Monitoring: Pitfalls and Problems
sage advice -- Once monitoring has started, be willing to adapt the
objectives and methods with reality. No matter what you
plan for it will invariably be different once you arrive in the
This unforeseen condition could be as simple as planning for 20 days of work and because of weather
or transportation problems you only get 12 days. Another example could be that
once you start collecting data you find that one of the measurements you want to make
plot takes twice as long as you thought it would. Therefore, depending on its
your dataset, it may not be worth the extra collection time, especially since
without the measurement you would be able to sample more plots. Another common
an unforeseen occurrence is that during preliminary analysis of your data
(before data collection is completed), you may find that the measurements you
have collected do not provide the necessary information to answer your
monitoring questions. In such cases, you will need to re-evaluate and revise your
measurements and methodology.
In general, monitoring is not perfect and many factors can ruin
your hard sought efforts. A few of the more common reasons include:
such as arranging fieldwork during late fall, when plants are dormant an
difficult to identify therefore obscuring what you are
trying to monitor.
Poor design by not taking measurements in all representative areas or not including an unaffected area
(i.e., a control) and thus not being able to assess whether
certain factors are responsible for what you are observing.
Inconsistent observations arise by
having more than one observer taking measurements, when a standard has not
been agreed to or is not being followed. For example, for ocular
estimation of shrub cover in a quadrat a standard could involve each observer
taking their measurements and comparing their independent results with the known
cover measure as measured in a more objective fashion.
Problems can occur when data is entered
incorrectly into data sheets or is typed incorrectly into the
Incorrect inferences about what
the data means can occur when it is not analyzed correctly because the person
does not have the necessary skills.
Finally, nature sometimes does that which we donít expect. We
could have the best plan and design in the world, and a wildfire could pass
through the week before we planned to sample.
** An excellent overview of common problems encountered in
monitoring is presented in
Appendix 1 of
Measuring & Monitoring Plant Communities.
Implement Monitoring as a Pilot Study
Photo - K. Launchbaugh
It is always a good idea to envision difficulties in data
collection or analysis. A successful monitoring protocol ensures
To avoid these pitfalls, it is wise to plan a
pilot study that includes a "real world"
trial of the monitoring protocol to expose problems while the protocol can
be revised to address these problems.
On pages 20-21 of
Measuring and Monitoring Plant Communities, Elzinga, Salzer,
and Willoughby suggest 4 steps to testing out a field protocol in a Pilot Study:
1. Collect field data and evaluate field methods.
Is the sampling unit you selected the right size or shape?
Is the transect the right length?
Is it difficult to determine the species of interest?
Can you get to the sites you want to examine?
2. Analyze pilot study data.
Are objectives of power and precision met?
How many sampling units or sites will you need to
examine differences between sites or repeated measures?
Is the level of difference or change you expected to see
3. Reassess time and resources.
Will you be able to conduct the study in the time you have
Do you have enough people or other resources to meet the
Can technologies or other resources be secured to meet the
goals of the project?
4. Review - Solicit review of the results of your pilot study.
Do the parties involved still agree with the way the
monitoring is proposed?
Will those involved be able to abide by the results?
Are there better ways to accomplish your monitoring