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68 results for “foraging strategy”
Figure 4 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 4 Available foraging hours and weather variables (temperature and solar radiation) for each simulation day throughout the year. Rain and wind variables are not shown, but were used to calculate the number of available foraging hours.
Figure 3 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 3 Example of nectar (in yellow on the left side) and pollen (in blue on the right side) spatial and temporal distribution through the season. In each snapshot, a brighter colour indicates a higher amount of the resource in the polygon. A total of 12 snapshots were taken every 30 days, starting on day 15 of the simulation.
Figure 2 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 2 The total mass of floral resources (i.e. sugar and pollen) in the studied landscape available to bees in all the simulations. The mass of floral resources was calculated, based on the production and phenology of the individual plant species comprising the habitats present in the studied landscape and the landscape composition. Pollen availability started on simulation day 20 and nectar was available from day 39.
Figure 1 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 1 Components in ALMaSS landscape model. The blue arrow represents the access to landscape information at a 1 m2 resolution. In this example, one element has woody habitats, while the other is an arable field. The information about each element depends on its type and the temporal factors described in the green boxes. The orange box shows some of the factors derived from the landscape element type, its management and the weather.
Figure 6 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 6 Results of the implementation of different scouting and foraging strategies on the performance of model colonies in terms of nectar collection. For each scouting strategy (i.e. distance, quality or random), four different foraging strategies (i.e. distance, energy efficiency, quality and random) were tested. The total amount of sugar collected, the mean number of daily foraging flights and their success were evaluated for all combinations of scouting and foraging strategies.
Data from: QuLinePlus: extending plant breeding strategy and genetic model simulation to cross-pollinated populations – case studies in forage breeding
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Data from: To eat and not be eaten: diurnal mass gain and foraging strategies in wintering great tits
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Cerebellum promotes sequential foraging strategies and contributes to the directional modulation of hippocampal place cells
<p>Cerebellum contributes to spatial coding in the hippocampus. When PKC-dependent mechanisms are impaired in cerebellar Purkinje cells, hippocampal place cells indeed lose their spatial selectivity in the dark, when the animal relies mostly on self-motion cues to navigate. This impairment in PKC-dependent cerebellar functions additionally leads to behavioral deficits in using both external and self-motion cues when learning a goal-oriented navigation task. However, it is unclear how cerebellum influences the exploration strategy used by an animal during free foraging in an open environment. Moreover, place cells are influenced by other navigational variables than positions, such as movement direction and speed. Does the cerebellum also play a role in the modulation of place cells by these other covariates? We recorded hippocampal place cells in mice with impaired PKC-dependent mechanisms (L7-PKCI) and in their littermate controls while they performed a foraging task where they could obtain a reward after visiting a subset of hidden locations. We found that L7-PKCI and control mice developed different foraging strategies: while control mice repeated reliable spatial sequences to maximize their rewards, L7-PKCI mice persisted to use a random foraging strategy. The sequence-based strategy was associated with more place cells exhibiting theta-phase precession and theta modulation. It was also correlated with a larger fraction of place cells that were modulated concomitantly by movement direction and speed. Finally, in the dark, the modulation of place cells by movement direction was markedly reduced in L7-PKCI mice, demonstrating that PKC-dependent cerebellar functions control how self-motion cues influence not only position but also direction coding in the hippocampus. Cerebellum thus contributes to developing optimal sequential paths during foraging behavior, which may reflect its impact on self-motion and theta signals contributing to place cells coding.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.