An organism eats all or part of a primary producer

Exploitative interaction; only one entity benefits

Herbivory

Interactions in ecological Communities

over half of individuals in world are herbs and herbivores

Transfer of Carbon from primary prod. is start of most food chains/webs

Herbivores "prey" cannot escape like predators prey

Plant Abundance and Herbivores

Why are there so many plants?

3 plausible reasons

Plants persist in high abundance because:

1) herbivore pops have self-regulatory mechanism that keep them from completely eliminating plants

2) Predation on herbivores keeps herbivs pop low

3) Not all plants are edible or nutritionally beneficial

*top-down regulation of primary producer biomass

Physical Defenses:  spines, thorns, thick cuticle/epidermal tissues

Secondary Metabolites:  Nicotine etc.

-Young plants have higher concentrations in high nutrition areas (shoots/leaves)

-higher concentrations near tissue surface also; 1° defense

Resource Availability Hypothesis

Coley et al. 1985

Fast Growing vs. Slow Growing plants

Herbivores prefer faster growing plants/faster growing parts of plants; due to less defense

Slow growing plants have better defense; more time invested to regrow leaves lost

Resource Availability Hypothesis

Investment of Defenses vs. realized Growth Rate

4 different species w/ different defense investments

A: invest little in defense; high realized growth rate

D: invest majority in defense; low realized growth rate

Physical Defense example (Cacti)

Evolutionary constraints

If plants over time are prefered by herbivores, then they will evolve to invest more into defense; more spines on grazed cactus

Plants that aren't grazed often will invest more into growth and fitness than defense.

Interactive Grazing Example

Caddisfly and periphyton

Hypothesis: can caddisflies control algal Biomass?

Results: Yes,

- elevated tiles showed no algal control; increased bacteria/algal biomass

-non-elevated show complete algal control

-both tiles showed a similar increase in other benthic inverts

Caddisfly Results

Elevated vs. Non-elevated

A: Elevated Tiles show low # of Caddisflies; non elevated show high # of Caddisflies

B: total biomass of other benthic inverts increased on both; tiles on bed show little more

C: Total bacteria on elevated was greater due to lower # of caddisflies

D: Algal biomass on elevated was greater; b/c no caddisfly 

Common when new herbivore in introduced into an area with little/no pre-existing herbivory

ex. introduction of ungulates (hoofed animals); large increase and subsequent collapse of herbivs pops

Np= # of plants

Nh= # of herbivores

Stage 1: introduction of Herbivores, Nh has a sharp increase, Np has a slight decrease

Stage 2: Nh still inceasing; Np starts sharp decrease

Stage 3: Nh exceeds K for food, Both pops drop dramatically

Stage 4: coevolution starts, Np and Nh even off

Irruptive Herbivory example

White et al. 2007

Proghorn antelope in yellowstone; long-term empirical evidence used

Predictions: exceeding carrying capacity and then reaching equilibration population much below peak abundances; evidence most from large herbivores

Irruption Herbivory Example

White et al. 2007

Results

1918-1946 period: irruptive pop dynamics; density dependence

1947-1966 period: culling replaced density-dependent mechanisms; intraspecific competition

1967-2006 period: Irruptive dynamics again (92-95) due to lag time of culling

Irruption Herbivory Example

White et al. 2007

Discussion

Shows evidence of large herbivore irruption dynamics; especially with high fecundity and delayed density responses

Incorportation of management of pops needs to be integrated intro irruptive models

long-term data sets are critical to show irruption

Non-interactive system

ex. Finches

*No interaction between herbivore number and vegetation biomass

food plant production determines bird density in an area, but not vice versa; UNIDIRECTIONAL effect

seed producing plants typically keep stable bird pop

Fruit producing plants force emmigration when resources are low in an area.

