Bumblebees gain fitness through learning

Despite the widespread assumption that the learning abilities of animals are adapted to the particular environments in which they operate, the quantitative effects of learning performance on fitness remain virtually unknown. Here we evaluate the learning performance of bumblebees (_Bombus terrestris_) from multiple colonies in an ecologically relevant associative learning task under laboratory conditions, before testing the foraging performance of the same colonies under the field conditions. We demonstrate that variation in learning speed among bumblebee colonies is directly correlated with foraging performance, a robust fitness measure, under natural conditions. Colonies vary in learning speed by a factor of nearly 5, with the slowest learning colonies collecting 40% less nectar than the fastest learning colonies. Such a steep fitness function suggests strong selection for higher learning speed in bumblebees. Demonstrating the adaptive value of differences in learning performance under the real conditions in which animals function represents a major step towards understanding how cognitive abilities of animals are tuned to their environment

On-the-go machine vision sensing of cotton plant geometric parameters: first results

Plant geometrical parameters such as internode length (i.e. the distance between successive branches on the main stem) indicate water stress in cotton. This paper describes a machine vision system that has been designed to measure internode length for the purpose of determining real-time cotton plant irrigation requirement. The imaging system features an enclosure which continuously traverses the crop canopy and forces the flexible upper main stem of individual plants against a glass panel at the front of the enclosure, hence allowing images of the plant to be captured in a fixed object plane. Subsequent image processing of selected video sequences enabled detection of the main stem in 88% of frames. However, node detection was subject to a high false detection rate due to leaf edges present in the images. Manual identification of nodes in the acquired imagery enabled measurement of internode lengths with 3% standard error

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Bee positive: the importance of electroreception in pollinator cognitive ecology

International audienceThe global atmospheric circuit generates a permanent electric field between the Earth surface and outer atmosphere (Rycroft et al., 2000). The ground and plants conductively linked to it are negatively charged (Bowker and Crenshaw, 2007), whereas animals build up positive charge as they move in contact with air molecules (Jackson and McGonigle, 2005). Electric fields emanating from plants and pollinators, such as bees, are believed to promote pollination by enabling pollen grains to " jump " from flowers to pol-linators and vice versa (Corbet et al., 1982). Two recent studies reveal that bees not only detect these electric fields but also learn to discriminate them, indicating that electroreception should be seriously considered alongside vision and olfaction when studying bee behavior and ecology. Writing in Science, Clarke et al. (2013) demonstrated that bumblebees (Bombus terrestris) detect electric fields around plants and learn to use them to decide whether or not to visit flowers. Using a Faraday pail to measure electric fields generated by bees and plants, the team described how a bee visit temporarily modifies the electric charge of (Petunia) flowers, suggesting that floral electric properties could be used by future visitors to assess the reward value without necessarily needing to probe the flower. To explore this possibility, the authors used differential conditioning in which bees were trained to associate an electrically charged feeder (30 V) with a sucrose reward (CS+) and an uncharged feeder with an aversive quinine solution (CS−). After extensive training (50 trials), bees chose the rewarding feeder in around 80% of trials. Similar levels of performance were observed when bees were trained with two feeders carrying the same charge but different electric field patterns (homogeneous vs. bull's eye shape), indicating that these insects can learn both the magnitude and geometry of an electric field. Bees learned to perform even better in discrimination tasks if the two feeders differed both in color (shade of green) and their electric field pattern compared to if they differed only in color. Natural electric fields around flowers may therefore contribute to the multimodal sources of information that bees use to learn and memorize floral rewards, in conjunction with color, pattern, shape, texture

Impacts of multiple stressors on bumblebee queens

OBJECTIVE: The aim of the study was to assess the effect of a topical decongestant on eustachian tube function in children with ventilation tubes because of persistent otitis media with effusion. STUDY DESIGN: A randomized, double-blinded, placebo-controlled study. METHODS: At the outpatient departments of a secondary referral hospital and a tertiary referral hospital, eustachian tube function was measured before and after intranasal administration of five drops of 0.05% xylometazoline hydrochloride or placebo in 80 randomly selected children with ventilation tubes because of otitis media with effusion. RESULTS: Xylometazoline nose drops had no effect on the ventilatory or the protective function of the eustachian tube. CONCLUSIONS: Topical decongestants do not have a positive effect on eustachian tube function in children. Therefore, the use of topical decongestants to prevent or treat otitis media with effusion in children is not justified and should be discouraged

Guards and thieves: antagonistic interactions between two ant species coexisting on the same ant-plant

1. We describe the simultaneous occupation of a rare understorey ant-acacia Acacia mayana by its guarding ant Pseudomyrmex ferrugineus, and an apparent opportunist parasite of the mutualism, the generalist ant Camponotus planatus. The two ant species occur together in 30.7% of the 26 mature A. mayana plants (23.5% of all trees (n = 34)) surveyed, but C. planatus is absent from saplings below 1 metre in height (n = 8). 2. While P. ferrugineus shows behaviour compatible with effective host tree defence, C. planatus does not attack phytophagous insects and appears ineffective as an ant-guard. C. planatus does however occupy swollen thorns (pseudogalls) on the host tree, and harvests nectar from extrafloral leaf nectaries. We propose that C. planatus is a parasite of the Acacia-Pseudomyrmex mutualism. 3. C. planatus does not harvest the second trophic reward produced by the tree for its Pseudomyrmex ant-guards, protein-rich food (Beltian) bodies. C. planatus lack the specialised larval adaptations needed to use Beltian bodies as brood food, suggesting that this resource is potentially more resistant to exploitation by generalists than extrafloral nectar. 4. In competition for access to nectaries, C. planatus effectively displaced P. ferrugineus in 99.8% of encounters. These results suggest not only that C. planatus is a parasite of this mutualism, but also that it is able to effectively counteract the aggression shown to other insects by the resident ant-guards