FW364 Ecological Problem Solving

1. FW364 Ecological Problem Solving Class 4: Quantitative Tools Sept. 11, 2013

2. Outline for Today Objectives for Today: Survey how and why models are used Survey different categories of models Goal for Today: Help you to “get the gist” of modeling in general (we will go into more detail later about most models discussed today) Help you to understand what’s so special about this monkey

3. Quantitative Tools • Why quantitative tools (math / models) are useful • Quantitative tools: • make our process and assumptions transparent • help us to understand natural systems (can often be not intuitive) • allow us to do virtual experiments (cheaper than real experiments) • make predictions that can be tested in the real world • strengthen adaptive management (predictions, understand outcome)

4. Quantitative Tools • Quantitative tools make our process and assumptions transparent • Process examples: • The DNR suggests a 25% reduction for walleye bag limit • Models can be presented in reports and at public meetings • that show exactly how the 25% reduction was calculated •  Models are helpful for showing that management • decisions are not arbitrary • Assumption examples: • Mass balance: Steady-state assumption: Inputs = Outputs • Predation: No predator saturation (satiation) • Harvest: No reduction in angler effort with reduced bag limit • Value: • Other researchers / managers / stakeholders know how results were obtained • Others can evaluate whether the results are valid given knowledge of process and assumptions

5. Quantitative Tools Quantitative tools help us to understand natural systems Help us to understand how aspects of the natural world are related SH TH * cFP Help us to handle complexity (work with or just deal with) = Equation allowed us to see how plant turnover time affects the amount of herbivore biomass that can be supported SP TP * FP

6. Quantitative Tools Quantitative tools allow us to do virtual experiments Virtual experiment example: What happens to salmon biomass if zebra mussel biomass doubles? What then happens to prey of salmon? What happens if another mussel (e.g., quagga mussels) invades? • We can answer these questions by altering model variables / parameters • Could also use “real” experiments, but there are limitations • Mesocosms - lose the complexity of food web • Experimental additions to lake - unethical for invasive species • Both take a lot of resources (time and money)

7. Quantitative Tools Quantitative tools make predictions that can be tested in the real world Built model of wolf population growth using 1999-2007 data Made predictions for 2008-2012 from those data Predictions can now be evaluated in 2013 Models can be refined as needed would know now if linear or non-linear was best model

8. Quantitative Tools Quantitative tools strengthen adaptive management Hypothetically, say the 2008-2012 data suggest population growth was linear Say our goal was 900 wolves by 2012 and we assumed in 2007 that population growth would be non-linear • We adjust our model to include linear growth • Perhaps introduce more wolves to account for slower population growth

9. Components of Models Variables: the quantities that change in amodel Dependent variable: The quantity that we want to estimate / predict (y) E.g., The amount of pollutant in the lake; population size Independent variables: variable being manipulated or followed (x) E.g., Time Functions: describe relationships between state variables E.g., Lynx abundance is a function of hare abundance Lynx abundance = f (hare abundance) Could be linear function (y = a + bx) lynx abundance = a + b * hare abundance Parameters: constants that specify functions Mediate the relationship between independent and dependent variables Typically numbers that we can hopefully estimate with real data E.g., Assimilation efficiency, per capita birth rate, survival probability a and b (above) are parameters

10. Model Complexity Model complexity range In general, different types of models fall somewhere along a simple-complex continuum Simple Complex More general, behavior easy to understand (why the model predicts what it does), unrealistic More specific, realistic Example: Salmon stocking models Simple: Total # salmon in lake = f(# naturally reproduced, # stocked salmon) Complex: Total # salmon in lake = f(# naturally reproduced, # stocked salmon, competition, harvest, # prey, # predators)

11. Model Complexity Which level of complexity do we use? Sometimes simple is best, some times complex, sometimes use both “Make everything as simple as possible, but not simpler” ~ Albert Einstein When the model is too complex, it can get very hard to understand the model results and connect them to assumptions  There is no point in constructing a model that is an exact representation of nature… …would be as hard to understand as the system we're trying to model! But there is a tendency to want to consider all the factors The art is figuring out how to simplify  what to leave out and still get at important processes

12. Pictorial Monkey Model Complexity MonkeyReality MonkeyModel 1 MonkeyModel 2 Monkey Monkey Monkey maybe human? DESIRED COMPLEXITY

13. Model Break! Let’s think about deer… http://mvhs1.mbhs.edu/mvhsproj/deer/deer.html

14. Types of Models • Model Categories: • Static vs. Dynamic • Discrete vs. Continuous • Deterministic vs. Stochastic • Analytical vs. Numerical Simulation

