Trading off short-term costs for long-term gains: how do bumblebees decide to learn morphologically complex flowers?

Keywords: Bombus impatiens bumblebee complex foraging handling time individual variation skill learning specialization Many animals learn skills that can take a long time to acquire. Such learned skills may have high payoffs eventually, but during the period of learning their net profitability is low. When there are other options available, it is not clear how animals decide to learn how to perform tasks that initially have low or no benefits. Bees in particular visit many types of flowers that vary in the time required to learn how to access their food rewards. We used bumblebees (Bombus impatiens) to address how individuals decide to persevere with learning to handle 'complex' flowers. We tested two hypotheses: (1) individuals have unlearned preferences for more complex flowers; (2) individuals use the absolute reward value of the flower to decide whether to learn to handle a particular flower type. We presented individual bees with mixed arrays of colour-distinct 'simple' and 'complex' flowers, either containing the same value of reward, or where the complex flowers contained twice the concentration of sucrose as the simple flowers. Foragers did not show any unlearned preferences towards the complex flowers, but instead preferred the simple flowers. The strongest initial preferences were for flower colour (purple over pink). Our second hypothesis was supported, because when the purple complex flowers contained a higher reward than the simple flowers, more bees persevered with visiting them, foraging on them exclusively by the end of the test period. There was significant variation between individuals in whether they learned to handle, and how much they visited, complex flowers. These results highlight the complex interplay between unlearned biases and environmental feedback in making decisions about what to learn

Red foliage color reliably indicates low host quality and increased metabolic load for development of an herbivorous insect

Abstract Plant chemical defense and coevolved detoxification mechanisms in specialized herbivorous insects are fundamental in determining many insect-plant interactions. For example, Brassicale plants protect themselves from herbivory by producing glucosinolates, but these secondary metabolites are effectively detoxified by larvae of Pierid butterflies. Nevertheless, not all Brassicales are equally preferred by these specialist herbivores. Female Pieris butterflies avoid laying eggs on anthocyanin-rich red foliage, suggesting red color is a visual cue affecting oviposition behavior. In this study, we reared P. brassicae larvae on green and red cabbage leaves, to determine whether foliage color reliably indicates host plant quality. We did not find a difference in survival rates or maximal larval body mass in the two food treatments. However, larvae feeding on red cabbage leaves exhibited significantly lower growth rates and longer durations of larval development. Interestingly, this longer development was coupled with a higher consumption rate of dry food matter. The lower ratio of body mass gain to food consumption in larvae feeding on red cabbage leaves was coupled with significantly higher (ca. 10 %) larval metabolic rates. This suggests that development on red foliage may incur an increased metabolic load associated with detoxification of secondary plant metabolites. Energy and oxygen allocation to detoxification could come at the expense of growth and thus compromise larval fitness as a result of extended development. From an evolutionary perspective, red foliage color may serve as an honest defensive cue, as it reliably indicates the plant's low quality as a substrate for larval development

Knowledge-Based Potential for Positioning Membrane-Associated Structures and Assessing Residue-Specific Energetic Contributions

The complex hydrophobic and hydrophilic milieus of membrane-associated proteins pose experimental and theoretical challenges to their understanding. Here we produce a non-redundant database to compute knowledge-based asymmetric cross-membrane potentials from the per-residue distributions of Cβ, Cγ and functional group atoms. We predict transmembrane and peripherally associated regions from genomic sequence and position peptides and protein structures relative to the bilayer (available at http://www.degradolab.org/ez). The pseudo-energy topological landscapes underscore positional stability and functional mechanisms demonstrated here for antimicrobial peptides, transmembrane proteins, and viral fusion proteins. Moreover, experimental effects of point mutations on the relative ratio changes of dual-topology proteins are quantitatively reproduced. The functional group potential and the membrane-exposed residues display the largest energetic changes enabling to detect native-like structures from decoys. Hence, focusing on the uniqueness of membrane-associated proteins and peptides, we quantitatively parameterize their cross-membrane propensity thus facilitating structural refinement, characterization, prediction and design

Effects of Natural Habitat and Season on Cursorial Spider Assemblages in Mediterranean Vineyards

