EcoService Models Library (ESML)
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Variables Details
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Burn management
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EM Identity and Description (* Note that run information is shown only where run data differ from the "parent" entry for that variable)
EM-777 | |
Document Author
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Buckles, B. J., and A. N. Harmon-Threatt |
Document Year
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2019 |
Variable General Info (* Note that run information is shown only where run data differ from the "parent" entry for that variable)
Soil PC1 ?Comment:The remaining soil data, which are both multivariate and nested, were reduced to the first principle component to create a univariate explanatory variable (herein referred to as “soil PC1”) using a duality diagram similar to a nested principle component analysis as implemented in the ade4 package (Dray & Dufour, 2007). Texture was converted to a numeric variable in order to be included in the analysis and all data were centred and scaled. Soil PC1 explained 24.6% of the total variability in soil conditions. Bare ground rank, temperature and moisture had the greatest PCA loadings on soil PC1 with 0.641, 0.538 and −0.514 respectively. This suggests that as soil PC1 increases there is more bare ground, higher temperatures and less soil moisture. |
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Variable ID
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18919 |
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Qualitative-Quantitative
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Cardinal-Ordinal
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Variable Typology (* Note that run information is shown only where run data differ from the "parent" entry for that variable)
Soil PC1 ?Comment:The remaining soil data, which are both multivariate and nested, were reduced to the first principle component to create a univariate explanatory variable (herein referred to as “soil PC1”) using a duality diagram similar to a nested principle component analysis as implemented in the ade4 package (Dray & Dufour, 2007). Texture was converted to a numeric variable in order to be included in the analysis and all data were centred and scaled. Soil PC1 explained 24.6% of the total variability in soil conditions. Bare ground rank, temperature and moisture had the greatest PCA loadings on soil PC1 with 0.641, 0.538 and −0.514 respectively. This suggests that as soil PC1 increases there is more bare ground, higher temperatures and less soil moisture. |
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Predictor-Intermediate-Response
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Predictor Variable Type
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Response Variable Type
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Data Source/Type
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Variable Classification Hierarchy
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Variable Spatial Characteristics (* Note that run information is shown only where run data differ from the "parent" entry for that variable)
Soil PC1 ?Comment:The remaining soil data, which are both multivariate and nested, were reduced to the first principle component to create a univariate explanatory variable (herein referred to as “soil PC1”) using a duality diagram similar to a nested principle component analysis as implemented in the ade4 package (Dray & Dufour, 2007). Texture was converted to a numeric variable in order to be included in the analysis and all data were centred and scaled. Soil PC1 explained 24.6% of the total variability in soil conditions. Bare ground rank, temperature and moisture had the greatest PCA loadings on soil PC1 with 0.641, 0.538 and −0.514 respectively. This suggests that as soil PC1 increases there is more bare ground, higher temperatures and less soil moisture. |
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Spatial Extent Area
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Spatially Distributed?
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Observations Spatially Patterned?
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Spatial Grain Type
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Spatial Grain Size
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Spatial Density
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EnviroAtlas URL
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Variable Temporal Characteristics (* Note that run information is shown only where run data differ from the "parent" entry for that variable)
Soil PC1 ?Comment:The remaining soil data, which are both multivariate and nested, were reduced to the first principle component to create a univariate explanatory variable (herein referred to as “soil PC1”) using a duality diagram similar to a nested principle component analysis as implemented in the ade4 package (Dray & Dufour, 2007). Texture was converted to a numeric variable in order to be included in the analysis and all data were centred and scaled. Soil PC1 explained 24.6% of the total variability in soil conditions. Bare ground rank, temperature and moisture had the greatest PCA loadings on soil PC1 with 0.641, 0.538 and −0.514 respectively. This suggests that as soil PC1 increases there is more bare ground, higher temperatures and less soil moisture. |
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Temporal Extent
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Temporally Distributed?
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Regular Temporal Grain?
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Temporal Grain Size Value
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Temporal Grain Size Units
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Temporal Density
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Variable Values (* Note that run information is shown only where run data differ from the "parent" entry for that variable)
Soil PC1 ?Comment:The remaining soil data, which are both multivariate and nested, were reduced to the first principle component to create a univariate explanatory variable (herein referred to as “soil PC1”) using a duality diagram similar to a nested principle component analysis as implemented in the ade4 package (Dray & Dufour, 2007). Texture was converted to a numeric variable in order to be included in the analysis and all data were centred and scaled. Soil PC1 explained 24.6% of the total variability in soil conditions. Bare ground rank, temperature and moisture had the greatest PCA loadings on soil PC1 with 0.641, 0.538 and −0.514 respectively. This suggests that as soil PC1 increases there is more bare ground, higher temperatures and less soil moisture. |
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* ?Comment:Soil PC1 values, means and SD were provided in graphs by management treatment. Bee resources (i.e. Soil PC1, floral richness, FQI and floral abundance) as well as bee abundance and richness were analysed with generalised least squares (GLS) models with maximum likelihood estimation from the nlme package (Pinheiro et al., 2018), due to unequal variances between management treatments for some response variables. GLS can run a standard linear model or be adjusted for a different spread in each management type. Explanatory variables were removed to identify the most predictive variables using backward elimination, a procedure that compared models using likelihood ratio tests. Management was the most predictive variable for Soil PC1. |
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Min Value
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-0.7 |
Max Value
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2.8 |
Other Value Type
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Other Value
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0.95 |
Variable Variability and Sensitivity (* Note that run information is shown only where run data differ from the "parent" entry for that variable)
Soil PC1 ?Comment:The remaining soil data, which are both multivariate and nested, were reduced to the first principle component to create a univariate explanatory variable (herein referred to as “soil PC1”) using a duality diagram similar to a nested principle component analysis as implemented in the ade4 package (Dray & Dufour, 2007). Texture was converted to a numeric variable in order to be included in the analysis and all data were centred and scaled. Soil PC1 explained 24.6% of the total variability in soil conditions. Bare ground rank, temperature and moisture had the greatest PCA loadings on soil PC1 with 0.641, 0.538 and −0.514 respectively. This suggests that as soil PC1 increases there is more bare ground, higher temperatures and less soil moisture. |
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Variability Expression Given?
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Variability Metric
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Variability Value
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Variability Units
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Resampling Used?
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Variability Expression Used in Modeling?
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Variable Operational Validation (Response Variables only; * Note that run information is shown only where run data differ from the "parent" entry for that variable)
Variable ID
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Validated?
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Validation Approach (within, between, etc.)
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Validation Quality (Qual/Quant)
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Validation Method (Stat/Deviance)
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Validation Metric
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Validation Value
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Validation Units
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Use of Measured Response Data
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