scholarly journals Wind and obstacle motion affect honeybee flight strategies in cluttered environments

2020 ◽  
Vol 223 (14) ◽  
pp. jeb222471
Author(s):  
Nicholas P. Burnett ◽  
Marc A. Badger ◽  
Stacey A. Combes

ABSTRACTBees often forage in habitats with cluttered vegetation and unpredictable winds. Navigating obstacles in wind presents a challenge that may be exacerbated by wind-induced motions of vegetation. Although wind-blown vegetation is common in natural habitats, we know little about how the strategies of bees for flying through clutter are affected by obstacle motion and wind. We filmed honeybees Apis mellifera flying through obstacles in a flight tunnel with still air, headwinds or tailwinds. We tested how their ground speeds and centering behavior (trajectory relative to the midline between obstacles) changed when obstacles were moving versus stationary, and how their approach strategies affected flight outcome (successful transit versus collision). We found that obstacle motion affects ground speed: bees flew slower when approaching moving versus stationary obstacles in still air but tended to fly faster when approaching moving obstacles in headwinds or tailwinds. Bees in still air reduced their chances of colliding with obstacles (whether moving or stationary) by reducing ground speed, whereas flight outcomes in wind were not associated with ground speed, but rather with improvement in centering behavior during the approach. We hypothesize that in challenging flight situations (e.g. navigating moving obstacles in wind), bees may speed up to reduce the number of wing collisions that occur if they pass too close to an obstacle. Our results show that wind and obstacle motion can interact to affect flight strategies in unexpected ways, suggesting that wind-blown vegetation may have important effects on foraging behaviors and flight performance of bees in natural habitats.

2009 ◽  
Vol 212 (16) ◽  
pp. 2604-2611 ◽  
Author(s):  
J. T. Vance ◽  
J. B. Williams ◽  
M. M. Elekonich ◽  
S. P. Roberts

Author(s):  
Rachael Bis ◽  
Huei Peng ◽  
Galip Ulsoy

In order to autonomously navigate in an unknown environment, a robotic vehicle must be able to sense obstacles, determine their velocities, and follow a clear path to a goal. However, the perceived location and motion of the obstacles will be uncertain due to the limited accuracy of the robot’s sensors. Thus, it is necessary to develop a system that can avoid moving obstacles using uncertain sensor data. The method proposed here is based on a certainty occupancy grid—which has been used to avoid stationary obstacles in an uncertain environment—in conjunction with the velocity obstacle concept—which allows a robot to avoid well-known moving obstacles. The combination of these two techniques leads to velocity occupancy space: a search space which allows the robot to avoid moving obstacles and navigate efficiently to a goal using uncertain sensor data.


2015 ◽  
Vol 218 (17) ◽  
pp. 2728-2737 ◽  
Author(s):  
J. D. Crall ◽  
S. Ravi ◽  
A. M. Mountcastle ◽  
S. A. Combes

2018 ◽  
Vol 285 (1870) ◽  
pp. 20172140 ◽  
Author(s):  
Keng-Lou James Hung ◽  
Jennifer M. Kingston ◽  
Matthias Albrecht ◽  
David A. Holway ◽  
Joshua R. Kohn

The western honey bee ( Apis mellifera ) is the most frequent floral visitor of crops worldwide, but quantitative knowledge of its role as a pollinator outside of managed habitats is largely lacking. Here we use a global dataset of 80 published plant–pollinator interaction networks as well as pollinator effectiveness measures from 34 plant species to assess the importance of A. mellifera in natural habitats. Apis mellifera is the most frequent floral visitor in natural habitats worldwide, averaging 13% of floral visits across all networks (range 0–85%), with 5% of plant species recorded as being exclusively visited by A. mellifera . For 33% of the networks and 49% of plant species, however, A. mellifera visitation was never observed, illustrating that many flowering plant taxa and assemblages remain dependent on non- A. mellifera visitors for pollination. Apis mellifera visitation was higher in warmer, less variable climates and on mainland rather than island sites, but did not differ between its native and introduced ranges. With respect to single-visit pollination effectiveness, A. mellifera did not differ from the average non- A. mellifera floral visitor, though it was generally less effective than the most effective non- A. mellifera visitor. Our results argue for a deeper understanding of how A. mellifera , and potential future changes in its range and abundance, shape the ecology, evolution, and conservation of plants, pollinators, and their interactions in natural habitats.


