Measurements for Terrestrial Vegetation

Charles D. Bonham

• Publisher: Wiley-Blackwell
• Year: 2013
• Print ISBN: 9780470972588
• eBook ISBN: 9781118534526
• Edition: 2
• Topic: Biological Sciences
• Subtopic: Botany
• Index: Biological Sciences

Measurements for Terrestrial Vegetation, 2nd Edition presents up-to-date methods for analyzing species frequency, plant cover, density and biomass data. Each method is presented in detail with a full discussion of its strengths and weaknesses from field applications through statistical characteristics of bias and use of the correct probability distribution to describe and analyze data. This practical book also covers the use of satellite imagery to obtain measurement data on cover, density and biomass. Field data collection includes current applications of statistical sampling and analysis designs that should be used to obtain and analyze these data.

This new and thoroughly updated edition of a classic text will be essential reading for everyone involved in measuring and assessing vegetation and plant biomass, including researchers and practitioners in vegetation science, plant ecology, forestry, global change scientists and conservation scientists.

• Provides a comprehensive catalogue of sampling, surveying and measuring techniques in vegetation science
• Updated to include new technologies and developments in the field
• New coverage of prediction models for large areas, including satellite mapping and remote sensing techniques
• Includes up-to-date applications of statistical sampling and analysis designs used to obtain and analyse data Reviews the strengths and weaknesses of each technique, allowing an informed choice of alternative approaches
• Clear diagrams to explain best-practice in methodology 

 The companion website for this book can be found at www.wiley.com/go/bonham/measurements

1

Introduction

Variation in the morphology of plants resulted in the grouping of plants into broad categories on the basis of life-forms. Major life-forms are represented by terms such as “tree,” “shrub,” grass,” and “forb.” These life-forms often provide a basis to describe major terrestrial plant communities (Odum and Barrett 2005). Life-forms of plants or plant species can be described by a number of characteristics such as biomass, frequency, cover, and density. Some life-forms or plant species are, perhaps, better described by certain characteristics of measure than are others. A combination of objectives of a study and species involved will determine what characteristics are to be measured for an effective description of vegetation. To see this more clearly, consider the measurements of cover, which are estimates of relative areas that a plant controls to receive sunlight. In comparison, biomass directly indicates how much vegetation is present, and particular species indicate the amount of forage available to herbivores in the area. Density describes how many individual stems or plants occur per unit ground area, while frequency describes the dispersal or distribution of a species over the landscape. Each of these species characteristics has a distinct use in vegetation characterization and description. Often, several measures are used in combination for an in-depth description of vegetation (Bonham 1983).

Frequency, cover, density, and biomass are expressed in quantities per unit of area and these are units associated with equipment. Points, plots, and tape measures are often used to obtain these quantities of measure. On the other hand, a measurement technique includes the process used to obtain the measure, specifically location of the observation, clipping, observing a hit by a point, and summarization of the data. Thus, methods used in measurement are ways of doing things to obtain the measure. Often the terms “methods” and “techniques” are used synonymously in the vegetation literature to include pieces of equipment. Thus, one reads phrases such as “the method used was a 0.5 m2 quadrat.” No distinction is made here between techniques and methods because both describe or imply a process used in obtaining a measure of given vegetation characteristics.

Measurements of vegetation characteristics have been made for more than a century, and methods developed to obtain these measurements are numerous. Few methods are comparable, even for measuring the same characteristic of vegetation, such as cover. This is true because objectives to obtain the measure differ. Yet, comparisons of vegetation characteristics over time and space are often necessary. If comparisons are to be made, then comparable sampling methods must be used to obtain the measure. For instance, if 100 plots of 0.1 m2 area are measured for cover, then 10 m2 of area have been measured. To compare these results to the use of a 50 m length fine point transect, the number of transects measured has to be associated with an area of 10 m2. The same area measured would then be comparable (Bonham and Clark 2005). More on this is presented in the individual chapters on measures.

There are basically two objectives to be considered in the selection of measurements and methods used to obtain measurements. For example, one objective may be to describe the characteristics of the native vegetation at a given time period, and then again at a later date, to assess changes. The second objective is to describe vegetation characteristics so that the measurement can be used as a standard or a baseline. Sampling intensity has to be the same for both time periods; that is the sampling process has to sample the same area although the equipment area has changed. Otherwise, all descriptions of the vegetation would be relative to different areas measured and not subject to valid comparisons. The two objectives are compatible and should not be considered as competing for resources, especially monetary, in order to be attained.

The purpose in a study of methods to obtain vegetation measurements is twofold in nature: (1) to make proper choices of appropriate methods used to estimate a characteristic of vegetation, that is, frequency, cover, density, and biomass, and (2) to properly select and utilize a sample design that will provide unbiased estimates of the characteristic measured. One will gain efficiency in carrying out both of these objectives simultaneously only through proper use of methods in the field, followed by effective data analysis procedures.

No known set of techniques is free of disadvantages for any measurement or set of measurements. Rather, selection of a technique should be made with perhaps an understanding that certain modifications may be needed to optimize its use. Modifications of equipment to reduce effects of biased estimates or to limit disadvantages are frequently described in the literature. In general, however, all methods used to measure vegetation are related to land areas. That is, frequency, cover, density, and biomass amounts are correlated not only for certain species but also by the fact that each is estimated with reference to a land area and, in some cases, volume occupied.

