DOI: 10.5061/dryad.vt4b8gtzk
title: Olfaction in the Anthropocene: NO3 negatively impacts floral scent and nocturnal pollination

description: There is growing concern about sensory pollutants impacting ecological
 communities. Anthropogenically enhanced oxidants (O3 and NO3) rapidly
 degrade floral scents, potentially reducing pollinator attraction to
 flowers. However, the physiological and behavioral impacts on pollinators
 and plant fitness are unknown. Using a nocturnal flower-moth system, we
 find that atmospheric concentrations of NO3, but not O3, eliminate flower
 visitation by moths, and it is the reaction of NO3 to a subset of
 monoterpenes that reduces the scent’s attractiveness. Global atmospheric
 models of floral scent oxidation reveal that pollinators in certain urban
 areas may have a reduced ability to perceive and navigate to flowers.
 These results illustrate the impact of anthropogenic pollutants on an
 animal’s olfactory ability and indicate that such pollutants may be
 critical regulators of global pollination.
description: The GEOS-Chem chemical transport model was used for simulations
 driven by assimilated MERRA-2 (Modern-Era Retrospective analysis for
 Research and Applications, Version 2) meteorological fields
 (<em>33</em>). GEOS-Chem is an open-access global atmospheric
 chemistry model that can simulate tropospheric and stratospheric
 oxidant–aerosol chemistry, aerosol microphysics, and budgets of various
 gases, including ozone and nitrate radicals. The model uses analyzed winds
 and other meteorological variables produced by Goddard Earth Observing
 System (GEOS) simulations of the NASA Global Modeling and Assimilation
 Office (GMAO) with assimilated meteorological observations and couples
 these measurements with the chemistry. The recent model advances and
 assimilation of observed meteorological fields via MERRA-2 have led to
 accurate simulation of ozone and nitrate radical concentrations in the
 models (<em>51</em>). In the present day
 simulation, we use GEOS-Chem version 12.1.0 driven by MERAA-2 meteorology
 at a horizontal resolution of 2° × 2.5° (lat × long) with 47 vertical
 levels. The simulation period was from March 2012 to June 2014, with the
 first year as a spin-up period to allow for the accumulation of
 intermediate chemical reservoir species. These years were representative
 of the conditions of our study period, and have detailed information on
 the physicochemical environment. A reference simulation was conducted
 based on the public version 13.2.1 of GEOS-Chem
 (https://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_13.2.1). The
 HOx –NOx –VOC–O3–BrOx tropospheric chemistry chemical mechanism in the
 reference simulation is described in Mao et al. (<em>52,
 53</em>) with recent updates for biogenic volatile organic compound
 (VOC) chemistry (<em>54, 55</em>). The Model of Emissions of
 Gasses and Aerosols from Nature v2.1 (MEGAN) (<em>56</em>) was
 used to derive biogenic emissions, and the Global Fire Emissions Database
 (GFED4) was used for open fire emissions. For anthropogenic emissions of
 various gaseous and aerosol species`, the CEDS (Community Emission Data
 System) inventory was the basis (<em>57</em>) but combined
 with local inventories in Asia, USA, Canada, Mexico, Europe, and Africa.
