Transcript Positive and negative species interaction

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.
Client– 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
𝜎2 ≪ 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
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
𝐿 3/2 −3/2
𝑤=𝑏
𝑁
𝑎
𝑤 = 𝑐𝑁 −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
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
𝑁𝑡+1 = 𝑟 𝑡+1 𝑁0
𝑁𝑡+1 = 𝑟𝑁𝑡
1
Nt/Nt+1
𝑁𝑡
=
𝑁𝑡+1
𝑁𝑡+1 =
1/r 𝑦 = 𝑚𝑥 + 𝑏
Nt
K
1
1−𝑟
𝐾
𝑁𝑡+1 =
𝑁𝑡 +
1
𝑟
𝑟𝑁𝑡
𝑟−1
1 + 𝐾 𝑁𝑡
𝑁𝑡+1 =
𝑟𝑁𝑡
1 + 𝑎𝑁𝑡
𝑟𝑁𝑡
1 + 𝑎𝑁𝑡
𝑏
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 – Volterra model of interspecific competition
Alfred James
Lotka (18801949)
𝑑𝑁
𝐾−𝑁
= 𝑟𝑁
𝑑𝑡
𝐾
𝑑𝑁1
𝐾1 − 𝑁1 − 𝛼𝑁2
= 𝑟𝑁1
𝑑𝑡
𝐾
N = N + α𝑀
𝑑𝑁2
𝐾2 − 𝑁2 − 𝛽𝑁1
= 𝑟𝑁2
𝑑𝑡
𝐾
Vito Volterra
(1860-1940)
At equilibrium: dN/dt = 0
𝐾1 − 𝑁1 − 𝛼𝑁2 = 0
If competitive strength
differs one species vanishes
𝐾1 − 𝑁1 − 𝛼𝑁2 = 𝐾2 − 𝑁2 − 𝛽𝑁1
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
𝐾1 − 𝑁1 − 𝛼𝑁2
= 𝑟𝑁1
𝑑𝑡
𝐾
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
Holling’s optimal foraging theory
𝐷𝑒𝑛𝑠𝑖𝑡𝑦𝑓𝑜𝑜𝑑 𝑡𝑡𝑟𝑎𝑣𝑒𝑙
𝐹𝑜𝑜𝑑 𝑖𝑛𝑡𝑎𝑘𝑒 ∝
1 + 𝑎𝐷𝑒𝑛𝑠𝑖𝑡𝑦𝑓𝑜𝑜𝑑 𝑡ℎ𝑎𝑛𝑑𝑙𝑖𝑛𝑔
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
Hudson’s 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 – 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
= −𝑒𝑁
𝑑𝑡
𝑑𝑁
= 𝑟𝑁 − 𝑎𝑃𝑁
𝑑𝑡
e: mortality rate of the predator
𝑑𝑃
= 𝑓𝑎𝑁𝑃 − 𝑒𝑃 r: reproductive rate of the prey
𝑑𝑡
faN: reproductive rate of the predator
𝑑𝑁
𝑟
𝑑𝑃
𝑒
f: predator efficieny
=
0
→
𝑃
=
=0→𝑁=
𝑑𝑡
𝑎
𝑑𝑡
𝑓𝑎
aP: mortality rate of the prey
The equilibrium abundances of prey and predator 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
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
𝑑𝑁
= 𝑟𝑁 − 𝑎𝑃𝑁
𝑑𝑡
Lotka Volterra cycles with fixed
parameters a, e, f, r.
𝑑𝑃
= 𝑓𝑎𝑁𝑃 − 𝑒𝑃
𝑑𝑡
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, trichomes…
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