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)

Simple model illustrating the 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)

This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.

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 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)

Simple model illustrating the 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)

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)

This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.

This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.

This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.

This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.

Simple model illustrating the 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)

Simple model illustrating the 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)

This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.

This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.

This simulation shows how algae, tadpole and dragonfly populations impact each other in a pond ecosystem.

This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.

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)

This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.

Eastern oyster growth model calibrated for Long Island Sound
Developed and implemented by Joao G. Ferreira and Camille Saurel; growth data from Eva Galimany, Gary Wickfors, and Julie Rose; driver data from Julie Rose and Suzanne Bricker; Culture practice from the REServ team and Tessa Getchis. This model is a workbench for combining ecological and economic components for REServ. Economic component added by Trina Wellman.

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)

This simulation shows how plant, deer and wolf populations impact each other in a deciduous forest ecosystem.

Ce modèle simule la dynamique de deux populations en interaction : une population de proies (X) et une population de prédateurs (Y). Il est inspiré des travaux fondateurs de Lotka et Volterra et permet de comprendre l'origine des cycles de population.

Ce modèle s'inscrit dans la suite de notre cours sur la dynamique des populations. Après avoir étudié la dynamique d'une seule population (exponentielle, logistique), ce modèle introduit la dynamique des communautés en couplant le destin de deux espèces.

Contrairement aux modèles précédents centrés uniquement sur le nombre d’individus (N), ce modèle explore comment les interactions trophiques (le fait de "manger" et d'"être mangé") créent des comportements émergents complexes, tels que les oscillations décalées et la stabilité du système.

Chaque population n'est pas isolée ; son taux de croissance ou de déclin dépend directement de l'abondance de l'autre.

Les Composants du Modèle :

Variables d’état (Stocks) :

• X (Proie) : Abondance de la population de proies.

• Y (Prédateur) : Abondance de la population de prédateurs.

Flux (représentant dX/dt et dY/dt) :

• Prey Births : Taux de croissance intrinsèque de la proie (rX).

• Prey Deaths : Mortalité de la proie, due à l'auto-limitation (bX2), à la prédation (cXY) et à la chasse (HX).

• Predator Births : Croissance du prédateur, qui dépend de sa capacité à convertir les proies mangées en nouveaux prédateurs (c′XY).

• Predator Deaths : Mortalité du prédateur, due à sa mort naturelle (mY) et à la chasse (HY).

Paramètres modifiables (Curseurs) :

• X (Proie) : Abondance initiale des proies.

  • Valeur initiale : 50

• Y (Prédateur) : Abondance initiale des prédateurs.

  • Valeur initiale : 15

• r (Taux de croissance des proies) : Taux de reproduction intrinsèque des proies.

  • Valeur initiale : 0.5

• b (Auto-limitation des proies) : Force de la compétition intraspécifique (l'effet logistique K).

  • Valeur initiale : 0

• m (Mortalité des prédateurs) : Taux de mortalité naturel (intrinsèque) des prédateurs.

  • Valeur initiale : 0.3

• c (Taux de prédation) : Efficacité de la chasse du prédateur sur la proie.

  • Valeur initiale : 0.02

• c_prime (Efficacité de conversion) : Capacité du prédateur à convertir une proie mangée.

  • Valeur initiale : 0.01

• H (Effort de Chasse) : Taux de mortalité externe (chasse, pêche) s'appliquant aux deux espèces.

  • Valeur initiale : 0

Indicateurs produits :

• Graphique temporel : Montre les oscillations et le décalage caractéristique entre le pic des proies et celui des prédateurs.

• Diagramme de phase : Montre la trajectoire du système (cercle, spirale) et révèle sa stabilité (neutre ou amortie).

• Abondance moyenne : Le niveau d'équilibre autour duquel les populations oscillent.

Votre Mission d'Exploration :

Votre objectif est de vous mettre dans la peau d'un écologue théoricien pour tester les fondements du modèle Lotka-Volterra et résoudre l'énigme de D'Ancona.

1. Validez les briques de base : Isolez les populations (Mission 1) pour vérifier la croissance logistique et le déclin exponentiel.

2. Recréez le "pendule" : Simulez le modèle original de Volterra (b=0) et explorez la stabilité neutre (Mission 2).

3. Testez la stabilité moderne : Ajoutez de l'auto-limitation (b>0) et observez la convergence vers un équilibre stable (Mission 3).

4. Explorez la physiologie : Testez l'effet d'un prédateur au "métabolisme lent" (Mission 4).

5. Résolvez l'énigme : Utilisez le modèle (b=0) et le curseur H (Chasse) pour recréer le "Paradoxe de la Chasse" (Mission 5).

Cliquez sur "SIMULATE" et explorez la dynamique fondamentale qui régit les interactions prédateurs-proies !