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eco final lab Flashcards

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Biology 101 flashcards
1
Q

What is a Naturalist?

A
  • an expert in or student of natural history
  • Someone who studies flora and fauna,
    fossils, geology
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2
Q

What is an Ecologist?

A

an environmental scientist who studies how organisms interact with their environment and how the environment functions.an expert in or student of ecology

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3
Q

Ricklefs’ definition of Ecology

A

The scientific study of the abundance and distribution of organisms in relation to other organisms and environmental conditions.

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4
Q

Geographical range

A

Where are organisms is present and where it is absent

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5
Q

Abundance

A

how many individuals of a particular organisms are in a certain area

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6
Q

Dispersal,

A

has a population of organisms been in an area for a long time or recently arrived

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7
Q

survival and reproduction

A

how long the organisms live and how often, how much, when they reproduce

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8
Q

a possible answer to the question is called a

A

hypothesis

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9
Q

Population

A

Represents:
- All possible individuals in a group of organisms

  • Impossible to measure an entire population
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10
Q

Sample

A

A subset of a population

  • Used to make inferences about the
    entire population
  • How should crayfish be selected?
    -randomly
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11
Q

Parameter

A

Some property of the entire population of interest

  • A measurable factor
    -mean or SD = descriptive statistics
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12
Q

Statistic

A

Mean and standard deviation of the
sample

  • Estimate of the parameter
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13
Q

For crayfish example:

A

Population: Crayfish in Aperture Pond

Sample: 50 crayfish from Aperture Pond

Parameter: Mean Weight of crayfish in Aperture Pond

Statistic: Mean Weight of 50 crayfish from Aperture Pond

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14
Q

Replication

A

More measurements makes a sample more
representative of the population

  • Sample size (n) is taken into account when
    determining confidence in the statistic
  • Important to be aware of pseudoreplication
  • Need to conduct random samples to avoid
    bias
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15
Q

pseudoreplication

A

where there is only a single replicate per treatment, but subsamples are taken from each area.

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16
Q

Bias

A

type of error

measurements are consistently wrong

leads to poor accuracy

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17
Q

bias problem 1

A

Biased sampling

Solution → random sample

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18
Q

bias problem 2

A

Biased measurements

Solution → calibrate equipment, consistency among observers

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19
Q

Accuracy vs. Precision

A
  • Accuracy
    = how close our mean is to the “true” mean
  • Precision
    = how close repeated measurements are to
    each other
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20
Q

Standard Error

A

Quantifies how much confidence we should have in our estimate of the ‘true’ mean of a population

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21
Q

Replication

A

The more we sample a population the more
likely we are to get closer to the ‘true’ mean

  • Increased sample size gives better estimate
    of population
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22
Q

Nominal measurement

A

named categories

allow only counts or frequency data

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23
Q

Ordinal measurements

A

ranks

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24
Q

Interval or Ratio measurements

A

interval: can - and + not x and /
(cant measure “no temp)

ratio: there is a physically meaningful zero
-height, weight, length

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25
Q
  • Discrete measurement
A

Only certain values possible

Nominal, ordinal, and interval/ratio data

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26
Q

Continuous measurement

A

height and weight, any value is possible

Interval/ratio data

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27
Q

Dr. Burg identifies the various species of chickadee found in riparian areas within Lethbridge county (data type P)

A

Nominal

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28
Q

Dr. McCune counts the number of snowberry shrubs within a quadrat (data type p)

A

Ratio and discrete

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29
Q

Dr. Hoover studies the internal hive temperature of honeybees (data type P)

A

Interval and continuous

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30
Q

Animal Sampling Methods

A

Destructive and invasive sampling

Noninvasive sampling

Ethics are very important!

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31
Q

Destructive and invasive sampling

A

Trapping/hunting/fishing with retention of catch

Mark/recapture

Blood/bodily fluid sampling

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32
Q

Noninvasive sampling

A

feathers, hair, molts, feces, road kill, tracks, etc

Trail cams

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33
Q

Ethics

A

Agencies in place to ensure animal welfare is
considered

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34
Q

abiotic factors

A

the physical and chemical characteristics of the environment

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35
Q

biotic factors

A

the influenece of other organisms through competition, predation, ect

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36
Q

microhabitat

A

a small area which differs somehow from the surrounding habitat.

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37
Q

transect

A

a line running through the population to be sampled, often along some sort of gradient in the environment

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38
Q

quadrat

A

easily transportable plot that is laid down on the surface being studied to define a standard sampling area

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39
Q

What is a hypothesis?

A

Hypothesis = observation + possible explanation/mechanism

  • More than one hypothesis can explain an
    observation
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40
Q

What is a prediction?

A

A statement that arises logically from a hypothesis

  • Often “if-then” statements
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41
Q

What makes a good hypothesis?

