Simple dynamic model of species gain and loss from individual trees as patches in the landscape, including removal of surrounding trees and changes in climatic stressors.
Simple dynamic model of species gain and loss from individual trees as patches in the landscape, including removal of surrounding trees and changes in climatic stressors.
There is a concern that Logging has an adverse effect on the experience of tourist mountain bikers looking for nature experiences in Derby, Tasmaina.    This model helps give more insight on the relationship between the forest industry and mountain tourism, showing that despite the changes and incre
There is a concern that Logging has an adverse effect on the experience of tourist mountain bikers looking for nature experiences in Derby, Tasmaina.

This model helps give more insight on the relationship between the forest industry and mountain tourism, showing that despite the changes and increase in logging activities with the aim of generating more income from timber, there can be a balance between mountain tourism and the forest industry.
 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
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. 

 This is a basic BIDE (birth, immigration, death, emigration) model.  Not all parts are implemented, however Birth and Death are.

This is a basic BIDE (birth, immigration, death, emigration) model.  Not all parts are implemented, however Birth and Death are.

 Overview:   The model shows the industry competition and relationship between Forrestry and Mountain Bike Trip in Derby, Tasmania. The aim of the simulation is to find a balance between the co-existence of these two industry.      How Does the Model Work?       Both industries will generate incomes
Overview: 
The model shows the industry competition and relationship between Forrestry and Mountain Bike Trip in Derby, Tasmania. The aim of the simulation is to find a balance between the co-existence of these two industry.

How Does the Model Work?

Both industries will generate incomes. Firstly, income is generated from the sale of timber through logging. In addition, income is also generated from the consumption of mountain bike riders. Regarding to the Forrestry industry, people cut down trees because there is a market demand for timber. The timber is sold for profits. However, the experience of mountain biking tourism is largely affected by the low regeneration rate of trees and the degradation of the environment and landscape due to tree felling. People have better riding experiences when trees are abundant and the scenery is beautiful. People's satisfaction and expectations depend on the scenery and experience. Recommendations of past riders will also impact the tourists amount.

Interesting Insights

The income generated by logging can provide a significant economic contribution to Tasmania, but excessive logging can lead to environmental problems and a reduction in visitors. Excessive logging can lead to a decline in tourism in the mountains, which will affect tourism. Despite the importance of forestry, tourism can also provide a significant economic contribution to Tasmania. The government should find a balance between the two industries while maintaining the number of tourists. 



A collaborative class project with each participant creating an animal/plant sub-model​ to explore the greater population/community dynamics of the Yellowstone ecosystem.
A collaborative class project with each participant creating an animal/plant sub-model​ to explore the greater population/community dynamics of the Yellowstone ecosystem.
This non-dimensionalized, sleekest most neatest model illustrates predator prey interactions using logistic growth for the moose population, for the wolf and moose populations on Isle Royale.   Thanks Scott Fortmann-Roe for the original model.  I've added in an adjustment to handle population sizes,
This non-dimensionalized, sleekest most neatest model illustrates predator prey interactions using logistic growth for the moose population, for the wolf and moose populations on Isle Royale.

Thanks Scott Fortmann-Roe for the original model.

I've added in an adjustment to handle population sizes, by dividing by moose carrying capacity.

Time is scaled by the moose birth parameter:
tau=bm*t

There are therefore only three parameters left to account for any dynamics:

beta = bw/bm (relative wolf to moose births)
delta = dm/bm (relative death to birth ratio for moose)
gamma = dw/bm (wolf deaths to moose births)

The equations are thus

dM/dtau = M [ (1-M) - delta W ]
dW/dtau = W [beta M - gamma ]

There is a stable equilibrium pair of population values, relative to the carrying capacity:

M^* = gamma / beta
W^* = (1-gamma / beta) / delta

I have a sleek version with a logistical growth term for the moose, at

http://www.nku.edu/~longa/classes/2018spring/mat375/mathematica/Moose-n-Wolf-InsightMaker-sleek.nb
A simple and easy to follow model of how fertility and mortality affect a population, using ferns as an example.
A simple and easy to follow model of how fertility and mortality affect a population, using ferns as an example.
Modeling forest succession in a northeast deciduous forest.
Modeling forest succession in a northeast deciduous forest.
 This is a basic model for use with our lab section.  The full BIDE options.

This is a basic model for use with our lab section.  The full BIDE options.

 Allison Zembrodt's Model    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 f
Allison Zembrodt's Model

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

equations I used in kill rate :

power model - 12*0.1251361120909615*([Moose]/[Wolves])^.44491970277839954*[Wolves]


Kill rate sqrt = 12*(0.0933207+.0873463*([Moose]/[Wolves])^.5)*[Wolves]


Holling Type III - ((0.986198*([Moose]/[Wolves])^2)/ (601.468 +([Moose]/[Wolves])^2))*[Wolves]*12


linear - 12*[Wolves]*(.400271+.00560299([Moose]/[Wolves]))


 This model is to be used with Mr. Roderick's AP biology activity on population growth. See steveroderick.net for a copy of the activity worksheet.        Use the sliders below to quickly change the initial values of components of the model.
This model is to be used with Mr. Roderick's AP biology activity on population growth. See steveroderick.net for a copy of the activity worksheet.

Use the sliders below to quickly change the initial values of components of the model.
    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 int

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.


This model describes how costs, income and ecosystem services change with stocking rate.
This model describes how costs, income and ecosystem services change with stocking rate.
  ​S-Curve + Delay for Bell Curve Showing Erlang Distribution      Generation of Bell Curve from Initial Market through Delay in Pickup of Customers     This provides the beginning of an Erlang distribution model      The  Erlang distribution  is a two parameter family of continuous  probability dis
​S-Curve + Delay for Bell Curve Showing Erlang Distribution

Generation of Bell Curve from Initial Market through Delay in Pickup of Customers

This provides the beginning of an Erlang distribution model

The Erlang distribution is a two parameter family of continuous probability distributions with support . The two parameters are:

  • a positive integer 'shape' 
  • a positive real 'rate' ; sometimes the scale , the inverse of the rate is used.

 The body of research and studies generated on the Fryingpan River between the 1940s and the present supports the development of a conceptual model of ecosystem responses to hydrological regime behavior and streamflow management activities. This conceptual model should encourage conversations about

The body of research and studies generated on the Fryingpan River between the 1940s and the present supports the development of a conceptual model of ecosystem responses to hydrological regime behavior and streamflow management activities. This conceptual model should encourage conversations about system behavior and collective understanding among stakeholders regarding connections between specific hydrological regime characteristics affected by management of Ruedi Reservoir and the ecological or biological variables important to local communities. For the sake of simplicity, the model includes mostly unidirectional relationships—feedback loops are exploded to reveal intermediate connections between variables. This approach increases the number of variables represented in the system, perhaps increasing its complexity at first glance. However, the primary benefit to the end user is that the model becomes more readable and explicit in its representation of system behavior. 

 

The conceptual model presented here likely differs by degrees from those held by the various investigators who considered Fryingpan River processes over the previous 80 years. However, it affectively aggregates the ideas main presented by each of those individuals. This model focuses on hydrological and biological variables and does not incorporate the entire diversity of human uses and needs for water from the Fryingpan River (e.g. hydropower production for the City of Aspen, revenue generated in the Town of Basalt by angling activities, etc.).  Rather it attempts to illustrate how the conditional state of important ecosystem characteristics might respond to reservoir management activities that impact typical spring flows, peak flow timing and magnitude, summer flows, fall flows, and winter flows.