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68 results for “foraging strategy”

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dryad32/100

Insights into short and long-term crop-foraging strategies in a chacma baboon (Papio ursinus) from GPS and accelerometer data

<p>Crop-foraging by animals is a leading cause of human-wildlife 'conflict' globally, affecting farmers and resulting in the death of many animals in retaliation, including primates. Despite significant research into crop-foraging by primates, relatively little is understood about the behaviour and movements of primates in and around crop fields, largely due to the limitations of traditional observational methods. Crop-foraging by primates in large scale agriculture has also received little attention. We used GPS and accelerometer bio-loggers, along with environmental data, to gain an understanding of the spatial and temporal patterns of activity for a female in a crop-foraging baboon group in and around commercial farms in South Africa over one year. Crop fields were avoided for most of the year, suggesting that fields are perceived as a high-risk habitat. When field visits did occur, this was generally when plant primary productivity was low, suggesting that crops were a 'fallback food'. All recorded field visits were at or before 15:00. Activity was significantly higher in crop fields than in the landscape in general, evidence that crop-foraging is an energetically costly strategy and that fields are perceived as a risky habitat. In contrast, activity was significantly lower within 100m of the field edge than in the rest of the landscape, suggesting that baboons wait near the field edge to assess risks before crop-foraging. Together this understanding of the spatiotemporal dynamics of crop-foraging can help to inform crop protection strategies and reduce conflict between humans and baboons in South Africa.</p>

opencc-zeroNov 2021View details →
dryad32/100

Foraging strategies, craniodental traits and interaction in the bite force of Neotropical frugivorous bats (Phyllostomidae: Stenodermatinae)

<p>1. Bats in the family Phyllostomidae exhibit great diversity in skull size and morphology that reflects the degree of resource division and ecological overlap in the group. In particular, the subfamily Stenodermatinae has high morphological diversification associated with cranial and mandibular traits that is associated with the ability to consume the full range of available fruits (soft and hard).</p> <p>2. Was analyzed craniodental traits and their relationship to the bite force in 343 specimens distributed in seven species of stenodermatine bats with two foraging strategies: nomadic and sedentary frugivory. We evaluated 19 traits related to feeding and bite force in live animals by correcting bite force with body size.</p> <p>3. We used a generalized linear model (GLM) and post hoc tests to determine possible relationships and differences between cranial traits, species, and sex. We also used Blomberg's K to measure the phylogenetic signal and Phylogenetic generalized least-squares (PGLS) to ensure the phylogenetic independence of the traits.</p> <p>4. We found that smaller nomadic, <i>A. anderseni</i> and <i>A. phaeotis</i> have a similar bite force to the large species <i>A. planirostris</i> and <u>A. lituratus</u>; furthermore, <i>P. helleri</i> registered a bite force similar to that of the sedentary bat, <i>S. giannae</i>. Our study determined that all the features of the mandible and most of the traits of the skull have a low phylogenetic signal. Through the PGLS we found that the diet and several cranial features (mandibular toothrow length, dentary length, braincase breadth, mastoid breadth, greatest length of skull, condyloincisive length and condylocanine length) determined bite force performance among Stenodermatiane.</p> <p>5. Our results reinforce that skull size is a determining factor in the bite force, but also emphasize the importance of its relationships with morphology, ecology, and phylogeny of the species, which gives us a better understanding of the evolutionary adaptions of this highly diverse Neotropical bat group.</p>

opencc-zeroAug 2022View details →
dryad32/100

Foraging strategies, craniodental traits and interaction in the bite force of Neotropical frugivorous bats (Phyllostomidae: Stenodermatinae)

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publicAug 2022View details →
dryad32/100

Prey density affects predator foraging strategy in an Antarctic ecosystem

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publicDec 2019View details →
dryad32/100

Data from: Study on the foraging behaviour of the European nightjar Caprimulgus europaeus reveals the need for a change in conservation strategy in Belgium

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publicJun 2017View details →
dryad32/100

Data from: Condition-dependent foraging strategies in a coastal seabird: evidence that the rich get richer

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publicNov 2018View details →
dryad32/100

Ants adjust their tool use strategy in response to foraging risk

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publicAug 2020View details →
dryad32/100

Data from: Foraging strategies of generalist and specialist Old World nectar bats in response to temporally variable floral resources

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publicAug 2017View details →
dryad32/100

Data from: Linking GPS telemetry surveys and scat analyses helps explain variability in black bear foraging strategies

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publicMay 2016View details →
dryad32/100

Data from: Molecular diet analysis finds an insectivorous desert bat community dominated by resource sharing despite diverse echolocation and foraging strategies

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publicFeb 2019View details →
dryad32/100

Data from: The early bird gets the worm: foraging strategies of wild songbirds lead to the early discovery of food sources

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publicOct 2014View details →
dryad32/100

Data from: Depth dependent dive kinematics suggest cost-efficient foraging strategies by tiger sharks

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publicAug 2020View details →
dryad32/100

Data from: Foraging strategy predicts foraging economy in a facultative secondary nectar robber

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publicFeb 2017View details →
dryad32/100

Ant foraging strategies vary along a natural resource gradient

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publicSep 2020View details →
dryad32/100

Data from: Metabolism and foraging strategies of mid-latitude mesozooplankton during cyanobacterial blooms as revealed by fatty acids, amino acids and their stable carbon isotopes

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publicSep 2019View details →
dryad32/100

Insights into short and long-term crop-foraging strategies in a chacma baboon (Papio ursinus) from GPS and accelerometer data

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publicNov 2021View details →
dryad32/100

Data from: Water-conscious management strategies reduce per-yield irrigation and soil emissions of CO2, N2O, and NO in high-temperature forage cropping systems.

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publicJul 2023View details →
dryad28/100

Data from: QuLinePlus: extending plant breeding strategy and genetic model simulation to cross-pollinated populations – case studies in forage breeding

Plant breeders are supported by a range of tools that assist them to make decisions about the conduct or design of plant breeding programs. Simulations are a strategic tool that enable the breeder to integrate the multiple components of a breeding program into a number of proposed scenarios that are compared by a range of statistics measuring the efficiency of the proposed systems. A simulation study for the trait growth score compared two major strategies for breeding forage species, among half-sib family selection and among and within half-sib family selection. These scenarios highlighted new features of the QuLine program, now called QuLinePlus, incorporated to enable the software platform to be used to simulate breeding programs for cross pollinated species. Each strategy was compared across three levels of HS family mean heritability (0.1, 0.5 and 0.9), across three sizes of the initial parental population (10, 50, and 100), and across three genetic effects models (fully additive model, a mixture of additive, partial and over dominance model, and a mixture of partial dominance and over dominance model). Among and within half-sib selection performed better than among half-sib selection for all scenarios. The new tools introduced into QuLinePlus should serve to accurately compare among methods and provide direction on how to achieve specific goals in the improvement of plant breeding programs for cross breeding species.

opencc-zeroDec 2017View details →
zenodo28/100

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.

opencc-by-4.0Mar 2024View details →
zenodo28/100

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).

opencc-by-4.0Mar 2024View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
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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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record