Positive and negative interactions

Predation

Interspecific competition 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.

Herbivory is a form of

parasitism

Symbiosis is any type of relationship where two individuals live together 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. Parasitoidism is a relationship between two individuals in which one member benefits while the other is not allowed to reproduce or to develop further Commensalism is a relationship between two individuals where one benefits and the other is not significantly affected.

Mutualism is any relationship between two individuals of different species where both individuals benefit.

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

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.

Intraspecific competition

Canis lupus

Contest (interference) competition is a form of competition where there is a winner and a loser

Mytilus edulis

Scramble (exploitation, diffuse) is a type of competition in which limited resources within an habitat result in decreased survival rates for all competitors.

Mate competition

Territoriality 𝜎 2 ≪ 𝜇 The variance in distance is much less than the mean distance Territories imply a more or less even distribution of individuals in space Territoriality is a form of avoidance of intraspecific competition Territory Home range Home ranges might overlap Overlap Home range Territory

Density dependent regulation and diffuse competition The stem self thinning rule Trees is a forst have certain distances to each others Leaf area L increases with plant density N L= l l N where L is the average leaf area per plant. This area and mean plant weight w increase with stem diameter by =aD 2 and w=bD 2 Therefore 𝐿 3/2 𝑤 = 𝑏 𝑎 𝑤 = 𝑐𝑁 −3/2 𝑁 −3/2 The -3/2 self thinning rule Modified from Osawa and Allen (1993)

Density dependent regulation of population size results from intraspecific competition Density independence Density dependence

Tribolium confusum

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

Vulpia fasciculata

Density independence Data from Ebert et al. 2000. Oecologia 122

Salmo trutta

Density dependence Density independence Data from Allen 1972, R. Int. Whaling Comm. 22.

Peak reproduction at intermediate densityy 1 𝑁 𝑡+1 = 𝑟𝑁 𝑡 1/r 𝑦 = 𝑚𝑥 + 𝑏 N t K 𝑁 𝑡+1 = 𝑟 𝑡+1 𝑁 0 𝑁 𝑡 𝑁 𝑡+1 = 1 − 𝐾 1 𝑟 𝑁 𝑡 + 1 𝑟 𝑁 𝑡+1 = 1 + 𝑟𝑁 𝑡 𝑟 − 1 𝐾 𝑁 𝑡 𝑁 𝑡+1 = 𝑟𝑁 𝑡 1 + 𝑎𝑁 𝑡 𝑁 𝑡+1 𝑟𝑁 𝑡 = 1 + 𝑎𝑁 𝑡 𝑏 First order order recursive function of density dependent population growth

Nicholson and Baily model

Competitive exclusion principle

In homogeneous stable environments competitive dominant species attain monodominancy.

Paramecium aurelia

Georgii Frantsevich Gause (1910-1986)

Paramecium caudatum

Joint occurrence Data from Gause 1943, The Struggle for Existence Applying this principle to bacterial growth Gause found a number of antibiotics

Interspecific competition

Tribolium confusum Tribolium castaneum

Temperature Humidity

Hot Temperate Cold Hot Temperate Cold Moist Moist Moist Dry Dry Dry Data from Park 1954. Phys. Zool. 27.

Percentage wins

Tribolium Tribolium confusum 0 castaneum 100 14 71 90 87 100 86 29 10 13 0 Two species of the rice beetle Tribolium grown together compete differently in dependence on microclimatic conditions.

The Lotka – Volterra model of interspecific competition 𝑑𝑁 𝑑𝑡 = 𝑟𝑁 𝐾 − 𝑁 𝐾 𝑑𝑁1 𝑑𝑡 = 𝑟𝑁1 𝐾1 − 𝑁1 − 𝛼𝑁2 𝐾 Alfred James Lotka (1880 1949) N = N + α𝑀 𝑑𝑁2 𝑑𝑡 = 𝑟𝑁2 𝐾2 − 𝑁2 − 𝛽𝑁1 𝐾 Vito Volterra (1860-1940) 𝐾1 − 𝑁1 − 𝛼𝑁2 = 0 At equilibrium: dN/dt = 0 𝐾1 − 𝑁1 − 𝛼𝑁2 = 𝐾2 − 𝑁2 − 𝛽𝑁1 If competitive strength differs one species vanishes Certain conditions allow for coestistence If carrying capacity differs one species vanishes The Lotka Volterra model predicts competitive exclusion

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

𝑑𝑁1 𝑑𝑡 = 𝑟𝑁1 𝐾1 − 𝑁1 − 𝛼𝑁2 𝐾 The model needs stable reproductive rates stable carrying capacities stable competition coefficients Grassland are highly diverse of potentially competing plants It needs also homogeneous environments Randomy fluctuating values of r, K, a , and b .

a > b K1 > K2

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

Competition for enemy free space (apparent competition)

Plodia interpunctella

Venturia canescens Ephestia kuehniella Extinction Data from Bonsall and Hassell 1997, Nature 388 Predator mediated competition might cause extinction of the weaker prey

Character displacement and competitive release 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)

Chalcosoma caucasus Chalcosoma atlas

Rhinoceros beetles 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.

