Stratified sampling also yields estimates for each stratum, providing information that better represents the area being sampled, and is therefore more ecologically meaningful. As an example, two strata may each have a significant trend for a given parameter, but the trends may be in opposite directions. Because stratified sampling ensures that some samples will be taken from each stratum, over the entire study area, it helps ensure that the overall estimate will be more representative on average than one obtained from a simple random sample. This advantage of stratified sampling will be realized even if optimal allocation is not used and the strata are defined arbitrarily with respect to the parameters of interest. In general, if the variability within individual strata is less than the overall variability in all combined strata, the standard errors obtained with stratified sampling will be less than those obtained from systematic sampling, which will be less than those obtained from simple random sampling.
Multistage sampling is another cost effective method of allocating sampling effort over large areas.
In this design, large primary sampling units are composed of smaller secondary units. For large-scale studies, third stage units may also be used. Within each of the selected first stage units, one or more second stage units are selected. In addition to increased sampling efficiency, this method allows intensive sampling to be done only at the second stage, while parameters that are inexpensive to measure can be obtained for the first stage units. This provides some level of monitoring over a wide area, and if problem areas are detected, the distribution of second stage units can be reallocated.