1.1.1 Status and trend: Basic questions

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As we describe further in step 2, indicators are the summary statistics on which management decisions are made.  The four basic indicators that are usually considered in the context of designing biological monitoring programs for salmon are: 1) abundance (or number of fish); 2) productivity (or the survival from one life stage to another); 3) diversity (or variability within and among populations in life history types, age structure, mean date of migration, etc.); and 4) spatial structure (or the relative distribution of fish within a specified area).  The latter two categories are indirect indicators of resilience.

Identifying the species that will be monitored is an important step in designing a monitoring program because it influences your spatial design, temporal design, and response design.  As an example, consider the differences between designing monitoring programs for fall Chinook salmon and coho salmon.  The spatial design may differ for the two species because fall Chinook tend to spawn in larger streams and rivers lower in watersheds while coho tend to spawn in smaller tributaries higher in the watersheds.  This difference in sampling frame may mean that you can conduct a census of all areas where fall Chinook may be found, whereas you may have to conduct a survey of a subset of potential areas where coho may be found.  The temporal design may differ between the two species because of differences in run timing, length of residency time in your sample frame, etc.  Response design may also differ between the two species because, for example, you may be able to conduct a count of live coho spawning in their smaller tributary streams, whereas you may not be able to see fall Chinook spawning in some of the larger, deeper rivers. 

Specifying the life stages of the species you intend to monitor is important in designing your monitoring program because it can dictate where you need to sample, how often you need to sample, and how you need to sample.  For example, if the life-stage of interest is the number of downstream migrating juvenile fish (e.g. smolts) you may be able to conduct a census by operating a trap, 24 hours a day, in the spring, at one location low in your sample frame.  If, however, your life-stage of interest are juveniles rearing throughout your sample frame, you will need to sample at more than one location at different times of the year.  

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It is not always clear to those implementing a monitoring program who the entities are that will ultimately use the gathered information.  If multiple entities need monitoring information and their needs with regards to the what, why, when, and where reasons for monitoring differ, an inadequate monitoring program may be designed.

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A clear understanding of the reasons that monitoring information is needed will assist you in developing many aspects of your monitoring program.  For example, the statistical rigor of information that is needed to evaluate cause and affect relationships (i.e. mechanisms) may be significantly higher than information that is needed to assess status and trend.  Describing why the information is needed in terms of how managers and other decision makers will use the information is useful because it will help you identify specifically what monitoring information is needed to satisify these entities

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An important consideration in evaluating the efficacy of information provided by any monitoring program is the degree of confidence you have in the information and the likelihood that the information will lead you to the wrong conclusions.  In general, the more certain you need to be about the information you are providing, the more resources will be needed to gather it.  Thus, you should specify the desired precision and/or statistical power of your monitoring program early in its development. This will help determine the most appropriate monitoring design for your situation, given your available resources.   

Precision can be described in terms of the coefficient of variation (CV), which is the standard deviation (SD) of the estimates divided by the mean estimate. Depending on the quantity estimated, typical CVs for salmon variables or parameters range from 0.3 to over 1, i.e., a SD about 30% of the mean to at least as large as the mean value. Small CVs reflect greater precision. See Crawford and Rumsey (2009) for an example of how CVs are used to establish precision needs for salmon monitoring.

Precision can also be described in terms of confidence intervals (CI), which are a range of values about a parameter estimate such that the probability that the true value of the parameter lies within the range is some fixed value, α, known as the confidence level. The upper and lower limits of the range are known as confidence limits. Confidence limits are calculated from the theoretical frequency distribution of the estimating function. The concept may be generalized to several parameters. A confidence region at level α contains the true values of the parameters with probability α.  See Snedecor and Cochran (1989) for more information on confidence intervals. 

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In order to adequately assess the resources needed to implement your monitoring program it is important that you have a clear understanding of the time span over which information will be gathered and reported.  Basic understandings of the total time span over which information is to be gathered as well as the time intervals over which results are to be reported, are important first steps in developing the temporal design of your monitoring program.

