Nicholson_etal_2024_LandscapesOfRisk
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These data include observed pesticide residues in pollens gathered by honey bees (<i>Apis mellifera</i>), bumble bees (<i>Bombus</i><i> </i><i>terrestris</i>), and mason bees (<i>Osmia bicornis</i>) across 41 sites in southern Sweden from 2019 through 2021 (site network centroid: 55°43'09.2"N, 13°47'12.2"E). From these observed compound concentrations we calculated toxicity-weighted exposure (<i>TWE</i>) to estimate the direct pesticide risk to bees (Knapp et al. 2023), where the <i>TWE</i> for each compound <i>(TWE</i><sub><em>i</em></sub>) is the ratio of its exposure value (<i>c</i><sub><em>i</em></sub>) and its respective acute toxicity endpoint (LD<sub>50i</sub>). We summed TWE<sub>i</sub>s to calculate risk (see Materials and Methods).We analyzed the landscape surrounding our sites at three spatial scales according to the average foraging range for our three genera (<i>Osmia</i>: 500; <i>Bombus</i>: 1,500; and <i>Apis: </i>2,000 m). Landscape metrics were: proportion agricultural land, the proportion of pesticide-treated area, calculated landscape crop diversity, the interspersion and juxtaposition index, edge density, mean patch area (see Materials and Methods).Using a national data set of pesticide use and agricultural parcel data we calculated landscape-level metrics of pesticide use: total landscape pesticide load, total landscape toxic load (see Materials and Methods).We also used the spatialized pesticide use data as input a previously developed and validated mechanistic model (Lonsdorf et al., 2024) to predict bees' pesticide exposure and subsequent risk (i.e., summed TWE<sub>i</sub>s, as above).These data were used to compare the ability of the three classes of landscape-scale variables to predict this observed pesticide risk: 1) landscape composition and configuration metrics, 2) landscape load based on national pesticide use data, and 3) predictions from the bee pesticide exposure model. For the second and third class we distinguish between potential and realized load and risk (see Materials and Methods).The associated paper has been been published as an article in <i>Journal of Applied Ecology</i>.The columns included in this data are:<b>SiteID </b>- Field Identifier for the sites used in this analysis.<b>BeeSpecies </b>- the bee species from which pollen samples were collected. BB: <i>Bombus terrestris</i>; HB: <i>Apis melifera</i>; SB: <i>Osmia bicornis.</i><b>Year </b>- When the study took place.<b>Month </b>- Month number of when pollen samples were collected.<b>ObservedPesticideRisk </b>- pesticide risk (summed toxicity weighted concentrations) observed in pollen samples.<b>TotalLandscapeLoad_Realized </b>- The amount of different compounds applied in the landscape within the foraging range of the bee species from which the pollen sample was collected. This variable is calculated for all compounds that <i>were detected</i> in the sample (i.e., realized load). A log transformed and z-standardized version of this variable is also included (this is what was used in statistical models).<b>TotalLandscapeLoad_Potential </b>- The amount of different compounds applied in the landscape within the foraging range of the bee species from which the pollen sample was collected. This variable is calculated for all compounds that <i>were screened for</i> in the sample (i.e., potential load). A log transformed and z-standardized version of this variable is also included (this is what was used in statistical models).