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ENVR 301 > QUIZ 2 > Flashcards

QUIZ 2 Flashcards

1
Q

discrete variables

(3)

A

dichotomous = binary (female/male)
categorical = nominal ( bus, bike, walk)
ordinal ( very satisfied –> neutral –> dissatisfied)

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2
Q

interval = numerical data

(i.e. differences are meaningful)

3

A

count
ratio ( height)
continuous (time)

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3
Q

measurement

A

precision vs accuracy
significant figures

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4
Q

describing distributions

A

univariate or bivariate

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5
Q

univariate

5 characteristics

A

normal
uniform
bimodal
U-shaped
skewed

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6
Q

descriptive stats

3

A

measures of central tendency
measures of variation
shape ( higher moments)

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7
Q

measures of central tendency

3

A

mean, median, mode

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8
Q

measures of variation

5

A

range, variance and standard deviation, quantiles, percentiles, inter-quartile

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9
Q

shape (higher moments)

3

A

variance, skewness, kurtosis

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10
Q

confidence

shown by 3

A

standard error
confidence interval ( credible interval)
statistical significance
scientific/ economic/ clinical significance

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11
Q

likert scale example

A

strongly disagree–> disagree–> neutral –> agree –> strongly agree
( this data is categorical, ordinal, dichotomous, continuous)

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12
Q

accuracy

A

true. consistent with the truth or objective (unbiased)

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13
Q

precise

A

detailed, specific, having low uncertainty, highly resolved

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14
Q

measurement e.g. the flood water is 35 cm deep, plus or minus half a cm

A

when we measure something in science, we always provide an estimate of our confidence along with the measurement

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15
Q

estimation e.g. ospreys live on average 38.4 +/- 5 years

A

when we statistically estimate something that was not measured directly, or infer something about the world through our research methods, we must also calculate a measure of confidence for what we report

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16
Q

communicating precision = probabilistic statements

A

between 31% and 37% with 95 % confidence ( i.e., e [31%,37%] 19 times out of 20)

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17
Q

displaying univariate qualitative (categorical) data

3

A

tables, bar charts, pie charts

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18
Q

displaying univariate continuous data

3

A

histogram, box plot, kernel density estimation (carpet plot)

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19
Q

bivariate plots: two categorical variable

1

A

paired bar plots

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20
Q

how to visualize –> bivariate plots : one categorical, one continuous variable

2

A

multiple histograms, box plots

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21
Q

visualize –> bivariate plots: two continuous variables

1

A

scatter plots

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22
Q

univariate measures of dispersion

5

A

range
standard deviation: average distance from the mean, coefficient of variation, index of dispersion, interquartile range

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23
Q

measures of shape

4

A

variance, skewness, kurtossis, L-moments

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24
Q

Skewness Left (negative), Right (positive)

A

(a) negative direction ( mean - median - mode)
(b) positive direction ( mode -median-mean)

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25
Q

confidence interval

A

the range within which we would expect the value of the statistics to fall, if we were to repeat the study with a very large sample

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26
Q

what does confidence depend on?

A

sample spread, sample size, ( the nature of the statistic you’re estimating )

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27
Q

credible intervals are a simpler concept from bayesian stats

A

the interval within which an unobserved parameter value falls with a particular probability, given available data, model, and preexisting knowledge
- best estimate of the true value of parameter

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28
Q

properties of a univariate variable

A

ex: describe (aspects of) the distribution of an unordered list of sample measurements

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29
Q

multivariate properties

A

-two things measured for each individual sampled
- describes (aspects of) the relationship between theme

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30
Q

univariate measures of central tendency

A

mean, median, mode

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31
Q

statistical significance

A

“within 3%, 19 times out of 20”
- closely related to confidence intervals = error bars

observed relationship is unlikely to be due to chance

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32
Q

scientific ( or economic) significance

A

an estimate of effect size put into context, for instance by competing effects, natural variation, or comparison to other costs or impacts

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33
Q

discrete variables

A

dichotomous = binary (female/male)
categorical = nominal ( bus, bike, walk)
ordinal ( very satisfied –> neutral –> dissatisfied)

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34
Q

Determine H & H0.

Are these outcomes equally likely?

