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35 results for “foraging model”
Data from: Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds
<p>For ecosystem models to be applicable outside their context of development, temporal and spatial transferability must be demonstrated. This presents a challenge for modeling intertidal ecosystems where spatiotemporal variation arises at multiple scales. Models specializing in tidal dynamics are generally inhibited from having wider ecological applications by coarse spatiotemporal resolution or high user competency. The Tidal Inundation Model of Shallow-water Availability (TiMSA) uniquely simulates tides to empirically derive a time-integrated measure of availability for a shallow water depth range defined by the user. To evaluate temporal and spatiotemporal transferability, we employed TiMSA at the development site in the Florida Keys and at novel sub-sites in the Florida Bay (application site) under a different time period (application period). We used foraging Little Blue Herons (<em>Egretta caerulea</em>) as the ecological unit with which to constrain the model's 'water depth window', i.e., range of water depths to estimate shallow-water availability. At the development site, temporally consistent water depth windows contrasted with interannual variation in shallow-water availability which revealed short-term changes in Little Blue Heron foraging habitat. At the application site, water depth accuracy varied by sub-site and was correlated with spatial error in bathymetric elevation. Although TiMSA parameters were sensitive to environmental temporal variation and uncertainty in spatial data, a spatially-explicit water depth window generated reliable estimates of shallow-water conditions over space and time at the development and application sites. By exploring the contributing factors to model error, we provide solutions to reduce uncertainty of TiMSA parameters at potential application sites and recommendations for addressing bathymetric inaccuracy in digital elevation models. Accurately quantifying spatiotemporal changes of shallow-water has implications for monitoring habitat conditions for tidally-influenced species and projecting future changes to coastal ecosystems in response to anthropogenic stressors and natural disturbances such as sea level rise.</p>
Data from: Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds
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Data and code to replicate: Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie
<p>Diet analysis integrates a wide variety of visual, chemical and biological identification of prey. Samples are often treated as compositional data, where each prey is analyzed as a continuous percentage of the total. However, analyzing compositional data results in analytical challenges, e.g., highly parameterized models or prior transformation of data. Here, we present a novel approximation involving a Tweedie generalized linear model (GLM). We first review how this approximation emerges from considering predator foraging as a thinned and marked point process (with marks representing prey species and individual prey size). This derivation can motivate future theoretical and applied developments. We then provide a practical tutorial for the Tweedie GLM using new package <i>mvtweedie</i> that extends capabilities of widely used packages in R (<i>mgcv</i> and <i>ggplot2</i>) by transforming output to calculate prey compositions. We demonstrate this approach and software using two examples. Tufted puffins (<i>Fratercula cirrhata</i>) provisioning their chicks on a colony in the northern Gulf of Alaska show decadal prey switching among sand lance and prowfish (1980-2000) and then Pacific herring and capelin (2000-2020), while wolves (<i>Canis lupus ligoni</i>) in Southeast Alaska forage on mountain goats and marmots in northern uplands and marine mammals in seaward island coastlines. </p>
When should bees be flower constant? An agent-based model highlights the importance of social information and foraging conditions
<p>1. Many bee species show flower constancy, i.e. a tendency to visit flowers of one type during a foraging trip. Flower constancy is important for plant reproduction, but the benefits of constancy to bees are unclear. Social bees, which often use communication about food sources, show particularly strong flower constancy.</p> <p>2. We aimed to better understand the benefits of flower constancy in social bees and how these benefits depend on foraging conditions. We hypothesised that sharing social information increases the benefits of flower constancy because social foragers share information selectively about high-quality food sources, thereby reducing the need to sample alternatives.</p> <p>3. We developed an agent-based model that allowed us to simulate bee colonies with and without communication and flower constancy in different foraging environments. By varying key environmental parameters, such as food source numbers and reward size, we explored how the costs and benefits of flower constancy depend on the foraging landscape.</p> <p>4. Flower constancy alone performed poorly in all environments, while indiscriminate flower choice was often the most successful strategy. However, communication improved the performance of flower constant colonies considerably in most environments. This combination was particularly successful when high-quality food sources were abundant and competition was weak.</p> <p>5. Our findings help explain why social bees tend to be more flower constant than solitary bees and suggest that flower constancy can be an adaptive strategy in social bees. Simulations suggest that anthropogenic changes of foraging landscapes will have different effects on the foraging performance of bees that vary in flower constancy.</p>
Limiting scaring activities reduces economic costs associated with foraging barnacle geese: results from an individual-based model
