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A collaborative class project with each participant creating an animal/plant sub-model​ to explore the greater population/community dynamics of the Yellowstone ecosystem.
YellowstoneEcoClassModel
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This model describes how costs, income and ecosystem services change with stocking rate.
Goat Management
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Food Security and Climate Change in East Africa
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This is a basic BIDE (birth, immigration, death, emigration) model.  Not all parts are implemented, however Birth and Death are.

First homework insight
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This model explains the difference between Mountain bikes riding compared to logging in the Tasmanian forests.
Logging allows the activity in the forest with a negative demand for timber providing an income (with the price variable). The deforestation variable shows us that over time, the forest will run out if the logging keeps going on this way.
Alternatively, mountain biking allows a demand of visitors who want to see the scenary. They increase the regional tourism which is good for the community as it involves other businesses around too. The charges paid by visitors and tourists allow an income for the activity which makes it productive over time and great for TAS.
As we stimulate the model, we can see that it is better to have more visitors and more tourists rather than more logging as it will be better over time.
Maylis - Simulation of Derby Mountain bikes riding versus logging
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​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.

S-Curve + Delay for Bell Curve by Guy Lakeman
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This is a basic BIDE (birth, immigration, death, emigration) model.  Not all parts are implemented, however Birth and Death are.

Clone of Bio 190: BIDE Model With Carrying Capacity
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Woodland caribou is a species at risk because of northward expansion of resource development activity.  Some herds are in dire condition and well below self-sustainability, while others are only moderately below self-sustaining levels.  Given limited conservation dollars, what are the most effective conservation actions, and how much money needs to be spent?  Which herds should be a priority for conservation efforts? The purpose of this model to provide insight into these difficult conservation questions.  

This model was developed by Rob Rempel and Jen Shuter at the Centre for Northern Forest Ecosystem Research, and was based in part on input from attendees of a modelling workshop ("Modelling the Caribou Questions") held at the 16th North American Caribou Workshop in Thunder Bay, Ontario, May 2016.
Caribou Conservation Triage-V2
3 months ago
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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.
Complex systems. Mountain bike riding versus logging in Derby, Tasmania
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Westley, F. R., O. Tjornbo, L. Schultz, P. Olsson, C. Folke, B. Crona and Ö. Bodin. 2013. A theory of transformative agency in linked social-ecological systems. Ecology and Society 18(3): 27. link

Clone of Transformative Agency in Social-Ecological System
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Systems Zoo model Z308 Forest Dynamics (Bossel, 2007)
Systems Zoo Z308: Forest dynamics
5 months ago
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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.


Bio103 Predator-Prey Model ("Lotka'Volterra")
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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 Midterm - Square Root Model
Insight diagram
Woodland caribou is a species at risk because of northward expansion of resource development activity.  Some herds are in dire condition and well below self-sustainability, while others are only moderately below self-sustaining levels.  Given limited conservation dollars, what are the most effective conservation actions, and how much money needs to be spent?  Which herds should be a priority for conservation efforts? The purpose of this model to provide insight into these difficult conservation questions.  

This model was developed by Rob Rempel and Jen Shuter, and was based in part on input from attendees of a modelling workshop ("Modelling the Caribou Questions") held at the 16th North American Caribou Workshop in Thunder Bay, Ontario, May 2016.
Clone of Caribou Conservation Triage-V2
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Overview
A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania.  Simulation borrowed from the Easter Island simulation.

How the model works.
Trees grow, we cut them down because of demand for Timber amd sell the logs.
With mountain bkie visits.  This depends on past experience and recommendations.  Past experience and recommendations depends on Scenery number of trees compared to visitor and Adventure number of trees and users.  Park capacity limits the number of users.  
Interesting insights
It seems that high logging does not deter mountain biking.  By reducing park capacity, visitor experience and numbers are improved.  A major problem is that any success with the mountain bike park leads to an explosion in visitor numbers.  Also a high price of timber is needed to balance popularity of the park. It seems also that only a narrow corridor is needed for mountain biking
Simulation of Derby Mountain biking versus logging. Version 2
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​Climate Sector Boundary Diagram By Guy Lakeman
 Climate, Weather, Ecology, Economics, Population, Welfare, Energy, Policy, CO2, Carbon Cycle, GHG (green house gasses, combined effects)

As general population is composed of 85% with an education level of a 12 grader or less (a 17 year old), a simple block of components concerning the health of the planet needs to be broken down into simple blocks.
Perhaps this picture will show the basics on which to vote for a sustained healthy future
Democracy is only as good as the ability of the voters to FULLY understand the implications of the policies on which they vote., both context and the various perspectives.   National voting of unqualified voters on specific policy issues is the sign of corrupt manipulation.

