microbial ecology theory [kompatibilitätsmodus] · microbial ecology theory ... loosing the fear...

1

Microbial ecologyTheory

Helmut HillebrandHelmut Hillebrand

Lecture aims

Understanding general ecological principles for population growth and species interactions

Understanding the use of

Ecology is the scientific Ecology is the scientific study of the interactions study of the interactions

that determine the that determine the distribution and distribution and

theory to explain microbial patterns of distribution and abundance of organisms

Realizing the value of microbial model ecosystems

Loosing the fear for math

abundance of organisms abundance of organisms (C.J. Krebs).(C.J. Krebs).

Why theory and models?

• Nature is complex

Theory and models simplify and order complexity to make it more understandable

• Theory and models generate predictions

This allow us to make testable hypotheses about the way populations, communities and ecosystems operate

• Mathematical models force us to be exact

Our assumptions and their consequences are spelled out

• Theory and models can be either tactical or strategic

Tactical models make precise predictions about a particular system

Strategic models give the general behaviour of many systems

2

Structure

Population dynamics Resource uptake and numerical response

Regulation of population size

C titi Competition Exploitative competition

Interference competition

Predator prey dynamics Two-species interactions

Reticulate food webs

Structure







Definition resources

Definition: Resources are environmental factors, which can be consumed and thus taken away from other individuals Nitrate, phosphate, light are important resources on

se gfor plants Space can be a resource, too, but is often a proximate

resource only

Temperature, pH are not resources, but factors Definition is context-dependent Light is an environmental factor for animals and a

resource for plants

Res

ourc

eup

take

and

num

eric

alre

sp

3


Plant functionsPhotosynthesis

6 CO2 + 6 H2O + 2802kJ

=> C6H12O6 + 6 O2

ResourcesLight, CO2, Water

Chlorophyll: N, Mg

Enzymes: N, Fe, Caonse

6 12 6 2

Nucleic acids: P Macronutrients

N, P, K, Ca, Mg, S

Micronutrients

Fe, Cl, Mn, B, Zn, Cu, Ni, Mo

For some plants

Na, Co, Si

Res

ourc

eup

take

and

num

eric

alre

sp


Animal functionsChemoorganoheterotroph

Resourcesorganic

compounds (prey items)

oxygenonse

C6H12O6 + 6 O2

=> 6 CO2 + 6 H2O + energy

oxygen

Res

ourc

eup

take

and

num

eric

alre

sp


Resources

CO2

H2S

onse

O2 or NO3-

Res

ourc

eup

take

and

num

eric

alre

sp

4


Essential resources Autotroph organisms

Substitutable resources Complementary

resources Higher concetrations lead on

se

gto better resource use Growth on mixcture faster

Antagonistic resources Higher concentrations of

R1 reduce the growth on R2, e.g., by inhibiting uptake Growth on mixture slower

than on single resources

Inhibition Toxicity at high

concentrations

Res

ourc

eup

take

and

num

eric

alre

sp

ZNGI: Zero net growth isoclinesPoints in parameter space where grwth = mortality

Definition resources Limiting resources: Liebig’s law

of the minimum 1840 THE RATE OF A REACTION PROCEEDS IN

PROPORTION TO THE AVAILABILITY OF THE REACTANT IN SHORTEST SUPPLY.

Essential resources are all resources not possibly

th i d b th i 0

10

20

30

40

50

60

70

80

90

Supply

Demand

onse

synthesised by the organism 0N P Si Light

0

5

10

15

20

25

30

N P Si Light

Supply /Demand

Res

ourc

eup

take

and

num

eric

alre

sp

Definition resources However, Liebig’s Law might not

hold on all scales of biological organization Co-limitation may be predominant in

Individuals = physiological co-limitation (R1 needed to take up R2)

Populations = some individuals limited b R1 th b R2

onse

Elser et al. 2007

by R1, others by R2 Communities = some species specialists

for R1, others for R2

Empirical evidence: Addition of N*P yields much higher growth responses of autotrophs than predicted from singl nutrients (Elser et al. 2007)

Theory: Conceptualization of co-limitation scenarios for factorial experiments (Ngai et al. submitted)

Res

ourc

eup

take

and

num

eric

alre

sp

Ngai et al. submitted

5

Functional response Functional

responses describe the uptake of a resource in dependence of resourceon

se

resource concentration

3 functional response types by Holling

Res

ourc

eup

take

and

num

eric

alre

sp

Functional response Michaelis-Menten

kinetics (= Holling’s type II)

Describes maximum uptake and half saturation constant

onse

Res

ourc

eup

take

and

num

eric

alre

sp

Type I Type II Type III

ILL ILL

Imax

ILL

onse

Functional response

Imax R2

km2 R2

Imax Rkm R

I < ILL : I Imax

ILLR

I > ILL : I Imax

I: Ingestion rate Imax: Maximum ingestion rate

km: Half saturation constant R: Prey abundance

ILL: Incipient limiting level; prey concentration where ingestion rate becomes maximal

Prey abundance

Res

ourc

eup

take

and

num

eric

alre

sp

6

Functional response The functional

response to light is a special case of Type II FR visualized by photosynthesis-irradiance curves

P-I-curves have aonse

P I curves have a number of important aspects Icrit Imax Iinh

Res

ourc

eup

take

and

num

eric

alre

sp

Functional response

Examples for Type I-IIIType 3: Bluebottle fly feeding on sugar droplets See reduced handling time andon

se

Type 1: Daphnia feeding on yeast cells

Type 2: Damselfly larvae feeding on Daphnia

handling time and increased searching efficiency leading to the sigmoidal functional response

Res

ourc

eup

take

and

num

eric

alre

sp

Functional response

Species can vastly differ in theioron

se

theior functional responses Plants FR

on light

Res

ourc

eup

take

and

num

eric

alre

sp

7

Numerical response

Resource uptake efficiency (functional response of the individual) affects also growth efficiency (numerical response of the population)

onse

Res

ourc

eup

take

and

num

eric

alre

sp

Numerical response

Somatic growth for microalgae very uncommon, therefore growth = population growth

