1.3 Setting Priorities, Avoiding Pitfalls and Problems

Video Presentation

Learning Guide

A Question of Priority

The majority of monitoring programs are going to be limited by resources and funding, and monitoring program objectives will probably not be able to address all species of interest.  This means priorities will need to be set defining which species or management issue should be given the most attention, and scale and intensity of sampling determined.  We will first discuss how to prioritize monitoring and management objectives, and then address the issue of how to decide upon scale and intensity of sampling.

Consider the scenario that you have been hired by the Bureau of Land Management to manage 20,000 acres that include forests and rangelands intersected by riparian areas. Within these areas you have an endangered species of owl, and 3 fish species of concern. You may also have considerable fine fuel build-up or areas where soil erosion is making slopes unstable above roads and streams. In summary, you have many management goals and concerns.  If you are asked to produce a monitoring protocol you first need to set your management priorities.

Thankfully, several organizations have produced guidelines on how to go about defining the priority of one species or management objective over another.

Established Priority Systems

An example of a ranking system is one developed to rank the abundance of species created by the Natural Heritage ProgramLinks to an external site.. The main levels of this system are:

• Critically Imperiled
• Imperiled
• Very Rare and Local
• Apparently Secure
• Demonstrably Secure
• Status Uncertain

In this system, the first 3 priority levels are defined by the number of individuals. For example, critically imperiled refers to < 5 occurrences or <1000 individuals; whereas imperiled refers to 6-20 occurrences and <3000 individuals.

The level “Status Uncertain” refers to instances where not enough information about that species exists to determine whether the species is at risk.

Inventory methods could be used to assess the current status of a species.  Monitoring protocols could be subsequently applied to evaluate whether the populations of those species are increasing or declining.

An example of how matrices could be developed using several biological and management criteria to rank importance of monitoring among species and situations is presented on page 32 and 33Links to an external site. in Measuring and Monitoring Plant Communities by Elzinga et al. (1998).  In this example, the following biological and management criteria have been included:

BIOLOGICAL CRITERIA:
• rarity
• taxonomic status
• sensitivity
• known decline
• extent of threats
• immediacy of threats

MANAGEMENT CRITERIA:
• existing conflict
• monitoring difficulty
• availability of management actions
• recovery potential
• public interest
• potential for crisis

Establish Your Own Priorities

There are many cases where a defined priority system does not exist for a specific monitoring task and you will need to set your own priorities. When selecting monitoring sites and activities it is imperative that they are POSSIBLE to accomplish and give USEFUL information.

Here are a few points that should be considered when initiating monitoring activities:

• Are important legal or policy considerations driving monitoring, such as sensitive or endangered species that occur in the area?
• Are there management activities pending for which it will be necessary to evaluate impact or effectiveness?
• Are there growing changes in land use activities, such as recreation, that might cause changes to the plant community?
• Are management decisions being made, such as a grazing permit renewal, for which monitoring information would be valuable to monitor outcomes?
• Is there growing public interest in a specific area, activity, or species for which monitoring data might be valuable to inform or resolve potential conflicts?
• Can areas be identified for which management activities can affect change in the plant community?
• What social, political, regulatory, or financial obstacles exist in relation to monitoring activities?

Scale and Intensity

There is rarely, if ever, enough time or money to conduct exhaustive monitoring activities. The extent to which we conduct monitoring will be limited in both space and time. As such, it is important before starting any monitoring protocol to know what our scale of assessment and level of intensity are going to be. Clearly this will depend on many factors beyond our available resources. For example, monitoring the annual foraging range of sheep may require measurements over a larger spatial extent than if you were interested in monitoring the feeding habitat of a deer mouse.  Level of intensity reflects the frequency of sampling and the amount of detail in the information gathered, as determined by the monitoring protocol.  For example, photo monitoring represents a lower level of intensity than a protocol calling for canopy cover, ground cover, and canopy gap measurements.

Scale* can be simply defined as the spatial extent of your monitoring program. Clearly, the scale at which our measurements are taken should correspond to our management objectives and include the necessary spatial extent. The scale at which we are making measurements should always be explicitly defined.  For example, is sampling to be conducted at the local or landscape scale?  Local scale generally refers to measurements taken at the scale of individual plants to patches or stands of small populations, while landscape scale generally encompasses measurements that are taken across larger areas, and may extend to include many large populations or even the geographic range of the species.  Similarly, if the monitoring objective is management oriented, local scale measurements may be taken within a distinct stretch of a riparian corridor (e.g., a 0.5 km stretch used for livestock crossings), compared to addressing questions about management impacts at a landscape scale (e.g., monitoring effects of salt cedar control over a 100 km stretch of the river).

