bio lec 2

Population Level Ecology

Introduction to Population Level Ecology

  • Focus shifts from cellular and atomic scale to ecosystem scale.

  • Today’s focus: population level ecology.

  • Recap of previous discussion on different ecological levels.

  • Aim: to understand data collection on populations and develop predictive models for population sizes.

Data Collection by Biologists

  • Wildlife Biology Perspective: Discussion framed largely from a wildlife biology standpoint.

    • Note: Applies to all living organism populations including plants, bacteria, and fungi.

Types of Data Collection

  • Census:

    • Definition: Counting every individual in a population.

    • Characteristics:

    • Very time-consuming and labor-intensive.

    • Used mainly for small or easily observable populations (e.g., elephants).

    • Important for rare or endangered species.

  • Sampling or Estimation:

    • Definition: Sampling a subset of a population to generalize about the whole.

    • Characteristics:

    • Typically used for larger populations where a census is impractical.

    • Useful for organisms that are hard to observe or patchily distributed.

    • Less intensive, cost-effective compared to a census, employing methods like quadrat sampling.

Metrics for Understanding Populations

  • Key metrics collected by biologists include:

    • Density:

    • Definition: Number of individuals per unit area.

    • Assesses how individuals are packed in a specific space and influences interactions among them.

    • Dispersal:

    • Arrangement of individuals within a habitat.

    • Influences interaction rates among population members.

    • Demography:

    • Definition: Statistical study of population characteristics (e.g., sex ratios, age ratios, birth/death rates).

    • Dynamics:

    • Observation of how population numbers change over time.

Detailed Metrics Explanation

  • Density:

    • Individuals per area is one of the first metrics studied.

    • Capital N is used to represent total population size.

  • Dispersal Patterns:

    • Three main types:

    • Clumped Distribution:

      • Individuals clustered in groups.

      • Often due to resource distribution (e.g., near water sources for animals).

      • Social animals like herds (e.g., elephants, wolves).

    • Uniform Distribution:

      • Individuals equally spaced, often due to competition or territoriality.

      • Example: Penguins maintain distance due to territory defense.

    • Random Distribution:

      • Individuals dispersed randomly, no predictable pattern.

      • Suggests lack of strong interactions among individuals.

  • Scale and Timing:

    • Scale of observation (wide vs. narrow) can influence apparent dispersal patterns.

    • Timing matters (e.g., birds congregating during mating season).

Demography in Depth

  • Definition: Analyzing age structure, sex ratios, and vital rates that affect population dynamics.

  • Vital Rates:

    • Fundamental rates influencing population size (births and deaths).

  • Age Structure Diagrams:

    • Graphical representation dissecting population by age categories and sex.

    • Predictions based on these structures can influence social, economic, and housing projections.

Survivorship Curves

  • Graphs illustrating survival rates at different ages.

  • Types of curves:

    • Type I: Low infant mortality with high survivorship until old age (e.g., humans).

    • Type II: Constant mortality rate throughout life (e.g., some birds).

    • Type III: High early mortality but those that survive tend to live long lives (e.g., some fish).

Reproductive Strategies

  • Comparison between r-selected and K-selected species:

    • r-selected species:

    • Produce many offspring, high mortality early in life.

    • Little parental care (e.g., insects).

    • K-selected species:

    • Fewer offspring, significant parental investment.

    • Generally lower early mortality (e.g., elephants).

Population Dynamics

  • Definition: How population sizes change over time.

  • Example: Moose and wolf populations on Isle Royale (1970-2019).

  • Dynamics affected by predator-prey relationships and environmental factors.

Isle Royale Case Study

  • Historical context summarizing the ecological history of wolves and moose on Isle Royale:

    • Wolves arrived in 1948, first studied in 1958.

    • Importance for understanding predator-prey dynamics in ecological studies.

  • Factors affecting population predictions:

    • Birth rates, death rates, sustainability of prey populations.

  • Basic population model:

    • Future Population = Current Population + Births – Deaths.

    • Need to consider per capita rates for accuracy.

Mathematical Population Models

  • Transformation to per capita rates:

    • Per capita birth = Total Births / Total Population

    • Per capita death = Total Deaths / Total Population

  • Effective modeling for population growth predictions:

    • Population growth modeled as:
      Population<em>t+1=Population</em>t+B−DPopulation<em>{t+1} = Population</em>t + B - D

    • Models can adjust for factors like immigration/emigration.

  • Lambda (λ): Growth rate metric.

    • If 0 < λ < 1: population decreases.

    • If λ=1λ = 1: population stable.

    • If λ > 1: population increases.

Advanced Population Model Development

  • Expansion of models to project beyond one year:

    • Multiple time steps introduce complexity (e.g., λn∗Populationcurrentλ^n * Population_{current} for n years).

    • Discrete population growth models work for fixed intervals (not continuous).

Conclusion and Future Directions

  • Previous calculations established groundwork for understanding population dynamics.

  • Readings and preparation required for future classes to refine models and expand understanding of various types of population growth models.

  • Follow-up calculations and discussions anticipated next class regarding specific populations, continuities, and complexities of ecological models.