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2.0.1 Balancing Sampling Effort Among Spatial, Temporal, and Response Designs

In designing the monitoring program, you will be faced with this problem:  You will have a fixed budget allocated to collecting data at each spatial unit (e.g, a site) during each temporal unit (e.g., year), and an expected budget for the study period.   You will know how much it costs to collect the data at a site (including travel costs).   Your challenge will be to optimize the allocation of sampling across the potential set of spatial and temporal units during the study period.  It might also be important to allocate effort to sampling within the temporal window to obtain an adequate estimate of your metric and/or to evaluate the variation introduced by your response design.

The following considerations are important for optimizing this allocation of sampling effort:

  • Degree of certainty - The level of confidence that you must have in the results of your monitoring program plays a significant role in determining the appropriate design.  In general, the degree of certainty in monitoring results is lowest for opportunistic designs, intermediate for model-based and survey designs, and highest for census designs.  It is lowest for opportunistic designs because it is difficult or often impossible to assess how well the chosen sample sites represent the domain for which inferences are intended.  Because of the non-statistical nature of sample site selection, it is often impossible to assess the degree of certainty of results from opportunistic sample sites because you cannot determine the precision or bias associated with inferences to entire populations obtained from data collected at opportunistic sample sites.  The degree of certainty is intermediate for model-based and survey based spatial designs because, although they do use selection methods that allow for the sampled sites to be representative, with an estimated precision and bias, the uncertainty around estimates derived from model or survey based sites is inflated because of statistical sampling error.  In addition, model based designs can be subject to unknown uncertainties associated with model assumptions.  The degree of certainty is highest for census designs because the sampling of all members of the target population (either via a fixed counting station or by sampling at all sites in the domain) results in no statistical sampling error or faulty assumptions about the representativeness of selected sites.
  • Cost - The cost of designs generally varies inversely in relation to their degree of certainty.  While the high degree of certainty provided by a complete census may be attractive, in many cases the cost associated with conducting a census over a large geographic area or for the entire study period will be prohibitive.  Because of the myriad of factors that are associated with estimating costs for different types of monitoring designs, we do not attempt to provide explicit guidance on the cost of different designs.  However, it is important that you keep in mind the need to adopt a design that is within the budget available to you.  This may mean that you will have to take a hard look and potentially revise your objectives for the degree of certainty you will obtain from your monitoring program, given the spatial, temporal and response designs that fall within your budget.   
  • Feasibility - Adopting a design that achieves your desired degree of certainty and is within your budget may result in an inappropriate design if you have not considered other factors that may affect the feasibilty of implementing the design.  For example, census or survey-based designs that require permission from landowners to access sites will not be appropriate if you are denied access to their lands.  As a result, you must realistically evaluate the feasibilty of implementing a particular design given the access constraints specific to your monitoring area.
  • Existence of a verified model - Choosing a model-based design will obviously not be an option if you lack an appropriate model that can guide your site selection process. 
  • Flexibility - It is a common occurrence that over the life of a monitoring program the goals and objectives, monitoring technologies, allocated budgets or other constraints may change.  Some designs are more amenable to the modification that may be necessary to meet these new challenges than others.  For example, an initial objective that requires an abundance estimate over a prescribed monitoring region might be changed to an objective that requires abundance estimates for specific populations within that region.  A spatial/temporal design that allows you to add or subtract sites without biasing your results is more desirable than one that requires an entirely new design.


A framework that can be used to balance the various competing choices in designing a monitoring program consists of a) understanding the importance of spatial and temporal components of variation (... click here to learn more about the components of variation); b) evaluating the accuracy of your estimates (i.e. precision and bias); c) the power (or certainty) supporting your conclusions; and d)costs.   The following citations will provide you with a good foundation underlying the concept of power (the likelihood of detecting an effect if the effect is present) and how it has been applied natural resource monitoring:

Fairweather, P.G.  1991.  Statistical power and design requirements for environmental monitoring.  Aust. J. Mar. Freshw.Res.  42:555-567.

Gerow, K. G. 2007. Power and sample size estimation techniques for fisheries management: assessment and a new computational tool. North American Journal of Fisheries Management 27:397–404.

Hatch, S.A.  2003.  Statistical power for detecting trends with applications to seabird monitoring.  Biol. Conserv. 111:317-329.

Link, W. A., and J. S. Hatfield. 1990. Power calculations and model selection for trend analysis: a comment. Ecology 71:1217–1220.

Peterman, R. M. 1990. Statistical power analysis can improve fisheries research and management. Canadian Journal of Fisheries and Aquatic Sciences 47:2–15.

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