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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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6
(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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The increased r means that the population is less
stable, and more variable in density
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There are many factors to consider – fishing, predation, competition, prey, climate etc
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Sub-populations are connected by dispersal
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For example a desert has
a low r and K, and a jungle has a high r and K
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Isolated uniform patches will have similar r
values
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Connected uniform patches with complete total random
dispersal are panmictic (any individual might breed with
another), and make a single population
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Emmigration from the bad patch is low as there are too few
organisms
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Stochasticity means that small populations are vulnerable to extinction
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Moderate dispersal in

Dispersal Heterogeneity Stochasticity Outcome
High
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High

High

Source-sink

metapopulations stabilises networks of
Low
extinction prone patches
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High dispersal synchronises
extinction prone patches
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There is heterogeneity in size as well as
Low
quality
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There is a golden zone of patch quality
Low
versus patch distance from the mainland
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Fewer patches are
therefore likely to be
occupied, and the population
will reach the extinction
threshold (the point at
which a population or
metapopulation,
Habitat fragmentation – corridors
experiences an abrupt
are slowly destroyed, so patch size
change in density or
decreases and overall size is lower
number – crash)
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Habitat fragmentation is a non-linear process, it happens slowly then all at once
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Summary: The logistic model is a simple representation of how demography translates into population
dynamics
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Adding spatial structure
makes a big difference to understanding: many populations rely on dispersal for persistence
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LIFE HISTORY I
OBJECTIVES: Understand that organisms are resource transformers, resource allocation budgets,
availability and allocation of resources, variation within and among individuals of allocation, and
genetic bias in resource allocation
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Allocation is different at different ages – young individuals invest in growth, older invest mainly
in reproduction
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Growth and
reproduction investment is reduced
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In this example it appears that on the worst
(lowest N) food, they have the best survival – but that
is due to trade-offs
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With high N food, the caterpillars grow fast but have few stores
to protect from stochasticity
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Remember that natural selection acts on individuals
Feeding patterns may change due to circumstances – may eat more for energy than growth if
predators are around
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Differences can have a genetic basis, eg the gene for a glycolytic enzyme which converts sugar to
ATP has two alleles, which work differently at different temperatures
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Life History Strategy – how you allocate your resources to maximise fitness (survival,
reproduction, maturation – when to invest in each?)
Fitness – the long term reproductive success of an individual (or relative proportion of your
genes in the next generation)
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Why are some humans so different? Answer with Timbergen’s four questions
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The bottom two are evolutionary – why it is like it is
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Mechanism – short limb bones, small skull, or
fewer cells
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Adaptive value – food limitation, more efficient
heat dispersal (SA:Vol), agility, high adult
Single Form
Developmental
mortality (mature earlier – so can reproduce
more)
4
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Therefore
individuals must have trade-offs – eg more young, but they will have reduced fitness? Or long
survival but low fecundity? Individuals are also not created equal
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Superficially you would say that everyone should have 8 eggs,
whereas reality ranges from 4-8
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If eggs are
added to a smaller clutch, then the survival and fecundity of the offspring is greatly affected
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Therefore the varying clutch sizes are explained by the varying
fitness of the parent birds
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Can be
antagonistic pleiotropy, where if trait A is improved, trait B is decreased
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A single mutation in a gene can increase the
sperm production hugely – but at the cost of a much slower maturation
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This is because of 3
components:
1
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Development – development is a function of body size, so takes longer in large flies
3
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If

you put everything in as functions of body size, it will give a curve
where the highest fitness is at an intermediate body size
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More accurately, at certain ages, size is a
function of growth, and growth is a function of resource levels
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Reaction norm – the above is an example of a reaction norm – a trait
which can evolve, which dictates the how the same genotype can
Age at maturity
produce different phenotypes in different environments
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Kung bushmen have a similar
strategy to 19th century Europeans
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Longevity is a life history trait (things which affect the life table of the organism)
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Interestingly
short term dietary restriction determines mortality rate (like Catholics) – you can switch to DR
and after a short period your mortality rates decrease
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When humans attempt to genetically modify organisms if often doesn’t work – there is a reason
that the strategy has occurred
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Another example is with the Japanese

Size at maturity

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GM relative mating success

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medaka fish – farmers modified it to grow faster and
produce larger fish
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However, the GM fish
have a much lower survival than the WT, and as there are
more GM than WT then the population declines (maybe to
extinction)
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Invasion – when the GM organism out competes the existing
WT and persists