Vegetative division of cells

Growth rate µ=birth-deathsonse t

NNµ t 0lnln

µ

Res

ourc

eup

take

and

num

eric

alre

sp

Numerical response Limiting resources reduce

growth rates If consumption tightly

coupled to growth processes (no storage): Monod Model

skS

Sµ

max

Monod

0.1

0.2

0.3

0.4

0.5

0.6

Gro

wth

rat

e

µmax

onse

Monod-Model (mathematically equivalent to Michaelis Menten)

Growth rate depends on external concentration of substrate

Examples: three species of desmid algae in chemostats

0

0 2 4 6 8 10 12 14

Substrate concentration

ks

Res

ourc

eup

take

and

num

eric

alre

sp

8

Numerical response

Typical values for µmax and half saturation constants ks for P, measured in P-limited chemostats

Diatoms with low, N-fixing cyanobacteria with high ks

skS

Sµ

max

onse

Species Ks (nmol L-1) µmax (d-1)

Nitzschia actinastroides 14 2.1

Scenedesmus protuberans 190 1.6

Selenastrum capricornutum 40 1.2

Aphanizomenon flos-aquae 50 1.3

Microcystis aeruginosa 400 1.1

Data from Kohl & Nicklisch 1988

Res

ourc

eup

take

and

num

eric

alre

sp

Numerical response

Limiting resources reduce growth rates If consumed nutrients are

stored and growth and consumption uncoupled: Droop-M d l

Q

qµ 0

max 1̂

Droop

0

0.1

0.2

0.3

0.4

0 2 4 6 8 10 12 14

Gro

wth

rat

e

onse

Model Growth rate depends on

internal concentration of nurtient

Example: Algal growth dependent on B12 concentrations Note difference in definition

µmax

0 2 4 6 8 10 12 14


Res

ourc

eup

take

and

num

eric

alre

sp

Numerical response

Typical values for µmax and minimum cell quotas Q0 for P, measured in P-limited culture

Species Q0 (fmol cell-1) µmax (d-1)

Q

qµ 0

max 1̂

onse

Species Q0 (fmol cell-1) µmax (d-1)

Cyclotella meneghiniana 10.7 0.69

Fragilaria crotonensis 3.34 0.9

Scenedesmus sp. 1.6 1.33

Anabaena flos-aquae 2.63 0.89

Microcystis aeruginosa 1.58 1.15

Data from Kohl & Nicklisch 1988

Res

ourc

eup

take

and

num

eric

alre

sp

9

0255075

(m)

Numerical response

Numerical response to light regulates onset of phytoplankton blooms in temperate seas

zmix

75100125150175200

Dep

th

J F M A M J J A S O N D

30

20

10

0

Pla

nkto

nbi

omas

s

Ligh

t A

vaila

bilit

y

Critical mixing depth (zcrit) Sverdrup 1953: P > R Mixing depth zmix decreases

through temperature Light intensity increases

during spring => zcrit

increases If zmix < zcrit onset of

bloom.

light

zcrit

zooplankton

Numerical response

50000400003000020000

Ph

ytop

lan

kto

nb

un

da

nce

/L

Spring bloom North Pacific (Sverdrup1953)

onse

5 10 15 20 25 30 4 9 14 19 24 27 4 9 14 19 24 29

MARCH APRIL MAY

100000

100

200

Mixed-layer depthCritical depth

P aD

epth

(m

)

Res

ourc

eup

take

and

num

eric

alre

sp

Numerical response

The chemical content of prey items in important minerals might constrainon

se

might constrain herbivore performance Urabe and Sterner

1996 PNAS

Res

ourc

eup

take

and

num

eric

alre

sp

10

Numerical response

Daphnia feeding on two algal food sources (Scenedesmus and Synechococcus)

The low fatty acid onse

content of Syn reduces somatic growth of juvenile Daphnia, unless they are amended by artificial sterols. Martin-Creuzberg et al.

2005 Oecologia

Res

ourc

eup

take

and

num

eric

alre

sp

Structure







Population growth

Discrete population growth Stepwise growth, often

by simultaneous reproduction (discrete 50

60

70

size discretep (

reproduction events): Many macroalgae have seasonal patterns of spawning

Continuous growth Reproduction and

mortality randomly distributed in time, most microalgae

0

10

20

30

40

0 2 4 6 8

Time

Po

pu

lati

on

continuous

Reg

ulat

ion

ofpo

pula

tion

size

11

Population growth

Net population growth = gross growth – mortality

r = µ - mµ In population

ecology, r is defined as the specific (per-capita) net growth rate

Ndt

dNr

1

Reg

ulat

ion

ofpo

pula

tion

size

Exponential growth: Examples

Nt = N0•ert ln(Nt) = ln(N0) + r•t

N0 = 100

Gotelli (2001)

Reg

ulat

ion

ofpo

pula

tion

size

Population growth Predicting population

size at time t

rdt

dN

N

1

dNdNNdNd

1lnln

trt eNN 0

Reg

ulat

ion

ofpo

pula

tion

size

trNN

dtrN

dtrNd

rdt

Nd

t

tt

)ln()ln(

)ln(

)ln(

)ln(

0

00

dtNdtdNdt

12

Population growth

ln(Nt ) ln(N0) r t

ln(2 N0) ln(N0) r tdouble

ln(2) ln(N0) ln(N0) r tdouble

ln(2) r tdouble

tdouble ln(2)

r

Gotelli 2001

Reg

ulat

ion

ofpo

pula

tion

size

Population growth Consequences of exponential growth

If you place one bacterial cell on the first plate of a checkerboard, how many do you get at plate 64 if the divide at every step

