Foraging Behavior – Comprehensive Study Notes

Feedback and course adjustments (Test 1)

• Thanked class for feedback; focus on improving next test based on common issues.
• Main issue identified: time management during the test and allocating time per question.
• Plan for Test 2:
  • Keep test framework similar but adjust question design to allow more time per question.
  • Increase clarity and time by reducing total questions (fewer questions, each worth more toward final grade).
  • Maintain balance between time available and number of questions.
• Revision resources discussion:
  • Resources currently used: book chapters at end of each lecture; optional Moodle quizzes.
  • Usage signal: less than a third of students used quizzes; resource investment vs. utility to students was questioned.
  • Invitation for anonymous feedback on what revision resources would be helpful; options to share now, later, or with Kendall.
• Guidance on what to study:
  • Learning outcomes should map to study focus; all test questions based on lectures, but students requested clearer guidance on what to study (which lectures or specific points).
  • Request for more explicit guidance on coverage (which parts of lectures or which details are essential).
• Guest lectures:
  • Plan to secure two more guest lectures; aim for clarity on learning objectives and key points.
  • Feedback highlighted that previous guest lecture lacked clarity on main takeaways; instructors will coordinate with guests to emphasize learning objectives.
  • Students can email the instructor or Kendall (anonymous Moodle comments) with thoughts.
• Open invitation for further feedback on any other points.
• Administrative note: pause and switch to Zoom; next sections to resume if time allows.

Course and lecture context: Foraging behavior (introduction to today’s lecture)

• Course shift: from primarily sexually selected traits (mating, parental care) to naturally selected traits focused on survival.
• Learning objectives:
  • Explore the diversity of feeding behavior and the constraints animals face.
  • Introduce optimal foraging theory and the marginal value theorem.
• Key concepts introduced:
  • Foraging strategies vary by ecological constraints and energy budgets.
  • Food acquisition involves trade-offs among search time, handling time, and giving-up time.
  • Foraging decisions are influenced by energy gains, nutritional needs, and risks.

Types of foraging and feeding strategies

• Filter feeders
  • Absorb nutrients from environment using body adaptations; consume live and dead material.
  • Typical organisms: invertebrates (e.g., krill, oysters, mussels); large vertebrates like baleen whales.
  • Adaptations include specialized filtering structures for waterborne foods.
• Detritivores / scavengers
  • Consume dead material; often include microbes within food; role in nutrient cycling.
  • Examples of diverse scavenging behaviors: dung beetles rolling balls of feces, burial of dung, feeding on carcasses or moldy material.
• Herbivory (plant-based feeding)
  • Broadly defined: consumption of plant parts or plant materials.
  • Examples:
  • Fruits and nectar (pollinators) as forms of herbivory.
  • Leaf miners that create marks inside leaves.
  • Leafcutter ants that harvest leaves to cultivate fungi for food (note: leaves themselves are not eaten directly).
  • Diverse herbivory strategies highlight complexity of plant-animal interactions.
• Predation (consuming other animals)
  • High energetic payoff but often challenging due to prey defenses and competition.
  • Predation strategies:
  • Trapping: spider webs capturing insects.
  • Aggressive mimicry: predators imitate signals to lure prey (e.g., bolas spiders).
  • Visual or stealth adaptations: tortuous tongue lure in snapping turtles; mimicry and camouflage to approach prey.
  • Predation via cues and detection: predator-prey signaling and detection (e.g., vole scent marking that can be detected by predators/birds via UV cues).
• Predator and prey co-adaptations
  • Predators adapt to detect, pursue, and capture prey; prey adapt to avoid detection and increase survival odds.
  • Examples include deception, camouflage, and cue-based avoidance.

Foraging constraints and decision variables

• Core idea: foraging success requires a balance between time spent and energy gained.
• Key time components:
  • Search time: time to locate food; depends on patch density, distribution, and conspicuity.
  • Handling time: time to capture and process food after finding it; depends on prey size/defenses and processing requirements.
  • Giving up time: decision time to abandon a patch and move to another patch; influenced by comparison of current patch profitability to expected profitability of alternatives.
• Patch dynamics and feeding decisions:
  • Patches can refer to environmental areas or specific food items.
  • If food is sparse or cryptic, search time increases; if food is dense or easy to harvest, search time decreases.
  • Handling time rises with more difficult prey (e.g., large, dangerous, or well-defended prey).
• Variation in foraging strategies:
  • High-variation species: candidates for inconsistent search/handling times (e.g., long-distance pursuits vs. opportunistic foragers).
  • Low-variation species: more predictable search/handling times (e.g., ambush predators like tigers; trap specialists; some deceivers).
• Profitability of food items:
  • Profitability defined as net energy gain: energy gained minus energy spent on searching/handling.
  • Example: crows dropping snail shells from height to break them and access nutrients.
  • Optimal drop height aligns with maximum energy efficiency: around 5 meters for large shells (empirical finding ~5.23 m).
  • Trade-offs: lower drops require more drops (higher total effort); higher drops require more energy to ascend and descend but may yield quicker access if optimal height is used.
  • When to choose lower-profit items (e.g., bread) over highly profitable items (meat): when meat is rare or hard to obtain; overall profitability may favor less energy-intensive options when the highest-value item is scarce.
• Nutritional and qualitative considerations in food choice:
  • Nutritional needs shape food selection beyond energy content (e.g., calcium for snail shells; nitrogen for spider silk).
  • Food item defenses (toxins, deterrents) influence profitability and risk assessment.
  • Food density and visibility affect profitability: small items can be profitable if abundant; high-visibility foods may be easier to find but may incur higher predation risk or deliberate defences (e.g., chemical defenses, conspicuous warnings).
• Patch dynamics and decision rules:
  • Patch dynamics influence when to switch patches; high patchiness may favour rapid switching; consistent patches may encourage longer foraging in a single patch.
  • Patch predictability and environmental density impact risk and profitability calculations.