<b>TotalLandscapeToxicLoad_Realized </b>- The amount of different compounds applied in the landscape <i>weighting by their toxicity</i> within the foraging range of the bee species from which the pollen sample was collected. This variable is calculated for all compounds that <i>were detected</i> in the sample (i.e., realized load). A log transformed and z-standardized version of this variable is also included (this is what was used in statistical models).<b>TotalLandscapeToxicLoad_Potential </b>- The amount of different compounds applied in the landscape <i>weighting by their toxicity</i> within the foraging range of the bee species from which the pollen sample was collected. This variable is calculated for all compounds that <i>were screened for</i> in the sample (i.e., potential load). A log transformed and z-standardized version of this variable is also included (this is what was used in statistical models).<b>TotalLandscapeRisk_Realized </b>- The level of different compounds that bees are predicted to encounter based on a spatially explicit model of landscape exposure (SEMLE) for bees (Lonsdorf et al. 2024). These exposure predictions are weighted by their toxicity and summed (as above). This variable is calculated for all compounds that <i>were detected</i> in the sample (i.e., realized load). A log transformed and z-standardized version of this variable is also included (this is what was used in statistical models).<b>TotalLandscapeRisk_Potential </b>- The level of different compounds that bees are predicted to encounter based on a spatially explicit model of landscape exposure (SEMLE) for bees (Lonsdorf et al. 2024). These exposure predictions are weighted by their toxicity and summed (as above). This variable is calculated for all compounds that <i>were </i><i>s</i><i>creened for </i>in the sample (i.e., realized load). A log transformed and z-standardized version of this variable is also included (this is what was used in statistical models).<b>propAg </b>- The proportion of agricultural land within the foraging range of the bee species from which the pollen sample was collected. A z-standardized version of this variable is also included (this is what was used in statistical models).<b>propTreatedAg </b>- The proportion of pesticide-treated agricultural land within the foraging range of the bee species from which the pollen sample was collected. A z-standardized version of this variable is also included (this is what was used in statistical models).<b>CropDiversity </b>- Shannon index of crop types (richness) and their coverage (evenness) within the foraging range of the bee species from which the pollen sample was collected. A z-standardized version of this variable is also included (this is what was used in statistical models).<b>iji </b>- interspersion and juxtaposition index (McGarigal & Marks, 1995). A z-standardized version of this variable is also included (this is what was used in statistical models).References:Knapp, J. L., Nicholson, C. C., Jonsson, O., de Miranda, J. R., & Rundlöf, M. (2023). Ecological traits interact with landscape context to determine bees’ pesticide risk. <i>Nature Ecology & Evolution</i>, <i>7</i>(4), 547-556.Lonsdorf, E. V., Rundlöf, M., Nicholson, C. C., & Williams, N. M. (2024). A spatially explicit model of landscape pesticide exposure to bees: Development, exploration, and evaluation. <i>Science of the Total Environment</i>, <i>908</i>, 168146.McGarigal, K., & Marks, B. J. (1995). Spatial pattern analysis program for quantifying landscape structure. <i>Gen. Tech. Rep. PNW-GTR-351. US Department of Agriculture, Forest Service, Pacific Northwest Research Station</i>, 1-122.Nicholson, C. C., Lonsdorf, E. V., Andersson, G. K. S., Knapp, J., Svensson, G. P., Gönczi, M., Jonsson, O., de Miranda, J. R., Williams, N. M., & Rundlöf, M. (2024). Landscapes of risk: a comparative analysis of landscape metrics for the ecotoxicological assessment of pesticide risk to bees. <i>Journal of Applied Ecology. </i>doi: TBD
