Habitat fragmentation has become one of the largest areas of research in conservation biology. Empirical studies into habitat fragmentation impacts typically measure ecological responses to metrics describing fragmentation processes, for example ‘distance to the nearest forest edge’, ‘forest fragment area’ and ‘landscape habitat amount’. However, these studies often fail to sample across representative ranges of fragmentation metrics characterising the study region. They therefore lack the data to account for correlation among multiple fragmentation metrics and for spatial autocorrelation among sample sites, which reduces the strength of derived predictive models.
Here, we draw on approaches used in the mining and soil science industry to develop standardised and repeatable protocols for designing optimised sampling schemes of biodiversity in fragmented landscapes that meet three criteria: the distance between sample sites is maximised to reduce spatial autocorrelation, the full range of values of the metrics of interest are sampled and the confounding effects of correlated metrics are minimised.
We show that our computational methods can optimise the placement of sample sites in fragmented landscapes to minimise, and in some cases to entirely avoid, over- or under-sampling of fragmentation metrics. Our method is flexible enough to cater for any continuous (e.g. maps of percentage tree cover) or categorical (e.g. habitat and land use types) fragmentation metric, and to simultaneously handle combinations of multiple fragmentation metrics and habitat types. We implement our methods as open-source code which includes options to mask invalid or inaccessible regions, update designs to adapt to unforeseen constraints in the field and suggest optimal numbers of sample sites for given design criteria.
Using a case study landscape, we demonstrate how the approach improves on manually generated sampling designs. We also show that the methods are flexible enough to be applied to landscape studies beyond the field of habitat fragmentation. We introduce our package as a novel research tool that is able to streamline the experimental design process for biodiversity sampling and monitoring at landscape scales, leading to improved data quality and representativeness.