Oliver L. Pescott , Gary D. Powney, Rob James Boyd

Indicators of biodiversity change across large extents of geographic, temporal and taxonomic space are frequent products of various types of ecological monitoring and other data collection efforts. Unfortunately, many such indicators are based on data that are highly unlikely to be representative of the intended statistical populations: they are biased with respect to their estimands. Where there is full control over sampling processes, individual units within a population have known response propensities, but these are unknown in the absence of any statistical design. This could be due to the voluntary nature of surveys or because of data aggregation. In these cases some degree of sampling bias is inevitable and we must do something to ameliorate it. One such option is poststratification to adjust for uneven surveying of strata assumed to be important for unbiased estimation. We propose that a similar strategy can be used for the prioritisation of future data collection: that is, an adaptive sampling process focused on actively increasing representativeness defined in terms of response propensities. This is easily achieved by monitoring the proportional allocation of sampled units in strata relative to that expected under simple random sampling. The allocation of new units is thus that which reduces the departure from randomness (or, equivalently, that equalising response propensities across population units), allowing an estimator to approach that level of error expected under random sampling. We describe the theory supporting this straightforward strategy, and demonstrate its application using the National Plant Monitoring Scheme, a UK-focused, structured citizen science monitoring programme with uneven uptake.