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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-777 | |
Document Author
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Buckles, B. J., and A. N. Harmon-Threatt |
Document Year
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2019 |
Bare ground rank |
Bee community composition (pre-management) ?Comment:Pre-management bee community composition was not reported in the paper. |
Management type ?Comment:Twenty 1 ha plots were evenly distributed across three management types—burning, burning and haying, or patch‐burn grazing. Plots were sampled in two patches in each grazed prairie site, one less than a year since the most recent fire (patch‐burn grazing‐Y1) and one that had not been burned during the previous 2 years (patch‐burn grazing‐Y3) resulting in a total of four treatments, entered as runs in ESML. |
Plant community composition (pre-management) ?Comment:Pre-management plant community composition and characteristics were not reported in the paper. |
Soil bulk density ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. Bulk density (g/cm3) was determined by drying an intact 2‐cm‐long portion of the soil sample for an additional 24 hr at 105°C and then dividing the dry mass by the sample's volume. |
Soil moisture ?Comment:To determine soil characteristics both at the soil surface and at the depths of nesting for bees, 17 ± 26 cm (Cane & Neff, 2011) soil cores were taken under the centre of each tent using a 2 cm in diameter and 30‐cm‐long soil probe when tents were removed. Soil temperature and moisture levels were measured at the soil surface and at 30 cm by inserting a digital thermometer (Cooper ATKINS®) and a soil moisture meter (EXTECH®) into the top and bottom of the soil core respectively. |
Soil pH ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. Soil pH was measured on 10 g of soil homogenised in 10 ml of distilled water after resting for 15 min using a digital pH meter (Mettler Toledo). To ensure accuracy the pH meter was calibrated prior to each use. |
Soil temperature ?Comment:To determine soil characteristics both at the soil surface and at the depths of nesting for bees, 17 ± 26 cm (Cane & Neff, 2011) soil cores were taken under the centre of each tent using a 2 cm in diameter and 30‐cm‐long soil probe when tents were removed. Soil temperature and moisture levels were measured at the soil surface and at 30 cm by inserting a digital thermometer (Cooper ATKINS®) and a soil moisture meter (EXTECH®) into the top and bottom of the soil core respectively. |
Soil texture ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. To determine soil texture, 15 g of soil was added to 15 ml of distilled water in a 25 cm2 flat bottomed cell culture flask and shaken vigorously. After settling for a minimum of 24 hr, the relative proportion of each layer of the clay, silt and sand was measured and used to determine the soil texture (USDA NRCS, 2016). |
Soil type ?Comment:Soil type and characteristics were not reported in the paper. |
Conservation coefficient (plant species) ?Comment:Using floral richness recorded in plots, we calculated Floristic Quality Index (FQI). FQI is a metric devised by botanists to assess the vegetative quality of a habitat. This metric combines the floral richness of the plot and the coefficient of conservatism (CC)… Using the CC for each plant species as designated in Ladd and Thomas (2015) for Missouri or Taft et al. (1997) for Illinois when absent from the Missouri list, we calculated the FQI for each plot as: FQI=mean CC∗√N where N is the native species richness and mean CC is the mean of the coefficient of conservatism. All invasive species were assigned a C of 0 and not included in the native species richness calculation as implemented by Maginel, Knapp, Kabrick, Olson, and Muzika (2016). |
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. |
Bee community composition ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Floristic Quality Index (FQI) ?Comment:Using floral richness recorded in plots, we calculated Floristic Quality Index (FQI). FQI is a metric devised by botanists to assess the vegetative quality of a habitat. This metric combines the floral richness of the plot and the coefficient of conservatism (CC), and thus provides a rank‐based assessment of the sensitivity of a plant species to disturbance (on a scale of 1–10). FQI can provide important information on the overall conservation status of the habitat. Using the CC for each plant species as designated in Ladd and Thomas (2015) for Missouri or Taft et al. (1997) for Illinois when absent from the Missouri list, we calculated the FQI for each plot as FQI=mean CC∗√N where N is the native species richness and mean CC is the mean of the coefficient of conservatism. All invasive species were assigned a C of 0 and not included in the native species richness calculation as implemented by Maginel, Knapp, Kabrick, Olson, and Muzika (2016). FQI was designed to evaluate the entire plant community, but we only evaluated the flowering community. |
Log(floral abundance) |
Plant community composition ?Comment:Differences in plant community composition relative to management was visualized in Non‐metric dimensional scaling (NMDS) ordination. Multivariate analysis showed management was the most important variable in determining plant community composition. |
Bee abundance ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Bee richness ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Bees nesting ?Comment:Beginning at 1900 the evening before bee and floral sampling, fifteen 0.36 m2 ground emergence tents (BugDorm BT2006, Taichung, Taiwan) were installed to capture nesting bees at each plot (Sardinas & Kremen, 2014; Sardinas et al., 2016). Tents were installed after dusk when female bees return to the nest after foraging. They were secured with stakes and soil on flaps to prevent overturning and limit insect escape, and kill jars were filled with soapy water. The proportion of tents capturing bees at a plot is herein referred to as “nesting rate”. |
Floral abundance ?Comment:Floral richness and abundance were estimated along five parallel 100‐m‐long transects within each 1‐ha plot separated by 10–15 m (Figure 1c). All blooming flowers encountered within 1 m of either side of the transect were identified to species and counted. Dense heads and inflorescences were counted as a single flower. Flowering plant species that were in bloom in the 1 ha sampling plots, but not encountered within a transect were recorded as present and assigned an abundance of 0.5. Adding a small abundance for these observed species allows more accurate richness counts and prevents species detected outside of transects to have minimal influence on total plot abundance so as not to skew abundance figures between plots. |
Floral richness ?Comment:Floral richness and abundance were estimated along five parallel 100‐m‐long transects within each 1‐ha plot separated by 10–15 m (Figure 1c). All blooming flowers encountered within 1 m of either side of the transect were identified to species and counted. |
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Variable ID
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18915 | 18910 | 18912 | 18909 | 18916 | 18913 | 18918 | 18914 | 18917 | 18911 | 19361 | 18919 | 18930 | 18934 | 18923 | 18920 | 18932 | 18931 | 18933 | 18922 | 18921 |
