Square‐grid sampling support to reconcile systematicity and adaptivity in periodic spatial surveys of natural populations

Abstract

AbstractAimsSpatially balanced sampling is the most efficient method for surveying continuous and spatially structured populations. The spatial sampling of large‐scale surveys is mostly based on grids whose properties drive and potentially limit the possibility of building flexible samples. Periodicity causes high sampling constraints when an increase in the frequency of information delivery is sought. The sampling stratification of the adaptive sampling intensity also conflicts with the grid‐based approach. Although some surveys seemingly exploit these properties, no formal developments have been made available in the survey sampling literature across the fields of application.MethodsWe define and demonstrate the geometric properties of square grids, demonstrate how they can be used to produce nested hierarchical grids compatible with multiple periodicity values of interest for natural monitoring, and adapt the sampling intensity across space and time. A simulation study was conducted to quantify how spatial balance can be traded slowly for sample size reduction.ResultsWe showed that square grids have geometric properties that can be exploited to cope with spatial flexibility in the sampling effort and the spatiotemporal coordination of samples. We also provide an original extension of this framework intended to tune the sampling effort gradually while preserving spatial systematicity. The simulation study showed that a nested hierarchical grid can be used to progressively reduce the sampling intensity while preserving regularity in the spatial arrangement of units.ConclusionsWe demonstrate the flexibility and diversity of sampling schemes that can be implemented with square grids, answering the need for periodicity and the coordination of multiple samples and the limits of their use.