of disturbance, stability, and recovery
The variance may help in defining an acceptable range of values for indicating recovery
Investigate the recovery of soil properties on reclaimed soils by incorporating both the mean and variance in our
assessment of similarity with regard to reference soils
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Wamsutter, Wyoming Elevation: 2052 m (6731 ft) Precipitation: 180 mm (7 in) Dominant vegetation:
Big sagebrush (Artemisia tridentata)Greasewood (Sarcobatus vermiculatus)
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< 1 year 4 years 28 years 35 years 54 years Undisturbed
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i = 1 in 12 observations
Pijk
j = 1 in 7 treatmentsk = 1 in 2 years
Bayesian Hierarchical Linear Mixed Model
Soil Property (P)Moisture
SOCTN
Microbial abundanceEtc.
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i = 1 in 12 observations
Pijk
j = 1 in 7 treatmentsk = 1 in 2 years
μjk τjk
Bayesian Hierarchical Linear Mixed Model
Data Model for the Likelihood
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i = 1 in 12 observations
Pijk
j = 1 in 7 treatmentsk = 1 in 2 years
μjk τjkβj
Bayesian Hierarchical Linear Mixed Model
Data Model for the Likelihood
Mean value of P within a treatment
Variability between treatments within years
αj(k)
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i = 1 in 12 observations
Pijk
j = 1 in 7 treatmentsk = 1 in 2 years
μjk τjk
αj(k)
βj
τατβ
Bayesian Hierarchical Linear Mixed Model
Prior Model
μβ
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i = 1 in 12 observations
Pijk
j = 1 in 7 treatmentsk = 1 in 2 years
μjk τjkβj
Bayesian Hierarchical Linear Mixed Model
Posterior Distribution
τβμβ
αj(k)
τα
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Bayesian Hierarchical Linear Mixed Model
Predictive Distribution of Future Observables
Huzurbazar et al. In Press SSSAJ
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Mean(central tendency)
Variance(spread)
Moisture
Total N =
Organic C =
Microbial abundance
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Future work: investigate sources of variability
Spatial approach (10 cm – 100 m)
Geostatistical parameter comparison
Assess degrees of heterogeneity
Link aboveground-belowground properties
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Thank you: Wyoming Reclamation and Restoration Center, Haub School and Environment and Natural Resources, Overland Pass Pipeline Company, ONEOK, Kinder
Morgan, El Paso Companies, Bureau of Land Management Rawlins Field Office, Rachana Giri Paudel, Darren Gemoets, Leann Naughton, Kurt Smith
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Soil properties vary in their response to disturbance and reclamation
Disturbed soils can differ from reference soils because of a property’s central tendency, or its spread of values
Longer recovery time increases the probability that a reclaimed soil property will be similar to a reference soil
Variability of a soil property tends to increase with recovery time and is generally high in reference soils
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Bayesian Hierarchical Linear Mixed Model
Posterior Distribution
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Table 1. General soil properties for each treatment, years combined. Values are sample mean, with standard error of the mean in parenthesis (n=6).
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