Bodey et al. 2009. Biol.Lett 5: 617 Raven Raven + Crows

Predation

Erigone atra

Generalist predator Canada lynx and snowshoe hare Specialist predator Polyphages Oligophages Monophages

Maximum yield Searching time Stopping point Trade-offs in foraging Animals should adopt a strategy to maximuze yield

Optimal foraging theory

Holling’s optimal foraging theory 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 𝑓𝑜𝑜𝑑 𝑡 𝑡𝑟𝑎𝑣𝑒𝑙 𝐹𝑜𝑜𝑑 𝑖𝑛𝑡𝑎𝑘𝑒 ∝ 1 + 𝑎𝐷𝑒𝑛𝑠𝑖𝑡𝑦 𝑓𝑜𝑜𝑑 𝑡 ℎ𝑎𝑛𝑑𝑙𝑖𝑛𝑔 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 and handling time 10 20 3 15 18 Predicted energy intake from travel time 11 4 17

Parus major

8 9 Cowie 1977

Specialist predators and the respective prey often show cyclic population variability Canada lynx and snowshoe hare Hudson’s Bay Company Data from MacLulick 1937, Univ. Toronto Studies, Biol. Series 43

Bracyonus calyciflorus

12 year cycle

Chlorella vulgaris

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. Data from Yoshida et al. 2003, Nature 424

The Lotka Volterra approach to specialist predators 𝑑𝑃 𝑑𝑡 = −𝑒𝑁 𝑑𝑃 𝑑𝑡 𝑑𝑁 = 𝑟𝑁 − 𝑎𝑃𝑁 𝑑𝑡 = 0 → 𝑁 = 𝑒 𝑓𝑎 𝑑𝑃 𝑑𝑡 = 𝑓𝑎𝑁𝑃 − 𝑒𝑃 𝑑𝑁 = 0 → 𝑃 = 𝑑𝑡 𝑟 𝑎 The equilibrium abundances of prey and predator e: mortality rate of the predator r: reproductive rate of the prey faN: reproductive rate of the predator f: predator efficieny aP: mortality rate of the prey a: attack rate In nature most predator prey relationships are more or less stable.

The Lotka Volterra models predicts unstable delayed density dependent cycling of populations 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 environment Habitat heterogeneity provides prey refuges and stabilizes predator and

prey populations

Functional response

Type II Holling response Type III Holling response

Microplitis croceipes

Type I response

Calliphora vomitoria

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

Microplitis croceipes Calliphora vomitoria

Variability, chaos and predator prey fluctuations 𝑑𝑁 = 𝑟𝑁 − 𝑎𝑃𝑁 𝑑𝑡 Lotka Volterra cycles with fixed parameters a, e, f, r.

𝑑𝑃 𝑑𝑡 = 𝑓𝑎𝑁𝑃 − 𝑒𝑃 Lotka Volterra cycles with randomly fluctuating parameters a, e, f, r.

Stochasticity tends to stabilize populations

Dynamic equilibrium

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.

Herbivory

Feeding Strategy

Frugivores Folivores Nectarivores Granivores Palynivores Mucivores Xylophages

Diet

Fruit Leaves Nectar Seeds Pollen Plant fluids, i.e. sap Wood

Example

Ruffed lemurs Koalas Hummingbirds Hawaiian Honeycreepers Bees Aphids Termites Plant defenses against herbivors 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.

Alcaloide (amino acid derivatives): nicotine, caffeine, morphine, colchicine, ergolines, strychnine, and quinine Terpenoide, Flavonoids, Tannins Mechanical defenses: thorns, trichomes… Mimicry Mutualism: Ant attendance, spider attendance

Digitalis

Negative feedback loops occur when grazing is too low Functions of herbivores in coral reefs Herbivorous fish (Diadema) Positive feedback loops occur when grazing is high Reduced structural complexity Decreasing fish recruitment Increased structural complexity Increasing fish recruitment Low coral cover Low grazing intensity High coral cover High grazing intensity Decreasing coral recruitment Hay and Rasher (2010) Increasing algal cover Overfishing of herbivorous fish might cause a shift to algal Increasing coral recruitment dominated low divesity communities Decreasing algal cover