Ecology: Study of interactions among organisms (biotic factors) and their physical environment (abiotic factors). Jaguar in Brazilian rain forest (Solomon.

Ecology: Study of interactions among organisms (biotic factors) and their physical environment (abiotic factors).

Jaguar in Brazilian rain forest(Solomon et. al. 1999)

Organism

Community

Ecosystem

Population

Raven & Johnson 1999

Populations as Units of Structure and Function

•The population has unique, discernable properties and is an important unit of biological investigation.

•Interests of population ecologists include

•to understand how and why population sizes change.

•to understand populations as functional subunits of communities.

•The population is an important level of organization for the study of evolution

•a population has common gene pool

•evolutionary change happens to populations

Population ecology has important applications:

•Conservation biology - predicting extinction risk, managing populations

•Understanding, predicting human population growth

Density and Dispersion

Population Density Number of conspecific individuals per unit area or volume at a given time

Random

Clumped

Uniform

Dispersion Distribution, or spacing of individuals relative to each other

Uniform dispersion: Individuals are dispersed more evenly than expected from random occurrence of a habitat. Explanations include:

•uniform territory sizes in relatively homogenous environments (e.g. penguins)

•allelopathy -- production of toxins that inhibit growth of nearby plants (e.g. desert creosote bush and saltbush)

Campbell 1993 (Solomon et. al. 1999)

Sunbathers in Sydney, Australia Humans often exhibit uniform distribution

Campbell 1993

Clumped dispersion (aggregation) by far most common in nature.

•Environmental conditions seldom uniform throughout even relatively small area.

•Reproductive patterns including sexual attraction, often favor clumping

•Behavior patterns often lead to active congregation in loose groups or in more organized colonies, schools, flocks, or herds

Population Dynamics; Changes in population size and demographics over time

Mathematical modeling is an important and widely-used approach to studying population dynamics

• Using equations to simulate population dynamics over time

•Illuminate complex processes and guide further research

•Vary in predictive ability; rarely are perfect approximations

•Mortality high during first year of life; mortality then dropped for several years, followed by a general increase

•Some of the fluctuation between years was probably related to annual rainfall; survival is related to seed production, which is closely correlated with rainfall

Life Table of the 1978 Cohort of Ground Finches on Isla Daphne

In drought years on the archipelago, seed production is low, nesting is low (most adults don’t breed), and adult survival is low

In years of heavy rainfall, seed production is high, most birds breed several times, and adult survival is high

•Deer live up to 16, and females can breed at 4

•Type I survival curve indicates a relatively consistent increase in the risk of mortality with age

•The growth rate for most populations is strongly dependent on age structure

Life table for Red Deer on island of Rhum, Scotland.

http://cervid.forsci.ualberta.ca/library/taxonomy/cervus_elaphus.htm

Raven and Johnson 1999

Raven and Johnson 1999

Biotic Potential and the Exponential Growth Model

•All species have potential for explosive, exponential growth; absent resource limitations, growth would be exponential

•Biotic potential (rmax) is the maximum rate at which a population could increase under ideal conditions

•Exponential growth has been demonstrated experimentally in bacterial and protist cultures and in some insects.

•At some point, environmental resisitance will curtail exponential growth; environmental limitations cause decreases in birth rates and increases in death rates Time

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eExample of rapidly increasing population. European loosestrife is now naturalized over thousands of square miles of North American wetlands. Introduced in 1860, it has had a negative effect on may native plant and animal species

Exponential growth in bacteria. Bacteria, dividing every 20 minutes, experience exponential population growth

Raven & Johnson 1999

Solomon et al 1999

Biotic potential and the Exponential Growth Model

Time

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•Rate of increase steadily accelerates, population increases exponentially.

b=per capita birth rated=per capita death rate

Think about (bN-dN) ...

•difference between births and deaths in absolute numbers

•determines if population will grow, be stable, or decline

Keeton & Gould 1993

dN = (bN-dN) dt

dt

dN

Time

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(Keeton & Gould 1993)

(b-d) is the per capita growth rate. It is the net rate of population change per individual

factor N out on right side of equation dN = (b-d)N dt

divide both sides by N to “see” the per capita growth rate (b-d)

dN dt = (b-d)----- N

back to...

dN = (b-d)N dt

Time

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ulat

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edN = (b-d)N dt

substitute r for b-d

dN = (r)N dt

r = b-d = net rate of population change per individual at a given moment

Under these conditions of maximum birth rate and minimum death rate r is designated rmax

dN = rmaxN dt

• rmax represents the intrinsic rate of increase, or biotic potential of the population