-ve

0

+ve

GM relative (to WT) juvenile viability

Summary: Resource allocation strategies are crucial to fitness
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Trade-offs can be difficult to demonstrate, especially with observational studies
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Phenotypic plasticity
allows organisms to make these tradeoffs
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Life History traits – age related patterns of reproduction and survival, the principle
components of fitness
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In the 19/20th century there was a large debate of continuous vs discontinuous variation,
and how these traits were inherited:
Continuous – promoted by biometricians, for traits such as birth weights
Discontinuous – or mendelian variation, for discrete traits such as eye colour
The debate was resolved by RA Fisher in 1918 who said that the phenotype of continuous
traits is formed from the additive effects of many mendelian genes (polygenic)
The individual phenotype is formed from the additive effects at all the loci, non-additive
dominance relationships at each locus, interactions among genes (or epistasis), maternal
and environmental effects, and the interactions between the genotype and the environment
𝑉𝑃 = 𝑉𝐺 + 𝑉𝐸 + 𝑉𝐺∗𝐸 , where the phenotypic variation in the population is equal to the
variation in the genotype, plus the variation in the environment, plus the variation in the
genetic interaction with the environment (which assumes that the genotypes distribute
randomly with the environment – false)
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If 𝑉𝐺 = 𝑉𝐴
(ignoring the non-additive dominance, epistatic, and maternal aspects), then (with
simplifying and substitutions etc):
𝑉

ℎ2 = 𝑉𝐴 – where the heritability (proportion of variation which is inherited
𝑃

genetically) equals the additive variation over the phenotypic variation
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If ℎ2 = 1 (the
maximum) then all phenotypic variation is purely genetic
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A study by Peter Grant looked at Galapagos finches to see if the bill size and shape was a
heritable trait
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The h² values were high (>0
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h² measures the ability of a population to respond to selection
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At the beginning of selecting for two opposite traits, h² = 1 and the response to selection is
good, but the genetic variation slowly decreases to the point where h² = 0 and selection
cannot change the population any further
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Despite strong directional selection, there is still plenty of genetic variation remaining
and speed keeps increasing
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There is still heritability for fitness, because of life histories
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This then comes to an equilibrium - removal is much faster than mutation
therefore FALSE (or not enough)
o Fluctuating selection – Ie a different ideal plan based on the environmental variation
(eg seasonal)
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Eggs laid in winter develop immediately,
whereas eggs laid in summer have a diapause to avoid the intense summer
predation
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Selection varies year to year favouring early or late depending on the earliness of
summer
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o Genetically based trade-offs – basically the idea that whilst both traits are highly
desirable, an organism cannot invest heavily in both – a compromise must occur
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In a constant environment, there would be little genetic
variation left, however in the wild there is rarely constant conditions
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For example dyslexia is a common genetic trait across many nationalities,
but the language structure in Italy masks the trait as it is a much easier
language (G*E interaction)
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In a study
where a RNA virus was used to create single, and then double, mutations,
actual fitness of double mutations was lower than the expected fitness of
double mutations (expected 2*single mutation)
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Eg red squirrel mothers give more
resources to pups based on what she perceives the future quality of their
environment will be (what competition they’ll face), from the previous
year’s cone density
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Eg male deer with high fitness have sons with a higher fitness, but
daughters with a lower fitness (mothers unrelated)
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Although an ecological periodic table has been searched for (saying X
organism in X environment leads to X life history), it is too difficult to be precise
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K selected organisms tend to be larger, longer
lived and in stable environments, whereas r selected tend to be small (or insects), have short
lives, and live in highly variable environments
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Many datasets don’t fit these two sets (although mosst do), so something must be missing
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The most important life history trait is
survival (sometimes by methods such as diapause and dormancy), and tolerance
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Summary: There is a strong selection for fitness, and low but not zero heritability of life history traits
due to mutations, fluctuating selection, trade-offs, non-additive genetic effects, and sexual
antagonism
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PHYLOGENY
OBJECTIVES: Understand that species are not independent, due to their evolutionary relationships,
and know the methods of obtaining phylogenies
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Characteristics may not be explained by the
most obvious explanation (necks for feeding at top of trees vs sexual selection)
Interspecific allometry – differences between species
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Doesn’t give much background
We want to analyse the relationships between species to explain their adaptations, but
species are not independent units
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Use Independent contrasts
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d1 gives what has
evolved since d3
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5), then
d1 would be (1, 2), d2 would be (4, 0
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5)
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d1
d1

d2

d3

d5

d4

d2
d3

2
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5
1

d3

0
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Gradual evolution means that branch
length need to be included
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Taxonomy – is NOT the same as phylogeny (evolutionary
relationships) – the classification of groups of animals
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Ridley’s method – for discrete characteristics
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The second value reconstructs ancestral changes back through
time
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H₁: Larger
species can run faster and jump further
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Calculate the ancestral values with a model of evolution
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Shows that size accounts for 50% of evolution of performance, and
leg length accounts for a further 25%
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Independent contrasts are the best way to negate the non-independence of species
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Described the niche as:
The range of environmental values necessary and sufficient for a species to survive
and complete its life cycle
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He was more focussed on the autecological (relationship
between each individual species and its environment)
Lots of evidence for this amongst plants, where the distribution limit of an organism is the
point at which the population cannot replace itself
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Looked more at the place of the animal in the community:
When an ecologist sees a badger, he should include in his thoughts some definite
idea of the animal’s place in the community to which it belongs just as if he had said
“there goes the vicar”
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He
believed that it was never seen in nature, and said that it was:
Where the organism would live in the absence of other species, a multidimensional
volume by resource density, habitats, etc
...
Although he said “N-Dimensional
Hypervolume” this is just being posh and is immeasurable
...
For
example a squirrel could use worms as food, but may preferentially use nuts as
rats also use worms and will outcompete them
...

