6.2 Patch selection and marginal value theorem

Patch selection and marginal value theorem are crucial concepts in animal foraging behavior. These ideas explain how animals decide where to feed and how long to stay, balancing energy gain with travel costs and risks.

Understanding these concepts helps us predict animal movements and habitat use. By applying these theories, we can better grasp how animals make decisions in complex environments, impacting their survival and reproduction.

Patch selection

• Patch selection is a key concept in optimal foraging theory that explains how animals make decisions about where to forage to maximize their energy intake
• Animals must balance the benefits of foraging in a particular patch with the costs of traveling between patches and the potential risks associated with each patch
• Patch selection strategies have important implications for understanding animal behavior, habitat use, and population dynamics

Foraging patches

• Foraging patches are distinct areas where animals search for and consume food resources
• Patches can vary in size, quality, and distribution across the landscape
• Examples of foraging patches include a clump of berries, a school of fish, or a section of a meadow with high plant density

Travel between patches

• Animals must expend time and energy to travel between foraging patches
• The distance between patches and the ease of travel (e.g., presence of obstacles or predators) can influence patch selection decisions
• Animals may use various navigation strategies (e.g., visual cues, memory) to efficiently move between patches

Patch quality assessment

• Animals assess the quality of foraging patches to determine which ones are worth exploiting
• Patch quality can be evaluated based on factors such as food density, food type, and presence of competitors or predators
• Animals may use sensory cues (e.g., visual, olfactory) or past experience to assess patch quality
• The ability to accurately assess patch quality can have significant effects on an animal's foraging success and fitness

Marginal value theorem

• The marginal value theorem (MVT) is a mathematical model that predicts how long an animal should spend foraging in a patch before moving to another one
• MVT assumes that animals aim to maximize their net rate of energy intake while foraging
• The model considers the trade-off between the benefits of continuing to forage in a patch and the costs of traveling to a new patch

Diminishing returns in patches

• As an animal forages in a patch, the rate of food intake typically decreases over time due to resource depletion
• This phenomenon is known as diminishing returns, where each additional unit of time spent in a patch yields less food than the previous unit
• The shape of the diminishing returns curve can vary depending on the type of resource and the foraging behavior of the animal

Optimal foraging time

• MVT predicts that there is an optimal amount of time an animal should spend in a patch before leaving to maximize its overall rate of energy intake
• The optimal foraging time depends on the initial quality of the patch, the rate of diminishing returns, and the travel time between patches
• Animals that leave patches too early or too late will have lower overall foraging efficiency

Patch-leaving threshold

• The patch-leaving threshold is the point at which an animal decides to leave a patch and move to another one
• According to MVT, an animal should leave a patch when the marginal rate of energy intake in the current patch drops below the average rate of energy intake for the entire habitat
• The patch-leaving threshold can be influenced by factors such as the animal's energy requirements, the availability of alternative patches, and the risk of predation

Energy intake vs time spent

• MVT predicts a relationship between the energy intake and the time spent in a patch
• The cumulative energy intake increases with time spent in a patch, but the rate of increase slows down due to diminishing returns
• The optimal foraging time is the point at which the slope of the cumulative energy intake curve equals the average rate of energy intake for the habitat

Factors influencing patch selection

• In addition to patch quality and travel costs, various other factors can influence an animal's patch selection decisions
• These factors can interact with each other and vary in importance depending on the species and the ecological context
• Understanding the relative influence of different factors on patch selection is crucial for predicting animal behavior and distribution

Predation risk

• The presence of predators can significantly affect patch selection, as animals must balance foraging benefits with the risk of being eaten
• Animals may avoid patches with high predation risk, even if they contain abundant food resources
• Predation risk can vary spatially and temporally, and animals may adjust their patch use accordingly (e.g., foraging in safer patches during times of high predation risk)

Competition

• Competition for resources can influence patch selection, as animals may avoid patches occupied by dominant competitors
• Intraspecific competition (between members of the same species) and interspecific competition (between different species) can both affect patch use
• The intensity of competition can depend on factors such as resource availability, population density, and the competitive abilities of the species involved

Patch distance

• The distance between patches can influence an animal's decision to travel to and forage in a particular patch
• Patches that are farther away may be less frequently visited, as the travel costs (in terms of time and energy) are higher
• However, if distant patches offer significantly better resources, animals may still choose to travel to them

Patch size

• The size of a foraging patch can affect its attractiveness to animals
• Larger patches may contain more resources and allow for longer foraging bouts, reducing the need for frequent travel between patches
• However, larger patches may also attract more competitors or predators, which can offset the benefits of increased resource availability

Applying marginal value theorem

• The marginal value theorem has been applied to various aspects of animal behavior and ecology, providing insights into foraging strategies, habitat use, and population dynamics
• Researchers have used MVT to develop mathematical models and generate testable predictions about animal behavior in different contexts
• The application of MVT has also informed conservation and management practices, helping to understand how human activities can affect animal foraging patterns and population viability