Not reported | Not reported | Not reported | Not reported | Not reported | Not reported | Not reported | Not reported | Not reported | Not reported | CC | Not reported | Not reported | FQI | Not reported | Not reported | Not reported | Not reported | Not reported | Not reported | Not reported | |
Qualitative-Quantitative
variable.detail.continuousCategoricalHelp
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Qualitative (Class, Rating or Ranking) | Qualitative (Class, Rating or Ranking) | Qualitative (Class, Rating or Ranking) | Qualitative (Class, Rating or Ranking) | Quantitative (Cardinal Only) | Quantitative (Cardinal Only) | Quantitative (Cardinal Only) | Quantitative (Cardinal Only) | Qualitative (Class, Rating or Ranking) | Qualitative (Class, Rating or Ranking) | Qualitative (Class, Rating or Ranking) | Quantitative (Cardinal Only) | Quantitative (Cardinal Only) | Quantitative (Cardinal Only) | Quantitative (Cardinal Only) | Quantitative (Cardinal Only) | Quantitative (Cardinal Only) | Quantitative (Cardinal Only) | Quantitative (Cardinal Only) | Quantitative (Cardinal Only) | Quantitative (Cardinal Only) |
Cardinal-Ordinal
variable.detail.cardinalOrdinalHelp
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Ordinal | Non-Ordinal | Non-Ordinal | Non-Ordinal | Cardinal | Cardinal | Cardinal | Cardinal | Non-Ordinal | Non-Ordinal | Ordinal | Cardinal | Cardinal | Cardinal | Cardinal | Cardinal | Cardinal | Cardinal | Cardinal | Cardinal | Cardinal |
Not applicable | Not applicable | Not applicable | Not applicable | g cm^-3 | % | unitless | °C | Not applicable | Not applicable | Not applicable | unitless | unitless | unitless | No. | unitless | No. | No. | No. | No. | No. |
Bare ground rank |
Bee community composition (pre-management) ?Comment:Pre-management bee community composition was not reported in the paper. |
Management type ?Comment:Twenty 1 ha plots were evenly distributed across three management types—burning, burning and haying, or patch‐burn grazing. Plots were sampled in two patches in each grazed prairie site, one less than a year since the most recent fire (patch‐burn grazing‐Y1) and one that had not been burned during the previous 2 years (patch‐burn grazing‐Y3) resulting in a total of four treatments, entered as runs in ESML. |
Plant community composition (pre-management) ?Comment:Pre-management plant community composition and characteristics were not reported in the paper. |
Soil bulk density ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. Bulk density (g/cm3) was determined by drying an intact 2‐cm‐long portion of the soil sample for an additional 24 hr at 105°C and then dividing the dry mass by the sample's volume. |
Soil moisture ?Comment:To determine soil characteristics both at the soil surface and at the depths of nesting for bees, 17 ± 26 cm (Cane & Neff, 2011) soil cores were taken under the centre of each tent using a 2 cm in diameter and 30‐cm‐long soil probe when tents were removed. Soil temperature and moisture levels were measured at the soil surface and at 30 cm by inserting a digital thermometer (Cooper ATKINS®) and a soil moisture meter (EXTECH®) into the top and bottom of the soil core respectively. |
Soil pH ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. Soil pH was measured on 10 g of soil homogenised in 10 ml of distilled water after resting for 15 min using a digital pH meter (Mettler Toledo). To ensure accuracy the pH meter was calibrated prior to each use. |
Soil temperature ?Comment:To determine soil characteristics both at the soil surface and at the depths of nesting for bees, 17 ± 26 cm (Cane & Neff, 2011) soil cores were taken under the centre of each tent using a 2 cm in diameter and 30‐cm‐long soil probe when tents were removed. Soil temperature and moisture levels were measured at the soil surface and at 30 cm by inserting a digital thermometer (Cooper ATKINS®) and a soil moisture meter (EXTECH®) into the top and bottom of the soil core respectively. |
Soil texture ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. To determine soil texture, 15 g of soil was added to 15 ml of distilled water in a 25 cm2 flat bottomed cell culture flask and shaken vigorously. After settling for a minimum of 24 hr, the relative proportion of each layer of the clay, silt and sand was measured and used to determine the soil texture (USDA NRCS, 2016). |
Soil type ?Comment:Soil type and characteristics were not reported in the paper. |
Conservation coefficient (plant species) ?Comment:Using floral richness recorded in plots, we calculated Floristic Quality Index (FQI). FQI is a metric devised by botanists to assess the vegetative quality of a habitat. This metric combines the floral richness of the plot and the coefficient of conservatism (CC)… Using the CC for each plant species as designated in Ladd and Thomas (2015) for Missouri or Taft et al. (1997) for Illinois when absent from the Missouri list, we calculated the FQI for each plot as: FQI=mean CC∗√N where N is the native species richness and mean CC is the mean of the coefficient of conservatism. All invasive species were assigned a C of 0 and not included in the native species richness calculation as implemented by Maginel, Knapp, Kabrick, Olson, and Muzika (2016). |
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. |
Bee community composition ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Floristic Quality Index (FQI) ?Comment:Using floral richness recorded in plots, we calculated Floristic Quality Index (FQI). FQI is a metric devised by botanists to assess the vegetative quality of a habitat. This metric combines the floral richness of the plot and the coefficient of conservatism (CC), and thus provides a rank‐based assessment of the sensitivity of a plant species to disturbance (on a scale of 1–10). FQI can provide important information on the overall conservation status of the habitat. Using the CC for each plant species as designated in Ladd and Thomas (2015) for Missouri or Taft et al. (1997) for Illinois when absent from the Missouri list, we calculated the FQI for each plot as FQI=mean CC∗√N where N is the native species richness and mean CC is the mean of the coefficient of conservatism. All invasive species were assigned a C of 0 and not included in the native species richness calculation as implemented by Maginel, Knapp, Kabrick, Olson, and Muzika (2016). FQI was designed to evaluate the entire plant community, but we only evaluated the flowering community. |
Log(floral abundance) |
Plant community composition ?Comment:Differences in plant community composition relative to management was visualized in Non‐metric dimensional scaling (NMDS) ordination. Multivariate analysis showed management was the most important variable in determining plant community composition. |
Bee abundance ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Bee richness ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Bees nesting ?Comment:Beginning at 1900 the evening before bee and floral sampling, fifteen 0.36 m2 ground emergence tents (BugDorm BT2006, Taichung, Taiwan) were installed to capture nesting bees at each plot (Sardinas & Kremen, 2014; Sardinas et al., 2016). Tents were installed after dusk when female bees return to the nest after foraging. They were secured with stakes and soil on flaps to prevent overturning and limit insect escape, and kill jars were filled with soapy water. The proportion of tents capturing bees at a plot is herein referred to as “nesting rate”. |
