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130 results for “foraging behaviour”
Data on anatomy, movement, and foraging behaviour of three cattle breeds of different productivity
<p>Given are</p> <ul> <li>the breed of the cattle (AH: Angus×Holstein, OB: Original Braunvieh, HC: Highland cattle),</li> <li>the age of the cows in months,</li> <li>the body weight at the beginning (Weight_1) and the end (Weight_2) of the experiment in kg,</li> <li>the summarised base of all eight claws of each cow in cm<sup>2</sup>,</li> <li>the average number of steps per hour as recorded by the pedometer,</li> <li>the average speed in m h<sup>-1</sup>,</li> <li>the ratio of the time spent lying as recorded by the pedometer,</li> <li>the evenness of space use calculated as Camargo’s index based on GPS positions,</li> <li>the evenness of forage selection calculated as Pielou’s evenness,</li> <li>the average forage quality indicator value (Briemle, Nitsche, and Nitsche 2002) of the selected diet,</li> <li>the ratio of broad leaved grasses, legumes, thistles and shrubs within the diet of each cow.</li> </ul> <p>All measurements conducted on the pastures are presented as averaged over all pastures (xxx_mean) and separatly for the three pastures (xxx_1, xxx_2, xxx_3).</p>
A refined method for studying foraging behaviour and body mass in group-housed European starlings.
<p>Datasets and R script corresponding to the following manuscript:</p> <p>A refined method for studying foraging behaviour and body mass in group-housed European starlings.</p> <p>Laboratory experiments on passerine birds have been important for testing hypotheses regarding the effects of environmental variables on the adaptive regulation of body mass. However, previous work in this area has suffered from poor ecological validity and animal welfare due to the requirement to house birds individually in small cages to facilitate behavioural measurement and frequent catching for weighing. Here we describe the social foraging system, a novel technology that permits continuous collection of individual-level data on operant foraging behaviour and body mass from group-housed European starlings (<em>Sturnus vulgaris</em>). We demonstrate rapid acquisition of operant key pecking, followed by foraging and body mass data from two groups of six birds maintained on a fixed-ratio operant schedule under closed economy for 11 consecutive days. Birds gained 6.0 ± 1.2 g (mean ± sd) between dawn and dusk each day and lost an equal amount overnight. Individual daily mass gain trajectories were non-linear, with the rate of gain decelerating between dawn and dusk. Within-bird variation in daily foraging effort (key pecks) positively predicted within-bird variation in dusk mass. However, between-bird variation in mean foraging effort was uncorrelated with between-bird variation in mean mass, potentially indicative of individual differences in daily energy requirements. We conclude that the social foraging system delivers refined data collection and offers potential for improving our understanding of mass regulation in starlings and other species.<strong> </strong></p>
Data from: Foraging behaviour and habitat-use drives niche segregation in sibling seabird species
<p>To mediate competition, similar sympatric species are assumed to utilise different resources, or the same but geographically separated resources. The two giant petrels (<em>Macronectes</em> spp.) are intriguing in that they are morphologically similar seabirds with overlapping diets and distributions. To better understand the mechanisms allowing their co-existence, we investigated intra- and interspecific niche segregation at Marion Island (Southern Indian Ocean), one of the few localities where they breed in sympatry. We used GPS tracks from 94 individuals and remote-sensed environmental data to quantify habitat-use, combined with blood carbon and nitrogen stable isotope ratios from 90 individuals to characterise their foraging habitat and trophic ecology. Females of both species made distant at sea foraging trips and fed at a similar trophic level. However, they used distinct pelagic habitats. In contrast, males of both species mainly foraged on or near land, resulting in significant sexual segregation, but high interspecific habitat and diet overlap. However, some males showed flexible behavioural strategies, also making distant, pelagic foraging trips. Using contemporaneous tracking, environmental and stable isotope data we provide a clear example of how sympatric sibling species can be segregated along different foraging behaviour dimensions.</p>
Figure 2 in Does nutritional status constrain adoption of more costly and less risky foraging behaviour in an Amazonian shelter-building spider?
Figure 2. Relationship between predicted probability of spider Hingstepeira folisecens (Hingston 1932) (Araneidae) exhibiting a pulling foraging behaviour to catch prey and body condition index (BCI – standardized residuals from a regression of abdomen volume on carapace area) in one region of Central Amazonia, Brazil. '1' represents occurrence of pulling behaviour and '0' represents absence of spider response or use of pursuing behaviour (n = 19).