9.22337E+18

Reg

ulat

ion

ofpo

pula

tion

size

Exponential growth: Assumptions1. Constant b and d

With increasing population numbers, birth and death rates do not change

There is also no random variation in b or d

2. No genetic structure

Population growth

Either the population is clonal and without genetic mutations, or the growth rate is the average of the different genotypes within the population

3. No age or size structure

b and d are independent of the age of the individual - a newborn has the same probability of giving birth or dying as an older member of the population

4. Continuous growth with no time lags

Both births and deaths occur continuously, and the rate of increase changes instantaneously as a function of the current population size

Reg

ulat

ion

ofpo

pula

tion

size

13

Population regulation

What limits population growth Density-independent control

External forces restrict abundances (e.g. disturbances or consumer presence) although resources are not limiting

0 3

0.4

0.5

0.6

0.7

0.8

Rat

es Mortality

Growth

Time

Ab

un

dan

ce

Dep

Indep

Density dependent control Resources become limiting at

higher density, the growth rate decreases and the population stands at a maximum abundance.

Intraspecific competition Carrying capacity K: the

abundance possibly attained due to the resource amounts present

0

0.2

0.4

0.6

0.8

1

1 25 50 75 100 125 150

Density

Rat

es Mortality

Growth

0

0.1

0.2

0.3

1 25 50 75 100 125 150

Density

R

Reg

ulat

ion

ofpo

pula

tion

size


Reg

ulat

ion

ofpo

pula

tion

size

Population regulation Density dependent control

Negative feedback of density on growth rate by carrying capacity K

10

20

30

40

50

Ab

un

da

nc

e

Abundance

CapacityN

0

0 2 4 6 8 10Time

0

0.2

0.4

0.6

0.8

0 2 4 6 8 10Time

r (g

row

th r

ate

)

trt

eN

NKK

N

K

NKNr

dt

dN

K

Nrr

max

0

0

00

max

1

1

Reg

ulat

ion

ofpo

pula

tion

size

14

Logistic Growth Rates: Example

200

250

300

20

25KK/2

0

50

100

150

0 20 40 60 80 100

N

Time

r = 0,3

r = 0,2

r = 0,10

5

10

15

0 50 100 150 200 250 300

dN/d

t

N K

K/2

Population regulationDensity decreases offspring massFish, J. Travis, FSU

Density decreases offspring survival in lizards

Density inreases egg mortality rate in copepodsOhman et al. Nature

Reg

ulat

ion

ofpo

pula

tion

size


Batch cultures represent a clear example of density dependencedependence Above yeast, below

phytoplankton (Pseudo-nitzschia)

Reg

ulat

ion

ofpo

pula

tion

size

15


Growth phases in batch culture

Logistic if r hreaches zero

at high densities

Decay at end if r <0

Reg

ulat

ion

ofpo

pula

tion

size

Population regulation The effect of

different densities may depend on key life-history traits.

Higher densities of barnacles reduce mortality. Even ythough high-density barnacles are significantly smaller, and produce less offspring, these individual-based negative effects of density are outweighed by the higher survival rate at the population level.

Leslie 2005, Ecology

Reg

ulat

ion

ofpo

pula

tion

size

Population growth Negative feedback between

growth rate and density often delayed by time-lag (e.g. by the production of resting stages)

Capacity K is not a stable, but oscillating point

0

20

40

60

80

100

120

abu

nd

ance

oscillating point Oscillations depend on extent

of time-lag τ If r*τ > 2: steep peaks with extended

minima If 2 < r*τ > π/2: stable oscillations If 1/e > r*τ < π/2: decreasing

oscillations If 1/e < r* τ: no oscilliations

0 5 10 15 20 25 30 35

time

0

100

200

300

400

500

600

0 10 20 30 40 50 60 70 80

TIME

Ab

un

dan

ce

r 0.15 T =2

r 0.4 T =2

r 0.4 T =4

r 0.6 T =4

K

NKNr

dt

dN tt

Reg

ulat

ion

ofpo

pula

tion

size

16

Environmental stochasticity is variation in the intrinsic growth rate brought on by

changing environmental conditions - e.g. warmer or colder, more or less food.

In this case, we replace the exact abundance and growth rate with a mean

abundance and mean growth rate, i.e.

Instead of this

Environmental Stochasticity

tInstead of this,

This:

Where

And W is a random variable producing white noise

Nt N0 ert

Nt

_

N0 er_

t

r_

r r2 W

Reg

ulat

ion

ofpo

pula

tion

size

Environmental stochasticity example

30

40

50

60

70

80

Nt

100

150

200

250

3000

4000

5000

6000

r = 0,5 r2 = 0,001 r = 0,5 r

2 = 0,002 r = 0,5 r2 = 0,011

0

10

20

30

0 2 4 6 8 10 12 14 16 18 200

50

0 2 4 6 8 10 12 14 16 18 20

Time0

1000

2000

0 2 4 6 8 10 12 14 16 18 20

When r2 > 2•r, then the chance of extinction is essentially 100%

Reg

ulat

ion

ofpo

pula

tion

size

Intraspecific competition

Density dependece intraspecific competition

Intraspecific competition results in a negative relationship between D

ensi

ty /

Bio

mas

spe

r a

rea

pdensity and individual biomass

Self-thinning law within organisms which can grow somatically

Density (individuals per area)

Bio

mas

sD

per

indi

vidu

alp

Time

Reg

ulat

ion

ofpo

pula

tion

size

17


Self thinning law w = average biomass

per individualC = constant associated with a particular speciesN = density of the populationk = parameter for the thinning relations = 3/2

Trajectories for populations over time

Reg

ulat

ion

ofpo

pula

tion

size


Self thinning law w = average biomass

per individualC = constant associated with a particular speciesN = density of the populationk = parameter for the thinning relations = 3/2