Floral abundance ?Comment:Floral richness and abundance were estimated along five parallel 100‐m‐long transects within each 1‐ha plot separated by 10–15 m (Figure 1c). All blooming flowers encountered within 1 m of either side of the transect were identified to species and counted. Dense heads and inflorescences were counted as a single flower. Flowering plant species that were in bloom in the 1 ha sampling plots, but not encountered within a transect were recorded as present and assigned an abundance of 0.5. Adding a small abundance for these observed species allows more accurate richness counts and prevents species detected outside of transects to have minimal influence on total plot abundance so as not to skew abundance figures between plots. |
Floral richness ?Comment:Floral richness and abundance were estimated along five parallel 100‐m‐long transects within each 1‐ha plot separated by 10–15 m (Figure 1c). All blooming flowers encountered within 1 m of either side of the transect were identified to species and counted. |
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Predictor-Intermediate-Response
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Predictor |
Predictor |
Predictor |
Predictor |
Predictor |
Predictor |
Predictor |
Predictor |
Predictor |
Predictor |
Predictor |
Intermediate (Computed) Variable |
Response |
Response |
Response |
Response |
Response |
Response |
Response |
Response |
Response |
Predictor Variable Type
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Time- or Space-varying Variable |
Time- or Space-varying Variable |
Time- or Space-varying Variable |
Time- or Space-varying Variable |
Time- or Space-varying Variable |
Time- or Space-varying Variable |
Time- or Space-varying Variable |
Time- or Space-varying Variable |
Time- or Space-varying Variable |
Time- or Space-varying Variable |
Constant or Parameter | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
Response Variable Type
variable.detail.resClassHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
Computed Variable |
Computed Variable |
Computed Variable |
Computed Variable |
Observed Variable |
Observed Variable |
Observed Variable |
Observed Variable |
Observed Variable |
Data Source/Type
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Local field/Lab data (e.g., particular to the EM study) | Local field/Lab data (e.g., particular to the EM study) | Local field/Lab data (e.g., particular to the EM study) | Local field/Lab data (e.g., particular to the EM study) | Local field/Lab data (e.g., particular to the EM study) | Local field/Lab data (e.g., particular to the EM study) | Local field/Lab data (e.g., particular to the EM study) | Local field/Lab data (e.g., particular to the EM study) | Local field/Lab data (e.g., particular to the EM study) | Map or database (e.g., wide coverage, wide availability, measured or modeled) | Derived from a look-up table | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Local field/Lab data (e.g., particular to the EM study) | Local field/Lab data (e.g., particular to the EM study) | Local field/Lab data (e.g., particular to the EM study) | Local field/Lab data (e.g., particular to the EM study) | Local field/Lab data (e.g., particular to the EM study) |
Variable Classification Hierarchy
variable.detail.vchLevel1Help
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5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
1. Policy Regarding Use or Management of Ecosystem Resources |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
2. Land Surface (or Water Body) Cover, Use, Substrate, or Metric |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
5. Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services |
--Physical/chemical characteristics of nonliving ecosystem components |
--Biological characteristics, processes or requirements of living ecosystem components |
--Land parcel-specific policies (e.g., regarding parcel use, development, protection, management) |
--Biological characteristics, processes or requirements of living ecosystem components |
--Physical/chemical characteristics of nonliving ecosystem components |
--Physical/chemical characteristics of nonliving ecosystem components |
--Physical/chemical characteristics of nonliving ecosystem components |
--Physical/chemical characteristics of nonliving ecosystem components |
--Physical/chemical characteristics of nonliving ecosystem components |
--Dominant soil or substrate type |
--Biological characteristics, processes or requirements of living ecosystem components |
--Physical/chemical characteristics of nonliving ecosystem components |
--Biological characteristics, processes or requirements of living ecosystem components |
--Ecosystem- or landscape-level metrics or indices of ecological condition, rarity or vulnerability |
--Biological characteristics, processes or requirements of living ecosystem components |
--Biological characteristics, processes or requirements of living ecosystem components |
--Biological characteristics, processes or requirements of living ecosystem components |
--Biological characteristics, processes or requirements of living ecosystem components |
--Biological characteristics, processes or requirements of living ecosystem components |
--Biological characteristics, processes or requirements of living ecosystem components |
--Biological characteristics, processes or requirements of living ecosystem components |
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----Physical/chemical characteristics of soils, substrates, rocks |
----Biological characteristics, processes or requirements of fauna |
----Land management practice goals or obligations |
----Biological characteristics, processes or requirements of flora and fungi |
----Physical/chemical characteristics of soils, substrates, rocks |
----Physical/chemical characteristics of soils, substrates, rocks |
----Physical/chemical characteristics of soils, substrates, rocks |
----Physical/chemical characteristics of soils, substrates, rocks |
----Physical/chemical characteristics of soils, substrates, rocks |
----Biological characteristics, processes or requirements of flora and fungi |
----Physical/chemical characteristics of soils, substrates, rocks |
----Biological characteristics, processes or requirements of fauna |
----Grassland/herbaceous condition, rarity or vulnerability |
----Biological characteristics, processes or requirements of flora and fungi |
----Biological characteristics, processes or requirements of flora and fungi |
----Biological characteristics, processes or requirements of fauna |
----Biological characteristics, processes or requirements of fauna |
----Biological characteristics, processes or requirements of fauna |
----Biological characteristics, processes or requirements of flora and fungi |
----Biological characteristics, processes or requirements of flora and fungi |
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------Other, multiple, unspecified or unclear |
------Pollinators (nonspecific) |
------Herbaceous plants (grasses, forbs) |
------Soil structural characteristics |
------Soil hydrologic characteristics |
------Soil chemistry, composition, and characteristics (non-contaminants) |
------Soil temperature |
------Soil chemistry, composition, and characteristics (non-contaminants) |
------Species richness and biodiversity indices |
------Other, multiple, unspecified or unclear |
------Pollinators (nonspecific) |
------Cover characteristics (canopy cover, herbaceous cover or leaf area) |
