Ecology

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A Primer of Ecology by Nicholas J. Gotelli.

Index:

• Ecology (Notes on a Summer School class taught by Dr. Amy Ritter.)
  1. Evolution, diversity, and biogeography.
  2. Biodiversity.
  3. Distribution; physiological ecology; body size and allometry.
  4. Behavioral and evolutionary ecology.
  5. Introduction to population growth models.
  6. Population growth models with intraspecific competition.
  7. Dispersion patterns and dispersal.
  8. Age-structured populations.
  9. Life history evolution
  10. Intraspecific competition: Lotka-Volterra competition model.
  11. Intraspecific competition: test and evolution.
  12. Consumers and predators.
• Evolution.
• The Books
• Links Elsewhere

Highlights of Ecology 2005 Summer School class ('Bio150')
taught at UCSC by the great scientist and teacher, Dr. Amy Ritter.

Text: A Primer of Ecology by Nicholas J. Gotelli.

1. Evolution, diversity, and biogeography.

1. Ecology as a way of understanding the natural world, how organisms are influenced by their environment, and why a species is in a certain location.
2. Levels of biological hierarchy: (1) Individual organism; (2) Populations; (3) Communities and the interactions between the species; (4) Ecosystems and how nutrients are cycled.
3. Ecological scientists detect interesting patterns in nature; ask 'why'; develop hypotheses about what causes these patterns; test hypotheses, to determine which produces the pattern.
4. Types of study: (1) Observations; (2) Experiments; (3) Comparative or literature-based study; (4) Purely theoretical model.
5. e.g. Duckling survival increases with initial brood size. Hypothesis 1 = safety in numbers. Hypothesis 2 = a healthier/stronger mom has more offspring who are (in turn) healthier.

2. Biodiversity.

1. Biodiversity:
   1. ecologically it appears in the variety of form and function (# of herbivores, flying creatures, etc.).
   2. taxonomic diversity shown in classification of species and evolutionary divisions.
2. How is diversity distributed around the world? And why? Distribution is not uniform and not ubiquitous, but in limited ranges with some overlap.
3. Distribution results from evolutionary history (long-term) and ecological factors (short-term).
4. Natural selection (N.s.): a process through which organisms adapt to their environment. N.s. acts on individuals, to determine whether they survive and whether they pass on their genes. But N.s. affects the makeup of populations.
5. In N.s.:
   1. Individuals show variation in some traits.
   2. Variation is heritable.
   3. There is a consistent relationship between the trait and the fitness (# of copies of your genes that you pass on) of an individual.
   When these conditions exist in a population:
   1. Genes for the favored trait will increase in frequency;
   2. Phenotypes (the expressed characteristics) will also change.
6. e.g. Grants' studies of the beak size of Galapagos finches:
   1. Beak size was variable (polygenic, coded for by multiple genes).
   2. Beak size was heritable, there being a correlation between beak size of offspring and its parents.
   3. Beak size had ecological consequences in the preferred size and hardness of seeds eaten.
   4. After a drought, population fell to 13%, with a bias to larger beaks - an ecological shift.
   5. Beak sizes of offspring averaged significantly less after the drought.
7. N.s. operates on the individual. So it's not working to ensure the survival of the species.
8. N.s. is an evolutionary process (there are genetic changes) but it is due to ecological interactions.
9. N.s. can be very strong and rapid (like the finches) and reversible (like the finches).
10. Adaptive radiation (such as in Darwin's finches): 1 mya common ancestor of 14 finches arrived on Galapagos and Cocos Islands; variations in beak sizes and shapes, body size and color; phenotype (morphological) variations related to differences in diet. Adaptive radiation occurs by:
    1. Empty region or niche is colonized by a species. e.g. the common-ancestor finch.
    2. Isolation of populations (e.g. on different islands) allow differences to occur. No continual migrants and gene flow.
    3. Occasional migrations (say every two centuries) between populations; at contact, competition between subspecies gives further differentiation (e.g. smaller beaked birds focus more on the smaller seeds).
11. Adaptive radiation: Cocos Island isolated and no speciation (significant morphological differentiation) occurs: some behavioral differentiation and specialization within species, which still interbreed.
12. Non-interbreeding may result from physical difference or psychological preferences. e.g. In African Rift Valley Lakes, the adaptive radiation in chiclids (who used to breed based on color and pattern cues) is being reversed as the lakes have become too polluted for them to see these cues.
13. Convergent evolution can also explain speciation: different taxonomic lineages converge on similar ecological roles, resulting from adaptation to similar environmental conditions. e.g. the placentals in the Americas and 'corresponding' marsupials in Australia. Certain conditions or niches favor specific adaptations regardless of taxonomic history; differences between the forms still exist, such as the mammalian form in the Americas and the marsupial form in Australia.
14. Biogeography: The geographic distributions of organisms among the six major biogeographic zones (with distinct flora and fauna): neoarctic; palaearctic; neotropical; afrotropical; oriental; australasian. Land masses: 225 mya, Pangea; 135 mya, Laurasia (north) and Gondwanaland (south america, africa, india, australia); 65 mya, Australia, India, South America are separate from the rest.
15. Biogeography: Wallace's Line (Wallace co-discovered natural selection). Compared two islands 26 miles apart: Bali (birds of Asian descent) and Lombok (birds of Australian descent). Asian taxa include monkeys, tigers, bears, woodpeckers. Australian taxa include marsupials, birds of paradise.
16. Mechanisms of Biogeographic similarities:
    1. Convergent evolution: different ancestors plus adaptation to local environment.
    2. Adaptive radiation: shared common ancestor plus differentiation due to local environment.
    3. Dispersal: can be a larger scale movement than (single-lifetime) migration.
    Examples of similar-looking taxa on multiple continents:
    1. Convergent evolution of unrelated species: e.g., nectar-feeding hummingbirds (America) and sunbirds (Africa).
    2. Adaptive radiation Distant relation with common ancestor from ~135 mya and the physical union of Gondwanaland: flightless ostrich (Africa), emu (Australia), and rhea (South America).
    3. Dispersal: closely related white cattle egrets in both South America and Africa; we have documented them migrating.
17. Climates and Biomes: planet-wide influence on plants, animals, and resulting communities of:
    1. Geographic variation in conditions, such as the 40% less solar radiation at the poles.
    2. Seasonal variation due to 23.5° tilt in Earth's axis.
    3. Low pressure and high rainfall at the equator; high pressure and low rainfall about 30° (where deserts tend to be) from the equator; trade winds, etc. result from the pressure difference.
18. Nine Biomes:
    1. Tundra. Extreme temperature range, no trees, permafrost half a meter down, very short (can be two months) growing season, low diversity.
    2. Boreal (northern coniferous) forest. Dramatic population cycles, fierce biting insects.
    3. Temperate deciduous forest. Trees shed leaves in fall; low diversity of trees; ephemeral spring flowers in under-story; shady under-story in summer.
    4. Temperate rain forest. Wetter than temperate deciduous forest; conifers dominate.
    5. Tropical rain forest. Enormous diversity; complex interactions between species; high predation; poor soil with nutrients locked in the trees.
    6. Tropical dry or seasonal forest. Higher proportion of deciduous trees than rain forest.
    7. Grasslands. No trees; often has high diversity or density of grazing mammals.
    8. Tropical savanna. Sparse trees and grasslands; abundant, diverse herbivores; predators.
    9. Desert. Dry.

