Positive and negative interactions

Predation

Interspecific competition

Herbivory is a form of parasitism

Competition is an interaction between individuals of the

same or of different species membership, in which the fitness of one is lowered by the

presence of the other.

Amensalism is a relationship between individuals where some

individuals are inhibited and others are unaffected.

Parasitism is any relationship between two individuals in which one

member benefits while the other is harmed but not killed or not allowed to

reproduce.

Mutualism is any relationship between two individuals of different species where

both individuals benefit.

Commensalism is a relationship between two individuals where one benefits and the other is not significantly affected.

Symbiosis is any type of relationship where two individuals live together

Parasitoidism is a relationship between two individuals in which one member benefits while the

other is not allowed to reproduce or to develop further

Mutualism is the way two organisms of different species exist in a relationship in which each individual benefits. Mutualism is the oposite to interspecific competition.

Client– service relationships

Pollination

Mutualism is often linked to co-evolutionary processes

Facilitation is a special form of commensalism and describes a temporal relationship between two or more species where one species benefits from the prior (and recent) presence of others.Facilitation generally increases diversity.

In plant succession early arriving plants pave the way for later arrviing by modifying soil condition.

Intraspecific competition

Scramble (exploitation, diffuse) is a type of

competition in which limited resources within an habitat result in decreased survival

rates for all competitors.

Mytilus edulis

Contest (interference) competition is a form of competition where there is a

winner and a loser

Canis lupus

Mate competition

Territoriality

Territories imply a more or less even distribution of

individuals in space

Territoriality is a form of avoidance of intraspecific competition

Territory

TerritoryHome range

Home range

Overlap

Home ranges might overlap

𝜎 2≪𝜇The variance in distance is much less than the mean distance

The stem self thinning rule

Trees is a forst have certain distances to each others

Leaf area L increases with plant density NL=lNwhere L is the average leaf area per plant. This area and mean plant weight w increase with stem diameter byl=aD2 and w=bD2

Therefore

𝑤=𝑏( 𝐿𝑎 )3 /2

𝑁 −3 /2

Modified from Osawa and Allen (1993)

Density dependent regulation and diffuse competition

The -3/2 self thinning rule

Density independence

Density dependence

Density dependent regulation of population size results from intraspecific competition

Vulpia fasciculata

Density independence

Density dependence

Tribolium confusum

Data from Ebert et al. 2000. Oecologia 122

Data from Bellows 1981. J. Anim. Ecol. 50

Density independence

Density dependence

K

1/r

Nt/N

t+1

Nt

𝑁𝑡+1=𝑟 𝑁𝑡 𝑁𝑡+1=𝑟 𝑡+1𝑁0

𝑦=𝑚𝑥+𝑏 𝑁𝑡+ 1=𝑟 𝑁𝑡

1+ 𝑟 −1𝐾 𝑁𝑡

1

𝑁𝑡+1=𝑟 𝑁 𝑡

1+𝑎𝑁 𝑡

First order order recursive function of density dependent population growth

Nicholson and Baily model

𝑁 𝑡

𝑁𝑡+1=1− 1𝑟𝐾 𝑁 𝑡+

1𝑟

𝑁𝑡+ 1=𝑟 𝑁𝑡

1+ (𝑎𝑁𝑡 )𝑏

Salmo trutta

Peak reproduction at intermediate densityy

Data from Allen 1972, R. Int. Whaling Comm. 22.

Competitive exclusion principleGeorgii Frantsevich Gause

(1910-1986)

In homogeneous stable environments competitive dominant

species attain monodominancy.

Paramecium aurelia Paramecium caudatum Joint occurrence

Applying this principle to bacterial growth Gause found a number of antibiotics

Data from Gause 1943, The Struggle for Existence

Interspecific competition

Tribolium confusum Tribolium castaneumTemperature Humidity Percentage wins

Tribolium confusum

Tribolium castaneum

Hot Moist 0 100Temperate Moist 14 86Cold Moist 71 29Hot Dry 90 10Temperate Dry 87 13Cold Dry 100 0Data from Park 1954. Phys. Zool. 27.

