Final Review

Population and Community Ecology Final Review

Dr. Hayes, KSC 

The final exam is cumulative and will include topics covered in the first semester. To review these please visit the Midterm Review.

Metapopulations and allee effects

  • Population growth trajectory for density independent growth is an exponential growth curve. dN/dt=rN
  • Population growth trajectory for negative density dependent growth. Logistic, S-shaped curve. dN/dt=rN(1-N/K)
  • Relationship of Per capita population growth and N:
  • Logistic models show population regulation.
  • Positive density dependence: when small populations show a lower per capita growth rates than bigger populations do. This is not a straight line-right skewed graph, the smaller population has an increasing per capita growth until it reaches a larger size when the negative density dependence takes over.
  • Allee Effect– due to positive density dependence.
    • Mechanisms:
      • Problems finding a mate at low densities.
      • Group formation-foraging success, detection, avoidance, saturation of predators.

Smaller populations at a greater risk for extinction than larger ones

  • Environmental changes can decrease population growth rates
    • Habitat loss, hunting, invasive species
  • Due to Allee Effect (smaller dN/dt)
  • More sensitive to randomness
    • Environmental stochasticity-good years and bad years
    • Demographic stochasticity- randomness that occurs when rates are applied to whole numbers. Ie. Average number of children an individual has, do not expect same outcome in each case.
  • Genetic reasons-inbreeding, inbreeding depression (reduction of fitness due to deleterious recessive traits that are passes to these offspring that have low fitness-lower birthrates and higher death rates occur), loss of genetic diversity.
  • Genetic rescue- introduce new individuals to increase genetic diversity.

Conservation of Genetic Diversity

  • Genetic diversity within individuals (heterozygosity) the proportion of gene loci in an individual that contains alternative forms of alleles.
  • Genetic diversity among individuals within a population
  • Smaller populations are more likely to become extinct-allele affects are more deleterious
  • Low genetic diversity-all are affected by the same abiotic/biotic factors, cannot respond to change
  • Florida Panthers-last surviving subspecies of puma found in southern North America. Only two populations: one in the everglades and the other is outside of Miami. Down to 25 animals total in 1990’s. Due to fitness consequences of inbreeding caused by very low populations (high parasites, low sperm counts, bobbed tails)
    • Four females from the texas subspecies were brought in which brought the population size from -30% yearly to +20%. Survivors were the pure hybrids who had the highest fitness. Florida offspring had lowest fitness. The backcrosses (hybrid+Miami/ hybrid+everglades) had an intermediate fitness.
    • Bring in new alleles to a population is called a “genetic rescue” to help stop the extinction vortex.

Interspecies Interactions

  • How do they affect each other?
  • What is the direction of the effect?
  • Competition: (-/-) negative effect on each individual on the other because they share a limiting resource.
  • Predation: (-/+) one individual (the predator) captures and kills another individual (prey); each predator kills multiple prey in their lifetime.
    • Exploitative interactions (predators, grazing, parasites (do not always kill host), pathogens(microorganisms/fungi), parasitoids (lay eggs on host, always kill host)).
    • Hairworm Life cycle- cricket drinks water that contains larva, larva grows inside cricket/eats it, when ready to emerge it influences behavior of host to make it jump into water because it makes it really thirsty through chemical manipulations. The drowned cricket will harbor the eggs of the next generation of parasites and infect the water supply.
  • Parasites influencing host behavior:
    • Cold or flu: symptoms of flu
      • Fever-indicates illness
      • Sneeze/cough- spreads illness
    • Rabies: increases aggressive behaviors to increase transmission through biting
    • Toxoplasmosis: due to protozoan parasite. Toxoplasma gondii infects warm blooded animals; primary host – cats
      • Mice that are infected behave differently. Will head toward smell of cat in a T-Maze.
  • Commensalism: (+/0) one species gains something, the other species is not affected
  • Dispersal mechanism
    • Plants
    • Barnacles on whale
    • Artic fox follows the polar bear
  • Mutualism: (+/+) both species have a net positive benefit from the interaction.
    • Flowers must produce excess pollen so that bees come and make honey, pollen is also spread.
    • Goby and alpheid shrimp

“blind goby and his shrimp” by Chris Thompson (CC reuse allowed) 