3. Distribution; physiological ecology; body size and allometry.

1. Scales of individual distributions: worldwide -> continental -> region -> cluster/population -> locality -> individual locations
2. Factors that determine the distribution of organisms, as measured by Leibig's law of the minimum:
   1. Previously used Leibig's law of the minimum, by hypothesizing that the distribution of an organism is determined by a single environmental factor for which the organism has the narrowest range of tolerance. e.g. temperature or moisture.
   2. Test Leibig's law of the minimum by measuring performance (surviving, growing, reproducing) as a function of the gradient of environmental condition. Reproductive range is the optimal range and range of persistence.
   3. In practice, a narrower curve is found in the field (which shows the ecological optimum in the natural context) compared to the broader curve in the lab (the physiological optimum in an idealized context).
   4. Problems include: (1) Examines the effect of only one environmental variable at a time; (2) Ignores other factors that could affect the organism's distribution; (3) Limits the factors to physical and chemical, ignoring interactions with other species.
3. Now look where organisms are NOT found (rather than where they are). Hypothesize that the distribution of organisms is determined by factors that prevent the organisms from entering or thriving in another area.
   Species distribution can be limited by:
   1. Barriers to dispersal. e.g. oceans, rivers, mountains, mobility of organism. e.g. gypsy moth absent from USA until introduced across Atlantic.
   2. Behavior. e.g. Habitat selection or choice or where they go. e.g. Kangaroo rats prefer open areas and pocket mice prefer sheltered areas.
   3. Ecological interactions with other organisms. e.g. competition, predation, mutualism, disease, alleopathy (excretion of toxins to inhibit others). e.g. red kangaroos rare in area unprotected them from dingos.
   4. Abiotic conditions. e.g. Temperature, pH, salinity. e.g. red kangaroos abundance decreases into desert conditions; southern species of barnacle thrives on the west coast (warmer) of the British Isles.
4. Combination of factors seen in the variation with elevation in Hawaii of malarial parasites in birds:
   1. Mosquitoes are not native to Hawaii, being limited in dispersal.
   2. Malaria was limited by the absence of mosquitoes, which are the vector.
   3. ~1900, mosquitoes introduced accidentally into Hawaii. Native birds lacked resistance and died if they got malaria.
   4. Because of colder temperatures at higher elevations about 1500 meters (an abiotic condition) mosquitoes were found only at lower elevations.
   5. Nonnative introduced birds cluster at lower elevations. These (starlings, pigeons, house finches, cardinals, etc.) evolved with resistance to malaria. So while the vector is common at lower elevations, the parasite lacks avian hosts below about 600 meters.
   6. Parasite maximum about 1300 meters.
5. Experiments on species distribution: (1) Take a species outside its range; if it dies, nothing is ruled out; if it grows, it had 'decided' not to go there. (2) For abiotic conditions, set up a sequence of plots with the variation of strength of the condition.
6. Questioning what factors limit distribution of a species:
   1. Is there a barrier to dispersal that makes an area inaccessible to an organism. Then we have a Dispersal factor.
   2. Is there a behavior that shows this habitat was not chosen? Then we have a Behavior factor.
   3. Is there predation, herbivory, parasites, etc? Then we have an Other Organisms factor.
   4. Is there a physical issue (temperature, light, moisture) or chemical factor (pH, salinity, nutrients). Then we have an Abiotic factor.
7. Body size: from the largest (the blue whale at 100 tons or 10^8 g) to the smallest (mycoplasma at 10^-13g). The range is 10^21. Compare the size of a human with 100 times the volume of the earth.
8. Elephant versus vole. Ostrich versus hummingbird.
9. Heart beats: for most organisms, the average # of heartbeats in a lifetime is equivalent. Elephant ~ 30 beats/min. Shrew ~ 1,000 beats/min.
10. Body size is:
    1. Structural size: length of bones, bodies. Answers evolutionary questions.
    2. Body mass (weight, surface area) answers ecological and physiological questions.
11. Within a species, body size is adapted to costs and benefits.
    1. Smaller size could protect from predation. Smaller size might be more likely when food is less available.
    2. Faster could protect from predation.
    3. Bigger and stronger could help in fighting for mates. Bigger could result in more babies. Bigger could protect in colder climates, with relatively low surface area per unit mass (helps maintain body temperature.
12. Sexual size dimorphism: larger males in elephant seals and other pinnipeds.
13. Sexual size: larger females are more fecund (crabs, snakes, scorpions).
14. Reverse sexual size dimorphism in birds: greatest female:male ratio for bird-eaters, next for fish eaters; falls to equal size for carrion eaters.
15. Reverse sexual size dimorphism in angler fish, where the tiny male attaches to the female and swims with her for the rest of his life.
16. Geographic rules of body-size patterns:
    1. Bergman's Rule: size increases toward the poles. True for 72% of 94 bird species and 65% of 149 mammals.
    2. Island Rule: animals that move from mainland to an island reduce in size if they were large on the mainland; e.g. pygmy elephant; 13 of 15 carnivores species are smaller; 9 of 11 artiodactyls (deer, elk) are smaller. and increase in size if they were small; e.g. giant rodents and lizards; 60 of 69 rodents are bigger.
    3. Size might get bigger with lack of predation and competition, or smaller with lack of resources.
17. Allometry: Many traits scale (log-log relationship) to body size (mass). Can describe by
    log(trait) = constant + ratio * log(mass), where table below shows ratio:

    Physiological:	Metabolic rate	0.75
    	Brain size	0.73
    	Heart rate	-0.25
    Ecological:	Mammal litter size	-0.30
    	Life span	0.25
    	Bird clutch mass	0.75
    Populations:	Density	-0.98

    
    e.g. Metabolic rate (oxygen consumption) proportional to mass-to-the-0.75 (across animals).
18. Mass-specific correlation #1: Mass-specific metabolism rate (cost to run each gram of tissue):
    metabolic rate / mass = constant * mass^0.75 divided by mass:

    Metabolic rate	0.75
    Mass	1.00
    Metabolic rate / mass:	-0.25

    
    i.e. Metabolic rate per unit of mass is proportional to mass-to-the-minus-0.25 (across animals).
    Implication: Diet (tinier organisms need energy-rich food).
19. Mass-specific correlation #2: Endurance by fasting is proportional to the fat stores of an animal (which are proportional to its mass) divided by the metabolic rate (which scales to the 0.75 value of mass):
    mass / metabolic rate = constant * mass divided by mass^0.75:

    Mass	1.00
    Metabolic rate	0.75
    Mass / Metabolic rate:	+0.25

    
    i.e. Metabolic rate per unit of mass is proportional to mass-to-the-plus-0.25 (across animals).
    Implication: Endurance increases with mass.
    Whales can fast for 8 months.
    Hummingbirds fast for only hours (going into torpor at night by lowering their metabolism rate).

