comparison of metapopulation and logistic population model (1).pptx

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1. Comparison of metapopulation and logistic population model Population models help ecologists understand how populations grow, decline, and interact with their environment.
Two important models are metapopulation models and the logistic population model, each describing different aspects of population dynamics.

2. Metapopulation model A metapopulation is a group of spatially separated subpopulations of the same species that interact through migration.
The concept, first introduced by Richard Levin in 1969, helps ecologists understand how species persist in fragmented landscapes
In a metapopulation:
Some subpopulations may go extinct over time.
Empty habitat patches can be recolonized by migrants.
The overall species can persist even if individual patches experience local extinctions

3. Features 1. Habitat Fragmentation: The population is spread across different patches of suitable habitat.
2. Local Extinction & Recolonization: Some subpopulations go extinct, but others are established through migration.
3. Migration Between Patches: Individuals move between patches, ensuring gene flow and recolonization.
4. Patch Occupancy Dynamics: The proportion of occupied patches changes over time based on extinction and colonization rates

4. Equation dp / dt = c(1-p)-ep P patch occupancy
c colonization rate
e extinction rate

5. Example Coral-reef Fishes: Marine and estuarine species, like coral-reef fishes, often exhibit metapopulation dynamics due to their large population sizes, high dispersal capacity during larval stages, and extensive distributions Butterflies: Populations of the checkerspot butterfly ( Euphydryas editha ) in California demonstrate metapopulation structure, with small satellite populations relying on a larger source population for new recruits

6. Applications Conservation biology (e.g., protecting species in fragmented landscapes)
Managing species in habitats affected by human activity (e.g., deforestation, urbanization)
Studying species that naturally occur in patches (e.g., amphibians in ponds, butterflies in meadows)

7. Logistic population model The logistic population model describes how a population grows in an environment with limited resources. Unlike exponential growth, where populations grow indefinitely, the logistic model accounts for competition, resource limitations, and environmental constraints. Rapid growth at first when resources are abundant.
Slower growth as population size increases due to competition.
Stabilization at the carrying capacity (K), where birth and death rates balance.

8. Carrying capacity is the maximum population that an area can support without degrading the environment

9. 1. Lag Phase: Population is small.
Growth is slow due to few reproducing individuals. 2. Exponential Growth Phase: Population grows rapidly.
Plenty of resources and little competition. 3. Deceleration Phase: Growth slows as resources become limited.
Competition for food, space, and mates increases.

10. 4. Stable Phase (Carrying Capacity):
Population size stabilizes at .
Birth rates equal death rates.

11. Equation dN / dt = rN (1-N/K) N = Population size
r = Intrinsic growth rate (birth rate minus death rate)
K = Carrying capacity (maximum population the environment can support) dN / dt = Change in population size over time

12. Example 1. Bacteria in a Petri Dish: At first, bacteria multiply rapidly (exponential growth).
As nutrients run out, growth slows and stops. 2. Fish in a Lake: Overfishing can reduce , leading to population crashes.
Proper management ensures stable fish populations.

13. Applications 1. Wildlife Conservation
Helps manage populations of endangered species.
Predicts sustainable hunting and fishing limits.
2. Human Population Growth
Some scientists suggest human population growth may follow logistic patterns due to resource limitations.

14. 3. Epidemiology
Models the spread of diseases, where infection rates slow down as more people gain immunity.
4. Agriculture and Fisheries
Determines sustainable farming and fishing practices to avoid overexploitation.