<ol> <li>With increasing numbers of large grazing birds on agricultural grassland, conflict with farmers is rising. One management approach to alleviate conflict allows foraging on dedicated agricultural land (accommodation areas) and nature reserves, combined with scaring on remaining agricultural land. Here, we examine the cost-effectiveness of these measures by studying the influence on barnacle goose distribution and associated economic damage.</li> <li>We present an individual/agent-based model of barnacle geese (<em>Branta</em> <em>leucopsis</em>) foraging on grasslands in Fryslân, the Netherlands. The model is parameterized using field observations and GPS-tracks and allows simulation of management scenarios, differing in scaring probability and accommodation area size, with different potential management costs. </li> <li>Our model shows that, while yield loss decreases with higher scaring probabilities, costs of damage appraisal increase because geese graze on more fields. With small accommodation areas, achieving high scaring probabilities takes more effort and could result in goose population decline. Total management costs are lowest without scaring activity. </li> <li> <em>Synthesis and applications</em>: Considering costs of active scaring and the need to maintain the barnacle goose population in a favourable conservation status, our model suggests that the most cost-effective scenario is to prevent disturbance of geese. A high scaring probability could be beneficial if applied in small areas, for example around sensitive crops or airfields. Scaring in large areas could result in costs outweighing benefits and a declining barnacle goose population.</li> </ol>
Data and code to replicate: Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie
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Data for: Modeling short-term energetic costs of sonar disturbance to cetaceans using high resolution foraging data
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When should bees be flower constant? An agent-based model highlights the importance of social information and foraging conditions
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Limiting scaring activities reduces economic costs associated with foraging barnacle geese: results from an individual-based model
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Modelled mid‐trophic pelagic prey fields improve understanding of marine predator foraging behaviour
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Data from: Delineating important killer whale foraging areas using a spatiotemporal logistic model
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Data from: Scale-dependent foraging ecology of a marine top predator modelled using passive acoustic data
1. Understanding which environmental factors drive foraging preferences is critical for the development of effective management measures, but resource use patterns may emerge from processes that occur at different spatial and temporal scales. Direct observations of foraging are also especially challenging in marine predators, but passive acoustic techniques provide opportunities to study the behavior of echolocating species over a range of scales. 2. We used an extensive passive acoustic dataset to investigate the distribution and temporal dynamics of foraging in bottlenose dolphins using the Moray Firth (Scotland, UK). Echolocation buzzes were identified with a mixture model of detected echolocation inter-click intervals, and used as a proxy of foraging activity. A robust modelling approach accounting for autocorrelation in the data was then used to evaluate which environmental factors were associated with the observed dynamics at two different spatial and temporal scales. 3. At a broad scale, foraging varied seasonally, and was also affected by sea-bed slope and shelf-sea fronts. At a finer scale, we identified variation in seasonal use and local interactions with tidal processes. Foraging was best predicted at a daily scale, accounting for site-specificity in the shape of the estimated relationships. 4. This study demonstrates how passive acoustic data can be used to understand foraging ecology in echolocating species, and provides a robust analytical procedure for describing spatio-temporal patterns. Associations between foraging and environmental characteristics varied according to spatial and temporal scale, highlighting the need for a multi-scale approach. Our results indicate that dolphins respond to coarser-scale temporal dynamics, but have a detailed understanding of finer-scale spatial distribution of resources.
Data from: How do foragers decide when to leave a patch? A test of alternative models under natural and experimental conditions
1. A forager's optimal patch-departure time can be predicted by the prescient marginal value theorem (pMVT), which assumes they have perfect knowledge of the environment, or by approaches such as Bayesian-updating and learning rules, which avoid this assumption by allowing foragers to use recent experiences to inform their decisions. 2. In understanding and predicting broader scale ecological patterns, individual-level mechanisms, such as patch-departure decisions, need to be fully elucidated. Unfortunately, there are few empirical studies that compare the performance of patch-departure models that assume perfect knowledge with those that do not, resulting in a limited understanding of how foragers decide when to leave a patch. 3. We tested the patch-departure rules predicted by fixed-rule, pMVT, Bayesian-updating and learning models against one another, using patch residency times recorded from 54 chacma baboons (Papio ursinus) across two groups in natural (n = 6,594 patch visits) and field-experimental (n = 8,569) conditions. 4. We found greater support in the experiment for the model based on Bayesian-updating rules, but greater support for the model based on the pMVT in natural foraging conditions. This suggests that foragers may place more importance on recent experiences in predictable environments, like our experiment, where these experiences provide more reliable information about future opportunities.5. Furthermore, the effect of a single recent foraging experience on patch residency times was uniformly weak across both conditions. This suggests that foragers' perception of their environment may incorporate many previous experiences, thus approximating the perfect knowledge assumed by the pMVT. Foragers may, therefore, optimise their patch-departure decisions in line with the pMVT through the adoption of rules similar to those predicted by Bayesian-updating.