4. Behavioral and evolutionary ecology.

1. Adaptation and natural selection: (1) Trait variation; (2) Trait must be heritable; (3) Trait has fitness consequences in terms of leaving copies of their genes, leading to changes in the genes and the phenotype.
2. Hard to study because: (1) N.s. mostly happened in the past; (2) we see traits but do not know how that maps to genetics; (3) things can be too complex.
   Therefore, focus on phenotypic selection: the fitness consequences of variation in a trait. Consider how this trait affects the fitness of the organism. [Selectionist thinking. Though traits could occur through other processes such as genetic drift.]
3. Two levels of analysis, e.g. to why birds sing in the spring:
   1. Functional level: to attract mates or defend territory. This is important to behavioral and evolutionary psychology.
   2. Proximate level: physiological mechanism due to changes in hormone levels or in weather or daylight, or due to what was learned from parents.
4. e.g. What factors constrain how many eggs a female bird lays? Lack (1950s) made a clutch-size optimality model. Robins tend to lay 4 eggs/nest. Why?
   Prediction: Robins do what is optimal to produce offspring, and get the maximum number of chicks that parents can adequately raise. The number of chicks is proportional to the fitness of the parents and limited by the amount of food the parents can provide.
   Observe the mode of 4 eggs laid for the # of chicks surviving to the next year.
   Test the model: add eggs to or remove eggs from a nest and measure how the survival of the chicks varies with clutch size. The same relationship as in nature supports the hypothesis, and a different relationship rejects the hypothesis. Some species match; others do not.
5. Optimal foraging is necessary. e.g. Snow bunting. Female sits on eggs 24*7. Male collects all food.
6. Optimal foraging strategy:
   1. Center-based forager goes to one or more food patches. Decides how long to stay in each patch, how many insects to carry, and when to return to the nest or go to another patch.
   2. Plot food gathered (insects, worms, etc) versus time spent (sum of travel time to patch and time in patch).
   3. Currency = maximum rate of food returned to the next.
   4. Constraint = rate of forage in each patch decreases with time.
   5. Graph shows a curve of diminishing return. The best value is from the tangent (marginal value theorem): the further away the patch, the more food must be brought back.
   6. Experiment with starlings in bird boxes, consuming worms in experimental feeders at different distances.
7. Risk-sensitive foraging. Whether animals gamble depend upon their condition prior to the experiment.

5. Introduction to population growth models.

1. Population: A group of individuals of the same species that can potentially interact, in the same location (place and time). Individual can be unitary (free-living, unique genotype, determinate form without budding or cloning. Or it can be modular (branched growth of repeating units which can break up into separate clonal units; the genet is the whole genetic organism, e.g. aspen trees that send up clonal shoots; the ramet is the individual unit or organism.
2. Malthus (1798) observed that population growth is geometric (exponential) when unchecked. Geometric growth: population doubles at each time step.
3. Continuous model of geometric population growth symbols:
   1. N = number of individuals in a population = population size.
   2. t = time.
   3. N_t = Population at time t.
   4. N₀= number of individuals in a population = population size.
   5. B = Number of births in the population.
   6. D = Number of deaths in the population.
   7. I = Number of immigrants in to the population.
   8. E = Number of emigrants from the population.
4. Assume that B and D occur continually, even though many spp. have seasons when they reproduce, and deaths may vary with season (such as increasing in the winter).
   1. It can be unrealistic but useful for a variety of 1-species populations and species interactions.
   2. Assume a closed population (I = E = 0). Then ΔN = B - D.
   3. For each time step, the change of population size (ΔN) equals the population growth rate (dN/dt).
   4. ΔN or dN/dt > 0 means a growing population.
   5. ΔN or dN/dt < 0 means a decreasing population.
   6. ΔN or dN/dt = 0 means a constant population.
5. Per capita definitions:
   1. b = per capita birth rate.
   2. d = per capita death rate.
   3. r = per capita growth rate = b - d.
   4. If b > d, then r > 0 and growth.
6. Examples of per capita growth rates:

   	r (per capita growth rates)	Doubling Time
   Virus (no lag time from birth to reproduction)	110,000	3.3 min.
   Bacterium	21,000	17 min.
   Hydra (unicellular, motile, freahwater organism)	124	2 days.
   Cow	0.365	1.2 years.
   Human	0.013	50 years.

7. On a graph of N versus t, dN/dt = slope:
   • dN/dt = B - D = bN - dN = rN
   • Also, r = population growth rate (dN/dt) divided by number of individuals (N).
   • Therefore
     N_t = N₀ * e^rt
   • e = 2.72. The natural log, ln(e^x) = x.
   • ln(N) is linear with time, while N is curved with time.
8. Example:
   1. b = 0.75 (3 offspring in 4 years).
   2. d = 0.25 (average life = 4 years).
   3. Then r = 0.50.
   4. Assume N₀ = 10.
   5. N₁ = N * e^0.5.
   6. N₂ = N * e¹ = 27.2
   7. N₄ = N * e².
9. For spp. with seasonal reproduction and non-overlapping generations (such as annual plants) we have the discrete model of geometric population growth, which spikes at birth times. Symbols:
   1. r_d = discrete per capita growth rate; constant proportion by which a population grows in unit time (in the discrete model); for infinitely small units of time, r_d = r.
   2. λ = finite rate of increase.
10. The discrete model of geometric population growth:
    1. N_t+1 = N_t + r_d * N_t
    2. N₁ = N₀ + r_d * N₀
    3. Then, N_t+1 = N₀ * (1 + r_d) ^t = N₀ * λ ^t
    4. λ > 1 means a growing population, and corresponds to r > 0.
    5. λ < 1 means a decreasing population, and corresponds to r < 0.
    6. λ = 1 means a constant population, and corresponds to r = 0.
11. Natural examples: Discrete generation and geometric growth (for first 4 years) of pheasants introduced to Protection Island in 1937; elephant seals with 20 survivors in 1890 and winter breeding has since grown with λ = 1.096, reaching 70,000 in the late 1970s.
12. Example calculation:
    1. b = 0.75 (3 offspring in 4 years).
    2. d = 0.25 (average life = 4 years).
    3. Then r_d = 0.50.
    4. λ = 1 + r_d = 1.50.
    5. Assume N₀ = 10.
    6. N_t+1 = N₀ * (λ) ^t = N₀ * λ ^t.
13. Add stochasticity (random variation) which can be: (a) environmental stochasticity (good and bad years); (b) demographic stochasticity (important only when population size is small, for then the birth and death rates become whole numbers; at the extreme, d=1.5 becomes 1 or 2 for the last female).
14. For stochastic growth, use the geometric mean (not the arithmetic mean) to estimate per capita growth. For N time periods, geometric mean is the Nth root of the product of the N λs. Example, with λ = 1.2 in a good year and λ = 0.8 in a bad year; if two bad years are followed by two good years, the arithmetic mean is 1 but the geometric mean shows that the population has declined by 9%:

    Year	λ	Nt = N0 * λ t
    zero (t=0)	n/a	100
    1st (t=1)	0.8	100 * 0.8 = 80
    2nd (t=2)	0.8	80 * 0.8 = 64
    3rd (t=3)	1.2	64 * 1.2 = 76
    4th (t=4)	1.2	76 * 1.2 = 91

