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OBJECTIVES: Understand that evolution drives organisms to be efficient in their search for
resources, the concept of a resource, the effects of resource distribution and density on
foraging, and basics of marginal value theorem
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Fundamental Principles of Ecology:
1 Organisms are distributed heterogeneously (variably) in space and time (migrations)
2 Organisms interact with their abiotic and biotic environments
3 Variation amongst organisms leads to heterogeneity in ecological patterns and
processes (water and nutrient cycles, succession)
4 Environmental conditions are heterogeneous in space and time (seasons)
5 Distribution of organisms and their interactions depends on contingencies (chance)
6 Resources are finite and heterogeneous in space and time
7 Birth and death rates are a consequence of interactions with the abiotic and biotic
environment
8 Ecological properties of species are a result of evolution (and determine
consequences)
An example of 3 and 8 is the variability in the markings of Harlequin ladybirds (invasive
species from Asia, arrived early 2000s, now most common ladybird in England)
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May result in similar
situations as the Peppered Moth in the industrial revolution
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Feeding lifestyles: Herbivores, Predators (not just mammalian – hoverfly larvae), Parasites,
Parasitoids (consume and kill hosts, normally insects, eg parasitoid wasps lay eggs in aphids
etc which hatch and emerge when the host is dead), Detritivores (eat dead stuff), Fungivores
(eg Fungus Gnat)
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Being the best at eating one thing will require specialised structures which may mean that
you cannot have the structures to be the best at eating another (eg canines vs molars)
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Cost and benefit determines individuals reproductive success > fitness
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s cannot break down aspen tree chemicals)
Developmental within-species variation - Otherwise known as size related – larger organisms
can eat larger food (Damselfish eat larger prey as the get larger in an allometric relationship
(how body size affects shape, behaviour, and anatomy)), (Daphnia Copepods filter feed larger
prey as they grow (tested with glass beads), but the relationship is negatively allometric)
Facultative within-species variation - May be due to availability (less choosy if there is more
food, eg Herons are generalists if there is low prey availability), or behaviour (there is a delay
when a predator is introduced to new prey as the figure out how to catch it, and form a
search image (this is not done if there is a lot of known prey), leading to switching
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Caught

Predator ignores rarer prey type as no search image,
and as the population increases it forms a search image
and catches more
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in environment
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Isometric scaling - when the relationship between two things stays constant, eg if a birds
head is ½ the size of its body and grows proportionally to the body so stays ½ the size of the
body regardless of the size of the bird
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Graphs are plotted with log
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If the value is lower than would be plotted with isometry, it is
negative allometry, eg the prey size increases at a slower rate than the predator size
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Efficiency is understood to mean E/t,
meaning that for maximum efficiency you can either maximise the energy per unit of time
E/t, or minimise the time per unit of energy E/t
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Eg Shore crabs (Carcinus
maenas) have negative allometry with the size of mussel they eat
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Therefore in order to forage efficiently the crabs will
mostly prey upon medium sized mussels
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Handling time may be related to predator size, therefore prey size and predator size follow
isometrically
Optimal strategies must always optimise benefit over cost, increasing fitness
Different values may be used to evaluate benefit/cost instead of time or energy, such as
nitrogen and water
Fitness = reproductive output
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This shows that selection should
favour more optimal foragers
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000+ workers per hive which do not reproduce (only queen – kinship
behaviour from last year, more related to each other than offspring)
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However, they visit
more and closer together flowers which would maximise the energy per time
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In order to eat optimally, the moose should eat the minimum
of each hard to eat food in order to survive, which is where the graphs cross
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Female moose must therefore move to the forest, and will
reabsorb some of their unborn offspring’s resources, reducing reproductive output
therefore fitness
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Functional response - the relationship between an individual’s rate of food consumption
and the density of the food
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May look exponential
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3 Line is S shaped to satiation – switch due to learning
(forming search image), graph made with numbers not percentage density
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Also known as sigmoid
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There
are three types (normally curves):
1 Measures survival of the predator (offspring)
2 Measures reproduction of predator
3 Measures aggregation of predator (how it moves in relation to prey)
In order to forage optimally you need to find high density/quality patches and stay there
until the resource has been depleted
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A
study with ants found that the more aphids were on a leaf, the more time they spent on
that leaf
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The optimal time depends on the travel (search) time between
patches (see where tangent meets the curve)
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Stay longer in poorer
environments (with longer search times)
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Most organisms are selective in what they eat, and selectivity is either obligatory or
facultative
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Optimal foraging theory assumes that short
term maximisation (usually energy) equates to a long term maximisation of fitness
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DEMOGRAPHY
OBJECTIVES: Understand the four key demographic processes (birth, death,
immigration, and emigration), life tables, and analyse key factors
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Biosphere
Ecosystem

