Download Predator-prey interactions

The major types of organism-organism
interactions
Predator-prey interactions
Dik Heg
1. Competition
Interspecific competition (competition between different species)
Intraspecific competition (competition within the same species)
2. Predation
Interspecific predation (predator-prey interactions)
Intraspecific predation (cannibalism, infanticide)
3. Cooperation
Interspecific cooperation (mutualism, symbiosis)
Intraspecific cooperation (kin selection, reciprocal altruism)
4. Parasitism
Interspecific parasitism (host-parasite interactions, e.g.
ectoparasites, endoparasites, viruses, pathogens)
Intraspecific parasitism (within-species brood parasitism, e.g. egg
dumping, sneaking)
I. Prey detection: stages of predation and
the antipredator defences
Overview
I. Prey detection
1. Encounter
rarity, hiding, activity budget
2. Detection
immobility, crypsis, confusion, sensory limits
3. Identification
masquerade, confusion, Mullerian mimicry, Batesian
mimicry, honest warning colorations
4. Approach (attack)
5. Subjugation (prevent escape)
6. Consumption
II. Optimal foraging theory
III. Predator-prey
population dynamics
Overview
Prey detection
1
Example apostatic selection
Prey selection
Predators might select prey based upon:
- Species
- Size
- Morph
etc. etc.
Apostatic selection:
predator preys on more common type of prey
Prey detection
Prey detection
Reasons for apostatic selection
1. Formation of search image
It takes time to learn to detect and handle prey: known prey preferred
2. Optimal search rate hypothesis
Optimal search speeds for prey 1 and 2 differ: first search for more
abundant prey 1, until 1 < 2, than switch search rate strategy to 2
3. Search intensity hypothesis (´stare duration´)
Prey species 2 might be more difficult to detect
Search rate increase: trade-off between increase in encounter
frequency and a reduction in detection probability
Prey detection
2
Cryptic prey
Cryptic prey:
colour, morphology and/or chemical profile resembles a
random sample of the background as perceived by the
predator (at the time and place at which the prey is most
vulnerable to predation)
Argentine horned frog
Prominent moth
Sandgrouse chicks
Leaflitter frog
Prey detection
Aposematic prey
Aposematic colouration (or sounds, odours etc.):
conspicuous colouration combined with noxiousness (incl.
unpalatability)
Predator training and hence protection more efficient when:
1. Higher density of the signal
2. Higher densities of the aposematic prey
3. Fewer or more similar aposematic colour patterns around
4. Clumped- or higher local densities of prey
Prey detection
3
Aposematic prey
Mimicry
Mimicry: two species resemble each other (in
morphology and often also in behaviour).
How can natural selection favour a defence that
operates during or after the subjugation and
consumption phases of predation?
Species 1
Species 2
Batesian mimicry
1. Kin selection (Fisher 1930)
2. Synergistic selection (Guilford 1985, Maynard-Smith 1989)
3. Combination with a common post-attack defence
(Wicklund & Jarvi 1982)
Müllerian mimicry
Batesian: one palatable species resembles unpalatable species
Müllerian: two unpalatable species resemble each other
Mimicry ring: species complex of groups of Batesian and Müllerian mimics
Prey detection
Example: pairs of
Batesian mimics
Unpalatable swallowtail
model species
Palatable female races
of Papilio memnon
Prey detection
Example: pairs of Müllerian mimics
from Ecuador and Northern Peru
Heliconius melpomene
H. erato
4
1. H. cydno morphs have increased survival when they match locally
abundant co-model species when transferred at low densities (a, c & d).
2. H. cydno morphs do not differ in survival when many H. cydno morphs
are transferred leading to high densities of both models (b).
Example: Müllerian mimics of Heliconius
Populations A:
`white` Müllerian mimics
Populations B:
`yellow` Müllerian mimics
Control
(normal transfer,
A to A or B to B)
Experiment
(reciprocal transfer,
A to B or B to A)
H. cydno
Co-models
Kapan (2001). Nature 409: 338-340
Kapan (2001). Nature 409: 338-340
The diet-switch model
II. Optimal foraging theory
(McArthur & Pianka 1966)
The aim of optimal foraging theory is to
predict the foraging strategy to be
expected under specified conditions
Major assumptions:
1. Natural selection on foraging in the past = present
2. High net rate of energy intake = high fitness
3. Experimental setup = close to natural environment
Specialist: only pursues profitable prey, but has to search more.
Generalist: also eats more and less profitable prey, has to search
less.
E = energy content
h = handling time prey
s
h
s = search time prey
ith prey = the next most-profitable prey type
s
h
Optimal strategy: predator should also pursue ith prey when:
Ei / hi > E / (s + h)
Optimal foraging theory
Optimal foraging theory
5
The diet-switch model
The constrained diet model
(McArthur & Pianka 1966)
(Belovsky 1978-1984)
Some general predictions from the model:
1. Predators with short handling times should be generalists.
2. Predators should be more generalistic in an unproductive
environment (low density = large search time).
