BIOL 370 Part II Flashcards
percent of population persisting vs time
-populations less than 100 have low probability of persisting >50 years
negative density dependence
- population growth is negatively effected by its density
- examples: crowding, predators and competition
sources of variation in population growth
- environmental stochasticity
- demographic stochasticity
stochasticity
- model in which parameters vary unpredictably with time
- random, chance events in nature
environmental stochasticity
- unpredictable environmental changes
- NOT: predictable ∆ like seasons; env’t trends
geometric mean
(π λ_i) ^ 1/n
incorporating stochasticity into population growth model implication
- makes pop growth slower than expected form constant growth
- variance in N_t increases w/ time
- variance in N_t proportional to both mean, variance of r
Extinction from environmental stochasticity likely if
var(r) >2r
var(r) is greater than 2r
what does a per capita birth rate of 0.2 mean
- for every z individuals we expect 0.2z new offspring in a year
- eg. 20 in a pop of 100
P_birth
= b/(b+d)
populations with high b and d
much higher demographic stochasticity than ones w/ low rates, even for same r
what changes in demographic stochasticity
only b and d, r stays same
P_death
= d/ (b+d)
P_extinction
= (d/b)^No
density dependence
birth and death rates are affected by density
the simplest model of density dependence
logistic growth
logistic growth assumptions
- linear density dependence in vital rates (b, d)
- decline in per-capita growth as density increases (negative dd)
exponential growth
dn/dt = rN_t
intrinsic rate of population increase r
r = per capita births b’ - per capita deaths d’
exponential growth with intrinsic pop growth
dN/dt = (b’-d’)N_t
exponential vs logistic growth, N vs t
exp: exponentially increasing
log: S-shape, increase to asymptote
exponential vs. logistic, dN/Ndt vs N
exp: linear (flat)
log: linear decreasing
logistic growth
dN/dt = rN_t (1 - (N_t / K)
theta logistic population growth
dN/dt = rN(1-(N/K) ^ θ)
θ = 1
- linear effects of density on pop growth rate
- logistic growth
θ > 1
- convex relationship
- density dependence stronger at high density
deterministic factors
- intrinsic (eg. density dependence)
- extrinsic (eg. seasonal ∆ in envt, long-term trends, species interaction)
sample variance
- sampling error
- adds undesired variability in pop. growth beyond true variation
recovery plans and population size
less than 50% of record plans include estimate of current pop size, N
why estimating population size is challenging
- expensive
- time consuming
- biological challenges
biological challenges to estimating pop size
- detectability
- mobility
- wide ranges
- non-uniform distribution
managing fisheries is like
managing a forest of invisible trees that move around
θ less than 1
- concave relationship
- density dependence stronger at low density
all animals are counted
census
-perfect detectability
number of individuals
abundance
abundance estimate
actual estimate, often accounting for detectability
index
some measure assumed to be proportional to abundance or density (relative abundance)
random sampling
- unbiased
- representative
- can be inaccessible
abundance per unit area
density
common wildlife abundance indices
- track count
- scat
- vocalizations
- # captured/observed per day
difficulties with abundance indices
- only reliable if standardized and w/o confounding variables
- index changes: temporal, spatial, technological, observer
abundance indices temporal changes
-diurnal vs nocturnal
-seasonal
temporal ∆ doesn’t necessarily mean a pop. ∆
abundance indices spatial changes
- schooling
- depth
- range contraction
estimating abundance w/ detectability
^N= c/ ^p c = observed count ^p = estimated detectability
common abundance estimation techniques
- distance sampling
- double sampling
- multiple observers
- mark-recapture
Lincoln-Peterson
mark-recapture methods N = ms/r m = number marked r = # w/ markings on 2nd sampling s = total # captured on 2nd sampling
marking individuals
radio telemetry, PIT tag, banding, elastomer, photography, genotyping
capturing individuals
physical or non-invasive (genetic hair/scat, photography)
Lincoln-Peterson assumptions
- closed population
- all individuals have equal opportunity of being caught