A

H: are the counts /frq. of each category as expected?
H0: the probability of each group is exactly equal (or equal)

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35
Q

Determine H & H0.

are groups different from each other?

A

H: are the means of X different in the two groups
H0: the mean of Xa equals the mean of Xb

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36
Q

Determine H & H0.

did the outcome change?

A

H:did the mean of outcome X change across the two measurements?
H0: the mean of X1 equals the mean of X2

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37
Q

Determine H & H0.

are these two outcomes correlated?

A

H: are they the same?
H0: X and Y are uncorrelated (orthogonal)

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38
Q

Determine H & H0.

are these variables related

A

H: is there a linear relationship between X and Y
H0: the coefficient of alpha in an OLS regression of Y on X (and Z…) is zero

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39
Q

Procedure for a statistical test

A

1-based on theory + H0 there is a stat of interest that you can calculate with sample
2-use theory to derive distribution of expected values (under H0). some assumption must be made
3- calculate the statistic actual value given sample
4-state likelihood of answer from sampling method, given that the H0 is true, if unlikely = reject H0

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40
Q

What is the null hypothesis (H0)?

A

-what you are trying to disprove
- strong results, eliminate the possibility of the H0
- no difference, no change, small difference, no effect

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41
Q

reject the null if ( statistical significance)

A

p-value is small or near zero

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42
Q

type I error for binary decisions

A

rejecting the null hypothesis when it is true ( false positive)

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43
Q

type ii error

A

failing to reject the null hypothesis when it is false (false negative)

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44
Q

the probability of a type I error is determined by ____

A

significance level
- with a 99% significance threshold, type I error is less likely than with a 95% confidence

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45
Q

the probability of a type ii can be computed for a particular test statistic (i.e. H0), if given______

A

population distribution (parameterization, mean and SD)
sample size (N) and alpha ( chance of type I error)

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46
Q

hypothesis test power

A

1 - ( the probability of a type ii error)
AKA - the probability that we correctly reject the null, when the null is false

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47
Q

frequentist statistics

A

developed before computers & calculators ( t-test, chi-square, F test)

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48
Q

bayesian statistics

A

rapidly developing framework for using prior expectation + evidence to make bets

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49
Q

measurement validity

A

how well your metric captures the underlying concept you are trying to measure

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50
Q

internal validity

A

the degree to which the design of an experiment controls extraneous variables

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51
Q

external validity

A

the degree to which effects found in an experiment generalize to other individuals, contexts, and outcomes
(ex: in sampling studies, are the times and places outside of the sampling frame represented accurately)

52
Q

Threats to external validity

interaction of selection and treatment

A

(unrepresentative responsiveness of treated pop.)

53
Q

Threats to external validity

interaction of setting and treatment

A

effect of the treatment may differ across geographic or institutional settings

54
Q

Threats to external validity

interaction of history and treatment

A

effect of the treatment may differ across time periods

55
Q

Threats to external validity

effect may not persist

A

-as individuals and institutions adapt over time to the treatment

56
Q

Threats to external validity

Partial- Equilibrium Effect

A

other components of the system also undergo related changes, reducing or eliminating the effect

57
Q

measuring biodiversity

total species list

A

most useful for well-studied groups such as birds and large mammals

58
Q

2 communities in which total counts are not feasibile

A

soil microbes + coral reefs

too diverse to count

59
Q

measuring biodiversity

subsample using quadrats or transects

A

count all species on several quadrats or transects of known size and extrapolate the average to the whole area

60
Q

reliability of quadrats depends on (3)

A

1- # of species in each quadrat must be determines exactly
2- area of quadrat known
3- quadrat counted must be reprsentative of the the whole area

some form of random sampling

61
Q

Quadrat sampling can be ___

type of os sampling (4)

A

random, systematic, stratified random or snowball sampling

62
Q

measuring biodiversity

time-restricted search // rapid biodiversity assessment

A

habitat is searched for a set period of time & species recorded

63
Q

measuring biodiversity

species discovery curves

A

construct a species accumulation curve

highly depedent on sampling effort ( time invested + ground covered)

64
Q

Quantifying density across transects

A

-relative density
-camera traps
-tracking stations

65
Q

capture-recapture methods

A

capture,mark, release
- in subsequent samples, the portion marked should be representative of proportion marked in whole population

