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

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

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

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

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?

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.

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

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

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?

Macroplot

300 acre wetland

200 acre property

Quadrat

Biological population?

Target population?

Sampled population?

Statistical population?

Plants have patchy distribution Dispersed by ants

Short distance: ~ 2 meters

Seeds found in clusters

Ant nestsLouse pincushion

2 m

Leucospermumtruncatlum

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

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

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

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

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

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

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.

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?

Individual plants or animals Plant or animal parts Quadrats (plots) Lines (transects) Points Point frames or point quadrats Distance (plotless) methods

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)

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

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?

Simple random sampling Stratified random sampling Systematic sampling (among other methods…see Table 8.1)

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

Random numbers table Random number generator

Calculator

Computer (e.g., Excel)

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

12

3

Fig. 8.7

Map of soil types at Carrizo Plain National Monument

Generated 10 samples

per strata

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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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

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

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

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

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

Vegetation transects Giant kangaroo rat precinct transects

Sampling locations at CPNM

GKR precincts

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?