Two species of the rice beetle Tribolium grown together compete differently in dependence on microclimatic conditions.

Alfred James Lotka (1880-1949)

Vito Volterra (1860-1940)

The Lotka – Volterra model of interspecific competition

𝑑𝑁𝑑𝑡 =𝑟𝑁 𝐾 −𝑁

𝐾

N  =  N  +  α𝑀

𝑑𝑁 1𝑑𝑡 =𝑟𝑁1 𝐾 1−𝑁 1−𝛼𝑁 2𝐾𝑑𝑁 2𝑑𝑡 =𝑟𝑁 2 𝐾 2−𝑁 2− 𝛽𝑁 1𝐾

At equilibrium: dN/dt = 0

𝐾 1−𝑁 1−𝛼𝑁 2=0 𝐾 1−𝑁 1−𝛼𝑁 2=𝐾 2−𝑁 2− 𝛽𝑁 1If competitive strength

differs one species vanishesIf carrying capacity differs

one species vanishesCertain conditions allow for

coestistence

The Lotka Volterra model predicts competitive exclusion

But the oberserved species richness is much higher than predicted by the model.

𝑑𝑁 1𝑑𝑡 =𝑟𝑁 1 𝐾 1−𝑁 1−𝛼𝑁 2𝐾

The model needsstable reproductive rates stable carrying capacitiesstable competition coefficients

It needs also homogeneous environments

a > b K1 > K2

Randomy fluctuating values of r, K, a, and b.

Unpredictability and changing environmental conditions as well as habitat heterogeneity and aggregation of individuals promote coexistence of many species.

Grassland are highly diverse of potentially competing plants

Competition for enemy free space (apparent competition)

Data from Bonsall and Hassell 1997, Nature 388

Plodia interpunctella Venturia canescens Ephestia kuehniella

Predator mediated competition might cause extinction of the weaker prey

Extinction

Character displacement and competitive release

Rhinoceros beetles

Chalcosoma caucasus

Chalcosoma atlas

Interspecific competition might cause species to differ more in phenotype at where where they co-occur than at sites where they do not co-occur (character displacement)

Interspecific competition might cause a lower phenotypic or ecological variability of two species at sites where both species compete.Competitive release is the expansion of species niches in the absence of interspecific competitors.

Raven Raven + Crows

Diet

ary

wid

th

Bodey et al. 2009. Biol.Lett 5: 617

Rave

n

Predation

Erigone atra

Generalist predator

Polyphages

Specialist predator

Monophages

Canada lynx and snowshoe hare

Oligophages

Trade-offs in foraging

Searching time

Prey

qua

lity Starvation

Maximum yield

Stopping point

Animals should adopt a strategy to maximuze yieldOptimal foraging theory

10

15

9

3

17 8

4

20

18

11

Parus major

Great tits forage at site of different quality

How long should a bird visit each site to have optimal yield?

Predicted energy intake from travel time

Predicted energy intake from travel and handling time

𝐹𝑜𝑜𝑑 𝑖𝑛𝑡𝑎𝑘𝑒∝𝐷𝑒𝑛𝑠𝑖𝑡𝑦 𝑓𝑜𝑜𝑑 𝑡𝑡𝑟𝑎𝑣𝑒𝑙

1+𝑎 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 𝑓𝑜𝑜𝑑𝑡h𝑎𝑛𝑑𝑙𝑖𝑛𝑔

Holling’s optimal foraging theory

Cowie 1977

Hudson’s Bay CompanyData from MacLulick 1937, Univ. Toronto Studies, Biol. Series 43

Data from Yoshida et al. 2003, Nature 424

Specialist predators and the respective prey often show cyclic population variability

12 year cycle

Canada lynx and snowshoe hare

Cycles of the predator follow that of the prey

Cycles might be triggered by the internal dynamics of the predator – prey interactions or by external clocks that is environmental factors of regular appeareance

Most important are regular climatic variations like El Nino, La Nina, NAO.

