Quant Bio Exam 2 Flashcards

(60 cards)

1
Q

Coalescent

A

theoretical model used in population genetics to describe the evolutionary relationships within a sample of alleles or sequences

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2
Q

Goal of Coalescent Theory

A

model of the distribution of gene divergence in a genealogy. It is used to estimate population genetic parameters (like population size, migration rates, and recombination rates) and to perform hypothesis testing

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3
Q

Total Branch Length in a Coalescent Tree

A

Total Branch Length (TBL) measures how much history genes share. It is usually calculated by weighting time intervals by the number of lineages in the epoch

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4
Q

Genetic Drift

A

Evolution without selection or mutation

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5
Q

Nearly Neutral Theory

A

explains how genetic drift (random chance) can be the dominant factor in evolution, even for nearly neutral mutations (have a slight effect on fitness) but are so weak that they behave essentially as neutral mutation

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6
Q

Genetic Drift and Beneficial Mutations

A

Even with a selective advantage, most beneficial mutations do not fix in the population

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7
Q

Key Prediction about Coding Regions

A

Synonymous (silent) sites should evolve faster than non-synonymous (replacement) sites because synonymous mutations are less likely to alter function and are therefore more likely to be neutral

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8
Q

Non-degenerate sites

A

All possible changes at this site are nonsynoymous

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9
Q

Two-fold degenerate

A

If one of the three ends changes the code can be one of two things

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10
Q

four-fold degenerate

A

all possible changes are synonymous

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11
Q

Neutral Theory

A

much of th egenetic variation present in populations are. a reflection of mutation and drift, two interacting processes

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12
Q

at a molecular level…

A

most evolution is nuetral

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13
Q

the coalecent is a model which

A

describes the relationship within a sample from the present

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14
Q

selection for benificial mutation is less common than

A

removal of harmful mutation and the fixation of nuetral mutations

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15
Q

EAF

A

ecosystem approach to fisheries

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16
Q

Why do we need an EAF management system?

A
  • Because of previous failures in ‘traditional’ fisheries management
  • fisheries must include management in peoples actions surrounding fish and Not just fish
  • concerns over global fish stock
  • EAF helps to manage the negative affects on ecosystems, non-target species, habitats and biodiversity
  • (broadly) EAF helps to consider the broader aspects surrounding fishing
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17
Q

What is ecosystem modeling?

A

a qualitative and quantitative representation of ecological interactions (e.g.,
predator-prey interactions, anthropogenic disturbances and natural disturbances)
between various ecosystem components (e.g., species or functional grou

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18
Q

Core purposes of ecosystem model

A
  • describing and understanding the current ecosystem
  • forecasting and hindcasting scenarios (e.g., climate change, fishing)
  • support management decision
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19
Q

Purpose of Ecosim

A

Ecosim is the temporally-dynamic model component of the EwE suite. It is used to explore changes in policy and environmental stressor impacts on the ecosystem over time

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20
Q

matrix population model

A

linear, deterministic model that produces exponential growth

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21
Q

core recursion equation

A

n_t+1 = A * n_t

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22
Q

A is called the projection matrix because

A

it projects the number of individuals in each age class at time t + 1 from the number of individuals in each age class at time t

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23
Q

Population vector

A

column vector, represents the number of individuals in each life stage (e.g., age, size, or other categories) of a population at a specific time

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24
Q

seven steps to modeling a biological problem

A

Formulating the question, determining ingredients, qualitative and quantitative description, analysis, checks, relating results

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25
matrix (A) position: Fecundity (F)
first row
26
matrix (A) position: Stasis (S) (self loops)
on the diagonal
27
matrix (A) position: Progression
below the diagonal
28
Sensitivity
absolute change in λ resulting from a small absolute change in a matrix element (a_ij)
29
Elasticity
proportional change in λ resulting from a small proportional change in a matrix element (a_ij)
30
Elasticities across all matrix elements sum to
1
31
Stochastic Population Growth Rate
for stochastic models, λ is typically calculated numerically as the mean of the log() of population ratios over a long simulation run
32
Environmental stochasticity
a concept within population dynamics that describes random environmental events that result in variation of population size
33
demographic stochasticity
the random fluctuation in population size caused by chance events in births and deaths
34
demographic stochasticity is typically (what?) in large populations
unimportant
35
hitchhiking effect
rapidly spreading beneficial mutation, which results in local removal of standing genetic variation (still, this is LESS COMMON than removal of harmful mutation and the fixation of nuetral mutations)
36
1 / 2 N_e (where N_e is the effective population size)
the probability that two randomly chosen alleles are copies of the same allele in the previous generation
37
N_e
effective population size
38
coalecence trees help us to understand
a populations history
39
Coalescent Trees
models trace the ancestry of sampled alleles/sequences backward in time to their common ancestor. They model the gene genealogy within a population or species
40
Phylogenetic Trees
trace the evolutionary history of species, populations, or divergent taxa (phylogeny) (they are tree reconstruction methods)
41
Coalescent Trees are driven by
stochastic processes, specifically genetic drift and population size (). They are often used for simulations to repeatedly draw samples under specific population parameters and are usually not inferred from data in the same way as phylogenies
42
Phylogenetic Trees construction
tree reconstruction methods inferred from the observed sequence divergence between taxa
43
Phylogenetic Trees: Branches
represent evolutionary time or the number of substitutions separating taxa.
44
Coalescent Trees: Branching
the point in the past where two ancestral lineages merge, branch lengths represent time epochs (usually in generations, scaled by N(sub_e) during which a specific number of lineages existed
45
effective population size (N e )
is the number of breeding individuals
46
census size
the actual count of individuals (N_c)
47
original Neutral Theory
most mutations that fix are either neutral (having no effect on fitness) or highly deleterious (and quickly removed)
48
The Vulnerability Parameter
expresses a prey's control in the food web
49
Low vulnerability rates
imply bottom-up control (predation rate is limited by how fast prey become vulnerable)
50
High vulnerability rates
imply top-down control (predator is in control of prey population)
51
Statistical Criteria for Best Fit: The best fit model is determined using two primary criteria:
Weighted Sum of Squared Residuals (SS): Measures the discrepancy between observed and predicted data and Akaike’s Information Criterion (AICc): AICc is a measure of model fit penalized by model complexity () (the number of parameters estimated). A lower AICc value indicates a 'better' fit of the model
52
flux
a flow rate of mass or energy
53
Four modes of energy exchange
1. Conduction 2. Convection 3. Radiation 4. Latent Heat Flux
54
Conduction
Heat transfer by direct contact between two bodies
55
Convection
Heat or mass transport by fluid (air) flow
56
Radiation
Transfer of energy by electromagnetic energy * No molecular contact * eg. sun vs shade
57
Latent heat flux
Movement of heat by water change phase (liquid to vapour) - Huge amount of E involved (sanity check)
58
Ohm’s Law
Describes flow of current through an electric circuit
59
W =
J/s
60