15. Static vs. Dynamic Static models assume system is at steady state E.g., mass balance; predator and prey populations at carrying capacities Often much easier to use: can build an equation for steady state as function of different parameters & see how parameters affect equilibrium e.g., how attack rate of predator affects carrying capacity Dynamic models provide a trajectory of some variable over time Can be used to predict both trajectories and equilibria More powerful, but more complex; e.g., population size over time Dynamic Static Carrying capacity Equilibrium Population size Time

16. Discrete vs. Continuous Discrete models useful for predicting quantities over fixed intervals Time is modeled in discrete steps; Intervening time is not modeled Good for populations that reproduce seasonally, like moose, salmon (don't use calculus) Extreme example: 13-year cicadas Continuous modelsuseful for continuous processes Can predict quantities at any time; time is a smooth curve Good for populations that breed continuously, like humans (apply calculus to solve for a point in time) Discrete Continuous Population Size Population Size

17. Discrete vs. Continuous How we use calculus in continuous models Population size, N Time, t • NtSome continuous function • dN/dt Derivative of that function (differential equation) • ΔN/ΔtPopulation growth rate • Derivative is the instantaneous slope of the N versus t function • Change in N over time [ΔN/Δt] • (like zooming in at a point on curve until straight line appears)

18. Deterministic vs. Stochastic Deterministic models useful for making exact predictions (no uncertainty) Stochastic models have uncertainty or error built in

19. Deterministic vs. Stochastic Deterministic models useful for making exact predictions (no uncertainty) E.g., population will be 5,564 in 3 years y = a + bx Very simple deterministic model if we want to know y(dependent variable), we simply plug in values for a, b (parameters or constants) and then vary x(xwill often be time) Advantage of Deterministic Models: Great as general tools for understanding ecological problems because they are simpler and easier to understand than stochastic models Drawback of Deterministic Models: There are no real-life situations in ecology where we can make exact predictions and expect them to be right

20. Deterministic vs. Stochastic Stochastic modelshave uncertainty or error built in Advantage of Stochastic Models: More realistic: the ecological world is messy I.e., not fully describable by sets of deterministic equations Our models never fit perfectly The scatter around the model we usually attribute to “random error”, but this really means “unexplained error” Scatter: Points do not fall perfectly along line Population Size

21. Deterministic vs. Stochastic Stochastic modelshave uncertainty or error built in Advantage of Stochastic Models: More realistic: the ecological world is messy I.e., not fully describable by sets of deterministic equations Our models never fit perfectly y = a + bx + error Stochastic model example Prediction (y) is based on a, b, x and error (stochasticity) Model predicts a range (cloud of points) for y (not a single value for each x) Using statistics we can put bounds on the likely values of y E.g., in 5 years we predict there will be between 1000 and 1500 wolves in the UP with 95% confidence

22. Analytical vs. Numerical Simulation Analytical models can be solved using algebra and calculus These are functions we are familiar with from math courses More general (can be applied in many contexts) e.g., All our mass balance problems (T = S/F) Some dynamic models (e.g., exponential population growth) Numerical simulation models cannot be solved using algebra and calculus Either because they have discontinuous functions Or because there are too many variables E.g., Stochastic models (have “randomness” involved)  Need a computer to solve iteratively: Plug in starting values (real numbers) Computer calculates output for each time step Advantage of Numerical Simulation Models: Greater realism, easier to use with available software (don't need to be a math whiz) Drawback of Numerical Simulation Models: Harder to understand behavior

23. Analytical vs. Numerical Simulation Example: Equation series without an analytical solution Fluid dynamics around a boat hull • Computers are used to plug in different combinations of numbers for the variables to determine what combinations work to make the equations balance • We’ll create complex conceptual models in Stella and let Stella do numerical simulations for us to solve them

24. Types of Models Where do our mass-balance models fit into these categories? • Model Categories: • Static vs. Dynamic • Discrete vs. Continuous … why? • Deterministic vs. Stochastic • Analytical vs. Numerical Simulation

25. Types of Models Where do our mass-balance models fit into these categories? • Model Categories: • Static vs. Dynamic •  Steady state (no time component) • Discrete vs. Continuous …why? •  No time component • Deterministic vs. Stochastic •  No uncertainty • Analytical vs. Numerical Simulation • Solved using algebra

26. Wrap-Up Monday: Starting population growth Chapter 1 in Text (if you want to read) Nt+1 = Nt