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. However, a key challenge for detecting change in ecosystem composition, structure and function is to obtain a representative sample of habitats. Representative sampling contributes to ecological validity when analysing large spatial surveys, but field resources are limited and representativeness may differ markedly from purely practical sampling strategies to statistically rigorous ones. Here, we report a post hoc assessment of the coverage of environmental gradients as a surrogate for ecological coverage by a continental-scale survey of the Australian Terrestrial Ecosystem Research Network (TERN). TERN's surveillance program maintains a network of ecosystem observation plots that were init ially established in the rangelands through a stratification method (clustering of bioregions by environment) and Ausplots methodology. Subsequent site selection comprised gap filling combined with opportunistic sampling. Firstly, we confirmed that environmental coverage has been a good surrogate for ecological coverage. The cumulative sampling of environments and plant species composition over time were strongly correlated (based on mean multivariate dispersion; r = 0.93). We then compared the environmental sampling of Ausplots to 100,000 background points and a set of retrospective (virtual) sampling schemes: systematic grid, simple random, stratified random, and generalised random-tessellation stratified (GRTS). Differences were assessed according to sampling densities along environmental gradients, and multivariate dispersion (environmental space represented via multi-dimensional scaling). Ausplots outperformed systematic grid, simple random and GRTS in coverage of environmental space (Tukey HSD of mean dispersion, p < .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 < .001), but the resulting sampling densities over-represented mesic coastal habitats. The Ausplots stratification by bioregions implemented under practical constraints represented complex environments well compared to statistically oriented or spatially even samples. However, potential statistical inference and power also depend on spatial and temporal replication, unbiased site selection, and accurate field measurements relative to the magnitude of change. A key conclusion is that environmental, rather than spatial, stratification is required to maximise ecological coverage across continental ecosystem observation networks.

Using generalised dissimilarity modelling and targeted field surveys to gap-fill an ecosystem surveillance network

“…We were able to use the comprehensive species composition and relative abundance data recorded in the large set of plots already established across Australia to inform further gap-filling surveys through compositional models. This approach contrasts with the original environmental stratification used to establish the TERN Ausplots network, which used environmental layers in the absence of ground data at that stage (Guerin et al 2020).…”

“…The size of the additional ecological space sampled was 172% that of the previous 181 plots, adding 8% over all establishment phase sampling, as measured by multivariate dispersion, which is significant given the law of diminishing returns and that coverage of the Australian terrestrial environment by establishment plots was already quite comprehensive (Fig. 5c; Guerin et al 2020).…”

“…Fewer studies have addressed optimising representation for targeted biological surveys (Funk et al 2005; Hortal & Lobo 2005; Ware et al 2018) or the design of monitoring networks (Stohlgren et al 2011; Rose et al 2016). Networks of ecosystem observation plots have traditionally been designed to maximise spatial or environmental coverage using stratification (Goring et al 2016; Guerin et al 2020). Even so, the problems of complementary reserve selection and gap-filling monitoring networks are, in essence, the same (Hopkins & Nunn 2010); both seek to maximise ecological coverage in the absence of fine-scale biodiversity survey data.…”

“…However, while environmental layers represent the broad environmental setting of ecological communities, they do not directly capture other key drivers of finer scale species compositional turnover, such as local disturbance regimes, microclimates, human impacts, and biotic interactions (Araújo et al 2001; Peres-Neto et al 2012; Goring et al 2016). The spatial structure of ecological similarity (i.e., the degree to which species are shared between locations in space or time) is well known and is the reason that unstratified, systematic sampling performs reasonably at sampling ecological variation (Peres-Neto et al 2012; Goring et al 2016; Guerin et al 2020). While local (patch level) drivers are opaque at large scales, ideally the purely spatial component of turnover would be included in assessments of surrogates.…”

“…Here, we combined the predictive power of GDM with practical considerations to strategically gap-fill an established network of ecosystem surveillance plots – TERN Ausplots (Sparrow et al 2019a). Ausplots were originally stratified to sample environmentally distinct bioregions across the Australian rangelands (Sparrow et al 2019b; Guerin et al 2020). However, due to an expansion in scope to all terrestrial bioregions and a practical limitation on the total number of sites that can be feasibly monitored, we aimed to select regions for survey that would efficiently increase ecological representation.…”