Numerical cognition in bees and other insects

The ability to perceive the number of objects has been known to exist in vertebrates for a few decades, but recent behavioral investigations have demonstrated that several invertebrate species can also be placed on the continuum of numerical abilities shared with birds, mammals, and reptiles. In this review article, we present the main experimental studies that have examined the ability of insects to use numerical information. These studies have made use of a wide range of methodologies, and for this reason it is striking that a common finding is the inability of the tested animals to discriminate numerical quantities greater than four. Furthermore, the finding that bees can not only transfer learnt numerical discrimination to novel objects, but also to novel numerosities, is strongly suggestive of a true, albeit limited, ability to count. Later in the review, we evaluate the available evidence to narrow down the possible mechanisms that the animals might be using to solve the number-based experimental tasks presented to them. We conclude by suggesting avenues of further research that take into account variables such as the animals' age and experience, as well as complementary cognitive systems such as attention and the time sense.This publication was funded by the German Research Foundation (DFG) and the University of Wuerzburg in the funding program Open Access Publishing. Shaowu Zhang was supported by the ARC-CoE in Vision Science

Large Scale Homing in Honeybees

Honeybee foragers frequently fly several kilometres to and from vital resources, and communicate those locations to their nest mates by a symbolic dance language. Research has shown that they achieve this feat by memorizing landmarks and the skyline panorama, using the sun and polarized skylight as compasses and by integrating their outbound flight paths. In order to investigate the capacity of the honeybees' homing abilities, we artificially displaced foragers to novel release spots at various distances up to 13 km in the four cardinal directions. Returning bees were individually registered by a radio frequency identification (RFID) system at the hive entrance. We found that homing rate, homing speed and the maximum homing distance depend on the release direction. Bees released in the east were more likely to find their way back home, and returned faster than bees released in any other direction, due to the familiarity of global landmarks seen from the hive. Our findings suggest that such large scale homing is facilitated by global landmarks acting as beacons, and possibly the entire skyline panorama.This study was supported by the ARC COE in Vision Sciences (CE0561903), ARC DP-0450535 to SWZ, MP, and HZ (http://www.vision.edu.au/). MP was supported by a grant of the German Excellence Initiative to the Graduate School of Life Sciences, Würzburg University (http://www.graduateschools.uni-wuerzburg.de/life_sciences). This publication was funded by the German Research Foundation (DFG) and the University of Wuerzburg in the funding programme Open Access Publishing. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Visually guided decision making in foraging honeybees

Honeybees can easily be trained to perform different types of discrimination tasks under controlled laboratory conditions. This review describes a range of experiments carried out with free-flying forager honeybees under such conditions. The research done over the past 30 or so years suggests that cognitive abilities (learning and perception) in insects are more intricate and flexible than was originally imagined. It has become apparent that honeybees are capable of a variety of visually guided tasks, involving decision making under challenging situations: this includes simultaneously making use of different sensory modalities, such as vision and olfaction, and learning to use abstract concepts such as "sameness" and "difference." Many studies have shown that decision making in foraging honeybees is highly flexible. The trained animals learn how to solve a task, and do so with a high accuracy, but when they are presented with a new variation of the task, they apply the learnt rules from the earlier setup to the new situation, and solve the new task as well. Honeybees therefore not only feature a rich behavioral repertoire to choose from, but also make decisions most apt to the current situation. The experiments in this review give an insight into the environmental cues and cognitive resources that are probably highly significant for a forager bee that must continually make decisions regarding patches of resources to be exploited

Number-Based Visual Generalisation in the Honeybee

Although the numerical abilities of many vertebrate species have been investigated in the scientific literature, there are few convincing accounts of invertebrate numerical competence. Honeybees, Apis mellifera, by virtue of their other impressive cognitive feats, are a prime candidate for investigations of this nature. We therefore used the well-established delayed match-to-sample paradigm, to test the limits of honeybees' ability to match two visual patterns solely on the basis of the shared number of elements in the two patterns. Using a y-maze, we found that bees can not only differentiate between patterns containing two and three elements, but can also use this prior knowledge to differentiate three from four, without any additional training. However, bees trained on the two versus three task could not distinguish between higher numbers, such as four versus five, four versus six, or five versus six. Control experiments confirmed that the bees were not using cues such as the colour of the exact configuration of the visual elements, the combined area or edge length of the elements, or illusory contours formed by the elements. To our knowledge, this is the first report of number-based visual generalisation by an invertebrate

Virus-like particle size and molecular weight/mass determination applying gas-phase electrophoresis (native nES GEMMA)