ABSTRACT: Spiders are potential natural enemies of insect pests in many crops, and their species composition in the crop may be influenced by nearby natural habitats. Here, we examined the effects of the habitat type (different sampling positions within the vineyard and in the nearby natural habitat) on spider assemblages in vineyards. Spider species richness, assemblage composition, and diversity were evaluated by means of pitfall traps in early and late summer, in three commercial vineyards and their adjacent natural habitats in a Mediterranean landscape in northern Israel. We collected 688 spiders, belonging to 25 families and 61 species and morphospecies. Spider richness differed in the two seasons; more species were documented in early summer (47) than in late summer (33). The natural habitat had the highest species richness, with 34 species, while three vineyard positions were inhabited by only 27–31 species each. The natural habitat assemblage differed from the vineyard assemblages, with 15 species that were found only in the natural habitat, yet 11 species were shared by both the natural habitat and all vineyard positions. Both season (early vs. late in the cropping season) and the habitat (vineyard vs. natural) affected the spider assemblage composition. The study documents the large diversity of spiders in a Mediterranean vineyard agroecosystem. The information that we provide here is critical in assessing the potential for conservation biocontrol, where natural habitats may be a source of natural enemies for nearby vineyards.info:eu-repo/semantics/publishedVersio

Speed-Accuracy Tradeoffs and False Alarms in Bee Responses to Cryptic Predators

SummaryLearning plays a crucial role in predator avoidance [1–3], but little is known about how the type of experience with predators molds future prey behavior. Specifically, is predator-avoidance learning and memory retention disrupted by cryptic coloration of predators, such as crab spiders [4, 5]? How does experience with different predators affect foraging decisions? We evaluated these questions by exposing foraging bumblebees to controlled predation risk from predators (robotic crab spiders) that were either cryptic or highly contrasting, as assessed by a quantitative model of bee color perception [6]. Our results from 3D tracking software reveal a speed-accuracy tradeoff [7]: Bees slow their inspection flights after learning that there is a risk from cryptic spiders. The adjustment of inspection effort results in accurate predator detection, leveling out predation risk at the expense of foraging time. Overnight-retention tests reveal no decline in performance, but bees that had experienced cryptic predators are more prone to “false alarms” (rejection of foraging opportunities on safe flowers) than those that had experienced conspicuous predators. Therefore, bees in the cryptic-spider treatment made a functional decision to trade off reduced foraging efficiency via increased inspection times and false-alarm rates against higher potential fitness loss from being injured or eaten

Trade-off between travel distance and prioritization of high-reward sites in traplining bumblebees

1.Animals exploiting renewable resource patches are faced with complex multi-location routing problems. In many species, individuals visit foraging patches in predictable sequences called traplines. However, whether and how they optimize their routes remains poorly understood

The large carpenter bees of central Saudi Arabia, with notes on the biology of Xylocopa sulcatipes Maa (Hymenoptera, Apidae, Xylocopinae)

The large carpenter bees (Xylocopinae, Xylocopa Latreille) occurring in central Saudi Arabia are reviewed. Two species are recognized in the fauna, Xylocopa (Koptortosoma) aestuans (Linnaeus) and X. (Ctenoxylocopa) sulcatipes Maa. Diagnoses for and keys to the species of these prominent components of the central Saudi Arabian bee fauna are provided to aid their identification by pollination researchers active in the region. Females and males of both species are figured and biological notes provided for X. sulcatipes. Notes on the nesting biology and ecology of X. sulcatipes are appended. As in studies for this species from elsewhere, nests were found in dried stems of Calotropis procera (Aiton) (Asclepiadaceae) and Phoenix dactylifera L. (Arecaceae)

The invasive ‘mothcatcher’ (Araujia sericifera Brot.; Asclepiadoideae) co-opts native honeybees as its primary pollinator in South Africa

Araujia sericifera is a South American milkweed which is an invasive/exotic species in South Africa. This species relies on pollinators for sexual reproduction and we show it has co-opted native South African honeybees as primary pollinators in its adopted country. Moths also visit the flowers of this species, but contribute little to its relatively high pollination success

Using neural networks and evolutionary information in decoy discrimination for protein tertiary structure prediction

Background: We present a novel method of protein fold decoy discrimination using machine learning, more specifically using neural networks. Here, decoy discrimination is represented as a machine learning problem, where neural networks are used to learn the native-like features of protein structures using a set of positive and negative training examples. A set of native protein structures provides the positive training examples, while negative training examples are simulated decoy structures obtained by reversing the sequences of native structures. Various features are extracted from the training dataset of positive and negative examples and used as inputs to the neural networks.Results: Results have shown that the best performing neural network is the one that uses input information comprising of PSI-BLAST [1] profiles of residue pairs, pairwise distance and the relative solvent accessibilities of the residues. This neural network is the best among all methods tested in discriminating the native structure from a set of decoys for all decoy datasets tested. Conclusion: This method is demonstrated to be viable, and furthermore evolutionary information is successfully used in the neural networks to improve decoy discrimination