------Herbaceous plants (grasses, forbs) |
------Pollinators (nonspecific) |
------Pollinators (nonspecific) |
------Pollinators (nonspecific) |
------Cover characteristics (canopy cover, herbaceous cover or leaf area) |
------Species richness and biodiversity indices |
Bare ground rank |
Bee community composition (pre-management) ?Comment:Pre-management bee community composition was not reported in the paper. |
Management type ?Comment:Twenty 1 ha plots were evenly distributed across three management types—burning, burning and haying, or patch‐burn grazing. Plots were sampled in two patches in each grazed prairie site, one less than a year since the most recent fire (patch‐burn grazing‐Y1) and one that had not been burned during the previous 2 years (patch‐burn grazing‐Y3) resulting in a total of four treatments, entered as runs in ESML. |
Plant community composition (pre-management) ?Comment:Pre-management plant community composition and characteristics were not reported in the paper. |
Soil bulk density ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. Bulk density (g/cm3) was determined by drying an intact 2‐cm‐long portion of the soil sample for an additional 24 hr at 105°C and then dividing the dry mass by the sample's volume. |
Soil moisture ?Comment:To determine soil characteristics both at the soil surface and at the depths of nesting for bees, 17 ± 26 cm (Cane & Neff, 2011) soil cores were taken under the centre of each tent using a 2 cm in diameter and 30‐cm‐long soil probe when tents were removed. Soil temperature and moisture levels were measured at the soil surface and at 30 cm by inserting a digital thermometer (Cooper ATKINS®) and a soil moisture meter (EXTECH®) into the top and bottom of the soil core respectively. |
Soil pH ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. Soil pH was measured on 10 g of soil homogenised in 10 ml of distilled water after resting for 15 min using a digital pH meter (Mettler Toledo). To ensure accuracy the pH meter was calibrated prior to each use. |
Soil temperature ?Comment:To determine soil characteristics both at the soil surface and at the depths of nesting for bees, 17 ± 26 cm (Cane & Neff, 2011) soil cores were taken under the centre of each tent using a 2 cm in diameter and 30‐cm‐long soil probe when tents were removed. Soil temperature and moisture levels were measured at the soil surface and at 30 cm by inserting a digital thermometer (Cooper ATKINS®) and a soil moisture meter (EXTECH®) into the top and bottom of the soil core respectively. |
Soil texture ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. To determine soil texture, 15 g of soil was added to 15 ml of distilled water in a 25 cm2 flat bottomed cell culture flask and shaken vigorously. After settling for a minimum of 24 hr, the relative proportion of each layer of the clay, silt and sand was measured and used to determine the soil texture (USDA NRCS, 2016). |
Soil type ?Comment:Soil type and characteristics were not reported in the paper. |
Conservation coefficient (plant species) ?Comment:Using floral richness recorded in plots, we calculated Floristic Quality Index (FQI). FQI is a metric devised by botanists to assess the vegetative quality of a habitat. This metric combines the floral richness of the plot and the coefficient of conservatism (CC)… Using the CC for each plant species as designated in Ladd and Thomas (2015) for Missouri or Taft et al. (1997) for Illinois when absent from the Missouri list, we calculated the FQI for each plot as: FQI=mean CC∗√N where N is the native species richness and mean CC is the mean of the coefficient of conservatism. All invasive species were assigned a C of 0 and not included in the native species richness calculation as implemented by Maginel, Knapp, Kabrick, Olson, and Muzika (2016). |
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. |
Bee community composition ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Floristic Quality Index (FQI) ?Comment:Using floral richness recorded in plots, we calculated Floristic Quality Index (FQI). FQI is a metric devised by botanists to assess the vegetative quality of a habitat. This metric combines the floral richness of the plot and the coefficient of conservatism (CC), and thus provides a rank‐based assessment of the sensitivity of a plant species to disturbance (on a scale of 1–10). FQI can provide important information on the overall conservation status of the habitat. Using the CC for each plant species as designated in Ladd and Thomas (2015) for Missouri or Taft et al. (1997) for Illinois when absent from the Missouri list, we calculated the FQI for each plot as FQI=mean CC∗√N where N is the native species richness and mean CC is the mean of the coefficient of conservatism. All invasive species were assigned a C of 0 and not included in the native species richness calculation as implemented by Maginel, Knapp, Kabrick, Olson, and Muzika (2016). FQI was designed to evaluate the entire plant community, but we only evaluated the flowering community. |
Log(floral abundance) |
Plant community composition ?Comment:Differences in plant community composition relative to management was visualized in Non‐metric dimensional scaling (NMDS) ordination. Multivariate analysis showed management was the most important variable in determining plant community composition. |
Bee abundance ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Bee richness ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Bees nesting ?Comment:Beginning at 1900 the evening before bee and floral sampling, fifteen 0.36 m2 ground emergence tents (BugDorm BT2006, Taichung, Taiwan) were installed to capture nesting bees at each plot (Sardinas & Kremen, 2014; Sardinas et al., 2016). Tents were installed after dusk when female bees return to the nest after foraging. They were secured with stakes and soil on flaps to prevent overturning and limit insect escape, and kill jars were filled with soapy water. The proportion of tents capturing bees at a plot is herein referred to as “nesting rate”. |
Floral abundance ?Comment:Floral richness and abundance were estimated along five parallel 100‐m‐long transects within each 1‐ha plot separated by 10–15 m (Figure 1c). All blooming flowers encountered within 1 m of either side of the transect were identified to species and counted. Dense heads and inflorescences were counted as a single flower. Flowering plant species that were in bloom in the 1 ha sampling plots, but not encountered within a transect were recorded as present and assigned an abundance of 0.5. Adding a small abundance for these observed species allows more accurate richness counts and prevents species detected outside of transects to have minimal influence on total plot abundance so as not to skew abundance figures between plots. |
Floral richness ?Comment:Floral richness and abundance were estimated along five parallel 100‐m‐long transects within each 1‐ha plot separated by 10–15 m (Figure 1c). All blooming flowers encountered within 1 m of either side of the transect were identified to species and counted. |
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Spatial Extent Area
variable.detail.spExtentHelp
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1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | Not recorded for Constant or Parameter Variables | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. |
Spatially Distributed?
