LURE 2009 SUMMER PROGRAM John Alford Sam Houston State University

Mathematical Modeling  Mathematical modeling is an area of applied mathematics concerned with describing and/or predicting real-world system behavior  Examples of real-world systems –an object moving in a gravitational field –stock market fluctuations –predator-prey interactions –nerve impluse propagation

Mathematical Modeling  Many familiar concepts in mathematics evolved from the desire to answer basic scientific questions using mathematical models –e.g. Newton used calculus to describe and predict how an object of a certain mass will move in response to applied forces

Mathematical Modeling  Why model?  Simplification of a complex system  Ease of manipulation (simulation as opposed to experimentation)  Gains in understanding of the system –helps to formulate new hypotheses –aids in design of new experiments

Mathematical Modeling  There is no perfect model! –a model should balance accuracy, flexibility, and cost  A general rule of thumb –increasing accuracy decreases flexibility and increases cost  Goal –construct a “sufficiently” accurate model with high flexibility and low cost

Mathematical Modeling  No model is perfect  The problem may never be “completely” solved –you need to get used to this if you are going to be doing research

Mathematical Modeling  Step 1: find a real-world problem –obtain data and general knowledge  perform experiments  search the literature (journals, books, etc.) –make simplifying assumptions and neglect some complexity

Mathematical Modeling  Step 2: formulate the model –research the literature for other models (don’t re-invent the wheel) –decide on the model type and form  equations- algebraic, differential, integral, difference, etc.  deterministic (non-random) or stochastic

Mathematical Modeling  Step 2: formulate the model (cont.) –model will include  variables represent system parts  parameters influence variables but are not influenced by them (typically)  equations describe behavior and relate variables and parameters

Mathematical Modeling  Step 3: analyze the model –run computer simulations –apply mathematical theories  Step 4: interpret and verify the results to explain or predict –iterate the model for improved accuracy

Mathematical Modeling Process

 Unstructured population models –populations are treated as “homogeneous green gunk” (Kot, Elements of Mathematical Ecology)  Structured population models –Include effects due to age, spatial location, genetic variation, etc. Population Ecology

 Unless otherwise stated, we will assume for this discussion that our population models are unstructured. Population Ecology

 The six axioms of Turchin –Conservation –Individualism –Upper density bound –Mass action –Biomass conversion –Max physiological rates

Population Ecology  We will now use the first two axioms to construct the exponential growth model for population growth

Exponential Growth Model  Population density  Rate of change of population  Per capita rate of change of population

Exponential Growth Model  Conservation –The number of organisms in a population can change only as a result of births, deaths, immigrations, and emigrations

Exponential Growth Model  Model Using Conservation –The rate of change of a closed population (i.e. no immigration or emigration) is the number of births minus the number of deaths

Exponential Growth Model  Individualism –Population mechanisms are individual based. That is, all population processes affecting population change (e.g. births, deaths, movement) are a result of what happens to individuals.

Exponential Growth Model  Model Using Conservation –birth and death rates can be expressed in per capita form

Exponential Growth Model  Model using conservation –Assume the per capita birth rate and the per capita death rate are constant equal to b and d respectively

Exponential Growth Model  Define the intrinsic rate of growth (net per capita growth rate) so that so that

Exponential Growth Model  Some Mathematics –Solution by separation of variables

Exponential Growth Model  Separate variables

Exponential Growth Model  Integrate to get to get

Exponential Growth Model  Malthus’ equation (1798) where where is the initial population density is the initial population density

Exponential Growth Model  Exponential population growth  For positive initial populations, there is no limit on population size as time increases

Exponential Growth Model  Exponential population decay  All initial populations (eventually) become extinct.

Exponential Growth Model  per capita growth rate vs. N (r>0)

Exponential Growth Model  growth rate vs. N

Exponential Growth Model  population growth vs. t

 Equilibrium solutions are constant solutions is an equilibrium for is an equilibrium forcheck Exponential Growth Model

 Equilibria may be stable or unstable –stability “means” any small perturbation results in a return (over time) to the equilibrium –instability “means” some small perturbation will not result in a return (over time) to the equilibrium  Equilibria and stability may depend on parameter(s) in the equation(s) Exponential Growth Model

–without emigration or immigration, populations that start at 0 stay at 0 –if per capita growth rate is positive, small perturbations from 0 result in large population changes

Exponential Growth Model –if per capita growth rate is negative, small perturbations from 0 result in population sizes returning to 0

Population Ecology  The six axioms of Turchin –Conservation –Individualism –Upper density bound –Mass action –Biomass conversion –Max physiological rates

Population Ecology  Limitations exponential growth –Constant per capita growth rate yields unlimited growth –Deterministic nature of the model ignores random (stochastic) effects which are (particularly) important at small population sizes –Model is unstructured and ignores temporal and spatial variability

Population Ecology  Factors that regulate growth of populations: biotic vs. abiotic –competition within and between species (biotic) –variation in the weather (abiotic)

Population Ecology  A.J. Nicholson, 1933, The Balance of Animal Populations, Journal of Animal Ecology –population densities are regulated by biotic factors such as competition and disease which affect high-density populations more than low-density populations

Population Ecology  The six axioms of Turchin –Conservation –Individualism –Upper density bound –Mass action –Biomass conversion –Max physiological rates

Logistic Growth Model  Per capita growth rate is positive for small population densities  Per capita growth rate is negative for large population densities  Per capita growth rate decreases as population increases (competition for resources including food, space)

Logistic Growth Model  per capita growth rate vs. N

Logistic Growth Model  Per capita growth rate is a linear function of the population density  Here r>0 and K>0 are parameters –r=intrinsic growth rate –K=carrying capacity

