Omnivory and Population Dynamics of the White-footed Mouse Peromyscus leucopus in Response to Pulsed Food Resources Pei-Jen Shaner University of Virginia Pulsed Food Resources 17 year cicadas Climate-driven events Acorn masting Annual seeding and fruiting Annual insect emergence Periodicity (year) Predictability Omnivory Stablizing Arthropod Community (Fagan 1997) Omnivores and Pulsed Food Resources (Holling 1965) Nutrient Balance and Pulsed Food Resources High Palatability Alt. Food Present Low Palatability Alt. Food Present No. of Sawfiles / Sq. Meters No. of Prey Eaten Total Food Eaten Sawfiles + Alt. Food (Raudenheimer and Jones 2006) Nutrient Balance and Pulsed Food Resources Protein Eaten (mg) Carbohydrate Eaten (mg) Compensatory ingestion of macronutrients in an omnivorous insect (Vickery et al. 1994) Nutrient Balance and Pulsed Food Resources Population Dynamics and Pulsed Food Resources (Elias et al. 2004) Population Dynamics and Pulsed Food Resources (Marcello et al. 2008) Dietary Shifts of the White-footed Mouse in Response to Pulsed Food Resources Questions Is the availability of foods with different nutritional values perceived by generalist consumer? Does relative availability of foods with different nutritional values drive dietary shifts in a generalist consumer towards foods of complementary quality? Marginal Value Theorem Giving-up density is based on marginal value theorem, as applied to animal optimal behaviors (Schoener 1971) dG/dt dL/dt Energy gain or lost per unit time Quitting harvest time Quitting harvest rate = Giving-up density Foraging time spent in a patch with a fixed amount of resource Giving-up Density (GUD) The food density remaining in a patch with a fixed initial food density, after a period of optimal foraging by a forager (Brown 1988) GUD is commonly used to measure: Perceived food availability Perceived predation risk GUD is based on marginal value theorem and is a surrogate to quitting harvest rate Quitting Harvest Rate (QHR) (Brown and Kotler 2004) the foragers instantaneous rate of being preyed upon F: the foragers fitness when survived predation the marginal fitness value of time p : the probability of surviving predation over a period of time marginal value of energy metabolic cost Quitting Harvest Rate (QHR) Quitting harvest rate increases with increased food availability the foragers instantaneous rate of being preyed upon F: the foragers fitness when survived predation the marginal fitness value of time p : the probability of surviving predation over a period of time marginal fitness value of energy metabolic cost Higher food availability Higher quitting harvest rate = Higher giving-up density Measuring Food Availability with GUD Higher food availabilityLower food availability *** * * *** ** **** * ** ** * * *** ** * *** * * * ** * * ** * **** * * * * * ** * * * ** * * * * * * * * * Measuring Food Availability with GUD Higher food availability Higher quitting harvest rate = Higher giving-up density Higher food availabilityLower food availability *** * * *** ** **** * ** ** * * *** ** * *** * * * ** * * ** * **** * * * * * ** * * * ** * * * * * * * * * ** GUD = 3GUD = 1 Stable Nitrogen Isotopes Stable nitrogen isotopic compositions increase with trophic level: Experimental Design Blandy Experimental Farm, VA Food addition in the summer of 2004: Millet seeds Mealworms Mixed seeds and worms Nutritional Values of Millet Seeds and Mealworms Food typeEnerg y (KJ/g) Protei n (%) Dry mass per item (g) Handlin g time per item (s) Protein gain per unit handling time (mg/s) Energy gain per unit handling time (KJ/s) Mealworm Millet seeds Mealworm: higher protein gain, lower energy gain Millet seeds: higher energy gain, lower protein gain Handling Time of Millet Seeds and Mealworms Simulating Natural Pulsed Food Resources Millet seeds addition in similar density as acorn mast: Measuring Giving-up Density Giving-up Density of Millet Seeds and Mealworms Consumption Rates of Millet Seeds and Mealworms Relative availability of mealworms increases Relative availability of millet seeds increases Dietary Shifts in Response to Food Complementarity Population Dynamics of the White-footed Mouse in Response