1.1 Historical brief

Measurements of vegetation date to antiquity. In the third century B.C., Theophrastus observed that certain relationships existed between plants and their environment. Thus, he was an early contributor to plant ecology in the quantitative sense. Still, centuries passed with qualitative assessments dominating vegetation descriptions, and plant species in particular. Geographic descriptions of vegetation occupied the interest of many naturalists in Europe, where only listings of species dominated their efforts. Indeed, lists of plants provided the beginning of vegetation characterization as a true quantitative approach. Emphasis was placed on such lists throughout the eighteenth and nineteenth centuries on the European continent. It was in Europe that Raunkiaer (1912) used the first known plot (0.1 m2) to obtain a quantitative measure of plant species frequency, although the work was still to describe geography of plants (Raunkiaer 1934).

The work of F. E. Clements in the United States increased precision in vegetation measurements. Clements, in 1905, coined the word “quadrat” for use in vegetation data collection. While the term technically defines a four-sided plot, its usage over time has been adulterated to include any plot shape, even a circle.

Gleason (1920) further advanced the concept of quantitative measurements. He described applications of the quadrat method in description of vegetation characteristics. In particular, Gleason (1922, 1925) developed a thorough explanation of species and area (or space) relationships. His concepts led to the idea that sample adequacy could be determined from the number of species encountered as a plot area increases. The well-known species–area curve is still used at present to determine relative sample adequacy. There are limitations to this method of determination of proper plot size; namely, each species would have a different size of plot.

Measurements and analysis of vegetation characteristics during the 1920s led to statistical applications to plant ecology. Kylin (1926) introduced the concept of “mean area” and defined it to be the inverse of density, that is, the number of individuals per unit of area. He was also among the first to present explanations for the relationship between density and frequency, which is of a logarithmic nature, not linear. Furthermore, species absence, not presence, determines density. Kylin's work followed that of Svedberg (1922) in Europe, and their approach to measures of vegetation was of a statistical nature that encouraged many others to examine vegetation characteristics from a quantitative point of view.

Cain (1934) and Hanson (1934) compared quadrat sizes, while Ashby (1935) gave an early introduction for the use of quantitative methods in vegetation descriptions and Ashby (1936) published on the topic of statistical ecology. Bartlett (1936) gave examples of statistical methods for use in agriculture and applied biology, but Blackman (1935) had previously introduced statistical methodology to describe the distribution of grassland species. These early studies in vegetation measurements emphasized the dispersion of individuals in plant communities. Thus, patterns of dispersal were very much in the forefront of most quantitative assessments of vegetation characteristics.

How plants are arranged spatially implies distance measures and, subsequently, pattern. This emphasis on pattern analysis began in earnest in the 1920s and reached a peak in the late 1940s and early 1950s (Greig-Smith 1983). Interest in species patterns was briefly rekindled in the 1960s as distance measures were again used for density estimates (Green 1966, Beasom and Houcke 1975). Many distance measures in plant patterns use a single, linear dimension, and distance measures have been referred to as “plotless” methods. The return to plotless methods in the United States was essentially driven by time–cost considerations needed for large-scale inventories of forests and rangeland resources. Recently, these methods have been referred to as “variable area” methods and this description for these methods is used here because, indeed, “area” is involved in all of them.

Since distance measurements included rigid assumptions about the distribution of individual plants, an understanding of patterns found in natural plant populations was necessary. Therefore, a great deal of effort was expended to develop acceptable modifications to variable area methods for use in the estimation of frequency, cover, density, and biomass. Thus, measurements of vegetation actually began to find a place in the work of professional plant ecologists from the 1920s onward. Today, many professionals in vegetation ecology have mastered the seemingly more difficult merger of this discipline with that of statistics.

1.2 Units of measure

The science of measurement, which is called metrology, has been a vital part of science, especially the physical sciences, for centuries. The science of metrology was given much attention during the nineteenth century because a better system of units and standards for measurements was needed to assist the field of physics (Pipkin and Ritter 1983).

The metrology of vegetation itself, however, is of even more recent origin. In the decade 1970–1979 there was major progress on the determination of fundamental constants needed to relate measured vegetation characteristics displayed by density, cover, and so forth to biological and ecological theory.

In the decade of the 1960s, the International Biological Program (IBP) introduced the integrated systems approach to study the interrelationships of organisms and their environment that operated in an ecosystem. Mathematical and statistical models formulated up to the present time have provided fundamental insight as to how such systems of organisms functioned individually and collectively. Thus, for example, constants for energy and nutrient transfer through a system were provided, which resulted in a clearer understanding of how measured characteristics described plant–environment relationships. For example, the amount of biomass accumulation by an individual species can be used to assess that species' role in nutrient utilization and recycling within the vegetation system as a whole.

Most vegetation measurements are now made in metric notation, which is used throughout this book. Table 1.1 provides a definition of the relationship that exists among linear, area, volume, and weight measures from the metric system. The volume measure is given in this form because some measures of weight of vegetation biomass should be reported as per unit volume occupied. Essential or fundamental constants used in measurements...

Table of contents

• Cover
• Title Page
• Copyright
• Dedication
• Preface
• About the companion website
• Chapter 1: Introduction
• Chapter 2: Sampling units for measurements
• Chapter 3: Statistical concepts for field sampling
• Chapter 4: Spatial sampling designs for measurements
• Chapter 5: Frequency
• Chapter 6: Cover
• Chapter 7: Density
• Chapter 8: Biomass
• Chapter 9: Monitoring and evaluation
• Appendix: Unit conversion tables
• Index