Determining plant diversity within interconnected natural habitat remnants (ecological network) in an agricultural landscape: a matter of sampling design?

In intensively used and human-modified landscapes, biodiversity is often confined to remnants of natural habitats. Thus, identifying ecological networks (ENs) necessary to connect these patches and maintaining high levels of biodiversity, not only for its conservation but also for the effective management of the landscape, is required. However, ENs are often defined without a clear a-priori evaluation of their biodiversity and are often never even monitored after their establishment. The objective of this study was to determine the adequate number of replicates to effectively characterize biodiversity content of natural habitats within the nodes of an EN in north-eastern Italy, based on vascular plant diversity. Plant communities within habitat types of the EN’s nodes were sampled through a hierarchical sampling design, evaluating both species richness and compositional dissimilarity. We developed an integrated method, consisting of multivariate measures of precision (MultSE), rarefaction curves and diversity partitioning approaches, which was applied to estimate the minimum number of replicates needed to characterize plant communities within the EN, evaluating also how the proposed optimization in sampling size affected the estimations of the characteristics of habitat types and nodes of the EN. We observed that reducing the total sampled replicates by 85.5% resulted sufficient to characterize plant diversity of the whole EN and by 72.5% to exhaustively distinguish plant communities among habitat types. This integrated method helped to fill the gap regarding the data collection to monitor biodiversity content within existing ENs, considering temporal and economic resources. We therefore suggest the use of this quantitative approach, based on probabilistic sampling, to conduct pilot studies in the context of ENs design and monitoring and more in general for habitat monitoring.