to Pulsed Food Resources Population Spatial Synchrony Spatial synchrony in population density or growth rate can be measured with cross-correlation coefficient Extrinsic factors such as large scale synchrony in weather conditions, food resource abundance, or predation can cause population spatial synchrony (Grenfell et al. 2004) Demographic Processes Contributing to Synchrony Experimental Design Population density (MNA) Survivorship (MARK) Reproduction (Percent breeding females) Juvenile Recruitment (number of juveniles per adult female) Blandy Experimental Farm, VA Three annual cycles: , , Sep-Oct of , 200 pounds of millet seeds added per grid. Measuring Population Synchrony Cross-correlation coefficient of population growth rates: (Bjrnstad et al. 1999) the mean growth rate for population i over T time periods the density of population i at time t, t = 1,2T the variance in growth rate for population i over T time periods the growth rate of population i at time t Measuring Population Synchrony Cross-correlation coefficient of population growth rates: cross-correlation coefficient between population i and j square root of the variance in growth rate for population i, j or number of pairs between population i, j growth rate for population i, j over t time periods Stable Carbon Isotopes Stable carbon isotopic compositions vary between C3 plants (-35 to -23) and C4 plants (-23 to -6): (%C3 + %C4) = 1, fractionation =1.4 (MacAvoy et al. 2005) Tracking Mouse Movement Fueled by Millet Seeds Population Synchrony Induced by Millet Seeds Survival Probability in Forest Populations Survival Probability a b % Breeding Females in Forest Populations % Breeding Females a b Juvenile Recruitment in Forest Populations No. Juveniles per Adult Female a b Juvenile Movement Fueled by Millet Seeds Adult Juvenile Conclusions Dietary Shift Driven by Food Complementarity Individual mouse responded to pulsed food resources of different nutritional values with dietary shifts towards complementary food types Spatial Synchrony Driven by Pulsed Food Resources Forest populations could potentially be synchronized by pulsed food resources Demographic Processes Contributing to Synchrony Female reproduction and juvenile dispersal fueled by pulsed food resources contributing to population synchronization Linking Individual Behavior to Population Dynamics and Ecosystem Processes Future Research: Linking Individual Behavior to Population Dynamics and Ecosystem Processes 1. How does pulsed food resources drive dietary shifts and population dynamics of omnivores? Temporal (long-term and short-term) and spatial (local and landscape scale) patterns of pulsed food resources Nutrient constraints on dietary shifts in omnivores in response to pulsed food resources of complementary nutritional values Demographic processes in omnivores in response to temporal and spatial patterns of pulsed food resources Linking Individual Behavior to Population Dynamics and Ecosystem Processes Future Research: Linking Individual Behavior to Population Dynamics and Ecosystem Processes 2. How do dietary shifts and population dynamics of omnivores affect food web processes? The effect of dietary shifts and population fluctuations in omnivores on alternative prey Degree of dietary shifts and patterns of population fluctuations among different omnivore species The strength of omnivory in a food web with multiple omnivore species Linking Individual Behavior to Population Dynamics and Ecosystem Processes Future Research: Linking Individual Behavior to Population Dynamics and Ecosystem Processes 3. How do dietary shifts and population dynamics of omnivores affect ecosystem processes? Pulsed food resources v.s. pulsed nutrient input (macro- and micro-nutrient input) Degree of dietary shifts in omnivores driven by compensatory intake of macro- and/or micro-nutrients The stablizing role of omnivory in ecosystems facing increased disturbances A Blueprint Future Research: A Blueprint Field and lab experiments Model systems Modeling and meta-analysis Isotope ecology Invertebrates ecologists Aquatic ecologists Real ecosystems Field observations Ecosystem ecologists Statisticians Mathematicians THANK YOU!