Effects of wind on honeybee and bumblebee foraging behaviour on multiple plant species
<p>Dataset of results used for two publications. It shows the foraging behaviours of honeybees and bumblebees on multiple plant species in different wind speeds,</p>
Foraging behaviour data for sympatric Ateles geoffroyi, Alouatta palliata, and Cebus imitator
<p><span>Senses form the interface between animals and environments, and their form and function provide a window into the ecology of past and present species. However, research on the senses used during foraging (e.g. smell, vision, touch, taste) by wild terrestrial frugivores is sparse. Here, we combine 26,094 fruit foraging sequences recorded from three wild, sympatric primates (<em>Cebus imitator, Ateles geoffroyi, Alouatta palliata</em>) with data on within- and between-species variation in colour vision, olfaction, taste, and hand anatomy. We hypothesize that dietary and sensory specialization shape foraging behaviours. We find that frugivorous spider monkeys (<em>Ateles geoffroyi</em>) sniff fruits most often, that omnivorous capuchins (<em>Cebus imitator</em>), the species with the highest measure of manual dexterity, uses manual touch most often, and that main olfactory bulb volume is a better predictor of sniffing behaviour than nasal turbinate surface area. We also identify an interaction between colour vision phenotype and use of other senses. Controlling for species, dichromats sniff and bite fruits more often than trichromats, and trichromats use manual touch to evaluate cryptic fruits more often than dichromats. Our findings help reveal how dietary specialization and sensory variation shape foraging behaviours, and inform methods for investigating relationships between behaviour and anatomy.</span></p>
Figure 3 in Factors affecting foraging behaviour, as seen in a nocturnal ground lizard, Goniurosaurus kuroiwae kuroiwae
Figure 3. Goniurosaurus kuroiwae kuroiwae (female with regenerated tail) encountered (untouched) on the trunk of a tree, about 1.2 m up from the ground, on the study site, Okinawajima Island (9 July 1999, 21:20 h). Note abundant millipedes near the gecko, apparently being ignored rather than eaten.
Figure 2 in Factors affecting foraging behaviour, as seen in a nocturnal ground lizard, Goniurosaurus kuroiwae kuroiwae
Figure 2. Goniurosaurus kuroiwae kuroiwae (female with complete tail) encountered (untouched) walking semierect on the study site, Okinawajima Island (13 August 1999, ca 22:30 h).
Figure 1 in Factors affecting foraging behaviour, as seen in a nocturnal ground lizard, Goniurosaurus kuroiwae kuroiwae
Figure 1. Goniurosaurus kuroiwae kuroiwae (a juvenile with complete tail) marked with numbered reflecting labels, on the study site, Okinawajima Island (6 August 1999, ca 02:30 h), after release following the process of measuring and marking.
Figure 7 in Elbow-joint morphology as a guide to forearm function and foraging behaviour in mammalian carnivores
Figure 7. Second principal component (PC2) of extinct giant mustelids and recent carnivorans plotted against body mass (kg). Hand-fitted arrows marking the two trajectories for recent carnivorans (filled circles) are shown. The postulated threshold at 21.5–25 kg, where carnivorans shift from small to large prey (Carbone et al., 1999), is shaded grey.
Figure 6 in Elbow-joint morphology as a guide to forearm function and foraging behaviour in mammalian carnivores
Figure 6. Second principal component (PC2) of recent carnivorans plotted against calculated body mass (kg). The postulated threshold at 21.5–25 kg, where carnivorans shift diet from small to large prey (Carbone et al., 1999), is shaded grey. Above this threshold, grapplers are clearly separated from nongrapplers on PC2. Hand-fitted arrows marking the two morphological trajectories, the top one for nongrapplers and the lower one for grapplers, are shown. For body mass see Appendix 1.
Figure 3. Second principal component mapped onto a in Elbow-joint morphology as a guide to forearm function and foraging behaviour in mammalian carnivores
Figure 3. Second principal component mapped onto a composite phylogeny for the order Carnivora. Carnivorans traditionally regarded as primarily using their forelimbs for locomotion (nongrapplers) are written in bold typeface. PC2 largely follows the phylogeny. Transitions are rare, but have occurred, e.g. in the cheetah (Acinonyx jubatus). Ancestral stages are reconstructed by minimizing the sum of squared changes. The value for the root is not reconstructed. The phylogeny is a composite from the following sources: Decker & Wozencroft (1991); Bryant, Russell & Fitch (1993); Tedford, Taylor & Wang (1995); Veron (1995); Masuda et al. (1996); Talbot & Shields (1996); Dragoo & Honeycutt (1997); Wayne et al. (1997); Flynn & Nedbal (1998); Seymour (1999); Flynn et al. (2000); Veron & Heard (2000); Gaubert, Veron & Tranier (2002); X. Wang (pers. comm.).
Figure 5 in Elbow-joint morphology as a guide to forearm function and foraging behaviour in mammalian carnivores
Figure 5. Moving average of PC2 variance body mass. Variation is low and uniform at small sizes. At around 10 kg variation starts to increase with size. There is a marked decrease in variation around 30–40 kg. The species are ranked according to body size and the average over a moving succession of ten increments calculated.