Trajectories for populations over time

Reg

ulat

ion

ofpo

pula

tion

size


Self-thinning law within species can be used to predict macroecologicalmacroecological density - size plots across species

Reg

ulat

ion

ofpo

pula

tion

size

18

The brightest and the muddiest point

Resources, resource types

Functional response, numerical response

P l ti th Population growth, µ, r

Density dependent regulation of population size, K


Structure







Interactions

Mutualism

Competition

A B+

+

A B-

-

Consumption Predation

Facilitation

A B

+

-

A B+

0

19

Competition

Competition is for limiting resources

Competition can be E l it ti titi

S1 S2indirect

Exploitative competition: Species interact not directly, but consume a commonly needed resources

Interference competition: Species defend territories or inhibit growth of comptitor by toxic compounds

R

S1 S2direct

Competition

Two species competing for the same resource do not

monocultures

coexist at equilibrium

Comptitive exclusion principle

Gause 1934

mixtures

Exp

loita

tive

com

petit

ion

Lotka-Volterra (or Gause-Volterra)competition equation

dN1

N1 dt r1

dN1

N1 dt r1

K1 N1

K1

If per-capita exponential growth of species 1 is

simply:

And logistic growth reduces this by (K1-N1)/K1

dN1

N1 dt r1

K1 N1 1,2 N2

K1

dN2

N2 dt r2

K2 N2 2,1 N1

K2

Then with competition, this is further reduced by

the competition factor, times the number of

species 2 present:

The logic for species 2 is the same:

20

L-V competition equation assumptions

1. Resources are in limited supply. If resources are not limiting, then any

number of species can coexist, regardless of how similar they are in

resource use.

2. Competition coefficients (1,2 and2,1) and carrying capacities (K1 and K2)

are constants that do not change with time or density.

3. Density dependence is linear. Adding an individual or either species

produces a strictly linear decrease in per capita population growth rates.

Competition - equilibrium conditions

At equilibrium

This means

dN1

N1 dt 0 and

dN2

N2 dt 0

0 K1 N1 1,2 N2

K1

and 0 K2 N2 2,1 N1

K2

K1 N112 N2 and K2 N2 2 1 N1

Or

In the logistic growth equation, at equilibrium K = N, but with competition the

abundance of species 1 is less than K: the equilibrium abundance is K,

minus the discounted abundance of species 2. The same is true for the

equilibrium abundance of species 2.

1 1 1,2 2 2 2 2,1 1

N1 K1 1,2 N2 and N2 K2 2,1 N1

Lotka-Volterra Competition Lotka-Volterra models Description of effect of

competitor 1 and 2 by adding coefficients α and β α12 is the per-captia effect

of species 2 on the growth of species 1

2

121222

2

2

1

212111

1

1

1

1

K

NNKr

Ndt

dN

K

NNKr

Ndt

dN

of species 1 α21 is the per-captia effect

of species 1 on the growth of species 2

12122

21211

0,

NKN

NKN

rmEquilibriu

Exp

loita

tive

com

petit

ion

21

Lotka-Volterra Competition Lotka-Volterra models

12122

21211

NKN

NKN

If S1 absent, N 2

Exp

loita

tive

com

petit

ion

If S2 absent, N 1 = K1

,= K2

If S1 increases, N 2 decreasesdown to K2/21

If S2 increases, N 1 decreasesdown to K1/12

Lotka-Volterra Competition Lotka-Volterra models

Competition results are based on relative ZNGI

Stableequilibrium, S 1 wins

If S1 absent, N 2

Stableequilibrium S 2 wins

Exp

loita

tive

com

petit

ion

,= K2

Lotka-Volterra Competition

If S1 absent, N 2

Scenario 3. K1> >K2/21 and K2> (K1/12) Interspecific competition stronger than intraspecificOutcome depends on the initial abundances of the two speciesYellow area: competitive exclusion of species 2 by species 1. Pink area: competitive exclusion of species 1 by species 2.

Exp

loita

tive

com

petit

ion

,= K2

Senario 4: Both species' K < other's (K/) (intraspecific competition stronger than interspecific). Joint trajectories always head toward intersection = stable equilibrium point coexistence regardless of the initial abundances.

22

Lotka-Volterra Competition

Lotka Volterra competition successfully used in models, but not predicitve (one has to do the experiment to obtain

) and not mechanistic

Monod

0.2

0.3

0.4

0.5

0.6

Gro

wth

rat

e

µmax

) and not mechanistic Most parsimonious

competition model by Tilman (1977, 1982) based on supply-specific growth rates in competing species Cf. numerical response

0

0.1

0 2 4 6 8 10 12 14


G

ks

Droop

0

0.1

0.2

0.3

0.4

0 2 4 6 8 10 12 14


Gro

wth

rat

e

Exp

loita

tive

com

petit

ion

Competition for 1 resource

S1

µ1

µ2

2

S2

Resource concentration

m1

R*1µ=m

Both species have positive growthS1 growsS2 dies

m2

R*2µ=m

Exp

loita

tive

com

petit

ion

R2

Competition for 2 resources

R2

S1:R1* S2:R1*S1 wins: R1 too low for S2

S1 wins: depletesR1 below S2:R1*

S2 wins:

CoexistenceS1 lim by R2S2 lim by R1

R1S1

R1

S1

S1:R2*

S2

S2:R2*

S1 has lower R* for both R1 and R2S1 excludes S2 irrespective of supply

S2

S1 has lower R* R1 & S2 for R2Competitive success depends on supplyratio


S2 wins: R2 too low for S1

Exp

loita

tive

com

petit

ion

23


Predictions from Tilman’s model: In well-mixed communities at equilibrium, the

number of coexisting species is equal or lower than the number of limiting resourcesg

The identity and number of coexisting species depends on the resource supply ratio

A prerequisite is that species have an inverse relationship between R* for 2 resources: A good competitor for R1 has to be a bad competitor for R2.