  • A positive effect that one individual has on another is called Facilitation
  • Interaction Web: diagram that shows the direction (sometimes strength) of interactions between pairs of species- includes consumption (predator/prey), competition, and facilitation.
  • The world is green because the predators keep the herbivores in check- “Green World Hypothesis”. Predators regulate herbivore populations-prevents them from eating all the plants
  • Paine: what happens if you remove the predator starfish? Within a year and a half- the number of species on the rocks decrease, mussels (the starfish prey) overtook the area. A single species of starfish controlled the composition of the area. “Keystone Species and Trophic Cascades”. These species have a huge impact on the ecosystems while other species do not……Not all species have the same impact on the food web. Remove the predator- the system simplifies itself.
    • Another experiment: sea urchins eat all the kelp. Why is nothing controlling the urchin populations? Loss of otters due to the fur trade caused urchins to take over. Sea otters control urchin populations, without otters the urchins will eat all of the kelp. Sea otters are keystone species-regulate the costal systems.
    • Otter populations decline due to orcas starting to eat otters. Whales that orcas normally eat were on the decline due to hunting. Otter populations in areas where orcas had no access remained the same.
  • Removal of predators has broad effects on the variation and number of species within a population.
  • Keystone Species: A species that has a disproportionate effect on the species within the community. Are often predators, less abundant within the community (rare).
  • Trophic Cascade: predator has a negative effect on herbivore which has a negative effect on the autotroph. The predator has a positive effect on the autotroph. The presence of the predator creates a diverse community.
  • Bottom up process: resources regulate abundance of a species
  • Top Down process: predators regulate their prey abundance and have positive effects on the prey of their prey. Reintroduction of wolves to Yellowstone.
  • Apparent Competition: a predator or pathogen that affects two different prey species, the two-prey species have a negative indirect interaction on each other. If one species increases in abundance, predation for the other decreases. They have an inverse relationship.
  • Coevolution: obligate and host-specific for parasites and hosts
  • Evolution of reduced virulence: rabbit populations in Australia- biological control via the introduction of a pathogen. In 1950 a virus was released and killed 99.8% of the rabbits. 600 million rabbits declined to 200 rabbits within two years. The rabbits became resistant to the lab strain and the virus became less virulent over time.

The Paradox of the Plankton

  • Refers to a paper by G. E Hutchinson (1961)
  • Points out the clash between observed diversity of photosynthetic plankton and Gause’s Principle of Competitive exclusion. How can so many species filling the same niche survive together?
  • To resolve the paradox: how do you explain the maintenance of diversity?
    • Isoclines are arranged for stable coexistence

Maintenance of species Diversity:

  • Intraspecific competition > interspecific competition (Connell 1978 Science)
  • Gradual change in the environment. The competitive rank varies with changing environmental conditions. Fluctuating pattern of who is the dominant competitor.
    • Species must not decline to extinction before the environment changes to favor it again.
  • Circular Networks: competitive hierarchy. A > B > C > A. A nontransitive network is independent from environmental conditions.
    • Similar to frequency dependent selection
  • Compensatory mortality on most dominant species. The mortality in competitive dominant Is greatest because it is more abundant.
    • Frequency dependent
    • Source of mortality….
      • Pathogen/host, predator/prey, disturbances
  • Predation:
    • Keeps population level of the dominant competitor below that which would cause competitive exclusion.
    • Creates patchiness in the environment- opens up spaces and resource that reduces the strength of competition.
    • Proportional predation: generalist predator that eats prey as they are encountered. The most abundant species/competitive dominant will be encountered more often and eaten.
    • “Switching” behaviors: predator prefers the prey species that is most abundant. The most common species eaten disproportionately more than uncommon ones. Rare species will become more abundant than the other and the predator switches to eating that species.
  • Keystone Predators: predator prefers the competitive dominant. By consuming the dominant competitor, biodiversity is maintained.
  • Janzen-Connell Hypothesis: to explain the diversity in tropical forests.
    • Seed dispersal patterns- interspecific interactions
      • Pathogens and parasites on seed. Specialization of parasite/pathogen will spread from the parent tree. So that seed mortality occurs most often to those seeds that are closest to the parent tree. Patchwork of differential mortality and species diversity.
    • Intermediate Disturbance Hypothesis: proposed to explain the maintenance of species diversity
      • Predicts that species diversity will be greatest at intermediate levels of disturbance (Intensity/frequency of disturbance)
        • The competitive dominants will survive when there are low disturbances
        • The most resistant will survive at high disturbances
      • Disturbances: abiotic factor that causes death/decreased fitness/population size of some individuals but not necessarily all.

Metapopulation Dynamics:

Assumptions of exponential and logistics population growth models:

  1. Populations are closed, no immigration/emigration
  2. All individuals contribute equally to the population growth, K (carrying capacity).
    1. Age, size, sex are not considered
  3. Effect of adding or losing an individual has an instantaneous effect on dn/dt
    1. Delayed density dependence with a time lag-new dynamics such as damped oscillations, chaos

How are populations connected to each other?

  1. Species occur in nature as networks of populations whose temporal and spatial dynamics are interconnected by dispersing individuals
  2. “metapopulation coined by Levins (1969)
    1. Meta: analysis of several analysis’s, a population of populations, etc.
      1. Scale out
    2. Many populations or suitable habitat patches are connected by dispersal across intervening matric of unsuitable habitat.

Metapopulation: a group of populations linked together by immigrations and emigrations and dispersal, able to persist despite local extinctions because of frequent recolonization events.

Model metapopulation dynamics:

  • P= fraction of occupied patches
  • Change in % occupied = births – deaths (pf occupied patches)
  • dP/dt = c(P)(1-P) – eP
  • c = “per patch” colonization rate à
    • c(P)(1-P) is representative of birth rate
      • c(P)= occupied patches
      • (1-P) = unoccupied patches
    • e = “per patch” extinction rate
  • When set equal to ZERO….find the equilibrium
    • P = 1- e/c
  • Metapopulation will persist if e/c is less than one ( extinction rate is less than colonization rate) (e<c )
  • What affects c and e?   (will be on the final exam)
    • Matrix habitat: the group of habitat patches within an unsuitable environment.
    • Distance between patches
    • Size of patches
    • Less hospitable matrix decreases C
    • Increase the distance between patches decreases C d
    • Make the patches smaller will increase e, decrease c
    • Destroy patches will increase e
  • Patches that are sinks will only thrive if there are sources from which migrants come from.
  • Patch area determines population size due to K.
    • The bigger patches have larger populations, have larger colonization rates.
    • Small patches are more likely to go extinct, due to small population size, inbreeding, etc.
  • Which patches are occupied changes on an almost yearly basis.