Optimal foraging theory and the marginal value theorem (MVT)

• Core idea: foraging decisions should maximize fitness by balancing energy intake with energy expenditure and risk.
• Components of foraging decisions:
  • Food selection: prioritize reverence to profitability, nutrition requirements, and optimal diet composition.
  • Searching strategies: consider patch dynamics, marginal value theorem, and search image (learned cues that guide search).
  • Costs of foraging: energy expenditure and predation/competition risk associated with searching and handling.
• Profitability and decision criteria:
  • Profitability of a food item can be examined via energetic gains and losses, alongside nutritional needs and defense risk.
  • Example of foraging profitability with crows and snails: empirical data show optimal drop height around 5.23 m for maximizing energy gain relative to energy spent (see below for a graphical interpretation).
  • The profitability discussion emphasizes a balance between search effort, handling effort, and energy gained, not only maximal energy content.
• Marginal Value Theorem (MVT): core concept and intuitive interpretation
  • MVT seeks the optimal Giving-Up Time (GUT) on a current patch by comparing the instantaneous rate of gain to the average rate of gain across the environment (including travel time between patches).
  • Graph interpretation (typical illustration):
  • Blue line: long travel times between patches (patchs are far apart).
  • Yellow line: short travel times between patches (patchs are close).
  • Red line: cumulative energy gain on the patch as a function of time spent on it.
  • The intercept where the slope of the gain curve on the current patch equals the overall average rate (including travel costs) indicates the GUT.
  • Practical takeaway: when travel costs are high (far patches), animals should stay longer on a rich patch before giving up; when patches are close, they switch earlier because travel costs are low.
  • Equational form (standard MVT): let G(t) be the cumulative gain from a patch as a function of time spent on it, and let T be travel time to the next patch. The optimal leaving time t* satisfies:
  • dG/dt|_{t=t} = G(t) / (T + t*)
  • equivalently, the instantaneous gain rate on the patch equals the average rate of gain across the environment including travel time.
• Real-world illustration of MVT: termite ants raiding termite mounds
  • Hypothesis: longer travel times to foraging patches should lead to longer investment on distant patches (more ants sent, longer foraging bouts) to maximize energy return.
  • Study design: vary mound size (small vs large) and distance from nest (10 m vs 30 m) to observe ants’ foraging effort and time spent on mounds.
  • Findings align with MVT expectations: further distant mounds invoke greater collective foraging effort and longer time spent foraging, supporting the idea that travel costs inform patch exploitation strategy.

Connections to broader concepts and implications

• Fitness and energy economics:
  • Foraging decisions are tightly linked to survival and reproductive success via energy intake and expenditure, resource density, and risk.
• Behavioral ecology relevance:
  • Foraging theory connects individual decision-making to population and ecosystem-level dynamics (e.g., patch dynamics influence predator-prey interactions and resource distributions).
• Ethical, philosophical, and practical implications:
  • Understanding foraging behavior informs conservation strategies, habitat management, and understanding how animals adapt to changing environments.
• Practical study tips and next topics:
  • Readings and case studies highlighted (e.g., MVT and patch dynamics).
  • Upcoming topics: habitats and territory; cooperation and altruism; data preparation and analysis activities in labs.

Quick recap of key terms and formulas

• Key terms:
  • Search time, handling time, giving up time, patch dynamics, patch density, patch predictability, visibility.
  • Profitability, net energy gain, optimal diet, patch dynamics, marginal value theorem.
• Core formulas and definitions:
  • Profitability of a food item: Profit = E{gain} - E{cost}
  • Net energy gain on a patch over time: G(t) where t is time spent on patch.
  • Marginal Value Theorem leaving condition:
  • rac{dG}{dt}igg|_{t=t^} = rac{G(t^)}{T + t^*}
  • equivalently, leave when the instantaneous rate of gain on the current patch equals the environment's average rate of gain (including travel time) across patches.
• Representative numerical examples:
  • Optimal snail-shell drop height for crows: approximately 5.23\,\mathrm{m} to maximize energy efficiency.
  • Cheetah chase distance (handling considerations): around 200\,\mathrm{m} before giving up due to energy costs.
  • Ant foraging distances in MVT example: 10\,\mathrm{m} vs 30\,\mathrm{m} patches to illustrate travel-time effects (and corresponding investment in foraging).

Next steps and readings

• Review the marginal value theorem and the associated graph until the left-right intercept logic and the impact of travel time are clear.
• Revisit the real-life termite ant example to connect theory with empirical data.
• Prepare for next lectures: habitats and territory; cooperation and altruism; data preparation and analysis.
• If you have ideas for revision resources, submit anonymous feedback to improve resources for Test 2.

Contact and questions

• If you have questions or want to discuss feedback, you can email the instructor or Kendall (Moodle offers anonymous channels).
• Bring data prepared and ready for the upcoming analysis-focused lab session.