Populations are dynamic (ever changing), and their variability depends on the timeMeta
Community
scale
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Sexual aphids have wings, asexual do
not
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Per capita growth rate: growth rate per individual (ie number of children born in relation to
population size, normally a log)
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Negative growth rates
can be caused by a decreased number of births/immigration, increased number of
deaths/emigration
Immigration and emigration is often ignored
Hornbeam trees produce lots of seeds in mast years, which leads to an increase in vole
populations the year after – bottom up regulation
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Size at next time period = size at this time period + births –deaths + immigration –
emigration)
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Populations increase when λ > 1 (normally when births > deaths),
are tending to extinction if λ < 1, and are static if λ = 1
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Here is a simple life cycle, where only the adults are reproductive (f
represents fecundity, Se the survival of the eggs, and Sj the survival
of the juveniles)
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The time from egg to adult is normally
counted as one time step
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The
only limitation on reaching the correct age is survival
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Stage-specific life history - organism must progress through stages
(progression determined by size), eg instars, before they can reach maturity
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Normally insects and plants
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Can
be indeterminate (will grow to infinity like fish) or determinate (stop at a certain age like us)
Reproductive strategies may be monoecious or dioecious (one or two sexes),
hermaphrodites (gastropod molluscs, polychaetes), sequential hermaphrodites (transition
from one gender to another), asexual, or alternate generations (eg asexual and sexual,
haploid and diploid)
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Whiptail
lizards and invertebrates may use parthenogenesis (apomixis for plants) - reproduction from
an unfertilised egg
Animal ecology tends to focus on
determinate growth and separate sexes
Life cycles can have overlapping
generations or non-overlapping
generations
Non-overlapping generations - usually
insects, where adults lay eggs and die
(therefore there are no adults of different
generations at the same time), discrete
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There is a
huge mortality event at the egg stage,
most likely due to dessication
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Previous generations also lay eggs but fecundity is
affected my age
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Low survival of fledglings as the parents
have left and they must learn to survive alone and
find own territories
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Eg: Great tits