Exp

loita

tive

com

petit

ion


Trade-offs make such inverse relationships probableprobable Diatoms of

Lake Michigan show an inverse ranking in their R* for Si and P

Exp

loita

tive

com

petit

ion


Tilman used 2 planktonic diatoms to test his model (Tilman 1977) Asterionella is a better competitor

for P, Cyclotella for Si

Exp

loita

tive

com

petit

ion

24


Predictions hold for 76 long-term competition experiments except for a few borderline cases (Tilman

1977)

Exp

loita

tive

com

petit

ion


tion

of b

iom

ass

Sommer 1985: P and Si limiting2 species survive

0.3 0.6 1 3 10 25

Si:N ratio

prop

ort

Dunaliella Chaetoceros

Nitzschia Asterionella

Stephanopyxis

modified from Sommer ‘96

Exp

loita

tive

com

petit

ion


Field evidence for Tilmans’ model

With increasing number of limiting resources, the relative frequency of high diversity peaks goes up.

Relative frequency distributions for phytoplankton diversity associated with samples in which different numbers of limiting resources were measured. Interlandi & Kilham 2001,

Ecology

Exp

loita

tive

com

petit

ion

25

Competition for light Competition for light

shows some important deviance from competition for mineral nutrients nutrients (Tilman 1982):

R*: µ = m; R* constant utri

en

t co

nce

ntr.

Biom

ass

R*

R*

Art 1

2

3

4

time

I out

Biom

ass

Iout*Iin Iout

R : µ = m; R constant independent of start concentration

light (Huisman & Weissing 1994, 1995, 1999): Iout*: µ=m; Ioutinfluenced by Iin

time

Nu R*

Supply

I in

I out

Art 1

2

3

4

Iout*

Iin Iout Competive light dominant depends on resource supply

Exp

loita

tive

com

petit

ion

Competition for light Empirical test of the Huisman-Weissing model

The species with the lowest Iout* wins all 2- and 4 species competition experiments

Exp

loita

tive

com

petit

ion

Competition for light Stomp et al. 2004 (Nature)

Coexistence by partitioning of the light spektrum Green and red pigmented

picocyanobacteria coexist in white light by partitioning wavelength, whereas they are unable to coxist ingreen or red light

Exp

loita

tive

com

petit

ion

26

Competition for light

Stomp et al. 2004 (Nature) Coexistence by partitioning of the light spektrum Adding a third cyanobacteria

(Toly) with flexible pigment composition leads tocomposition leads to coexistence via allocation to pigments using the less well used wavelelngths.

Exp

loita

tive

com

petit

ion

Competition for subsitutable resources

Prey as resource, e.g. protists competing for bacteria

N i t ti

R*prey1

peci

es

1

R*prey1 Species 1Species 2

Non-intersecting ZNGI => no coexistence, species 1 always wins R*prey2

Prey species 2

Pre

y sp

R*prey2

Exp

loita

tive

com

petit

ion

Competition for living prey

Competition for substitutable resources

Intersecting

R*prey1

peci

es

1

R*prey1 Species 1Species 2

S1 wins

S2 wins

coexistence

Intersecting ZNGI => stable coexistence possible R*prey2

Prey species 2

Pre

y sp

R*prey2

Exp

loita

tive

com

petit

ion

27

Competition for living prey Experimental test with two

prey species and two consumers

Rothhaupt 1988 Nature Brachionus rubens B.calcyciflorus

Exp

loita

tive

com

petit

ion

Competition for living prey Consumers can

coexist even on one food item if they are stoiciometrically limited bylimited by different resources within the food package

Hall 2004 Am Nat

Exp

loita

tive

com

petit

ion

Preventing competition

Predictions from Tilman’s model:

In well-mixed communities at equilibrium, the number of coexisting species is equal or lower than the number of limiting resources

The observed diversity is much higher, even in well-mixed communities with a small number of limiting resources

There have to processes preventing competitive exclusion

When is coexistence enhanced?

Ho can coexistence be explained mechanistically?

Exp

loita

tive

com

petit

ion

28


Stable

Pulsing resources increases the number of coexisting species

Temporal heterogeneity prevents competitive exclusion

Oscillating

Decreasing

Exp

loita

tive

com

petit

ion


Under stable temperatures, Colpidium and Paramecium do not always coexist

The competition effect of C on P is large especially at intermediate temperature.

Under fluctuating temperatures, this competition effect is reduced and the species coexist

Jiang & Morin 2007 JAE


Patchiness of resources in space increases the number of coexisting species: Spatial heterogeneity prevents g y pcompetitive exclusion Example: Snails feeding on

periphyton differ in the ability to utilize patches and find patches.

Intermediate patchiness allows more species to coexist (Chase et al. 2001)

Exp

loita

tive

com

petit

ion

29


Low mortality, butexclusion takes place

High mortality, butexclusion prevented

Disturbance frequencyDisturbance intensity

Div

ersi

ty

Exp

loita

tive

com

petit

ion

Connell 1978


Flöder & Sommer 1999Flöder & Sommer 1999

Exp

loita

tive

com

petit

ion


Disturbance and productivty interact

Response of either variable depends on thevariable depends on the other (Kondoh 2001) at high D, S increases

with P at low D, S decreases

with P at low P, S decreases

with D at high P, S increases

with D

Exp

loita

tive

com

petit

ion

30


0.1

0.8

1.5

D6 4

-1.82.87.412.0

SRESLAT

Stream periphyton-Disturbance measures-Productivity

-9.5 -0.2 9.1 18.4 27.7 37.0P

-0.6-11.0-6.4

Dep Var: S(reslat) N: 84 R2: 0.30

Source Coef t Pr Int 2.31 3.09 0.00P 0.42 4.86 0.00D 2.96 1.61 0.11P2 -0.02 -4.82 0.00D2 -4.59 -1.76 0.08P*D 0.60 3.46 0.00Cardinale & Hillebrand 2006

Exp

loita

tive

com

petit

ion


Competitive exclusion takes time

Processes reverting competitive dominance prevent competitive exclusion unstable coexistencecoexistence

Which mechanisms drive stable and unstable coexistence?