(Bio-)nanoparticle analysis employing a nano-electrospray gas-phase electrophoretic mobility molecular analyzer (native nES GEMMA) also known as nES differential mobility analyzer (nES DMA) is based on surface-dry analyte separation at ambient pressure. Based on electrophoretic principles, single-charged nanoparticles are separated according to their electrophoretic mobility diameter (EMD) corresponding to the particle size for spherical analytes. Subsequently, it is possible to correlate the (bio-)nanoparticle EMDs to their molecular weight (MW) yielding a corresponding fitted curve for an investigated analyte class. Based on such a correlation, (bio-)nanoparticle MW determination via its EMD within one analyte class is possible. Turning our attention to icosahedral, non-enveloped virus-like particles (VLPs), proteinaceous shells, we set up an EMD/MW correlation. We employed native electrospray ionization mass spectrometry (native ESI MS) to obtain MW values of investigated analytes, where possible, after extensive purification. We experienced difficulties in native ESI MS with time-of-flight (ToF) detection to determine MW due to sample inherent characteristics, which was not the case for charge detection (CDMS). nES GEMMA exceeds CDMS in speed of analysis and is likewise less dependent on sample purity and homogeneity. Hence, gas-phase electrophoresis yields calculated MW values in good approximation even when charge resolution was not obtained in native ESI ToF MS. Therefore, both methods-native nES GEMMA-based MW determination via an analyte class inherent EMD/MW correlation and native ESI MS-in the end relate (bio-)nanoparticle MW values. However, they differ significantly in, e.g., ease of instrument operation, sample and analyte handling, or costs of instrumentation.Leibniz AssociationEU Horizon 2020Indiana University Graduate Training Program in Quantitative and Chemical Biolog

Strategic green infrastructure planning in Germany and the UK: a transnational evaluation of the evolution of urban greening policy and practice

The evolution of Green Infrastructure (GI) planning has varied dramatically between nations. Although a grounded set of principles are recognized globally, there is increasing variance in how these are implemented at a national and sub-national level. To investigate this the following paper presents an evaluation of how green infrastructure has been planned for in England and Germany illustrating how national policy structures facilitate variance in application. Adopting an evaluative framework linked to the identification of GI, its development and monitoring/ feedback the paper questions the impacts on delivery of intersecting factors including terminology, spatial distribution and functionality on effective GI investment. This process reviews how changing policy structures have influenced the framing of green infrastructure policy, and subsequent impact this has on the delivery of green infrastructure projects

Terrestrial Very-Long-Baseline Atom Interferometry:Workshop Summary

This document presents a summary of the 2023 Terrestrial Very-Long-Baseline Atom Interferometry Workshop hosted by CERN. The workshop brought together experts from around the world to discuss the exciting developments in large-scale atom interferometer (AI) prototypes and their potential for detecting ultralight dark matter and gravitational waves. The primary objective of the workshop was to lay the groundwork for an international TVLBAI proto-collaboration. This collaboration aims to unite researchers from different institutions to strategize and secure funding for terrestrial large-scale AI projects. The ultimate goal is to create a roadmap detailing the design and technology choices for one or more km-scale detectors, which will be operational in the mid-2030s. The key sections of this report present the physics case and technical challenges, together with a comprehensive overview of the discussions at the workshop together with the main conclusions

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Kognition bei Honigbienen: Aspekte zu Lernverhalten, Gedächtnis und Navigation bei einem sozialen Insekt

Honeybees (Apis mellifera) forage on a great variety of plant species, navigate over large distances to crucial resources, and return to communicate the locations of food sources and potential new nest sites to nest mates using a symbolic dance language. In order to achieve this, honeybees have evolved a rich repertoire of adaptive behaviours, some of which were earlier believed to be restricted to vertebrates. In this thesis, I explore the mechanisms involved in honeybee learning, memory, numerical competence and navigation. The findings acquired in this thesis show that honeybees are not the simple reflex automats they were once believed to be. The level of sophistication I found in the bees’ memory, their learning ability, their time sense, their numerical competence and their navigational abilities are surprisingly similar to the results obtained in comparable experiments with vertebrates. Thus, we should reconsider the notion that a bigger brain automatically indicates higher intelligence.Honigbienen (Apis mellifera) furagieren an vielen verschiedenen Pflanzenarten, und navigieren über große Distanzen zu wichtigen Ressourcen. Die räumliche Lage von Futterquellen und potentiellen neuen Nistplätzen teilen sie ihren Nestgenossinnen mithilfe einer symbolischen Tanzsprache mit. Um all dies leisten zu können, haben sie ein reiches Repertoire von adaptiven Verhaltensweisen evolviert. Mehr und mehr Verhaltensweisen, die man nur bei Vertebraten vermutet hätte, werden auch bei der Honigbiene entdeckt. In meiner Dissertation habe ich einige der Mechanismen erforscht, die beim Lernverhalten, der Gedächtnisbildung, der numerischen Kompetenz und der Navigation eine wichtige Rolle spielen. Die Ergebnisse, die in meiner Dissertation erzielt wurden, zeigen dass Honigbienen keineswegs die einfachen, reflexgesteuerten Organismen sind, als die sie lange Zeit angesehen wurden. Die Komplexität die ich im Gedächtnis, der Lernfähigkeit, dem Zeitsinn, der numerischen Kompetenz und der Navigationsfähigkeit der Bienen gefunden habe, ist erstaunlich ähnlich zu den Ergebnissen, die in vergleichbaren Experimenten mit Vertebraten erzielt wurden. Deshalb sollten wir die allgemeine Annahme, dass ein größeres Gehirn automatisch höhere Intelligenz bedeutet, überdenken