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9. Recognizing and overcoming obstacles to successful field studies in vegetation science |
In
the great arc of field studies in vegetation science, the goal is to pose important
questions, design appropriate field studies in which to make observations of
vegetation, and interpret the data to answer the questions correctly.
This noble task is fraught with obstacles. The goal of this chapter is to inform you of common obstacles so that you may better overcome them in your own studies. In some ways, this is a summary of the material you have covered in many of the previous chapters. In case you want to review earlier material, there are links that direct you to relevant parts of the course. And many of the topics covered here have their counterpart in the chapter "How to conduct a vegetation study using sampling."
A
poorly-framed ecological question can
doom a vegetation study from the start. Meaningful questions come from the scientific
context. The more you understand the literature in vegetation science and the
more experience you have in the field, the better your questions will be. Your
scientific training as students (course work, independent study, discussion,
and field experience) gives you the tools to start framing incisive and important
questions.
There are some steps you can take to improve the quality of the questions you pose. The first step is to be explicit in stating your question! It is amazing how often field studies in vegetation science proceed with only an implicit, intuited scientific question.
An important benefit of making your question explicit is that it pins down the question. In my experience, most student projects develop from a group of loosely related questions. A problem that arises when these questions remain unstated is that some methods relate to one implicit question, while others relate to other implicit questions. It is difficult in such an amalgam to make sure that all the necessary data are being collected, and that no field time is wasted on collecting extraneous data. Once the questions are explicit, it becomes clear where the gaps are and what is superfluous.
Stating the question explicitly also makes it easier to compare your question to what is known in the literature. This essential step helps you refine your question based on what is already known. And it is always good to discover that the answer to your question is already known before you start your field work, and not when you submit your work for publication!
Having an explicit question makes it easier to get sound advice from colleagues and advisors. Many unproductive hours have been spent between student and advisor vainly trying to refine methods for the student's project, when they had differing ideas of the underlying scientific questions. Save yourself this frustration and make your questions explicit.
The
complexity of nature is a reason many of us chose to become vegetation scientists.
But this complexity is also a source of many difficulties in vegetation science
field studies. Spatial heterogeneity of vegetation dictates that you must make
replicate observations that encompass
the range of vegetation in the field. Temporal variability
adds another source of variability. The louder the noise of variability, the
more difficult in can be to undercover ecological signals. An essential contribution
of statistical sampling designs and methods of analysis
is to provide ways to increase the signal-to-noise ratio, to reduce the noise
of nature so the patterns of ecological interest more clearly emerge. Weak statistical
designs are inefficient and can produce biased answers.
Multi-species interactions also pose a challenge to the vegetation scientist. The study of competition is a prime example. Many studies have collected results that are consistent with one plant species suppressing another through competition. In an uncomfortably large number of these studies, the patterns actually arise not from competition but from herbivory or other trophic interactions. The problem here is that the ecological question ("does plant competition occur?") does not account for alternative explanations ("are the patterns more the result of competition or of herbivory?").
Often the ecological question concerns features of vegetation that are hard to measure. Unfortunately, no magic device has been invented that can read biomass by species in the field. As discussed in the Ecological Background section of the course, vegetation scientists must rely on more easily measured surrogates for biomass and productivity. Errors can arise when employing inappropriate surrogates. For example, it would be a mistake to use number of flowers to measure year-to-year variation in plant biomass, because many other factors (weather, pollinators, herbivores) help determine flowering intensity.
Earlier
in the course, I stated that "once you have carefully gone through the preparation
steps, the actual field work should be straightforward." True enough, but there
are still several things that can go wrong. Thankfully, they are all relatively
easy to prevent.
One problem is measurement error. An example of measurement error is using a tape that has stretched and gives incorrect values. Although this example is unlikely to occur (all modern field tapes are manufactured to resist stretching), instrument calibration should be a regular component of field work.
In vegetation science, the most likely type of measurement error occurs in estimating plant cover. The steps described in the chapter How to Measure can greatly reduce this source of measurement error.
Another problem during field work is the faulty recording of data. These recording errors can arise if instruments are misread (like reading the wrong side of a diameter tape!), if the values reported by one member of the crew are misheard by the data recorder, or if the data recorder hears one thing but writes down another. When the data are generated by an electronic device, recording errors can be reduced because data get transferred directly from device to computer. Even when data are human-generated, like in estimating plant cover, there are still ways to reduce recording errors. I recommend that when the person making the measurement announces the value ("Iris tenax, 13% cover"), the person recording the data repeat the value ("Iris tenax, 30% cover"), and the recorder confirm that this is right (either "check" or "that should be 13%, not 30%"). As dull as this might sound, it is well worth it when you eliminate errors in your data.
Another problem is when crew members just don't follow the prescribed procedures. (This is more widespread than you might think. I can recall no example, among the dozens of field studies I have been involved with, when everyone followed the procedures properly.) You can reduce this source of error by having the project leader produce clear and complete procedures in the first place, by devoting considerable time to training crew members, and by circulating among crews to head-off errors.
The
interpretation stage really has three parts: making the field data ready for
analysis, conducting the analysis, and using the results of the analysis to
answer the original questions.
If you have used data forms, the data must be transcribed into computer files for analysis. Transcription errors are common, but are easily fixed by proofreading. Proofreading hints are part of the chapter on Data Management.
Data analysis in vegetation science is a huge topic, much too big to cover here. There are several data analysis courses at Oregon State University useful for vegetation scientists. Click here if you want to see a list of some of these courses.
Data analysis programs like statistical programs and specialized programs like PC-ORD and the Cornell Ecology Programs are great tools for vegetation scientists. But these and other programs are so powerful that their improper use can easily introduce interpretation errors. Perhaps the most common error is using a type of analysis that seems to fit your data, but is really inappropriate. Results from inappropriate analyses will only mislead you. The solution is to thoroughly understand the purpose of the analysis and to recognize its underlying assumptions.
You might be discouraged by this compendium of hazards. You might be asking, With all these obstacles, how could any field study be successful in answering meaningful questions in vegetation science? But with recognition comes power. The more you know about problems that can arise, the better you can prepare yourself to overcome them.
Match obstacle to solution by completing the quiz "Obstacles." For BOT 540 students, apply your general understanding of obstacles in vegetation science to the specifics of your own field study by completing the exercise Overcoming obstacles.
© 2007 Mark V. Wilson and Oregon State University