Lab 4: Sampling design and methods - Sonoma State …€¦ · · 2010-05-14Lab 4: Sampling design...
Transcript of Lab 4: Sampling design and methods - Sonoma State …€¦ · · 2010-05-14Lab 4: Sampling design...
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Lab 4: Sampling design and methods
April 24, Stev 3059
Lab 5: Sampling in the field
April 25, 8am – 3pm
Lab 6: Data analysis and presentation
May 14, Stev 3059
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April 25 (Sunday) Meet at 8:00 am in Stev
3059
Departure for field promptly at 8:15 am
Bring lunch, snacks, water, hat, sunscreen, field shoes, etc.
Lab 5 – we’ll need your data! Data from field trip must
be submitted by 5/7
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Many questions require some level of quantitative information to answer
Examples from Geography? Biology? Environmental studies?
Well-designed studies are more efficient and cost-effective
Well-designed studies produce defensible data
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Poor design leads to inconclusive results Unreliable data complicates interpretation of
results Poorly managed data results in lost,
unused or inaccurately used data Data are not analyzed because
necessary skill set is lacking Remember: Natural systems are
dynamic, take these fluctations into account when creating study design
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What’s your question (goal)? Short-term? Long-term?
What is the most effective, accurate and efficient way of collecting the data? How much time, money, labor available?
What will be done with the data? How will it be managed?
How will it be analyzed? What will be done with the results?
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How many bird species are found at Crane Creek park?
Do insect communities vary on different plant species?
How do vegetation communities change along an elevation gradient?
What is the effect of removing an invasive plant species on native plant species?
What is the impact of trails on native vegetation?
etc.
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Descriptive studies Describing a pattern
Comparative studies Testing hypotheses by comparing two or more patterns
Experimental studies Testing hypotheses by manipulating one or more factors
of interest
Approach you take depends on goal, budget, time, etc.Each approach requires solid understanding of sampling
methods
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Descriptive studies
Comparative studies
Experimental studies
Test hypotheses? No Yes Yes
Ascribe causation?
No No Yes
Cost/inputs? Lower Lower Higher
Feasibility? Higher Higher Lower
Scale? Feasible at range of spatial scales
Feasible at range of spatial scales
Limited at larger spatial scales
Ethical issues Fewer – measuring existing patterns
Fewer – measuring existing patterns
Depends
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Sampling: process of selecting a part of something with the intent of showing the quality, quantity, style or nature of the whole
Used when measuring the whole is not practical or not feasible
How to select a portion of the whole? How to evaluate accuracy of sampling in
describing the whole?
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Macroplot
300 acre wetland
200 acre property
Quadrat
Biological population?
Target population?
Sampled population?
Statistical population?
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Plants have patchy distribution Dispersed by ants
Short distance: ~ 2 meters
Seeds found in clusters
Ant nestsLouse pincushion
2 m
Leucospermumtruncatlum
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1 2 3 4 5 6 7 8 9
1
2
3
4
5
6
7
8
9x y # Seeds
2 6 3
3 2 0
3 8 1
4 1 0
4 3 0
4 9 0
6 3 6
6 6 0
7 2 1
8 3 0
8 8 1
N = 11 cells sampled
X = 1.09 seeds/cell
µ = 121 seeds / 81 cells = 1.41 seeds/cell
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1 2 3 4 5 6 7 8 9
1
2
3
4
5
6
7
8
9x y # Seeds
2 1 1
2 8 0
3 4 3
3 6 9
4 3 0
4 6 7
6 4 0
7 2 1
7 7 2
8 5 0
8 8 1
N = 11 cells sampled
X = 2.18 seeds/cell
µ = 121 seeds / 81 cells = 1.41 seeds/cell
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Population parameters: Descriptive measures that characterize the populations
Assumed to be fixed (but often unknown)
Change only if population changes
= true population mean
σ2 = true population standard deviation
Sample statistics: Descriptive measures derived from a sample
X = sample mean
s = sample standard deviation
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Population mean ( ) =Sum of values for each individual
of the population
Number of individuals in the population
= X1 + X2 + …XN
N
X1 = value of the first individual of the population
X2 = value of the second individual of the population
X3 = value of the third individual of the population
N = number of individuals in the population
Or, more concisely:
= Σ Xi / N
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Sample mean (X) =
Sum of values of each observation in the sample
Number of samples (n)
Or,
X = Σ Xi / n
x y # Seeds
2 1 1
2 8 0
3 4 3
3 6 9
4 3 0
4 6 7
6 4 0
7 2 1
7 7 2
8 5 0
8 8 1
n = 11 cells sampled
X = 24 seeds/11 cells = 2.18 seeds/cell
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Accuracy: the closeness of a measured value to its true value Sampling designs should use most accurate
methods Precision: the closeness of repeated
measurements of the same quantity Repeatability
measures of precision: standard deviation, standard error, confidence interval, variance
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Sampling error: Due to chance
Occurs when sample information does not reflect true population information
Non-sampling error: Due to error associated with human mistakes
▪ Bias in selection of subsamples
▪ Inconsistent sampling effort
▪ Transcription, data entry errors
▪ Misidentification of species
▪ Reading instrument incorrectly
▪ etc.