Demography and Life Tables

  • Age structure: relative proportion of different age classes within a population
    • Size structure, stage structure
  • Population pyramid: separate sexes left/right, youngest are on bottom of pyramid.
  • Shape of pyramid describes the metapopulation
    • Pointy means rapid growth
    • Mound is stable growth
    • Smaller base indicates shrinking populations (bulbous)

Life tables:

  • X= age
  • Nx= number of individuals of age X
  • Fx= per capita fecundity of age X (average births per female age X)
  • Survivorship and survival rate
  • X=0 is the 1st age class (from birth up to the first age class)
  • X=3 is the 4th age class

****common to typically only use females since they are the ones that give births***

Cohort Life Table:

  • Cohort= group of individuals experiencing something together
  • Derived from a single group over time from birth. Monitor growth, survival, death as the group ages

Static Life Table:

  • Longer living organisms
  • Counts of individuals of different age classes at a single time step.
  • Used to infer past survivorship


  • Survivorship: (lx) the probability of surviving from birth to the beginning of age class X
    • Lx=Nx/N0
  • Survival Rate: (Sx) the proportion of individuals of age X that survive to the beginning of the next age class, (X+1).
    • Sx= Nx+1/N
  • Different species have different mortality curves.
    • Type 1: most die late in life (humans)
    • Type 2: die at uniform rate-likelihood of dying is constant
    • Type 3: most die at young age (sea turtles)
  • Fecundity (Fx) is the average number of offspring produced by a female while she is in a certain age class (per capita rate)


  • 1000 female fish as newborns are marked (cohort approach-following these organisms throughout life)
  • One week only 200 alive
  • At two weeks 40 alive
  • At 3 weeks 8 alive
  • At 4 weeks none were alive
  • Each female fish reproduces 200 eggs in their 4th week


X (week) Nx lx (Nx/N0) Sx (Nx+1/Nx) Fx R0
0 1000 1.0 (from birth to birth) 0.2 0 0
1 200 0.2 0.2 0 0
2 40 0.04 0.2 0 0
3 8 0.008 0 200 (0.008)(200)
4 0 0
= 1.6


  • R0 is the net reproductive rate = ∑lxFx
  • R0 > 1 indicates a growing population
  • Population projections: use time step table
  • PVA: population viability analysis

Matrix Models:

  • Age specific survival and fecundity X vector of individuals within each age structure
  • Fecundities are top row
  • Survival rates are elements of sub diagonal


Age 0 Age 1 Age 2 Age 3
Age 0 F0 F1 F2 F3
Age1 S0 0 0 0
Age 2 0 S1 0 0
Age 3 0 0 S2 0


  • Age Is not always the best indicator of demographic change.
  • Vital rates might be related to size or developmental stage.
  • Stage-Based Matrix Models


  • Seed → seedling → small adult → large adult
  • Egg/hatching → small juvenile → large juvenile → subadult → adult
  • Egg → pupa → cocoon → butterfly
  • Calf → immature female → mature female ⇔ mature female with a calf (reversal and changed can occur)

Example of matrix modeling:

  • Invasive bullfrogs
  • Negative effect on native fauna
  • Focus on removing tadpoles and breeding adults
  • Sensitivity Analysis: lambda was most influential by % tadpoles who metamorphize fastest. Concluded that removing the organisms before metamorphosis and the adults will fix the problem.


  • (-/-) interaction. The negative effect on each individual on the other because they share a limiting resource.
  • Scramble Competition: divisible resources
  • Contest Competition: resources cannot be divided between all of the individuals
  • Exploitative Competition: Negative effects on both species are due to their effect on the shared resource. Indirect effect due to effect on the abundance of the shared resource.
  • Interference Competition: physically interact with one another
  • Strength of competition between two species will depend on how limiting the resource is. How much overlap in resource use is there between species.
    • Density dependence and genetic diversity
    • Character displacement: niche portioning with a genetic component. Two species are found in sympatry where they mix but are allopatric in the rest of their habitats. Where species do not overlap in habitat will be more genetically similar but the populations that do overlap will be different à allows for variation in resource use and decreased competition/sharing of resources. A shift in the genetic composition due to competition (selection acts to reduce the strength of competition.
      • Evolutionary change in species traits that act to minimize competition.
    • Gausse’s Principle of competitive exclusion: species occupying the same niche cannot coexist in a stable environment. One is better adapted to access the resource. Often seen as a negative (inverse) relationship in abundance.
      • Gradient in density
      • Patchy/clumped
      • Apparent competition: two prey species negatively affect each other because they are a resource to a shared enemy.
    • Competitive Release: change in distribution when separate and together. Distribution of species changes when a potential competitor is removed.
    • Competition effects:
      • populations:
        • Abundance/density
        • Distribution
        • Demographic
      • Individuals:
        • Behavioral-feeding, foraging
        • Physiological- growth and reproductive
        • Morphological- body size and biomass

Modeling Competition:

Lotka and Volterra

Basic Approach:

  1. Derive an equation for population growth in the presence of competition
  2. Define the values at which population growth stops: set the equation to zero, plot isoclines (line of sameness) in phase space.
  3. Understand the zones, predict the outcome. If one species’ N goes to zero then you can conclude that there is competitive exclusion. Determine relative population size of each species. Plot the two isoclines together. IF the isoclines do not cross…the species on the outside always outcompetes the other.

