1. Introduction to Vegetation Inventory, Assessment, and Monitoring
The purpose of this section is to explore steps in designing and conducting vegetation monitoring projects. Specific concepts and tools will complete the story in subsequent sections of this course.
2. Sampling Principles
This unit focuses on the principles of sampling: why we sample, the relationship between population parameters and sample statistics, accuracy and precision, types of error and their causes, and using confidence intervals to make inferences about populations. Very simply, we sample so that we can gather accurate and precise information about populations, and to make inferences about populations with confidence.
3. Sampling Design
This module focuses on the elements of sampling design. Sampling design encompasses all of the practical components of a sampling endeavor: where to sample, what to sample, and how to sample!
4. Monitoring Implementation, Data Quality, and Best Practices
Data management is fundamental to any type of data gathering activity. It is a process that includes many steps, each of which provide opportunities to introduce non-sampling errors related to human error. This module focuses on the best management practices that can be used to reduce or eliminate potential errors associated with data management.
5. Indicators, Methods, Descriptors, and Covariates
This section explores the distinctions between indicators and methods, introduces the concepts of site descriptors and covariates that are used to help classify and interpret monitoring data.
This module focuses on plant density: what it is, how it is measured, and how density data are used by land managers to inform resource management decisions. Very simply, density is defined as the number of individuals per unit area, and reflects the closeness of individuals.
This module focuses on plant frequency: what it is, how it is measured, and how frequency data are used by land managers to inform resource management decisions. Very simply, frequency measurements record the presence of species in quadrats or plots placed repeatedly across a stand of vegetation. Frequency reflects the probability of finding a species at any location in the vegetated area.
This module focuses on cover: what it is, how it is measured, and how cover data are used by land managers to inform resource management decisions.
9. Vegetation Height and Structure
This module focuses on vegetation structure: what structure represents, how it is measured, and how information about vegetation structure is used to inform resource management decisions. Very simply, vegetation structure refers to the three-dimensional arrangement of plants and plant materials on a site or across a landscape. Vegetation structure is primarily influenced by plant cover on horizontal and vertical planes.
10. Biomass and Production
This module focuses on plant biomass: what it is, how it is measured, and how biomass data are used by land managers to inform resource management decisions.
This module focuses on plant utilization: what it is, how it is measured, and how utilization data are used by land managers to inform resource management decisions.
12. Composition, Diversity, Similarity
This module focuses on plant community diversity: how it is described, how it is measured, and how diversity is interpreted by land managers to inform management decisions.
13. Remote Sensing for Vegetation Monitoring and Assessment
Remote sensing techniques offer many opportunities to inform, supplement, and sometimes replace traditional field-based aproaches to vegetation assessment and monitoring. This module explores ways in which remote sensing can be used in monitoring and provides example applications.
14. Assessment and Monitoring Programs
This module explores some established rangeland assessment and monitoring programs, describes their protocols, and discusses how the collected data are used in management decision making.
2.1: Introduction to Sampling
Note: The symbol “” is generally used to represent the sample mean in formulas and the standard error is represented by “S” with “” as a subscript. In this video, the bar over the x was inadvertently omitted from the symbols for the mean and standard error.
To add captions to this video click the CC icon on the bottom right side of the YouTube panel and select English: Corrected captions.
Introduction to Sampling Principles
Sampling is selecting a part of something with the intent of showing the quality, style, or nature of the whole. We sample because we want to learn about a population or an area of interest, such as a species of concern of a specific management unit. Why don’t we just do a complete census of the area or population that we are interested in? Conducting an accurate census is difficult, time consuming, and often impossible.
For example, assume we are interested in cheatgrass (Bromus tectorum), an invasive annual grass that is problematic on western rangelands (Figure 1). Because it is an annual plant, we are concerned about its reproductive potential and need to make some inferences about population size and the total number of seeds produced by the population each year. To conduct a complete census of all cheatgrass plants in the western United States, we would need to locate and measure every single cheatgrass plant in every single population.