66
Q

biodiversity indicators

A

an indicator species’status reflects the status of other species in a community

67
Q

ex: taxon based indicator species

A

woodpeckers are indicators of forest bird diversity

68
Q

rapid inventories for conservation

A

conservation tool designed to assess the biological importance of priority sites for conservation

69
Q

conservation priority sites

A

plants, birds, mammals, fishes, frogs, geology, soil diversity, water characteristics

HUMAN DIMENSIONS

70
Q

observational methods for behaviour sampling

A

ad lib
focal animal
scan sample
one-zero sampling

71
Q

method descriptions

focal animal (pair or group)

A

restricts data recording during sample period to one animal, pair, or group

72
Q

method descriptions

ad-lib

A

opportunistic sampling with no constraints

73
Q

method descriptions

one-zero

A

record the occurence (1) or nonoccurence (0) of selected behaviours during sequential sample intervals

74
Q

method descriptions

instantaneous or scan

A

record behaviour of an individual (instantaneous) or group (scan) at sequential, predetermined points in time

75
Q

selection of animal

A
  • random
  • systematic
  • stratified
76
Q

indirect measures

A
  • stress hormones
  • ketones
  • c-peptide
77
Q

habitat selection

how organisms evaluate + select an environment from the different alternatives available

A

non-random distribution

behavioural component (choice)

78
Q

how to determine habitats

A

correlative studies
- assume species density is proportional to the quality of habitat

Problem!!! Source - Sinks

79
Q

ideal free distribution theory

A

competing individuals distribute themselves between reosurce patches in such a way that each individual receives the same payoffs

80
Q

ideal free distribution theory assumptions

equality

A
  • first animals use the best patch
  • no difference in competitive ability
  • no territoriality
81
Q

ideal despotic distribution theory

reality?

A
  • competitors exclude others w/ aggression
  • 1st arrivals establish in high quality habitats + defend resources
  • lower quality habitats fill up with later arrivals
  • if full, future arrivals must take over or be excluded
  • fewer competitors and higher intake rates in richer habitats
82
Q

What is ethics?

A

study of general nature of morals and moral choices

morals: conforming to established ideas of right and wrong

unwritten guidelines

83
Q

ethics vs laws

laws are….

A

formal, written standards
- designed to apply to everyone
- enforced by gov agencies
- interpreted by courts

impossible to pass laws to cover every possibility

84
Q

unethical behaviour

A
  • isn’t necessarily illegal
  • not all illegal behaviour is unethical
  • not conforming to approved standards
85
Q

amoral behaviour

A
  • no sense of right or wrong
  • no interest in moral consequences
86
Q

which system of ethics works best?

A
  • no universal agreement
  • ethical decisions are greatly influenced by personal ethics
87
Q

McGill & Unethical research

Project MK Ultra

A
  • CIA mind control experiment
  • unconsenting patients
  • sensory deprivation, LSD, electroshock therapy, and other mind control methods

devastating effects on patients invoved

88
Q

Research ethics board at McGill

A

proper certification required before research if working with
- human participants, animals, radioactive or biohazardous materials

89
Q

Enforcing ethics

A
  • no data can be collected without or prior to an approval
  • funding withheld until approval
90
Q

General rule of ethics requirements

A

always required with children
assume any human subjects

EXCEPTION–> designated representatives of organizations

91
Q

Research ethics board-1 (REB-1)

A

law, arts (except: social work, information studies), engineering, management, continuing studies, religious studies, science (except : psych)

92
Q

REB-2

A

linguistics, psych, music, social work, information studies, education + research involving competent adults

93
Q

REB -3

A

all faculties (except med and dentistry) involving minor or uncompetent adults (unable to consent)

94
Q

REB-4

A

agricultural and environmental sciences involving competent adults

95
Q

application process

Required human research ethics training

A

tri-council policy statement 2

all students, faculty, staff, investigators

96
Q

application process ii

who can apply as a prinicpal investigator?

