bio 597 - foraging theory sp06people.cst.cmich.edu/gehri1tm/mammalogy/bio 597 - foraging...
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Foraging Ecology
SOLITARY HUNTERS
Myrmecophagy– anteaters– pangolins– numbat– echidna– aardvark– aardwolf
SOLITARY HUNTERSMyrmecophagy
– Prevalent in tropics, why?
– Narrow niche, so how to avoid competition?
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SOLITARY HUNTERSMyrmecophagy
– Avoid competition?
SOLITARY HUNTERSMyrmecophagy
– banded tamandua– eat ants without sting
(Azteca ants)– ants secrete skin
irritant chemical from abdomen
– Nasutitermes termite soldiers & terpenoidcompounds vs. winged termites
SOLITARY HUNTERSInsectivory
– Bats• 70% eat insects• 3,000 to 7,000 per night per bat• big brown bat maternal colony
of 150 can eat 38,000 cucumber beetles, 1,600 June bugs, 19,000 stinkbugs, 50,000 leafhoppers
• Relation to agricultural pest insects – corn rootworms & cucumber beetles
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SOLITARY HUNTERS
Insectivory– Bats
• frequency modulated
• 5-10 per sec• 10 millisec long• shortened duration
& higher frequency (feeding buzz)
SOLITARY HUNTERSInsectivory
– Shrews
Preuss et al.
Foraging in Shrews
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# Vi
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Foraging in Shrews
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Predator No Predator
# Vi
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SOLITARY HUNTERS
Planktivory– Mysticete (baleen)
whales
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Gray whale
SOLITARY HUNTERS
Carnivory
SOLITARY HUNTERS
Carnivory: Weasels– Costs of being a weasel
• large SA:Volume ratio• must be active• fur is short, little fat• 2x energy demand• caches of prey• rougher on females?
– size dimorphism
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SOLITARY HUNTERSCarnivory: Weasels
– Costs of being a weasel– Vaudry et al. 1990. Holarctic Ecology 13:265-268.– Captive trials with female & male short-tailed weasels– Females 1/3 the time (searching & handling) for meadow
voles– Females unable to handle short-tailed shrews
King, C.M. 1989. in Carnivore behavior, ecology, and evolution
SOLITARY HUNTERSCarnivory: Weasels
– Inter-specific competition
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Foraging Theory: Predation & Herbivory
1- Many prey items with variable nutritional values
2- Prey items vary in spatial distribution and abundance
3- Variable costs of capturing and processing prey items
4- Forager has limited amounts of time & energy
5- Forager's choices may affect its fitness
Optimality theory
• we expect that natural selection yields efficient, economic animals; maximizing benefits or minimizing costs, thus maximizing net energy/time (e/t)
.....WHY????
Optimality theory
• foraging theory = optimal foraging theory = optimality theory
• includes: – prey selection– optimal diets– marginal value
theorem– central place foraging– optimal group size
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Optimal Foraging Model Components:
1.) Decision assumptions: refers to the type of choice forager makes or that natural selection makes for it
e.g., – pursue or not after
encountering prey item
– stay in habitat patch or move on
“The wolf is kept fed by its feet.”
Optimal Foraging Model Components:
2.) Currency assumptions: used to evaluate choices
e.g., – # prey items
captured/unit time– relates to profitability
of prey item– often express as net
energy gain/unit time
Optimal Foraging Model Components:
2.) Currency assumptions: used to evaluate choices
* time minimizers: minimize time needed to gain a fixed amount of energy
* energy maximizers: maximize amount of energy gained in a fixed time period
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Optimal Foraging Model Components:
3.) Constraint assumptions: factors that limit choices & currencies that might be obtained
e.g., – intrinsic limitations of
the animal (color vision)
– extrinsic limitations due to environment acting on animal
Optimal Foraging Model ComponentsOwen-Smith. 1994. Ecology 75:1050-1062
kudu (Tragelaphus strepsiceros)• Hand-reared, free-ranging• Winter dry season
• expand diet, include evergreen & unpalatable
• increase proportion of palatable tree spp.
• extended feeding
• Targeted energy requirements with least overall cost
• Elastic foraging times & digestive capacity
Optimal Foraging Models
Prey Models• Optimal diet & prey
selection• Predators choose the most
profitable prey item(s)• Gains = nutritional value
of prey item (energy = e)• Costs = handling time, t
(subdue & eat times = h) + search times (s)
• Net gain for given prey item = e/h
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Optimal Foraging Models
Prey Models• If ignore 1st prey item
with reward e1/h1, then must spend time searching (s) for 2nd prey item with reward e2/h2
• Optimal strategy = pursue 1st prey item if:
e1/h1 > e2 /(s2+h2)
Optimal Foraging Models
Prey Models• Handling times (h)
relatively short = generalist (wide diet breadth)
• Handling times (h) relatively long = specialist
• Poor habitat (e.g., low prey abundance), then search times (s) long =
• What about high prey abundance?