- no effect of marking on recapture or survival
- complete mixing of marked/unmarked
- marks not lost
sources of variation in population abundance estimates
- deterministic factors
- process error
- observation/ measurement error
process error
stochasticity
sensitivity
how much lambda changes/ change in given vital rate; absolute change
problem w/ sensitivity
survival and fecundity are on different scales
elasticity
proportional λ∆ in a given vital rate
-how a small, proportional change in rate will affect overall pop growth
sensitivity and elasticity, importance of survival
- survival more important than fecundity
- survival relatively more important in longer-lived organisms
adult survival in long-lived organisms
- highest elasticity
- lowest variability
relative strength of forces driving African cheetah population
inside protected areas:
- lower cub survival due to lion, hyena
- higher adult survival less human conflict
- adult survival key for pop. growth
a large increase in a vital rate (eg. 10%) with low elasticity
can outweigh a small change in a vital rate w/ high elasticity
viability
probability of extinction
PVA
population viability analysis
population viability analysis
quantitative assessment of probability a pop. will become extinct or quasi-extinct w/i specified time frame
Types of PVA
- count-based
- demographic
- metapopulatin
- spatially-explicit (powerful, but dada hungry)
- individual-based (computationally intensive)
count-based PVA
- unstructured population
- uses time-series of abundance or density
demographic PVA
- structure population
- uses matrix-models, sensitivity analysis, etc.
quasi-extinction
or pseudo-extinction
- easier to estimate than extinction probability
- chance that pop. will hit some critical minimum threshold
- can be used to reflect management options
example of quasi-extinction use
-species are listed on SARA if pop. drops below threshold of 50 individuals
PVA goals
- Assess extinction risk (probabilities of single pop., relative risk of multiple pops.)
- guide management (ID key life stages as management target)
PVA tools
population models
simplicity vs realism trade-off
simplicity: low data demand, few assumptions, broadly applicable, unrealistic, limited biological insight
realism: high data, many assump., narrowly applicable, realistic, biologically detailed
what can we learn from Number of individuals (N_t) vs time (t), simple unstructured PVA
- range of values pop. is likely to take
- estimated probability of extinction (or pseudo-extinction)
usefulness of PVAs dependent on
- data quality
- assumption that future pop. dynamics will be similar to present
Demographic PVA
- incorporates age/stage structure and age/stage specific vital rates
- may also include: sex-specific rate, variance/covariance in vital rate, dd, inbreeding depression, allee effects, env’t/demo stochasticity, animal behaviour
Key PVA points
- remain aware of data quantity, quality
- always show CIs around variability estimate
- view viability metrics as relative not absolute
- shorter predictions more realistic
- keep simple, but be aware of what is left out
- consider multiple models
- consider PVAs as work in progress
PVA simple model example
breeding pairs owls vs year
-count individuals over time
-estimate trend
project forward
PVA demographic structure example
- add life stages: fledgling, juvenile, sub-adult, adult
- add competition from other species, barred owl
- spatial structure: potentially suitable owl habitat
Sumatran orangutan demographic PVA
-add year-to-year stochasticity, rare catastrophic events
what can be learned from orangutan PVA
- complexity can always be added (dd, competition, allee, spatial complexity)
- if there are data it can be modelled
questions to ask about habitat fragmentation
- how many protected areas
- what configuration
- how big should each be
- what shape should each be
landscape ecology
-study of how spatial patterning of landscapes affects behaviour, populations, diversity of organisms, as well as the functioning of ecosystems
IBT equilibrium theory
equilibrium where colonization = extinction
which type of islands will have greatest equilibrium number of species under IBT
large, near source population
why IBT is important for designing protected areas
-extinction rate increases for small fragments isolated from source pop.
