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Positive and negative interactions. I nterspecific 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. Predation. Herbivory is a form of parasitism. - PowerPoint PPT Presentation
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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)