6. Population growth models with intraspecific competition.

1. Limited resources lead to reductions in reproduction, growth, or survival: there are population consequences.
2. Intraspecific: within 1 species. Interspecific: between different species.
3. Mechanisms:
   1. Exploitation: competition through reduction of resource levels.
   2. Interference: prevention by other individuals of use of resources (through dominance, established territories, etc.).
4. Density dependence (# individuals per area unit): previously individual birth and death rates were held constant with total population or density. But birth rate can fall and death rate can rise with density.
5. Observation of intraspecific competition:
   1. # of young per female in an area decreases when the # of females increases.
   2. Proportion of juveniles surviving in an area decreases when the # of adults increases.
   3. # of floaters (males without territories) in an area increases with the # of males with territories.
   4. Across the globe for humans, birthrate falls as density rises.
6. Experiment of intraspecific competition:
   1. Establish plots with different amounts of resources in each plot. Look for density in each plot.
   2. Have the same resources in multiple plots, and manipulate density. Check the birth and death rates to see if they change with density.
7. When growth (r) is 0, population is at the maximum density that can be supported by resources, resulting in the carrying capacity population (K).
8. Mechanisms of intraspecific density-dependence:
   1. Space depletion.
   2. Resource depletion.
   3. Cannibalism (intraspecific predation).
   4. Allee effect: reverse density-dependence where the probability of mating increases with density.
9. Mechanisms of interspecific density-dependence:
   1. Parasitism, predation, disease, competition with other species.
   2. Search-image predation: a predator focuses on the prey that it sees the most often, so the denser prey is more likely to be attacked than the less dense prey.
10. Types of density-dependence from experiment with flour beetles, where different numbers of eggs were places in aliquots of flour, and the # of individuals that survived to the adult stage was measured:
    1. Density independence: increase of # surviving with density.
    2. Under-compensating density dependence: increase of # surviving with density, but not as fast as during density independence.
    3. Over-compensating density dependence: decrease of # surviving with density.
    Also we find Exact-compensating density dependence: no matter what initial density, the same number survives.
11. Continuous model of population growth, with addition of density dependence:
    1. Logistic growth: dN/dt = r*N.
    2. Assume linear negative density dependence: join (0,r) to (K,0). Slope = rise/run = -(r/K).
       Per capita growth becomes r - (r/K)*N = r*(1 - N/K)
    3. Logistic growth with density dependence: dN/dt = r*N ( 1 - N/K).
    4. N(t) becomes a sigmoidal (S-shaped) curve, with inflection point (maximum dN/dt) at K/2.
    5. Inflection point was once used for (now over-fished) fisheries to maximize harvest as well as growth.
12. Discrete model of population growth, with addition of density dependence:
    1. Discrete model: λ = 1 + r_D
    2. Discrete model: N_t+1 = N_t * λ = N_t *(1 + r_D)
    3. Incorporating density dependence, N_t+1 = N_t * r_D * (1 - N_t / K)
    4. A graph shows oscillations in the value of N, of overshooting and undershooting because of the generations and the time-lag between them.
       1. r_D < 2.0: dampened oscillations.
       2. r_D between 2.0 and 2.49: two-point limit cycle.
       3. r_D between 2.49 and 2.57: two-point limit cycle.
       4. r_D > 2.57: chaos. [Chaos is deterministic not stochastic. but very small differences in N₀ lead to very different trajectories.]

7. Dispersion patterns and dispersal.

1. Dispersion: Distribution of individuals in a population. e.g. clumped, uniform, or random.
2. Dispersal: Spreading or movement of individuals away from each other, usually during a particular life stage. e.g. seeds carried by the wind; Pelagic larval stages of marine organisms.
3. Migration: Mass movement of individuals, often entire populations that move from one region to another. e.g. migratory birds; elk.
4. Types of dispersion in one location:
   1. Random. The null pattern. Rare in nature except if environment is very homogenous.
   2. Uniform; hyperdispersed. Competition, territoriality.
   3. Clumped; aggregated. Heterogeneous. Or sociality (as in deer) of social groups. Or seed shadows (seeds dropped near an adult plant).
5. Scale dependence of type of dispersion. e.g. Tern colonies are clumped across an island (1 km apart) but uniform within a colony (2 m apart).
6. If two sites have similar densities, they may have similar amounts of resources. If one has greater density, it may have more resources or fewer predators.
7. Ideal-free distribution model: a form of density-dependence that can lead to a pattern of relatively higher density of a mobile organism at one site compared to another. Assumes that each individual wants to do the best that it can. An organism's decision where to settle depends on (1) intrinsic quality of the habitat or food patch and (2) the behavior of others.
8. Ideal-free distribution model assumption: once equilibrium has been reached (all individuals have decided where to settle), everybody should be getting the same amount of resources. Assumes no territoriality.
9. Ideal-free distribution model test on coots by B. Lyons: one patch is given one unit of food and the other patch is given five units of food. At the start each patch has roughly the same coot density. After about an hour coots on the rich patch are about five times that on the sparse patch.
10. Types of movement:
    1. Migration. Two-way. Dispersal followed by a return to the origin.
    2. Natal dispersion. Movement away from birthplace by the young (young birds, marine larvae). This is the 'default' meaning of dispersal.
    3. Other (non-natal) types of dispersal.
11. Dispersal mechanisms for animals:
    1. Active: fly, walk, swim.
    2. Passive: air or water currents (ballooning spiderlings; larvae of marine organisms).
    3. Phoresy (hitch hiking). e.g. flower mites on hummingbirds.
12. Dispersal mechanisms for plants:
    1. Adhesion to animals: sticky or velcro seeds.
    2. Floatation: coconut seeds.
    3. Explosive projections: California poppy.
    4. Bribery of animal dispersers: fruit.
    5. Wind.
13. Reasons for natal dispersion:
    1. Avoid competition with your kin.
    2. Respond to change or instability of habitat across generations. Local resources can get used up or environment can change.
    3. Avoid inbreeding which is costly to fitness (susceptibility to disease).
    4. Predation may be less elsewhere.
14. Evolutionary Stable Strategy:
    1. Dispersal is evolutionarily successful.
    2. When dispersal is a rare strategy it can invade any others.
    3. Cannot be invaded by a non-dispersal strategy.
    4. Dispersal polymorphism: plants produce seeds of different sizes; insects have winged forms (in ephemeral ponds) and token-wing forms (in permanent wet lands).
15. Dispersal and mating issues:
    1. Avoid inbreeding depression of fitness.
    2. Use distance apart as a proxy for the inter-relatedness of plants.
    3. Wasser and Price measured delphiniums in the Rockies. Hand-pollinated with pollen from very close, intermediate, and remote delphiniums. Measured fitness by the NUMBER of seeds produced: 1 meter apart gave inbreeding depression. 10 meters apart was good. 100 meters was too far away and gave a slight 'out breeding' depression, due to the local adaptation effect of plants close together.
    4. Quail: prefer to mate with cousins more than siblings and more than non-related quail.
16. Population sink: λ < 1. Local death rate exceeds birth rate, so we get a decline. But bad habitat can still be stable due to immigration from good habitat.
17. Population source: λ > 1. Local birth rate exceeds death rate, so we get a increase toward the carrying capacity, and dispersal to population sinks.
18. Blue tit (a chickadee):

    	Deciduous Forest	Evergreen Forest
    Habitat quality	Good (more food)	Poor (less food available)
    Population growth (based on b + d only)	λ = 1.09	λ = 0.87
    Density	6 * density in Evergreen	1 *
    Dispersal	100 *	1 *

    Density difference suggests that food is 6* more available in deciduous forest than evergreen. But stability of IFD (Ideal Free Distribution) model would expect λ = 1 in both populations.
19. Species-area relationship: non-linear increase in species richness as island area is increased, proportional to the square of the area.

8. Age-structured populations.

1. Per capita birth (b) and death (d) rates vary with the age of an individual: e.g. juveniles do not have babies (b = 0); adults might have greater probability of survival than juveniles.

2. Differences in per capita birth (b) and death (d) rates across age classes can affect population growth.

3. We incorporate age structure into population models by using life tables to summarize b and d schedules. This lets us predict population growth more accurately and determine which age classes are the most critical to population growth.

