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.

Automated classification of bees and hornet using acoustic analysis of their flight sounds

“…Byrne et al (1988) showed that the smaller size of homopterous insects has higher wingbeat frequency, and Burkart et al (2011) demonstrated that the frequency of wing beat of bees was in a certain range which was anatomically determined and correlated to the size of the bees. Miller-Struttmann et al (2017) investigated the relationship between the sound characteristics of flight sounds and wing length of bumble bees, and found a negative relationship between wing length and the fundamental frequency of flight sounds of bumble bees. The wing length of V. simillima xanthoptera and A. mellifera are 31.76 mm (Byun et al 2009) and 9.3 mm (Ruttner 1988), respectively.…”

“…So far, acoustic/vibrational analysis has not been extensively used to detect insects, but our results point to various applications. For example, acoustic/vibrational analysis could be used to replicate the studies of Miller-Struttmann et al (2017), who analyzed the buzzing of bumble bees visiting two alpine forbs to evaluate pollination services, and of Potamitis et al (2015), who analyzed wing beats of insect pests to predict the arrival of the pests. We used only a single microphone, but placing multiple microphones in a wide range of places would enable us to study animal movements in the field, and evaluate how they use their habitat over a wide range of areas and time periods (Blumstein et al 2011).…”

“…For pollination in greenhouses, monitoring the activity of bees is useful in managing their activity and knowing when to replace nest boxes (Fisher and Pomeroy 1989;Morandin et al 2001). Detection of insects can also be used to better understand the biodiversity of pollinators and their habitat use (Miller-Struttmann et al 2017;Hill et al 2018). Monitoring insect activity and detecting insects are thus useful in both agricultural production and ecological research.…”

“…Recently, Gradišek et al (2017) tried to discriminate bumblebee species using the acoustic features of their flight sounds, and found that the different species differed in their flight sounds. In this way, acoustic/ vibrational-based monitoring technology is becoming popular, but previous studies have focused on the Cicadae and Orthoptera (Obrist et al 2010) or specific bee species such as bumble bees (De Luca et al 2014;Gradišek et al 2017;Miller-Struttmann et al 2017), and, to our knowledge, there are still few studies that focus on identifying different types of bees by their sounds. Especially, in practical sense, distinguishing predators and pollinators are important for beekeepers or ecologist so that investigating whether the acoustic analysis can identify bee species into functional group is informative.…”

Insect Flower Visitors and their Role in Mangrove Pollination: A Study from East Coast of India

“…Xylocopa varipuncta (carpenter bee) has been identified to carry pollens of exclusive mangroves like, A. alba, L. racemosa, S. caseolaris, S. ovata and R. apiculata in the mangrove community of Setiu Wetlands, Terengganu (Azmi et al, 2012). Bumble bee visited flowers show increased seed set than when plant excluded from it (Miller-Struttmann, 2017). Carpenter bees have long tongue which gathers mainly pollen, but not nectar.…”