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An affordable apparatus for fine-controlled emulation of buzzing frequencies of bees for the testing hypothesis in buzz interactions.

ABSTRACT: The buzzing foraging behavior of female bees for pollen harvesting called the attention of early pollination biologists. Flower types that demand this buzzing behavior comprise about 20,000 species of different and phylogenetically unrelated plant taxa, suggesting that it had independently evolved many times among the flowering plants. Between the late 1970s and early 1980s, theoretical papers had modeled the energetics of buzz pollination, but, up to this moment, no hypothesis was experimentally tested concerning the theoretical basis of the energetics of buzz pollination. We present a cost-effective and simple apparatus, including a digital and highly accurate frequency generator, and a device for the transference of buzz-frequency energy to the receptive floral unity. The receptive floral unities may comprise the entire or partial androecium, or the tubular corolla, or, in some cases, the whole flower. This apparatus can be easily used in both laboratory and field conditions of research, as natural air currents are avoided, and the response of pollen liberation can be quantitatively measured by pollen grain counts that can be captured by adhesion in slide poured with an isosmotic lactate-glycerol media. The maximum displacement of the hardwire beam/claw system was 0.1170 ± 0.0006 mm @ 150 Hz; 0.021 ± 0.003 mm @ 250 Hz; 0.010 ± 0.001 mm @ 350 Hz; 0.0058 ± 0.0001 mm @ 450 Hz; and 0.0082 ± 0.0005 mm @ 550 Hz. Hypothesis contrasting frequency emission and pollen liberation measured as pollen grain counts may be tested in a species flower type by simple linear regression if pollen counts are normally distributed, or ordinal logistic regression, with non-normal counts. The comparison among different flower-type requirements can be tested through appropriate statistical methods for both normally and non-normally distributed pollen grain counts.

SUBMITTER: Rodrigues EV

PROVIDER: S-EPMC6106199 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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An affordable apparatus for fine-controlled emulation of buzzing frequencies of bees for the testing hypothesis in buzz interactions.

Rodrigues Ernani V EV Riguette Júlia R JR Pereira Henrique R C HRC Tesch Juliétty A JA Silva Ary G AG

Ecology and evolution 20180712 15

The buzzing foraging behavior of female bees for pollen harvesting called the attention of early pollination biologists. Flower types that demand this buzzing behavior comprise about 20,000 species of different and phylogenetically unrelated plant taxa, suggesting that it had independently evolved many times among the flowering plants. Between the late 1970s and early 1980s, theoretical papers had modeled the energetics of buzz pollination, but, up to this moment, no hypothesis was experimentall ...[more]

PMID: 30151180

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Project description:Bee-mediated pollination greatly increases the size and weight of tomato fruits. Therefore, distinguishing between the local set of bees-those that are efficient pollinators-is essential to improve the economic returns for farmers. To achieve this, it is important to know the identity of the visiting bees. Nevertheless, the traditional taxonomic identification of bees is not an easy task, requiring the participation of experts and the use of specialized equipment. Due to these limitations, the development and implementation of new technologies for the automatic recognition of bees become relevant. Hence, we aim to verify the capacity of Machine Learning (ML) algorithms in recognizing the taxonomic identity of visiting bees to tomato flowers based on the characteristics of their buzzing sounds. We compared the performance of the ML algorithms combined with the Mel Frequency Cepstral Coefficients (MFCC) and with classifications based solely on the fundamental frequency, leading to a direct comparison between the two approaches. In fact, some classifiers powered by the MFCC-especially the SVM-achieved better performance compared to the randomized and sound frequency-based trials. Moreover, the buzzing sounds produced during sonication were more relevant for the taxonomic recognition of bee species than analysis based on flight sounds alone. On the other hand, the ML classifiers performed better in recognizing bees genera based on flight sounds. Despite that, the maximum accuracy obtained here (73.39% by SVM) is still low compared to ML standards. Further studies analyzing larger recording samples, and applying unsupervised learning systems may yield better classification performance. Therefore, ML techniques could be used to automate the taxonomic recognition of flower-visiting bees of the cultivated tomato and other buzz-pollinated crops. This would be an interesting option for farmers and other professionals who have no experience in bee taxonomy but are interested in improving crop yields by increasing pollination.

Project description:Geographic variation in sensory traits is usually influenced by adaptive processes because these traits are involved in crucial life-history aspects including orientation, communication, lineage recognition and mate choice. Studying this variation can therefore provide insights into lineage diversification. According to the Sensory Drive Hypothesis, lineage diversification may be driven by adaptation of sensory systems to local environments. It predicts that acoustic signals vary in association with local climatic conditions so that atmospheric attenuation is minimized and transmission of the signals maximized. To test this prediction, we investigated the influence of climatic factors (specifically relative humidity and temperature) on geographic variation in the resting frequencies of the echolocation pulses of Geoffroy's horseshoe bat, Rhinolophus clivosus. If the evolution of phenotypic variation in this lineage tracks climate variation, human induced climate change may lead to decreases in detection volumes and a reduction in foraging efficiency. A complex non-linear interaction between relative humidity and temperature affects atmospheric attenuation of sound and principal components composed of these correlated variables were, therefore, used in a linear mixed effects model to assess their contribution to observed variation in resting frequencies. A principal component composed predominantly of mean annual temperature (factor loading of -0.8455) significantly explained a proportion of the variation in resting frequency across sites (P < 0.05). Specifically, at higher relative humidity (around 60%) prevalent across the distribution of R. clivosus, increasing temperature had a strong negative effect on resting frequency. Climatic factors thus strongly influence acoustic signal divergence in this lineage, supporting the prediction of the Sensory Drive Hypothesis. The predicted future increase in temperature due to climate change is likely to decrease the detection volume in echolocating bats and adversely impact their foraging efficiency.

Dataset Information

An affordable apparatus for fine-controlled emulation of buzzing frequencies of bees for the testing hypothesis in buzz interactions.

Publications

An affordable apparatus for fine-controlled emulation of buzzing frequencies of bees for the testing hypothesis in buzz interactions.

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