faunal relaxation
reduction in diversity following a reduction in habitat area or creation of habitat island within formerly continuous habitat
faunal relaxation in national parks
extinctions after park establishment vs area of park
- larger parks = less extinctions
- most parks are not large enough to support MVP
SLOSS
- single large or several small
- depends on overlap
nested diversity
-collection of species found in a given small area overlaps extensively w/ other areas and large areas contain these species plus more
edge effects
- more light, wind
- could result in dessication
habitat lost to edge effect, shape and size
- too small and all of core habitat is lost
- shapes that maximize SA:V minimize core habitat, i.e. long slender rectangle
metapopulation
- set of spatially isolated pop.’s of same species that interact on some level
- small subpops w/i fragments
- prone to local extinction
- connected by dispersal
metapopulation regional persistence
extinctions must be balanced by colonizations
Theoretical spatial ecology
- homogenous space
- focus on population dynamics
metapopulation spatial ecology
- habitat is suitable on a patch basis
- consider only patch sub populations
landscape spatial ecology
- landscapes is a complex mosaic varying in suitability, area, isolation, shape
- less emphasis on modelling pop dynamics
example of meta population with regional persistence
- Glanville fritillary, butterfly
- small populations of specialists
- high local extinction and colonization
habitat suitability
- not all patches are equal
- source-sink metapopulations
source-sink metapopulations
maintained only by dispersal from elsewhere
turnover of patch occupancy
- some habitat patches newly colonized, some extinctions, emmigration, immigration
- fraction colonized changing
key metapopulation concepts
- unoccupied, suitable habitat can be very important
- reduced dispersal success can result in extinction
- critical threshold for habitat destruction
- patch arrangement and connectivity just as important as absolute #
- local events depend on regional context
critical threshold for habitat destruction
-metapop can become extinct long before all habitat patches are destroyed
aiding patch colonization
dispersal corridors
eg. wildlife overpass
corridor experiment results
corridor increased SR in native species but not exotics
potential problems with corridors
- straight edge effect (long thin rectangle)
- increased exposure, predation
SLOSS Hawaii, Galapagos
- rank islands from smallest-largest and largest-smallest
- plot cumulative species vs. cumulative area
- find more species for less area in both s-l lines
- lots of small patches probably better
conservation genetics can
- ID unique evolutionary lineages
- monitor dispersal, movements
- estimate pop. size
- trace genetic changes through t
evolutionary change in population is a function of
amount of genetic diversity available
genetic issues in conservational biology
- deleterious effects of inbreeding
- loss of genetic div. and ability to evolve
- fragmentation, reduction in gene flow
- genetic drift overriding natural selection
- accumulation of deleterious mutations
- adaptation to captivity
- resolving taxonomic uncertainties
- defining management issues
- outbreeding depression
problems with genetic adaptation to captavity
adverse effects on reintroduction success
primary level of biodiversity
diversity measured at the level of genes, or quantitative genetic traits
reduced genetic diversity causes
lower ability to withstand extremes
genetic variation depends on
- the species/population
- genome/ chrmosome region
- part of the gene
- whether sequence/ nucleotide codes for anything
Theodosius Dobzhansky
Nothing in biology makes sense except in the light of evolution
adaptive radiation
- morphological and genetic diversity from founder species (ancestor)
- diversification of a group of organisms into forms filling different ecological niches
types of genetic markers
- allozymes
- microsatellite DNA
- single nucleotide polymorphisms
- direct DNA sequencing
allonymes
- protein variants
- grind up organism, liberate DNA, gel electrophoresis, compare protein differences
- indirect way of looking at DNA changes w/o examining DNA
microsatellite DNA
- genetic variants in a section of b.p.’s
- short repeated sequences = microsatellite
- examine size of microsatellite w/ PCR
single nucleotide polymorphisms
- SNPs
- single bp change
- many thousands per individual
- used for individual-tailored medical treatments
DNA sequencing improvements
- large scale high throughput
- lower cost/b.p.
- lower cost/genome
- single molecule sequencing
- portable sequencer
cost of getting genome sequenced
ca. $1000 US
Hardy-Weinberg assumptions
- diploid organism
- sexual reproduction
- nonoverlapping generations
- identical allele frequencies in both sexes
- random mating
- large pop
- no migration, mutation, selection
frequency of heterozygotes
2pq
frequency of homozygotes
q^2
or
p^2
frequency of heterozygotes maximum when
p = q = 1/2
forces that effect allele frequencies
- migration
- mutation
- drift
- selection
raw material of diversity and evolution
mutations
vast majority of mutations
deleterious
germ cell vs somatic cell
- gametes arise from germ cells
- somatic cells are all other cells besides reproductive
polymorphism rate
- much lower in nature than we expect
- mutations are rare
fate of a mutation
- die out or persist, quickly or slowly
- depends on factors that enhance or downgrade mutation effect (drift, mutation, selection, etc)
- depends on effect on fitness, genome neighbours
natural selection requirements
- must be phenotypic variation in pop.
