Concept 53.5 Notes: Density-Dependent Regulation of Populations

Density-Dependent vs Density-Independent Factors

  • Density-independent factors: birth and/or death rates do not change with population density; variation is driven by external abiotic factors (e.g., temperature, precipitation).
  • Density-dependent factors: birth and/or death rates change as population density changes; higher density often reduces per-capita birth or increases per-capita death.
  • Per-capita rates: let b(N)b(N) be the per-capita birth rate and d(N)d(N) the per-capita death rate. Density dependence means db/dNdb/dN, dd/dNdd/dN may be nonzero.
  • Population growth is governed by the difference between births and deaths. With immigration/emigration considered (often assumed zero or equal), population grows if b(N)>d(N)b(N) > d(N) and declines if d(N)>b(N)d(N) > b(N).
  • Equilibrium density QQ occurs when b(Q)=d(Q)b(Q) = d(Q); at this point per-capita growth is zero and the population can stay stable if other fluxes (immigration/emigration) balance as well.
  • Model insight: total births B=b(N)imesNB = b(N) imes N and total deaths D=d(N)imesND = d(N) imes N; at equilibrium B=DB = D.
  • Density-independent shocks can cause large, abrupt changes in abundance (e.g., drought causing high mortality), but cannot consistently push a large population down or a small population up over time.
  • Regulation vs stability: a population is regulated when density-dependent factors cause declines at high density or increases at low density, providing negative feedback.

Birth, Death, and Equilibrium in Population Growth

  • When density is low: b(N)>d(N)b(N) > d(N) → population grows.
  • When density is high: d(N)>b(N)d(N) > b(N) → population declines.
  • Equations to formalize: per-capita growth rate r(N)=b(N)−d(N)r(N) = b(N) - d(N); population growth if r(N)>0r(N) > 0, decline if r(N)<0r(N) < 0; equilibrium at r(N)=0r(N)=0.
  • Simple density-dependent model (birth rate changes with density; death rate constant): at equilibrium density QQ, b(Q)=db(Q) = d; below QQ, births exceed deaths; above QQ, deaths exceed births.

Mechanisms of Density-Dependent Regulation

  • Competition for resources: higher density reduces reproductive rates (e.g., more crowding means fewer nutrients, water).
    • Practical note: farmers use fertilizers to mitigate resource limitation and maintain yields.
  • Disease: transmission rate often increases with crowding, raising mortality and/or reducing birth.
  • Predation: predation pressure can rise with prey density, reducing survival.
  • Territoriality: space becomes limiting, preventing further increases in density.
  • Intrinsic factors: physiological and hormonal changes at high density can suppress reproduction.
  • Toxic wastes: accumulation of toxins (e.g., ethanol in yeast) can limit growth and survival at high density.
  • These mechanisms exemplify negative feedback that slows growth as density rises.

Population Dynamics: Stability, Fluctuation, and Cycles

  • Population sizes fluctuate year to year due to multiple factors (biotic and abiotic).
  • Stability vs fluctuation: long-term studies show large-mammal populations can be variable (e.g., Isle Royale moose and wolves).
  • Population cycles vary by taxa:
    • Small herbivores (voles, lemmings): ~3–4 year cycles.
    • Some birds (ruffed grouse, ptarmigans): ~9–11 year cycles.
    • Snowshoe hare–lynx: ~10-year cycles in northern forests.
  • Cycles often arise from predator–prey dynamics and resource availability, with time-lagged responses.

Predator–Prey Cycles: Experimental Evidence

  • Hare cycles: two main hypotheses—winter food shortage vs predator–prey interactions.
  • Experimental test ( Yukon, 20 years ): extra winter food increased density but did not stop cycles; cycles persisted, suggesting food shortage alone does not drive cycles.
  • Predator exclusion (electric fences): reducing predators nearly eliminated the collapse phase; predation appears essential to hare cycles.

Immigration, Emigration, and Metapopulations

  • Immigration and emigration affect population dynamics, especially when many local populations are linked in a metapopulation.
  • Metapopulation: discrete habitat patches vary in size, quality, and isolation; local extinctions can be recolonized by immigrants.
  • Persistence arises from a balance of local extinctions and recolonizations.

Case Study: Glanville Fritillary Metapopulation and Dispersal Genetics

  • Species: Glanville fritillary butterfly (Melitaea cinxia) across Åland Islands (~500 occupied patches, ~4,000 suitable patches).
  • Key concept: movement between local populations allows recolonization and persistence of the metapopulation.
  • Dispersal is influenced by genotype at the Pgi gene (phosphoglucoisomerase), which affects glycolysis and dispersal ability.
  • Findings: heterozygous individuals disperse farther than homozygotes, especially in cooler conditions in the morning, implying a fitness advantage for heterozygotes in movement and colonization.

Summary Takeaways

  • Population size is shaped by density-dependent (regulated) and density-independent factors.
  • Equilibrium density QQ occurs when b(Q)=d(Q)b(Q) = d(Q); regulation requires negative feedback that slows growth as density increases.
  • Mechanisms of density dependence include competition, disease, predation, territoriality, intrinsic physiology, and toxins.
  • Population dynamics show cycles and fluctuations driven by complex interactions; experimental evidence highlights the role of predators in generating cycles.
  • Metapopulations emphasize immigration/emigration and patch dynamics; dispersal genetics (e.g., Pgi) can influence connectivity and persistence of the network of populations.

Key Concepts to Remember

  • Density-dependent vs density-independent factors
  • Equilibrium density QQ and conditions b(Q)=d(Q)b(Q) = d(Q)
  • Mechanisms of regulation and negative feedback
  • Population cycles and predator–prey dynamics
  • Metapopulations and the role of dispersal genetics in connectivity