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FW364 Ecological Problem Solving PowerPoint Presentation

FW364 Ecological Problem Solving

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## PowerPoint Slideshow about 'FW364 Ecological Problem Solving' - dillon

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Presentation Transcript

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

- 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)

- 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

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

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)

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

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

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

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)

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

Pictorial Monkey Model Complexity

MonkeyReality

MonkeyModel 1

MonkeyModel 2

Monkey

Monkey

Monkey

maybe human?

DESIRED COMPLEXITY

- Model Categories:
- Static vs. Dynamic
- Discrete vs. Continuous
- Deterministic vs. Stochastic
- Analytical vs. Numerical Simulation

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

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

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)

Deterministic models useful for making exact predictions (no uncertainty)

Stochastic models have uncertainty or error built in

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

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

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

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

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

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

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

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