DOI: 10.5061/dryad.wwpzgmsx0
title: Data from: <em>Vespula pensylvanica</em> locate odor sources across diverse natural wind conditions

description: Many organisms across ecosystems track odor plumes to locate mates and
 food. This behavior holds particular ecological significance for flying
 animals, such as bees and birds, due to their critical role in crop
 pollination. In flying insects, the task of localizing an odor source is
 particularly challenging due to the complicated dynamics associated with
 wind flow and odor plume dispersion through spatially complex
 environments. Though prior studies have discussed the role of wind
 dynamics in the foraging and flight behaviors of bees, our knowledge of
 how wind characteristics influence insects’ success and strategies to
 locate odor sources remains an open area of investigation. Here, we tested
 whether certain wind conditions were more favorable for foraging insects
 by comparing yellowjacket arrival times and corresponding wind conditions
 across three distinct natural environments. Our results indicated that
 Vespula pensylvanica were capable of locating odor sources across the full
 range of observed wind conditions, without any clear preferences. This
 suggests that insects have adapted strategies to perform odor localization
 tasks across the full spectrum of natural wind that they may encounter.
 These experiments provide insight into key considerations for future wind
 tunnel experiments which seek to better resolve insect plume tracking in
 understudied flow conditions.
description: For information on specific methodologies involved in data
 collections, see the associated article.
description: # *Vespula pensylvanica* locate odor sources across diverse natural wind
 conditions \[Access this dataset on Dryad]
 ([10.5061/dryad.wwpzgmsx0)](10.5061/dryad.wwpzgmsx0) ## Description of the
 data and file structure ### The data provided in this repository includes:
 * Raw wind files (.bin format) collected over a ~3 month period
  (raw_wind_data.zip) * Processed wind files (.hdf format), which can be
 directly read in any Python script
 (farm_preprocessed_hdfs.zip, urban_preprocessed_hdfs.zip, forest_preprocessed_hdfs.zip) * Simplified wind and arrival/trigger data for ease of analysis, as the combined preprocessed wind data is a large dataset (files_used_in_analysis.zip) Examples of how to use these files can be found in the associated GitHub repository.  ### Files and variables #### File: raw_wind_data.zip **Description:** all raw wind files collected (in .bin format), separate directories are included for each environment ("farm", "verdi" (i.e. the forest location), and "urban") and subdirectories within each environment directory descriptively segment daily/weekly periods of recording (with format MMDDYYYY).  Each binary file contains the data_type variable, with values: ``` millis - time since recording started (in milliseconds) lat - gps latitude lon - gps longitude gps_time - hour/minute/second of data, recorded by GPS (UTC) gps_date - year/month/day of data, recorded by GPS (UTC) S2 - Horizontal wind speed (m/s) wind - String variable with all recorded measurements, including wind speed (S2), horizontal wind direction (D), temperature (T), E-W wind velocity (U), N-S wind velocity (V), Z wind velocity (W), air density (AD). ``` #### File: farm_preprocessed_hdfs.zip **Description:** Corresponding preprocessed wind files for the farm environment resulting from using the processing script in our Github repository with all raw wind .bin files. A list of any files that were corrupt/erroneous are also included as a separate .csv file. Each hdf corresponds to a preprocessed .bin file. #### File: urban_preprocessed_hdfs.zip **Description:**  Corresponding preprocessed wind files for the urban environment resulting from using the processing script in our Github repository with all raw wind .bin files. A list of any files that were corrupt/erroneous are also included as a separate .csv file. Each hdf corresponds to a preprocessed .bin file. #### File: forest_preprocessed_hdfs.zip **Description:** Corresponding preprocessed wind files for the forest environment resulting from using the processing script in our Github repository with all raw wind .bin files. A list of any files that were corrupt/erroneous are also included as a separate .csv file. Each hdf corresponds to a preprocessed .bin file. Below is a list of variable names used in these preprocessed hdf files: ``` lat - gps latitude lon - gps longitude year - year of data recording month - month of data recording day - day of data recording hour - hour of data recording (UTC) minute - minute of data recording (UTC) second - minute of data recording (UTC) time_epoch - Epoch time S2 - Horizontal wind speed (m/s) D - Horizontal wind direction (0-360 degrees) T - Temperature (in Celcius) U - E-W wind velocity component (m/s) V - N-S wind velocity component (m/s) W - Z wind velocity component (m/s) ``` #### File: files_used_in_analysis.zip **Description:** All files that are directly used in our data analysis and figure generation notebook (see Github repository for all code/analysis). Within this .zip file there are multiple files: * **File: forest_df_6am_to_9pm.parquet** **Description**: A concatenation of all files in 'verdi_preprocessed_hdfs.zip', alongside the recorded trigger events in 'forest_master_sheet.csv'. Only wind data between 6am to 9pm is included in an effort to keep file size manageable, and nighttime wind recordings were not relevant to the analysis.  * **File: farm_df_6am_to_9pm.parquet** **Description:** A concatenation of all files in 'farm_preprocessed_hdfs.zip', alongside the recorded trigger events in 'farm_master_sheet.csv'. Only wind data between 6am to 9pm is included in an effort to keep file size manageable, and nighttime wind recordings were not relevant to the analysis.  * **File: urban_df_6am_to_9pm.parquet** **Description:** A concatenation of all files in 'urban_preprocessed_hdfs.zip', alongside the recorded trigger events in 'urban_master_sheet.csv'. Only wind data between 6am to 9pm is included in an effort to keep file size manageable, and nighttime wind recordings were not relevant to the analysis.  Relevant variable names in these preprocessed parquet files: ``` lat - gps latitude lon - gps longitude year - year of data recording month - month of data recording day - day of data recording hour - hour of data recording (UTC) minute - minute of data recording (UTC) second - minute of data recording (UTC) time_epoch - Epoch time S2 - Horizontal wind speed (m/s) D - Horizontal wind direction (0-360 degrees) T - Temperature (in Celcius) U - E-W wind velocity component (m/s) V - N-S wind velocity component (m/s) W - Z wind velocity component (m/s) trap_number - 'nan' if there was no arrival at that time, '1' or '2' during arrival events ``` * **File: urban_master_sheet.csv** **Description**: A list of all yellowjacket arrival times at the urban site throughout the period of data collection, and which trap they entered. * **File: farm_master_sheet.csv** **Description**: A list of all yellowjacket arrival times at the farm site throughout the period of data collection, and which trap they entered. * **File: forest_master_sheet.csv** **Description:** A list of all yellowjacket arrival times at the forest site throughout the period of data collection, and which trap they entered. Relevant variable names in these csv files: ``` date_time - local date + time of arrival (PDT) time_epoch - epoch time trap_number - '1' or '2' depending on which trap the insect entered ``` * **File: small_urban_10000.parquet** **Description**: Wind data merged with urban trap arrival times - only 5000 frames before and after the arrival time are kept for each trigger arrival in an effort to reduce data size (frame rate is 10Hz). * **File: small_forest_10000.parquet** **Description**: Wind data merged with forest trap arrival times - only 5000 frames before and after the arrival time are kept for each trigger arrival in an effort to reduce data size (frame rate is 10Hz). * **File: small_farm_10000.parquet** **Description:** Wind data merged with farm trap arrival times - only 5000 frames before and after the arrival time are kept for each trigger arrival in an effort to reduce data size (frame rate is 10Hz). Relevant variable names in these preprocessed parquet files: ``` lat - gps latitude lon - gps longitude year - year of data recording month - month of data recording day - day of data recording hour - hour of data recording (UTC) minute - minute of data recording (UTC) second - minute of data recording (UTC) time_epoch - Epoch time S2 - Horizontal wind speed (m/s) D - Horizontal wind direction (0-360 degrees) T - Temperature (in Celcius) U - E-W wind velocity component (m/s) V - N-S wind velocity component (m/s) W - Z wind velocity component (m/s) trap_number - 'nan' if there was no arrival at that time, '1' or '2' during arrival events ``` * **File: urban_daily_stats.hdf** **Description**: Randomly sampled 5 minute statistics (mean wind speed, mean wind direction, standard deviation in wind speed, standard deviation in wind direction) taken across the full 'urban_df_6am_to_9pm.parquet' dataset. * **File: forest_daily_stats.hdf** **Description**: Randomly sampled 5 minute statistics (mean wind speed, mean wind direction, standard deviation in wind speed, standard deviation in wind direction) taken across the full 'forest_df_6am_to_9pm.parquet' dataset. * **File: farm_daily_stats.hdf** **Description:** Randomly sampled 5 minute statistics (mean wind speed, mean wind direction, standard deviation in wind speed, standard deviation in wind direction) taken across the full 'farm_df_6am_to_9pm.parquet' dataset. Relevant variable names in these preprocessed hdf files: ``` avg_speed - 5 min average horizontal wind speed (m/s) avg_dir - 5 min average horizontal wind direction (0-360 degrees) avg_AD - 5 min average air density (kg/m^3) std_speed - 5 min stand deviation in horizontal wind speed (m/s) std_dir - 5 min standard deviation in direction (0-90deg) std_AD - 5 min standard deviation in air density (kg/m^3) avg_temp - 5 min average temperature (C) trap_number - 'nan' if there was no arrival at that time, '1' or '2' during arrival events time_epoch - epoch time TKE - 5 min turbulent kinetic energy environment - 'urban', 'farm' or 'forest' local_time - Local time (PDT) HMS_local - Hours, Minutes, Seconds (PDT) hours - local hour at time of recording TI - 5 minute average turbulence intensity ``` * **File: NOAA_daily_averages.csv** **Description**: Daily averages for Reno Tahoe Airport weather station (KRNO) available from NOAA's NCEI database. Relevant variable names in this file: ``` NAME - Weather station name/state/country LATITUDE - Latitude of station LONGITUDE - Longitude of station ELEVATION - Elevation of station DATE - Date of weather measurement PRCP - Daily total precipitation (inches) ``` ### Code/software All relevant processing and analysis scripts can be found in our public GitHub repository: [https://github.com/JaleesaHoule/YellowJacketTracking](https://github.com/JaleesaHoule/YellowJacketTracking) or the archived release of this GitHub repository: [https://doi.org/10.5281/zenodo.17443918](https://doi.org/10.5281/zenodo.17443918)