Exp

loita

tive

com

petit

ion

Species coexistence

Coexistence mechanisms function in two major ways (Chesson 2000, AREES). They may be

(a) equalizing because they tend to minimize average fitness differences between species, or

(b) stabilizing because they tend to increase (b) stabilizing because they tend to increase negative intraspecific interactions relative to negative interspecific interactions.

Only stabilizing mechanisms result in stable coexistence, i.e. higher intraspecific than interspecific competition is a necessary prerequisute for stable coexistence

Exp

loita

tive

com

petit

ion

31

Species coexistence Stabilizing mechanisms require trade-offs

R* for different resources

Trade-off in resource competition (R*) versus ability to withstand (and sustain) predation (P*) (further depends on predator population dynamics)

Exp

loita

tive

com

petit

ion

Species coexistence

Some of these trade-offs require fluctuations

Colonization-competition trade-off

Cadotte 2006

Exp

loita

tive

com

petit

ion

Species coexistence Unstable

coexistence mechanisms comprise equalizing mechanisms, i.e., at

io

factors reducing fitness differences

This become more and more important as more the species show niche overlap Chesson & Kuang

2008, Nature

Exp

loita

tive

com

petit

ion

Niche overlap

Fitn

ess

ra

32

Competition for >2 resources

Assumed that same mechanisms prevail, but until recently not tested

Models by Huisman and Weissing predict h ti fl t ti t 3 ( tchaotic fluctuations at > 3 resources (next

slides) No competitive exclusion Problems: Parameter space, assumptions,

no experimental test so far

Exp

loita

tive

com

petit

ion


Huisman & Weissing 1999, 2001

A high number of species coexist on 3 limiting resources

Exp

loita

tive

com

petit

ion

Competition for >2 resources The outcome of competition in

this model is truely chaotic

Competition of 5 species, outcome blue = species 1–3 win; yellow = species 1, 4, and 5 win. In the speckled areas, the outcome of competition is very sensitive to the initial conditions. B magnified from A

Huisman & Weissing 2001

Exp

loita

tive

com

petit

ion

33


Only experimental test so far

Beninca et al. 2008: a, Food-web structure of

Cyclopoids Calanoids, Rotifer, Protists

web structure of the mesocosm experiment. b–g, Time series of the functional groups in the food web

Exp

loita

tive

com

petit

ion

Pico, Nano, diatoms DIN SRP

Bacteria Benthic meiofauna

Structure








Allelopathy also in aquatic communities: Myriophyllum spicatum plants inhibit growth of a cyanobacterium (Anabaena) Leu et al. 2003

Inte

rfer

ence

com

petit

ion

34


Temperature increases aggression in stream fish as well oxygenated sites become scarcebecome scarce

In streams where they cooccur, S. malma deters S. leucomaenis from suitable sites (a) Taniguchi and

Nagano 2000

Inte

rfer

ence

com

petit

ion


Inte

rfer

ence

com

petit

ion

Interference competition Competition between fungi and bacteria

Treatments: Fungi only (F), F and bacteria (F+B) inoculated simultenously, pregrown B ( B were allowed to grow before addition of F), subs: additional substrate

Bacteria reduce fungal biomass, especially if pregrown. Fungi reduce bacteria compatred to F-free control (Mille-Lindblom et al 2006 Oikos

Inte

rfer

ence

com

petit

ion

35


Competition between animals and bacteria (Burkepile et al. 2006 Ecology)

Crabs feed less on aged carrion, but only if this is colonized by mocrobes. Aged carrion with antibiotics is used as much as fresh

Inte

rfer

ence

com

petit

ion

Aged

The brightest and muddiest points

Competition as a -/-interaction

Lotka-Volterra models and Tilman’s model Mechanistic

R2



S2 wins:

CoexistenceS1 lim by R2S2 lim by R1

Mechanistic understanding of competition

Competitive exclusion and how it is prevented Fluctuating

environments, spatial heterogeneity, evolution


R1S1 S2



Structure







36

Interactions

Mutualism

Competition

A B+

+

A B-

-

Consumption Predation

Herbivory

Parasitism

Facilitation

A B

+

-

A B+

0

Effects of consumers

Net growth rates of prey decline with increasing consumer biomass

DaphniaKagami et al. 2002, Oecologia

Pre

dato

rpr

eydy

nam

ics

Effects on phytobenthos

Consumer presence reduces attainable biomass Grazer benthic

Ch

loro

ph

yll a

(µ

g c

m-2

)

a b c

0

5

10

15

20

25

NE

2 )

25

Grazer benthic microalgae in Lake Erken, Sweden After Peters et al. 2007

JNABS

Ch

loro

ph

yll a

(µ

g c

m-2

0

5

10

15

20 NW

Ch

loro

ph

yll a

(µ

g c

m-2

)

0

5

10

15

20

25

ex op ex ep ex op

SE

Pre

dato

rpr

eydy

nam

ics

37

Effects of consumers

S1

µ1

m2

Resource concentration

m1

R* R**µ=m1 µ=m2

Consumer presence reduces prey net growth rate and increases minimum R*

Pre

dato

rpr

eydy

nam

ics

Predator-prey dynamics

Lotka-Volterra model predicts oscillations µ: prey growth rate c: consumption rate a: coefficient determining how many prey is

needed for one offspring d: death rate NNcNµ

dt

dNcppp

p At equilibrium (both = 0) you

can calculate the minimum density of prey needed for existence of consumer and the maximum number of consumer allowing prey to exist

