Stocktaking the environmental coverage of a continental ecosystem observation network

Abstract

Field‐based sampling of terrestrial habitats at continental scales is required to build ecosystem observation networks. A key challenge for detecting change in ecosystem composition, structure, and function within these observatories is to obtain a representative sample of habitats. Representative sampling across a continent contributes to ecological validity when analyzing spatially distributed data. However, field resources are limited, and actual representativeness may differ markedly from theoretical expectations. Here, we report a post hoc evaluation of the coverage of environmental gradients as a surrogate for ecological representativeness by a continental‐scale survey undertaken by the Australian Terrestrial Ecosystem Research Network (TERN). TERN’s surveillance program maintains a network of ecosystem observation plots initially established in the rangelands through a stratification method (clustering of bioregions by environment) and application of the Ausplots survey methodology. Subsequent site selection comprised gap‐filling and opportunistic sampling. We confirmed that environmental coverage was a good surrogate for ecological representativeness. The cumulative sampling of environments and plant species composition over time were strongly correlated (based on mean multivariate dispersion; r = 0.93). We compared environmental sampling of Ausplots to 100,000 background points and a set of retrospective (virtual) sampling schemes: systematic grid, simple random, stratified random, and generalized random‐tessellation stratified (GRTS). Differences were assessed according to sampling densities along environmental gradients, and multivariate dispersion. Ausplots outperformed systematic grid, simple random, and GRTS in coverage of environmental space (Tukey HSD of mean dispersion, P < 0.001). GRTS site selection obtained similar coverage to Ausplots when employing the same bioregional stratification. Stratification by climatic zones generated the highest environmental coverage (P < 0.001), although resulting sampling densities over‐represented mesic coastal habitats. The Ausplots bioregional stratification implemented under practical constraints represented complex environments well, compared to statistically oriented or spatially even samples. Potential statistical power also depends on replication, unbiased site selection, and accuracy of field measurements relative to the magnitude of change. Consistent with previous studies, our stocktake analysis confirmed that environmental, rather than spatial, stratification is required to maximize ecological coverage across continental ecosystem observation networks, and the approach to establishing TERN Ausplots was robust. We recommend targeted gap‐filling to complete sampling.

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