Data from: Exploring foraging decisions in a social primate using discrete choice models
There is a growing appreciation of the multiple social and nonsocial factors influencing the foraging behavior of social animals, but little understanding of how these factors depend on habitat characteristics or individual traits. This partly reflects the difficulties inherent in using conventional statistical techniques to analyze multi-factor, multi-context foraging decisions. Discrete choice models provide a way to do so, and we demonstrate this by using them to investigate patch preference in a wild population of social foragers (chacma baboons, Papio ursinus). Data were collected from 29 adults across two social groups encompassing 683 foraging decisions over a six-month period, and the results interpreted using an information theoretic approach. Baboon foraging decisions were influenced by multiple nonsocial and social factors, and were often contingent on the characteristics of the habitat or individual. Differences in decision-making between habitats were consistent with changes in interference competition costs but not changes in social foraging benefits. Individual differences in decision-making were suggestive of a trade-off between dominance rank and social capital. Our findings emphasize that taking a multi-factor, multi-context approach is important to fully understand animal decision-making. We also demonstrate how discrete choice models can be used to achieve this.
Including population and environmental dynamic heterogeneities in continuum models of collective behaviour with applications to locust foraging and group structure Data and Code
<p>This dataset includes all data used for the creation of "Including dynamic population and environmental heterogeneity in continuum models of collective behaviour with applications to locust foraging and group structure" as well as a snapshot of the code used.<br><br>Each zip should be unzippable and the code should operate with only the contents of the zip file.</p>
Data from: Central place foragers and moving stimuli: a hidden-state model to discriminate the processes affecting movement
1. Human activities can influence the movement of organisms, either repelling or attracting individuals depending on whether they interfere with natural behavioural patterns or enhance access to food. To discern the processes affecting such interactions, an appropriate analytical approach must reflect the motivations driving behavioural decisions at multiple scales. 2. In this study, we developed a modelling framework for the analysis of foraging trips by central place foragers. By recognising the distinction between movement phases at a larger scale and movement steps at a finer scale, our model can identify periods when animals are actively following moving attractors in their landscape. 3. We applied the framework to GPS tracking data of northern fulmars Fulmarus glacialis, paired with contemporaneous fishing boat locations, to quantify the putative scavenging activity of these seabirds on discarded fish and offal. We estimated the rate and scale of interaction between individual birds and fishing boats and the interplay with other aspects of a foraging trip. 4. The model classified periods when birds were heading out to sea, returning towards the colony or following the closest boat. The probability of switching towards a boat declined with distance and varied depending on the phase of the trip. The maximum distance at which a bird switched towards the closest boat was estimated around 35 km, suggesting the use of olfactory information to locate food. Individuals spent a quarter of a foraging trip, on average, following fishing boats, with marked heterogeneity among trips and individuals. 5. Our approach can be used to characterise interactions between central place foragers and different anthropogenic or natural stimuli. The model identifies the processes influencing central place foraging at multiple scales, which can improve our understanding of the mechanisms underlying movement behaviour and characterise individual variation in interactions with a range of human activities that may attract or repel these species. Therefore, it can be adapted to explore the movement of other species that are subject to multiple dynamic drivers.
Data from: Central place foragers and moving stimuli: a hidden-state model to discriminate the processes affecting movement
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Through the taste buds of a large herbivore: foodscape modeling contributes to an understanding of forage selection processes
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Data from: How do foragers decide when to leave a patch? A test of alternative models under natural and experimental conditions
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Data from: Scale-dependent foraging ecology of a marine top predator modelled using passive acoustic data
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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.