A
  • Has to be testable.
  • Must be able to falsify our idea wrong.
  • Builds on previous knowledge.
    -Should make sense in terms of what we already know
    -We always try to build on previous knowledge
    -Background - introduction of a research paper
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42
Q

Phenotypic Variation

A
  • Differences in phenotype from one individual to another within a species.
  • Environmental factors can play an important
    role
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43
Q

inductive logic

A

a method of drawing conclusions from specific observations to reach general conclusions.

44
Q

deductive logic

A

a logical process that involves drawing specific conclusions from general ideas or premises

45
Q

Phenotype:

A
  • observable physical and behavioural
    characteristics
  • expression of genotype as influenced by
    environment
46
Q

Phenotypic plasticity

A
  • Ability of a single genotype to produce
    multiple phenotypes
47
Q

Reaction Norm

A

the range of phenotypic variation that occurs when organisms with the same genotype are exposed to different environmental conditions:

48
Q

Mechanisms for phenotypic plasticity

A

1) Tolerance
2) Acclimatization
3) Developmental Response
4) Ecotypes

49
Q

Tolerance

A
  • Non-adaptive
  • REVERSIBLE
  • Optimal conditions = healthy phenotype
  • Marginal conditions = stressed phenotype
50
Q

Acclimatization

A

Adaptive

  • REVERSIBLE
  • Physiological change or metabolic adjustments
  • Morphological change

eg. rock ptarmigan seasonal
colour change

51
Q

Developmental Response

A

Adaptive
-Each phenotype has an advantage in its
local environment

  • NOT reversible!
52
Q

Ecotype

A

Phenotypic variation resulting from underlying genotype

  • Different genetic variants of the same
    species
  • Usually separated geographically
  • Underlying genotype produces same phenotype even if environmental conditions change
53
Q

Microclimate effects on phenotypic variation

A

phenotypic variation can be shown in same
species due to differences in the local climate (microclimate)

  • This can be seen even on the same
    genetic individual
54
Q

Leaf area and microclimate

A

Leaves have to be able to collect sunlight
efficiently = large leaves?

  • Leaves are prone to drying out due to
    evapotranspiration – small leaves?
  • Variation occurs across species due to
    adaptations for specific environments
  • Variation can occur on same plant due to
    microclimate effects within that plant

=Plasticity !

55
Q

type 1 error

A

null hypo is false, and its really true

56
Q

type 2 error

A

h0 is true but its really false

57
Q

parametric

A

normally distributed

58
Q

when to use significant

A

when p-value <0.05 and h0 is rejected

59
Q

Distribution

A

Abundance within range

  • Abiotic and biotic factors
  • Spatial scales (global to local)
60
Q

Plant Sampling

A

Comparing percent cover of non-grasses on lawn grass vs. native grass

Measure at random coordinates

61
Q

Plant Sampling Field Protocols

A

Measure in native grass and cultivated lawn
grass

  • Obtain random coordinates from instructor
  • Find those spots within each plot area
  • Lay down gridded quadrat
  • Count all squares that have non-grass species
    present (covering at least 50% of square)
  • Calculate percent cover:

of squares with non-grass/ total # of squares in grid x 100

62
Q

Mark – Recapture Sampling

A

Non-destructive sampling method

  • Commonly used for vertebrates or invertebrates that have small populations
  • Minimizes impact of studies on the
    ecosystem
  • Allows for estimation of population size
  • Also used for other types of studies
63
Q

How to Mark – Recapture Sampling

A
  • Capture some subset of the population
  • Count them, mark them and release them
  • Some time later capture a subset of population
  • Count how many are marked
64
Q

Caveats

A

All individuals must have equal chance of recapture

  • Marking must stay on
  • Must be no immigration or emigration
  • Must be no mortality
  • Marking must not harm individual or affect its chance of recapture
65
Q

Estimated Population Size

A

N = (M*C)/R

66
Q

N = (M*C)/R

A

N = estimated # of individuals in pop.
M = # marked in 1st capture
C = # in 2nd capture
R = # recaptured (those with a mark in 2nd capture)

67
Q
  • Observational studies
A

Compare areas/individuals with natural
variation in the variable we are interested in.

68
Q
  • Experiments
A

Manipulate the variable we are interested in,
and hold other variables constant.

69
Q

Independent variable

A

*the variable we think has some effect on the organism we are studying

*the manipulated variable in an experiment.
-forms the basis for treatments

70
Q

*Dependent variable

A

*the variable which we think is influenced by the independent variable.

*the response variable in an experiment.

71
Q

*Controlled variables

A

Things that need to be held constant in an experiment because they could affect our results.

  • If not controlled they may be confounding
    factors.
  • If we can’t keep them constant, we can
    randomize them between treatments.
72
Q

*Control group (or control treatment)

A
  • A group which is not manipulated; or manipulated in the same way as the treatment, but minus the actual treatment itself
  • May serve as a comparison.
  • Can be used to check for influence of experimental techniques.
  • Not all studies/experiments need a control group!
73
Q

Example Study

A

Study Organism:
pheasants

Manipulated variable:
tail length (by gluing on feather).

Response variable:
matings obtained

Controlled variables:
male size, male colour, etc.

