The three spacing patterns — know the cause, not just the picture
3 rows| Pattern | What causes it | Examples from the text | How common |
|---|---|---|---|
| Random | Individuals do not interact strongly AND the environment is not patchy | Some tree species in Panamanian rainforests | Rare in nature — the pattern that almost never wins |
| Uniform | Competition: animal territoriality, plant shading and root competition, creosote-bush soil toxins | Territorial birds; creosote bush | Common where a resource is evenly spread and defensible |
| Clumped | Patchy resources (soil type, moisture) plus social grouping — flocks, herds, prides | Most organisms; group living for predator defense and reduced travel cost | The most common pattern |
Survivorship curves — the graph question that shows up every year
3 rows| Type | Shape on a log axis | Where the deaths happen | Classic example | Life history tilt |
|---|---|---|---|---|
| Type I | Flat then drops off a cliff | Mortality rises steeply late in life | Humans (also many animals and protists) | K-selected: few offspring, heavy parental care |
| Type II | Straight diagonal line | Equal probability of dying at any age | Hydra; annual bluegrass after its first months | Intermediate |
| Type III | Plunges immediately, then flattens | Enormous early mortality, then very low once established | Oysters | r-selected: vast numbers of tiny offspring |
r-selected versus K-selected life history adaptations (Table 54.3)
10 rows| Trait | r-selected | K-selected |
|---|---|---|
| Age at first reproduction | Early | Late |
| Life span | Short | Long |
| Maturation time | Short | Long |
| Mortality rate | Often high | Usually low |
| Offspring per reproductive episode | Many | Few |
| Reproductions per lifetime | Few | Many |
| Parental care | None | Often extensive |
| Size of offspring or eggs | Small | Large |
| Position relative to K | Far below carrying capacity, resources abundant | At or near K, competition stiff |
| Examples in the text | Dandelions, aphids, mice, cockroaches | Coconut palms, whooping cranes, whales, humans |
Exponential versus logistic growth
8 rows| Feature | Exponential growth | Logistic growth |
|---|---|---|
| Equation | dN/dt = rᵢN | dN/dt = rN[(K − N)/K] |
| Assumption | No limits; immigration equals emigration | Growth always limited by one or more environmental factors |
| Curve shape | J-curve — accelerating without bound | S-curve (sigmoidal), leveling off at K |
| Role of K | None — K does not appear | The bracket is the fraction of K still unused |
| Growth rate as N rises | Number added per unit time keeps increasing | Slows, reaching dN/dt = 0 when N = K |
| If N exceeds K | Not defined in this model | (K − N) is negative → negative growth back down toward K |
| Density dependence | Absent | Present — this is the whole difference |
| Where you see it | New habitat with abundant resources: dandelions in North America, algae in a new pond, cats on a bird island | Fur seals on St. Paul Island; lab Bosmina (which overshot first) |
Density-dependent versus density-independent regulation
8 rows| Factor | Which type | Why |
|---|---|---|
| Competition for food near K | Density-dependent | Fewer resources per individual as numbers rise → lower birth rate, higher death rate |
| Predators concentrating on a common prey | Density-dependent | Mortality rises specifically because the prey became abundant |
| Accumulation of toxic wastes | Density-dependent | Waste output scales with the number of individuals |
| Stress behavior in crowded rodents | Density-dependent | More fighting, less breeding — hormonal response to crowding |
| Emigration and locust swarming | Density-dependent | Crowding itself triggers departure, lowering local growth |
| Difficulty finding mates when sparse | Density-dependent (Allee effect) | Positive feedback — growth rate falls as density falls |
| An extremely cold winter or drought | Density-independent | Weather hits every individual regardless of how many there are |
| A tornado, storm, or volcanic eruption | Density-independent | Catastrophe is unrelated to population size; growth becomes erratic, not sigmoidal |
Exam traps
11 pairsQuestions get built out of near-misses. If you can state each difference in one sentence, you will not lose those points.
Norm of reactionvsEvolutionary adaptation
A norm of reaction is one genotype making different phenotypes in different environments — the differences are nongenetic and are not passed to the next generation. Evolutionary adaptation is a change in gene frequencies across generations by natural selection. Thicker snail shells near predators = plasticity. Tibetan oxygen-uptake mutations = evolution.
Random spacingvsUniform spacing
Random means no strong interaction and no environmental patchiness — it is rare in nature. Uniform means individuals are actively pushed apart, usually by competition (territories, shading, creosote-bush toxins). If the question mentions territories or competition, the answer is uniform, not random.
Source populationvsSink population
A source sits in good habitat, maintains itself, and exports dispersers. A sink sits in poor habitat, has a negative growth rate, and survives only on immigration. Cut the dispersal and the sink goes extinct while the source persists — that is the definitional test.
Type II survivorshipvsType III survivorship
Type II = constant death rate at every age → a straight diagonal line (hydra). Type III = catastrophic early mortality then low mortality → a curve that plunges then flattens (oysters). Both look "low" at the right end of the graph; what differs is when the deaths occur.
SemelparityvsIteroparity
Semelparity = one enormous "big bang" event, then death (annual plants, most insects, Pacific salmon). Iteroparity = repeated reproduction across many seasons, which requires not overtaxing yourself in any single episode. Semelparity is favored when survival to another season is unlikely or the cost of breeding is huge.
Cost of reproductionvsThe calories used to reproduce
The exam definition of cost of reproduction is the reduction in future reproductive output caused by reproducing now — not the energy content of eggs and not the calories burned during breeding. The collared flycatcher experiment measures exactly this: extra eggs this year → smaller clutch next year.
Exponential growthvsLogistic growth
Both can depend on birth and death rates. The real difference is that only logistic growth includes density-dependent effects through the [(K − N)/K] term. Exponential gives a J-curve with no ceiling; logistic gives an S-curve that flattens at K.
rvsK
r is a rate — how fast the population changes per individual per unit time. K is a number of individuals — the ceiling the environment can support. Changing resources changes K; changing birth and death rates changes r. Do not describe K as "how fast" anything happens.
Density-dependent factorvsDensity-independent factor
Ask "would this hit harder if the population were bigger?" Competition, disease, focused predation, waste buildup, and crowding-driven emigration all say yes → density-dependent. A cold winter, drought, tornado, or eruption says no → density-independent. The classic exam trick is a hot summer with fewer cool burrows than individuals — the shortage makes it density-dependent.
Allee effectvsOrdinary negative feedback
Ordinary density dependence is negative feedback: more individuals → lower growth rate. The Allee effect is the reverse — fewer individuals → lower growth rate, because mates are hard to find and groups can no longer deter predators. It is positive feedback and the reason small endangered populations may never recover.
R₀vsHow deadly a pathogen is
R₀ measures transmission, not lethality. Nipah virus kills 40%+ of the people it infects but its R₀ is only about 0.48, so it never causes big outbreaks. SARS-CoV-2 killed a far smaller fraction but started with R₀ of 2–3, so it spread worldwide. Only R₀ > 1 produces a growing outbreak.