A model of resource partitioning between foraging bees based on learning.
Ontology highlight
ABSTRACT:
SUBMITTER: Dubois T
PROVIDER: S-EPMC8351995 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Every evening, from late spring to mid-summer, tens of thousands of hungry lactating female lesser long-nosed bats (Leptonycteris yerbabuenae) emerge from their roost and navigate over the Sonoran Desert, seeking for nectar and pollen [1, 2]. The bats roost in a huge maternal colony that is far from the foraging grounds but allows their pups to thermoregulate [3] while the mothers are foraging. Thus, the mothers have to fly tens of kilometers to the foraging sites-fields with thousands of Saguaro cacti [4, 5]. Once at the field, they must compete with many other bats over the same flowering cacti. Several solutions have been suggested for this classical foraging task of exploiting a resource composed of many renewable food sources whose locations are fixed. Some animals randomly visit the food sources [6], and some actively defend a restricted foraging territory [7-11] or use simple forms of learning, such as "win-stay lose-switch" strategy [12]. Many species have been suggested to follow a trapline, that is, to revisit the food sources in a repeating ordered manner [13-22]. We thus hypothesized that lesser long-nosed bats would visit cacti in a sequenced manner. Using miniature GPS devices, aerial imaging, and video recordings, we tracked the full movement of the bats and all of their visits to their natural food sources. Based on real data and evolutionary simulations, we argue that the bats use a reinforcement learning strategy that requires minimal memory to create small, non-overlapping cacti-cores and exploit nectar efficiently, without social communication.
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Project description:Despite widespread interest in the potential adaptive value of individual differences in cognition, few studies have attempted to address the question of how variation in learning and memory impacts their performance in natural environments. Using a novel split-colony experimental design we evaluated visual learning performance of foraging naïve bumble bees (Bombus terrestris) in an ecologically relevant associative learning task under controlled laboratory conditions, before monitoring the lifetime foraging performance of the same individual bees in the field. We found appreciable variation among the 85 workers tested in both their learning and foraging performance, which was not predicted by colony membership. However, rather than finding that foragers benefited from enhanced learning performance, we found that fast and slow learners collected food at comparable rates and completed a similar number of foraging bouts per day in the field. Furthermore, bees with better learning abilities foraged for fewer days; suggesting a cost of enhanced learning performance in the wild. As a result, slower learning individuals collected more resources for their colony over the course of their foraging career. These results demonstrate that enhanced cognitive traits are not necessarily beneficial to the foraging performance of individuals or colonies in all environments.
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Project description:Brain structure and learning capacities both vary with experience, but the mechanistic link between them is unclear. Here, we investigated whether experience-dependent variability in learning performance can be explained by neuroplasticity in foraging honey bees. The mushroom bodies (MBs) are a brain center necessary for ambiguous olfactory learning tasks such as reversal learning. Using radio frequency identification technology, we assessed the effects of natural variation in foraging activity, and the age when first foraging, on both performance in reversal learning and on synaptic connectivity in the MBs. We found that reversal learning performance improved at foraging onset and could decline with greater foraging experience. If bees started foraging before the normal age, as a result of a stress applied to the colony, the decline in learning performance with foraging experience was more severe. Analyses of brain structure in the same bees showed that the total number of synaptic boutons at the MB input decreased when bees started foraging, and then increased with greater foraging intensity. At foraging onset MB structure is therefore optimized for bees to update learned information, but optimization of MB connectivity deteriorates with foraging effort. In a computational model of the MBs sparser coding of information at the MB input improved reversal learning performance. We propose, therefore, a plausible mechanistic relationship between experience, neuroplasticity, and cognitive performance in a natural and ecological context.
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Project description:BackgroundIndividual bees exhibit complex movement patterns to efficiently exploit small areas within larger plant populations. How such individual spatial behaviours scale up to the collective level, when several foragers visit a common area, has remained challenging to investigate, both because of the low resolution of field movement data and the limited power of the statistical descriptors to analyse them. To tackle these issues we video recorded all flower visits (N = 6205), and every interaction on flowers (N = 628), involving foragers from a bumblebee (Bombus terrestris) colony in a large outdoor flight cage (880 m2), containing ten artificial flowers, collected on five consecutive days, and analysed bee movements using networks statistics.ResultsBee-flower visitation networks were significantly more modular than expected by chance, indicating that foragers minimized overlaps in their patterns of flower visits. Resource partitioning emerged from differences in foraging experience among bees, and from outcomes of their interactions on flowers. Less experienced foragers showed lower activity and were more faithful to some flowers, whereas more experienced foragers explored the flower array more extensively. Furthermore, bees avoided returning to flowers from which they had recently been displaced by a nestmate, suggesting that bees integrate memories of past interactions into their foraging decisions.ConclusionOur observations, under high levels of competition in a flight cage, suggest that the continuous turnover of foragers observed in colonies can led to efficient resource partitioning among bees in natural conditions.
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Project description: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 bumble-bees (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 bumble-bee colonies is directly correlated with the foraging performance, a robust fitness measure, under natural conditions. Colonies vary in learning speed by a factor of nearly five, with the slowest learning colonies collecting 40% less nectar than the fastest learning colonies. Such a steep fitness function is suggestive of strong selection for higher learning speed. Partial correlation analysis reveals that other factors such as forager body size or colour preference appear to be negligible in our study. Although our study does not directly prove causality of learning on foraging success, our approach of correlating natural within-species variation in these two factors represents a major advance over traditional between-species correlative analyses where comparability can be compromised by the fact that species vary along multiple dimensions.
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