• rmax varies widely among species

(Keeton & Gould 1993)

Time

Pop

ulat

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dN = rmaxN dt

Rate of population growth is a function of r and N; N is related to the number of breeders

N increases with each generation; therefore so does the rate of increase -- dN/ d t

Its due to this accelerating rate of increase that the slope of the curve becomes steeper and steeper

(Keeton & Gould 1993)

Population growth predicted by the exponential model. The exponential growth model predicts unlimited populations increase under conditions of unlimited resources. This graph compares growth in populations with two different values of rmax:1.0 and 0.5

•All other things being equal, a population with a higher intrinsic rate of increase will grow faster than one with a lower rate of increase

•The value of rmax for a population is influenced by life history features, such as

•age at onset of reproductive capability

•number of young produced

Human population growth. During the last 1000 years, the human population (globally) has been growing nearly exponentially

(Solomon et al 1999)

No population can continue to increase exponentially indefinitely

Environmental resistance:

•Environment imposes limits on population growth

•Food; water; disease; shelter from elements, predators…...

Carrying capacity (K):

•Theoretical maximum population size that can be maintained indefinitely (assumes unchanging environment)

•In reality, K changes with changes in environmental conditions

Logistic population growth:

•Populations can be modeled taking carrying capacity of environment into account using the “logistic growth equation”

•dN/dt = rN [(K-N)/K]

Num

ber

of In

divi

dual

s (N

)

The term (K-N)/K causes growth in the simulated population to respond to environmental resistance

•When N is small compared to K, [(K-N)/K] is close to 1 and growth is nearly exponential

•When N is large compared to K, [(K-N)/K] approaches 0, as does

population growth

Time

Some assumptions and simplifications of the logistic model that either are not true for most populations or do not apply equally to all populations

•Each individual added to a population at a low level (N) has the same negative effect on population growth rate at low population at a high level (N)

•Each individual exerts its negative effects immediately at birth

•All individuals have equal effect on the population

•Populations approach carrying capacity smoothly – don’t overshoot it

•Carrying capacity is constant

How well does the logistic growth model fit the growth of real populations?

Experimental populations (bacteria, yeast, Paramecia…)

•Some show sigmoidal growth fairly well, but conditions do not approximate nature (predators, competitors lacking).

•Some, not all, experimental populations stabilize at some carrying capacity, and most experimental populations deviate unpredictably from a smooth sigmoidal curve

Natural populations

•Introduced populations and decimated, recovering populations show growth patterns that generally support the concept of carrying capacity that underlies logistic population growth

Logistic Population Growth

http://www.pinnipeds.fsnet.co.uk/species/species.htm

A fur seal population on St. Paul Island, Alaska The numbers of male fur seals with harems were reduced to very low numbers due to huntin untill 1911. After hunting was banned, the population increased diramatically and now oscillates around an equilibrium number, presumably the islands carrying capacity for this species (Campbell 2000)

Raven & Johnson 1999

Raven & Johnson 1999

Logistic Population Growth – Overshooting K

•Lag time in many populations before negative effects of increasing population are realized

•Hypothetical example: food becomes limiting, but birthrate not immediately affected because females use energy reserves to continue producing eggs for a period; population may then overshoot carrying capacity

•Real life: In many of the populations that show sigmoidal-type growth, they oscillate around K, or at least overshoot it the first time

Num

ber

of s

heep

(in

thou

sand

s)Growth curve of the sheep population of Australia. Smooth curve is the hypothetical curve about which real curve fluctuates

(Keeton & Gould 1993)

Population Growth & Life Histories*

•Conditions of high population density may favor life history traits different from those favored at low population density

•High population size and life history

•High population size; limited resources, slow or zero population growth

•Traits favored may be those that enable organisms to survive and reproduce with few resources

•Competitive ability and high efficiency at resource use may be favored in populations that tend to remain at or near their carrying capacity

•Low population size and life history

•Low population size; abundant resources, rapid population growth

•Traits favored may be those that promote rapid reproduction; ie high fecundity, early maturity; efficiency of resource use not as important

* Life history

•Life history of an organism includes birth, growth to reproductive maturity, reproduction, and death

•“Life history traits” are characteristics that affect an organisms schedule of reproduction and death.

