This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.
Plant, Deer and Wolf Population Dynamics
Small replicator equation setup (2d) with prisoner's dilemma payoff matrix (can be adjusted): (dx/dt)_i = x_i*((A*x)_i-x^T*A*x)
Prisoner's dilemma with replicator equation
This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.
Plant, Deer and Wolf Population Dynamics - ISD OWL
This is a demonstration of how logistic growth can be modeled with either one or two stocks. However, the two-stock case shows how the implementation of the carrying capacity is somehow less arbitrary than in the one-stock case.
Logistic Growth: One and Two Stocks
Wolf and Deer population interaction geog 166
Wolves & Deer
First order negative feedback
This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.
Out 2025 - 3. Cervo e Lobo - Presa Predador - modelo da internet
Project wildlife populations (2)
This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.
Out 2025 - 4. Plantas, Cervo e Lobo - modelo da internet
keep on from here
http://www.suryatech.com/pages/wildlifemanagement-2.pdf
Modified Lotka–Volterra model (plants, preys, predators)
Eastern oyster growth model calibrated for Long Island Sound
This is a one box model for an idealized farm with one million oysters seeded (one hectare @ a stocking density of 100 oysters per square meter)
1. Run WinShell individual growth model for one year with Long Island Sound growth drivers;
2. Determine the scope for growth (in dry tissue weight per day) for oysters centered on the five weight classes)
3. Apply a classic population dynamics equation:
dn(s,t)/dt = -d[n(s,t)g(s,t)]/ds - u(s)n(s,t)
s: Weight (g)
t: Time
n: Number of individuals of weight s
g: Scope for growth (g day-1)
u: Mortality rate (day-1)
4. Set mortality at 30% per year, slider allows scenarios from 30% to 80% per year
5. Determine harvestable biomass, i.e. weight class 5, 40-50 g (roughly three inches length)
Eastern oyster population model Long Island Sound
with the carrying capacity instead of mortality rate.
K = Carrying capacity (g m-2)
Project wildlife populations (5)
Project wildlife populations (4)
Project wildlife populations
Eastern oyster growth model calibrated for Great Bay.
Developed and implemented by Joao G. Ferreira and Camille Saurel; growth data, driver data, and culture practice from Phil Trowbridge, Ray Grizzle, and Suzanne Bricker.
This is a one box model for an idealized farm with one million oysters seeded (one hectare @ a stocking density of 100 oysters per square meter)
1. Run WinShell individual growth model for one year with Great Bay growth drivers;
2. Determine the scope for growth (in dry tissue weight per day) for oysters centered on the five weight classes)
3. Apply a classic population dynamics equation:
dn(s,t)/dt = -d[n(s,t)g(s,t)]/ds - u(s)n(s,t)
s: Weight (g)
t: Time
n: Number of individuals of weight s
g: Scope for growth (g day-1)
u: Mortality rate (day-1)
4. Set mortality at 30% per year, slider allows scenarios from 30% to 80% per year
5. Determine harvestable biomass, i.e. weight class 5, 40-50 g (roughly three inches length)
REServ Eastern oyster Great Bay
This is a one box model for an idealized farm with one million oysters seeded (one hectare @ a stocking density of 100 oysters per square meter)
1. Run WinShell individual growth model for one year with Long Island Sound growth drivers;
2. Determine the scope for growth (in dry tissue weight per day) for oysters centered on the five weight classes)
3. Apply a classic population dynamics equation:
dn(s,t)/dt = -d[n(s,t)g(s,t)]/ds - u(s)n(s,t)
s: Weight (g)
t: Time
n: Number of individuals of weight s
g: Scope for growth (g day-1)
u: Mortality rate (day-1)
4. Set mortality at 30% per year, slider allows scenarios from 30% to 80% per year
5. Determine harvestable biomass, i.e. weight class 5, 40-50 g (roughly three inches length)
IDREEM example oyster population model
This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.
CURSO MAR 25 - Grama, Cervo e Lobo - Presa Predador - modelo da internet
Eastern oyster growth model calibrated for Long Island Sound
This is a one box model for an idealized farm with one million oysters seeded (one hectare @ a stocking density of 100 oysters per square meter)
1. Run WinShell individual growth model for one year with Long Island Sound growth drivers;
2. Determine the scope for growth (in dry tissue weight per day) for oysters centered on the five weight classes)
3. Apply a classic population dynamics equation:
dn(s,t)/dt = -d[n(s,t)g(s,t)]/ds - u(s)n(s,t)
s: Weight (g)
t: Time
n: Number of individuals of weight s
g: Scope for growth (g day-1)
u: Mortality rate (day-1)
4. Set mortality at 30% per year, slider allows scenarios from 30% to 80% per year
5. Determine harvestable biomass, i.e. weight class 5, 40-50 g (roughly three inches length)
Clone of Eastern oyster population model Long Island Sound
This simulation shows how algae, tadpole and dragonfly populations impact each other in a pond ecosystem.
Algae, Tadpole and Dragonfly Population Dynamics
This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.
Plant, Deer and Wolf Population Dynamics G-IV Intro
Small replicator equation setup (2d) with prisoner's dilemma payoff matrix (can be adjusted): (dx/dt)_i = x_i*((A*x)_i-x^T*A*x)
Clone of Prisoner's dilemma with replicator equation
Matching pennies with replicator equation