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What is appropriate population of interest? What is appropriate sampling unit? What is appropriate sampling-unit size and
shape? How should sampling units be positioned? How many sampling units should be
sampled? Should sampling units be permanent or
temporary?
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Individual plants or animals Plant or animal parts Quadrats (plots) Lines (transects) Points Point frames or point quadrats Distance (plotless) methods
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Three criteria:1) Use random, unbiased sampling method2) Position sampling units to achieve good
dispersion throughout population being sampled
3) Ensure sampling units are independent (i.e., not spatially correlated)
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Samples drawn randomly from population
vs. subjective, preferential selection of samples
Fundamental assumption of statistical analyses and required for making statistical inferences
Example of randomly chosen sampling points
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Sampling units spaced far enough apart so that measurements are not spatially correlated
Spatial autocorrelation can occur at different scales
Why is autocorrelation a problem? How far apart is enough?
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Simple random sampling Stratified random sampling Systematic sampling (among other methods…see Table 8.1)
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Simple random sample of 100 1m x 1m plots Note how some portions of region did not get
sampled just due to chance
Fig. 8.6
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Random numbers table Random number generator
Calculator
Computer (e.g., Excel)
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Dividing population into two or more subgroups (strata)
Based on soil type, slope, vegetation community, elevation, etc.
Random samples taken in each stratum
Sampling effort can be proportional to area of each stratum
e.g. you might want to consider stratifying by
aspect here
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12
3
Fig. 8.7
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Map of soil types at Carrizo Plain National Monument
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Generated 10 samples
per strata
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Sampling points placed at systematic intervals along randomly-located starting points
Can be analyzed as random sample if done correctly
What is the appropriate unit of sampling in this situation?
Macro Plot
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#
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# # # # ###
# ## ## #
# ####
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# ### #
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# ### #
######## ##
# ### ###### # ##
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##### ### ############
## ## # ####
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##### #### ### ## #### #
## ##### #####
## # #### ##
#### ##
1000 0 1000 Meters
Soils / SuelosOld alluvium / Aluvion viejoRecent alluvium / Aluvion recienteResidual
Stream-associated / Suelo de quebradasSwamp / Pantano
# Species present / Presencia del especies
N
Dipteryx panamensis
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Census counts
Population size
Population structure?
Density
Number of individuals in a given area
Only appropriate for species with recognizable individuals
Percent cover
Amount of area covered by a species
Frequency
% of possible plots within an area containing a certain species
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Quadrats (= plots) Count number of
individuals within quadrat
Size and shape of quadrat important – Why?
Distance measures Measure distance of individuals from a
random point
Used for largely scattered populations, such as trees, with a random distribution
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Expressed as percentage of sampling units occupied by the target species (e.g. species of interest)
e.g. number of cells in a quadrat occupied / total number of cells in quadrat
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Visual estimates
% cover within quadrat
Must select cover classes (e.g., species)
e.g. 0-25%, 26-50%, 51-75%, 76-100%
Line intercepts
Used to measure canopy cover
Point intercepts
Best for grasses and herbaceous vegetation
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Vegetation transects Giant kangaroo rat precinct transects
Sampling locations at CPNM
GKR precincts
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Groups of 2-3 people (randomly selected, of course!)
Objective: Quantify the abundance of ‘daisies’ located outside of Stevenson Hall You decide which method you will use
Various field equipment available to you Calculate mean and standard deviation for
your samples Quantify non-sampling (observer) error?