- variation must result in fitness differences
- variation must be heritable
types of selection
purifying selection
positive selection - directional, balancing
purifying selection
removes deleterious mutations
random genetic drift
- chance/ random event allele frequency fluctuation
- drift direction unpredictable (especially in small pop.)
- reduces variation w/i pop.
- causes populations to diverge from one another
how does random genetic drift reduce population variability
- causes lost off alleles
- increase homozygosity
- decreases heterozygosity
random genetic drift example
x # marbles in a jar
- some fraction passed on at random
- unique combinations for each sample repitition
- smaller sample = higher chance of misrepresenting true pop.
favours beneficial mutations
positive selection
fixation of a beneficial mutation
directional selection
genetic bottlenecks
- relatively large pop is reduced to very small # by catastrophic event
- non-natural selection related
- bottleneck survivors likely have low level of genetic diversity and usually carry non-representative collection of source pop. alleles
founder effect
small # individuals form a pop. with low diversity
- may be positive, negative, or all new
- rare alleles present more often due to bottleneck effect
inbreeding coefficient
probability that alleles in an individual are identical by descent, homozygosity
number of eggs that fail to hatch vs inbreeding coefficient
increasing exponentially as inbreeding increases
inbredding coefficient vs generations for N=i
- for i = low population increase exponentially until completely inbred
- for i = 500 linear increase
balancing selection
maintains polymorphisms
MVP
- minimum viable population
- smallest population that will not exacerbate inbreeding effects
% homozygosity vs # generations of inbreeding
smaller pop. increases to complete homozygosity exponentially
F = 1/4
brother-sister matings
Effective population size
N_e
- N_census > N_e
- not everyone in pop. contributes to reproduction
- fluctuation of N_e influences genetic drift
- difficult to measure
N_e : N_c
- generally 0.1 - 0.2
- i.e. for every 5-10 indiv. only 1 breeding individual
fixation index
- a measure of the difference in the allele
- increased in small populations
F statistics
-useful to summarize reduction in heterozygosity at different scales and due to different processes
F = 1/16
means children of first cousins
reduction in heterozygosity due to
- bottlenecks
- founder effects
- population structure
- inbreeding
barrier to migrations
- subdivide populations
- decrease heterozygosity
isolated population
- can see reduced gene flow
- in real life we can not
without barriers to migration we expect to see
HW ratios of homozygosity : heterozygosity
Sewall Wright’s F statistic
- reduction in heterozygosity at one level of of pop. hierarchy relative to another level
- popular, useful measure of pop. differentiation
F_ST levels
0-0.05 = little structure 0.05 - 0.15 = moderate 0.15 - 0.25 = high >0.25 = very high 1 = full homozygosity, no breeding
no gene flow =
genetic divergence among subpopulations
F_ST =
1/(4Nm + 1)
N_e*m = number of migrants
N = drift
m = migration
Equilibrium fixation index F^ vs number of migrants per generation (Nm)
- negative exponential
- high F = very great divergence
- low F = little divergence
migration
- reduces pop. structure
- can balance drift
Ne*m =~
(1 - F_ST)/ 4F_ST
F_ST = (H_T - H_s) / H_T
H_s = average heterozygosity of sub pop.
H_T = average allele frequency
the 50/500 rule
- N_e should be at least 50 to avoid inbreeding depression (loss of fitness)
- N_e should be at least 500 to avoid eroding evolutionary potential (evolve and adapt to env’t ∆)
revised 50/500 rule
- not good enough
- Ne >100 required to limit inbreeding depression to 10% over 5 generations
- Ne >1000 required to retain evolutionary potential
phylogeny
history of descent of a group of taxa from their common ancestors
-includes order of branching, absolute ages of divergence