Derive Equation:

  • Population growth with negative density dependence: logistic equation
    • dN/dt=rN(1-N/K)
  • Population growth with negative density dependence and competition: Lotka-Volterra equation
    • dN1/dt=r1N1[1-(αN2+N1/K1)]
    • dN2/dt=r2N2[1-(βN1+N2/K2)]
  • α = the competition coefficient
  • β = competition coefficient

Set the equations equal to zero

  • 0= 1 – (αN2+N1/K1)
  • 1= (αN2+N1/K1)
  • K1 = αN2+N1

The equation of the line that describes the isocline for species 1:

N2 = (-1/α) N1 + K1

Y  =   m   X     +   b

Isocline for species 2:

N2 = K2 – βN1

“Isoclines” by WikiMedia Commons (CC BY-SA 4.0)

  • In an unstable coexistence, “r” per capita population growth rate has an effect on the outcome.
  • Two species cannot use the same limiting resource in the same way and coexist in a stable environment indefinitely.
  • When the strength of interspecific competition is weaker (smaller βand α) than intraspecific competition, then coexistence in more likely.
  • Outcome of competition is determined by the relationship between the two species’ carry capacity (K) and the competition coefficients, not the per capita growth rate (r) or initial population sizes (N)


  • (-,+) immediate consumption of one organism (prey) by another (predator), predator individual consumes many prey individuals in its lifetime.
  • Effects of Predation on Prey:
    • Population response: change in population size/density, distribution/range, age/size/genetic distribution, change in population dynamics (population regulation).

Modeling predator-prey dynamics:

  • Without a predator the prey will grow exponentially
  • For Prey:
    • dN/dt = rN – (predation rate)( # predators) (N of prey)
    • 0 = rN – (predation rate)( # predators) (N of prey)
    • rN = (predation rate)( # predators) (N of prey)
    • # of Predators = r/predation rate
  • Without prey the predators will experience population decline.
  • For Predators:
    • dN/dt = (conversion rate)(N of prey)( N of predators) – (predators)(death rate)

Create Equation:

  • Prey: dx/dt = αx – βxy
  • Predator: dy/dt = δxy – γy
  • αx= exponential growth of prey species
  • βxy= predation rate by predator
  • δxy= consumption of prey
  • γy= death rate

Set Equal to 0

  • 0 = αx – βxy
  • Y= α/β (zero growth for prey species) – prey abundance is dependent only on the predator abundance
  • Predator population is dependent on prey abundance
  • Cyclic changes in abundance


Huffaker’s Mites Experiment:

  • Introduced the prey mite onto orange and then a few days later he added a predator.
    • Predators rapidly consumed all prey
  • Created patches in the orange by covering them. The predator mites ate all the prey mites.
  • Then created barriers and found some oscillations in dynamics for 7 months before the predator consumed all the prey.
  • Coexistence was not maintained.
  • Conclusions:
    • Oscillations are seen in predator-prey relationships
    • Very difficult to create coexistence
    • Factors that lead to longer oscillations: increasing dispersal of prey and slowed dispersal of predators, smaller surface area, increased DD of prey, barriers and refuge
    • Space is critical

“Huffaker’s Balancing Act” by Ecomotion Studios (Standard YouTube License)

Isle Royale:

  • Predator prey dynamics in a natural island system
  • Moose have been present for over 100 years
  • Wolves were introduced in 1950 due to ice bridges
  • 1958 biologists began studying the island.
  • Data shows that as moose populations increase the predation rate decreases. This is a positive density dependent relationship, an allee affect.
  • Low populations of moose are more vulnerable to predation events.
  • An increasing predation rate negatively affects the moose population growth rate. Supports the theory that wolves have a top-down effect on the moose density.
  • Predator preference determines the predator’s effect on the community and population dynamics.
    • Keystone predation
    • Switching predators
  • Studies showed that wolves typically prey on the calves and old moose.
    • Wolves affect dn/dt of moose due to consuming calves.

Adaptions from Predation Events:

  • Predation can affect the traits of individuals, both within their lifetimes and in forms of selection across generations.
  • Adaptations in prey species to avoid predators – structural defenses
  • Silica on grass as a defense against herbivores
    • Predators will adapt such as horses who feed on the grass- teeth continually grow throughout the lifetime.
  • Camouflage: cryptic coloration (occurs in both prey and predators)
  • Batesian mimicry: a harmless animal looks like a poisonous/dangerous animal
  • Mullerian mimicry: a poisonous species looks like another poisonous/dangerous species
  • Chemical defenses: bright coloration (aposomatic) indicates poisonous species – can be mimicked by other species.
  • Secondary metabolites in plants: phenolics, terpenes, and alkaloids (cafferine, cocaine, nicotine, morphine) to kill off predators
  • Constitutive defenses: the defense mechanism/trait is always expressed
  • Induced defenses: trait is only expressed if/when there a cue/signal
    • Nicotine acts like acetylcholine and kills insects. Clipping/grazing on the tobacco leaves induces the roots to produce more nicotine and send them to the leaves.
    • Induced responses are common in plants due to their relationship with grazers who do not completely kill the plant. Animal predators kill the prey immediately.
    • They can be reversible or irreversible traits
  • Why aren’t all defenses constitutive?
    • Life history theory: a trait will become fixed in a population unless there is a cost associated with it.
    • There must be reliable cues
  • When a plant is grazed on (tissue damage), hormones are released to signal the presence of a grazer and the plant will upregulate its defenses. The wound on the plant will release VOC (volatile organic compounds) that signals to other plants to increase their own secondary metabolite.
    • There is also communication between plants via mycorrhizal networks (within the soil). Fungal species attaches to the roots and connects them to other plants.
    • The VOC can also signal to an enemy of the grazer- attract the predator of you predator