 More details about the simulations are described in
 (<em>58</em>). For the preindustrial
 GEOS-Chem model run, we used the GEOS-Chem chemical transport model
 version 13.2.1 (<em>50</em>) driven by assimilated meteorology
 from MERRA-2 (Modern-Era Retrospective analysis for Research and
 Applications, Version 2) from the year 2013 at a horizontal resolution of
 4°x 5° and 72 vertical levels up to 0.01 hPa. GEOS-Chem contains detailed
 HO<sub>x</sub>-NO<sub>x</sub>-VOC-ozone-halogen-aerosol chemistry (<em>59</em>) including isoprene chemistry from (<em>60</em>) and reactive uptake of NO<sub>2</sub>, NO<sub>3</sub>, and N<sub>2</sub>O<sub>5</sub> by aerosols from (<em>61, 62</em>). Global anthropogenic emissions of pollutants including VOCs, NO<sub>3</sub>, and various aerosol species are from the Community Emissions Data System (CEDS) v2 inventory from (<em>63</em>). Biogenic VOC emissions are from the Model of Emissions of Gasses and Aerosols from Nature v2.1 (MEGAN) (<em>56, 64</em>) and biogenic soil NO<em><sub>x</sub></em> emissions are from (<em>65</em>). Open fire emissions are from Global Fire Emissions Database (GFED4.1)(<em>66</em>). Dry deposition for aerosols and gasses is based on a resistance-in-series scheme described in in (<em>67</em>). The wet deposition scheme is described in (<em>68</em>) for water-soluble aerosols, and (<em>69</em>) for gases. To simulate a preindustrial atmosphere, all anthropogenic emissions are turned off (<em>sensu</em> (<em>70</em>)), and this was run alongside the 2013 and 2021 simulations with all emissions turned on. The preindustrial simulation had 12 months of spin-up to equilibrate the model to preindustrial conditions. More details about the simulation are described in Jongebloed et al. (2023) (<em>70</em>). The O<sub>3</sub> and NO<sub>3</sub> emissions and meteorology were averaged monthly allowing a conservative estimate of NO<sub>3</sub> and O<sub>3</sub> levels. Although these reactive compounds vary over the 24 h period, calculating the monthly average provides a conservative estimate for modeling the effects on the floral volatiles and flower recognition distances. Changes to the GEOS-Chem chemical mechanism and model physics between version 12.1.0 and version 13.2.1 preclude direct comparison of model output from the two different versions. We use the version 12.1.0 present-day simulation as the primary representation of present-day NO<sub>3</sub> and O<sub>3</sub> because this simulation was run at higher resolution (2° × 2.5°) compared to the preindustrial simulation run with 13.2.1 (4° × 5°). To avoid influence from model updates when comparing version 13.2.1 preindustrial simulation to the version 12.1.0 present-day simulation, a factor map was created by dividing the preindustrial simulation by the 2013 simulation with all emissions turned on. This factor map was then applied to the 2013 map from GEOS-Chem version 12.1.0 for preindustrial comparisons. To examine the 2013 ozone and NO<sub>3</sub> emissions to current levels, we compared model runs of O<sub>3</sub> and NO<sub>3</sub> for 2019 and 2021, relative to 2013. We find that the increase in O<sub>3</sub> and NO<sub>3</sub> from the preindustrial to present day are orders of magnitude larger than the interannual variability between 2013, 2019, and 2021, especially in regions within or near high anthropogenic pollutant emissions, such as urban areas. Global trends across years showed similar O<sub>3</sub> and NO<sub>3 </sub>levels, with the secular increase in anthropogenic NOx leading to little variability in NO<sub>3</sub> over the last ten years across large portions of North and South America, Europe, and megacity regions, especially compared to pre-industrial conditions. These results reflect prior work in troposphere O<sub>3</sub> (<em>71, 72</em>), and NO<sub>3</sub> (<em>30, 73</em>). However, natural NOx sources in the form of biomass burning caused year-to-year temporal and spatial variability in the importance