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Intermediate life cycles - in theory
may be non-overlapping, but may
have dormant seeds which become
active and contribute to the new
generation (therefore technically
being adults from previous
generations)
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This is an
example of a sink population with
the buried seed bank in sand sune
and invading seeds acting as
immigration
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Cohort life tables - follow cohort through full life, the most informative but harder to obtain
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lx is the percentage of the original cohort sample which is still alive
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Kx is the killing factor, the difference in
lognx from one stage to the next
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bx is the fecundity per stage
(zero until maturity), then (assuming a ratio of
1:1 m:f, divided by 2 and multiplied by the
number of adults to give the number of births)
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Immigration and emigration ignored
in both types of table
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Static is as good as cohort if the survival rates between stages are constant
(never true)
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Can get an idea from life tables whether the population is viable or not
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Curves are drawn from lx
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May also draw fecundity curves from bx values
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Do not draw curves for dispersal (against age) as
populations are often assumed to be closed
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Organisms disperse to reduce competition and
inbreeding
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Negatives of dispersal are
outbreeding (breeding too far from original territory, so genes are no longer well adapted,
reducing offspring survival), as the organism doesn’t have the local adaptations
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Dispersal may also be in time via seedbanks (takes
advantage of heterogeneity in time)
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Can
add up K values for each stage and plot to give the relative intensity of mortality
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Growth rate is determined by λ in discrete time, and r in continuous time
Exponential growth:
o In discrete time, λ= Nt+1/Nt or Nt+1= λNt (time is treated as a discrete
variable/may remain constant)
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r is the instantaneous,
intrinsic rate of population increase, and is called the Malthusian
parameter
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The Malthusian growth model is a model for exponential increase, named after Thomas
Malthus, who discovered that humans were following this pattern in 1998
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From life tables we can get mathematical values for survival (lx) and fecundity (bx) (and
time)
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If R0 = 1, the population is at replacement rate (not growing or
shrinking)
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R0 = λ in non-overlapping
generations
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X is equal to time
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As a rule,
doubling time increases as r is smaller, or as the body size is larger
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Matrix models are more accurate as they do not lump ages or stages together (eg in terms
of survival (s or lx) or fecundity (f or bx))
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𝑁1 (𝑒𝑔 𝑒𝑔𝑔𝑠)
𝑓1 𝑓2 𝑓3 𝑓4
𝑁2 (𝑙𝑎𝑟𝑣𝑎𝑒)
𝑠
0 0 0
With a Leslie matrix, A = 1
and Nt =
𝑁3 (𝑝𝑢𝑝𝑎𝑒)
0 𝑠2 0 0
0 0 𝑠3 0
𝑁4 (𝑎𝑑𝑢𝑙𝑡𝑠)
To calculate Nt+1 it is simply (F1 x N1)+(F2 x N2)+(F3 x N3)+(F4 x N4) = N1t+1, and so on
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8 = (0x14)+(0
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8x12)+(0
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4x4)+(0x0)
and N2t+1 = 8
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6x14)+(0x8)+(0x12)+(0x4)+(0x0)+(0x0) etc

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The northern right whale is the most endangered large whale, with less than 300
individuals
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Data shows that predominantly the
mature whales with calves have a decreasing survival probability since 1980
(may be due to a vulnerability due to low manoeuvrability around boats)
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In this case a small
change of 2 female whales saved per year would tip the population into positive growth
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Stochasticity - noise (what happens if parameters vary randomly)
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Has a larger impact
on smaller populations, but still significant
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Have most effect on smaller population sizes, may be mostly irrelevant to large sizes
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5, and one with a
fluctuating growth rate of λ1=1 λ2=2, and both with a starting population of 100
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5x1
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This shows that stochasticity matters
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λ = 𝑛√𝜆1 𝜆2 … 𝜆𝑛
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Genotypes with low growth
rate variance are favoured by natural selection
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Eg plants
producing seeds with varying germination times, forming a seed bank, so if an event kills all
germinated seeds, there are still seeds carrying its genes in the seedbank
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(Reminder that fitness =
reproductive output = surviving offspring)
Demographic heterogeneity - variation of survival and reproduction amongst individuals
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Demographic stochasticity - the variation of population growth rate due to births, deaths,
sex ratio, and dispersal
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So far the models have assumed constant r/λ, constant birth and death rates, closed
population and unlimited resources, which leads to exponential growth
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High densities
lead to decreased births, and increased deaths, and therefore a slowing population growth
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(population increases 4
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For example,
every larva needs 2 bugs or they’d die, so if there are a finite number
of 40 bugs, and everyone shared, the environment could support 20
larva
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However, with contest competition every larva gets as much as it can
for itself, therefore fewer larvae will survive
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This regulates the population size
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K is
the x intercept of the line
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rN is related to the y
intercept
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Summary: population dynamics focuses on four main processes (births, deaths, immigration,
emigration), which can be combined into simple models
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Growth is rarely
exponential, and density dependence is important
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The logistic equation