4. Demography:
   1. Is the study of age structure in populations.
   2. Once was limited to humans.
   3. Is a big business: life insurance companies use demography to construct life tables for human populations and to figure out which age classes have the highest b and d schedules (and are thus the most costly).
   4. Originates from the Greek demos ('common people').

5. To build a life table, start with births and deaths by age:
   1. Concentrate on a cohort of individuals (a group of individuals born at the same time or in a given period, such as one year). Follow the cohort through their lives, and count the number that survive each year.
   2. Concentrate on the females and female offspring; we can reasonable model population growth by counting only the females; knowing the gender ratio, we know the overall number. The number of females limits the birth rate because of the gestation period.
   3. Count the number of babies that each female has at each age.

6. Cohort life tables: Fecundity schedule shows fecundity, b(x). Here is an example of an iteroparous fecundity schedule, for organisms that (like humans) reproduce multiple times in their lifetimes:

   Year	Age = x	Fecundity = b(x)
   1	0	0
   2	1	2
   3	2	3
   4	3	1
   5	4	0
   6	5	0

   Compare with this semelparous fecundity schedule, for organisms (like spider, octopus, or salmon) that reproduce only once in their lifetimes:

   Age = x	Fecundity = b(x)
   0	0
   1	0
   2	0
   3	0
   4	0
   5	Many

7. Cohort life tables: From the fecundity schedule, we develop cohort life tables showing l(x), the survivorship schedule, which is S(x), the number of survivors up to a given age class, divided by S(0), the number born initially:

   Age = x	S(x)	Survivorship schedule, l(x) = S(x) / S(0)
   0	100	1.0
   1	80	0.8
   2	70	0.7
   3	40	0.4
   4	0	0.0

   From this you can see where the mortality tends to occur.

8. Cohort life tables. From the survivorship schedule, we calculate g(x), the survivorship probability from one age class to the next, which is l(x)/l(0):

   Age = x	S(x)	Survivorship schedule, l(x) = S(x) / S(0)	Survivorship probability, g(x) = l(x + 1) / l(x)
   0	100	1.0	.8/1 = 0.8
   1	80	0.8	.7/.8 = 0.875
   2	70	0.7	.4/.7 = 0.57
   3	40	0.4	0/.4 = 0
   4	0	0.0	-

9. Cohort life tables: Types of survivorship curves in nature, plotting ln[l(x)] versus Age(x):
   1. Type I: High survivorship at young and intermediate ages. Much lower survivorship at older ages. e.g. humans and other mammals with a lot of parental investment. The line is convex to the upper right.
   2. Type II: Mortality rate constant throughout life. Rare in nature; a few birds have this, except during the chick and egg stage. The line is straight.
   3. Type III: Very low survivorship for young age classes; higher for older age classes. e.g., insects, flowering plants, marine invertebrates, marine organisms with dispersive larvae, turtles. The line is convex to the lower left.

10. Cohort life tables: We want to calculate r, the per capita growth rate for each age class, using per capita birth (b) and death (d) rates for each age class. But first we calculate:
    1. R_o, the net reproductive rate, and
    2. G, the generation time.

11. Cohort life tables: R_o, the net reproductive rate
    = mean number of female offspring per female over her lifetime
    = Net reproductive rate
    = Sum of the product of survivorship schedule and fecundity for each age class.
      x=k
    = Σl(x)b(x)
      x=0
    Thus we get the potential reproduction rate discounted by the chances of surviving to those age classes.

12. R_o, the net reproductive rate, is similar to λ (r+1) because it tells us the per capita growth rate of the population:

    If Ro > 1.0	the net surplus of offspring makes the population increase.
    If Ro < 1.0	mortality exceeds reproduction and the population decreases.
    If Ro = 1.0	the population is stable.

13. Cohort life tables: G, the average generation length
    = Average age of the parents of all offspring produced by a single cohort
    = [ Σl(x)b(x)x]/R_o

14. Cohort life tables: calculate the intrinsic rate of increase, r:
    N_t = N₀ * exp ^rt
    The above is for continuous growth with no limiting K.
    If t=1 generation, we can substitute G for t.
    Therefore, N_G = N₀ * exp ^rG
    But N_G / N₀ ~ R₀
    Therefore, R₀ ~ exp ^rG
    Therefore, ln(R₀) ~ rG
    Therefore:

    r ~ ln(R0)/G

15. Cohort life tables: V(x), reproductive value
    The relative number of offspring that remain to be born to individuals of a given age. Varies with age based on (a) total number of offspring that are yet to be produced and (b) also on the chance of surviving to the next age classes.
    Low reproductive value if:
    1. Chances of surviving to that age are low; and
    2. Subsequently the individual does not produce a lot of babies.
    
    Wildlife management might remove the oldest and the youngest deer by hunting.
    V_x = babies at age x plus (chance of surviving to the next age times the number of babies at that age) summed over all ages
    = b_x + Σ(l_t / l_x) * b_t
    

16. Cohort life table: example to estimate r. From the survivorship schedule, we calculate g(x), the survivorship probability from one age class to the next, which is l(x)/l(0):

    Age, x	Survivorship, S(x) (Number surviving at age x.)	Fecundity, b(x) (Mean number of babies per x-age female.)	Survivorship schedule, l(x)=S(x)/S(0) (Proportion of the original cohort that is alive at age x.)	l(x)*b(x) (Per capita growth rate per generation.)	l(x)*b(x)*x (Multiplying the per capita growth rate per generation by the age.)	g(x)=l(x + 1)/l(x) Survivorship probability from S(x) to S(x+1)
    0 (new born)	1000	0	1	0	0	0.8
    1	800	2	0.8	1.6	1.6	400/800 = 0.5
    2	400	3	0.4	1.2	2.4	100/400 = 0.25
    3	100	1	0.1	0.1	0.3	0
    4	0	0	0	0	0.0	0

R₀ = Net Reproductive Rate = mean number of female offspring produced per female over her lifetime
= number of babies per female per generation
= sum (for x=0 to x=3) of l(x)*b(x)
= 0 + 1.6 + 1.2 + 0.1 = 2.9

G = Generation Time = Average age of the parents of all offspring produced by one cohort
= Σ(l(x) * b(x) * x ) / R_O
= (0 + 1.6 + 2.4 + 0.3)/2.9

r = Per Capita Growth Rate per Year = Intrinsic rate of increase
~ ln(R_O)/G
~ ln(2.9)/1.48 = 0.72 per year.
This is r (not λ) and is >0. So the population is growing.