Bracyonus calyciflorus

Chlorella vulgaris

The Lotka Volterra approach to specialist predators

𝑑𝑃𝑑𝑡 =−𝑒𝑁 𝑑𝑁

𝑑𝑡 =𝑟𝑁 −𝑎𝑃𝑁𝑑𝑃𝑑𝑡 = 𝑓𝑎𝑁𝑃−𝑒𝑃

𝑑𝑁𝑑𝑡 =0→𝑃=

𝑟𝑎

𝑑𝑃𝑑𝑡 =0→𝑁=

𝑒𝑓𝑎

The Lotka Volterra models predicts unstable delayed density dependent cycling of

populations

The equilibrium abundances of prey and predator

e: mortality rate of the predatorr: reproductive rate of the preyfaN: reproductive rate of the predatorf: predator efficienyaP: mortality rate of the preya: attack rate

In nature most predator prey relationships are more or less stable.

Any deviation from the assumption of the Lotka Volterra model tends to stabilize population:• Prey aggregration• Density dependent consumption• Functional responses

Environmental heterogeneity and predator prey cycles

Eotetranychus sexmaculatus

Typhlodromus occidentalis

Simple unstructured environment

Heterogeneous environmentHabitat heterogeneity provides prey refuges and stabilizes predator and

prey populations

Functional response

Type II Holling response Type III Holling response

Predator attak rates are not constant as in the Lotka Volterra model

Calliphora vomitoriaMicroplitis croceipes

Type I response

Microplitis croceipes Calliphora vomitoria

Variability, chaos and predator prey fluctuations

Lotka Volterra cycles with fixed parameters a, e, f, r.

Lotka Volterra cycles with randomly fluctuating parameters a, e, f, r.

𝑑𝑁𝑑𝑡 =𝑟𝑁 −𝑎𝑃𝑁 𝑑𝑃

𝑑𝑡 = 𝑓𝑎𝑁𝑃−𝑒𝑃

Any factor that provides not too extreme variability into parameters of the predator prey interaction tends to stabilize populations.Fixed parameter values cause fast extinction.

Stochasticity tends to stabilize populations

Dynamic equilibrium

Herbivory

Feeding Strategy Diet Example

Frugivores Fruit Ruffed lemurs

Folivores Leaves Koalas

Nectarivores Nectar Hummingbirds

Granivores Seeds Hawaiian Honeycreepers

Palynivores Pollen Bees

Mucivores Plant fluids, i.e. sap Aphids

Xylophages Wood Termites

Plant defenses against herbivors

Alcaloide (amino acid derivatives): nicotine, caffeine, morphine, colchicine, ergolines, strychnine, and quinineTerpenoide, Flavonoids, Tannins Mechanical defenses: thorns, trichomes…MimicryMutualism: Ant attendance, spider attendance Digitalis

Many plants produce secondary metabolites, known as allelochemicals, that influence the behavior, growth, or survival of herbivores. These chemical defenses can act as repellents or toxins to herbivores, or reduce plant digestibility.

Functions of herbivores in

coral reefs

Negative feedback loops occur when grazing is too low

Increasing algal cover

Decreasing coral recruitment

Low coral cover

Low grazing intensity

Decreasing fish

recruitment

Reduced structural

complexity

Positive feedback loops occur when grazing is high

Decreasing algal cover

Increasing coral recruitment

High coral cover

High grazing intensity

Increasing fish

recruitment

Increased structural

complexity

Herbivorous fish (Diadema)

Overfishing of herbivorous fish might

cause a shift to algal dominated low divesity

communities

Hay and Rasher (2010)