Chapter 8: Foraging behaviour Flashcards

(53 cards)

1
Q

sensory modality

A

mechanism in which information gets to the animal

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2
Q

what do fish use mechanoreceptors for?

A

detecting body position and information about water pressure/movement

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3
Q

lateral line system

A

used by fish for “hearing”, detecting their body position and for information about water pressure/movement. made of neuromasts, which are mechanoreceptors that provide hydrodynamic information

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4
Q

catfish prey tracking research questions

A

how to nocturnal catfish find food in the dark?

do catfish use chemical or hydrodynamic cues to track prey in the dark?

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5
Q

catfish prey tracking hypothesis

A

catfish use their lateral line system to detect cues provided by the wake of their prey

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6
Q

catfish prey tracking prediction

A

catfish will follow the wake of their prey while hunting

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7
Q

catfish prey tracking methods

A

use infrared video to track movement of both predator and prey
classify movement as path following, head on, or attack on stationary guppy

manipulate lateral line or external gestation, classify hunting success and movement sequences of fish

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8
Q

catfish prey tracking results

A

intact catfish relied mostly on wake following
lateral line inhibited (ablated) fish used a head on attack mode. hunting success SEVERELY reduced
taste ablated used more head on and wake following. hunting success mildly reduced

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9
Q

what’s interesting about research into snakes’ heat sensing abilities

A

help inform tech for military, search and rescue, police

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10
Q

bees foraging senses research question

A

is foraging more efficient when multiple senses are used?
(bees tend to use colour + shape along with odour to find flowers)

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11
Q

bee foraging senses methods

A

trained individual bees to feed on artificial flowers
treatments:
- visual cue
- olfactory cue
- shape and olfactory cue

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12
Q

bee foraging senses results

A

bees trained to use both visual and odour cues (multimodal) had highest feeding performance

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13
Q

bee foraging senses conclusion

A

use of multimodal cues facilitates efficient feeding

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14
Q

evolutionary arms race and visual predation:

A

natural selection favours more cryptic prey, behaviour or morphology that avoids detection.
natural selection favours predators that can better detect hidden/cryptic prey

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15
Q

trout foraging research question

A

how does cryptic prey colouration affect trout foraging efficiency

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16
Q

trout foraging hypothesis

A

prey that match their background will be harder for predators to detect, and predator hunting efficiency will increase with experience

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17
Q

trout foraging predictions

A
  1. trout will find non cryptic prey faster
  2. trout will find cryptic prey faster with experience
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18
Q

trout foraging methods

A

2 aquaria, identical except for bottom: one was brown plastic + grains, other was green plastic + grains.
placed prey in aquarium. it was cryptic on brown, conspicuous on green.
record time until fish found food item
tested half with cryptic half with conspicuous prey

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19
Q

trout foraging results

A

found non cryptic prey faster
search time decreased with experience

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20
Q

trout foraging conclusion

A

background colour matching can benefit prey by reducing predator hunting efficiency
predator search
predator search efficiency for cryptic prey increases over time

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21
Q

optimal foraging theory, and 2 common examples

A

assumes that fitness increases with energy intake rate
behaviour that maximizes fitness is optimal behaviour
diet model
patch use model

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22
Q

diet model assumptions

A
  1. foragers maximize fitness by maximizing energy intake rate
  2. food items are encountered one at a time in proportion to abundance
  3. food items can be ranked by their profitability (energy/handling time)
23
Q

diet theory variables

A

food items: define energy and handling time
diets: define how specialized or general the diet is
mean search time: how much search time for each food item
mean handling time: how much time each type of forager spends handling food

24
Q

optimal diet model solving

A

average energy equation

25
optimal diet model graphical solution
find line that minimizes time spent on one item and handling time per item. middle is best, since theres some specialization to speed up handling time but not so much where the food is hard to find.
26
crow diet research question
why do crows reject some clams while foraging?
27
crow diet hypothesis
crows attempt to maximize energy intake rate according to optimal diet model
28
crow diet prediction
crows should eat all clams greater than 29mm and reject all clams smaller than this.
29
crow diet methods
measured handling times of differently sized clams measured energy content of different sized clams
30
crow diet results
clams over 30mm were almost always eaten, clams under 28 mm almost always rejected
31
diminishing returns
when forager enters a food patch theres an initial high harvest rate, but as patch is depleted the harvest rate declines
32
optimal patch use model assumptions
1. foragers attempt to maximize energy intake rate 2. all patches are identical 3. travel time between patches is constant 4. instantaneous harvest rate declines as forager depletes a patch (forager experiences diminishing returns)
33
what does optimal patch use model predict?
the optimal time to spend exploiting each patch
34
graphical solution to optimal patch use model
line that goes from start (beginning of travel time) will meet the curve. wherever it meets is the optimal time
35
marginal value theorem
for fixed travel time Tt, energy intake rate is maximized when forager spends Tp time in patch short travel time = short patch time long travel time = long patch time
36
costs when feeding:
- energetic costs -predation risk costs -missed opportunity costs
37
updated patch use model
predicts that patches with identical benefits and costs should be harvested at the same rate
38
give up density
amount of food remaining in a patch after foraging
39
fruit bat research question
how do fruit bats exploit food patches that differ in food amount?
40
fruit bat hypothesis
bats will attempt to maximize their energy intake rate
41
fruit bat prediction
bats will equalize give up density across patches
42
fruit bat methods
outdoor flight cage with feeding stations each station had 3 artificial feeders that differed in amount of food record give up density in all feeders for 3 days feeders had rubber pieces inside to make extracting food challenging
43
fruit bat results
graph mimics the graphical solution for optimal patch use mode. as food harvested increased, so did time in patch but it began to level off. bats equalized giving up densities at 3 feeders in each station
44
kangaroo rat methods
individuals feed from food patches filled with sand patches in open or covered with brush manipulated presence/absence of predator urine near patches measured give up density in patches
45
kangaroo rat results
GUD lowest under shrub highest when predator scent present
46
kangaroo rat conclusion
foraging behaviour is affected by predation risk costs as predicted by updated optimal patch use model
47
food patch estimation
optimal patch use model assumes animals know quality of encountered patches, when in reality the animal will combine sample information about the patch with prior knowledge about other patch types using bayesian estimation
48
bayesian foraging bees research question
can bees estimate food patch quality in a bayesian manner?
49
bayesian foraging bees hypothesis
foragers combine prior knowledge of distribution of patch types with sample information to estimate patch quality
50
bayesian foraging bees predictions
bees trained in uniform environment will exploit patches differently than bees trained in high variance environment
51
bayesian foraging bees methods
trained bees to feed from artificial patches half in uniform, half in high variance environment observed propensity to depart a patch (estimate of patch quality)
52
bayesian foraging bees results
high variance bees would have high propensity to stay in patch uniform bees would have low propensity to stay in patch
53
bayesian foraging bees conclusion
bees appear to combine prior knowledge of patch type distribution with sample information to estimate patch quality (bayesian foraging)