Figure 1. A stand of cheatgrass (Bromus tectorum). Photo courtesy of Cassondra Skinner @ USDA-NRCS PLANTS Database.
How could we ensure that we quantified seed production throughout the entire growing season? How much time and effort would this require? Even if we scaled our area of study back to a management unit such as a 500-hectare pasture, and we had unlimited time and resources, obtaining an accurate and complete census of this variable would be impossible! However, information about population size and reproductive potential is important to assist managers as they prioritize management actions and prevent cheatgrass invasions. How might we go about providing this information? We sample and the results of sampling can be used to make inferences about the population as a whole.
Population is a term that has two different meanings in natural resource management. In the statistical sense, a population is the group of individuals or area about which one wishes to make statistical inferences. In this context, the population may be a population of plants, of animals, a community, or an area of land. In essence, the population in the statistical sense represents the “sampling universe” from which we select our sample to measure. In the biological sense, “population” refers to a biological population, which includes all individuals of a species that occupy a particular area and are capable of exchanging genetic material.
To further illustrate these two populations, consider the following. To make generalizations about cheatgrass seed production, we might record the number of seeds found on plants in 100 quadrats (1 m2) in the McCone pasture (Figure 2). We want to make inferences by sampling the cheatgrass population located within that pasture, and there are 10,000 possible 1 m2 quadrats that we could measure in the 1 hectare (1 ha) exclosure. The statistical population encompasses all the possible plots within that pasture boundary, our “sampling universe”.
Figure 2. Map showing a statistical population of cheatgrass (McCone Pasture Exclosure) and a biological population of cheatgrass, the Canyon Creek Watershed.
Alternatively, the biological population of cheatgrass may be all of the cheatgrass located within the Canyon Creek watershed. In this case, the statistical population (cheatgrass within the pasture) is a subset of a specified biological population (cheatgrass in the watershed). At other times, the statistical population may encompass the entire biological population or even multiple biological populations. Such would be the case if we wanted to sample and make statistical inferences about cheatgrass across the entire state of Montana.
A sample is a subset of sampling units selected from a population. In sampling, we take measurements of a variable of interest and use the results to make inferences about the population. In the cheatgrass example above, the 100 quadrats measured for seed production was the sample, and the variable measured was density, or the number of seeds per 1 m2 quadrat.
A sampling unit is a discrete entity or member of the population from which we take measurements. Essentially, the population consists of all the possible sampling units that could be measured, and the sample represents the sub-sample of sampling units that were selected for measurement. In the example above, each 1 m2 quadrat was a sampling unit, and the measured variable was the number of seeds counted in each quadrat. The number of sampling units is considered the sample size (n).
Examples of common sampling units used for vegetation measurements are:
- quadrat – plot of a known area and dimensions, with a distinct boundary
- transect – a line of known distance
- point -a quadrat of smallest possible dimensions, theoretically dimensionless, usually measured at the intersection of cross-hairs or the tip of a highly-sharpened steel pin
- individual – either the whole plant or specific plant parts such as stems or flowers
The values that we obtained when we measured each sampling unit are the values that we use in our data analysis. This is very straight-forward in the case when a single point, quadrat or plot is the sampling unit, because there is one measured value per sampling unit. However, sometimes we collect data using multiple points positioned along a transect line, or we place multiple quadrats along a transect line and take a measurement in each quadrat.
For example, if we estimate cover by measuring 25 points along each of 10 transects, is the number of sampling units 10 transects or 250 points? In other words, do we analyze the data based on 250 individual point measurements, or do we calculate an average value of the 25 points per transect and base our analysis on the values of the 10 transects? In this case, the points were used to clearly describe conditions along each transect and the transect is the sampling unit. Our choice of sampling unit depends on our objectives for sampling, and the sampling unit needs to be clearly defined in the monitoring or sampling protocol.