Facilitation and Mutualism:

  • Facilitation: a positive interaction between two individuals
  • Mutualism: two different species facilitate each other, typically involved the exchange of protection for resources (Ants that farm aphids, lichens)
  • Facilitation is strongest when there is abiotic stress.
  • Consumer pressure and abiotic stress are inversely related
  • Associational Defense is highest in response to large consumer density. (Safety in numbers)


  • Individuals of different species, living in the same place at the same time, some interact strongly with each other, some do not.
  • Frederic Clements: “organismic” view”, community is like a super organisms, tightly bound functional unit with discrete boundaries, that communities were predictable due to conditions. Deterministic-the output is due to the factors of the input.
  • Henry Gleason: “Individualistic”, communities are fortuitous associations of species, adaptations allowed them to live under particular biotic/abiotic conditions found in a place, the role of chance
  • Robert Whittaker (1970): environmental gradient analysis and tested mountain ranges, the Gleason model was shown to be more accurate.
  • Ecotone: there is a boundary between 2 community types, set by an abrupt change in abiotic conditions
  • Succession: the change is species composition over time after a disturbance
    • Primary Succession: sequence of communities developing on a newly exposed habitat devoid of life and soil
    • Secondary Succession: recovery of a disturbed site that has soil and perhaps seeds
    • Chronosequence: space for time substitution
    • Pioneer Species: first stage in succession
    • Climax Community: final stage of recovery of an ecosystem
  • Disturbance: an abiotic event that kills or damages some individuals but may allow others to grow and reproduce.
  • Succession is explained by:
    • Type of interaction between early and late stage prganims
    • Which species can establish directly after the disturbance
      • Facilitation Model: (Clements) only pioneer species are able to survive the early abiotic conditions. These species modify the environment so that other species to establish. Later species typically are better competitors and outcompete the pioneers.
        • Primary succession on a rock exposed by glacier recession. Lichens live on the rocks which allows for mosses etc.
      • Tolerance Model: any species can establish (no pioneers, just good dispersers), there is no facilitation between species. There is a sequence of change reflects who gets there first and who can outcompete those who have been established. Stable when no new species can invade. Later species are better competitors and can tolerate lower resource levels.
      • Inhibition Model: any species can establish, no facilitation. Early colonists make the habitat less suitable for others. Priority effects- dependent on who got to the habitat first, replacement occurs when the species dies or there is a disturbance.
        • Marine fouling organism. Bryozoans inhibit colonization by sponges and tunicates.
        • Alternate stable states: when two ecological communities can form under very similar environmental conditions.
      • Stability: the ability of an ecological community to defy change (resistance) or rebound from change (resilience) after a disturbance.
        • Threshold– tipping point, change in abiotic factors that lead to a change in community structure.
        • Hysteresis: different pathways to create biological communities.

Species Diversity and its Effect on Ecosystem Function and Stability:

  • Diversity has been observed to produce more productive and stable communities.
  • Effect of Diversity on Productivity: Tilman Cedar Creek LTER experiment
    • 168 9X9 meter plots
    • Treatment: 1, 2, 4, 8, 16 different savanna species planted into the plots
    • Weeded to maintain the treatments over time
    • Primary Productivity: accumulation of biomass, rate that new plant growth is produced
      • Study saw that over time there is an increase in biomass, plots with multiple species saw greater increase in biomass.
    • Stability: study saw that the more diverse plots were stable over time
      • WHY?….
    • Complementarity: different species vary in traits and how they utilize resources. This creates less competition for resources. Traits are complementary to one another.
    • Portfolio Effect: diversity of species stabilizes productivity of a community during changing environmental factors. Monocultures can be wiped out due to a large negative disturbance.
    • Sampling Effect: diverse plots have a greater likelihood of containing species that are inherently better suited to an environment/have a greater productivity rate.

Diversity within a Population:

Plant Genetic Diversity Predicts Community Structure and Governs an Ecosystem Process

  • By Crutsinger et. al.
  • Genetic Diversity affects community structure → richness of arthropods; herbivores/predators
  • Genetic Diversity affects ecosystem function → ANPP above ground annual primary productivity, the measure of the annual plant growth
  • Solidago altissi was studied à differential genotypes for the plant, reproduces clonally.
    • 21 genotypes of the plant
  • Experimental Design:
    • Plots with 12 individuals
    • 1, 3, 6, or 12 genotypes in each plot
  •  Results:
    • Monoculture plots showed greater variability biomass, herbivore richness, and predator richness
    • Diverse plots showed greater species richness in consumer species
  • Discussion:
    • Many Individuals Hypothesis: Greater genetic diversity lead to greater productivity, and increased herbivore and predator richness. There were greater population sizes and increased consumer species richness. Rarefication of the data supported this hypothesis.
    • Resource Specialization Hypothesis: Increased plant diversity leads to an increase in the types of available resources which will lead to an increased abundance of specialist consumers.