Climate Sector Boundary Diagram of Guy Lakeman
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This is a basic BIDE (birth, immigration, death, emigration) model.  Not all parts are implemented, however Birth and Death are.

Clone of Bio 190: BIDE Model With Carrying Capacity
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This model has two main components. First is modelling the change in population composition as non-First Nations immigration increases with the opening of new mines in the region. The second is modelling the increasing income disparity between First Nations and non-First Nations as mining jobs are disproportionately gained by non-First Nations workers.

Northern Ontario Demographic and Income Trend Model
Insight diagram
Woodland caribou is a species at risk because of northward expansion of resource development activity.  Some herds are in dire condition and well below self-sustainability, while others are only moderately below self-sustaining levels.  Given limited conservation dollars, what are the most effective conservation actions, and how much money needs to be spent?  Which herds should be a priority for conservation efforts? The purpose of this model to provide insight into these difficult conservation questions.  

This model was developed by Rob Rempel and Jen Shuter at the Centre for Northern Forest Ecosystem Research, and was based in part on input from attendees of a modelling workshop ("Modelling the Caribou Questions") held at the 16th North American Caribou Workshop in Thunder Bay, Ontario, May 2016.
Testing of Caribou Conservation Sub-Models v2
3 months ago
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Basic wolf population dynamics
Wolf population model
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Clone of Predator-Prey Interactions (Wolf & Moose)
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This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

Experiment with adjusting the moose birth-rate to simulate Over-shoot followed by environmental recovery
Royal Island- Resilience
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Overview

It is a model simulating logging and adventure tourism (mountain bike riding) competition in Derby, Tasmania. It is a chance for northeast Tasmania to become an exciting, new, world-class product for the mountain bike tourism industry, which drives local economic development.

Simulation borrowed from the Easter Island simulation.

How the model works

l  Trees grow; we cut them down because of demand for Timber and sell the logs.

l  The mountain bike visits depend on previous experience and suggestions.

l  Previous experience and suggestions depend on the number of trees compared to visitors and adventure number of trees and users. Park capacity limits the number of mountain bike trail users.

l  The employment opportunity depends on the mountain bike demand and demand for Timber.

Interesting Insights

Mountain biking appears to be unaffected by heavy logging. The visitor experience and numbers are improved by reducing park capacity. The main issue is that any success with the mountain bike park increases visitor numbers. A high timber price is also required to balance the park's popularity. Mountain biking appears to require only a narrow corridor; that is, single-track mountain bike trails are enough. The employment is a measure of the economic acting, a recession or growth trends.

BMA708 Marketing Insights into Big Data_Complex Systems_Mountain bike riding versus logging in Derby, Tasmania
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​Predator-prey models are the building masses of the bio-and environments as bio masses are become out of their asset masses. Species contend, advance and scatter essentially to look for assets to support their battle for their very presence. This model is designed to represent the moose and wolf population on Isle Royal. The variables include moose population, wolf population, moose birth rate, wolf birth rate, moose death proportionality constant, and wolf death proportionality constant. This model was adapted from https://insightmaker.com/insight/3A0dqQnXXh8zxWJtkwwAH9/Lotka-Volterra-Model-Prey-Predator-Simulation.

 Looking at Lotka-Volterra Model:

The well known Italian mathematician Vito Volterra proposed a differential condition model to clarify the watched increment in predator fish in the Adriatic Sea during World War I. Simultaneously in the United States, the conditions contemplated by Volterra were determined freely by Alfred Lotka (1925) to portray a theoretical synthetic response wherein the concoction fixations waver. The Lotka-Volterra model is the least complex model of predator-prey communications. It depends on direct per capita development rates, which are composed as f=b−py and g=rx−d. 

A detailed explanation of the parameters:

  • The parameter b is the development rate of species x (the prey) without communication with species y (the predators). Prey numbers are reduced by these collaborations: The per capita development rate diminishes (here directly) with expanding y, conceivably getting to be negative. 
  • The parameter p estimates the effect of predation on x˙/x. 
  • The parameter d is the death rate of species y without connection with species x. 
  • The term rx means the net rate of development of the predator population in light of the size of the prey population.

Reference:

http://www.scholarpedia.org/article/Predator-prey_model

https://insightmaker.com/insight/3A0dqQnXXh8zxWJtkwwAH9/Lotka-Volterra-Model-Prey-Predator-Simulation

Lotka-Volterra Model: Moose-Wolf Simulation