𝑑𝑁
𝑑𝑡

= 𝑟𝑁 (1 −

𝑁
𝐾

) shows the

rate of population change over time – the growth rate
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It makes a
sigmoid curve
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If

𝑁
𝐾

is 0, the population size is

Carrying capacity (K)
𝑑𝑁

Pop
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This is related to resource availability
At higher prey population densities, there are also higher numbers of natural
enemies –eg parasitoid wasps and gall flies, the wasps lay eggs in the gall fly
larvae and decrease the survival of the flies – regulates population density
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Density dependance can be negative – eg in competition for food where the food available
determines not only survival and immediate fecundity but also age at maturity (more food, grow
faster, reach maturity sooner, have longer window of reproduction therefore higher LRS)
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Later on
Unsustainable
however, the density decreased growth rate due to easier
transmission of disease
MSY
Allee Effect – positive density dependance, where the fitness
increases with increasing population size, eg with humans and the
industrial revolution
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Net recruitment (number of births minus number of deaths) is
highest at intermediate densities (middle of pop
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As the population size approaches the carrying
capacity, the growth slows
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Where the growth rate
is negative, harvesting is still sustainable because taking
from here reduces density dependant effects
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Chaos
– not mathmatically predictable without knowing the
equation, no apparent pattern
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Stable – has
logistic growth with an equilibrium, may still have damped
oscilations due to overshooting the carrying capacity
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Abiotic fluctuations can also be responsible – large scale and low frequency via the climate, such
as El Nino southern oscillation (temperature changes in the pacific) or random events such as
the North Atlantic oscillation (pressure changes in the atlantic leading to changes in storm paths
etc, with no apparent pattern)
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Environmental
effects such as global warming
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𝑁

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2
𝜎𝐾

2

– the average population size = the

average carrying capacity – half the variance of K (var = 𝜎 2 )
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capacity (smoother following of K)
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r determines the response time to a
variable (∝ 1/𝑟)
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For example if a mahogany trees produces seeds at

whatever rate it wants, then the density dependance which acts on the seeds is what affects
reproduction in 100 years (generation)
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Numerical response – change in consumer density according to food density
Functional response – change in consumer’s rate of consumption according to food density
A low ᴛr gives a smooth logistic graph, an average ᴛr gives damped oscillations, and a high ᴛr
gives cycles with a period of 4ᴛ (the general rule of cycles)
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An example of interactions between ages and stages is egg
cannibalism in flour beetles
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Without egg cannibalism, all life stages were
relatively stable
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It is an allometric relationship –
Contest
different body sizes gives different responses to types of
Growth rate – r (∝ body size)
competition
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Fishing removes larger therefore older fish as smaller fish escape the nets
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Ultimate - Why

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Proximate - How

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What are priorities for some species are not for others – eg aphids have so much access to
sugar that they excrete it so that they can keep eating in search of N2
Gypsy moths actually survive longer on a low
nitrogen diet, as more N2 is stored
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On aspen leaves,
the moths have to invest more into defence against
chemicals, so cannot invest as highly in growth and
storage
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If the food is low quality, the
individuals invest less into growth, and more into storage, in the hopes of
waiting the bad times out
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It is somewhat irrelevant to growth how much energy you have – you cannot grow without
nitrogen
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Eg Daphnia grow neck spines in the presence of Phantom midges, which
reduces fitness (increases survival but decreases birth rate and fecundity) if there is actually no
predation, but increases if the predator is truly there
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This accords different
benefits to different larvae – the ones with the low temperature gene survive better at low
temperatures but lay fewer eggs and have a shorter life expectancy
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Parameters affecting this are λ, the finite rate of increase, 𝑅0 /LRS,
the net reproductive rate, and r, the intrinsic rate of increase
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The top two are
proximate explanations – how it works
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It’s important to consider all types of answers not just assume natural selection
Pygmy humans may be smaller because:
1
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Ontogeny – growth hormones suppressed,
poor nutrition, or slow growth
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Phylogeny – common small ancestor, or
convergent evolution of humanlike traits
Pygmy trait is genetic in origin, polygenic, adaptive
(selected for), and convergent (arose in different
ways in different populations)