Herbivory in Aquatic Systems

Cyr and Pace 1993 example

Trends in aquatic/terrestrial:

1) herbivore interactions are stronger in aquatic; but lacking in meta-analysis

2) Herbivory increases linearly with primary poducers

3) pattern of herbivory with increasing productivity is same in both; elevations differ (aquatics graze faster)

4) herbivore biomass increased with productivity in both

parasite/host interactions are similar to predator/prey

Effects of parasite/diease is density DEPENDENT

Red Queen Hypothesis

(alice wonderland)

Parasites/Diseases try to "Outwit" hosts

-complex life cycles; multiple hosts (ex. worms)

-alter host behavior

-Biochemically "mask" itself, once in host

Evolutionary arms race; newly emerged strategy by one will lead to selection pressures on the other

Complex Ecological Interactions example

Mange in Red Fox

Mange spread in norways through foxes

Hare populations flucuated before mange outbreak

Post-mange outbreak, hare population increased dramatically as fox population declined

Handicap Hypothesis

(Hamilton & Zuk 1982)

1) Parasites will reduce host fitness

2) parasite resistancec is genetic thus inherited

3) Parasite resistance is signaled by the extent of elaboration or ornaments

4) Females will prefer males with the most elaborate signals

Handicap Hypothesis in nature example

Barn Swallows

Moller (1988-1990)

Females prefer male swallows with long pointed tail feathers

-"cost" of longer tails: males with "experimentally" lengthened tails caught fewer insects than shorter tails

-Number of mitess per nest was postively correlated with number of mites per male

-Genetic Component: chicks from long-tailed males had lower mite burden; even when transfered to "foster" nest with short-tailed males.

Extra Limb Formation in Amphibians

Ultraviolet Radiation

UV-B causes genetic damage; kill larvae and eggs, eye damage, induce some limb deformities

Problems with increases UV-B levels hypothesis

-leads to missing limbs or digits; not extra limb formation

Behavioral adaptation: amphibians take refuge in shade

Overall: not as effective as thought to be problem for extra limb fomation

Extra Limb Formation in Amphibians

Environmental Pollutants

Pesticides can kill amphibians: Methoprene replaced DDT

Can cause limb deformations: bread down products can also cause limb deformations

Problems with methoprene hypothesis:

1) break down quickly in environment

2) mostly causes missing limbs too (like UV-B)

Extra Limb Formation in Amphibians

Parasites

(Ruth and Sessions)

Malformed frogs had cysts nears areas of limb formation

Hypothesis: cysts were associated with a parasitic trematode (fluke)

Experiment: Inserted small, sterile glass beads into larval limb forming areas to simulate cyst formation

Resuls: caused extra limb formation

Extra Limb Formation in Amphibians

Hypothetical effects of trematode parasites

Amphibian Extra limb formation

Interactive Effects

Concept of multiple stressors on systems

Eutrophication: ponds with high levels of infestation are also polluted with manure and fertilizers; creates lots of periphyton and therefore snails

Recent data found that presence of increased pesticides causes suppression of amphibian immune response and increased occurence of limb abnormalities

**Synergistic Effects**

Munz et al. 2009

Modeled Outbreak dynamics of Zombies

assumptions and design

Assumptions: Zombies are slow moving and if your bit, you become infected

3 groups in basic models:

S= susceptible individuals; living individuals

R= removed people/zombies; S death/bitten/dead zombies

Z= zombies; through resurrection/infected indiv.

Growth of each populations expressed

Π= birth rate

β= trasmission parameter

σ= natural death rate

ζ= resurrection parameter

α= zombie defeat parameter

Munz et al. 2009

Latent Infection Senario

Period of latency in which you don't become a zombie for 24 hours after being bitten

-adds a new group of people; the infected (I)

Takes longer for zombies to take complete control

[image]         [image]

Munz et al. 2009

Quarentine Model

Isolate infected people; infected/zombies locked up

[image]       [image]

Munz et al. 2009

Treatment Senario

Infected or zombies can be given cure; but can get reinfected

[image]        [image]

Munz et al. 2009

Impulsive Eradication Senario

Kill zombies quickly and hard in pulses; kill progressively greater precentages of zombies through time

[image]

Munz et al. 2009

Overall

Outbreak of zombies is likely to be disastrous and wipe out the living unless dramatic action is taken; sufficiently frequent attacks with increasing force is only way to wipe out zombies

Modeling is useful to think about diease outbreak; figure out best way to take care of problem, then overkill with it

Endosymbiotic Hypothesis

(Margoilis and Fester 1991)