Apidologie ◽  
2009 ◽  
Vol 40 (4) ◽  
pp. 441-449 ◽  
Author(s):  
Robert Brodschneider ◽  
Ulrike Riessberger-Gallé ◽  
Karl Crailsheim

2021 ◽  
Author(s):  
Nicholas Burnett ◽  
Marc Badger ◽  
Stacey Combes

Bees flying through natural landscapes encounter physical challenges, such as wind and cluttered vegetation. The influence of these factors on the flight performance of bees remains unknown. We analyzed 548 videos of wild-caught honeybees (Apis mellifera) flying through an enclosure containing a field of vertical obstacles that bees could fly within (through open corridors, without maneuvering) or above. We examined how obstacle field height, wind presence and direction (headwinds or tailwinds) affected altitude, ground speed, and side-to-side casting (lateral excursions) of bees. When obstacle fields were short, bees flew at altitudes near the midpoint between the tunnel floor and ceiling. When obstacle fields approached or exceeded this midpoint, bees typically, but not always, increased their altitudes to fly over the obstacles. Bees that flew above the obstacle fields exhibited 40% faster ground speeds and 36% larger lateral excursions than bees that flew within the obstacle fields, likely due to the visual feedback from obstacles and narrow space available within the obstacle field. Wind had a strong effect on ground speed and lateral excursions, but not altitude. Bees flew 12-19% faster in tailwinds than in the other wind conditions, but their lateral excursions were 19% larger in any wind, regardless of its direction, than in still air. Our results show that bees flying through complex environments display flexible flight behaviors (e.g., flying above versus within obstacles), which affect flight performance. Similar choices in natural landscapes could have broad implications for foraging efficiency, pollination, and mortality in wild bees.


2011 ◽  
Vol 7 (4) ◽  
pp. 499-501 ◽  
Author(s):  
Emily Baird ◽  
Eva Kreiss ◽  
William Wcislo ◽  
Eric Warrant ◽  
Marie Dacke

To avoid collisions when navigating through cluttered environments, flying insects must control their flight so that their sensory systems have time to detect obstacles and avoid them. To do this, day-active insects rely primarily on the pattern of apparent motion generated on the retina during flight (optic flow). However, many flying insects are active at night, when obtaining reliable visual information for flight control presents much more of a challenge. To assess whether nocturnal flying insects also rely on optic flow cues to control flight in dim light, we recorded flights of the nocturnal neotropical sweat bee, Megalopta genalis , flying along an experimental tunnel when: (i) the visual texture on each wall generated strong horizontal (front-to-back) optic flow cues, (ii) the texture on only one wall generated these cues, and (iii) horizontal optic flow cues were removed from both walls. We find that Megalopta increase their groundspeed when horizontal motion cues in the tunnel are reduced (conditions (ii) and (iii)). However, differences in the amount of horizontal optic flow on each wall of the tunnel (condition (ii)) do not affect the centred position of the bee within the flight tunnel. To better understand the behavioural response of Megalopta , we repeated the experiments on day-active bumble-bees ( Bombus terrestris ). Overall, our findings demonstrate that despite the limitations imposed by dim light, Megalopta —like their day-active relatives—rely heavily on vision to control flight, but that they use visual cues in a different manner from diurnal insects.


Zootaxa ◽  
2021 ◽  
Vol 4979 (1) ◽  
pp. 155-165
Author(s):  
JURATE DE PRINS

After twenty years of publishing the taxonomic journal Zootaxa, it is now the right time to synthesize its achievements and its contribution to the taxonomic knowledge of the Afrotropical Lepidoptera taxa. These contributions were made thanks to the exploration of natural habitats, museum collections, historic libraries, private holdings and phylogenetic investigations by many professional and amateur lepidopterists throughout the world. Zootaxa was introduced as a novel publication model with its innovative, community-based, and democratic approach to speed up the process of taxonomic publications. The fast-developing technology and especially the accessibility of digital taxonomic tools prepared the basis for novel links and inter-relationships. The global digital revolution and especially the overwhelming embrace of digital technology in Africa made an essential switch in the way we work: it became possible to present taxonomic information in a way that is searchable, consultable, illustrative, updatable, correctable and, most importantly—open and accessible to everyone. In this article, the trends of an increase in Afrotropical moth biodiversity knowledge published in Zootaxa and other journals are shortly discussed. Data are retrieved from the online taxonomic relational database Afromoths (www.afromoths.net). 


Robotica ◽  
2014 ◽  
Vol 33 (3) ◽  
pp. 463-497 ◽  
Author(s):  
Michael Hoy ◽  
Alexey S. Matveev ◽  
Andrey V. Savkin

SUMMARYWe review a range of techniques related to navigation of unmanned vehicles through unknown environments with obstacles, especially those that rigorously ensure collision avoidance (given certain assumptions about the system). This topic continues to be an active area of research, and we highlight some directions in which available approaches may be improved. The paper discusses models of the sensors and vehicle kinematics, assumptions about the environment, and performance criteria. Methods applicable to stationary obstacles, moving obstacles and multiple vehicles scenarios are all reviewed. In preference to global approaches based on full knowledge of the environment, particular attention is given to reactive methods based on local sensory data, with a special focus on recently proposed navigation laws based on model predictive and sliding mode control.


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