9. Life history evolution.

1. Life history (L.H.):
   1. Concerns births and deaths and the related timing and tradeoffs.
   2. Life table data describe an organism's L.H.
   3. Decisions include how much to invest in current versus future reproduction.
   4. Why do some organisms (such as elephants) have long slow lives while others (shrews, etc.) have very short lives?
   5. Why do some organisms start reproducing soon while others take longer to mature?
   6. Why do some organisms (plants, ectotherms (like snakes)) have indeterminate growth while others (endotherms) have determinate growth?
   7. Why do some organisms have many offspring while others have very few?
   8. Why do some organisms (such as the plovers whose chicks walk as soon as they are hatched) have precocial offspring? And why do others (such as robin chicks that require high parental care) have altricial offspring?
   9. Why have some organisms iteroparous reproduction (like the yuccas that live many years and reproduce often) while others have semelparous reproduction (like agave, which lives many years and reproduces only once).
   10. Why do some organisms (like flower annuals) live and reproduce in a single year while others (like flower perennials) live and reproduce for many years.
2. Important for wildlife management to determine which stage(s) of L.H. are the most crucial for population growth. Spotted owl L.H. led to change in logging practices.
3. L.H. for some organisms are size-based instead of age-based. Useful when difficult to determine age.
4. L.H. of an organism is the patterns of reproduction, growth, and survival of an organism, as related to the organism's age or size.
5. Approach 1 to studying evolution of L.H. is r and K selection (MacArthur and Planka).

   r-and-K selection theory can be used to explain variation in life history traits (e.g., seed number) in terms of the how traits affect: (i) population growth rate or (ii) an organism's ability to compete in a crowded world. r-selected species live in habitats where disturbances (storms, fire, etc.) keep their population below carrying capacity. E.g. arctic organisms. Natural selection favors traits that allow for a rapid population growth rate (r-selection) (e.g., large numbers of offspring, small offspring, or early maturation). K-selected species are typically at carrying capacity (K-selection). Their selection favors traits that enable such organisms withstand competition and use resources efficiently (e.g. few babies, large babies, or late maturation). E.g., tropical organisms.

   
   Did humans develop as r-selected but are now K-selected?

   	r-selected	K-selected
   Density and K	Low density and well below K	High density near K.
   Stability of environment	Unstable	Stable
   Select for traits that give:	Rapid population growth.	Ability to deal with crowded conditions (compete, use less resources, fast).
   Number of offspring	High	Low
   Time to maturity (to first reproduction)	Early	Late
   Offspring size	Small. (Put effort into number and release kids early, and therefore small.)	Big
   Interspecific competition	Low	High
   Efficiency of resource use	Low	High

6. Problems with r and K selection:
   1. Most species do not fit completely into r or K strategy types for all traits.
   2. No relationship to life tables, population growth, or fitness of individuals.
   3. Density-dependent selection (expected in K environment) has never been found in any studies in nature. We have not found adaptation to density dependence in reducing the number of offspring.
7. Approach 2 to studying evolution of L.H. is Optimality:
   1. Assume that L.H. traits evolve to maximize lifetime fitness.
   2. Thus, optimal L.H. maximizes fitness, giving the highest population growth to individuals with that strategy.
   3. The best L.H. for an organism is determined by: (1) Extrinsic mortality rate (the underlying chance of dying, and the organism cannot control this; (2) Tradeoffs of limited resources between competing demands.
   4. Birds can often raise more chicks than the average number of eggs that they lay. This refutes a model that predicts highest survival occurs for the average number of eggs laid. The more chicks, the more resources used by the parent, which then has less resources for next year. Therefore, we need to measure fitness based on more than one reproductive bout.
   5. Tradeoff 1: Current versus future reproduction. Studies where parents raise more than the optimal number of chicks show that for the bigger clutches:
      Smaller chicks occur (68% of studies).
      Fewer chicks survive (57% of studies).
8. Optimality Approach symbols:
   1. B_A = Number of offspring (babies) per mother for an annual.
   2. B_P = Number of offspring (babies) per mother for a perennial.
   3. S = Survival of adults.
   4. S_o = Offspring survival to adulthood - juvenile survivorship.
   5. λ_A = N₁/N₀ = B_A * S_o
   6. λ_P = N₁/N₀ = B_P * S_o + S
9. Optimality approach: When is it better to be an annual?:
   1. λ_A > λ_P
   2. Therefore B_A - B_P > S / S_o
   3. Better to be an annual when annuals can have lots of babies relative to perennial.
   4. Also better when adult survivorship is low or juvenile survivorship is high.
10. Optimality approach: Why we wait to mature. Individuals in a species with higher annual survival pay a lower cost of waiting.
    Prediction: spp. with higher annual survival will show later ages of maturation than other spp.
    Studies show a positive relationship between survival and age of maturity:

    	Age at maturity (years).	Annual survival (%).
    Temperate passerines, ducks, galliforms	1	40-60%
    Swifts, herons, geese, owls, shorebirds	2	60-80%
    Ospreys, cormorants	3	80-90%
    Most seabirds	4-7	80-95%
    Albatrosses	9-10	95%

11. Optimality approach: Why we age. Aging (senescence) is physiological deterioration of organisms with age and decline in survival and reproduction with age. Hypotheses:
    1. Only weak selection exists against bad genes with effects late in life, because those genes don't have strong effects on overall fitness. This would lead to mutation accumulation.
    2. Genes with good effects early but bad effects later can be preserved. Selecting for such genes gives "antagonistic pleiotropy" contrasting the effects of one gene.
    We can select for longer life spans (such as in the lab with fruit fly). Zoo life spans are longer than in the wild. Species with higher baseline mortality age have an earlier onset of mortality: they have even less selection against genes with bad late effects because fewer animals reach the age when the side effects are experienced.

10. Intraspecific competition: Lotka-Volterra competition model.

1. Species interactions:

   Types of interaction	Effect on species 1	Effect on species 2
   Competitor	-ve	-ve (or 0 in asymmetrical competition).
   Consumer-resource e.g., preditor-prey	- (resource)	+ (consumer)
   Mutualism e.g., coral & xoozanthellae	+	+ (or 0 effect)
   Detritivore	0 (already dead)	+ (eats the dead)

2. Tansley field experiment on competition:
   1. Bedstraw A grows in acid soil and B in basic soil.
   2. But when planted separately, each species can grow in each type of soil: refutes to the 'soil type' hypothesis.
   3. Each species thrived more in the soil where typically found: supports the 'soil type' hypothesis.
   4. When species are grown together, one species out-competed the other and took over.
   5. Each species was completely superior in its own soil type.
3. Competitive exclusion:
   1. Presence/absence of a species was determined by competition.
   2. Environmental conditions affect the outcome of the competition.
   3. Competition is a population-level process. The superior competitor causes the local extinction of the inferior competitor.
4. Asymmetrical competition found through salamander experiment in Appalachians:
   1. Salamanders C and D co-occur and overlap in diet.
   2. Treatments: (1) is the control (C and D); (2) is the removal treatment where C is removed; (3) is the removal treatment where D is removed.
   3. C has a negative effect on D: when sp. C removed, density of D increases compared with density of D in the control.
   4. D does not affect C: when sp. D removed, density of C did not change compared with density of C in the control.
5. Stable coexistence:
   1. Competition can be asymmetrical: one species can have a stronger effect on the other than vice versa.
   2. Even if two species compete, they can coexist.
   3. Competition is a population-level process, because it changes the overall density: C suppresses the density of D.
6. Lotka-Volterra competition models illustrate:
   1. Link between species interactions and population processes.
   2. How to study the outcome of competition (whether species coexist or exclude each other).
7. Recipe for Lotka-Volterra competition model:
   1. Start with 2 species, each with its own carrying capacity (logistic model).
      dN₁/dt = r₁N₁ (1 - N₁/K₁)
      dN₂/dt = r₂N₂ (1 - N₂/K₂)
   2. Species compete by using some of each other's K.
   3. Competition coefficients denote how much each species uses up of the other species' K:
      α = alpha, per capita effect of species #2 on the population growth of species #1. An individual of sp.2 is 'worth' times sp.1 in terms of its effect on the growth rate of sp.1.
      K₁ = N₁ + αN₂
      α = 1 : Intra-species competition = inter-species competition.
      α > 1 : Inter-species competition is greater.
      α < 1 : Intra-species competition is greater.
      β = beta, per capita effect of species #1 on the population growth of species #2.
   4. For each species, K is approached through a combination of the density of each species.
      Add the contribution of species #2 (+ αN₂) to the logistic equation for species #1:
      dN₁/dt = r₁N₁ (1 - (N₁ + αN₂)/K₁)
      Also add the contribution of species #1 (+ βN₁) to the logistic equation for species #2:
      dN₂/dt = r₂N₂ (1 - (N₂ + βN₁)/K₂)
   5. An isocline (line of zero population growth) can show population stability of the 2 species.
      Isocline = a set of abundances for which the growth rate (dN/dt) is 0.
      • On a 'state-space' graph, with axes that are the abundances of the two species (N₁ and N₂), plot the isocline for species #1 as a straight line of slope -1/α between (0, K₁/α) and ( K₁, 0):
        N₁ = K₁ - αN₂
        N₁ can increase at any point to the left of its isocline.
      • Likewise plot the isocline for species #2 as a straight line of slope -β between (0, K₂) and ( K₂/β, 0):
        N₂ = K₂ - βN₁
        N₂ can increase at any point below its isocline.