Intraspecific Mutualism example 1

Pied Wagtails

Individuals (usually pairs; can be non-mating) join together to defend a winter feeding territory

individual feeding rate is higher with partner than alone

cost of sharing < benefits of sharing

Intraspecific Mutualism

Kin Selection

Intraspecific Mutualism

Kin Cooperation

Indirect Fitness: relatives contain some of your genes, so their survival indirectly affects your indiv fitness

Based upon Coefficient of Relatedness (r): proportion of genetic material shared by relatives

ex: diploid, sexually reproducing genes

-parent -- child: .50

-grandparent--child: .25

-full siblings: .50

Interspecific Mutualism

Facultative

Interspecific Mutualism

Obligate

Interspecific Mutualism

Plant - Mycorrhizae Association

Arbuscular:  sites of exchange between fungi and plant; occur within root tissues (most common)

Ectomycorrhizal:  fungus forms a net-like sheath (mantle) around the root

Plant: gains additional access to water and immobile inorganic nutrients in soilds

Fungus:  supplied with root exudates in the form of CHOs (photosynthetic byproducts)

Interspecific Mutualism

Mycorrhizae - Plant water balances

Improve ability of plant to extract water from soil

Higher leaf water potential > increased transpiration rate > increased water uptake

Plants supplied with more phosphorus = roots more efficient at taking up water

Interspecific Mutualism

Plants - Seed Dispersers

Plants depend upon birds and mammals to disperse seeds

Fruit Defense by plants and selection of dispersers:

-fruit present when: fruit preds are lowest/dispersers abundant

-ripen fruit slower; reduces availability

-fruits nutritionally unbalanced

-chemical/mechanical defenses

Interspecific Mutualism

plants - seed dispersers example

whitebark pink and clark's nutcracker (bird)

The bird collects seeds and transports to new location 3-5 at a time to form a cache.

1 bird can store up to 32000 seeds per year

Most seeds not used; thus leaving seeds dispersed in favorable environments

Interspecific Mutualism

Animal Example

Acacia Trees - Ants

~700 spp of acacia, most associated with 1 species of ant

Ants live in the tree, will attack and killo ther plants and animals around the tree

In turn, Plant sources food (carbs, proteins, lipids) for ants

Conflicts:

1) acacia need pollinators, but ants kill anything not acacia

2) ants dont go to flowers, food sources aren't near flowers, so pollinators and succesfully forage and disperse

Interspecific Mutualism

Coral - Zooxanthellae

Coral Reefs are similar to tropical rain forests (highly productive systems; nutrient poor envrionment)

Zooxanthellar living in coral provide color; under stressful situations, zooxanthellae lose pigments and die causing coral bleaching

Interspecific Mutualism

Causes of Coral Bleaching

Temperature: corals and zooxanthellae are temp sensitive; 1-2 degree change for 5-6 weeks can lead to significant bleaching

Solar Intensity: changes (positive and negative)

Freshwater Intrusion

Pesticides/herbicides

Parasites

Coral Bleaching

Corals and Decapod Mutualists

Pocilloproa/Acropora: decapod mutualistic relationship

Crabs and shrimp protect corals from predators

Corals supple crabs/shrimp with shelter, nutritious muscous and fats bodies

Predator rate decreses with crustaceans present

Like Herbivory, but prey are usually mobile and can seek refuge

Predation

Snowshoe Hare - Lynx example

Food supply: Hares have high pop growth rates, can deplete primary food source in an area, also stimulate production of secondary metabolites

Predators: 60-70% of hare mortality is from predation during times of HIGH DENSITY

Lynx pops: show Type II functional response

-Numerical response: lynx densities respond to change in hare densities

Mathematical Expression of Predator-Prey Cycles

assumptions

Predator and prey pops will cycle together

Assumptions:

-prey pops grow at exponential rates

-prey pops limited by predators (logistic)

ΔN_prey/Δt = [r_preyN­_prey]- [pN_preyN_predator]

Prey Population Equation

[r_preyN­_prey]= exponential prey growth

p= predation rate (indiv. probability of being eaten)

N_prey= # of prey

N_predator= # of predators

ΔN_pred/Δt = [cpN­_preyN_pred] – [d_predN_pred]