Foundation Species:

  • A species that creates a habitat, a species that creates biogenic structures
  • For Example: sea grass, kelp forests, and coral reefs
  • Genetic Diversity of these species creates ecosystem stability/productivity

Fragmentation and Habitat Loss:

  • Effects on Species Diversity: Large creatures/predators are more likely to be disrupted/lost
  • Heterozygosity: genetic diversity within individuals- genes that contain alternative forms of alleles
  • Fragmentation tends to  diversity between population because it can decrease genetic flow but within a population there is a decrease in genetic diversity.
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Midterm Review

Population and Community Ecology

“Butterfly” by Genuine Creators (CC0 1.0)

The scientific study of interactions that determine the distribution and abundance of organisms.

  • Coevolution: when two species evolve in response to the other. “reciprocal evolution”
    • Examples:
      • A pathogen and its host- the host must develop immunity or it will die off, the pathogen must respond to these changes to continue thriving in/on the host.
      • Predator/prey relationships- specialists, selective food choices

Experimental design:

  • Observational studies detect correlations and relationships in the environment,  correlations are a linear association between two quantitative variables
  • Experimental studies detect causation due to the manipulation of variables by the experimenter to test a hypothesis and run statistical analysis on.
  • Replicates: observations or experimental units allow us to test whether the patterns we see are general and repeatable.  What ‘counts’ as a replicate depends on the scale of inference we are trying to achieve.
    • For example: one male and one female each run 30ft 20 times and times recorded.
      • If the scale is dependent on the individual him/herself then each has 20 replicates.
      • If the scale is dependent on gender then there is only one male and one female and therefor there is only one replicate.
      • If there were three males and three females then there would three replicates for each gender
    • In experiments, the control is the group to which you compare your treatment group(s); without it, you have nothing to compare to. A good control should be EXACTLY like the experimental group in all ways, EXCEPT the factor being tested.
    • Types of variables:  continuous (numerical and can be used in arithmetic, decimal places) vs. categorical or discrete (specific group, type, or item, characteristic, etc. that is finite/nonnumerical)
    • Predictor variable the factor that is being tested/manipulated, placed on the X-axis.
    • response variable– the factor that is measured, placed on the Y-axis.
    • IF the data are not normally distributed then the data can be transformed using a square-root function
    • General hypothesis: a testable statement that is inferred due to deductive/inductive reasoning  
    • prediction or specific hypothesis: IF…..THEN…… statement that states the general hypothesis in the “IF” clause and the experiment/manipulation in the “THEN” clause.
    • Null hypothesis in inferential statistical tests: States that there is no difference between groups, that the results are coincidental/random.
    • Descriptive stats: describes tendencies in the data set such as center (mean/median/mode), spread (variance, standard deviation, IQR, standard error). Describes the samples.
    • inferential stats: Allow you to make predictions about larger groups, predict future population sizes. (Correlation/regression/confidence intervals etc.)
    • Strengths of observational vs. experimental studies
      • Both can be time consuming/costly/fail
      • Observational studies are a good place to start when beginning to study a population
      • Experiments are useful in testing specific hypothesis where factors can be manipulated
      • Orthogonal design= all possible combinations of two or more factors are tested for in an experiment.
      • independent vs. paired t-test
        • A paired T-Test is used when there is correlation/association between the variables being tested, or the same individual is used to compare to itself before/after the manipulation
        • An independent T-test is used when the data points collected cannot be associated/linked with another data point.
      • one-tailed vs. two-tailed tests
        • One tailed-the hypothesis gives a direction of difference
        • Two-tailed there is not direction of difference
      • assumptions of parametric statistics
        • Independent/replicated samples
          • If not met then it is a fault with the experimental design
        • Normally distributed- ie. Frequency histogram
          • If not normal then do the square root/log of the data or use a nonparametric test
        • The groups should have equal variance
          • Intrinsic to the populations
  • Inductive Reasoning: going from one or several specific examples to infer a general truth
  • Deductive Reasoning: the process of going from a general truth to a specific prediction


  • Mechanisms of microevolution:
    • Genetic drift: random changes in gene frequencies within a population over time. More likely to occur in smaller populations because changes are more dramatic if certain individuals are eliminated.
    • Gene Flow: Intermixing of two different populations, individuals move from one population to another and breed.
    • Mutations: random changes to DNA that cause phenotypic variations that are passed onto offspring
    • Selection: those with the most well adapted traits for their environment will survive and pass on their genes.
  • Local adaptation: when after several generations in a population an advantageous trait acts to increase fitness and respond to stress so that the individuals within that populations with the trait can tolerate that stressor more.
  • Allopatric speciation: two populations are separated by a physical barrier.
  • Traits as adaptations, and constraints on adaptations
    • Such as genetic constraints, history of species, energetic trade offs, etc.)
  • Phenotypic plasticity: one gene is responsible for two phenotypes due to varying environments of different populations.
  • Acclimation: a compensation for stress, is not a genetic change, occurs within individuals and is reversible.
  • Transgenerational plasticity = maternal effects:
  • Phylogenetic conservatism: The tendency of species to retain ancestral traits.
  • Phenology: the timing of seasonal events such as budding in tree species, plant germinations, and reproductive cycles. Cues that species use are temperature, amount of sunlight/day length.