本数据集包含2019年至2021年间，瑞典南部41个样点（样点网络中心点坐标：55°43'09.2"N，13°47'12.2"E）中，西方蜜蜂（Apis mellifera）、熊蜂（Bombus terrestris）及壁蜂（Osmia bicornis）采集的花粉内观测到的农药残留。 基于观测到的化合物浓度，本研究计算了毒性加权暴露（toxicity-weighted exposure, TWE）以评估蜂类面临的直接农药风险（Knapp等，2023）：单种化合物的毒性加权暴露（TWE<sub>i</sub>）为其暴露浓度（c<sub>i</sub>）与对应急性毒性终点（半数致死剂量LD<sub>50i</sub>）的比值。最终风险通过对所有TWE<sub>i</sub>求和得到（详见材料与方法）。 本研究根据三种蜂属的平均觅食范围，在三个空间尺度上分析样点周边的景观特征：壁蜂属（Osmia）为500米，熊蜂属（Bombus）为1500米，蜜蜂属（Apis）为2000米。所涉及的景观指标包括：农田占比、农药处理农田占比、景观作物多样性、镶嵌与邻接指数（interspersion and juxtaposition index, IJI）、边缘密度及平均斑块面积（详见材料与方法）。 利用全国农药使用数据与农业地块数据集，本研究计算了景观尺度的农药使用指标：总景观农药负荷与总景观毒性负荷（详见材料与方法）。 本研究同时将空间化的农药使用数据作为输入，导入此前开发并验证的机理模型（Lonsdorf等，2024），以预测蜂类的农药暴露水平及后续风险（即前述的TWE总和）。 本数据集用于对比三类景观尺度变量对观测到的农药风险的预测能力：1）景观组成与配置指标；2）基于全国农药使用数据的景观负荷；3）蜜蜂农药暴露模型的预测结果。对于第二类与第三类变量，我们区分了潜在负荷/风险与实际实现负荷/风险（详见材料与方法）。 相关研究论文已发表于《应用生态学杂志》（Journal of Applied Ecology）。 本数据集包含以下字段： 1. **站点ID（SiteID）**：本次分析所用样地的唯一标识符。 2. **蜂类物种（BeeSpecies）**：采集花粉样本的蜂种。其中BB代表熊蜂（Bombus terrestris），HB代表西方蜜蜂（Apis mellifera），SB代表壁蜂（Osmia bicornis）。 3. **年份（Year）**：研究开展的年份。 4. **月份（Month）**：采集花粉样本的月份（以数字表示）。 5. **观测农药风险（ObservedPesticideRisk）**：花粉样本中观测到的农药风险，即毒性加权浓度总和。 6. **实际景观总负荷（TotalLandscapeLoad_Realized）**：采集花粉样本的蜂种觅食范围内，景观中实际施用的各类农药化合物总量。该变量仅针对样本中**检测到**的化合物计算（即实际实现负荷）。本数据集同时提供该变量的对数转换与Z标准化版本（为统计模型所用）。 7. **潜在景观总负荷（TotalLandscapeLoad_Potential）**：采集花粉样本的蜂种觅食范围内，景观中理论上可施用的各类农药化合物总量。该变量针对样本中**纳入检测筛查**的所有化合物计算（即潜在负荷）。本数据集同时提供该变量的对数转换与Z标准化版本（为统计模型所用）。 8. **实际景观总毒性负荷（TotalLandscapeToxicLoad_Realized）**：采集花粉样本的蜂种觅食范围内，景观中各类农药按其毒性加权后的总量。该变量仅针对样本中**检测到**的化合物计算（即实际实现负荷）。本数据集同时提供该变量的对数转换与Z标准化版本（为统计模型所用）。 9. **潜在景观总毒性负荷（TotalLandscapeToxicLoad_Potential）**：采集花粉样本的蜂种觅食范围内，景观中各类农药按其毒性加权后的理论总量。该变量针对样本中**纳入检测筛查**的所有化合物计算（即潜在负荷）。本数据集同时提供该变量的对数转换与Z标准化版本（为统计模型所用）。 10. **实际景观总风险（TotalLandscapeRisk_Realized）**：基于蜜蜂景观暴露空间显式模型（spatially explicit model of landscape exposure, SEMLE，Lonsdorf等，2024）预测的、蜂类将接触的各类化合物水平，经毒性加权后求和（如前文所述）。该变量仅针对样本中**检测到**的化合物计算（即实际实现负荷）。本数据集同时提供该变量的对数转换与Z标准化版本（为统计模型所用）。 11. **潜在景观总风险（TotalLandscapeRisk_Potential）**：基于蜜蜂景观暴露空间显式模型（SEMLE，Lonsdorf等，2024）预测的、蜂类将接触的各类化合物水平，经毒性加权后求和（如前文所述）。该变量针对样本中**纳入检测筛查**的所有化合物计算（即潜在负荷）。本数据集同时提供该变量的对数转换与Z标准化版本（为统计模型所用）。 12. **农田占比（propAg）**：采集花粉样本的蜂种觅食范围内，农田所占的比例。本数据集同时提供该变量的Z标准化版本（为统计模型所用）。 13. **农药处理农田占比（propTreatedAg）**：采集花粉样本的蜂种觅食范围内，经农药处理的农田所占的比例。本数据集同时提供该变量的Z标准化版本（为统计模型所用）。 14. **作物多样性（CropDiversity）**：采集花粉样本的蜂种觅食范围内，作物类型的香农指数（涵盖物种丰富度与覆盖均匀度）。本数据集同时提供该变量的Z标准化版本（为统计模型所用）。 15. **镶嵌与邻接指数（iji）**：根据McGarigal & Marks（1995）的定义计算的景观镶嵌与邻接指数。本数据集同时提供该变量的Z标准化版本（为统计模型所用）。 ### 参考文献 1. Knapp, J. L., Nicholson, C. C., Jonsson, O., de Miranda, J. R., & Rundlöf, M. (2023). 生态性状与景观背景交互作用决定蜂类的农药风险. *Nature Ecology & Evolution*（《自然-生态学与进化》）, 7(4): 547-556. 2. Lonsdorf, E. V., Rundlöf, M., Nicholson, C. C., & Williams, N. M. (2024). 蜜蜂景观农药暴露空间显式模型：开发、探索与评估. *Science of the Total Environment*（《总环境科学》）, 908: 168146. 3. McGarigal, K., & Marks, B. J. (1995). 用于量化景观结构的空间格局分析程序. *Gen. Tech. Rep. PNW-GTR-351. US Department of Agriculture, Forest Service, Pacific Northwest Research Station*, 1-122. 4. Nicholson, C. C., Lonsdorf, E. V., Andersson, G. K. S., Knapp, J., Svensson, G. P., Gönczi, M., Jonsson, O., de Miranda, J. R., Williams, N. M., & Rundlöf, M. (2024). 风险景观：用于蜂类农药风险生态毒理学评估的景观指标对比分析. *Journal of Applied Ecology*（《应用生态学杂志》）. doi: TBD