•“r-Selected populations” likely to be found in variable environments in which population densities fluctuate, or in “open” habitats where individuals likely to face little competition

•“K-selected populations” likely to be living at a density near the limit imposed by their resources

•Life history traits do often vary in ways shown in table

•No demonstration of direct relationship between population growth rate and specific life history traits; concepts of r and K selection are mainly useful as hypothetical models

Population ecology and the evolution of life history traits

•Because of the varying pressures of natural selection, life histories show high variability

•among species and higher taxa

•among populations within species

•even among individuals within a population

•Patterns exist in the way in which life history traits vary

•Life histories often vary in parallel with environmental patterns

•Life histories often vary with respect to each other (eg, delayed maturity & high parental investment tend to correlate with low fecundity and low mortality) Relationship between adult mortality and annual

fecundity in 14 bird species Birds with high probability of dying during any given year usually raise more offspring each year than those with a low probability of dying. Wandering albatross; lowest fecundity (~.2 offspring/yr – single surviving offspring every 5 yrs) & lowest mortality. Tree sparrow; >50% chance of dying from one breeding season to another, produces average of 6 offspring per year

Organisms have finite resources to invest in components of their life history; Trade-offs between investments in reproduction and survival are a consequence

•Selection favors (heritable) life history traits that allow individuals to maximize lifetime reproductive success; these traits will become more common in a population

•Natural selection can not simultaneously “maximize” all the life history traits that can potentially contribute to the greatest lifetime reproductive output, because organisms have finite resources to invest; this mandates trade-offs. eg few, well-provisioned offspring vs many offspring, each with little chance of survival.

•Trade-offs have been demonstrated:

•between investing in current reproduction and survival (eg seed beetles)

•between investing in current reproduction and future reproduction

Manipulating fecundity of female seed beetles by denying access to males or egg-laying sites causes a trade off in adult longevity and fecundity

Manipulating clutch size in collared flycatchers (add or remove eggs) results in direct trade off between number of chicks raised that year and the next year’s fecundity (no effect of current fecundity on adult survival in this study)

Population Regulation: Two categories of factors affect population size

Density-independent factors

•Any environmental factor that affects population size, but its effect is not influenced by population size

Density-dependent factors

•Any environmental factor that affects population size and its effect is influenced by population size

Density-independent factors

Typically abiotic, often weather-related

•e.g. in insects, winters kill off all individuals except eggs and dormant larvae

Often random (unpredictable)

•e.g., blizzard, flood, fire

Effects may be indirectly related to density

•e.g., social animals often able to endure weather by collective behavior -- sheep huddling in snow storm

(Solomon et. al. 1999)

Density dependent factors

Important density dependent factors

•Competition, predation, disease

Effects are proportional to population size

•Density-dependent factors exert stronger effect as population increases

Solomon et. al. 1999

Effect of lizard presence on spider density. Tropical islands with lizards typically have few spiders. Spiller and Schoener (UC-Davis) tested the effect of presence of lizards on spider population density on Bahamian Islands

•12 Bahamian islands; all islands have native spider populations, 4 have lizards, 8 do not have lizards

•Lizards introduced in enclosures on 4 islands without native lizard populations.

•After 7 years, spider densities were higher in lizard-free islands (enclosures).

•Species diversity of spiders was also greater on lizard free islands.

•Due to predation (lizards eat spiders), or competition (lizards and spiders compete for insect food)?

Age interval (in months)

Number alive at beginning of age interval

Proportion alive at beginning of age interval

Number dying during age interval

Death rate for age interval

No. seeds produced per ind. during age interval

•Age-specific demographics for 9-12 month cohort•death rate = 172/316=.544•per capita birth rate = 430

Age interval (in months)

Number alive at beginning of age interval

Proportion alive at beginning of age interval

Number dying during age interval

Death rate for age interval

No. seeds produced per ind. during age interval

•Cohort members began reproducing after 3 months of age; •Then reproduced throughout life; all dead by 2 years of age•Probability of age-specific survival decreases with age•Can calculate age-specific birth rates and death rates

Time

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factor N out on right side of equation

N = (b-d) t N

(Keeton & Gould 1993)

…and dividing both sides by N gives the per capita growth rate (b-d)...

Time

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N = rN t

Under these conditions of maximum birth rate and minimum death rate:

r is designated rmax

rmax represents the intrinsic rate of increase, or biotic potential of the population

rmax varies widely among species

(Keeton & Gould 1993)

Time

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N = rmaxN t

Under these conditions of maximum birth rate and minimum death rate:

r is designated rmax

rmax represents the intrinsic rate of increase, or biotic potential of the population

rmax varies widely among species

(Keeton & Gould 1993)