variable.detail.spDistributedHelp
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Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Not recorded for Constant or Parameter Variables | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Observations Spatially Patterned?
variable.detail.regularSpGrainHelp
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Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Not recorded for Constant or Parameter Variables | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Spatial Grain Type
variable.detail.spGrainTypeHelp
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not recorded for Constant or Paarameter Variables | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
variable.detail.spGrainSizeHelp
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1 ha | 1 ha | 1 ha | 1 ha | 1 ha | 1 ha | 1 ha | 1 ha | 1 ha | 1 ha | Not recorded for Constant or Parameter Variables | 1 ha | 1 ha | 1 ha | 1 ha | 1 ha | 1 ha | 1 ha | 1 ha | 1 ha | 1 ha |
Spatial Density
variable.detail.spDensityHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not recorded for Constant or Parameter Variables | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EnviroAtlas URL
variable.detail.enviroAtlasURLHelp
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Acres of Land Enrolled in the Conservation Reserve Program (CRP) | GAP Ecological Systems |
Bare ground rank |
Bee community composition (pre-management) ?Comment:Pre-management bee community composition was not reported in the paper. |
Management type ?Comment:Twenty 1 ha plots were evenly distributed across three management types—burning, burning and haying, or patch‐burn grazing. Plots were sampled in two patches in each grazed prairie site, one less than a year since the most recent fire (patch‐burn grazing‐Y1) and one that had not been burned during the previous 2 years (patch‐burn grazing‐Y3) resulting in a total of four treatments, entered as runs in ESML. |
Plant community composition (pre-management) ?Comment:Pre-management plant community composition and characteristics were not reported in the paper. |
Soil bulk density ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. Bulk density (g/cm3) was determined by drying an intact 2‐cm‐long portion of the soil sample for an additional 24 hr at 105°C and then dividing the dry mass by the sample's volume. |
Soil moisture ?Comment:To determine soil characteristics both at the soil surface and at the depths of nesting for bees, 17 ± 26 cm (Cane & Neff, 2011) soil cores were taken under the centre of each tent using a 2 cm in diameter and 30‐cm‐long soil probe when tents were removed. Soil temperature and moisture levels were measured at the soil surface and at 30 cm by inserting a digital thermometer (Cooper ATKINS®) and a soil moisture meter (EXTECH®) into the top and bottom of the soil core respectively. |
Soil pH ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. Soil pH was measured on 10 g of soil homogenised in 10 ml of distilled water after resting for 15 min using a digital pH meter (Mettler Toledo). To ensure accuracy the pH meter was calibrated prior to each use. |
Soil temperature ?Comment:To determine soil characteristics both at the soil surface and at the depths of nesting for bees, 17 ± 26 cm (Cane & Neff, 2011) soil cores were taken under the centre of each tent using a 2 cm in diameter and 30‐cm‐long soil probe when tents were removed. Soil temperature and moisture levels were measured at the soil surface and at 30 cm by inserting a digital thermometer (Cooper ATKINS®) and a soil moisture meter (EXTECH®) into the top and bottom of the soil core respectively. |
Soil texture ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. To determine soil texture, 15 g of soil was added to 15 ml of distilled water in a 25 cm2 flat bottomed cell culture flask and shaken vigorously. After settling for a minimum of 24 hr, the relative proportion of each layer of the clay, silt and sand was measured and used to determine the soil texture (USDA NRCS, 2016). |
Soil type ?Comment:Soil type and characteristics were not reported in the paper. |
Conservation coefficient (plant species) ?Comment:Using floral richness recorded in plots, we calculated Floristic Quality Index (FQI). FQI is a metric devised by botanists to assess the vegetative quality of a habitat. This metric combines the floral richness of the plot and the coefficient of conservatism (CC)… Using the CC for each plant species as designated in Ladd and Thomas (2015) for Missouri or Taft et al. (1997) for Illinois when absent from the Missouri list, we calculated the FQI for each plot as: FQI=mean CC∗√N where N is the native species richness and mean CC is the mean of the coefficient of conservatism. All invasive species were assigned a C of 0 and not included in the native species richness calculation as implemented by Maginel, Knapp, Kabrick, Olson, and Muzika (2016). |
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. |
Bee community composition ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Floristic Quality Index (FQI) ?Comment:Using floral richness recorded in plots, we calculated Floristic Quality Index (FQI). FQI is a metric devised by botanists to assess the vegetative quality of a habitat. This metric combines the floral richness of the plot and the coefficient of conservatism (CC), and thus provides a rank‐based assessment of the sensitivity of a plant species to disturbance (on a scale of 1–10). FQI can provide important information on the overall conservation status of the habitat. Using the CC for each plant species as designated in Ladd and Thomas (2015) for Missouri or Taft et al. (1997) for Illinois when absent from the Missouri list, we calculated the FQI for each plot as FQI=mean CC∗√N where N is the native species richness and mean CC is the mean of the coefficient of conservatism. All invasive species were assigned a C of 0 and not included in the native species richness calculation as implemented by Maginel, Knapp, Kabrick, Olson, and Muzika (2016). FQI was designed to evaluate the entire plant community, but we only evaluated the flowering community. |
Log(floral abundance) |
Plant community composition ?Comment:Differences in plant community composition relative to management was visualized in Non‐metric dimensional scaling (NMDS) ordination. Multivariate analysis showed management was the most important variable in determining plant community composition. |
Bee abundance ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Bee richness ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Bees nesting ?Comment:Beginning at 1900 the evening before bee and floral sampling, fifteen 0.36 m2 ground emergence tents (BugDorm BT2006, Taichung, Taiwan) were installed to capture nesting bees at each plot (Sardinas & Kremen, 2014; Sardinas et al., 2016). Tents were installed after dusk when female bees return to the nest after foraging. They were secured with stakes and soil on flaps to prevent overturning and limit insect escape, and kill jars were filled with soapy water. The proportion of tents capturing bees at a plot is herein referred to as “nesting rate”. |