11. Intraspecific competition: test and evolution.

1. Step 6 of Recipe for Lotka-Volterra competition model (continued): [See steps 1 to 5 above.]
   Superimpose the two isoclines - one for each species - to determine the outcome of competition:
   1. sp.1 wins when its isocline is above that of sp.2 for all densities below both of their solo carrying capacities. Even if sp.2 appears temporarily, sp.1 always takes over.
   2. sp.2 wins when the isocline of sp.1 is below that of sp.2 for all densities below both of their solo carrying capacities.
   3. Stable coexistence when sp.1 has the steeper isocline. Coexistence requires:
      K₂ / β > K₁ and
      K₁ / α > K₂
   4. Populations are in equilibrium when dN₁/dt = 0 = dN₂/dt
   5. Four different outcomes, depending on K₁, K₂, α, and β.
   6. Suppose the isocline for sp. 1 is always above and to the right of that for sp. 2,
      K₁/α > K₂ and
      K₁ > K₂/β.
      Then sp. 1 wins, driving sp. 2 to local extinction.
   7. Suppose the isocline for sp. 2 is always above and to the right of that for sp. 1,
      K₂ > K₁/α and
      K₂/β > K₁.
      Then sp. 2 wins, driving sp. 1 to local extinction.
   8. Suppose the isoclines cross with:
      K₁/α > K₂ and
      K₂/β > K₁.
      Then sp. 1 and sp. 2 reach the same combined or shared carrying capacity.
   9. Suppose the isoclines cross with:
      K₁ > K₂/β and
      K₂ > K₁/α.
      Then sp. 1 and sp. 2 are in unstable exclusion, with one or the other outcompeting, depending on initial conditions.
2. The more similar are two species in their use of resources, the more precarious is their coexistent: α ~ β ~ 1 has a high overlap in resource use. [If α = β = 1, then one species would out-compete.]
3. Lotka-Volterra competition model fails and populations do not reach equilibrium:
   • Disturbances (treefall in rainforest, inter-tidal wave exposes bare rock); fast-lived spp. or inferior competitors (e.g. sea palms).
   • Third species - a predator that affects the superior competitor (e.g. sea stars graze on mussels, allowing a diverse community to take over).
4. Test for competition:
   • Remove one sp. and see if the population of the other sp. increases.
   • Check for possible 3rd species (predator, parasite, etc.) causing 'apparent competition.' You have to identify the 3rd species and either remove it or look for correlation with its number. Otherwise it's a possible confounding factor.
   • Competition observed in a third of experimental competition studies, half of which were asymmetric. But most did not rule out apparent competition.
5. Evolutionary response to competition:
   • Each sp. has its range of resources in which it can persist (survive -> grow -> reproduce).
   • Before competition, two spp. are brought together, suppose they have big overlap. With many generations, character displacement increases and the overlap decreases.
   • This results in niche differentiation (the spp. become more specialized); resource partitioning; and character displacement.
   • If the spp. are similar but using separate resources, it suggests past competition.

12. Consumers and predators.

1. Types of consumers:
   1. Predators remove prey from the prey population.
   2. Parasitoids capture prey, lay eggs on or in it, and the offspring feed on the prey and kill it. e.g. wasps and flies will use caterpillars and spiders.
   3. Herbivores eat plants, seeds, or leaves: they do not consume the entire plant.
   4. Parasites consume part of an organism.
   5. Social parasites steal caretaking of other species, e.g. brood parasites in birds, fish, insects.
2. Predators regulate prey:
   1. Predator-removal studies lead to an increase in prey density (usually).
   2. Regulation is through density-dependence in per capita growth rate. Predation increases with increase in prey density. Therefore the death rate (d) increases with prey density, due to predation.
   3. Many predator and prey populations show very regular fluctuations in density.
3. Lotka-Volterra predator-prey models:
   1. Construct model without intraspecific competition in prey.
   2. See if we get cyclic fluctuations in prey density without this form of density dependence.
   3. Assume 1 species of prey (victim, V) and 1 species of predator (P).
   4. α = fraction of encounters that lead to the death of the prey.
      dV/dt = rV = B - D
      With predator deaths, add ( αV * P )
      At stability (the prey isocline) dV/dt = 0
      and so the prey isocline is: P = r/α
   5. β * V = per capita growth rate of predators.
   6. β * V * P = intrinsic growth rate of predators.
   7. q = per capita death rate of prey.
      dP/dt = β * V * P - q * P
      At stability (the predator isocline) dP/dt = 0
      and so the prey isocline is: V = q/β
   8. If victims exceed the threshhold, predators grow.
   9. On a P-V diagram, the isocline moves counter clockwise around the point where the prey and predator isoclines intersect.
4. Predator behavior (responses to prey) that add stability to predator-prey models:
   1. Numerical response: Population level. How predator density responds (increases) to changes in the (increasing) prey density. e.g. graph of NUMBER of PREY and of PREDATOR as a function of YEAR.
   2. Functional response: Individual level. Number of prey eaten per predator. e.g. graph of PREDATOR KILL RATE as a function of PREY DENSITY.
5. Type I predator:
   1. Consumes more prey as prey density increases.
   2. Number of prey eaten per predator increases linearly with prey density.
   3. Prey mortality rate is constant with prey density.
6. Type II predator:
   1. Consumes more prey as prey density increases, but at a decreasing rate, because of satiation and handling time.
   2. Number of prey eaten per predator increases but levels off with prey density.
   3. Prey mortality rate falls with prey density.
7. Type III predator:
   1. Consumes more prey at low prey density but decreases at high prey density.
   2. Number of prey eaten per predator increases in an S-curve, with an inflection point at optimal prey density.
   3. Prey mortality rate peaks at the inflection point for optimal prey density.
   4. At low prey density, predators ignore rare prey and focus on more common prey. There are also refuges for prey, a limited number of safe sites, which get filled up as the prey density increases. So predation rate increases.
   5. At high prey density, (a) satiation and (b) handling time.
8. Cycling of lemming populations in Fennoscandia (Finland, Sweden):
   1. In the north, 5-year cycle, factor of 150 between minima and maxima.
   2. In the south, 2-year cycle, factor of 2 between minima and maxima.
   3. The north has more specialized predators such as the weasel preying on the arctic vole. Type II functional response.
   4. The south has more generalist predators: cats, birds of prey, foxes. These are prey switchers; focus on the prey when it increases. Type III functional response, which can stablize prey population.
9. Evolutionary response to predation:
   1. Coevolution: reciprocal evolutionary response.
   2. Aposomatism: warning coloration to advertise toxicity.
   3. Mimicry: similar phenotype.
      Mullerian. Different species that are each toxic converge on the same signal (reinforcement).
      Batesian. Non-toxic species mimic a toxic sp.
   4. Natural selection favors those individuals with innate ability to recognize toxic spp. N.s. leads to an innate response.

