DOI: 10.5061/dryad.t1g1jwtdm
title: Data from: Induced flow cools hovering bumble bees

description: Understanding how flying insects manage heat exchange is critical for
 predicting their survival in dynamic thermal environments. To fly, insects
 propel air downwards to offset body weight, inducing airflow over their
 bodies. Remarkably, the potential cooling effect of this self-generated
 airflow is largely unstudied. We measured induced airflow and wingbeat
 kinematics for hovering bumble bees (Bombus impatiens) across a range of
 body sizes and then measured the cooling effect of flows of the same
 magnitude in a vertical wind tunnel. We combined these data in heat
 balance models to predict transient and equilibrium body temperatures of
 hovering bumble bees with and without self-generated wind. Measured
 self-induced airflow was substantial (up to 1 m s-1), and varied with body
 size and wingbeat kinematics, contributing significantly to thermal
 stability. Without this self-induced airflow, bees of all sizes rapidly
 overheated across a range of environmental conditions, highlighting the
 importance of this overlooked heat-loss mechanism in the heat budget of
 flying insects. Our findings suggest that shifts in wingbeat kinematics
 required for altered force production not only affect energetics and,
 therefore, heat production but also alter the induced flow and associated
 convective heat loss.
description: # Data from: Induced flow cools hovering bumble bees Glass, Petranek,
 Dillon ## Data files: ### anemHSVfinal.csv readings from hot wire
 anemometer and associated high-speed video data * date: experiment date *
 video.top: name of high-speed video recorded from above used for kinematic
 analyses * timecode: timepoint in the video used for kinematic analyses *
 anem.csv: name of the anemometer csv file * mass.g: bee mass in grams,
 measured immediately after a flight recording * max.anem: maximum
 anemometer reading (0-1024 for Arduino DAC) during the flight sequence *
 wbf: wingbeat frequency measured from high-speed video * str.amp.L: stroke
 amplitude of left wing * str.amp.R: stroke amplitude of right wing * tc.s:
 time constant for anemometer * bee.color: marked color of bee (to keep
 track of multiple flights) * sus: whether flight was "sus"tained
 (stable, minimal pitch, yaw, rotation): yes, no, maybe * bee.id: unique
 number of the measured bee * slide.id: identity of the glass slide holding
 the mounted wing * winglen.mm: length of the mounted wing in mm * keep:
 whether or not to include in final analysis ###
 BillardonDarveau2019JEB.csv raw data provided by the authors for CO2
 production and body mass for B. impatiens workers (not included with the
 original paper). The only columns we use are: * Mass(mg): mass in mg *
 VCO2: rate of CO2 production, mL/hour ### cc_processed.csv data from
 cooling curves of freshly killed bees in a vertical wind tunnel. The
 estimated slope is used for subsequent hc and Nu estimates * date: date of
 trial * csv: file containing per second temperatures for the bee core and
 the surrounding air * bee: unique id (for cooling curve experiments) *
 mass.g: mass in gram, measured just prior to experiment * flow.ms: the
 velocity of flow in the vertical wind tunnel (m/s) * trt.order: order in
 which a given bee experienced different flow settings * bee.tc:
 thermocouple implanted in bee * ambient.inside.tc: thermocouple in wind
 tunnel just above bee * ambient.outside.tc: thermocouple outside wind
 tunnel in room * notes: * readfile: (see csv, this is just a coding
 convenience) * Ta.mean: mean temperature (K) for inside thermocouple
 (again a coding convenience) * int: intercept of the cooling curve fit *
 slope: slope of the cooling curve fit * r2: R-squared of the cooling curve
 fit ### hmvar1000.csv model outputs for estimated heat exchange (different
 modes and net), change in temperature, and body temperature through time
 for 1000 simulated bees at each combination of 6 masses
 (50,100,150,200,250,300 mg), 3 ambient temperatures (15, 25, 35 C), and 2
 levels of solar radiation (200, 1000 W/m2) for 10 minutes (600 seconds).
 Note that these data are generated by code in GlassInducedFlow.R but the
 output is provided here as a convenience because the simulations take a
 while to run (~19 hours on a fast desktop). * met: metabolic heat
 production (W) * sol: solar heat load (W) * lwr: long wavelength radiation
 (W) * con: convective heat exchange due to induced flow (W) * net: net
 heat exchange (met+sol+lwr+con), W * netnocon: net heat exchange without
 induced flow (met+sol+lwr) * sec: seconds * dT: change in body temperature
 (C) * Tb: body temperature (C) * dT.nc: change in body temperature (C)
 under netnocon conditions * Tb.nc: body temperature (C) under netnocon
 conditions * mass: mass, mg, one of 6 levels * Ta: ambient temperature
 (C), one of 3 levels * solar: W/m2, one of 2 levels * bee: bee number
 (1000 per combination of levels) ## Scripts: ### GlassetalInducedFlow.R
 -main script that does all analyses ### InducedFlowFunctions.R -functions
 used by the main script, which sources this script