Predator Population Equation

c= prey to pred conversion rate (constant)

p= predation rate (constant)

d= predator death rate (constant)

[cpN­_preyN_pred] = Rate of prey "converted" into preds

[d_predN_pred] = # of predator deaths

Lotka-Volterra

Predator-prey Isocline

Area A (dN/dt < 0): too many preds; decrease prey growth

 (dN/dt=0): ZNGI for prey

Area B (dN/dt > 0): not many preds; increase prey growth

Rosenzweig- MacArthur Pred-prey isocline

dN/dt=0: prey ZNGI (unimodal)

Right side = prey crowding

Left side = too many preds feeding on few prey

Point A: large # of preds, small # of prey; prey decrease

Point B: small # preds, small # prey; prey increase

Rosenzweig and MacArthur Predator Isocline

Assumptions

Predators are limited by amount of prey, when prey are low; thus when prey # are high, preds should increase

When pred #s get too high, factors like territoriality or prey refuges can stop pred growth rate

Rosenweig- MacArthur predator-prey isocline with Predator only isocline superimposed

A = pred and prey both decrease

B= Prey decrease, pred increase

C= prey increase, pred decrease

D= Both increase

*Oscillation around intersection

Predation Refuges

In Lab

Intially preds would eat all prey; local extinction

Corrections:

-Had to provide refuges to keep pred/prey from exhibiting large oscillations or local extinction

-In presence of refuges, must *immigration* of new prey to keep pred pops from crashing

[image]

Predation Refuges

Aquatic Protozoan Lab Experiments

A: absence of refuges & immigration; both pops go extinct

B: Adding refuge allowed prey to persist, but preds went extinct

C: Both immigration and refuge; maintained oscillations of pred-prey interactions

Space: prey move to a location hard to preds to access

Numbers: being within a large group of individuals decreases individual probability of being eaten

-Predator Satiation: prey # so high, preds can't consume all (ex. periodical cicadas)

Size: handling costs are too high is prey gets too big

(ex. Bass, bream, minnow in lake; minnows driven very low numbers by bream)

Abundnace and Diversity of species

Community definitions

Community: an association of interacting species inhabiting the same area

Community Structure: attributes of a community that describe the conditions of the community (ie: # of species, relative abundance, etc)

Abundance and Diversity of Species

Guild definition

To simplify communitites, species of similiar types are grouped into Guilds

Grouped By: feeding niche, reporductive characteristics, habitat use

Lognormal Distribution of Species

 Trend

A few rare species

A lot of moderately abundant species

few really common species

*Pattern appeared to be repateable across plant and animal communities

Lognormal Distribution of Species

x-axis: # of individuals/ percent cover (plants)

y-axis: # of species

Bell-shaped or "normal" distribution

*Sample size must be adequate enough to show trend

Causes of Lognormal Distribution

Sequential Breakage Hypothesis

Hierarchical niche structure

The proportion of niche space occupied by each species is proportional to its abundance and the probability that a niche fragment can be further subdivided

*addition of species into the community is sequential and not "all at once"

Shannon-Wiener Diversity Index

H'= -Σp_i (lnp_i)

H'= shannon-weiner diversity index

p_i= proportion of species i

lnp_i= natural log of proportion of species i

*starts at 0 (1 sp in community) and increases

Measures of Diversity

Rank - Abundance Curves

Graphic representation of richness and evenness

plots relative abundance as a function of their ranked abundance (most abundant = 1)

Length of Line = species Richness

Slope of line = species evenness

Distinguish between, within and across habitat patterns

β-diversity compares diversity between areas/habitats/environmental gradients

Sorensens's β-Diversity Index

β= # of unique species at both sites

S₁= # of spp. at first site

S₂= # of spp. at second site

c= # of spp. shared between sites

Values range from 0 (no shared spp) to 1 (all shared spp.)