Unrealistic, lacks feedbacks, e.g. on µ

c

µN

ca

dN

mEquilibriu

NdNNcadt

dNdt

c

p

cpcc

*

*

Pre

dato

rpr

eydy

nam

ics


1. Prey are not food or resource limited.

2. The only source of mortality for prey is predation.

Lotka-Volterra: Assumptions

Pre

dato

rpr

eydy

nam

ics

3. Predator growth rate is only dependent on the number of prey they have consumed.

4. Predators have a density independent mortality rate.

5. The predation rate is a linear function of prey density, without a maximum.

38


Pre

dato

rpr

eydy

nam

ics


Pre

dato

rpr

eydy

nam

ics

Predator-prey dynamics Examples for

predator-prey oscillations

Pre

dato

rpr

eydy

nam

ics

39

Seasonal succession in plankton as an example of predator-prey dynamics

Pre

dato

rpr

eydy

nam

ics

Empirical tests of L-V models

Predator = Paramecium caudatumPrey: Didinium nasutum

Gause (1934)

Pre

dato

rpr

eydy

nam

ics


Lotka-Volterra: Reduced encounter rates

Luckinbill LS (1973) Ecol 54:1320-1327P

reda

tor

prey

dyna

mic

s

40

Lotka-Volterra: Increased prey growth rates

Control


(the paradox of enrichment)

Increased resources

(bacteria) for Paramecium

Luckinbill LS (1973) Ecol 54:1320-1327P

reda

tor

prey

dyna

mic

s

Lotka-Volterra: Reduced prey growth ratesFinally, stable cycles!


Luckinbill LS (1973) Ecol 54:1320-1327

Pre

dato

rpr

eydy

nam

ics

Spatial predator-prey dynamics

Huffaker 1958: Predator and prey (2 mites) only coexist in a spatiallyheterogeneous world, whereas they drive each other to extinction in a homogenous world. P

reda

tor

prey

dyna

mic

s

41


Parasitoid on beetle. Persistence time increased if environment was separated in cells, more cells = more persistence. However, effect collapsed if the cells were undivided. Bonsall et al. 2002

Predatory ciliate Didinium persisted longer in metacommunities (Arrays) whereas increasing the volume to the same total did not enhance perseistence (Holyoak and Lawler 1996)

Pre

dato

rpr

eydy

nam

ics


Pre

dato

rpr

eydy

nam

ics

Structure







42

Interactions in reticulate food webs

Dissolution of complex webs in modules

Ret

icua

late

food

web

s

Keystone Predation

Keystone predators are species which have a much larger impact on the community as could be expected from their abundance

A classic keystone species is a predator that prevents a particular herbivorous species from eliminating dominant plant species or other competitors

Ret

icua

late

food

web

s

Keystone Predation

A classic example for keystone predation Paine et al. 1974

8090

100MytilusInverts

010203040506070

% c

ove

r

1963 +P 1973 +P 1963 -P 1973 -P

Treattment/year

AlgaePisaster

Sessile invertebrates

Mytilus AlgaeComp -

Ret

icua

late

food

web

s

43

Intraguild predation

Holt & Polis 1997Holt & Huxel 2007

Ret

icua

late

food

web

s

Intraguild predation Enriching the basal resource leads

to the extinction of the IG prey (although this is the better competitor) as the IG predator increases in abundance

Diehl & Feissel 2001: Equilibrium densities of (a) the intraguild ( ) gpredator, (b) the intraguild prey, and (c) the resource as functions of enrichment (increasing resource-carrying capacity) for a Lotka-Volterra model of intraguild predation. Different foodweb compositions are: all three species (solid black line), resource with intraguild predator (solid gray line), resource with intraguild prey (open line), and resource alone (dashed line).R

etic

uala

tefo

odw

ebs

Intraguild predation Diehl & Feissel 2001

Symbols: food-web compositions: R + Igprey + IG predator (black circles), R + IGpred (gray squares), and R + Ig prey (open circles). Enrichment scales indicate the concentration of protozoan pellet in the growth medium.

Blepharisma IG predatorTetrahymena IG preyBacteria resource

t e g o t ed u

A: IG pred increases with enrichment in both treatments

B: IG prey increases if alone with resources, but decreases if IG pred is present ( C)

D: IG prey is better competitor for R (draws abundance down more then IG pred, open circle versus square). Bacteria increase with enrichment when alone with IG pred, but do not profit if IG prey is there.

Ret

icua

late

food

web

s

44

Apparent competition

S1 S2--

Competition

Rand 2003, Ecology. Salt marsh: Atriplex is more damaged if more Salicornia is present, leading to reduced Atriplex survival

R

S1 S2

C

--


Atriplex survival when herbivores present (black dots)

Ret

icua

late

food

web

s


Prey isoclines: Without predator (P=0), prey equlibrates at R = R*. Increasing P increases R

d d l di t thneeded, leeding to the ZNGI Ni=0. Given that total resources are fixed, resources not in the prey are either free or in the oredator, so the system is cnstrained by the mass balance constraint (MBC) line.

Ret

icua

late

food

web

s


Ret

icua

late

food

web

s

45


Ret

icua

late

food

web

s

Associational resistance

Cleaning Fucus from fouling organisms increases probability of being grazed

Jormalainen et al. 2008

Ret

icua

late

food

web

s

Associational resistance

Preference for epiphyte-loaded host species

C

Test by Wahl & Hay 1995, Oecologia

Host species brown algae (1 pref. to 6 non.pref.)

Epiphyte (A: preferred, F: non-preferred)

Preference for epiphyte-free host species

Grazer species choose epiphyte-loaded associations if the epiphyte is more preferred than the host-plant. Grazers choose epiphyte-free host species if epiphytes are less preferred than host.

S-i S-e+

Ret

icua

late

food

web

s

46

Selectivity and defense

Daphnia (and other filter feeders) are unable to graze on large phytoplankton

The proportion of such inedible prey increases with grazer presence, especially with enrichment.

