1) Ecological processes operating on large spatio-temporal scales are difficult to disentangle with traditional empirical approaches. Alternatively, researchers can take advantage of “natural” experiments, where experimental control is exercised by careful site selection. Recent advances in developing protocols for designing these “pseudo-experiments” commonly do not consider the selection of the focal region and predictor variables are usually restricted to two. Here we advance this type of site selection protocol to study the impact of multiple landscape scale factors on pollinator abundance and diversity across multiple regions. 2) Using datasets of geographic and ecological variables with national coverage, we applied a novel hierarchical computation approach to select study sites that contrast as much as possible in four key variables, while attempting to maintain regional comparability and national representativeness. There were three main steps to the protocol: i) selection of six 100 km x 100 km regions that collectively provided land cover representative of the national land average, ii) mapping of potential sites into a multivariate space with axes representing four key factors potentially influencing insect pollinator abundance, and iii) applying a selection algorithm which maximised differences between the four key variables, while controlling for a set of external constraints. 3) Validation data for the site selection metrics were recorded alongside the collection of data on pollinator populations during two field campaigns. While the accuracy of the metric estimates varied, the site selection succeeded in objectively identifying field sites that differed significantly in values for each of the four key variables. Between variable correlations were also reduced or eliminated, thus facilitating analysis of their separate effects.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)