Grinnell was aiming to understand environmental tolerances, whereas Elton
and Hutchinson thought that the study of communities was paramount
...


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•

Grinnell didn’t believe competitors
were important, but Hutchinson
thought there was a huge difference
with or without the predator
...

Microhabitats within environments
permit coexistence
...
With Hutchinson, selection
should minimise the overlap, or lead
to niche complementarity (if overlap
increases in one dimension, it should decrease in another)
...

Competition can have a large effect
...

Whilst both can survive in cold/dry and hot/moist environments (with total overlap), if you
put both species in together, they have altered niches where one species ends up at either
extreme with little overlap
...

Species packing: how many species can be packed in along a niche axis depends on the niche
width
...
This assumes that resources shared are minimised, and that the niche overlap
is proportional to the competition coefficient (effect of one species on another)
Critical point – the point at which competitive exclusion occurs
...
This shows
that significant overlap is tolerated – 54% (found from integration of overlap) of resources
can be shared before hitting the critical point

Realistically resources are rarely constant, so the carry capacity varies
...

An uncertain environment has less tolerance for overlap (and will never exceed 54%)
Hutchinson hypothesised that there was a certain minimum size difference which would
prevent competitive exclusion
...
3
...

For each size group of fruit there is a group of pigeons
...
33
...
Competition leads to a variation in their size ratios,
which means that they use different food/prey sizes (handling times) – and each size larger
is approximately 1
...

Character displacement – when phenotypic differences between similar species are
accentuated when the two species are coexisting, but the full range is displayed when the
species is alone
...
However, where both species occur their
bill sizes diverge to either extreme of the range
Another example is found in stickleback: if there is one species in the lake then it will have
variable morphology and will be a generalist
...

2D niche packing has the same ideas as earlier, with the x y axes being
resource types
...

It is possible to predict the niche packing if we know the number of
species and the variation in the environment
Studies with desert lizards on three continents looked at habitat use, food type, limiting
factors, and number of species to predict the maximum tolerable overlap
...


Summary: Autecological vs Synecological nice, and the fundamental and realised niches
...
Vacant niches are common
...
The idea that
decomposers are limited by their food supply, plants are limited by their resource supply,
herbivores are limited by predation, and predators are limited by their food supply
...
Drawn from observations that
fossil fuels accumulate slowly (therefore most stuff is decomposed), herbivores rarely can
eat all available food and only normally occurs with predator removal (so herbivores are
controlled by predation), and mass devastation events are low for plants (therefore must be
controlled by resources)
...

Predicts competition between decomposers, plants, and predators, but not herbivores

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Therefore, says that herbivore populations must be kept below the carrying capacity by
natural enemies
...

HSS is strongly supported in terrestrial and freshwater systems, but not in marine systems –
may be an artefact or due to unrepresentative studies
...
Test by adding/removing species
...
6% of species showed evidence of competition (whether always or
sometimes), with all systems showing it (Schoener 1983)
There are 6 mechanisms of competition:
Consumptive – species compete by consuming limited resources – most common
...
flavifrons is removed from a system, B
...
Eg,
if you remove an anemone and barnacles will cover its space, but if you remove the
barnacles then the anemone will reinvade
...

Overgrowth – mainly a marine phenomenon, where one organism physically grows
on top of another, denying it resources
...

Chemical – antibiotics in bacteria, or allelopathy in plants
...
Also occurs in animals
...
If they
encounter another species at the food, they will release irritants
...
Eg, with competitive
exclusion – two species cannot coexist in the same territory
...
Different
from territoriality as they harm each other whenever they meet
...

Maternal effects can change competition
...
Mountain bluebirds try to colonise before their sister
species, western bluebirds
...

Competitive exclusion – where one species is eliminated from the environment by another
using the same resources
...

