EcoService Models Library (ESML)
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EM-777: Bee diversity in tallgrass prairies affected by management and its effects on above‐ and below‐ground resources
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EM Identity and Description
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
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 |
Not reported | |
Qualitative-Quantitative
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Quantitative (Cardinal Only) |
Cardinal-Ordinal
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Cardinal |
unitless |
Variable Typology
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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Intermediate (Computed) Variable |
Predictor Variable Type
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Not applicable |
Response Variable Type
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Not applicable |
Data Source/Type
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Not applicable |
Variable Classification Hierarchy
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5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
--Physical/chemical characteristics of nonliving ecosystem components |
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----Physical/chemical characteristics of soils, substrates, rocks |
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------Other, multiple, unspecified or unclear |
Variable Spatial Characteristics
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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1000-10,000 km^2. |
Spatially Distributed?
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Yes |
Observations Spatially Patterned?
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Yes |
Spatial Grain Type
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area, for pixel or radial feature |
Spatial Grain Size
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1 ha |
Spatial Density
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Not applicable |
EnviroAtlas URL
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Variable Temporal Characteristics
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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2012-2016 |
Temporally Distributed?
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Not applicable |
Regular Temporal Grain?
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Not applicable |
Temporal Grain Size Value
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Not applicable |
Temporal Grain Size Units
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Not applicable |
Temporal Density
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Not applicable |
Variable Values
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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unitless ?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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Varies by run; view runs to see value |
Max Value
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Varies by run; view runs to see value |
Other Value Type
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Mean |
Other Value
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Varies by run; view runs to see values |
Variable Variability and Sensitivity
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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Yes | |
Variability Metric
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Variability Value
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Variability Units
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Resampling Used?
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Not applicable | |
Variability Expression Used in Modeling?
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Yes |
Variable Operational Validation (Response Variables only)
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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