Ret

icua

late

food

web

s

Selectivity and defenses Some species

induce colony-formation in the presence of grazers

Long et al. 2007

Phaeocystis may change size by many orders of magnitude when it shifts from small cells of 4–6 µm to large colonies of up to 30,000 µm in diameter. Single cells are consumed by ciliates but not copepods, whereas colonies are consumed by copepods but not ciliates. We demonstrate that chemical cues associated with each of these grazers induce consumer-specific, but opposing morphological transformationsbut opposing, morphological transformations.

Ret

icua

late

food

web

s

Selectivity and defense Defenses can be morphological (stronger

cell walls), chemical (toxins), life-history changes (dormancy)

Defenses can be inducible Inducible defenses under variable

herbivory; Structural defenses under permanent herbivorypermanent herbivory

Toth et al. 2005: Algae produces phlototannins under grazing, but more so at the basal shots (meristems!). This leads to lowered number and percentage of viable eggs of the grazer.

Ret

icua

late

food

web

s

47


Some grazer species can cope with inducible defenses

Stachowicz and Hay 1999

Ret

icua

late

food

web

s


The presence of this grazer shifts competitive domiknance between seaweeds and corals

Stachowicz and Hay 1999

Ret

icua

late

food

web

s

Trophic cascades Cascading interactions in food webs

Without predators herbivores control plant biomass. With predators, herbivores are reduced and plants can grow

Predators have positive effects on plants: Assumes linear food chains Lake pelagic food chains (after Carpenter et al.

1987)

Effects of planktivorous fish, Brett & Goldman 1996, PNAS)

Sea otter – sea urchin – kelp, Estes et al. 1998, Science

Ret

icua

late

food

web

s

48

Trophic cascades

System comparison of trophic cascades in aquatic andaquatic and terrestrial habitats

Shurin et al. 2002, Ecology Letters

Ret

icua

late

food

web

s

Trophic cascades

Trophic cascade of copepods on the components of the mcirobial food webmcirobial food web

Zöllner et al. 2003 FWB

Ret

icua

late

food

web

s

Trophic cascades

Trophic cascades may transcend ecosystem boundariesboundaries Knight et al.

2005, Nature

Ret

icua

late

food

web

s

49

Resources versus consumer

Is the world top-down or bottom-up controlled?

Ret

icua

late

food

web

s


Experimental test by manipulating both resources and consumer presence

0.06

0.08

0.1

me

(mm

3/cm

2)

presence.

Algal example: Grazer presence and resources have simultaneous impact on algal biovolume Hillebrand et

al. 2000, MEPS

0

0.02

0.04

no low med high

nutrient treatment

Alg

al b

iovo

lum

Ret

icua

late

food

web

s

Resources versus consumer Plant biomass, growth and

reproduction is either limited by resources or by being grazed down.

The relative importance of both can be addressed in meta-analysis. Hawkes & Sullivan 2001 Hillebrand 2002; 2005

Ret

icua

late

food

web

s

50


Gruner et al. 2008

Ret

icua

late

food

web

s

Resources versus consumer Competition and

consumer presence reduce the survival of plant species and ultimativelyand ultimatively reduce diversity.

Competition and consumer presence may have interactive effects.

Hillebrand et al. 2007

Ret

icua

late

food

web

s

Resources versus consumer Competition and

consumer presence reduce the survival of plant species and ultimativelyand ultimatively reduce diversity.

Competition and consumer presence may have interactive effects.

Hillebrand et al. 2007

Ret

icua

late

food

web

s

51


Oksanen-Fretwell model predicts an increase in trophic levels with increasing productivity. The highest trophichighest trophic level is always bottom-up controlled, the next one top-down, the next again bottom-up

Oksanen et al. 1981, Fretwell 1977

Ret

icua

late

food

web

s

Resources versus consumer Oksanen-Fretwell model is an equilibrium

model and experimental tests are difficult since they need to include reproduction on all trophic levels. Moreover, the model neglects the presence of omnivores and prey inedibility. Inedible algae may disrupt the sequence

○ No fish ● fish

Consequently, short term experiments show a strong top-down, but comparative studies a strong bottom-up component

Leibold et al. 1997

Ret

icua

late

food

web

s

Carpenter and Kitchell 1993

Food webs

Martinez 1997

vs.

Ret

icua

late

food

web

s

52

Food webs

Important information about trophic structure can be assembled from scaling laws within the food web (e.g. by comparing links per species and connectivity (proportion of links realized) Williams and Martinez 2004

Ret

icua

late

food

web

s

Food webs

Other have used stable isotope techniques to understand the generation of different maximum food chain lengths. Post et al. 2000,

Nature

Ret

icua

late

food

web

s

Food Webs

Microbial food webs

Ret

icua

late

food

web

s

53

Microbial food web

Two classic studies: Pomeroy 1974 and Azam et al. 1983

Ret

icua

late

food

web

s

Microbial food webs

Microbial food webs

Increased DOC supply increased total periphyton biomass in almost all experiments, whereas increased P supply incresed total biomass only if algae were present.

54

Microbial food webs

The effects of DOC and P on the ratio of heterotrophic to autotrophic abundance strongly depended on trophic structure, where additional resources enhanced the autotroph component when the basal heterotrophs were limited by low organic C or by strong consumer pressure.

The brightest and the muddiest point

Lotka Volterra models of consumption

Oscillations and spatial dynamics

Food web moduls: apparent competition, k t d ti i t ild d tikeystone predation, intraguild predation, associational resistance, trophic cascades


Resources versus consumer control

Food webs and food web properties

Microbial food webs

Thank you

Literature Begon, Harper & Townsend Ecology 4th edition

Blackwell/Wiley

Morin Community Ecology Blackwell/Wiley Morin Community Ecology Blackwell/Wiley

Exam Types of questions

microbial ecology theory [kompatibilitätsmodus] · microbial ecology theory ... loosing the fear...

Documents