Floral abundance ?Comment:Floral richness and abundance were estimated along five parallel 100‐m‐long transects within each 1‐ha plot separated by 10–15 m (Figure 1c). All blooming flowers encountered within 1 m of either side of the transect were identified to species and counted. Dense heads and inflorescences were counted as a single flower. Flowering plant species that were in bloom in the 1 ha sampling plots, but not encountered within a transect were recorded as present and assigned an abundance of 0.5. Adding a small abundance for these observed species allows more accurate richness counts and prevents species detected outside of transects to have minimal influence on total plot abundance so as not to skew abundance figures between plots. |
Floral richness ?Comment:Floral richness and abundance were estimated along five parallel 100‐m‐long transects within each 1‐ha plot separated by 10–15 m (Figure 1c). All blooming flowers encountered within 1 m of either side of the transect were identified to species and counted. |
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Temporal Extent
variable.detail.tempExtentHelp
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2012-2016 | 2012-2016 | 2012-2016 | 2012-2016 | 2012-2016 | 2012-2016 | 2012-2016 | 2012-2016 | 2012-2016 | 2012-2016 | Not recorded for Constant or Parameter Variables | 2012-2016 | 2012-2016 | 2012-2016 | 2012-2016 | 2012-2016 | 2012-2016 | 2012-2016 | 2012-2016 | 2012-2016 | 2012-2016 |
Temporally Distributed?
variable.detail.tempDistributedHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not recorded for Constant or Parameter Variables | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
Regular Temporal Grain?
variable.detail.regularTempGrainHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not recorded for Constant or Parameter Variables | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
Temporal Grain Size Value
variable.detail.tempGrainSizeValHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not recorded for Constant or Parameter Variables | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
Temporal Grain Size Units
variable.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not recorded for Constant or Parameter Variables | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
Temporal Density
variable.detail.tempDensityHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not recorded for Constant or Parameter Variables | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
Bare ground rank |
Bee community composition (pre-management) ?Comment:Pre-management bee community composition was not reported in the paper. |
Management type ?Comment:Twenty 1 ha plots were evenly distributed across three management types—burning, burning and haying, or patch‐burn grazing. Plots were sampled in two patches in each grazed prairie site, one less than a year since the most recent fire (patch‐burn grazing‐Y1) and one that had not been burned during the previous 2 years (patch‐burn grazing‐Y3) resulting in a total of four treatments, entered as runs in ESML. |
Plant community composition (pre-management) ?Comment:Pre-management plant community composition and characteristics were not reported in the paper. |
Soil bulk density ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. Bulk density (g/cm3) was determined by drying an intact 2‐cm‐long portion of the soil sample for an additional 24 hr at 105°C and then dividing the dry mass by the sample's volume. |
Soil moisture ?Comment:To determine soil characteristics both at the soil surface and at the depths of nesting for bees, 17 ± 26 cm (Cane & Neff, 2011) soil cores were taken under the centre of each tent using a 2 cm in diameter and 30‐cm‐long soil probe when tents were removed. Soil temperature and moisture levels were measured at the soil surface and at 30 cm by inserting a digital thermometer (Cooper ATKINS®) and a soil moisture meter (EXTECH®) into the top and bottom of the soil core respectively. |
Soil pH ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. Soil pH was measured on 10 g of soil homogenised in 10 ml of distilled water after resting for 15 min using a digital pH meter (Mettler Toledo). To ensure accuracy the pH meter was calibrated prior to each use. |
Soil temperature ?Comment:To determine soil characteristics both at the soil surface and at the depths of nesting for bees, 17 ± 26 cm (Cane & Neff, 2011) soil cores were taken under the centre of each tent using a 2 cm in diameter and 30‐cm‐long soil probe when tents were removed. Soil temperature and moisture levels were measured at the soil surface and at 30 cm by inserting a digital thermometer (Cooper ATKINS®) and a soil moisture meter (EXTECH®) into the top and bottom of the soil core respectively. |
Soil texture ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. To determine soil texture, 15 g of soil was added to 15 ml of distilled water in a 25 cm2 flat bottomed cell culture flask and shaken vigorously. After settling for a minimum of 24 hr, the relative proportion of each layer of the clay, silt and sand was measured and used to determine the soil texture (USDA NRCS, 2016). |
Soil type ?Comment:Soil type and characteristics were not reported in the paper. |
Conservation coefficient (plant species) ?Comment:Using floral richness recorded in plots, we calculated Floristic Quality Index (FQI). FQI is a metric devised by botanists to assess the vegetative quality of a habitat. This metric combines the floral richness of the plot and the coefficient of conservatism (CC)… Using the CC for each plant species as designated in Ladd and Thomas (2015) for Missouri or Taft et al. (1997) for Illinois when absent from the Missouri list, we calculated the FQI for each plot as: FQI=mean CC∗√N where N is the native species richness and mean CC is the mean of the coefficient of conservatism. All invasive species were assigned a C of 0 and not included in the native species richness calculation as implemented by Maginel, Knapp, Kabrick, Olson, and Muzika (2016). |
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. |
Bee community composition ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Floristic Quality Index (FQI) ?Comment:Using floral richness recorded in plots, we calculated Floristic Quality Index (FQI). FQI is a metric devised by botanists to assess the vegetative quality of a habitat. This metric combines the floral richness of the plot and the coefficient of conservatism (CC), and thus provides a rank‐based assessment of the sensitivity of a plant species to disturbance (on a scale of 1–10). FQI can provide important information on the overall conservation status of the habitat. Using the CC for each plant species as designated in Ladd and Thomas (2015) for Missouri or Taft et al. (1997) for Illinois when absent from the Missouri list, we calculated the FQI for each plot as FQI=mean CC∗√N where N is the native species richness and mean CC is the mean of the coefficient of conservatism. All invasive species were assigned a C of 0 and not included in the native species richness calculation as implemented by Maginel, Knapp, Kabrick, Olson, and Muzika (2016). FQI was designed to evaluate the entire plant community, but we only evaluated the flowering community. |