Gause demonstrated competition and used simple mathematical models to predict the
outcome
...

o Separately, P
...
caudatum grow fine, but if grown together then P
...
aurelia’s metabolic waste products
...
aurelia
wins (P
...
bursaria and P
...
bursaria has symbiotic algae so can survive in the
deoxygenated microenvironment of the sediment)
o P
...
aurelia have no stable equilibrium or set winner – the outcome
depends on the starting ratio of species

•

Population convergence – where any starting ratio of species leads to the same stable
equilibrium (as with P
...
aurelia)

•

Lotka-Volterra Model -

𝑑𝑁1
𝑑𝑡

𝑁

= 𝑟1 𝑁1 (1 − 𝐾1 −
1

𝑎21 𝑁2
),
𝐾1

or the change in size of species one
...

There is a mirror equation for species two:

𝑑𝑁2
𝑑𝑡

𝑁

= 𝑟2 𝑁2 (1 − 𝐾2 −
2

𝑎12 𝑁1
)
...

So if these are the zero growth isoclines for two species:

= 0
...
If the
lines do cross then the two species can coexist regardless of the starting ratio, but if the
carrying capacities are on the outside then it is unstable (as interspecific competition is more
important than intraspecific, and both species are strong competitors)
These ZNGIs are plotted with how the competition (coefficient) effects the carrying capacity
...

•

12

Gause proved this with his ciliates, and proved that mathematical models have a place in
ecology

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Despite these predictions holding well in labs, it can still be difficult to explain coexistence in
natural systems – usually due to extra variables
In reality the ZNGIs are curved – but shape is largely irrelevant for predictions
...

Competition is an important force in shaping natural systems and there are 6 types
...
Marine → pre-emptive, fresh water → consumptive,
terrestrial → consumptive
...

The lotka-volterra model is the logistic model plus interspecific competition
...
The
competition coefficient is most likely related to the niche overlap
...

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The naturalistic fallacy – how we are isn’t necessarily how we ought to be (eg cannot derive
ought from is/was)
Biological determinism – the idea that all human behaviour is innate, encoded by genes and
other biological attributes (genes vs environment) – not true
...
Variation in age of onset is only 37% genetic
...
We may not be
evolving much, because we separate survival of the fittest and sexual selection - natural
selection is both
Bateman gradient – quantifies differences between sexes in anisogamy – the statistical
relationship of sex and mating success
...

Human decisions make sense in an ecological sense – women prefer older therefore more
established men, men prefer younger and inherently more fertile women
...

Consists of (2) different but equally rewarding strategies
...
Jack coho
salmon are non-migratory and take 2 years to mature (so
are smaller)
...
This is stable because the Jack need
the Hooknose, and they are kept in check because their LRS

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decreases with fewer Hooknose (therefore fewer territories to invade)
In gall aphids, fitness is increased with larger leaf size but is negatively impacted by density
...

Lots of different viewpoints about whether humans are unique, involving religion, politics,
culture etc
...
Largely discredited
...
What may be unique is our consciousness, and
awareness of impending doom (our own death)
Ecologically longevity, menopause (rare in other animals), cooking and homosexuality are
factors which may set us apart
...

We have moderate sexual dimorphism, with men having certain physical differences (10%
taller, 20% heavier, 50% stronger, 100% handgrip)
...

Sociosexual orientation – an
individual’s preferred mating
strategy – monogamous or
promiscous
...
Whilst
monogamy might increase
offspring survival, it costs in
lost mating opportunities
...

Conspicuous consumption – obvious wastes of time/money which demonstrate fitness
...

Longevity is a consequence of towns forming (allee effect – cost of defense shared), and
modern medicine
Grandmother effect – women with grandmothers still alive have a higher
fitness
...
The menopause allows the grandmother
to step in after weaning (rather than still bearing own children), sharing
the costs of raising the child with the mother (who is then more free to
have more children, increasing fitness), increasing the survival rate of the
child
...
The
increasing fitness promotes an earlier menopause
...
Overlap between mother and adult
offspring can select against late-life reproduction, stopping fertility ages
increasing with longevity
...
This makes the effect strongest in patrilocal dispersals
...
Leads to shorter and easier perimenopause (pre) and later menopause
...

This shows that conflict is enough to ‘cause’ the menopause – not necessarily the
grandmother effect
...
May be linked to an increase in offspring in females on the maternal side (Xlinked allele which increases female LRS at cost of male LRS)
...
Has led to a
massive decrease in
family size, and
therefore average LRS
...
It
has decreased due to
better nutrition
(modern !Kung women
are equivalent to 1850 Glasgow factory workers)
...
Happens because there is not
enough energy to menstruate and breastfeed
(therefore is not effective in well-nourished
women), and in !Kung who breastfeed for 4 years
(long time) limits their LRS considerably
...


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•

The birth interval provides short term population regulation, with the age at maturity
constituting the longer term control
...
The agriculture → industrial
transition traded increased offspring quality
for decreased quantity
...
Poorer
families have earlier reproduction, and less
parental investment, which pushes more
towards a fast strategy
...
There is no such thing as the nature-nurture divide
...
Humans have ESS too

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