yrs<-10 1. Use the given info below to answer the following questions: How did stochasticity influence your predictions of bananaquit population growth compared to the deterministic model? Provide data from your model runs to support your answer. Based on work I did on bananaquits (a small bird) in the Caribbean from 2002-2012, I have point estimates of the in normal years as 1.11. If we assume a starting population of 500 birds, let's grow the population for 10 years at this rate. n<-500 lam<1.11 N
yrs<-10 1. Use the given info below to answer the following questions: How did stochasticity influence your predictions of bananaquit population growth compared to the deterministic model? Provide data from your model runs to support your answer. Based on work I did on bananaquits (a small bird) in the Caribbean from 2002-2012, I have point estimates of the in normal years as 1.11. If we assume a starting population of 500 birds, let's grow the population for 10 years at this rate. n<-500 lam<1.11 N
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
![yrs<-10
1. Use the given info below to answer the following questions: How did stochasticity influence your
predictions of bananaquit population growth compared to the deterministic model? Provide data
from your model runs to support your answer.
Based on work I did on bananaquits (a small bird) in the Caribbean from 2002-2012, I have point estimates of the
in normal years as 1.11. If we assume a starting population of 500 birds, let's grow the population for 10 years at this
rate.
n<-500
lam<1.11
N<n
for (i in 1 yrs){N[i+1]<-lam*N[i]}
plot(seq(0,10),N,type=1',col='blue',xlab="Year",ylab="Population Size")
title(paste('Final population size=',round (N[11],0)))
Final population size=1420
Population Size
600 1000 1400
Year
6 8 10
We see an upcurving line of growth, which is really a small portion of the
geometric (the discrete version of exponential) growth curve, with an estimate of 1420 bananaquits after 10 years.
After running this same code 10 more times, below are the graphs for the best-case scenario (highest number),
worst-case scenario (lowest number), and final number for all years. Compare this with the predicted number based
on the deterministic model.
Test #4 (Best Case Scenario)
Final population size=1420
Test #8 (Worst Case Scenario)
Population Size
600 1000 1400
°
2
4
6
8
10
Year
Population Size
450
Population Size
099 099
Test #10: (Last test/Result)
Final population size=456
300
450
600
TITTTTT
0
4
10
Year
Final population size=456
TIT
0
M
2
4
6 8 10
Year](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F296fef9a-c947-42b6-a55f-96737f4868cc%2F977bd4a4-fe8f-4d08-8610-4c20de78658e%2Fhhu38qf_processed.png&w=3840&q=75)
Transcribed Image Text:yrs<-10
1. Use the given info below to answer the following questions: How did stochasticity influence your
predictions of bananaquit population growth compared to the deterministic model? Provide data
from your model runs to support your answer.
Based on work I did on bananaquits (a small bird) in the Caribbean from 2002-2012, I have point estimates of the
in normal years as 1.11. If we assume a starting population of 500 birds, let's grow the population for 10 years at this
rate.
n<-500
lam<1.11
N<n
for (i in 1 yrs){N[i+1]<-lam*N[i]}
plot(seq(0,10),N,type=1',col='blue',xlab="Year",ylab="Population Size")
title(paste('Final population size=',round (N[11],0)))
Final population size=1420
Population Size
600 1000 1400
Year
6 8 10
We see an upcurving line of growth, which is really a small portion of the
geometric (the discrete version of exponential) growth curve, with an estimate of 1420 bananaquits after 10 years.
After running this same code 10 more times, below are the graphs for the best-case scenario (highest number),
worst-case scenario (lowest number), and final number for all years. Compare this with the predicted number based
on the deterministic model.
Test #4 (Best Case Scenario)
Final population size=1420
Test #8 (Worst Case Scenario)
Population Size
600 1000 1400
°
2
4
6
8
10
Year
Population Size
450
Population Size
099 099
Test #10: (Last test/Result)
Final population size=456
300
450
600
TITTTTT
0
4
10
Year
Final population size=456
TIT
0
M
2
4
6 8 10
Year
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