3. Predators should ignore insufficiently profitable ith prey
irrespective of their density (formula is si independent).
Example using linear programming with herbivore moose Alces
alces:
Consumption Energy value Sodium
(gram)
per gram
per gram
Aquatic plants
A
3.8 kJ
0.003 gram
Terrestrial plants
T
4.25 kJ
0.000 gram
How much A and T can and should the moose eat to survive?
Optimal foraging theory
Maintenance requirement:
14000 kJ < 3.8A + 4.25T
Sodium requirement:
2.57 gram < 0.003A
(minimum energy needed for survival per day)
(Only aquatic plants contain sodium, gram
needed per day)
Optimal foraging theory
Constraints, limitations, currencies to be optimized
are very often individual (phenotypic/genotypic),
population and/or species specific!
Predator present
Predator
(gram of wet food per day which can be
maximally processed, aquatic plants contain 5x
more water)
Number of fish
Digestive limitation:
32900 gram > 20A + 4.04T
No predator present
% Vegetation cover where prey is feeding
Optimal foraging theory
largemouth bass Micropterus salmoides
Prey
bluegill sunfish Lepomis macrochirus
Optimal foraging theory
6
The functional response
The numerical response
Functional response (Solomon 1949): the relationship between
individual‘s consumption rate (P) and local food density (N)
Numerical or aggregative response: the relationship between the
local consumer density and local food density.
Three types of functional responses (Holling 1959):
Type 1
Type 2
Type 3
No handling time
Max P = maximum throughput
At large N: predator
only handling prey
Switching, search image,
increased (handling) efficiency
Optimal foraging theory
Patch use: the marginal value theorem
Interference: functional
response and numerical
response interact
Given:
prey distributed in patches (which might differ in quality)
prey within patches are depleted by foragers
forager pays travel cost to reach other patch
This will lead to animals
distributing themselves over the
habitats or patches until fitness:
Question: at what point should a forager leave a patch?
Equal: Ideal free distribution
Unequal: Ideal despotic distribution
(dominants occupy best patches or have less
negative effects from interference)
Optimal foraging theory
Optimal foraging theory
7
Patch use: the marginal value theorem
III. Predator-prey population dynamics
The basic dynamics of predator-prey and plant-herbivore
systems: a tendency towards cycles. Why??
Fig. 10.1c Begon p.370
Optimal foraging theory
Population dynamics
The Lotka-Volterra Model
The Lotka-Volterra Model
Description of the changes in abundance of predators and prey:
P = number of predators (or consumers)
N = number of prey (or biomass)
Predator numbers are assumed to decline exponentially through
starvation in the absence of prey, q is the mortality rate:
Without predators, exponential increase in N per time step t:
dP = -qP
dt
dN = rN
dt
This is counteracted by predator birth, which depends on
predator consumption a´PN and the efficiency f of converting
this food into predator offspring:
As predator or prey densities increase, more predators encounter
prey (PN) and prey will be consumed with ´attack rate´ a´:
dP = fa´PN - qP
dt
dN = rN – a´PN
dt
Population dynamics
Population dynamics
8
The Lotka-Volterra Model
Predator abundance (P)
The Lotka-Volterra Model
Prey abundance (N)
Despite the underlying tendencies, predator-prey cycles are not
always to be expected!
Population dynamics
Parasitoid present
Population dynamics
Example: effects of piscivore predator on group-living in the
Lake Tanganyika cichlid Neolamprologus pulcher
Piscivore:
Lepidiolamprologus elongatus
host Plodia interpunctella
No parasitoid present
parasitoid Venturia canescens
Group-living fish:
Neolamprologus pulcher
Population dynamics
9
Lake Tanganyika
10
Cages (2x2m)
Piscivore (control, medium, large)
Survival
(a)
breeder males
breeder females
large helpers
medium helpers
small helpers
17 19
29
63
survival
42
medium helpers
23
20
22
0.8
24
48
95
45
40
0.6
large helpers
1.0
survival
1.0
92
47
0.4
control
(b)
medium
0.8
small helpers
0.6
0.4
large
5
predator treatment
10
15
20
25
number of adults
60
Reproduction
(a)
Feeding rate
50
Large helpers
number of offspring
70
fry
60
juveniles
50
40
30
20
Medium helpers
10
0
Feeding rate (bites/minute)
40
30
20
10
0
60
(b)
50
40
30
20
n = 18
20
18
control medium large
18
20
18
control medium large
10
0
0
predator treatment
20
40
60
80
100
Mean distance from shelter (cm)
11