Flight of the bumble bee: Buzzes predict pollination services

Abstract

Multiple interacting factors drive recent declines in wild and managed bees, threatening their pollination services. Widespread and intensive monitoring could lead to more effective management of wild and managed bees. However, tracking their dynamic populations is costly. We tested the effectiveness of an inexpensive, noninvasive and passive acoustic survey technique for monitoring bumble bee behavior and pollination services. First, we assessed the relationship between the first harmonic of the flight buzz (characteristic frequency) and pollinator functional traits that influence pollination success using flight cage experiments and a literature search. We analyzed passive acoustic survey data from three locations on Pennsylvania Mountain, Colorado to estimate bumble bee activity. We developed an algorithm based on Computational Auditory Scene Analysis that identified and quantified the number of buzzes recorded in each location. We then compared visual and acoustic estimates of bumble bee activity. Using pollinator exclusion experiments, we tested the power of buzz density to predict pollination services at the landscape scale for two bumble bee pollinated alpine forbs (Trifolium dasyphyllum and T. parryi). We found that the characteristic frequency was correlated with traits known to affect pollination efficacy, explaining 30-52% of variation in body size and tongue length. Buzz density was highly correlated with visual estimates of bumble bee density (r = 0.97), indicating that acoustic signals are predictive of bumble bee activity. Buzz density predicted seed set in two alpine forbs when bumble bees were permitted access to the flowers, but not when they were excluded from visiting. Our results indicate that acoustic signatures of flight can be deciphered to monitor bee activity and pollination services to bumble bee pollinated plants. We propose that applications of this technique could assist scientists and farmers in rapidly detecting and responding to bee population declines.

Fig 1. Spectrogram of a typical flight buzz by a Bombus balteatus queen.

A spectrogram represents the energy of the audio signal within time-frequency bins, where the magnitude of each bin corresponds to the energy within a frequency range during a narrow time frame. The lowest band (approximately 175 Hz in this example) corresponds to the wing beat frequency. The sound is a harmonic series with energy at integer multiples of the wing beat frequency (known as the 1^st harmonic or fundamental frequency).

Fig 2. Characteristic frequency of flight buzzes for workers and queens of two alpine bumble bee species.

Buzz frequency was negatively related to wing length (indicated by the red line) (y = 259.2–7.57x), an estimate of body size that is not biased by pollen and nectar loads of the bees.

Fig 3. Characteristic frequency is related to bumble bee tongue length.

(A) Variation in characteristic frequency for two alpine bumble bees, Bombus balteatus and B. sylvicola is explained by tongue length (y = 238.2–7.96x), indicating that acoustic signals reflect functional trait diversity. (B) Literature reports of tongue length for workers and queens of 17 bumble bee species also correlate with characteristic frequency (y = 5.48 + e^-0.376x). Tongue length measurements correspond to the weighted means of published accounts for each species and caste combination (S1 Table).

Fig 4. The number of buzzes recorded is correlated with the number of bees counted during visual observations.

Fig 5. Acoustic surveys predict pollination services in two alpine flowering forbs.

When flowers of Trifolium dasyphyllum (circles) and T. parryi (triangles) are left open to bumble bee visitors, buzz abundance (A) predicts the average number of seeds per plant in each of three 0.01 km² plots. When bumble bee pollinators are excluded, buzz abundance fails to predict seed set (B), illustrating that acoustic surveys document pollination services by bumble bees. Gray shading around the line represents the standard error of the mean [47].