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Specifying the total geographic extent and discreet spatial scales over which inferences are to be made from the information gathered by your monitoring program is an important early step in the development of the spatial design of your monitoring program.  The total geographic extent specifies the spatial extent across which people will wish to make inferences based on the information gathered by your monitoring program.  The discrete spatial scale specifies the length of sampling units.  For example, monitoring of the abundance of salmon spawning in a large river system (the total geographic extent) may be estimated based on counts obtained at randomly selected survey sites that are 1 mile in length (the discrete spatial scale).

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A statistical population defines the entirety that is the focus of your monitoring; the population could be discrete (e.g., all the lakes in Oregon), or continuous (e.g., the stream network in the Fraser watershed).  You might want to determine the number of lakes in Oregon with rainbow trout, or how many adult Chinook occur in the Fraser River network.  If a census cannot be implemented to achieve your objectives, then sampling the statistical population (selecting locations that represent the statistical population) allows making inferences about the population based on measurements made at the chosen locations.  Confusion can arise when statisticians (with one definition of a population) talk with fishery biologists (with another definition of a population); click here for a discussion of the distinction between a statistical population and a biological population..  

To see more information on comparisons of biological and statistical populations ...click here.

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 Metrics are calculated from the measurements or observations that you make while implementing your sampling protocols.  For example, your sampling protocol might involve counting the number of live fish observed on the spawning grounds at regular intervals over the course of the spawning season. Your metric for these “measurements” might be to calculate an area-under-the-curve estimate of the total number of live spawners.  Metrics can themselves be equivalent to the indicators you previously identified, or they may be an intermediate step between field measurements/observations and your indicators of interest.  For example, if your indicator is the abundance of spawners in a particular river, and if you can count all fish that enter the river to spawn as they pass over a counting weir, then your metric (the total number of fish counted passing the weir) would be equivalent to your target indicator.  On the other hand, if you can only assess spawner abundance by counting spawning fish at a sub-sample of all the potential spawning sites, then your metrics (the average number of spawning fish observed/length of spawning habitat surveyed) might be an intermediate step to developing your target indicator of total abundance.  

Identifying the metrics you will be collecting is an important component of developing your response design. The frequency with which you will need to collect information on your metrics has an important influence on the development of your temporal and response design.  It also can influence your spatial design.  For example, if you have a metric that requires frequent data collection over the course of your reporting interval, then due to budget limitations you may need to choose a design that allows you to sample at fewer sites than a design for metrics for which data can be collected less frequently.

 

We often hear that variability affects our ability to describe the metrics for which we’ve designed our monitoring programs.  However, the term "variability" connotes so much that before we can make any progress evaluating how variability affects estimation, we need to describe what we mean by “variability”.  When we consider a monitoring program whose goals include describing ecological resources that are distributed across regions across years, and for which our measurements are imperfect, organizing variability helps us focus our attention on those aspects of variation that are of specific interest, and those that impose imprecision or bias on our descriptions.  The following is one way of decomposing variability to assist us in evaluating which components are important and which are distractions (that might be amendable to management by appropriate monitoring designs). The context of this brief overview is an interest in the status and trends of metrics among sites across years; think of a 'sites-by-years' matrix of metric scores for which status and trends information is desired.Consult the following for additional insights: Dauwalter, et al., 2009; Urquhart, et al., 1998; Urquhart and Kincaid, 1999; Wagner, et al., 2007; Wiley, et al., 1997.

 

Components of Variability

You will need information on four broad categories of variability that will be useful for designing your monitoring program.  The information might be available from existing studies or you might design your monitoring program to obtain the necessary data during its first few years.  The following text uses "year" as the temporal unit for ease of discussion and because readers are likely familiar with programs that use "year" as the primary temporal unit. 