Whittaker's β diversity index

β= # of unique species at both sites

S= total # od spp. at both sites

α= mean # of species at each site

Diversity declines as one moves away from low latitudes (equator)

Ant spp:

Brazil= 222

Utah= 63

Arctic Alaska= 3

Fish spp:

Lake Tanganyika, Africa= 214

All of Europe = 192

Biota in tropics are likely to evolve faster due to constant favorable environment and freedom from historical disaster

Longer time left unperturped = more time for spp colonization and speciation events

Recent studies indicate that speciation rates are greater at lower latitudes

Factors Affecting Diversity

Evolutionary Speed

 more time and more rapid evolution leads to higher diversity

-longer time an ecosystem is left unperturbed, the longer the time period to evolve unique fauna

 -Older systems that are subject to low levels of perturbation can exhibit high diversity and endemism (unique to one geographic area)

ex. Lake Baikal; very old deep lake; 580 endemic species

Factors Affecting Diversity

Geographic Area

Ecosystem Area

areas of larger size and areas that are more physically/biologically complex for more niche space.

more area = more species

Described as:

S = cA^z

s= spp richness

A= area

z= quantifies scaling of richness with area

c= taxon- and envrionment-specific constant (slope)

Species Area Relationship

x-axis: area

y-axis: species richness

As area increases, species richness increases

Ecosytem Area Diversity

Smith et al. 2005

Intro

Species- Area relationships: one of the most universal patterns in ecology

S=cA^z

Most studies have found that Z varies with the scale of the system considered

Not univseral

Mostly examined in terrestrial system

Ecosystem Area Diversity

Simth et al. 2005

Expermient

Phytoplankton and Z: phytoplankton are most widespread and abundanct primary producers in the world

little evidence that phytoplankton subscribes to the species-area relationship (z=0)

-small size, rapid colonization might negate S-A relationship

Ecosystem Diversity

Smith et al. 2005

Results

Zn= .114

Ze= .139

Zpooled= .134

generall lower range for values in the literature for other organisms (.116 to .669)

*Indicates small body size and rapid immigration rates

Ecosystem Diversity

Smith et al. 2005

Conclusions

Natural patterns in microorganisms are the same for macro-organisms

Model/artifical systems can be used to examine determiants of biodiversity

Factors Affecting Diversity

Geographic Area

Spatial Heterogeneity

Increase in habitat complexity - more niche space for species

-at a local scale, increased habitat complexity can lead to higher species diversity

Factors Affecting Diversity

Interspecific Interactions

Competition, Predation and Parasitism

Competition could create smaller, more specialized niches over time, thus preventing substantial Niche overlap

Presence or absence of predators/parasites can affect the diversity of communities at a local scale

Factors Affecting Diversity

Interspecific Interactions

Pain (1966)

Pisaster exclusion example

Removal of Pisaster from rocky intertidal communities caused species richness (S) to decline from 15 to 8.

Mytilus (prey): out-competed other species (mainly barnacles) for space (limiting resource in this community)

Pisaster predation on Mytilus keeps community more diverse

Factors Affecting Diversity

Ambient Energy

Ambient Engery Hypothesis

Though that energy availability generates and maintains species richness

*Climate Driven: solar radiation, temperature and water

Ambient Energy Hypothesis: More stable/favorable climates lead to increased productivity and higher diversity

Ambient Energy and Biodiversity

North American Organisms

x-axis: Ambient Energy (temp)

y-axis: species richness (S)

Trees: show type 3 functional response increase to temp

Mammals: show type 2 functional response increase

Factors Affecting Biodiversity

Productivity and Resource Availability

Initial hypothesis: greater the resrouce availability, the greater the diversity

Diversity is often high in nurtrient-poor habitats

ex. TRF, Reefs etc.

[image]

Soil Fertility and Diversity

x-axis: relative soil fertility

y-axis: # of plant species

Higher number of species are found in areas with lowest soil fertility

Factors affecting Diversity

Productivity

Across communities: productivity and diversity often display a non-linear relationship (unimodal)

[image]

Factors affecting Diversity

Undisturbed vs. Disturbed

In UNdisturbed envrionment: predicted that community will eventually consist of few species that competitively exclude others

This assumes community is in a state of equilibrium and there are no large perturbations

In natural systems perturbation occurs via: floods, etc

Factors affecting Diversity

Intermediate Disturbance Hypothesis

Connell 1978

High frequency = low spp diversity; community always trying to "recover" from perturbation

Intermediate frequency= highest diversity; wide variety of spp will colonize, but there is not time for competitve exclusion to occur