Sampling is the process of selecting a group of sampling units to gather information about variables of interest in such a way that you can make inferences about the total population. A variable is a characteristic whose value can vary from one sampling unit to another. The variables that are most commonly measured when sampling rangeland vegetation are the attributes Density, Frequency, Cover, Biomass, Plant Height, and Utilization. Variables can be measured or estimated and can be quantitative as in the case of plant height measured in inches or qualitative as in the case of “heavy”, “moderate” or “light” used to describe levels of grass utilization.
Decisions about which variables to measure need to be carefully considered in the context of management objectives. Collecting data is time consuming and expensive, so we need to ensure that the variables measured will provide necessary information, so that we can avoid measuring useless variables. For example, we could measure the leaf length on cheatgrass plants, but this information would be of little use when developing a management plan for the population.
Describing Populations and Samples
Population parameters are descriptive measures that characterize a population. Population parameters are assumed to be fixed, but their exact values are unknown because we cannot measure each member of the population. For example, we know that a cheatgrass population produces a certain number of seeds each year, but we do not know what that quantity is. If the population is growing, then it follows that cheatgrass seed production will increase in the next year, because the population changed. Population parameters change only if the population changes and are denoted with Greek letters, such as μ and ơ.
Sample statistics are descriptive measures that characterize a sample. We sample to obtain an unbiased estimate of population parameters: remember, population parameters are fixed but unknown or unknowable! We use sample statistics to estimate the population parameters. We expect sample statistics to change if the population changes. However, sample statistics may also change from sample to sample due to the nature of selecting a subset of sampling units from the population. Sample statistics are denoted with Roman letters such as or s.
Common Parameters and Statistics
The most common parameters and statistics that we use to describe populations and samples are estimates of central tendency and estimates of variability.
The most common measures of central tendency are:
The mean is the most commonly reported measure of central tendency and is derived by summing the values obtained from each sampling unit and dividing by the total number of sampling units.
For example, if the number of seeds produced by 5 individual cheatgrass plants were 3, 4, 4, 7, and 13, the mean would be calculated as:
Note: Population means are denoted by the Greek letter µ, and sample means are denoted by .
Although means are commonly reported and are relatively easy to comprehend, the mean is sensitive to extreme values, or outliers. As such, the mean may be somewhat misleading when the dataset includes outliers. In the example above, if the last measured value was 39 seeds instead of 13, the mean would be 11.4 seeds per plant. In this case, the mean value clearly is not representative of the sample.
The median is the middle measurement in a dataset when all measurements are ordered from smallest to largest. Essentially, this means that an equal number of values are distributed above and below the median value. From the original example of seed counts, the median value of 3, 4, 4, 7, and 13 equals 4. In general, the median is a more robust measure of central tendency than the mean in cases where the dataset includes extreme outliers.
The mode is the value that occurs most often in the sampling. In the above example, the mode equals 4. Another way to consider the mode is that it reveals areas of concentrated values in the dataset. For example, if we collected data on 20 sampling units with the following values:
1, 2, 3, 3, 3, 3, 4, 4, 5, 6, 8, 8, 8, 8, 8, 8, 8, 8, 9, 10
we could report the mode to reveal the concentration of values at 8. In addition, this dataset appears to be bimodal (Figure 3), with measurement concentrations at 3 and 8.
Figure 3. The bimodal distribution is shown with peaks at 3 and 8.
We are also interested in describing the variability of the data.
Variability refers to the amount of spread that we observe in the measured values: are they clustered close together, or are they widely scattered around the mean? Some variables exhibit relatively little variation in nature, whereas other variables may assume a wide range of values. We use the following descriptive measures to describe the variability of populations and samples:
- Variance (σ 2 for populations and s2 for samples) is a measure of variability or dispersion that takes both the magnitude of the deviation, or distance of individual values from the mean, and the frequency of these deviations into consideration. Variance is not often reported as a sample statistic because the units of variance are squared, so they do not match the units of the mean. For example, if the mean plant height for a pasture is 4 ft, the variance might be 0.25 ft2.