• Spatial or site-to-site variation: The inherent ability of different locations or sites to support the resource of interest varies across the region of interest. Some locations have a greater capacity to support a higher level of the target metric than others, e.g., some locations have a much greater capacity to support high abundances of particular salmon populations than others. Describing this fundamental site to site variation (e.g, frequency distribution of abundance across sites) allows us to estimate various properties of the indicator such as its totals (total number of fish), central tendencies (mean, median) with implications with respect to precision of making these estimates. Well designed sample surveys allow us to estimate properties of this spatial variation with known precision.  (Note that if we are interested in the properties of a single site, spatial variation is irrelevant).
• Synchronous temporal variation (across years): Some environmental driving processes affect the “level” of a metric consistently at all locations throughout the domain of interest during a particular study period.  For example, “good” ocean conditions might result in relatively high abundances in a salmon population at all sites in its domain compared with abundances following “poor” ocean conditions.  Or the thermal regime in streams during warm years can be higher across all streams in the domain compared with the thermal regime during cold years.  This synchronous temporal component of variation can significantly reduce trend detection capability. An unfortunate aspect of this component is that refining the monitoring design (e.g., increasing sample size by adding sites to the survey) does little, if anything, to reduce its effect.  Two options are available: estimate the temporal pattern in the driving process (allowing removal or factoring out its effect in the analytical phase), or extending the temporal monitoring interval.
• Interaction variation: We are all aware that metric values at all sites do not increase or decrease by the same amount year to year—the year to year variation among sites is variable, with metrics at some sites increasing more than at others while at other sites, metric scores decrease, some more than others.  This “independent” temporal variation among sites is captured as a sites-by-years interaction.  In general, interaction variation is much higher than synchronous temporal variation and can affect both status estimation and trend detection.  However, its effect on trend detection can be managed by the number of sites incorporated into the survey.
• Residual variation:  We lump the remaining variation into a “residual” term that describes the variation in our ability to quantify the metric at a site during a year (during the temporal unit). Residual variation arises from our inability to perfectly describe the natural situation because our measurement methods (e.g., mark-recapture) or instruments (stream-flow gauges) have inaccuracies.  Our sampling crews do not apply field protocols in exactly the same way.   As well, we cannot sample all locations simultaneously (variation within the index widow).  If necessary, residual variation can be divided into these parts: 
• Variation in repeated applications of the measurement protocol at the same time and location by the same sampling team or introduced by the analytical methods
• Variation introduced by different crews applying the measurement protocol
• Temporal variation during the interval over which the sites are monitored within the index period.

Like interaction variation, residual variation can affect both estimates of status and trend, and similar procedures can be used to adjust for its effects. Its effect on status estimation can be corrected with a procedure called deconvolution (Kincaid, et al., 2004). If residual variation is a relatively large part of total variation, decomposing residual variation might lead to improvements, for example, in the particular field protocol, or in greater field crew training. Spending time improving field protocols or on team training might not be cost effective if residual variation is relatively low, or if residual variation is primarily a result of temporal variation during the index window.

If you do not have good information on any of these four categories, do not despair.  This is the purpose of "pilot studies" (i.e. to conduct preliminary field sampling). The resulting initial estimates of these four quantities are then used to refine your design.  Consider developing your survey designs in an adaptive manner, first with some priority given to estimating four important components of variation, either as part of an initial survey, or as you can compile from existing information.  Develop sound cost estimates.  The following general framework will help get you going:  Select 30 to 50 sites using randomization in the site selection process; adopt a panel design to allocate sampling across years (e.g, some sites sampled annually, some sampled less frequently; ....click here for more information about panel designs).   After about five years, you will have a reasonable amount of information to evaluate spatial, temporal, and (interaction plus residual) variation.  Incorporating revisits to sites within a year will allow you to separate interaction from residual variation; revisiting 10 sites each year during the index window yields an effective sample size of 50 to separate residual from interaction variation.  Evaluate the potential for a “crew” effect by having different crews sample the same location at nearly the same time.  This five year set of data will allow you to make reasonable estimates of the components of variation that can then be used to optimize your allocation of sampling effort across sites, years, and index periods.

Synchronous temporal variation (across years): 

Interaction variation: 

Residual variation: