Insight diagram
STEM-SM combines a simple ecosystem model (modified version of VSEM; Hartig et al. 2019) with a soil moisture model (Guswa et al. (2002) leaky bucket model). Outputs from the soil moisture model influence ecosystem dynamics in three ways. 
(1) The ratio of actual transpiration to maximum evapotranspiration (T/ETmax) modifies gross primary productivity (GPP).
(2) Degree of saturation of the soil (Sd) modifies the rate of soil heterotrophic respiration.
(3) Water limitation of GPP (by T/ETmax) and of soil nutrient availability (approximated by Sd) combine with leaf area limitation (approximated by fraction of incident photosynthetically-active radiation that is absorbed) to modify the allocation of net primary productivity to aboveground and belowground parts of the vegetation.

Ecosystem dynamics in turn influence flows of water in to and out of the soil moisture stock. The size of the aboveground biomass stock determines fractional vegetation cover, which modifies interception, soil evaporation and transpiration by plants.

References:
Guswa, A.J., Celia, M.A., Rodriguez-Iturbe, I. (2002) Models of soil moisture dynamics in ecohydrology: a comparative study. Water Resources Research 38, 5-1 - 5-15.

Hartig, F., Minunno, F., and Paul, S. (2019). BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics. R package version 0.1.7. https://CRAN.R-project.org/package=BayesianTools

Argentina: Simple Terrestrial Ecosystem Model - Soil Moisture (STEM-SM)
Insight diagram
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

Clone of Final Midterm Student version of A More Realistic Model of Isle Royale: Predator Prey Interactions
Insight diagram
Overview: 
This simulation will show the relationship between tree logging forestry and how this can affect mountain biking tourism in Derby Park Tasmania. The main goal of this simulation is to show these two industries can co-exist in the same environment, or increase in demand or production in one sector will affect the result of another.  

Function of the model:
In comparison there are both pros and cons for both sectors working correspondently. Demand for derby park is caused by individual past experience when visiting the park or friends recommendation which increase in the number of demands. Increase in demands will increase in the number of visitors. When visitors visits the park they require make a purchase a bike and pay the park for using the park facilities. All this will adds up to bikers total spending when visiting Derby. When consumer spend it is booting the economy especially in the tourism sector. Similarly tree logging will also contribute financially towards the Tasmania economy. The regeneration stage is relatively low compare to the logging rate. The growth will not cover the loss which can cause some level of damage in the scenery of the park, affecting tourist to view when mountain biking. Visitors overall experience will have the impact towards the demand for mountain biking in derby park, if visitors experience is satisfied they will come back to visit again or visit with group of friends, even words of mouth recommendation will also increase the level of demand of visiting Derby. 

Some key insights base on the simulation:
Based on the simulation of the two models we can see there are some key changes.
Tree logging increase will cause the disturbance of the natural scenery, thus change the overall experience of the visitors, decrease in the level of demand. Tree logging will also have negative impact towards the overall tourist experience thus affect the park facility and track. The natural scenery and the overall experience can affect their experience and if they would continue to recommend this area to friends to increase the demand. 

Assignment 3 Derby mountain biking and the effect of logging - 586984
Insight diagram
STEM-SM combines a simple ecosystem model (modified version of VSEM; Hartig et al. 2019) with a soil moisture model (Guswa et al. (2002) leaky bucket model). Outputs from the soil moisture model influence ecosystem dynamics in three ways. 
(1) The ratio of actual transpiration to maximum evapotranspiration (T/ETmax) modifies gross primary productivity (GPP).
(2) Degree of saturation of the soil (Sd) modifies the rate of soil heterotrophic respiration.
(3) Water limitation of GPP (by T/ETmax) and of soil nutrient availability (approximated by Sd) combine with leaf area limitation (approximated by fraction of incident photosynthetically-active radiation that is absorbed) to modify the allocation of net primary productivity to aboveground and belowground parts of the vegetation.

Ecosystem dynamics in turn influence flows of water in to and out of the soil moisture stock. The size of the aboveground biomass stock determines fractional vegetation cover, which modifies interception, soil evaporation and transpiration by plants.

References:
Guswa, A.J., Celia, M.A., Rodriguez-Iturbe, I. (2002) Models of soil moisture dynamics in ecohydrology: a comparative study. Water Resources Research 38, 5-1 - 5-15.

Hartig, F., Minunno, F., and Paul, S. (2019). BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics. R package version 0.1.7. https://CRAN.R-project.org/package=BayesianTools

Clone of Simple Terrestrial Ecosystem Model - Soil Moisture (STEM-SM)
Insight diagram
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

Experiment with adjusting the initial number of moose and wolves on the island.
Clone of (3) Copy of "Isle Royale: Predator Prey Interactions"
Insight diagram
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

Experiment with adjusting the initial number of moose and wolves on the island.
Clone of Clone of Clone of (3) Copy of "Isle Royale: Predator Prey Interactions"
Insight diagram
​Physical meaning of the equations
The Lotka–Volterra model makes a number of assumptions about the environment and evolution of the predator and prey populations:

1. The prey population finds ample food at all times.
2. The food supply of the predator population depends entirely on the size of the prey population.
3. The rate of change of population is proportional to its size.
4. During the process, the environment does not change in favour of one species and genetic adaptation is inconsequential.
5. Predators have limitless appetite.
As differential equations are used, the solution is deterministic and continuous. This, in turn, implies that the generations of both the predator and prey are continually overlapping.[23]

Prey
When multiplied out, the prey equation becomes
dx/dtαx - βxy
 The prey are assumed to have an unlimited food supply, and to reproduce exponentially unless subject to predation; this exponential growth is represented in the equation above by the term αx. The rate of predation upon the prey is assumed to be proportional to the rate at which the predators and the prey meet; this is represented above by βxy. If either x or y is zero then there can be no predation.

With these two terms the equation above can be interpreted as: the change in the prey's numbers is given by its own growth minus the rate at which it is preyed upon.

Predators

The predator equation becomes

dy/dt =  - 

In this equation, {\displaystyle \displaystyle \delta xy} represents the growth of the predator population. (Note the similarity to the predation rate; however, a different constant is used as the rate at which the predator population grows is not necessarily equal to the rate at which it consumes the prey). {\displaystyle \displaystyle \gamma y} represents the loss rate of the predators due to either natural death or emigration; it leads to an exponential decay in the absence of prey.

Hence the equation expresses the change in the predator population as growth fueled by the food supply, minus natural death.


Clone of Prey&Predator
Insight diagram
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

Clone of Final Midterm Student version of A More Realistic Model of Isle Royale: Predator Prey Interactions
Insight diagram

Dynamic simulation modelers are particularly interested in understanding and being able to distinguish between the behavior of stocks and flows that result from internal interactions and those that result from external forces acting on a system.  For some time modelers have been particularly interested in internal interactions that result in stable oscillations in the absence of any external forces acting on a system.  The model in this last scenario was independently developed by Alfred Lotka (1924) and Vito Volterra (1926).  Lotka was interested in understanding internal dynamics that might explain oscillations in moth and butterfly populations and the parasitoids that attack them.  Volterra was interested in explaining an increase in coastal populations of predatory fish and a decrease in their prey that was observed during World War I when human fishing pressures on the predator species declined.  Both discovered that a relatively simple model is capable of producing the cyclical behaviors they observed.  Since that time, several researchers have been able to reproduce the modeling dynamics in simple experimental systems consisting of only predators and prey.  It is now generally recognized that the model world that Lotka and Volterra produced is too simple to explain the complexity of most and predator-prey dynamics in nature.  And yet, the model significantly advanced our understanding of the critical role of feedback in predator-prey interactions and in feeding relationships that result in community dynamics.The Lotka–Volterra model makes a number of assumptions about the environment and evolution of the predator and prey populations:

1. The prey population finds ample food at all times.
2. The food supply of the predator population depends entirely on the size of the prey population.
3. The rate of change of population is proportional to its size.
4. During the process, the environment does not change in favour of one species and genetic adaptation is inconsequential.
5. Predators have limitless appetite.
As differential equations are used, the solution is deterministic and continuous. This, in turn, implies that the generations of both the predator and prey are continually overlapping.[23]

Prey
When multiplied out, the prey equation becomes
dx/dtαx - βxy
 The prey are assumed to have an unlimited food supply, and to reproduce exponentially unless subject to predation; this exponential growth is represented in the equation above by the term αx. The rate of predation upon the prey is assumed to be proportional to the rate at which the predators and the prey meet; this is represented above by βxy. If either x or y is zero then there can be no predation.

With these two terms the equation above can be interpreted as: the change in the prey's numbers is given by its own growth minus the rate at which it is preyed upon.

Predators

The predator equation becomes

dy/dt =  - 

In this equation, {\displaystyle \displaystyle \delta xy} represents the growth of the predator population. (Note the similarity to the predation rate; however, a different constant is used as the rate at which the predator population grows is not necessarily equal to the rate at which it consumes the prey). {\displaystyle \displaystyle \gamma y} represents the loss rate of the predators due to either natural death or emigration; it leads to an exponential decay in the absence of prey.

Hence the equation expresses the change in the predator population as growth fueled by the food supply, minus natural death.


Clone of Bio103 Predator-Prey Model ("Lotka'Volterra")
Insight diagram
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

Clone of Final Midterm Student version of A More Realistic Model of Isle Royale: Predator Prey Interactions
Insight diagram
​Physical meaning of the equations
The Lotka–Volterra model makes a number of assumptions about the environment and evolution of the predator and prey populations:

1. The prey population finds ample food at all times.
2. The food supply of the predator population depends entirely on the size of the prey population.
3. The rate of change of population is proportional to its size.
4. During the process, the environment does not change in favour of one species and genetic adaptation is inconsequential.
5. Predators have limitless appetite.
As differential equations are used, the solution is deterministic and continuous. This, in turn, implies that the generations of both the predator and prey are continually overlapping.[23]

Prey
When multiplied out, the prey equation becomes
dx/dtαx - βxy
 The prey are assumed to have an unlimited food supply, and to reproduce exponentially unless subject to predation; this exponential growth is represented in the equation above by the term αx. The rate of predation upon the prey is assumed to be proportional to the rate at which the predators and the prey meet; this is represented above by βxy. If either x or y is zero then there can be no predation.

With these two terms the equation above can be interpreted as: the change in the prey's numbers is given by its own growth minus the rate at which it is preyed upon.

Predators

The predator equation becomes

dy/dt =  - 

In this equation, {\displaystyle \displaystyle \delta xy} represents the growth of the predator population. (Note the similarity to the predation rate; however, a different constant is used as the rate at which the predator population grows is not necessarily equal to the rate at which it consumes the prey). {\displaystyle \displaystyle \gamma y} represents the loss rate of the predators due to either natural death or emigration; it leads to an exponential decay in the absence of prey.

Hence the equation expresses the change in the predator population as growth fueled by the food supply, minus natural death.


Clone of Prey&Predator
Insight diagram
This model is a modified version of the 'Very Simple Ecosystem Model' (VSEM; Hartig et al. 2019). Controls have been added to gross primary productivity (GPP) and heterotrophic respiration (Rhetero) based on evapotranspiration rates.

Reference:
Hartig, F., Minunno, F., and Paul, S. (2019). BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics. R package version 0.1.7. https://CRAN.R-project.org/package=BayesianTools
Clone of Very Simple Ecosystem Model with Evapotranspiration (VSEM-ET)
Insight diagram
This model is a modified version of the 'Very Simple Ecosystem Model' (VSEM; Hartig et al. 2019). Controls have been added to gross primary productivity (GPP) and heterotrophic respiration (Rhetero) based on evapotranspiration rates.

Reference:
Hartig, F., Minunno, F., and Paul, S. (2019). BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics. R package version 0.1.7. https://CRAN.R-project.org/package=BayesianTools
Very Simple Ecosystem Model with Evapotranspiration (VSEM-ET) for a Tropical Deciduous Forest in Mexico
Insight diagram
See basic Dystopia model first. Idea here is to add a second "clean" energy source, which is is not constrained by pollution. The expectation, of course, is that, as the pollution constraint kicks in, this can take over the drive from the demand that would otherwise remain unsatisfied...
Dystopia-2: simple energy system "clean transition" model
Insight diagram
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

Clone of Clone of Final Midterm Student version of A More Realistic Model of Isle Royale: Predator Prey Interactions
Insight diagram
STEM-SM combines a simple ecosystem model (modified version of VSEM; Hartig et al. 2019) with a soil moisture model (Guswa et al. (2002) leaky bucket model). Outputs from the soil moisture model influence ecosystem dynamics in three ways. 
(1) The ratio of actual transpiration to maximum evapotranspiration (T/ETmax) modifies gross primary productivity (GPP).
(2) Degree of saturation of the soil (Sd) modifies the rate of soil heterotrophic respiration.
(3) Water limitation of GPP (by T/ETmax) and of soil nutrient availability (approximated by Sd) combine with leaf area limitation (approximated by fraction of incident photosynthetically-active radiation that is absorbed) to modify the allocation of net primary productivity to aboveground and belowground parts of the vegetation.

Ecosystem dynamics in turn influence flows of water in to and out of the soil moisture stock. The size of the aboveground biomass stock determines fractional vegetation cover, which modifies interception, soil evaporation and transpiration by plants.

References:
Guswa, A.J., Celia, M.A., Rodriguez-Iturbe, I. (2002) Models of soil moisture dynamics in ecohydrology: a comparative study. Water Resources Research 38, 5-1 - 5-15.

Hartig, F., Minunno, F., and Paul, S. (2019). BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics. R package version 0.1.7. https://CRAN.R-project.org/package=BayesianTools

Clone of Simple Terrestrial Ecosystem Model - Soil Moisture (STEM-SM)
Insight diagram
​Physical meaning of the equations
The Lotka–Volterra model makes a number of assumptions about the environment and evolution of the predator and prey populations:

1. The prey population finds ample food at all times.
2. The food supply of the predator population depends entirely on the size of the prey population.
3. The rate of change of population is proportional to its size.
4. During the process, the environment does not change in favour of one species and genetic adaptation is inconsequential.
5. Predators have limitless appetite.
As differential equations are used, the solution is deterministic and continuous. This, in turn, implies that the generations of both the predator and prey are continually overlapping.[23]

Prey
When multiplied out, the prey equation becomes
dx/dtαx - βxy
 The prey are assumed to have an unlimited food supply, and to reproduce exponentially unless subject to predation; this exponential growth is represented in the equation above by the term αx. The rate of predation upon the prey is assumed to be proportional to the rate at which the predators and the prey meet; this is represented above by βxy. If either x or y is zero then there can be no predation.

With these two terms the equation above can be interpreted as: the change in the prey's numbers is given by its own growth minus the rate at which it is preyed upon.

Predators

The predator equation becomes

dy/dt =  - 

In this equation, {\displaystyle \displaystyle \delta xy} represents the growth of the predator population. (Note the similarity to the predation rate; however, a different constant is used as the rate at which the predator population grows is not necessarily equal to the rate at which it consumes the prey). {\displaystyle \displaystyle \gamma y} represents the loss rate of the predators due to either natural death or emigration; it leads to an exponential decay in the absence of prey.