Low frequency= low spp diversity; competitive exclusion

How species interactions in communities lead to changes in community structure

Community ecologists examine biotic factors which affect comm. structure

A summary of the feeding interactions in a community of interacting species

Elton (1927)- first proposed idea of "trophic pyramid;" only 4-5 links

Lindeman (1942)- viewed ecosystem as an "energy transforming system;" energy transferred from PP to Herbivs to Preds

Going to be on test

Food web Terminology

Top predators: species eaten by nothing else

Basal Species: feeds on nothing in the food web (plants)

Intermediate Species: spp that have both preds and prey

Tophic Species: spp that have exact same preds/prey

Omnivore: feeds on multiple trophic levels

Cannibal: feeds on its own spp

Donors: contribute energy to next level thru feeding

Recipients: Gains energy from previous level thru feeding

Going to be on test

Food web terminology - interactions

Interactions: any feeding relationship (line on diagram)

Possible Interactions: defined as [s(s-1)/2] where s=species

Connectance: # of actual interactions in a food web divided by possible interactions

Linkage Density: avg # of link per species in the food web

COmpartment: subgroup of spp in web with strong linkages among member of the compartment and only weak linkages with other groups of spp.

Energetic Hypothesis: transfer of energy is inefficient in food chains; "use up" energy quickly

Dynamical Stability Hypothesis: longer food chains are not stable so that pop fluctuations at lower trophic spp (prey) have a big effect on upper levels (preds); predicts that in highly perturbed systems, chain lenth should be shorter

*Linkage Density tends to remain constant*

# of food chain links vs. # of species

x-axis: # of species

y-axis: links per species

links per species increase as species diversity increases

Predator - Prey Ratios in Food webs

x-axis: # of predator species

y-axis: # of prey species

positive correlation

Human Impacts on Food chain length

Overfishing example

Pauly et al. 1998

fisheries often concetrate on top consumers in food webs; ex. tuna or cod

Shows systematic global trends in overfishing in marine and freshwater

Strong Interactions and Keystone Species

Paine (1966, 69)

Pisaster example

Paine suggested that feeding activites of a select number of species can have a large impact on community structure; lead to the first definition of a keystone species to be mostly about species diversity

Removal of the starfish (as top pred) at 2 seperate foodwebs reduced the number of species at both sites

Not common; conceptually good, but not experimentally

-context dependent effects: keystone spp may not be dominant controlling agents in all parts of their range

Strong interactions and non-native species

nile-perch in lake victoria

400 spp of highly adapted feeding niche cichlids

introduction of nile-perch: ate nearly all native fish

Strong response because: prey fishes not adapted to deal with the introduced predator (lack of co-evolution)

Species that has a strong impact on the structure and function of a community, but its biomass or abundance is large; ex. redwoods and corals

Community Dominance Index (CDI): estimates the percent of the abundance contributed by the most abundant species in the community

Keystone vs dominant species

x-axis: relative biomass of spp

y-axis: total impact of spp

A: Species with low biomass, but large effects of comm structure; ex. starfish (keystone)

B: Keystone spp influence on a comm is disproportionate to its biomass

C: Dominant spp have significant influence on community structures by virtue of high biomass

Dominant are assumed to be: competitive dominants; competitive exclusion of others

Can also be a function of biotic or abiotic factors in the community; ex. predation

most lakes with no or low fish; predation is dominanted by zooplankton

When there are abundant fish that eat zooplankton; the largest species of zooplankton are eaten first then the smaller groups become the dominant species

but invert preds select smaller zooplankton; so could be both competition and predation leading to dominance of large zooplankton in fishless systems

Size - Efficiency Hypothesis

Zooplankton in fishless systems example

larger zooplankton feed more efficiently on algae than small zooplankton and can eat larger algae than smaller zooplankton

Therefore, because zooplankton are more efficient herbivores, thus can out-compete small zooplankton in fishless systems

Size - Efficiency Hypothesis Test

x-axis: body size

y-axis: threshold food level

Other factors

Zooplankton in fishless systems example

Invert preds in lakes often select smaller zooplankton species

Invert preds are in high abundance in fishless lakes (larger than zooplankton)

So could be both competition and predation leading to dominance of large zooplankton in fishless systems