- Standard deviation (σ for populations and s, or SD, for samples) is a measure of how much the individual measurements in the sample diverge from the mean. The standard deviation is commonly used to report variability of samples because the units for standard deviation are the same as those of the mean. For example, in the plant height example from above, the standard deviation would equal 0.5 ft. Mathematically, the standard deviation is simply the square root of the variance.
- Standard error (SE, also reported as sx) is another measure of deviation from the mean, but standard error takes into account the number of sampling units (sample size, n) that were measured. The larger the data set, the smaller the standard error will be. Mathematically, the standard error is calculated as the standard deviation divided by the square root of the sample size: s/ √ n
- A coefficient of variation (CV) is another way to describe the variability of a sample. It is simply the calculation of the standard deviation divided by the mean (s / ); for ease of interpretation, the CV is often converted to percent by multiplying by 100. The advantage of coefficients of variation is that they do not have units, so they are not sensitive to the magnitude of measurements. We often will use CV’s to compare variability of two or more samples for which the magnitudes of the means are very different.
Let’s consider a situation where the mean weight of two species (coyotes and mule deer) are quite different in magnitude, and we want to be able to describe how variable the measured weights of individuals in the samples were. Based on sampling efforts, we determine the mean and standard deviation for weight of adult coyotes and mule deer (Table 1). If used the standard deviation to determine which mammal exhibits greater variability in adult weights, it would be easy to assume that the weight of adult mule deer is more variable than that of coyotes because 40 lbs is obviously larger than 5 lbs. However, given that the mean weight of mule deer is around 10 times greater than coyotes, we would expect that the standard deviation of large measurements is going to be greater than that for smaller measurements. By calculating the coefficient of variation for the two populations, we account for the difference in the magnitude of the mean values because the measure of variability (s) is relative to the mean ( ). Smaller CV’s indicate less variability, and by comparing the CV’s we see that mule deer weights are actually less variable than coyote weights.
Table 1. Comparison of the weight of individual adults of two mammal species, coyote (Canis latrans) and mule deer (Odocoileus hemionus). The values given are mean ( ), standard deviation (s), and coefficient of variation (CV).
In another example, we compare measurements of variability using data collected in 3 plots at 3 different sites. In this case, we counted the number of cheatgrass seeds produced in each plot (Table 2). Note that the mean number of seeds for the 3 sites is the same ( = 14 seeds/plot), but by comparing the different measures of variability, we can see that seed production is most variable at the Ridge Top Pasture.
Table 2. Cheatgrass seeds produced in 3 plots at 3 different sites on the Round Top Ranch.
Accuracy and Precision
Two important concepts in sampling are the accuracy and precision of the data.
- Accuracy is the closeness of a measured value to its true value. Remember that we cannot know the true mean value for a population, but through sampling we estimate the population mean with the sampling mean.
- Precision is the closeness of repeated measurements to each other.
A convenient way to visualize the concepts of accuracy and precision is to envision a target and target practice (Figure 4). The bull’s-eye of the target represents the true population mean, or the true value, and each “x” represents a measurement from a sample, or in the target analogy, a shot at the target. In the shot pattern from the target on the left, the shots are fairly evenly distributed around the bull’s-eye, and their average value would be close to the center: the shooting is accurate, but not very precise. In the center target, the shots are closely grouped together, but they are not very close to the bull’s-eye, and if you averaged the values for those shots, it would be quite different from the true population mean. In this case, the shooting is precise, but not accurate. In the target on the right, the shots are closely grouped and their average location is centered near the bull’s-eye, or true population mean. In this case, the shooting is both precise and accurate.
Figure 4. A graphical illustration demonstrating the concepts of accuracy and precision.
The target analogy is convenient for learning about the concepts of accuracy and precision, but we don’t actually collect samples by shooting at them! So let’s apply these concepts to the data collected on the Round Top Ranch (Table 2). Let’s start by ranking the three datasets in terms of their precision. When repeated measurements are similar in value, this is indicated by low estimates of variability. The measurements from the East Hill Pasture were the most precise because this dataset has the smallest values for all of the estimates of variability, and the measurements from the Ridge Top Pasture were the least precise, or most variable. We can use sample statistics such as standard deviation, standard error, and coefficient of variation to estimate the precision of our sampling.
