Lecture 8 populations and logistic growth (1)

37
Lecture Outline Density Independent and Density Dependent growth and the carrying capacity The differential logistic equation for continuous growth and Maximum Sustained Yield The difference equation for logistic continuous growth. The logistic equation for discrete growth

description

This is a powerpoint on a biological topic of population and logisitics growth. This lecture was given to me in one of my classes and is my teacher's material.

Transcript of Lecture 8 populations and logistic growth (1)

Page 1: Lecture 8 populations and logistic growth (1)

Lecture Outline• Density Independent and Density Dependent

growth and the carrying capacity

• The differential logistic equation for continuous growth and Maximum Sustained Yield

• The difference equation for logistic continuous growth.

• The logistic equation for discrete growth• r and extinction

Page 2: Lecture 8 populations and logistic growth (1)

Organization & Equations

Logistic

Continuous Discrete

Differential Difference Difference

dN/dt = rN (1-N/K) Nt = K/[1+ [(K – N0)/N0] e -(rt)] Nt+1 = Nt + R0 Nt (1 – Nt/K)

Page 3: Lecture 8 populations and logistic growth (1)

Density Independence, page 140 in McConnell

• Factors that effect b and d do not depend on the population size.

Density Dependence

• Factors that effect b and d do depend on population size.

Page 4: Lecture 8 populations and logistic growth (1)

Recruitment = larvae leave the plankton and attach to solid substrate.

• Which is density dependent?

Page 5: Lecture 8 populations and logistic growth (1)

As N increases, r will decrease to zero.

This is density dependence.

r

N K

Page 6: Lecture 8 populations and logistic growth (1)

Organization & Equations

Logistic

Continuous Discrete

Differential Difference Difference

dN/dt = rN (1-N/K) Nt = K/[1+ [(K – N0)/N0] e -(rt)] Nt+1 = Nt + R0 Nt (1 – Nt/K)

Page 7: Lecture 8 populations and logistic growth (1)

Logistic growth is density dependent growth.

• dN/dt = rN (1-N/K)

Page 8: Lecture 8 populations and logistic growth (1)

How does (1-N/K) work?

• Population 1: K = 100 individuals and N = 5

• Population 2: K = 1500 and N = 1000

Page 9: Lecture 8 populations and logistic growth (1)

What does the curve look like for continuous populations growing under density dependence?

Page 10: Lecture 8 populations and logistic growth (1)

• What is the value of K?• What does the upper curve tell us?

Page 11: Lecture 8 populations and logistic growth (1)

Maximum Sustained Yield…

• Fisheries managers tried to harvest enough fish to maintain the maximum growth rate or maximum yield = K/2.

• Do you think this worked? Why or Why not?

Page 12: Lecture 8 populations and logistic growth (1)

Ed Ricketts, John Steinbeck, and Cannery Row

“Ricketts was accepting because he just listened and tended to turn whatever people said into something that sounded brilliant.” Ricketts was Steinbeck’s friend and philosophical/intellectual father.

1930s = 500,000 tons of sardines/year1952 = gone

Page 13: Lecture 8 populations and logistic growth (1)

Practice

• Suppose a population of ravens is growing according to the continuous logistic equation. If K=200 and r = 0.1 individuals/(individual · year-1), what is the maximum growth rate of this population over a year?

• dN/dt = rN(1-N/K)

Page 14: Lecture 8 populations and logistic growth (1)

Organization & Equations

Logistic

Continuous Discrete

Differential Difference Difference

dN/dt = rN (1-N/K) Nt = K/[1+ [(K – N0)/N0] e -(rt)] Nt+1 = Nt + R0 Nt (1 – Nt/K)

Page 15: Lecture 8 populations and logistic growth (1)

What if you want to predict the pop size at some point in time?

• The integrated form of the differential logistic equation for continuous reproduction. The difference equation.

• Nt = K/[1 + [(K – N0)/N0] e-rt]

Page 16: Lecture 8 populations and logistic growth (1)

An overview of the procedure.

• Create a phase-plane plot with Nt+1 on the Y-axis and Nt on the X-axis

• Calculate the function line using the difference equation.

• Draw the equilibrium line where Nt+1 = Nt

• Use “cob webbing” to determine the stability properties of a population…more in a minute.

Page 17: Lecture 8 populations and logistic growth (1)

What is a phase plane plot?

0

100

200

300

400

500

600

0 100 200 300 400 500 600

N t

Nt+

1

Function line

Equilibrium line

Page 18: Lecture 8 populations and logistic growth (1)

How do we calculate data for the function line?

Use the difference equation . Example: K = 500; r = 0.5; N0 = 10.

• Nt = K/[1 + [(K – N0)/N0]e-rt]

N1 = K/[1 + [(K – N0)/N0]e-rt] = 500/[1+[(500-10)/10] e-0.5*1 ]= 16N2 = K/[1 + [(K – N0)/N0]e-rt] = 500/[1+[(500-10)/10] e-0.5*2 ] = 27

• N3 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 42• N4 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 66• N5 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 100• N6 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 145• N7 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 202• N8 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 264• N9 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 324• N10 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 372• N11 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 417• N12 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 446• N13 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 466• N14 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 479• N15 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 487• N16 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 492• N17 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 495• N18 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 497• N19 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 498• N20 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 499• N21 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 499• N22 = Nt = K/[1 + [(K – N0)/N0]e-rt] = 500

0

100

200

300

400

500

600

0 5 10 15 20 25

Time

Nt

Page 19: Lecture 8 populations and logistic growth (1)

How do we plot the function line in phase plane?

