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Positive and negative interactions Interspecific competition Predation 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. Clientβ service relationships Pollination Mutualism is often linked to coevolutionary processes In plant succession early arriving plants pave the way for later arrviing by modifying soil condition. 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 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. Contest (interference) competition is a form of competition where there is a winner and a loser Mate competition Territoriality π2 βͺ π 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 Overlap Territory Home range Home range Home ranges might overlap 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=lN where L is the average leaf area per plant. This area and mean plant weight w increase with stem diameter by l=aD2 and w=bD2 Therefore πΏ 3/2 β3/2 π€=π π π π€ = ππ β3/2 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 Density independence Vulpia fasciculata Data from Ebert et al. 2000. Oecologia 122 Data from Allen 1972, R. Int. Whaling Comm. 22. Salmo trutta Density dependence Peak reproduction at intermediate densityy Density independence ππ‘+1 = π π‘+1 π0 ππ‘+1 = πππ‘ 1 Nt/Nt+1 ππ‘ = ππ‘+1 ππ‘+1 = 1/r π¦ = ππ₯ + π Nt K 1 1βπ πΎ ππ‘+1 = ππ‘ + 1 π πππ‘ πβ1 1 + πΎ ππ‘ ππ‘+1 = πππ‘ 1 + πππ‘ πππ‘ 1 + πππ‘ π First order order recursive function of density dependent population growth Nicholson and Baily model Georgii Frantsevich Gause (1910-1986) Competitive exclusion principle In homogeneous stable environments competitive dominant species attain monodominancy. Paramecium aurelia 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 Temperature Humidity Hot Temperate Cold Hot Temperate Cold Moist Moist Moist Dry Dry Dry Tribolium castaneum Percentage wins Tribolium Tribolium confusum castaneum 0 100 14 86 71 29 90 10 87 13 100 0 Data from Park 1954. Phys. Zool. 27. Two species of the rice beetle Tribolium grown together compete differently in dependence on microclimatic conditions. The Lotka β Volterra model of interspecific competition Alfred James Lotka (18801949) ππ πΎβπ = ππ ππ‘ πΎ ππ1 πΎ1 β π1 β πΌπ2 = ππ1 ππ‘ πΎ N = N + Ξ±π ππ2 πΎ2 β π2 β π½π1 = ππ2 ππ‘ πΎ Vito Volterra (1860-1940) At equilibrium: dN/dt = 0 πΎ1 β π1 β πΌπ2 = 0 If competitive strength differs one species vanishes πΎ1 β π1 β πΌπ2 = πΎ2 β π2 β π½π1 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. ππ1 πΎ1 β π1 β πΌπ2 = ππ1 ππ‘ πΎ The model needs stable reproductive rates stable carrying capacities stable competition coefficients It needs also homogeneous environments Grassland are highly diverse of potentially competing plants 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 Chalcosoma caucasus 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. Rhinoceros beetles Chalcosoma atlas Raven Dietary width 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) Bodey et al. 2009. Biol.Lett 5: 617 Raven Raven + Crows Predation Erigone atra Canada lynx and snowshoe hare Specialist predator Generalist predator Oligophages Polyphages Monophages Trade-offs in foraging Prey quality Stopping point Starvation Maximum yield Animals should adopt a strategy to maximuze yield Optimal foraging theory Hollingβs optimal foraging theory π·πππ ππ‘π¦ππππ π‘π‘πππ£ππ πΉπππ πππ‘πππ β 1 + ππ·πππ ππ‘π¦ππππ π‘βπππππππ Searching time Great tits forage at site of different quality How long should a bird visit each site to have optimal yield? 10 Predicted energy intake from travel and handling time 20 Predicted energy intake from travel time 18 15 3 11 4 Parus major 17 8 9 Cowie 1977 Specialist predators and the respective prey often show cyclic population variability 12 year cycle Canada lynx and snowshoe hare Hudsonβs Bay Company Data from MacLulick 1937, Univ. Toronto Studies, Biol. Series 43 Bracyonus calyciflorus Chlorella vulgaris Cycles of the predator follow that of the prey Cycles might be triggered by the internal dynamics of the predator β 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 e: mortality rate of the predator ππ = ππππ β ππ r: reproductive rate of the prey ππ‘ faN: reproductive rate of the predator ππ π ππ π f: predator efficieny = 0 β π = =0βπ= ππ‘ π ππ‘ ππ aP: mortality rate of the prey The equilibrium abundances of prey and predator a: attack rate ππ = βππ ππ‘ ππ = ππ β πππ ππ‘ 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: β’ Prey aggregration β’ Density dependent consumption β’ Functional responses Environmental heterogeneity and predator prey cycles Eotetranychus sexmaculatus Typhlodromus occidentalis Simple unstructured environment Habitat heterogeneity provides prey refuges and stabilizes predator and prey populations Heterogeneous environment Functional response Type II Holling response Type III Holling response Type I response Microplitis croceipes Calliphora vomitoria Predator attak rates are not constant as in the Lotka Volterra model Microplitis croceipes Calliphora vomitoria Variability, chaos and predator prey fluctuations ππ = ππ β πππ ππ‘ Lotka Volterra cycles with fixed parameters a, e, f, r. ππ = ππππ β ππ ππ‘ 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 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 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, trichomesβ¦ Mimicry Mutualism: Ant attendance, spider attendance Digitalis Negative feedback loops occur when grazing is too low Functions of herbivores in coral reefs Positive feedback loops occur when grazing is high Herbivorous fish (Diadema) 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 Increasing coral herbivorous fish might recruitment cause a shift to algal dominated low divesity communities Decreasing algal cover