More species, less effort: designing and comparing sampling strategies to draft optimised floristic inventories

Floristic inventories are an essential part of basic and applied research in botany. Despite a long history in floristic investigations, they are still conducted following non-objective (preferential) sampling approaches. Accordingly, final outputs (i) are extremely variable in the quality and quantity of collected data, (ii) are fully dependent on the researcher ability, and (iii) miss any possibility to perform statistical analyses. The aim of this work is to explore the drafting of a floristic inventory by means of probabilistic approaches, based on geostatistical designs aimed to locate samplings units (plots) in the study area. We planned, carried out and then compared two different sampling strategies: (i) ‘basic strategy’, a stratified random sampling design based solely on a spatial optimization criterion (no prior information is available), and (ii) ‘advanced strategy’, a sampling design based on the maximisation of the spectral heterogeneity among sampling units, quantified in terms of Normalized Difference Vegetation Index values (NDVI). The strategy that maximises collected floristic information was assessed based on a combination of descriptive and quantitative statistics, such as the completeness of the floristic inventory, the steepness of the rarefaction curves, the sampling time effort, and the plot contribution to the total β diversity. The 'advanced strategy' detected more species than the 'basic strategy' in all the sampling sites. Again, the rarefaction curve obtained with 'advanced strategy' is steeper in accumulating species in respect to the 'basic strategy'. The analysis of the contribution of each plot to the total β diversity showed that the 'advanced strategy' selects sampling units having a more homogeneously distributed contribution among plots, and that they are better spatially arranged across the study area to capture environmental peculiarities of sampling sites. Accordingly, the 'advanced strategy' is more effective than the 'basic' one in drafting a species inventory, in the face of just a little more effort in the design of the sampling strategy. The algorithm proposed to perform the 'advanced strategy', proposed here for the first time, can be profitably and freely applied to every geographic location and vegetation context.