Fleming, 2014, plant ecology research lesson, S5 Lab 3
Lab 3: Field Sampling Methods
Goals
Ecological data come in many forms, and we will look at some in this class. Structural, floristic,
dominance, population, and pattern data are all useful. These exercises are aimed at demonstrating some
of these concepts. This lab introduces you to some major concepts and defines terms that are useful in
plant ecology field studies. We will discuss some important constraints and explore ways in which
vegetation is sampled on the landscape. The role of sample size is explored, and we’ll consider the basic
species-area relationship as a prelude to later lectures and field work. Finally, the effect of quadrat
shape, location and number will be explored.
Overview
Sampling vegetation is neither easy nor straight-forward. Here, we will only scratch the surface of
the topic!
Before you start a study or vegetation in the field, you must consider:
1. The purpose of the study, since what you describe varies with your overall objectives;
2. The scale of the study, since there is a general tradeoff between size and detail;
3. The habitat you are in, since structure and diversity impact methods, and;
4. The available resources, which include finances, equipment, labor, and time.
Before beginning a field survey, consider these questions:
1. Is it really relevant or necessary to identify all of the plants present?
2. Can I identify all the plants I need to identify?
3. What environmental data can I collect? What are the relevant environmental data, and how do I
compensate for the fact that what I can do, what I need to do, and what I can afford to do are all different?
4. What kind of analysis will I perform, and will my data be amenable to these analytical methods?
5. Does it matter what season I’m doing the study in?
6. I may be perfect and precise, but how do I deal with the five other people (e.g. temporary helpers)
collecting my data? Will they be as precise and discriminating as me?
7. How do I balance speed, accuracy, and relevance (that is, making sure I get data appropriate for my
study)?
Some Useful Definitions
1. Quadrat: A sample unit, typically with four sides. It can be large or small in area. It is usually
square, but rectangular and even round (circat?) plots are common. Circles are easier to set up, have the
minimum edge and lowest internal variation as a circle generally mimics the shape of a plant’s canopy
and potentially reduces the likelihood of capturing nearby plant canopies. Rectangles have higher internal
variation (if you lay a rectangle over a circular plant canopy to capture it, you may well capture some
nearby plant canopies as well) which reduces between-sample variance and may be desirable, but have
longer edges and are harder to set up in the field. Squares are a compromise. A good sampling method,
often used by government agencies, is to use large circular plots, and sub-sample inside them with
quadrats.
Various measures of vegetation are determined within the area sampled. Some people only use
“presence/absence” data for species they encounter. Others are more thorough and quantify species
abundance, cover, stem density, or biomass. This analysis is often done by pooling data from many
plots to give you a snapshot of the plant community.
We will use the term plot to refer to a large sample unit (area to be sampled), and quadrat to
refer to the sub-sample unit nested within the plot. The two need not have the same sample shape.
Fleming, 2014, plant ecology research lesson, S5 Lab 3
2. Transect: A line or belt that is placed through vegetation. Vegetation often changes
significantly along a transect. Species data are determined along the transect in several ways, e.g.,
recording the plants that hit the tape (intercept), recording cover in a series of non-contiguous quadrats
(belt) or in quadrats arrayed at regular or random intervals.
Note that the contiguous array of quadrats used frequently in past ecological studies presents
serious statistical problems, generally referred to as auto-correlation. As ecologists have become better
statisticians over the last 100 years, we now know that each quadrat should be independent. Looking
back at earlier studies of plant ecology that used contiguous quadrats along transects, we see that the same
plant will often occur in both, destroying the notion of independent occurrences. We can solve this
problem for most situations by alternating sides of the transect and allowing a standard distance between
quadrats.
3. Plotless methods: These methods are point-to-point distance measures. They are used in stem
mapping, special pattern analysis, and can be used to determine density and frequency characteristics. We
will not consider them in this class.
Purpose and Scope
The purpose or scope of the study will dictate many of the decisions about experimental design. You
should be very clear on your purpose before you attempt to design your study. What is the “sample
universe” of your study? That is, you should be clear about the boundaries in space, time, character, and
situation. For example, a sample universe (maybe better: study system?) can be defined as all sites
within a state park (geographic) provided that they are covered by forest (character) at least 50 years old
(time) that show no signs of recent disturbance (time/situation/character) and located on alluvial soil
(character). All sites failing to meet these criteria are not within the sampling universe (system) and are
excluded from the study.
How many samples are needed? Again, it depends! Is this an observational study or a manipulative one?
Is this a broad-scale study or are you setting up a baseline for future monitoring? The solution is
generally to sample as intensively as you can in the time you have. This strategy should be to cover the
universe in a sparse pass first, then fill in details as time permits. Sampling 20% of your universe is a
good goal.
Where will the plots be placed? This depends on your statistical constraints. Are they lax or stringent?
Did you use random, stratified random, plotless, or haphazard sampling methods? Will you need to
testify in court about your findings? How large are the plots (see below)? What time and money
constraints are there? As you can see, there will always be trade-offs in terms of sample number and
placement. The key thing to consider is how you can justify your sample number and placement.
What type of data are you collecting? Can you afford to spend much time or money to do destructive
sampling such as biomass or primary productivity? Will simple cover or presence/absence data suffice?