0

100

200

300

400

500

600

0 100 200 300 400 500 600

N t

Nt+

1

Function line

Equilibrium line

Page 20: Lecture 8 populations and logistic growth (1)

What is cobwebbing?

0

100

200

300

400

500

600

0 100 200 300 400 500 600

N t

Nt+

1

Page 21: Lecture 8 populations and logistic growth (1)

Stability properties?

• What is an equilibrium point?

Page 22: Lecture 8 populations and logistic growth (1)

General Concept of Equilibrium• It is a balance between opposing forces…births –

deaths.

• Non-equilibrium: conditions are constantly changing. A stable balance cannot be achieved.

• Equilibrium forces produce a stable balance.

• Non-equilibrium forces disrupt a stable balance.

Page 23: Lecture 8 populations and logistic growth (1)

The Allee Effect: A special case of density dependence.

• Some species require a minimum number of individuals or they spiral to extinction.

– Some species need a certain group size to find a mate (whales) or to hunt (wolves), care for young (elephants), reproduce (mayflies), and avoid predators (flocking birds).

– r is negative below a critical N (Allee density) and then becomes positive as N increases past the critical N.

Page 24: Lecture 8 populations and logistic growth (1)

What are the stability properties of the Allee effect?

• How many equilibrium points with an Allee effect?

• Which are stable which are unstable?

Page 25: Lecture 8 populations and logistic growth (1)

Organization & Equations

Logistic

Continuous Discrete

Differential Difference Difference

dN/dt = rN (1-N/K) Nt = K/[1+ [(K – N0)/N0] e -(rt)] Nt+1 = Nt + R0 Nt (1 – Nt/K)

Page 26: Lecture 8 populations and logistic growth (1)

How to solve for Nt+1 when a population has discrete growth

• What happens as r increases in the continuous equation?

• What happens as r increases in the discrete equation?

Page 27: Lecture 8 populations and logistic growth (1)

A population with discrete reproduction

• A population of mayflies started with 2 individuals, with a carry capacity of 100 and an r = 0.2. The population reproduces once each year. What is the population size at Nt+1.

• Nt+1 = Nt + R0 Nt (1 – Nt/K):

• Nt+1 = 2+(.2)(2) [1- (2/100)]• Nt+1 = 2+.4 [1-.02]• Nt+1 = 2+(.4)(.98)

– the population is increasing at 98% of the exponential rate• Nt+1 = 2.392

• Now what?

Page 28: Lecture 8 populations and logistic growth (1)

Your turn.

• A population with discrete reproduction has a growth rate of 3.0. They reproduce once per year, K = 1500 and Nt = 20.

• What is the population size after two generations?

Nt+1 = Nt + R0 Nt (1 – Nt/K)

Page 29: Lecture 8 populations and logistic growth (1)

Discrete growth has a built-in time lag.

• In the continuous growth model we assume that per capita growth changes instantaneously when an individual is born or dies.

• Discrete populations do not adjust instantaneously. There is a time lag before N begins to apply a brake to R0 when N overshoots K.

Page 30: Lecture 8 populations and logistic growth (1)

Kiabab deer herd in northern Arizona

??

Present

Wolves exterminated

Page 31: Lecture 8 populations and logistic growth (1)

What is the relationship between r and the stability of a discrete population?

Pg 130 in McConnell

Page 32: Lecture 8 populations and logistic growth (1)

Dampening, stable limit, complex stable limit, chaos

Time

R0 < 2.02.0 < R0 < 2.5

2.5 < R0 < 2.57 R0 > 2.57

Page 33: Lecture 8 populations and logistic growth (1)

How do you know the stability characteristics?

• The outcome of cobwebbing is determined by the slope of the function line where it crosses the equilibrium line.

Page 34: Lecture 8 populations and logistic growth (1)

Now we have 4 types of population “stability”.

1. Smooth increase to K…constancy over time.

2. Dampened oscillations…constancy with minor variation around K.

3. Stable cycles… continual variation around K over time.

4. Chaos…this is not stable

Page 35: Lecture 8 populations and logistic growth (1)

Large r and Chaos• Chaos = a non-repeating, drastic fluctuation in

population size with a simple deterministic model…shocking.

Lord Robert May1936 – presentOxford University

Attractor = K

Bifurcation 4 8 Chaos

Page 36: Lecture 8 populations and logistic growth (1)

General Meaning• Deterministic, meant that the future behavior was fully determined by

the initial conditions, with no random elements involved.

• “The phenomenon of deterministic chaos…very simple and purely deterministic laws or equations can give rise to dynamic behavior that not merely looks like random noise, but is so sensitive to initial conditions that long-term prediction is effectively impossible. This ended the Newtonian dream that if the system is simple (very few variables) and orderly (the rules and parameters are exactly known) then the future is predictable.” Robert May (2007)– Theoretical Ecology (2007)

Page 37: Lecture 8 populations and logistic growth (1)

Sibly et al. 2007. Ecology Letters 10:1-7

• Many return rates are ≈1. Between 0.5 & 2 is considered stable.

• <0.5 and disturbance rates may exceed return rates resulting in extinction. >2.0 can result in chaotic behavior and thus, extinction.

Closed circles = mammalsOpen cirlce = insectsPlus signs = birdsOpen squares = bony fishA total of 634 populations

The return rates are an approximation of r