Logistic Growth Model  Growth rate is quadratic function of N

Logistic Growth Model  growth rate vs. N

Logistic Growth Model  Exercise 1: solve to get the Verhulst (1838) model of population growth where where

 For positive initial populations, the limiting population is carrying capacity (K) WHY (mathematically and biologically)? WHY (mathematically and biologically)? Logistic Growth Model

 N vs. t

Logistic Growth Model  N vs. t

Logistic Growth Model Equilibria  There are two equilibria for the logistic model –carrying capacity –zero population density

Logistic Growth Model Equilibria  Multiply right side  Assume –to get

Logistic Growth Model Equilibria  Because r>0, previous slide shows that a small perturbation of N away from zero will grow exponentially (approximately)  Zero equilibrium is unstable

Logistic Growth Model Equilibria  Let  Substitute and do algebra

Logistic Growth Model Equilibria  Assume to get

Logistic Growth Model Equilibria  Because r>0, previous slide shows that a small perturbation of away from zero will decay exponentially back to zero  decays to zero N decays to K  K is a stable equilibrium

Population Ecology Interacting Species: aphid infestation

Population Ecology  A common way ecologists classify species interactions between two species is by denoting positive, negative, or zero (neutral) pairings. (+,+), (+,-), etc.

Population Ecology  A consumer-resource or trophic interaction is a (+,-) pairing between two species  Examples of consumer-resource interactions –predator-prey (e.g. fox and rabbit) –herbivore-plant (e.g. leaf-mining fly and hydrilla)

Population Ecology  The last three axioms of Turchin –Mass action –Biomass conversion –Max physiological rates  These may be used to derive models of consumer-resource interactions

Population Ecology  Mass action –At low resource densities the number of resource individuals encountered and captured by a single consumer is proportional to resource density ( N is resource density, a is constant ) ( N is resource density, a is constant )

Population Ecology  Biomass conversion –The amount of energy that an individual consumer can derive from captured resources to be used for growth, maintenance, and reproduction, is a function of the amount of captured biomass

Population Ecology  Maximum physiological rate –No matter how high the resource density is, an individual consumer can ingest resource biomass no faster than some upper limit imposed by its physiology (e.g. the size of its gut)

Population Ecology  Mass action and max physiological

Population Ecology  Ecologists call the functional rate at which each predator captures prey as it depends on prey density the functional response.  C.S. Holling (ca 1960) described three types of functional response relations  Each of Holling’s functional response relations obey Turchin’s mass action and max physiological axioms

Population Ecology  Holling’s functional response Type II (a, b constants) (a, b constants)

Population Ecology

 Holling’s functional response Type II (a, b constants) (a, b constants)

Population Ecology  Holling’s functional response Type III (a, b constants) (a, b constants)

Population Ecology  LURE Students –Analyze the following two models for a predator-prey interaction. Treat the number of predators P as a (constant) bifurcation parameter (for now). –Consider r and K to be fixed (for now) –Interpret your results biologically

Population Ecology  Model 1  Model 2

Population Ecology  The number of predators is not (typically) fixed but changes in time.  This requires a separate differential equation to describe predator density.

Lotka-Volterra Model  A classic predator-prey model due to Lotka and Volterra (ca 1925)

Lotka-Volterra Model  r, c, b, m are all positive constants  N is prey (resource) density, P is predator (consumer) density

Lotka-Volterra Model  Lotka and Volterra model has oscillatory solutions (why biologically?)

Lotka-Volterra Model  The first equation describes the rate of change of prey (resource) density  Let’s consider each term

Lotka-Volterra Model  The first term shows that in the absence of predation the prey grow exponentially (if P=0) (if P=0)

Lotka-Volterra Model  The second term describes the loss (minus sign) of prey due to predators  The loss is proportional to both the number of prey and the number of predators (linear consumption rate)

Lotka-Volterra Model  The second equation describes the rate of change of predator (consumer) density  Let’s consider each term

Lotka-Volterra Model  The first term describes the gain of predators due to prey (equals the loss of prey due to predators)  The second term shows that the predator population decreases exponentially in the absence of prey (if N=0) (if N=0)

Rosenzweig MacArthur Model  A classic predator-prey model due to Rosenzweig and MacArthur (ca 1963)

Population Ecology  Can you describe qualitative differences in these two models?? Lotka-Volterra Rosenzweig-MacArthur Lotka-Volterra Rosenzweig-MacArthur

Population Ecology  A plant-herbivore system can be thought of as a type of predator (=herbivore) and prey (=plant) system  The Rosenzweig-MacArthur model has been used to describe plant-herbivore* dynamics where the variables become V=vegetation biomass N=herbivore density * the herbivore here is assumed to be a mammalian grazer * the herbivore here is assumed to be a mammalian grazer

Population Ecology  The Rosenzweig-MacArthur model for plant-herbivore (mammal) system

Population Ecology  Stability analysis of the Rosenzweig- MacArthur model for plant-herbivore system yields the paradox of enrichment (Turchin): As plant standing biomass (= K) is increased, the dynamics of the system become increasingly less stable (i.e. small parameter changes become more likely to result in large qualitative changes in the dynamics) As plant standing biomass (= K) is increased, the dynamics of the system become increasingly less stable (i.e. small parameter changes become more likely to result in large qualitative changes in the dynamics)

Population Ecology  Other models (Turchin) account for plant re-growth as there is a part of the plant that the herbivore typically does not consume (i.e. underground biomass)

Population Ecology  Still other models (Edelstein- Keshet) use dependent variables to describe the system in terms of plant quality (rather than plant density) and herbivore density

Population Ecology  LURE students  Research (google) plant- herbivore models

Population Ecology  LURE students  Propose a plant herbivore model that will account for insect herbivory and plant quality variations (e.g. via fertilizer or sunlight variation)