Log(floral abundance) |
Plant community composition ?Comment:Differences in plant community composition relative to management was visualized in Non‐metric dimensional scaling (NMDS) ordination. Multivariate analysis showed management was the most important variable in determining plant community composition. |
Bee abundance ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Bee richness ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Bees nesting ?Comment:Beginning at 1900 the evening before bee and floral sampling, fifteen 0.36 m2 ground emergence tents (BugDorm BT2006, Taichung, Taiwan) were installed to capture nesting bees at each plot (Sardinas & Kremen, 2014; Sardinas et al., 2016). Tents were installed after dusk when female bees return to the nest after foraging. They were secured with stakes and soil on flaps to prevent overturning and limit insect escape, and kill jars were filled with soapy water. The proportion of tents capturing bees at a plot is herein referred to as “nesting rate”. |
Floral abundance ?Comment:Floral richness and abundance were estimated along five parallel 100‐m‐long transects within each 1‐ha plot separated by 10–15 m (Figure 1c). All blooming flowers encountered within 1 m of either side of the transect were identified to species and counted. Dense heads and inflorescences were counted as a single flower. Flowering plant species that were in bloom in the 1 ha sampling plots, but not encountered within a transect were recorded as present and assigned an abundance of 0.5. Adding a small abundance for these observed species allows more accurate richness counts and prevents species detected outside of transects to have minimal influence on total plot abundance so as not to skew abundance figures between plots. |
Floral richness ?Comment:Floral richness and abundance were estimated along five parallel 100‐m‐long transects within each 1‐ha plot separated by 10–15 m (Figure 1c). All blooming flowers encountered within 1 m of either side of the transect were identified to species and counted. |
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Not applicable ?Comment:Bare ground was assessed using a ranked scale from 0 to 4 (0:0%, 1:1%–25%, 2:26%–50%, 3:51%–75% and 4: >75%). |
Not applicable | Not applicable | Not applicable | Not reported | Not reported | Not reported | Not reported | Not applicable | Not applicable | Not applicable |
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. |
Not reported ?Comment:The Morisita dissimilarity was used for bees. The Morisita dissimilarity matrix is more robust in under sampled datasets, which would be expected for bees. Multivariate analysis results for effects of resources and management on bee community composition after backward selection showed that management and Soil PC1 had the greatest effects. |
unitless ?Comment:Values captured from linear regression figure. 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. Soil PC1 was the most predictive variable for FQI. |
Not reported ?Comment: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. Management was the most predictive variable for Log(floral abundance). |
Not reported |
Not reported ?Comment: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. Management and Soil PC1 were the most predictive variables for Bee abundance. |
Not reported ?Comment: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. Log(floral abundance) and Floral richness were the most predictive variables for Bee richness. |
Not reported ?Comment:Logistic regression of management and resource availability on bee nesting within plots showed nesting was significantly predicted by soil PC1 and floral abundance. |
Not reported ?Comment:Floral abundance was logged for analysis. |
Not reported ?Comment: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). Soil PC1, Log (floral abundance) and Management were the most predictive variables for Floral richness. |
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Min Value
variable.detail.minEstHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not reported | Not reported | Not reported | Not reported | Not applicable | Not applicable | Not applicable | Varies by run; view runs to see value | Not reported | Varies by run; view runs to see value | Not reported | Not reported | Not reported | Not reported | Not reported | Not reported | Not reported |
Max Value
variable.detail.estHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not reported | Not reported | Not reported | Not reported | Not applicable | Not applicable | Not applicable | Varies by run; view runs to see value | Not reported | Varies by run; view runs to see value | Not reported | Not reported | Not reported | Not reported | Not reported | Not reported | Not reported |
Other Value Type
variable.detail.natureOtherEstHelp
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Mean | Not applicable | Not applicable | Not applicable | Not applicable | Mean | Not applicable | Mean | Not applicable | Not applicable | Not applicable | Mean | Not applicable | Not applicable | Mean | Not applicable | Mean | Mean | Mean | Not applicable | Mean |
Other Value
variable.detail.otherEstHelp
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Varies by run; view runs to see values | Not applicable | Not applicable | Not applicable | Not reported | Varies by run; view runs to see value | Not reported | Varies by run; view runs to see value | Not applicable | Not applicable | Not applicable | Varies by run; view runs to see values | Not reported | Not reported | Varies by run; view runs to see value | Not reported | Varies by run; view runs to see value | Varies by run; view runs to see values | Varies by run; view runs to see value | Not reported | Varies by run; view runs to see value |
Bare ground rank |
Bee community composition (pre-management) ?Comment:Pre-management bee community composition was not reported in the paper. |
Management type ?Comment:Twenty 1 ha plots were evenly distributed across three management types—burning, burning and haying, or patch‐burn grazing. Plots were sampled in two patches in each grazed prairie site, one less than a year since the most recent fire (patch‐burn grazing‐Y1) and one that had not been burned during the previous 2 years (patch‐burn grazing‐Y3) resulting in a total of four treatments, entered as runs in ESML. |