Figure 2 in Elbow-joint morphology as a guide to forearm function and foraging behaviour in mammalian carnivores
Figure 2. Thin-plate spline comparison of wolverine (Gulo gulo) against grey wolf (Canis lupus) graphically illustrating the difference in shape of the distal humerus articulation between carnivorans scoring high (C. lupus) on the second principal component (PC2) and low (G. gulo). The broken line shows the outline of the articulation of G. gulo. The thin-plate spline is calculated from the consensus configurations of the two species (G. gulo, reference species, N = 6; C. lupus, N = 5; Bookstein, 1991) generated through generalized least squares (GLS) orthogonal Procrustes analysis (Rohlf & Slice, 1990).
Figure 1 in Elbow-joint morphology as a guide to forearm function and foraging behaviour in mammalian carnivores
Figure 1. Results of the principal component analysis of recent carnivorans. PC1 plotted against PC2 (A), PC2 against PC3 (B). For species scores see Appendix 1.
Inhibitory control, exploration behaviour and manipulated ecological context are associated with foraging flexibility in the great tit
<p class="MsoCommentText">Organisms are constantly under selection to respond effectively to diverse, sometimes rapid, changes in their environment, but not all individuals are equally plastic in their behaviour. Although cognitive processes and personality are expected to influence individual behavioural plasticity, the effects reported are highly inconsistent, which we hypothesise is because ecological context is usually not considered.</p> <p class="MsoCommentText">We explored how one type of behavioural plasticity, foraging flexibility, was associated with inhibitory control (assayed using a detour-reaching task) and exploration behaviour in a novel environment (a trait closely linked to the fast-slow personality axis). We investigated how these effects varied across two experimentally manipulated ecological contexts, food value and predation risk.</p> <p class="MsoCommentText">In the first phase of the experiment, we trained great tits <i>Parus major</i> to retrieve high value (preferred) food that was hidden in sand so that this became the familiar food source. In the second phase, we offered them the same familiar hidden food at the same time as a new alternative option that was visible on the surface, which was either high or low value, and under either high or low perceived predation risk. Foraging flexibility was defined as the proportion of choices made during four minute trials that were for the new alternative food source.</p> <p>Our assays captured consistent differences among individuals in foraging flexibility. Inhibitory control was associated with foraging flexibility - birds with high inhibitory control were more flexible when the alternative food was high value, suggesting they inhibited the urge to select the familiar food and instead selected the new food option. Exploration behaviour also predicted flexibility – fast explorers were more flexible, supporting the information gathering hypothesis. This tendency was especially strong under high predation risk, suggesting risk aversion also influenced the observed flexibility because fast explorers are risk prone and the new unfamiliar food was perceived to be the risky option. Thus, both behaviours predicted flexibility, and these links were at least partly dependent on ecological conditions.</p> <p class="MsoCommentText">Our results demonstrate that an executive cognitive function (inhibitory control) and a behavioural assay of a well-known personality axis are both associated with individual variation in the plasticity of a key functional behaviour. That their effects on foraging flexibility were primarily observed as interactions with food value or predation risk treatments also suggests that the population level consequences of some behavioural mechanisms may only be revealed across key ecological conditions.</p>
Variation in foraging activity influences area-restricted search behaviour by bottlenose dolphins
<p>Area-restricted search (ARS) behaviour is commonly used to characterise spatio-temporal variation in foraging activity of predators, but evidence of the drivers underlying this behaviour in marine systems is sparse. Advances in underwater sound recording techniques and automated processing of acoustic data now provide opportunities to investigate these questions where species use different vocalisations when encountering prey. Here, we used passive acoustics to investigate drivers of ARS behaviour in a population of dolphins, to determine if residency in key foraging areas increased following encounters with prey. Analyses were based on two independent proxies of foraging: echolocation buzzes (widely used as foraging proxies), and bray calls (vocalizations linked to salmon predation attempts). Echolocation buzzes were extracted from echolocation data loggers and bray calls from broadband recordings by a convolutional neural network (CNN). We found a strong positive relationship between the duration of encounters and the frequency of both foraging proxies, supporting the theory that bottlenose dolphins engage in ARS behaviour in response to higher prey encounter rates. This study provides empirical evidence for one driver of ARS behaviour and demonstrates the potential for applying passive acoustic monitoring in combination with deep learning-based techniques to investigate the behaviour of vocal animals. </p>
Data from: Foraging behaviour and habitat-use drives niche segregation in sibling seabird species
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Variation in foraging activity influences area-restricted search behaviour by bottlenose dolphins
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Using motion-detection cameras to monitor foraging behaviour of individual butterflies
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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