of NO<sub>3</sub> regions distant from anthropogenic urban sources (Canada, areas in N. Eurasia)(<em>30</em>). To further examine the relationship between the population size around large urban areas and preindustrial impacts and pollinator detection distances, we performed regression models between these variables and the population of the top 98 megacities (Table S13). Regression models were compared based on their adjusted-R<sup>2</sup> and Akaike information criterion (AIC). Based on the R<sup>2</sup> and AIC scores, only second or third-degree quadratic regressions were used to prevent data over-fitting. <strong>GEOS-Chem code availability:</strong> GEOS-Chem version 12.1.0 code is publicly available at http://wiki.geos-chem.org/GEOS-Chem_12#12.1.0 (https://doi.org/10.5281/zenodo.1553349, International GEOS-Chem User Community, 2018). GEOS-Chem version 13.2.1 code is publicly available at https://doi.org/10.5281/zenodo.5500717. 2019 GEOS-Chem 13.2.1 benchmark output was used for creating NO<sub>3</sub> comparison maps to determine the interannual variation in NO<sub>3</sub> concentrations. 2021/2013, 2019/2013, and 2021/2019 comparison maps were made by dividing the July NO<sub>3</sub> maps from the GEOS-Chem 13.2.1 simulations. The bottom vertical layers of the model output were used in computing the pollinator impacts maps. The data was processed, and the maps were plotted using MATLAB R2020b. Data from January 2013 was used to investigate the pollinator impacts in the summer in the southern hemisphere, and data from July 2013 was used to investigate the pollinator impacts in the summer in the northern hemisphere. For the pre-industrial comparisons map, values over the oceans were masked by creating a mask using a modified version of the global-land-mask 1.0.0 package by toddkarin in Python, using the ‘Natural Earth’ map database. The land/water values in each grid cell were summed, and if there were any land values in the grid cell, the grid cell was classified as a land grid cell. Antarctica was also excluded from consideration by masking all grid cells below -56° in latitude. Mean O<sub>3</sub> and NO<sub>3</sub> values over the northern latitudes were calculated by averaging all values over land between 10° and 50° latitude in the July 2013 GEOS-Chem model simulation. A plot of reaction rate ratios of various monoterpenes, sesquiterpenes, and green leaf volatiles (<em>74</em>)(Table S12, Fig. S8B) at the mean northern latitude concentrations of NO<sub>3</sub> (2.6 ppt) and O<sub>3</sub> (37 ppb) was generated. The O<sub>3</sub> reactivity ratio was obtained by dividing the O<sub>3</sub> reaction rate by the NO<sub>3</sub> reaction rate. The NO<sub>3</sub> reactivity ratio was obtained by dividing the NO<sub>3</sub> reaction rate by the O<sub>3</sub> reaction rate. The monoterpenes used in the model are commonly emitted from flowers in many regions of the world, including North America, Europe, Central Asia, the Middle East, and southern Africa (<em>7, 11, 35-37</em>).
description: # Olfaction in the Anthropocene: NO3 negatively impacts floral scent and
 nocturnal pollination
 [https://doi.org/10.5061/dryad.vt4b8gtzk](https://doi.org/10.5061/dryad.vt4b8gtzk) Includes data on chemical analytical results (GCMS, ToF), behavior, and field measurements. ## Description of the data and file structure File list: --- * Fig2D_ToFdata.txt Fig2D_ToFdata.txt corresponds to the ion counts from the Figure 2D scent in reaction to the ozone and nitrate radicals. Column 1 is the time (minutes) and Column 2 is the ion counts. --- * Fig_S2A_Megachile_raw_GCEAD_trace.atf * Fig_S2B_Hyles_raw_GCEAD_trace.atf * Fig_S2B_Manduca_raw_GCEAD_trace.atf GC-EAD measurements in Figure S2A and S2B. In the files, Column 1 is the timestamp (sec.), Column 2 is the EAD response (mV), and Column 3 is the GC trace (V). --- * FigS3_20180711_OPALL_6700000048751741_180719.csv * FigS3_20180725_OPALL_FD00000048732241_180730.csv * FigS3_20180801_OPALL_6700000048751741_180806.csv Figure S3A data files, corresponding to the time, temperature, and humidity measurements at the field site. In each file, Column 1 is the date time (ibutton format), Column 2 is the temperature (Celsius), and Column 3 is the humidity (RH).  --- * FigS3_O3_LOG32a.txt * FigS3_O3_LOG33a.txt * FigS3_O3_LOG36a.txt Figure S3 data files, corresponding to the ozone measurements at the field site. In each file, Column 1 is the ozone concentration, Column 5 is the day, and Column 6 is the time (24h clock).  --- * Fig_S4_Table_S9_VOCUS_analysis_and_plotting.ipynb Jupyter notebook code for analysis of the VOCUS data. --- * Fig_S4_aldoxime_NO3_oxidation_benzene_cation_CIMS.txt * Fig_S4_eucalyptol_NO3_oxidation_benzene_cation_CIMS.txt * Fig_S4_monoterpene_NO3_oxidation_benzene_cation_CIMS.txt Each data file corresponds to the data of the ion counts for aldoxime, eucalyptol, or monoterpene quantification from the ToF-CIMS experiment plotted in Figure S4. In each file, Column 1 is the time (sec.), and Column 2 is the ion count for the different compounds. --- * Fig_S5C_Manduca_dose_response_wind_tunnel_behavior_data.xlsx * Fig_S5_Manduca_Hyles_EAG_dose_response_mean_of_largest.csv Fig_S5C_Manduca_dose_response_wind_tunnel_behavior_data.xlsx and Fig_S5_Manduca_Hyles_EAG_dose_response_mean_of_largest.csv are the data for the behavioral results and electroantennogram results plotted in Figure S5. The behavior corresponds to the total number of tested moths, and the number that fed from the flowers. The EAG results corresponds to the data from the EAG deflections and the tested stimulus (flower odor) concentration. --- * FigS6_CIMS_data.xlsx Excel file with the Chemical Ionization MS data in Figure S6. Columns 1 through 11 correspond to yyyy, mo, dd, hh, mm, ss, C10H16O4 (ion counts), C10H18O4 (ion counts), C10H15NO6 (ion counts), C10H17NO6 (ion counts), and C10H17NO7 (ion counts). --- * Table_S1_anemometer_data.txt Table_S1_anemometer_data file provides the concatenated data for the 3D wind velocity measurements from the anemometer during the sampling times, and corresponds to the data in Table S1. Column 1 is the u wind velocity (cm/s); Column 2 is the v (cm/s), Column 3 w (cm/s), and Column 4 is the temperature (Kelvin). Data was collected at 32 Hz. --- The TableS1_FigS3_CAPS files are the NO2 measurements during the field trials. Each file corresponds to a sampling day. In each file, Column 1 corresponds to IgorTime (hh:mm:ss dd-mmm-yyyy), NO2(counts), RawLoss (zeroed counts),Pressure (psi),Temperature (Kelvin), and Signal(counts). Column 2 corresponds to Status(binary),LastBaseline(counts),Time(year-month-day-hour-min-sec.), and Column 3 is the Timestamp(year-month-day-hour-min-sec.). * TableS1_FigS3_CAPS_201807260002.dat * TableS1_FigS3_CAPS_201807260824.dat * TableS1_FigS3_CAPS_201807262126.dat * TableS1_FigS3_CAPS_201807262128.dat * TableS1_FigS3_CAPS_201807270315.dat * TableS1_FigS3_CAPS_201807270316.dat * TableS1_FigS3_CAPS_201808012201.dat * TableS1_FigS3_CAPS_201808020000.dat * TableS1_FigS3_CAPS_201808020814.dat * TableS1_FigS3_CAPS_201808022050.dat * TableS1_FigS3_CAPS_201808030000.dat * TableS1_FigS3_CAPS_201808110204.dat * TableS1_FigS3_CAPS_201808110220.dat * TableS1_FigS3_CAPS_201808110408.dat * TableS1_FigS3_CAPS_201808110429.dat * TableS1_FigS3_CAPS_201808120121.dat * TableS1_FigS3_CAPS_201808120122.dat --- * TableS8_MSdata_tic_front.csv TableS8_MSdata_tic_front.csv corresponds to the GCMS data from the floral scent analysis (total ion counts). Column 1 is the time (minutes) and Column 2 is the total ion counts. --- * Table_S10.xlsx The data table (also shown in the Supplementary Information in the manuscript Table S10) representing the floral visitation data. --- ## Sharing/Access information Data is available for download on this site. ## Code/Software Available on the doi cited in the manuscript. Also available on riffelllab github repo: [https://github.com/riffelllab/Chan-et-al](https://github.com/riffelllab/Chan-et-al)