Lec 20 (higher level)

Why might optimal foraging theory predict longer residence times in low-quality patches within fragmented habitats?

Fragmentation increases travel time between patches, raising the marginal value threshold. Even poor patches become “worth it” relative to the high travel cost. [p.3]

Explain how OFT integrates both energetic cost and predation risk in determining when an organism should leave a patch.

As marginal food intake declines, the gain must be weighed not only against travel cost but also against exposure to predators during movement—shifting optimal departure times.

Why do crows dropping nuts on pavement illustrate a behavioral adaptation to human-created landscapes?

Pavement provides a hard surface that increases cracking success, showing behavioral flexibility in exploiting anthropogenic substrates.

Why does using car traffic to break nuts require advanced learning, and what does the American crow data suggest about it?

It requires prediction of car timing; data show crows drop nuts regardless of car timing, suggesting incomplete learning. [p.2]

Describe why indirect predator effects (behavioral fear) can be as ecologically important as direct predation.

Fear alters prey distribution, foraging, and habitat use, reshaping vegetation and entire food webs. [p.4–5]

Why is the claim “wolves changed the rivers” considered an over-simplification?

Many interacting factors shape river geomorphology; wolves influence vegetation and ungulates but are only one part of a complex cascade. [p.5]

Explain why the LV model predicts perpetual cycles but real ecosystems do not always follow this pattern.

LV assumes no carrying capacity, homogeneous space, and perfect predator efficiency, all unrealistic in nature. [p.7]

How does prey hiding behavior transform the predator functional response away from LV’s Type I assumption?

Hiding increases search time, producing saturating Type II/III functional responses.

Why did Huffaker need to increase spatial complexity to produce predator–prey cycles?

Complexity generated refuges and delayed predator access, allowing time-lagged dynamics required for oscillations. [p.8]

Why do the lynx–hare cycles persist even with numerous predators and resource fluctuations?

The system’s resilience arises from multi-trophic interactions where multiple predators and resource limits reinforce cyclic dynamics. [p.10]

Explain why hare reproductive output suggests a bottom-up component to their cycles.

Reproductive peaks coincide with periods of high food availability rather than predator release. [p.9]

How can chronic stress from predators generate multi-year prey population cycles?

Stress suppresses reproduction and immune function, reducing population growth even without direct predation. [p.9–10]

Why is the hare decline phase considered mostly predator-driven?

Declines align with increased predator efficiency and high predation pressure. [p.10]

Why can’t the lynx–hare cycle be explained solely by top-down processes despite strong predation effects?

Resource limitation, winter food scarcity, and competition also affect hare density, adding bottom-up feedback. [p.10–11]

What does the lynx–hare system reveal about using simple models (e.g., LV) for real-world predictions?

Simple models capture broad patterns (cycles) but fail to incorporate resource limits, stress, multiple predators, and heterogeneity—factors necessary for accurate predictions. [p.11]