Operationalizing Scale: Functional and Observational - Are There Different Types of Scale?

Functional scale, also referred to as the “scale of the phenomenon” (MA 2005), or the “absolute scale” (Turner and Gardner 2015), is the magnitude or rate of a process of interest (Cumming et al. 2006), referring to the extent or duration over which a phenomenon has an impact (MA 2005). For example, grazing by a herd of bison or plant invasions occur in specific dimensions of time and space. Functional scale reflects the “true” scale at which a system property (e.g., forage availability) affects a process of interest (e.g., grazing) (Li and Reynolds 1995; Fuhlendorf et al. 2017).

Observational scale, also referred to as arbitrary scale (Weins 1989), is a human construct used for measurement and consists of grain and extent (MA 2005) (Figure 5). Observational scale may or may not accurately depict a functional scale and can influence how patterns are interpreted. Remember, functional scale reflects the scale(s) at which a system property affects a process (Li and Reynolds 1995), thus proper selection of scale affects our ability to understand and manage ecological processes. All observational scales are arbitrary--just because observational scales seems “right” provides no assurance that they perfectly represent the functional scale (Weins 1989). Selection of useful observational scales requires thinking about the functional relationships between a system property and the scale at which that property influences a process of interest. For example, what scales are relevant to a beetle versus a bison selecting a home range? Or a germinating acorn in a forest? Additionally, many natural phenomena are influenced by multiple scales and therefore require multiple observational scales to understand (e.g., real world examples provided earlier in this lesson).

Some considerations when selecting an observational scale

• How big of an area or window of observation (i.e., extent) is needed to represent the process of interest? Considerations should include the amount of area or time needed to represent the full range of variability of the process of interest. For example, to understand seasonal weather patterns, an observation window of many years is needed.
• What grain size is needed to make appropriate observations? Considerations include the size of what is being measured (e.g., grasses versus trees), how often a process of interest occurs, and the rate at which it occurs. For example, studying events that happen very quickly (i.e., earthquakes) may require that observations are taken over the course of seconds rather than hours or days.
• What is an appropriate extent and grain combination? Some studies may require large extents with coarse grain, while, others may require large extents with fine grain. For example, seed dispersal may occur over a large area (i.e., extent), but germination may depend on very fine differences in soil, moisture, and light exposure that can only be appropriately represented with a small grain size.
• What other scales are important? Processes at finer or broader scales may influence a studies results. For example, apparent population growth rates in wildlife populations could be driven by factors occurring at broader scales compared to the observational scale used in a study.