A

NO undergrad, profs can apply for students (if human participants is needed)

97
Q

submission deadlines

minimal risk

A
  • submitted at any time
  • review ~4-6 weeks
98
Q

submission deadlines

projects with greater risk +involve minors

A
  • 1st friday of every month
  • REB meets 4 weeks after that
  • no board meetings in july /aug
99
Q

informed consent

A
  • explicitly obtained (written or verbal)
  • voluntarily given
  • can off compensation BUT must be mentioned +approved by REB
100
Q

confidentiality

A
  • identities or sensitive info can not be revealed in publication
  • all data must be securely stored (paper+ ecopy)
  • kept for 7 years
  • specific ways of getting rid of data
101
Q

confidentiality example

Poaching traps in Kibale National Park

A
  • serious repercussions for locals who hunt for subsisdy or artisanal trade

minimize legal repercussions

102
Q

Why overfishing an interdisciplinary problem?

A
  • fish (species & abundance)/ ecology
  • managerial practices (fishing/econ)
  • regulations (institutionalization of a field)/ politics
103
Q

lake victoria’s fisheries

A
  • dynamic eco-history of the lake victoria basin
  • high population density
  • supports Africa’s largest inland fishery
104
Q

history of fishing in the area

A
  • intensified with new tech
  • intro of new species influenced faunal compositon & balance
105
Q

fish that flourished

even with gigantic nile perch “mputa”

A
  • nile tilapia “ngege” ( has become abundant, commercially important )
  • “mukene”, biomass 6x
106
Q

changes

lake victoria system composition

A

multi-species system exploting native fish –> 3 species almost all catch ( 2 are non-native)

107
Q

Similar changes have occured in which other lakes in the victoria basin?

A

Lake Nabugabo
Kyoga

108
Q

Changes in perch population

A
  • increased fishing
  • decrease in stock
  • decrease in size
109
Q

lake ecology

not homogeneous

A

understanding habitat diversity is key to understand relative fish abundance and reproduction

110
Q

ecological morphology

A

refers to resource availability (oxygen, nutrients, protection etc..)

111
Q

Biodiversity Banks (3)

A

satellite lakes, rcky refugia, wetland refugia

112
Q

Beach management Units

A

promising solution/control of socioecological system

113
Q

BMUs structure

A

new institutions that regulate fishing in the lakes
- to fish, fisherman must register
- control gear used
- no territorial jurisdiction
- no jurisdiction over capture (type or quantity)

114
Q

Overfishing is class case of …?

A

tragedy of the commons or open access resources

115
Q

Collective Action Problem (CAT)

A

predicts a resource managed in common has more probability to be sustainably managed with certain characteristics

116
Q

increasing probability of sustainable management

A
  • small size (group +resources)
  • well-defined boundaries
  • high level of dependence on resource
  • fair allocation
  • locally devised access and rules
  • clear territoriality
  • resource harvest restrictions match recovery
117
Q

flaws of BMU design

ID by CAT predictors

A
  • lack of territorial jurisdiction
  • no regulations on harvest
  • fisheries highly unpredictables (spatial distribution, compositition..)

BMUs = insufficient institutional step

118
Q

Why are fisheries so hard to manage?

A
  • difficult to institutionalize, and generate territorial jurisdictions?
119
Q

Data required to manage fisheries?

A
  • ecological distributions of resources (ecological map, fish presence+mobility, ethnoecological mapping)
  • spatial distributions of the fishing effort
  • trade networks
120
Q

How to create a map of ecological structure?

in lake nabugabo

A
  • ecological structure (depth+ shoreline ecotones: lilies, grass, forest edge)
  • quantify fish movement (tag)
  • etnoecological mapping (cognitive map +interviews)
121
Q

Collecting data on fishing effort

in lake Nabugabo

A
  • use ecological map as base, recall interview designs to determine where BMU members fished during 5 days of each month
122
Q

example of fishing recall qs

A
  • where did you fish today? how long? why there?
123
Q

catch landing surveys

A

both BMUs
boats, type of fish & gear

124
Q

expected results

Lake Nabugabo

A

define specific ecological patches with significant fish species distribution ==> define exclusive territorial jurisdictions for the BMUs*

worth defending

125
Q

consequences of no predictable pattern

in fishing efforts

A
  • cannot design functional institutional structure
  • no incentive for collective action

ENVR 301 (8 decks)

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