eco final lab Flashcards
What is a Naturalist?
- an expert in or student of natural history
- Someone who studies flora and fauna,
fossils, geology
What is an Ecologist?
an environmental scientist who studies how organisms interact with their environment and how the environment functions.an expert in or student of ecology
Ricklefs’ definition of Ecology
The scientific study of the abundance and distribution of organisms in relation to other organisms and environmental conditions.
Geographical range
Where are organisms is present and where it is absent
Abundance
how many individuals of a particular organisms are in a certain area
Dispersal,
has a population of organisms been in an area for a long time or recently arrived
survival and reproduction
how long the organisms live and how often, how much, when they reproduce
a possible answer to the question is called a
hypothesis
Population
Represents:
- All possible individuals in a group of organisms
- Impossible to measure an entire population
Sample
A subset of a population
- Used to make inferences about the
entire population - How should crayfish be selected?
-randomly
Parameter
Some property of the entire population of interest
- A measurable factor
-mean or SD = descriptive statistics
Statistic
Mean and standard deviation of the
sample
- Estimate of the parameter
For crayfish example:
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
Replication
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
pseudoreplication
where there is only a single replicate per treatment, but subsamples are taken from each area.
Bias
type of error
measurements are consistently wrong
leads to poor accuracy
bias problem 1
Biased sampling
Solution → random sample
bias problem 2
Biased measurements
Solution → calibrate equipment, consistency among observers
Accuracy vs. Precision
- Accuracy
= how close our mean is to the “true” mean - Precision
= how close repeated measurements are to
each other
Standard Error
Quantifies how much confidence we should have in our estimate of the ‘true’ mean of a population
Replication
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
Nominal measurement
named categories
allow only counts or frequency data
Ordinal measurements
ranks
Interval or Ratio measurements
interval: can - and + not x and /
(cant measure “no temp)
ratio: there is a physically meaningful zero
-height, weight, length
- Discrete measurement
Only certain values possible
Nominal, ordinal, and interval/ratio data
Continuous measurement
height and weight, any value is possible
Interval/ratio data
Dr. Burg identifies the various species of chickadee found in riparian areas within Lethbridge county (data type P)
Nominal
Dr. McCune counts the number of snowberry shrubs within a quadrat (data type p)
Ratio and discrete
Dr. Hoover studies the internal hive temperature of honeybees (data type P)
Interval and continuous
Animal Sampling Methods
Destructive and invasive sampling
Noninvasive sampling
Ethics are very important!
Destructive and invasive sampling
Trapping/hunting/fishing with retention of catch
Mark/recapture
Blood/bodily fluid sampling
Noninvasive sampling
feathers, hair, molts, feces, road kill, tracks, etc
Trail cams
Ethics
Agencies in place to ensure animal welfare is
considered
abiotic factors
the physical and chemical characteristics of the environment
biotic factors
the influenece of other organisms through competition, predation, ect
microhabitat
a small area which differs somehow from the surrounding habitat.
transect
a line running through the population to be sampled, often along some sort of gradient in the environment
quadrat
easily transportable plot that is laid down on the surface being studied to define a standard sampling area
What is a hypothesis?
Hypothesis = observation + possible explanation/mechanism
- More than one hypothesis can explain an
observation
What is a prediction?
A statement that arises logically from a hypothesis
- Often “if-then” statements
What makes a good hypothesis?
- 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
Phenotypic Variation
- Differences in phenotype from one individual to another within a species.
- Environmental factors can play an important
role
inductive logic
a method of drawing conclusions from specific observations to reach general conclusions.
deductive logic
a logical process that involves drawing specific conclusions from general ideas or premises
Phenotype:
- observable physical and behavioural
characteristics - expression of genotype as influenced by
environment
Phenotypic plasticity
- Ability of a single genotype to produce
multiple phenotypes
Reaction Norm
the range of phenotypic variation that occurs when organisms with the same genotype are exposed to different environmental conditions:
Mechanisms for phenotypic plasticity
1) Tolerance
2) Acclimatization
3) Developmental Response
4) Ecotypes
Tolerance
- Non-adaptive
- REVERSIBLE
- Optimal conditions = healthy phenotype
- Marginal conditions = stressed phenotype
Acclimatization
Adaptive
- REVERSIBLE
- Physiological change or metabolic adjustments
- Morphological change
eg. rock ptarmigan seasonal
colour change
Developmental Response
Adaptive
-Each phenotype has an advantage in its
local environment
- NOT reversible!