Optimal diet model

• The optimal diet model is an extension of MVT that predicts which food items an animal should include in its diet to maximize energy intake
• The model considers the energy content of different food types, the time required to handle and consume them, and their abundance in the environment
• Animals are predicted to prefer food items that provide the highest net energy gain per unit time, while ignoring less profitable items

Habitat selection

• MVT can be applied to understand how animals select habitats based on the distribution and quality of resources
• Animals are expected to prefer habitats that offer the highest overall rate of energy intake, considering the costs of travel between patches within the habitat
• Habitat selection models based on MVT can help predict animal distribution patterns and identify important foraging areas for conservation

Anthropogenic disturbances

• Human activities, such as habitat fragmentation, resource exploitation, and urbanization, can alter the distribution and quality of foraging patches
• MVT can be used to predict how animals may respond to these disturbances, such as changes in patch use, diet composition, or foraging behavior
• Understanding the effects of anthropogenic disturbances on animal foraging patterns is crucial for developing effective conservation and management strategies

Conservation implications

• Insights from MVT can inform conservation efforts by identifying critical foraging habitats and predicting the impacts of habitat loss or alteration on animal populations
• Conservation strategies based on MVT may focus on maintaining a network of high-quality foraging patches, minimizing travel costs between patches, and reducing the effects of anthropogenic disturbances
• MVT can also help predict the potential for human-wildlife conflicts, such as crop raiding or livestock predation, by understanding the factors that influence animal foraging decisions

Experimental studies

• Experimental studies have been conducted to test the predictions of the marginal value theorem and investigate patch selection behavior in various animal species
• These studies typically involve manipulating patch quality, travel costs, or other factors thought to influence foraging decisions
• Experimental approaches allow researchers to control for confounding variables and establish causal relationships between environmental factors and animal behavior

Giving-up density (GUD)

• Giving-up density is an experimental technique used to measure the perceived quality of a foraging patch
• GUD is the amount of food remaining in a patch when an animal decides to leave it
• Higher GUDs indicate lower patch quality, as animals are willing to leave more food behind before moving to another patch
• GUD experiments have been used to study the effects of predation risk, competition, and other factors on patch selection

Measuring patch quality

• Researchers can measure patch quality using various methods, such as assessing food density, energy content, or intake rates
• Direct observations of foraging behavior, such as the number of food items consumed or the time spent in a patch, can provide estimates of patch quality
• Remote sensing techniques, such as satellite imagery or drone surveys, can be used to map the distribution and characteristics of foraging patches across landscapes

Testing predictions of MVT

• Experimental studies can test specific predictions of MVT, such as the relationship between patch residence time and patch quality
• Researchers may manipulate patch quality by adding or removing food resources, or by altering the perceived risk of predation using predator cues (e.g., scent, vocalizations)
• Comparing observed foraging behavior with the predictions of MVT can help validate the model and identify factors that influence patch selection decisions

Limitations and challenges

• Experimental studies of patch selection can be challenging due to the complexity of animal behavior and the difficulty of controlling all relevant variables
• The spatial and temporal scales of foraging decisions may vary among species and ecological contexts, requiring careful design of experiments
• Measuring patch quality and animal behavior in natural settings can be logistically difficult and time-consuming
• Interpreting the results of experimental studies may require consideration of additional factors, such as individual variation, social interactions, and environmental stochasticity

Extensions of MVT

• The marginal value theorem has been extended and modified to account for various ecological factors and foraging scenarios
• These extensions aim to improve the realism and predictive power of the model by incorporating additional variables and constraints
• Researchers have developed specialized models to address specific foraging contexts, such as central place foraging, resource renewal, and multi-prey systems

Central place foraging

• Central place foraging refers to a scenario where animals return to a fixed location (e.g., nest, den) between foraging bouts
• The central place foraging model extends MVT by considering the costs of traveling between the central place and foraging patches
• Animals are predicted to select patches that maximize the net rate of energy delivery to the central place, rather than just the rate of energy intake while foraging

Resource renewal rates

• MVT assumes that patches are depleted as animals forage in them, but in some cases, resources may renew over time
• The resource renewal rate can influence patch selection decisions, as animals may benefit from revisiting previously depleted patches
• Models incorporating resource renewal can predict how animals adjust their foraging behavior in response to changes in resource availability and renewal rates

Multiple prey types

• In natural environments, animals often encounter multiple types of prey with different energy contents, handling times, and encounter rates
• Multi-prey models extend MVT by considering how animals allocate their foraging effort among different prey types to maximize overall energy intake
• These models can predict diet composition, prey switching behavior, and the effects of prey availability on patch selection

Non-depleting patches

• In some foraging scenarios, patches may not be depleted by the foraging activity of animals
• Non-depleting patches, such as nectar sources for pollinators or aerial insects for insectivores, require different foraging strategies than depleting patches
• Models for non-depleting patches consider factors such as encounter rates, handling times, and the spatial distribution of resources to predict optimal foraging behavior