Are individuals easily identifiable (ex: trees) or not (ex: grasses in the Central Valley)? How accurately
can you describe such traits as cover or species identification? Does species frequency give you any
helpful information?
SAMPLE PLACEMENT
Most people sample wherever they can access a representative community of interest. A truly random
sample would be to look at a map of the area you want to sample, lay a numbered grid over it and
randomly generate numbers for the map cell you would sample. But, what if an area you are supposed to
sample has a cliff, or is in a river?
Fleming, 2014, plant ecology research lesson, S5 Lab 3
PLOT SIZE
There are two things to consider here: (1) how large should your basic sample be? (2) how many of them
should there be? When you have multilayered vegetation these concerns become less distinct. Your plots
should sample good representations of the ambient vegetation, and you should subsample with a number
of quadrats that gives your data good statistical power. Some rules of thumb (e.g. a lot of ecologists have
used these successfully in different vegetation) to accomplish this are:
Vegetation Type
Forests
Tall shrub
Forest understory
Dwarf shrub
Grasslands, herb fields
Weeds
Mosses, lichens
Plot Size Range (m2)
200-1000 (ex: 20m x 50m)
100-400 (ex: 10m x 10m, 20m x 20m)
50- 200 (5m x 10m, 10m x 20m)
25-50 (5m x 5m, 5m x 10m)
25-100 (5m x 5m, 10m x 10m)
20-100 (4m x 5m, 10m x 10m)
1-4 (1m x 1m, 2m x 2m)
Vegetation Type
Trees
Shrubs
Herbs, Grasses
Quadrat Size Range (m2)
100-250 (10m x 10m, 10m x 25m)
16-25 (4m x 4m, 5m x 5m)
1 (1m x 1m)
It is usual to use no more small samples/quadrats than would total the sampling area/plots given above.
For example, for trees you would need at least 2 – 5 100m2 samples, while for herbs you would need
about 10 – 15 1m2 samples. Again, strive to sample about 20% of the sample universe.
For those who truly prefer a statistical method for determining sample size (useful for quantitative studies
tracking change over time or comparisons to other systems) you might do the following: Pick a common
species in your system, and then determine its cover in a series of quadrats of appropriate size for the
growth form of that species. Then, plot the MEAN percent cover of that species as a function of quadrat
number. This technique is called a running average. When this metric stabilizes, that is about the
number of quadrats needed to accurately sample the universe. Alternately, you could plot the standard
error (SE) of the mean percent cover as a function of quadrat number. When SE drops to < 15% of the
mean cover or less, the sample number is adequate. Do this for a few species and take the larger sample
size as an adequate number of samples to capture your universe accurately.
SAMPLE SHAPE
As stated above, circles are best for minimizing edge effects (technically, this is the ratio of edge length
to quadrat area; if this ratio is low, then we may better capture entire plants within our quadrats, rather
than only portions of them) and rectangles have lower between-sample variance. Squares are a good
compromise.
TYPES OF DATA
As stated above, plant species data can be as simple as “present or not”, or as complicated as cover for
each species in each quadrat. Other data like vegetation structure (physiognomy) and phenological (time
of life history events) data, habitat classification, density, stem diameter, etc. can be collected.
Fleming, 2014, plant ecology research lesson, S5 Lab 3
SPECIES AREA CURVE
The species-area curve (SAC) concept is useful for answering the question: How many species are in the
sampling universe? Immigration and local extinction of species influence this, but in general as sampled
area increases, so does # species observed. However, if the number of samples is large enough, we often
see a leveling off of species encountered (species richness is asymptotic to large numbers of samples).
One important limitation of the species-area curve concept is that it only tells if species are present; it says
nothing about species abundance. This concept is useful, however. It has been used for over 200 years.
1772 was first published use of this concept!
If you are math oriented, there is a SAC equation: S = cAz where S = # spp; c = constant depends on taxa
and region studied; A = area; z = constant generally between 0.2 – 0.35. If expressed as a log function
(logS = logc + z*logA) then the curve becomes a line.
For today:
With your lab group, sample plant species in a CSU Stan lawn area. This area can have trees or not, but
should not include walkways, parking lots, flower beds, etc. Use transects and non-contiguous quadrats
(e.g., you will sample your lawn twice). We have quadrats of two sizes to use, 1m2, and 0.25 m2 (50cm
on a side). If you use a 1m2 quadrat frame, your transect should be at least 50 meters long; if you use the
smaller 0.25 m2 quadrat, then a 20 meter transect will suffice. Sample some lawn or field area on
campus, and sample enough quadrat areas to show the leveling off of species encountered. Stay within
your sample universe! No fair sampling, say, 6 quadrats in one lawn then running across the street to the
citrus orchard to finish sampling there.
If you don’t know what the lawn species are, use the morphospecies concept from last week!
From your data, construct two graphs: (1) a species area curve that shows number of species captured as a
function of area you sampled, and (2) a running average of percent cover of a dominant species as a
function of area sampled. Your data and graphs are due next week at start of lab (of course, if you finish
earlier, I’ll take it!).
You are seeking to answer the question:
How many quadrats does it take to accurately sample (e.g. show the leveling off of species vs. area) the
CSU Stan lawns?