Ecotype
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
Microclimate effects on phenotypic variation
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
Leaf area and microclimate
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 !
type 1 error
null hypo is false, and its really true
type 2 error
h0 is true but its really false
parametric
normally distributed
when to use significant
when p-value <0.05 and h0 is rejected
Distribution
Abundance within range
- Abiotic and biotic factors
- Spatial scales (global to local)
Plant Sampling
Comparing percent cover of non-grasses on lawn grass vs. native grass
Measure at random coordinates
Plant Sampling Field Protocols
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
Mark – Recapture Sampling
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
How to Mark – Recapture Sampling
- 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
Caveats
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
Estimated Population Size
N = (M*C)/R
N = (M*C)/R
N = estimated # of individuals in pop.
M = # marked in 1st capture
C = # in 2nd capture
R = # recaptured (those with a mark in 2nd capture)
- Observational studies
Compare areas/individuals with natural
variation in the variable we are interested in.
- Experiments
Manipulate the variable we are interested in,
and hold other variables constant.
Independent variable
*the variable we think has some effect on the organism we are studying
*the manipulated variable in an experiment.
-forms the basis for treatments
*Dependent variable
*the variable which we think is influenced by the independent variable.
*the response variable in an experiment.
*Controlled variables
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.
*Control group (or control treatment)
- 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!
Example Study
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
MVT
used to understand foragers who live in a “patchy” environment
-when should an animal leave a patch of food and search for a new patch? (MTV)
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
Central place foraging (CPF)
when animals have to travel to get food then come back to their home (bees)
correlation
as one factor changes, another does too
Optimal give up time (GUT)
when the time it takes for them to find food takes too long so they give up
independent variable
the factor that we think is the cause, or the manipulated variable (treatments)
dependent variable
the factor that we believe is the effect, or the responding variable
Benefits of Foraging?
Food, mates, shelter, etc
Costs of Foraging?
Loss of energy, failure to
reproduce, death
Factors forager ‘considers’?
Food source, travel time, search
time, predators, wear and tear
Functional Response
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
Type I Functional Response
Simplest response
- Linear relationship
- Predator keeps up with increases in prey
- Assumes search and handling time are negligible
- Basis of Lotka-Volterra Model
Type II Functional Response
- 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
Type III Functional Response
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
Holling’s Disc Equation
Pe = a’TsN
but search time decreases as prey numbers increase, so not constant
So equation changes for every
individual predation circumstance
Ts = Ttot - ThPe
search time is dependent on total time searching and handling time for each prey
lotka-volterra model
incoroprates cycles in abundance of predator and prey populations to show a lag in predator numbers
Competition
- 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
Interference Competition
- Direct and physical interference while collecting resource
-Fighting over a piece of food between squirrels
Exploitation Competition
- One organism uses up the resources
- No longer available for others
-Foraging hummingbird empties all nectar from patch of flowers
Intraspecific Competition
- Between organisms of the same species
- Important population dynamics regulator
- Flock of cedar waxwings feeding on the same
tree filled with mountain ash berries
Interspecific Competition
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
Why quantify biodiversity?
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
Taxon Richness
- Number of unique taxa in a given area
- Species richness (S)
- the number of species in an area of
interest
- the number of species in an area of
- Doesn’t show whole picture
Shannon’s diversity index
measure of entropy
s = species richness
pi = relative abundance to the ith species
higher H’ = higher diversity
Pielou’s evenness index
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
Rank Abundance Distributions
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)
Species Accumulation Curves
- As we sample more, likelihood of getting a new species decreases
- Y-axis = cumulative # of species recorded
- X-axis = # of individuals sampled
speices richness
the number of species
what is science
the process we got the knowledge
a way of learning about the world around us
Decriptive statistics
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.
correlation
a mutual relationship or connection between two or more things
Nonparametric tests serve as a
an alternative to parametric tests
A parametric test assumes the data-
data follows a known distribution that can be modeled using parameters,