Plant community composition (pre-management) ?Comment:Pre-management plant community composition and characteristics were not reported in the paper. |
Soil bulk density ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. Bulk density (g/cm3) was determined by drying an intact 2‐cm‐long portion of the soil sample for an additional 24 hr at 105°C and then dividing the dry mass by the sample's volume. |
Soil moisture ?Comment:To determine soil characteristics both at the soil surface and at the depths of nesting for bees, 17 ± 26 cm (Cane & Neff, 2011) soil cores were taken under the centre of each tent using a 2 cm in diameter and 30‐cm‐long soil probe when tents were removed. Soil temperature and moisture levels were measured at the soil surface and at 30 cm by inserting a digital thermometer (Cooper ATKINS®) and a soil moisture meter (EXTECH®) into the top and bottom of the soil core respectively. |
Soil pH ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. Soil pH was measured on 10 g of soil homogenised in 10 ml of distilled water after resting for 15 min using a digital pH meter (Mettler Toledo). To ensure accuracy the pH meter was calibrated prior to each use. |
Soil temperature ?Comment:To determine soil characteristics both at the soil surface and at the depths of nesting for bees, 17 ± 26 cm (Cane & Neff, 2011) soil cores were taken under the centre of each tent using a 2 cm in diameter and 30‐cm‐long soil probe when tents were removed. Soil temperature and moisture levels were measured at the soil surface and at 30 cm by inserting a digital thermometer (Cooper ATKINS®) and a soil moisture meter (EXTECH®) into the top and bottom of the soil core respectively. |
Soil texture ?Comment:Soil samples were dried at ambient temperature (20°C, ~45% humidity) for one month to reduce moisture content before measuring bulk density, soil texture and pH on surface and bottom section of cores. To determine soil texture, 15 g of soil was added to 15 ml of distilled water in a 25 cm2 flat bottomed cell culture flask and shaken vigorously. After settling for a minimum of 24 hr, the relative proportion of each layer of the clay, silt and sand was measured and used to determine the soil texture (USDA NRCS, 2016). |
Soil type ?Comment:Soil type and characteristics were not reported in the paper. |
Conservation coefficient (plant species) ?Comment:Using floral richness recorded in plots, we calculated Floristic Quality Index (FQI). FQI is a metric devised by botanists to assess the vegetative quality of a habitat. This metric combines the floral richness of the plot and the coefficient of conservatism (CC)… Using the CC for each plant species as designated in Ladd and Thomas (2015) for Missouri or Taft et al. (1997) for Illinois when absent from the Missouri list, we calculated the FQI for each plot as: FQI=mean CC∗√N where N is the native species richness and mean CC is the mean of the coefficient of conservatism. All invasive species were assigned a C of 0 and not included in the native species richness calculation as implemented by Maginel, Knapp, Kabrick, Olson, and Muzika (2016). |
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. |
Bee community composition ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Floristic Quality Index (FQI) ?Comment:Using floral richness recorded in plots, we calculated Floristic Quality Index (FQI). FQI is a metric devised by botanists to assess the vegetative quality of a habitat. This metric combines the floral richness of the plot and the coefficient of conservatism (CC), and thus provides a rank‐based assessment of the sensitivity of a plant species to disturbance (on a scale of 1–10). FQI can provide important information on the overall conservation status of the habitat. Using the CC for each plant species as designated in Ladd and Thomas (2015) for Missouri or Taft et al. (1997) for Illinois when absent from the Missouri list, we calculated the FQI for each plot as FQI=mean CC∗√N where N is the native species richness and mean CC is the mean of the coefficient of conservatism. All invasive species were assigned a C of 0 and not included in the native species richness calculation as implemented by Maginel, Knapp, Kabrick, Olson, and Muzika (2016). FQI was designed to evaluate the entire plant community, but we only evaluated the flowering community. |
Log(floral abundance) |
Plant community composition ?Comment:Differences in plant community composition relative to management was visualized in Non‐metric dimensional scaling (NMDS) ordination. Multivariate analysis showed management was the most important variable in determining plant community composition. |
Bee abundance ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Bee richness ?Comment:Using multiple sampling methods should provide a more complete picture of the bee community during the sampling time (Geroff, Gibbs, & McCravy, 2014). All bees were later identified to species. These data resulted in three response variables that are tested here: bee community composition, bee richness and bee abundance. |
Bees nesting ?Comment:Beginning at 1900 the evening before bee and floral sampling, fifteen 0.36 m2 ground emergence tents (BugDorm BT2006, Taichung, Taiwan) were installed to capture nesting bees at each plot (Sardinas & Kremen, 2014; Sardinas et al., 2016). Tents were installed after dusk when female bees return to the nest after foraging. They were secured with stakes and soil on flaps to prevent overturning and limit insect escape, and kill jars were filled with soapy water. The proportion of tents capturing bees at a plot is herein referred to as “nesting rate”. |
Floral abundance ?Comment:Floral richness and abundance were estimated along five parallel 100‐m‐long transects within each 1‐ha plot separated by 10–15 m (Figure 1c). All blooming flowers encountered within 1 m of either side of the transect were identified to species and counted. Dense heads and inflorescences were counted as a single flower. Flowering plant species that were in bloom in the 1 ha sampling plots, but not encountered within a transect were recorded as present and assigned an abundance of 0.5. Adding a small abundance for these observed species allows more accurate richness counts and prevents species detected outside of transects to have minimal influence on total plot abundance so as not to skew abundance figures between plots. |
Floral richness ?Comment:Floral richness and abundance were estimated along five parallel 100‐m‐long transects within each 1‐ha plot separated by 10–15 m (Figure 1c). All blooming flowers encountered within 1 m of either side of the transect were identified to species and counted. |
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Bee community composition | Floristic Quality Index (FQI) | Log(floral abundance) | Plant community composition | Bee abundance | Bee richness | Bees nesting | Floral abundance | Floral richness | |||||||
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