Sampling populations:

  • Census: A count of every individual in a population
  • Area Based Sampling: use of randomly placed quadrats of a certain size to estimate the number of individuals in a population
  • distance-based sampling: An estimate of population size based upon the distance between two individuals of a population.
    • Nearest Neighbor Sampling: distance of two individuals within a population from a random point to determine distribution.
    • Individuals along a straight line are sampled
  • random vs. haphazard sampling:
    • Random sampling employs a random number generator to choose locations for quadrat placement
    • Haphazard sampling is biased in that the investigator influences the location to sample with the quadrat
  • uniform vs. stratified random sampling:
    • Uniform sampling employs the use of transects, sampling is done at regularly set intervals.
    • Stratified Random sampling is when a habitat is divided based upon perceived differences, the samples within each zone are randomly chosen.
  • Mark recapture method: use a trap to catch specimen, tag them, and then release them, then repeat. Monitor how many are recaptured vs. new specimen to estimate population size.
    • Total # marked / Population size = # marked in second group /total in second group
    • Assumptions of the model:
      • Probability of recapturing a marked mouse does not change
      • Assume marking does not increase mortality in specimen- otherwise will never recapture a mouse and it will seem as if there is an infinite population size
      • Mixed organisms, not territorial species- or will always catch the same ones, small population size
      • All individuals are equally likely to be caught- naïve and elderly mice, or is there division of labor?
    • Population dispersionpatterns: how organisms are arranged on the landscape
      • clumped or aggregated- small groups are spread across the landscape, herding animals.
      • Random- organisms have no pattern is dispersion, based on resources.
      • uniform/regular usually seen with territorial species.
    • fundamental niche: the area where the abiotic factors of an environment would allow for a species to thrive
    • realized niche: the actual location of the species dues to abiotic/ biotic factors such as interspecies interactions.
    • R-selected life history- many offspring, rapid reproduction, instantaneous growth-bunnies
    • K selected life history- long living, few offspring, high parental care, long gestation-tigers
    • Bergman’s Rule: animals become bigger at higher altitudes and as they move away from the equator
    • Allen’s Rule: vertebrate endotherm that live in cold environments have shorter appendages to decrease the escape of heat.
      • The artic fox has short ears while the desert fox has long ears
      • Volume to surface area and the need for heat conservation: as surface area increases there is a larger surface for heat exchange.

“Arctic Fox Glowing” by Eric Kilby (CC BY-SA 2.0)

Describing Communities

  • Assemblage: a group of different species living in the same habitat, sharing/competing for resources, and interacting with abiotic/biotic factors.
  • Richness: The number of different species in a habitat
  • Evenness: The relative abundance of each species in a habitat
  • Rank-abundance curve: the relative abundance of each species in your sample (pi for all species i), and ranking them from most abundant to least abundant. Then you simply graph that as a line graph, with rank on the x-axis and abundance on the y.
  • Rarefaction– to compare two sites with different sampling effort.  Basically, rarefaction uses either a resampling methods (bootstrapping, or resampling with replacement, or jack-knifing, resampling without replacement). to predict how many species we WOULD have found at site A if we’d only sampled 40 individuals rather than 80 – it ‘rarifies’ the bigger data set, so that the two can be compared to each other.
  • Alpha diversity: diversity within a site/habitat, due to the role of dispersal and chance events.
  • Beta diversity: diversity between sites, the shift in composition
  • Gamma diversity: the diversity of an entire region,
  • The historic processes (e.g. geography, continental drift, adaptive radiation) and broad climatic factors (e.g. regional patterns of rainfall, temperature) are the main drivers of biodiversity
  • At local spatial scales (e.g. comparing one site to another, within a region), species’ particular physiological tolerances and their interactions with other species (competition, predation, etc.), act to determine how many species (and which ones) are present at any given site.