Evolution

Buy: Evolution by Edward J Larson.

The Books

A Primer of Ecology by Nicholas J. Gotelli.

10 Minute Ecologist by John Janovy, Jr.

A heads-up for "the CEO on his way to a public hearing on an environmental statement", for the concerned citizen reading about ecological issues in the newspapers, and for the ecology student (who might be interested in question 17). These are the questions:

1. How do we humans view the world? (1) We are large and tend to see big things and miss small things. (2) We are short-sighted and tend to see and be concerned with things that are close in space and time, rather than distant factors. We share this with most species. Our language and culture (inherited from our human ancestors) gives us some power to act of longer distances and time.

2. What is a species? Initially defined by physical structure or by interbreeding ability. Might eventually be distinguished by DNA. "Taxonomy is the science of classification; systematics is the science of classification as it is applied to questions of evolutionary relationships."

3. What is biodiversity? "It is easier to preserve the diversity of an area by preserving the habitat than to focus on the welfare of one or a few endangered species" as long as the habitat is large enough. The diversity index reflects the number of species in a habitat as well as the relative distribution of those species. As the earth's population increases, humans take over more land for agriculture, replacing the variety of species with a single crop (e.g. Zea mays or corn in the USA's Midwest).

4. What is dirt? Soil, a complex mixture of organic and inorganic material.

5. What is water? Fresh, brackish, and salt. The water cycle is "the movement of water through and over the ground, through living organisms and into the air, and back into the ground, where it again becomes available for use by organisms. "

6. What is air? Not just the gases but the particles. Variations in pressure of air leads to winds. Tendency of organisms to lose moisture in air has led to water-conserving features like skin, exoskeletons, and kidneys.

7. Who eats whom?
   • Producers. Plants.
   • Consumers. Carnivores (animals, etc) eat the producers and other consumers. Primary consumers are herbivores.
   • Decomposers. Bacteria and fungi break down dead producers and consumers

8. Who beats whom? "Usually we compete most strongly with those most like us." "The species utilizing a common resource are called guilds" and members of the guild compete for the common resource. "Competition is still considered one of the causes of evolutionary diversification, although certainly not the only cause."

9. What is an ecosystem? "A combination of all the biological and physical properties of the natural world." It is characterized by flow and cycle.

10. Why are the tropics so complicated? The tropics have large supplies of heat and moisture, both of which lead to complex environments:

    Panama	2.5 acres	476 tree species.	Averaging 190 tree species/acre.
    North American temperate forest.	2.5 acres.	Under 20 tree species.	Averaging 8 tree species/acre.
    Your neighborhood.	A couple of city blocks ~ 2.5 acres.	How many tree species do you count?
    Malaysia	About 100 acres	Over 800 tree species.	Averaging 8 tree species/acre.
    Panama	About 100 acres	300 tree species.	Averaging 3 tree species/acre.

11. Why is the arctic so fragile? By 'fragile' he means "easily disrupted and slow to recover from a large disturbance". Its recovery from disturbance is short compared with the human attention span of weeks and months rather than centuries and millennia.

12. Why study islands?

    "Much of the natural environment is distributed into patches of varying sizes - individual trees, lakes and ponds, fallen logs, the space under rocks, caves - and all of these kinds of places can, theoretically, be considered islands."

13. How is real estate really divided up? Into biomes, "one of several major kinds of ecological communities, dominated by certain plant types." Be observant.

14. How many is too many? Check out his talking bacteria, oblivious to the limit to resources.

15. How long is short? Or how short is long?

    "One of the secrets to acquiring ecological knowledge and insights is learning how to shift one's thoughts easily from today to yesterday to the middle of the next century, then back to the Pleistocene."

16. What good is a swamp?
    1. "Swamps are places where ... billions and billions of ... organisms live, breeze, and die. Nature's terrestrial food pyramid ... rests in part on the wetlands." "Insects are the glue that holds life on earth together."
    2. Many migrating birds succeed because of swamps, leading to subsequent breeding success, with eco-tourism benefits.
    3. In swamps, we learn how "nature replenishes and detoxifies our wastes."
    Janovy quotes a 1996 Audubon article that reported "the 454,000 acres of California wetlands ... were worth about $10 billion per year, while the Louisiana coastal marshes contribute $2 billion per year in seafood alone."

17. Why are ecologists such nerds? Janovy suggests that this name-calling arises from people who "don't want to hear these predictions" by ecologists about the finite resources of the earth and the time before we have used them up at our growths in human population and its use of resources. While admitting that he is making sweeping generalizations that may be in error, Janovy claims that ecologists "don't care much about the things most of us care deeply about", which he lists as "sports, sex, politics, advertisements, and religion". He also says:

    "The mental preoccupation with timeless questions puts ecologists in somewhat the same category as poets, other types of free thinkers, mystics, and dreamers."

18. Why do scientists argue?

    "Scientific arguments: (1) they are a regular and normal aspect of science, and (2) scientists don't usually make controversial predictions without ... preliminary data or observations."

19. Do we humans live by the same rules as beetles? Yes.

20. Did god make the earth in seven days? No. Earth has been constructed for over 4 billion years:

    "Geophysical and biological processes have been at work here for several billion years, and they are still at work... Year began as a gas cloud about 4.6 years ago."

    Science cannot determine whether there is a constructor, still less whether there is or was an Intelligent Designer: those are religious beliefs but they are not scientifically provable or disprovable.

Evolution by Edward J Larson.

'Evolution' by Edward J Larson.

Lots of history of both the science and the religion, including the evangelical legislation of terror to protect the young from the idea that monkeys, slugs, and microbes are all God's children. The book is also good background for Evolutionary Psychology.

Links

A Primer of Ecology by Nicholas J. Gotelli.

• The Ecology Footprint quiz. Check it out to calculate what is your own impact on Gaia.

  Population Connection's Reporter (Winter 2004) gives:

  1. The current average American ("Eusan") footprint is over 10 hectares (25 acres).
  2. The current worldwide average footprint is 2.4 hectares (6 acres), which exceeds the available acreage.
  3. "Prof. David Pimentel estimates that the Earth can support from one to two billion people with a U.S. standard of living, good health, nutrition, prosperity, personal dignity, and freedom. ... The scenarios below [adapted from Prof. Ross McCluney's on-line data] estimate the shifts in quality of life as world population continues to grow, if we maintain current patterns of consumption."

  This table is adapted from Population Connection's Reporter (Winter 2004):

  World Population	Standard of living worldwide
  2 billion	Average U.S.A. standard of living (health, nutrition, personal freedom)
  0.5 billion	Same affluence as 2 billion, but with more freedom of action
  4 billion	Same affluence as 2 billion, but with many restrictions, requiring recycling, limits, rationing
  6 billion	Europe and U.S.A. inhabitants live at level of 2 billion population; rest of the world lives at current prosperity level of Mexico
  20 billion	Average standard of living at current prosperity level of Mexico
  40 billion	Average standard of living at current "prosperity" level of Northwest Africa

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