EcoService Models Library (ESML)
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Variables Details
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Low planted plant diversity
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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-774 | |
Document Author
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Cusser, S. and K. Goodell |
Document Year
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2013 |
Variable General Info (* Note that run information is shown only where run data differ from the "parent" entry for that variable)
Robustness of networks ?Comment:To determine how network architecture affects network stability, we calculated the robustness of each network. Rather than describing network architecture itself, robustness quantifies a network’s ability to retain its structure following the removal, or extinction, of species (Dunne et al. 2002). We simulated extinction by removing plant species and observing which pollinators were left without forage resources. Pollinator species were considered to go “extinct” when all of their plant hosts had been removed from the network. We used the technique developed by Borgo et al. (2007) to quantify the robustness with a single parameter r, which ranges from 0 to 1. A network in which r approaches 0 is considered fragile, such that even if a very few plants are eliminated, most pollinators would go extinct. Likewise networks with r approaching 1 are considered robust. In a robust network, most pollinators survive even if a large fraction of the plant species is eliminated. Values used for linear regression on distance from remnant forest. |
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Variable ID
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18877 |
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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)
Robustness of networks ?Comment:To determine how network architecture affects network stability, we calculated the robustness of each network. Rather than describing network architecture itself, robustness quantifies a network’s ability to retain its structure following the removal, or extinction, of species (Dunne et al. 2002). We simulated extinction by removing plant species and observing which pollinators were left without forage resources. Pollinator species were considered to go “extinct” when all of their plant hosts had been removed from the network. We used the technique developed by Borgo et al. (2007) to quantify the robustness with a single parameter r, which ranges from 0 to 1. A network in which r approaches 0 is considered fragile, such that even if a very few plants are eliminated, most pollinators would go extinct. Likewise networks with r approaching 1 are considered robust. In a robust network, most pollinators survive even if a large fraction of the plant species is eliminated. Values used for linear regression on distance from remnant forest. |
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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)
Robustness of networks ?Comment:To determine how network architecture affects network stability, we calculated the robustness of each network. Rather than describing network architecture itself, robustness quantifies a network’s ability to retain its structure following the removal, or extinction, of species (Dunne et al. 2002). We simulated extinction by removing plant species and observing which pollinators were left without forage resources. Pollinator species were considered to go “extinct” when all of their plant hosts had been removed from the network. We used the technique developed by Borgo et al. (2007) to quantify the robustness with a single parameter r, which ranges from 0 to 1. A network in which r approaches 0 is considered fragile, such that even if a very few plants are eliminated, most pollinators would go extinct. Likewise networks with r approaching 1 are considered robust. In a robust network, most pollinators survive even if a large fraction of the plant species is eliminated. Values used for linear regression on distance from remnant forest. |
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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)
Robustness of networks ?Comment:To determine how network architecture affects network stability, we calculated the robustness of each network. Rather than describing network architecture itself, robustness quantifies a network’s ability to retain its structure following the removal, or extinction, of species (Dunne et al. 2002). We simulated extinction by removing plant species and observing which pollinators were left without forage resources. Pollinator species were considered to go “extinct” when all of their plant hosts had been removed from the network. We used the technique developed by Borgo et al. (2007) to quantify the robustness with a single parameter r, which ranges from 0 to 1. A network in which r approaches 0 is considered fragile, such that even if a very few plants are eliminated, most pollinators would go extinct. Likewise networks with r approaching 1 are considered robust. In a robust network, most pollinators survive even if a large fraction of the plant species is eliminated. Values used for linear regression on distance from remnant forest. |
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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)
Robustness of networks ?Comment:To determine how network architecture affects network stability, we calculated the robustness of each network. Rather than describing network architecture itself, robustness quantifies a network’s ability to retain its structure following the removal, or extinction, of species (Dunne et al. 2002). We simulated extinction by removing plant species and observing which pollinators were left without forage resources. Pollinator species were considered to go “extinct” when all of their plant hosts had been removed from the network. We used the technique developed by Borgo et al. (2007) to quantify the robustness with a single parameter r, which ranges from 0 to 1. A network in which r approaches 0 is considered fragile, such that even if a very few plants are eliminated, most pollinators would go extinct. Likewise networks with r approaching 1 are considered robust. In a robust network, most pollinators survive even if a large fraction of the plant species is eliminated. Values used for linear regression on distance from remnant forest. |
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* ?Comment:Values used for linear regression on distance from remnant forest. |
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Min Value
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0.43 |
Max Value
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0.58 |
Other Value Type
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Other Value
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Variable Variability and Sensitivity (* Note that run information is shown only where run data differ from the "parent" entry for that variable)
Robustness of networks ?Comment:To determine how network architecture affects network stability, we calculated the robustness of each network. Rather than describing network architecture itself, robustness quantifies a network’s ability to retain its structure following the removal, or extinction, of species (Dunne et al. 2002). We simulated extinction by removing plant species and observing which pollinators were left without forage resources. Pollinator species were considered to go “extinct” when all of their plant hosts had been removed from the network. We used the technique developed by Borgo et al. (2007) to quantify the robustness with a single parameter r, which ranges from 0 to 1. A network in which r approaches 0 is considered fragile, such that even if a very few plants are eliminated, most pollinators would go extinct. Likewise networks with r approaching 1 are considered robust. In a robust network, most pollinators survive even if a large fraction of the plant species is eliminated. Values used for linear regression on distance from remnant forest. |
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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)
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Variable ID
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18877 | ||
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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