Ecology 8310 Population (and Community) Ecology Patch selection (e.g., Marginal Value Theorem) Prey selection (optimal diet theory) Moving beyond feeding.

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Presentation transcript:

Ecology 8310 Population (and Community) Ecology Patch selection (e.g., Marginal Value Theorem) Prey selection (optimal diet theory) Moving beyond feeding (energy intake): predation risk (u/g)

Patch selection: Consider a forager moving among many patches during a foraging bout (rodent among seed caches, pollinator among flowers, etc.) Which patches does it feed in? For how long? (when does it leave?) How are these decisions altered by patch density? Or the quality of other patches?

Patch selection:

GOAL: Maximize rate of net energy gain (intake – losses / time)

Patch selection: Cumulative energy intake

Patch selection: Marginal Value Theorem: Leave when: dg/dT = E n *

Time Travel Time Search Time in Patch Cumulative Energy gain Slope = Energy gain/Time Point of diminishing returns Time to leave! Another way to look at this (when there is only 1 patch type) Which strategy yields the greatest E/T?

Time Travel Time Search Time in Patch Cumulative Energy gain What if patches are denser (travel time is less)? Leave earlier when travel time is shorter. SparseDense

Patch selection: Predictions of MVT: 1.Leave at a fixed MV (indep. of patch quality 2.Stay in higher quality patches longer 3.Skip patches in which dg/dt| t=0 < E n * 4.As the density of patches increase… a.Reduce residency time b.Drop low quality patches from diet 5.Variants: a.Giving up density (uniform among patches) b.Giving up time (time since last prey taken)

Prey selection:

Consider a forager moving WITHIN a patch Which prey does it attack? How are these decisions altered by prey density? …or the types of prey available? Assume: energy maximization

Prey selection: Assume a forager has T s units of search time available. How can we express the rate of net energy gain to that forager during the foraging bout? Two main components: Net energy gained (intake – losses) Time expended (searching and handling) E n /T = Net energy gained / Time expended

Prey selection:

The solution and insights: Rank prey by e i /h i (=profitability) Predator can only decide on P i 's (attack, don't attack, sometimes attack). Optimal P i is either 0 or 1 (attack or don't attack) RULE: Attack if e i /h i >E*/T; else ignore. [As in MVT] Inclusion of a prey type in the diet is only a function of density of MORE valuable prey. Examine graphically….

From Richardson and Verbeek (1986) A test:

Laboratory test: Bluegill sunfish Based upon Werner and Hall (1974)

Prey selection: Field (lake) From Mittelbach (1981)

Can we use the diet model to predict field patterns (growth and habitat use)?

Habitat selection: "Field" (Ponds) From Werner et al (1983) Open water Vegetation

Problems? Early models were very simple Ignored complexities (e.g., capture probability, search images, nutrients, etc.) Other species ….

Lakes: Open water often the most profitable. Used by large bluegill. BUT small bluegill use the vegetated habitat. Why?

Can we incorporate predation risk into our predictive framework?

Old rule: maximize "g" (growth) New rule: minimize  /g (risk to growth)

Other implications of this work (stage-structure in bluegill)?

Small Bluegill Large Bluegill Littoral Invertebrates Zooplankton

Small Bluegill Large Bluegill Littoral Invertebrates Zooplankton