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All individuals have a finite amount of X, so cannot invest optimally in everything
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An example of this is that
Collared Flycatchers often have varying clutch sizes (despite the fitness of the parent bird being
unaffected by large clutches)
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This is explained by manipulating the clutch size
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It is
better to be cautious and still have your genes in the next generation, than have too many and
be unable to feed them all
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Pleiotropy – when one gene influences more than one seemingly unrelated traits
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This leads to selfregulating trade offs
An example of this is in self-hermaphroditic worms
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Selfing Hermaphrodite – fertilises own eggs (has both male and female parts)
Body size – The optimal body size (in Drosophila) is intermediate
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Reproduction – larger flies can hold more eggs (fecundity is a function of body size)
2
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Survival – more time for mortality before they can lay eggs, as they cannot defend
themselves (not relevant in captivity)
Phenotypic Plasticity – variation in optimal body size according to competition (eg competition)

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𝑥=𝑚𝑎𝑥

= ∫𝑥=𝑡

𝑒 −𝑟𝑥 𝑙𝑥 𝑏𝑥 𝑑𝑥 where t is the age of maturity
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This assumes
however, no competition
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If these
functions are used then the optimum size at maturity decreases with
fewer resources, and the optimum age at maturity increases
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This has happened in humans – earlier maturity and larger bodies is not a result of evolution,
rather a result of increased resources and phenotypic plasticity
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The reaction norm’s ideal is different than observed, as it
evolved and been shaped by the past environment not current
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Dietary
restriction can increase longevity (in the same mechanisms as the gypsy moth)
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Humans change the goalposts – artificially changing the carrying capacity of environments (eg in
farmland, kestrels breed and die earlier than in the forest, due to higher young mortality)
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For example, if you modify mosquitos so that they don’t carry the
malaria parasite, then they have expended energy on being immune therefore reducing their
fitness (as the parasite does not affect them overly)
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These large fish were sexually selected
for, so persisted in the population
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Trojan Genes – seemingly harmless genes introduced to
create a beneficial GM organism, which end up devastating
the organism’s population
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There are always trade-offs, jack of all trades
master of none
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Multiple life
histories represent the optimal tradeoffs which maximise fitness in varying situations
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LIFE HISTORY II
OBJECTIVES: Understand what Life History traits are, polygenic characters, responses to selection,
how heritability influences this, maintenance of genetic variation, and complicating factors
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Contain many heritable characteristics
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The last term is assumed to not exist
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If ℎ2 = 0
then all variation is environmental and no evolution is possible
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The assumptions made are
bad, but they give a good model
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When the parent average was plotted against the offspring average, then the
slope of regression was equal to the heritability
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4), showing
that much of the variation between individuals is genetic
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Selection will lead to a
response, but only up to a limit (glass floor/ceiling) where any remaining variation must be
environmental
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Fisher’s Paradox – states that the characters most important to fitness are the ones least
able to evolve, as they are strongly selected for
r² - correlation coefficient, gives the amount of variation in y explained by the variation in x
There may be confusing seeming statistics, such as with racehorse heavily selected for
speed
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This is because all horses are improving, not just winners
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Heritability is found in wild
populations, but will soon decrease if faced with strong directional selection in the lab
What maintains genetic variation for fitness?
o Mutation-selection balance – Selection removes variation, mutation puts it back in
again
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Eg with copepod crustaceans detection of the photoperiod
(winter/summer) is a highly heritable trait
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If they switch egg type too early they lose out in competition as they
could have had more offspring, but if they switch too late their offspring are eaten

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