Marjo 2025-09-16
Lecture 7_population Ecology_2025.pdf

Estimating Population Size

Unitary Organisms

Direct Count

Not usually possible except in specific locations

Estimating population

Based on probability

Example: In Åland, students usually get 60% of the population in a transect

Mark-recapture method

!Lecture _population Ecology_2025, p.11

Quadrant

Pick a small area and count all organisms in it, then extrapolate to entire study area

Accumulation curve

• Total number of unique genotypes taken from environmental samples
  !Lecture 7_population Ecology_2025, p.13

Modular organisms

• Difficult to define what is an individual
• Must evaluate the fraction of organisms within a sample are that are detected
  • e.g. percent ground cover by this organism
  • use genetic tools to isolate individuals

Population Growth Rate

!Lecture _population Ecology_2025, p.16
Determined by:

• Births
• Deaths
• Immigration
• Emigration
  Mathematical modesls can predict future population sizes given current information:

Models

Discrete-time growth rate models

• (discrete periods where population can increase
• e.g. birds with one generation per year and synchronous reproduction
• geometric growth
• Simplified system - change in population is difference between births and deaths
• $\lambda$ is the proportional change over the unit of time. When given the exponent of t, we can predict that many units in the future
  !Lecture _population Ecology_2025, p.21

Continuous Time growth rate models (more complicated)

• Exponential growth
• Incorporates density-dependence (carrying capacity)
  • High population density influences both birth and death rates of sparrows in a study

Simplifications used in above models

• By ignoring immigration and emigration (or estimating the rate of dispersal)
  • factor this rate into the model per generation
• Assume no age structure - younger populations will increase faster
  • This can be solved by making a matrix data structure and apply different growth rates to age categories (age specific fecundity / survivorship)

Factors used in models

Density-Dependent factors

• Disease, competition, food availability

Density-independent

• Temperature, moisture, unpredictable disturbances, habitat loss

Summary

Simple models can help predict growth rates, but more complex models are needed to include other influences like dispersal, age structure and environmental influences.

Life History Adaptations

Life history traits are adaptations affecting:

Spatially Structured Populations

• A Metapopulations is a group of spatially separated populations which sometimes interact (dispersal)
• Small populations are prone to extinction, but can be recolonized by other populations
• Metapopulation (the system as a whole) persists in a dynamic equilibrium between local extinctions and recolonizations

Ilko Hansi 1999

used a model to test his mathematical theories on the butterfly populations of Åland. He found:

• Local extinctions and recolonization is influenced by:
  • Size of habitat and population density
  • Habitat connectivity
  • Stochasticity (random events)
  • Inbreeding
• Applications for conservation biology with small patches
  !Lecture _population Ecology_2025, p.50