Population Growth Models

  • When left unchecked population growth is geometric (staircase shape, births occur only in spirts) or exponential (J shaped curve, continuous births and deaths, constant rate of growth)
    • Resources are a population check ***limit growth***
  • Terms that are used:
    • N – population size, # of individuals
    • Nt – Number of individuals at time “t”
    • N0 – initial population size, population size as t=0
    • “n” – sample size
  • Factors that change “N”
    • Nt+1 = Nt + Births – deaths (in a closed population)
    • Immigration (added)
    • Emigration (migrated away-subtract)
    • Nt+1 = Nt + b (birth rate, per capita rate)X N– d(death rate per capita) Nt
      • Nt+1 = Nt + (b-d) X Nt
    • Nt+1 – Nt change in population size =∆N
    • (b-d) = r ***instantaneous per capita rate of increase****
      • Which is the r selected life history
    • ∆N/∆t = rN
  • dN/dt = rN ***only equation that describes a change in a population size***
    • if r= 0 then no population growth
      • r>0 is population growth
      • r<0 is population decline
  • Assumptions of the model (dN/dt = rN)
    • closed population
    • b and d are constant
    • r is constant
    • all individuals contribute equally to population growth rate “r”
      • but what about populations that differ in relative number of individuals who can contribute (ie. Stage of life/ability to reproduce/sex ratios)
      • but what about resource limits/competition?
  • Integrate model for the differential equation- can predict population sizes based on time
    • Nt=N0ert
    • Can figure out a population size at any time “t”
    • Also describes compounded interest
  • Discrete Model- geometric (staircase shape)
    • Nt+1 = Nt(ƛ)
      • ƛ=1 is a constant/stable population size, ƛ<1 = decreasing populations size, ƛ>1 = increasing population size
    • general form: NttN0
    • predict population size at any time (t)
    • discrete intervals of growth, populations where births occur at regular intervals, non-overlapping generations
    • ƛ is population growth rate
    • ratio of population sizes
      • dimensionless, but associated with a particular time step.
    • Ranges from 0 to infinity
  • Differential Model- Exponential: (J shaped)
    • Nt=N0ert
    • growth occurs continuously, births are year-round, use calculus and differential models., overlapping generations.
    • “r” is the instantaneous per capita rate of increase
    • # individuals/per individual; instantaneous
    • Negative to positive infinity range
    • Stability is when “r”=0
  • To convert between discrete and differential equations:
    • ƛtN0 = N0ert
  • Take natural log of data and plot it on the graph, the slop of the line is “r”
  • logistic growth:  (s-shaped)
    • When there are limited resources and competition the birth rate and/or death rate change.
      • Less births
      • More death (not enough food, space, increased illness)
    • To model this: d N/dt= r N (1-(N/K))
    • S-shaped curve
    • If the birth rate increases so that “r” increases then the population will reach K faster.
    • Assumptions of logistic growth curve:
      • Closed population
      • All individuals contribute equally to the population size/r
      • All individuals use resources and contribute equally to carrying capacity (K)
      • Instantaneous affect
  • When the birth and death rates intercept there is a density dependent in the population growth. Occurs when per capita population growth rate changes with population density
    • Negative density dependence is when r is decreasing because birth rate is decreasing. Increase of death rate/decrease in birth rate as population density changes. Due to intraspecific competition. Form of regulation.
    • When b=d then r=0, graph plateaus
  • K is the carrying capacity, the population size that the environment can sustain stably, is the intercept of death and birth rates. To keep population stable at K.
    • Increase resources will increase K
    • The Y-value where the s-shaped graph plateaus
  • Delayed density dependence: population growth rate is affected by population size at some point in the past-time lag in density dependence. (deterministic model-logistic)
    • d N/dt= r N (1-(N(lag) /K))
    • if no or small r X lag then model appears to have no lag
    • if there is a medium r X lag model has damped oscillations until it evens out at K.
    • if there is a large r X lag model limit cycle around K, large oscillations about K.
    • Very large r X lag creates chaos
    • dynamics are intrinsic to the population itself à shape of curve is internal property of the population. (abiotic and biotic factors affect the population).
  • Deterministic models: (geometric, exponential, logistic, delayed dd) no randomization, the output directly depends on the input. Determined preexisting constants and known values.
  • Stochasticity- randomness. Extrinsic factors that influence population size such:
    • Environment: change in climate, conditions are more or less favorable for any given year for each species. Affects all populations regardless of their size.
    • Demographic: randomness that results from applying a rate (ie. “b”, “d”, “r”) to real whole numbers. Most impact on small populations.
      • Similar to genetic drift
    • There are an Infinite number of outcomes (iterations), similar pattern for a single input.
    • Randomness can hurt population growth (make is smaller at any time when compared to deterministic model).
    • Geometric mean: √(a * b *c *….)
      • Bad years hurt population growth more than good years help it.
      • Is always less than the deterministic mean.
    • Population growth trajectory for density independent growth is an exponential growth curve. dN/dt=rN
    • Population growth trajectory for negative density dependent growth. Logistic, S-shaped curve. dN/dt=rN(1-N/K)
    • Relationship of Per capita population growth and N:
      • X-axis is N
      • Y-axis is r
      • Exponential relationship is horizontal line
      • Logistic relationship is a decreasing line. (negative density dependence)
    • Logistic models show population regulation.
    • Positive density dependence: when small populations show lower per capita growth rates than bigger populations do. This is not a straight line-right skewed graph, the smaller population has an increasing per capita growth until it reaches a larger size when the negative density dependence takes over.
      • 1/N dN/dt vs. N
    • Allee Effect– due to positive density dependence.
      • Mechanisms:
        • Problems finding a mate at low densities.
        • Group formation-foraging success, detection, avoidance, saturation of predators.

Smaller populations at a greater risk for extinction than larger ones.

  • Environmental changes can decrease population growth rates
    • Habitat loss, hunting, invasive species
  • Due to Allee Effect (smaller dN/dt)
  • More sensitive to randomness
    • Environmental stochasticity-good years and bad years
    • Demographic stochasticity- randomness that occurs when rates are applied to whole numbers. Ie. Average number of children an individual has, do not expect same outcome in each case.
  • Genetic reasons-inbreeding, inbreeding depression (reduction of fitness due to deleterious recessive traits that are passed to these offspring that have low fitness-lower birthrates and higher death rates occur), loss of genetic diversity.
  • Genetic rescue- introduce new individuals to increase genetic diversity.






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