Irene Martín-Forés, Greg Guerin, Samantha Munroe, Ben Sparrow

In an era of unprecedented ecological upheaval, accurately monitoring ecosystem change at large spatial scales and over long-time frames is an essential to effective environmental management and conservation. However, economic limitations often preclude revisiting entire monitoring networks at a high enough frequency to accurately detect ecological changes. Thus, a prioritisation strategy is needed to select a subset of sites that meets the principles of complementarity and representativeness of the whole ecological reality. Here, we applied two well-known approaches for conservation design, the ‘minimum set’ and the ‘maximal coverage’ problems, to develop a strategic monitoring prioritisation procedure that compares potential monitoring sites using a suite of alpha and beta biodiversity metrics. To accomplish this, we created a novel function for the R environment that easily performs biodiversity metric comparisons and site prioritisation on a plot-by-plot basis. We tested our procedures using plot data provided by the Terrestrial Ecosystem Research Network (TERN) AusPlots, an Australian long-term monitoring network of 774 vegetation and soil monitoring plots. We selected 250 plots and 80% of the total species recorded for the maximal coverage and minimum set problems, respectively. We compared the results of each approach in terms of ecological complementarity (species accumulation) and the spatial and environmental representativeness of the plots selected by the different biodiversity metrics. We repeated the selection process for clusters of plots to incorporate logistic constraints for field expeditions. We found that prioritisation based on species turnover (i.e. selection of the most dissimilar plots in terms of species composition but ignoring species richness) maximised ecological complementarity and spatial representativeness, while also providing high environmental coverage. Species richness was an unreliable metric for spatial representation, whereas plot selection based on corrected weighted endemism failed to capture ecological and environmental variation. Range-rarity-richness was a more balanced metric in terms of complementarity and representativeness. Prioritisation based on species turnover is desirable to cover the maximum variability of the whole network.
Synthesis and applications: Our results inform monitoring design and conservation priorities, which should consider changes in the turnover component of the beta diversity instead of being based on univariate metrics.