Foraging Ecology SOLITARY HUNTERS SOLITARY HUNTERS. Myrmecophagy. Myrmecophagy. anteaters pangolins numbat echidna aardvark aardwolf

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1 Foraging Ecology SOLITARY HUNTERS Myrmecophagy anteaters pangolins numbat echidna aardvark aardwolf SOLITARY HUNTERS Myrmecophagy Prevalent in tropics, why? Narrow niche, so how to avoid competition?

2 SOLITARY HUNTERS Myrmecophagy Avoid competition? SOLITARY HUNTERS Myrmecophagy banded tamandua eat ants without sting (Azteca ants) ants secrete skin irritant chemical from abdomen Nasutitermes termite soldiers & terpenoid compounds vs. winged termites SOLITARY HUNTERS Insectivory 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

3 SOLITARY HUNTERS Insectivory Bats frequency modulated 5-10 per sec 10 millisec long shortened duration & higher frequency (feeding buzz) SOLITARY HUNTERS Insectivory Shrews Foraging in Shrews Foraging in Shrews # Visits # Visits High Low 0 Predator No Predator Preuss et al. SOLITARY HUNTERS Planktivory Mysticete (baleen) whales

4 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

5 SOLITARY HUNTERS Carnivory: Weasels Costs of being a weasel Vaudry et al Holarctic Ecology 13: 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 in Carnivore behavior, ecology, and evolution SOLITARY HUNTERS Carnivory: Weasels Inter-specific competition

6 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

7 Optimal Foraging Model Components: The wolf is kept fed by its feet. 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 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

8 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 Components Owen-Smith Ecology 75: 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 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

9 Prey Models If ignore 1 st prey item with reward e 1 /h 1, then must spend time searching (s) for 2 nd prey item with reward e 2 /h 2 Optimal strategy = pursue 1 st prey item if: e 1 /h 1 > e 2 /(s 2 +h 2 ) 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? Prey Models Other associated factors: Prey switching Search image

10 Patch 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? 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 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

11 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 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

12 Central-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 Foraging Distance (km) Travel Time (min) Optimal Load Size (mg) Observed Load Size (mg) 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 X 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

13 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 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 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)

14 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 Owen-Smith Ecology 75: kudu (Tragelaphus strepsiceros)

15 Optimal Group Size Adaptation to maximize energy intake by reducing search & handling times and/or predation risks Benefits of Group Living to Prey: 1) Predator has difficulty finding scattered groups or individual lost in group 2) More eyes & ears 3) Group intimidation Benefits of Group Living to Prey: 4) Which individual to chase? 5) Avoid being the victim

16 Benefits of Group Living to Predators: 1) Better able to locate food (information exchange) 2) Increased success 3) Cooperative strategies 4) Catch larger prey (felids vs. canids) 5) Able to compete with other species Benefits of Group Living to Predators: 1) Better able to locate food (information exchange) 2) Increased success 3) Cooperative strategies 4) Catch larger prey (felids vs. canids) 5) Able to compete with other species 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

17 Benefits of Group Living to Predators: 3) Cooperative strategies 4) Catch larger prey (felids vs. canids) 5) Able to compete with other species 6) Increased survival Bekoff and Wells 1980 COOPERATIVE HUNTERS African Wild Dogs (Lycaon pictus) pep rallies COOPERATIVE HUNTERS African Wild Dogs (Lycaon pictus) pep rallies den guards regurgitation of food coursing strategy

18 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 HUNTERS Cats Rush distance is prey-dependent

19 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

20 FACTORS INFLUENCING FORAGING STRATEGIES Habitat Bats gleaners (whispering bats) <1.5 ms, <80db sit-and-wait strategy detection via audition very high sensitivity & differentiation FACTORS INFLUENCING FORAGING STRATEGIES Prey Density Functional response

21 Weasels & Habitat Fragmentation ESLI M 0.6 a r = Coyote P = Fox 0.3 Long-tailed weasel 0.2 Striped Skunk 0.1 Opossum Raccoon Domestic Cat ESLI K Habitat Occupancy (%) 80 b r = P = Raccoon Coyote 20 Weasel Opossum 10 Fox Cat Skunk ESLI K 20 Opossum Cat Gehring and Swihart Skunk Fox 10 Raccoon Biological Conservation Weasel 5 109: ESLI M Gehring and Swihart Journal of Mammalogy 85: Matrix Tolerance (%) c r = 0.57 P = 0.09 Coyote Weasels & Habitat Fragmentation Gehring and Swihart Journal of Mammalogy 85: Weasels & Habitat Fragmentation Habitat Forest Relative abundance of small mammals a 5.78 SE 4.08 Species richness a 1.29 SE 0.36 Relative biomass of small mammals a SE Number of rabbit pellet groups b 4.14 SE 2.73 Field Grass Fencerow Ditch a b Fencerow > Field, Grass, Ditch Fencerow and Forest > Field, Grass, Ditch Gehring and Swihart Journal of Mammalogy 85:

22 Weasels & Habitat Fragmentation Source SEX Time of Day (TDAY) SEASON F- value P-value < <0.001 Type of Element (ELEM) Home-range Size (HRS) Prey Biomass (PB) Number of Pellet Groups (RAB) <0.001 < Gehring and Swihart Journal of Mammalogy 85: Weasels & Habitat Fragmentation Mean hourly rate (m/h) CC CFI CG FIFI FOFI FOFO FOG GFI GG Habitat type pairwise combination Gehring and Swihart Journal of Mammalogy 85: Weasels & Habitat Fragmentation Crepuscular Diurnal Nocturnal Mean hourly rate (m/h) CC CFI CG FIFI FOFI FOFO FOG GFI GG Pairwise combination of habitat types Gehring and Swihart Journal of Mammalogy 85:

23 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 PREDATION Predator Efficiency: Cats, Dogs Wolves: 7% Wild dogs: 34-85% Coyotes: 28-51% Cats: 35% THE COSTS OF PREDATION Mortality Risks

24 Distribution Patterns Ideal Free Distribution (IFD) Assume 2 habitats, 1 rich and 1 poor, relative to resources How should competitors distribute themselves? Distribution Patterns Ideal Free Distribution (IFD) Distribution Patterns Ideal Despotic Distribution (IDD) Life is never that simple Why wouldn t the IFD apply in all cases?