Control group:
males with tails cut and then glued back to the same length males with uncut tails

74
Q

MVT

A

used to understand foragers who live in a “patchy” environment

75
Q

-when should an animal leave a patch of food and search for a new patch? (MTV)

A

an animal should leave when the rate at which it is gaining energy on the patch drops below the average rate of energy gain in the habitat

76
Q

Central place foraging (CPF)

A

when animals have to travel to get food then come back to their home (bees)

77
Q

correlation

A

as one factor changes, another does too

78
Q

Optimal give up time (GUT)

A

when the time it takes for them to find food takes too long so they give up

79
Q

independent variable

A

the factor that we think is the cause, or the manipulated variable (treatments)

80
Q

dependent variable

A

the factor that we believe is the effect, or the responding variable

81
Q

Benefits of Foraging?

A

Food, mates, shelter, etc

82
Q

Costs of Foraging?

A

Loss of energy, failure to
reproduce, death

83
Q

Factors forager ‘considers’?

A

Food source, travel time, search
time, predators, wear and tear

84
Q

Functional Response

A

Rate of intake as a function of food density

  • Change in consumption rates as a response to increasing density of prey
  • Key element of modern population ecology

All 3 types observable in nature

  • Type II best describes the natural response of many predators
85
Q

Type I Functional Response

A

Simplest response

  • Linear relationship
  • Predator keeps up with increases in prey
  • Assumes search and handling time are negligible
  • Basis of Lotka-Volterra Model
86
Q

Type II Functional Response

A
  • Intake rate decreases as prey density increases, then reaches a plateau
  • Predator is limited by handling time
  • As more prey available, predator consumes more, but thus handling time increases
  • Eventually consumption rate remains constant due to handling time and satiation
87
Q

Type III Functional Response

A

Similar to Type II, but initial prey intake low

  • Predators increase their search activity with increasing prey density
  • Due to learning of predator, prey switching and preys’ ability to hide when low density
88
Q

Holling’s Disc Equation

A

Pe = a’TsN

but search time decreases as prey numbers increase, so not constant

So equation changes for every
individual predation circumstance

89
Q

Ts = Ttot - ThPe

A

search time is dependent on total time searching and handling time for each prey

90
Q

lotka-volterra model

A

incoroprates cycles in abundance of predator and prey populations to show a lag in predator numbers

91
Q

Competition

A
  • Major way in which organisms interact
  • Occurs when organisms need the same
    resources, for resources that are limiting
  • Both do worse when together than when apart
92
Q

Interference Competition

A
  • Direct and physical interference while collecting resource

-Fighting over a piece of food between squirrels

93
Q

Exploitation Competition

A
  • One organism uses up the resources
  • No longer available for others

-Foraging hummingbird empties all nectar from patch of flowers

94
Q

Intraspecific Competition

A
  • Between organisms of the same species
  • Important population dynamics regulator
  • Flock of cedar waxwings feeding on the same
    tree filled with mountain ash berries
95
Q

Interspecific Competition

A

Between organisms of the different species
-Different grasses and shrubs competing for the same soil resources on a north-facing coulee slope

  • Important population dynamics regulator across many organisms
  • One species usually ends up driving the other
    species’ population dynamics
  • Competitive exclusion principle important in
    explaining how species co-exist given this type
    of competition
96
Q

Why quantify biodiversity?

A

Impossible to sample entire communities
* estimate with quantitative techniques

  • Not just # of species
    • species relative abundance important too
  • Allows hypothesis testing
    * statistical analyses
  • Conservation efforts
    * quantitative answers to biodiversity related questions
97
Q

Taxon Richness

A
  • Number of unique taxa in a given area
  • Species richness (S)
    • the number of species in an area of
      interest
  • Doesn’t show whole picture
98
Q

Shannon’s diversity index

A

measure of entropy

s = species richness

pi = relative abundance to the ith species

higher H’ = higher diversity

99
Q

Pielou’s evenness index

A

measures evenness alone

s = species richness

pi= relative abundance to the ith species

can only be number 0-1 closer to 1 means more even

100
Q

Rank Abundance Distributions

A

Indices reduce large amounts of information to one number

  • Can show more information with rank
    abundance distributions
  • Y-axis = each species’ relative abundance
  • X-axis = rank of each species
    (most common to rarest)
101
Q

Species Accumulation Curves

A
  • As we sample more, likelihood of getting a new species decreases
  • Y-axis = cumulative # of species recorded
  • X-axis = # of individuals sampled
102
Q

speices richness

A

the number of species

103
Q

what is science

A

the process we got the knowledge

a way of learning about the world around us

104
Q

Decriptive statistics

A

They give us a way to describe our data – to summarize it and help us to see any patterns that are present.

-measures of central tendency
- measures of variation.

105
Q

correlation

A

a mutual relationship or connection between two or more things

106
Q

Nonparametric tests serve as a

A

an alternative to parametric tests

107
Q

A parametric test assumes the data-

A

data follows a known distribution that can be modeled using parameters,

Bio lab exam (4 decks)

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