In a similar way, the level of intensity in our measurements will vary depending on our specific management objectives.  Intensity refers to the frequency of sampling and the level of detail at which sampling is conducted.  Frequency of sampling will largely be determined by whether we are collecting qualitative or quantitative data (refer to Lesson 2 of this module).  However, the greatest determinants for sampling frequency are time and financial resources.  The same is true for the level of sampling detail.  This is because the greater the detail of sampling, the longer the time to collect samples from each location.  For example, if we take a photo-point and conduct ocular estimates of grass cover in two 1m x 1m frames, the total time of equipment set-up and sample collection might be 10 minutes.  If we increase our measurement detail by taking four photo-points, and five 50m line-point intercept transects, we may increase the time it takes us for set-up and sample collection to two hours.

* The word scale has many meanings depending on your application. Examples include: (1) cartographic scale: the ratio of true distance in the field to distance on a map; (2) Landscape ecology: the bracket of space (and/or time) that you would expect to observe an object or process; in addition to of course geological or anatomical features!

Pitfalls and Problems

Some 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 field!

This unforeseen condition could be as simple as planning for 20 days of work and because of weather or transportation problems you only actually work 12 days. Another example could be once you start collecting data you find that one of the measurements you want to make at each plot takes twice as long as you thought it would. Therefore, if the measurement is not of high value to your dataset, it may not be worth the extra collection time.  This is especially true if, without the additional measurement, you would be able to sample more plots. Another common and 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:

• Poor planning such as arranging fieldwork during late fall, when plants are dormant and 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 protocol has not been agreed upon or is not being consistently applied. Clearly, observers need to be well-trained on how to conduct the measurement methods, and calibration is needed to ensure that any measurements based on ocular estimation are consistent (see Observer Training and Calibration below).
• Problems can occur when data are entered incorrectly, either when recording on data sheets or when data are typed incorrectly into the spreadsheets or analysis software.
• Incorrect inferences about the meaning of the results can occur when the data are not analyzed correctly. This is more likely to occur if the person analyzing the data does not have sufficient knowledge of applied statistics.
• Finally, nature sometimes behaves in a way that we don’t expect. We could have the best plan and design in the world, yet a wildfire could pass through the study area shortly before we planned to sample.

** An excellent overview of common problems encountered in monitoring is presented in Appendix 1 of Elzinga et al., 1998.

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 that: Necessary and useful information will be collected. Time and resources will not be wasted by collecting data that are not related to the objectives or does not give the intended information. To avoid these pitfalls, remember the critical importance of pilot studies in the monitoring plan!  Conducting a “real world” trial of the monitoring protocol helps to expose problems at a time when the protocol can be revised to address these problems.

Elzinga et al. (1998; on pages 20-21 Measuring and Monitoring Plant CommunitiesLinks to an external site.) 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 we selected the right size or shape?
• Is our transect the correct length?
• Is it difficult to determine the species of interest?
• Can we get to the sites we 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 realistic?

3. Reassess time and resources.

• Will we be able to conduct the study in the time you have allotted?
• Do we have enough people or other resources to meet the objectives?
• Can technologies or other resources be secured to meet the goals of the project?

4. Review – Solicit review of the results of our 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 objective?

Record Keeping for Monitoring

There are two primary record keeping elements that must be considered for success of a monitoring program: documenting the plan and protocol, and recording plot metadata.

Properly document the monitoring plan and protocol and methods used for collecting data.  This is especially important for long-term monitoring projects.  The likelihood of having changes in personnel and observers is very high, and if protocols are not clearly and articulately defined, inaccurate data may be collected with severe consequences to management.

Plot metadata is the basic information regarding the collection of data at individual monitoring (Figure 1 and 2).  Basically, metadata includes information about sampling location within a study area, the date that data were collected and the names of the observers, often specifying who had the role of observer and who recorded the data.  Metadata sometimes includes additional site-specific information related to soils or ecological site, or measurement information such as the bearing or direction of transect placement, verification of transect length and distance between points measured.  Essentially, each data sheet needs to include sufficient metadata to link the data on the sheet to the time and place where the information was recorded:  without this basic level of identification, a data sheet could quickly be rendered useless if it can’t be properly identified!  It is also important to remember that both qualitative and quantitative data collection require adequate detail and completeness of metadata.

Figure 1: Example of a partial data sheet with properly completed metadata (adapted from , 1996).

Figure 2: Partial datasheet showing completed sample-site metadata (adapted from Rangeland Health Handbook).

Observer Training and Calibration

In order to assure sufficient accuracy and consistency in the data, observers need to be properly trained. Observer training should ideally be led by expert trainers and assure that everyone collecting data understands the sampling methodologies and monitoring protocol.  Observations by all people collecting samples should be calibrated amongst themselves and with objectively collected measurements.  Calibration of all data gatherers and trainers is necessary in assuring data quality.  It improves consistency in the data collected between different gatherers over varying time periods and increases the integrity of the data by eliminating bias as much as possible.  Calibration should be carried out at frequent intervals throughout the monitoring season, or whenever measurements are to be collected in a new ecosystem.