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

Guerin, Greg R.; Williams, Kristen; Leitch, Emrys; Lowe, A. J.; Sparrow, Ben

doi:10.1101/2020.06.01.107391

Cited by 2 publications

(3 citation statements)

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“…More sophisticated algorithms could weight potential locations by distance or accessibility, or seek to iteratively complement the environmental coverage of an accumulating sample (Kumar et al 2016, Albuquerque and Beier 2018). Indeed, a recent aim of Ausplots sampling has been to gap‐fill the network of monitoring plots with surveys targeting regions predicted to be the most ecologically different in a generalized dissimilarity modeling framework (Guerin et al 2020 b ).…”

Section: Discussionmentioning

confidence: 99%

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Stocktaking the environmental coverage of a continental ecosystem observation network

Guerin¹,

Williams

Sparrow³

et al. 2020

Ecosphere

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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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Section: Discussionmentioning

confidence: 99%

“…However, the resulting sample was biased toward more mesic, coastal habitats that are already better sampled by historical monitoring. Targeted gap‐filling using a smaller number of complementary sites can now fine tune the ecological coverage of Ausplots (Guerin et al 2020 b ).…”

Section: Discussionmentioning

confidence: 99%

Stocktaking the environmental coverage of a continental ecosystem observation network

Guerin¹,

Williams

Sparrow³

et al. 2020

Ecosphere

Self Cite

View full text Add to dashboard Cite

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“…All data were extracted from public databases. Site and expedition data are available via from the Figshare repository https://doi.org/10.25909/13332257 (Guerin, Williams, Leitch, et al., 2020). Environmental layers are available from the CSIRO Data Access Portal (https://data.csiro.au/dap).…”

Section: Data Availability Statementmentioning

confidence: 99%

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

Guerin

Williams

Leitch

et al. 2020

Journal of Applied Ecology

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Effective ecosystem management requires spatially distributed measurements that are representative of ecological diversity. When considering which sites complement existing conservation or monitoring networks, there are many strategies for optimising ecological coverage in the absence of ground observations. However, such optimisation is often implemented theoretically in conservation prioritisation frameworks and real‐world implementation is rarely assessed, particularly for monitoring networks. We assessed the performance of adding new survey sites informed by predictive modelling in gap‐filling the ecological coverage of the Terrestrial Ecosystem Research Network's (TERN) continental network of ecosystem surveillance plots, Ausplots. We constructed a generalised dissimilarity model (GDM) in which plant species composition in 531 sites was fitted to environmental parameters. We combined predicted nearest‐neighbour ecological distances for locations across Australia with practical considerations to select regions for gap‐filling surveys of 181 new plots across 18 expeditions. We iteratively tracked the reduction in mean nearest‐neighbour distances in GDM space, and increases in the actual sampling of ecological space, using cumulative multivariate dispersion. The generalised dissimilarity model explained 34% of deviance in species compositional turnover as a function of geographic distance, soil P, aridity, actual evapotranspiration and rainfall seasonality, among 17 significant predictors. We targeted identified gap regions in the Australian jurisdictions of Queensland, New South Wales and Western Australia for surveys in addition to opportunistic or project‐based gap‐filling over 2 years. Approximately 20% of the land area of Australia received increased servicing of biological representation, corresponding to a reduction in mean nearest‐neighbour ecological distances from 0.38 to 0.33 in units of compositional dissimilarity. The corresponding increase in sampled ecological space was 172% more than that achieved from the previous 181 plots. Synthesis and applications. Increases in the representation of ecosystems included in surveillance networks can be achieved efficiently using objective methods for site selection and appraisal. Scaling of environmental variables through ecological models supports practical sampling decisions, while optimising putative survey locations via their ecological distance to a nearest neighbour is useful when the aim is to increase inclusion of habitats. Iterations between modelled gaps and field campaigns provide a pragmatic compromise between theoretical optima and real‐world decision‐making.

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

Abstract: 13

Cited by 2 publications

References 44 publications

Stocktaking the environmental coverage of a continental ecosystem observation network

Stocktaking the environmental coverage of a continental ecosystem observation network

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

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