Optimal Foraging Models
Prey Models• Other associated factors:
• Prey switching• Search image
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Optimal Foraging ModelsPatch Models
• Marginal value theorem, optimal patch residence times
• Clumped or patchy prey distributions
• How long should a predator stay in 1 patch before moving to another patch?i.e., What is the “marginal
value” of a patch such that it becomes more profitable to move on to another patch?
Optimal Foraging Models
Patch Models• Maximize e/t• But, also factor in travel
time (T) between patches• So, maximize e/(T+s)• T = long, stay in patch
longer• Low energy patches =
shorter patch residence times
Optimal Foraging Models
Patch Models• Maximize e/t• But, also factor in travel
time (T) between patches• So, maximize e/(T+s)• T = long, stay in patch
longer• Low energy patches =
shorter patch residence times
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Optimal Foraging Models
Patch Models• Maximize e/t• But, also factor in travel
time (T) between patches• So, maximize e/(T+s)• T = long, stay in patch
longer• Low energy patches =
shorter patch residence times
Optimal Foraging ModelsPatch Models
• Maximize e/t• But, also factor in travel
time (T) between patches• So, maximize e/(T+s)• T = long, stay in patch
longer• Low energy patches =
shorter patch residence times
Optimal Foraging Models
Patch Models• Maximize e/t• But, also factor in travel
time (T) between patches• So, maximize e/(T+s)• T = long, stay in patch
longer• Low energy patches =
shorter patch residence times
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Optimal Foraging ModelsCentral-Place Foraging
Models• Extension of M-V
Theorem• Capture prey, then must
bring food load back to a central place, e.g., nest, den, or cache
• Factor in:• Outbound journey• Search time/handling
time• Return journey
Optimal Foraging Models
Central-Place Foraging Models
• Ability to orient or navigate to find way back to central place
• General Patterns:1) If patch quality constant
– Load size & patch residence time increase with increased distance of patch from central place
65.6523.181.0
55.2431.430.45
52.6320.320.1
Observed Load Size
(mg)
Optimal Load Size(mg)
Travel Time(min)
Foraging Distance
(km)
Optimal Foraging Models
Central-Place Foraging Models
• Ability to orient or navigate to find way back to central place
• General Patterns:2) Increase rate of net
energy delivered to central place by shortening return trip– Cognitive mapping
X
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Optimal Foraging Models
Central-Place Foraging Models
• Ability to orient or navigate to find way back to central place
• General Patterns:2) Increase rate of net
energy delivered to central place by shortening return trip– Cognitive mapping
Optimal Foraging Models
Central-Place Foraging Models
• General Patterns:3) If predation risk while
foraging– Forage closer to
central place, shorten patch residence times, deliver smaller loads
e.g., gray squirrel• Balance predation
risk with energy gain
Optimal Foraging Models
Central-Place Foraging Models
• General Patterns:3) If predation risk while
foraging– Forage closer to
central place, shorten patch residence times, deliver smaller loads
e.g., gray squirrel• Balance predation risk
with energy gain• Tendency to carry food
item decreases with distance of food from cover (travel time) and increases with size of item (handling time)
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Linear Programming
• Multivariate approach
• Consider constraints that result in optimal diet
– Time– Nutritional needs– Energy needs
Linear Programming
• Multivariate approach
• Consider constraints that result in optimal diet
– Time– Nutritional needs– Energy needs
Optimal Foraging ModelsOwen-Smith. 1994. Ecology 75:1050-1062
kudu (Tragelaphus strepsiceros)
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Optimal Foraging Models
Optimal Group Size• Adaptation to
maximize energy intake by reducing search & handling times and/or predation risks
Optimal Foraging Models
Benefits of Group Living to Prey:
1) Predator has difficulty finding scattered groups or individual lost in group
2) More eyes & ears3) Group intimidation
Optimal Foraging Models
Benefits of Group Living to Prey:
4) Which individual to chase?