Hence the equation expresses the change in the predator population as growth fueled by the food supply, minus natural death.


Clone of Prey&Predator
Insight diagram
This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.
Clone of Plant, Deer and Wolf Population Dynamics
Insight diagram
STEM-SM combines a simple ecosystem model (modified version of VSEM; Hartig et al. 2019) with a soil moisture model (Guswa et al. (2002) leaky bucket model). Outputs from the soil moisture model influence ecosystem dynamics in three ways. 
(1) The ratio of actual transpiration to maximum evapotranspiration (T/ETmax) modifies gross primary productivity (GPP).
(2) Degree of saturation of the soil (Sd) modifies the rate of soil heterotrophic respiration.
(3) Water limitation of GPP (by T/ETmax) and of soil nutrient availability (approximated by Sd) combine with leaf area limitation (approximated by fraction of incident photosynthetically-active radiation that is absorbed) to modify the allocation of net primary productivity to aboveground and belowground parts of the vegetation.

Ecosystem dynamics in turn influence flows of water in to and out of the soil moisture stock. The size of the aboveground biomass stock determines fractional vegetation cover, which modifies interception, soil evaporation and transpiration by plants.

References:
Guswa, A.J., Celia, M.A., Rodriguez-Iturbe, I. (2002) Models of soil moisture dynamics in ecohydrology: a comparative study. Water Resources Research 38, 5-1 - 5-15.

Hartig, F., Minunno, F., and Paul, S. (2019). BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics. R package version 0.1.7. https://CRAN.R-project.org/package=BayesianTools

Clone of Simple Terrestrial Ecosystem Model - Soil Moisture (STEM-SM)
Insight diagram

Dynamic simulation modelers are particularly interested in understanding and being able to distinguish between the behavior of stocks and flows that result from internal interactions and those that result from external forces acting on a system.  For some time modelers have been particularly interested in internal interactions that result in stable oscillations in the absence of any external forces acting on a system.  The model in this last scenario was independently developed by Alfred Lotka (1924) and Vito Volterra (1926).  Lotka was interested in understanding internal dynamics that might explain oscillations in moth and butterfly populations and the parasitoids that attack them.  Volterra was interested in explaining an increase in coastal populations of predatory fish and a decrease in their prey that was observed during World War I when human fishing pressures on the predator species declined.  Both discovered that a relatively simple model is capable of producing the cyclical behaviors they observed.  Since that time, several researchers have been able to reproduce the modeling dynamics in simple experimental systems consisting of only predators and prey.  It is now generally recognized that the model world that Lotka and Volterra produced is too simple to explain the complexity of most and predator-prey dynamics in nature.  And yet, the model significantly advanced our understanding of the critical role of feedback in predator-prey interactions and in feeding relationships that result in community dynamics.The Lotka–Volterra model makes a number of assumptions about the environment and evolution of the predator and prey populations:

1. The prey population finds ample food at all times.
2. The food supply of the predator population depends entirely on the size of the prey population.
3. The rate of change of population is proportional to its size.
4. During the process, the environment does not change in favour of one species and genetic adaptation is inconsequential.
5. Predators have limitless appetite.
As differential equations are used, the solution is deterministic and continuous. This, in turn, implies that the generations of both the predator and prey are continually overlapping.[23]

Prey
When multiplied out, the prey equation becomes
dx/dtαx - βxy
 The prey are assumed to have an unlimited food supply, and to reproduce exponentially unless subject to predation; this exponential growth is represented in the equation above by the term αx. The rate of predation upon the prey is assumed to be proportional to the rate at which the predators and the prey meet; this is represented above by βxy. If either x or y is zero then there can be no predation.

With these two terms the equation above can be interpreted as: the change in the prey's numbers is given by its own growth minus the rate at which it is preyed upon.