Note: Sampling Precision is different than Measurement Precision!
It is important to consider the difference between sampling precision (discussed above) and measurement precision. When we measure something, we make decisions about how precise the measurement will be – for example, do we measure length to the nearest centimeter (cm), the nearest 0.5 cm, or the nearest millimeter (mm)? Decisions about measurement precision depend on the object being measured and our purpose for measuring them. For example when measuring the height of herbaceous plants or small-statured shrubs, we often measure to the nearest cm. It would take extra time to measure to the nearest 0.5 cm, and it might be difficult to consistently measure plant heights at that scale. It would be very impractical to try to measure trees to the nearest cm. Certain plant characteristics, such as leaf length and width, may need to be measured more precisely in order to accurately determine species identification.
When sampling, we often strive for precision in our estimates. However, you should be aware that increasing sampling precision is generally achieved by changing some aspect of the sampling design (e.g., increasing sample size or changing quadrat shape – see Module 3 on Sampling Design), and sampling precision is NOT affected by increasing measurement precision.
Sources of Error in Sampling
Unfortunately, we cannot use statistics to determine whether these measurements were accurate. Remember that the actual population parameters are fixed, but unknown. We cannot use statistics to tell how accurately we estimated the true population parameters. In the cheatgrass example above (Table 2), we cannot be sure that true population mean for the number of cheatgrass seeds produced per plot was actually equal to 14. In part this is due to the nature of sampling. Because our sample is a randomly selected sub-set of the population, we could get an estimate for the mean that is quite different from the true population simply because the sample consisted of a group of “atypical” individuals or sampling units. Therefore, when we sample, the estimate of variability from the sample reflects both the inherent variation that is characteristic of the population, and variation due to the random selection of sampling units in the sampling process. The difference between the sample statistics and the true population parameters is called error.
There are two types of error associated with sampling:
- Sampling error
- Non-sampling error
Sampling error is due to chance. Since the sampling process involves chance selection of a sub-set of individuals or sampling units, some samples are “more” representative of the population, and some samples are “less” representative of the population. Therefore, the difference between the sample statistic and population parameter is not due to technique or procedure, but is different just by chance! We can estimate the amount of sampling error statistically because it is a measure of variation between all the samples. Sampling error affects the precision of our samples. In general, sampling error can be reduced by increasing the sample size, n, and by ensuring that we use an efficient sampling design to increase the precision of our estimates.
Note: sampling error is sometimes referred to as “random error”.
Non-sampling error is measurement error that results from using faulty equipment, incorrect or inconsistent application of measurement techniques, or a variety of mistakes that can be attributed to “human error”. Human errors can include misidentification of species, poor data recording, data entry errors, subjective selection of sampling units, subjective decisions while recording data, etc. Non-sampling errors affect the accuracy of our estimates because the measurements are faulty or suspect. Non-sampling error may result in measurements or estimations that are unpredictable in the direction of the error; sometimes the values are high and sometimes they are low. In many cases, non-sampling error results in bias, a consistent over- or under-estimation of a value. It is for this reason that non-sampling error is also called systematic error.
Unfortunately, since non-sampling error affects accuracy, it cannot be detected statistically. Therefore we need to take actions to eliminate the potential for non-sampling errors. Using well calibrated equipment, ensuring that observers are properly trained to employ the sampling technique and follow the sampling protocol are a few ways to reduce sampling error. In addition, ensuring that the selection of sampling units is done randomly and not subjectively also reduces the potential for this type of error. In general, we assume that in the absence of bias and non-sampling error, precision will lead to accuracy.
The following questions are designed to test your knowledge and understanding of vegetation measurements for monitoring. These questions are for your own benefit: scores are not recorded.
The following questions are designed to test your knowledge and understanding of vegetation measurements for monitoring. These questions are for your own benefit: scores are not recorded.