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65 results for “Foraging: ecology”
Data from: MetaBARFcoding: DNA-barcoding of regurgitated prey yields insights into Christmas Shearwater (Puffinus nativitatis) foraging ecology at Hōlanikū (Kure Atoll), Hawaiʻi
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Data from: Trait-based functional dietary analysis provides a better insight into the foraging ecology of bats
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The effects of foraging ecology and allometry on avian skull shape vary across levels of phylogeny
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Data from: Marine foraging ecology influences mercury bioaccumulation in deep-diving northern elephant seals
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Foraging and spatial ecology of a polydomous carpenter ant (Camponotus leydigi) in tropical cerrado savanna: A natural history account
<p>Carpenter ants (genus <i>Camponotus</i>) are considered to be predominantly omnivorous, mixing several feeding habits that include predation, scavenging of animal matter, and plant-derived resources. Nitrogen acquisition is crucial for the nutritional ecology of ant colonies since growing larvae require sustainable protein provisioning. Here, we investigate the foraging ecology and the spatial nesting structure of the carpenter ant <i>Camponotus leydigi</i> in Brazilian cerrado savanna. By marking workers from different nests with distinct colors, we revealed that <i>C. leydigi</i> occupies physically separated but socially connected nests (up to 30 m apart), a phenomenon known as polydomy. Observational data on aboveground internest movements in <i>C. leydigi</i> corroborate cooperative exchanges between nest units and confirm several types of social connections, including internest transfer of liquid and solid food, transport of colony members (brood, workers), movement of solitary workers, and internest recruitment. Polydomous <i>C. leydigi</i> allocate foragers throughout 1,700 m<sup>2</sup>, feeding mostly on termites and plant-derived exudates. Influx of exudates is threefold higher compared to solid food. Uric acid pellets excreted by lizards comprise 20% of the solid diet in <i>C. leydigi</i>, a rare quantitative assessment of this peculiar type of nitrogen complementation in ants. Based on video recordings, we hypothesize that nest decentralization in <i>C. leydigi</i> may reduce foraging constraints caused by overt interference by the aggressive ant <i>Ectatomma brunneum</i>, which regularly blocks nest entrances. Our field study enhances the importance of natural history data to clarify selective pressures underlying the evolution of particular behavioral patterns (nutritional and nesting habits) in ants.</p>
Data from: How phylogeny and foraging ecology drive the level of chemosensory exploration in lizards and snakes
The chemical senses are crucial for squamates (lizards and snakes). The extent to which squamates utilize their chemosensory system, however, varies greatly among taxa and species' foraging strategies, and played an influential role in squamate evolution. In lizards, Scleroglossa evolved a state where species use chemical cues to search for food (active-foragers), while Iguania retained the use of vision to hunt prey (ambush-foragers). However, such strict dichotomy is flawed since shifts in foraging modes have occurred in all clades. Here, we attempted to disentangle effects of foraging ecology from phylogenetic trait conservatism as leading cause of the disparity in chemosensory investment among squamates. To do so, we used species' tongue-flick rate (TFR) in absence of ecological relevant chemical stimuli as a proxy for its fundamental level of chemosensory investigation, i.e. baseline TFR. Based on literature data of nearly 100 species and using phylogenetic comparative methods, we tested whether and how foraging mode and diet affect baseline TFR. Our results show that baseline TFR is higher in active than ambush foragers. Although baseline TFRs appear phylogenetically stable in some lizard taxa, that is a consequence of concordant stability of foraging mode: when foraging mode shifts within taxa, so does baseline TFR. Also, baseline TFR is a good predictor of prey chemical discriminatory ability, as we established a strong positive relationship between baseline TFR and TFR in response to prey. Baseline TFR is unrelated to diet. Essentially, foraging mode, not phylogenetic relatedness, drives convergent evolution of similar levels of squamate chemosensory investigation.
Figure 7 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 7 Results of implementing different scouting and foraging strategies on the performance of model colonies in pollen collection. Three different foraging strategies (i.e. distance, quality or random) were tested for each scouting strategy (i.e. distance, quantity and random). The total amount of collected pollen, the mean number of daily foraging flights, the number of foraging flights and their success were evaluated for all combinations of scouting and foraging strategies.
Figure 5 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 5 Scout, recruit and foragers behaviour rules. Without private and social information, model bees become scout bees. When there is no private information because they never performed a foraging flight or because the flight was unsuccessful, model bees become recruits and will search for social information. If model bees have private information, they are considered forager bees even if no social information is available in the colony. In the presence of social information, scout and forager bees can change foraging locations (50% chance).
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.
Supplementary material 1 from: Dupont SM, Guinnefollau L, Weber C, Petit O (2019) Impact of artificial light at night on the foraging behaviour of the European Hamster: consequences for the introduction of this species in suburban areas. Rethinking Ecology 4: 133-148. https://doi.org/10.3897/rethinkingecology.4.36467
: Data type: statistical data
Data from: How phylogeny and foraging ecology drive the level of chemosensory exploration in lizards and snakes
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Data from: Exploring the nature of ecological specialization in a coral reef fish community: morphology, diet, and foraging microhabitat use
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Data from: Foraging mode, relative prey size and diet breadth: a phylogenetically-explicit analysis of snake feeding ecology
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Data from: Seasonal and annual differences in the foraging ecology of two gull species breeding in sympatry and their use of fishery discards
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Foraging and spatial ecology of a polydomous carpenter ant (Camponotus leydigi) in tropical cerrado savanna: A natural history account
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Data for Environmental and ecological drivers of eye size variation in a freshwater predator: a trade-off between foraging and predation risk
<p>CodeZenodo contains code for the final analysis included in the publication along with the code to generate figures. </p> <p>Envt_vars contains information about the lakes</p> <p>Fish_SI contains stable isotopes, eye size, and body length for each fish</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.