Predators

The predator equation becomes

dy/dt =  - 

In this equation, {\displaystyle \displaystyle \delta xy} represents the growth of the predator population. (Note the similarity to the predation rate; however, a different constant is used as the rate at which the predator population grows is not necessarily equal to the rate at which it consumes the prey). {\displaystyle \displaystyle \gamma y} represents the loss rate of the predators due to either natural death or emigration; it leads to an exponential decay in the absence of prey.

Hence the equation expresses the change in the predator population as growth fueled by the food supply, minus natural death.


Clone of Predator-Prey Model ("Lotka'Volterra")
Insight diagram
​Physical meaning of the equations
The Lotka–Volterra model makes a number of assumptions about the environment and evolution of the predator and prey populations:

1. The prey population finds ample food at all times.
2. The food supply of the predator population depends entirely on the size of the prey population.
3. The rate of change of population is proportional to its size.
4. During the process, the environment does not change in favour of one species and genetic adaptation is inconsequential.
5. Predators have limitless appetite.
As differential equations are used, the solution is deterministic and continuous. This, in turn, implies that the generations of both the predator and prey are continually overlapping.[23]

Prey
When multiplied out, the prey equation becomes
dx/dtαx - βxy
 The prey are assumed to have an unlimited food supply, and to reproduce exponentially unless subject to predation; this exponential growth is represented in the equation above by the term αx. The rate of predation upon the prey is assumed to be proportional to the rate at which the predators and the prey meet; this is represented above by βxy. If either x or y is zero then there can be no predation.

With these two terms the equation above can be interpreted as: the change in the prey's numbers is given by its own growth minus the rate at which it is preyed upon.

Predators

The predator equation becomes

dy/dt =  - 

In this equation, {\displaystyle \displaystyle \delta xy} represents the growth of the predator population. (Note the similarity to the predation rate; however, a different constant is used as the rate at which the predator population grows is not necessarily equal to the rate at which it consumes the prey). {\displaystyle \displaystyle \gamma y} represents the loss rate of the predators due to either natural death or emigration; it leads to an exponential decay in the absence of prey.

Hence the equation expresses the change in the predator population as growth fueled by the food supply, minus natural death.


Clone of Prey&Predator
Insight diagram
This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.
Clone of Plant, Deer and Wolf Population Dynamics
Insight diagram
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

Clone of Final Midterm Student version of A More Realistic Model of Isle Royale: Predator Prey Interactions
Insight diagram
​Physical meaning of the equations
The Lotka–Volterra model makes a number of assumptions about the environment and evolution of the predator and prey populations:

1. The prey population finds ample food at all times.
2. The food supply of the predator population depends entirely on the size of the prey population.
3. The rate of change of population is proportional to its size.
4. During the process, the environment does not change in favour of one species and genetic adaptation is inconsequential.
5. Predators have limitless appetite.
As differential equations are used, the solution is deterministic and continuous. This, in turn, implies that the generations of both the predator and prey are continually overlapping.[23]

Prey
When multiplied out, the prey equation becomes
dx/dtαx - βxy
 The prey are assumed to have an unlimited food supply, and to reproduce exponentially unless subject to predation; this exponential growth is represented in the equation above by the term αx. The rate of predation upon the prey is assumed to be proportional to the rate at which the predators and the prey meet; this is represented above by βxy. If either x or y is zero then there can be no predation.

With these two terms the equation above can be interpreted as: the change in the prey's numbers is given by its own growth minus the rate at which it is preyed upon.

Predators

The predator equation becomes

dy/dt =  - 

In this equation, {\displaystyle \displaystyle \delta xy} represents the growth of the predator population. (Note the similarity to the predation rate; however, a different constant is used as the rate at which the predator population grows is not necessarily equal to the rate at which it consumes the prey). {\displaystyle \displaystyle \gamma y} represents the loss rate of the predators due to either natural death or emigration; it leads to an exponential decay in the absence of prey.

Hence the equation expresses the change in the predator population as growth fueled by the food supply, minus natural death.


Clone of Prey&Predator