5) Avoid being the victim
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Optimal Foraging Models
Benefits of Group Living to Predators:
1) Better able to locate food (information exchange)
2) Increased success3) Cooperative strategies4) Catch larger prey (felids
vs. canids)5) Able to compete with
other species
Optimal Foraging Models
Benefits of Group Living to Predators:
1) Better able to locate food (information exchange)
2) Increased success3) Cooperative strategies4) Catch larger prey (felids
vs. canids)5) Able to compete with
other species
Optimal Foraging Models
Benefits of Group Living to Predators:
1) Better able to locate food (information exchange)
2) Increased success• Golden jackals &
Thomson’s gazelle• Spotted hyaenas &
wildebeest calves
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Optimal Foraging ModelsBenefits of Group
Living to Predators:3) Cooperative strategies4) Catch larger prey
(felids vs. canids)5) Able to compete with
other species6) Increased survival
Bekoff and Wells 1980
COOPERATIVE HUNTERS
African Wild Dogs (Lycaon pictus)– pep rallies
COOPERATIVE HUNTERSAfrican Wild Dogs
(Lycaon pictus)– pep rallies– den guards– regurgitation of food– coursing strategy
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COOPERATIVE HUNTERS
C. Orca Whales (Orcinus orca)– pods (matrilines)– females teach young to strand
Optimal Foraging
Ecological Considerations:1) Foraging Strategies –
related to energetic cost of foraging
• Sit-and-Wait (ambush) hunter• Prey densities low or
prey dispersed• Long search times, but
low energy costs• Generally high handling
costs• Occipital crunch or
suffocation hold
SOLITARY HUNTERSCats
– Rush distance is prey-dependent
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Optimal Foraging
Ecological Considerations:
1) Foraging Strategies –related to energetic cost of foraging
• Search-and-Pursuit hunter (& variations)• Prey densities higher
or prey clumped• Less search times, but
huge energy costs• Solitary or group
hunters
FACTORS INFLUENCING FORAGING STRATEGIES
Habitat– Lynx
• sparse cover: stalk• dense cover: ambush
FACTORS INFLUENCING FORAGING STRATEGIES
Habitat– Bats
• open areas– low frequency, long distance calls
• around foliage– constant, higher frequencies– sensitive to insect wings
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FACTORS INFLUENCING FORAGING STRATEGIES
Habitat– Bats
• gleaners (whispering bats)
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Weasels & Habitat Fragmentation
Gehring and Swihart. 2003. Biological Conservation 109:283-295.
Gehring and Swihart. 2004. Journal of Mammalogy 85:138-145.
ESLIK
0 10 20 30 40 50 60
ESL
I M
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Coyote
StripedSkunk
FoxLong-tailed
weasel
DomesticCat
Opossum Raccoon
ESLIK
0 10 20 30 40 50 60
Hab
itat O
ccup
ancy
(%)
0
10
20
30
40
50
60
70
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ESLIM
0.0 0.1 0.2 0.3 0.4 0.5
Mat
rix T
oler
ance
(%)
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10
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Coyote
Skunk
WeaselFox
Opossum
Cat
Raccoon
OpossumCat
Fox
Weasel
Skunk
Coyote
Raccoon
a
b
c
r = 0.95P = 0.0005
r = 0.57P = 0.09
r = -0.66P = 0.10
Weasels & Habitat Fragmentation
Gehring and Swihart. 2004. Journal of Mammalogy 85:138-145.
Weasels & Habitat Fragmentation
0.670.6735.5957.500.671.671.532.56Ditch
0.683.00126.18402.470.212.335.1217.66Fencerow
0022.8878.550.321.001.424.33Grass
0031.8291.720.090.911.765.04Field
2.734.1468.33125.370.361.294.085.78Forest
SENumber of
rabbit pellet groupsb
SERelative biomass of small mammalsa
SESpecies richness
a
SERelative abundance of small mammalsa
Habitat
a Fencerow > Field, Grass, Ditchb Fencerow and Forest > Field, Grass, Ditch
Gehring and Swihart. 2004. Journal of Mammalogy 85:138-145.
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Weasels & Habitat Fragmentation
Gehring and Swihart. 2004. Journal of Mammalogy 85:138-145.
0.6650.2Number of Pellet Groups (RAB)
0.0068.2Prey Biomass (PB)
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Optimal Foraging
Ecological Considerations:
2) Competition –• competitors may
decrease abundance/encounter rates of prey – forcing spp. to expand its diet to lower ranking prey or type of prey patches visited
THE COSTS OF PREDATIONPredator Efficiency: Cats, Dogs
– Wolves: 7%– Wild dogs: 34-85%– Coyotes: 28-51%– Cats: 35%
THE COSTS OF PREDATIONMortality Risks
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Distribution Patterns
Ideal Free Distribution (IFD)
• Assume 2 habitats, 1 rich and 1 poor, relative to resources
• How should competitors distribute themselves?
Distribution PatternsIdeal Free Distribution
(IFD)
Distribution PatternsIdeal Despotic
Distribution (IDD)• Life is never that
simple• Why wouldn’t the IFD
apply in all cases?