TERN, Australia’s land observatory: addressing the global challenge of forecasting ecosystem responses to climate variability and change

Cleverly, James; Eamus, Derek; Edwards, Will; Grant, Mark; Grundy, M. J.; Held, Alex; Karan, Mirko; Lowe, A. J.; Prober, Suzanne M.; Sparrow, Ben; Morris, Beryl

doi:10.1088/1748-9326/ab33cb

Cited by 40 publications

(39 citation statements)

References 147 publications

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“…Targeted monitoring programs are usually designed as ‘one‐off’ studies that rarely produce information at the scale governments require for regional or continental decision making (Lindenmayer & Likens, 2010). As a result these studies are most commonly conducted by an individual research laboratory, a single branch of a government department or management agency, or localised land managers, although networks of studies fit well with research infrastructure programs (Peters et al ., 2014; Cleverly et al ., 2019).…”

Section: An Explanation Of Monitoring Typesmentioning

confidence: 99%

“…Over time they build the knowledge base to inform strategic use of targeted monitoring to underpin the mechanistic understanding of environmental change. Further, given the scale at which these programs operate, they are ideal to include in fundamental national research infrastructure programs, with many programs internationally already doing this (Herrick et al ., 2010; Toevs et al ., 2011; Peters et al ., 2014; Cleverly et al ., 2019; Oliva et al ., 2019; Sparrow et al ., 2020).…”

Section: An Explanation Of Monitoring Typesmentioning

confidence: 99%

“…Remote‐sensing techniques used in landscape monitoring can be applied consistently across vast areas, with some authors (Pettorelli et al ., 2014 a ; Luque et al ., 2018) claiming that remotely sensed data is the only way to obtain standardised biodiversity information over large areas in reasonable time periods. This spatial focus means that this type of monitoring is often conducted by national research infrastructures (Cleverly et al ., 2019), or groups of international cooperation such as the Group on Earth Observation (http://earthobservations.org), NASA and other similar groups.…”

Section: An Explanation Of Monitoring Typesmentioning

confidence: 99%

“…(2008). Subsequently such systems are beginning to form with research infrastructure programs such as NEON (USA) (Kao et al ., 2012; Peters et al ., 2014), TERN (Australia) (Cleverly et al ., 2019), and SAEON (South Africa) (van Jaarsveld et al ., 2007), with international synthesis and cooperation being supported by GEO BON, the Global Environmental Research Infrastructure (GERI) group, the Forum on International Cooperation among Environmental Research Infrastructures (FIERI), and the Environmental Research Infrastructures (ENVRI) initiative.…”

Section: Why Our System Needs To Consider All Monitoring Types?mentioning

confidence: 99%

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Effective ecosystem monitoring requires a multi‐scaled approach

et al. 2020

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Ecosystem monitoring is fundamental to our understanding of how ecosystem change is impacting our natural resources and is vital for developing evidence‐based policy and management. However, the different types of ecosystem monitoring, along with their recommended applications, are often poorly understood and contentious. Varying definitions and strict adherence to a specific monitoring type can inhibit effective ecosystem monitoring, leading to poor program development, implementation and outcomes. In an effort to develop a more consistent and clear understanding of ecosystem monitoring programs, we here review the main types of monitoring and recommend the widespread adoption of three classifications of monitoring, namely, targeted, surveillance and landscape monitoring. Landscape monitoring is conducted over large areas, provides spatial data, and enables questions relating to where and when ecosystem change is occurring to be addressed. Surveillance monitoring uses standardised field methods to inform on what is changing in our environments and the direction and magnitude of that change, whilst targeted monitoring is designed around testable hypotheses over defined areas and is the best approach for determining the causes of ecosystem change. The classification system is flexible and can incorporate different interests, objectives, targets and characteristics as well as different spatial scales and temporal frequencies, while also providing valuable structure and consistency across distinct ecosystem monitoring programs. To support our argument, we examine the ability of each monitoring type to inform on six key types of questions that are routinely posed for ecosystem monitoring programs, such as where and when change is occurring, what is the magnitude of change, and how can the change be managed? As we demonstrate, each type of ecosystem monitoring has its own strengths and weaknesses, which should be carefully considered relative to the desired results. Using this scheme, scientists and land managers can design programs best suited to their needs. Finally, we assert that for our most serious environmental challenges, it is essential that we include information from each of these monitoring scales to inform on all facets of ecosystem change, and this is best achieved through close collaboration between the scales. With a renewed understanding of the importance of each monitoring type, along with greater commitment to monitor cooperatively, we will be well placed to address some of our greatest environmental challenges.

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Section: An Explanation Of Monitoring Typesmentioning

confidence: 99%

Section: An Explanation Of Monitoring Typesmentioning

confidence: 99%

Section: An Explanation Of Monitoring Typesmentioning

confidence: 99%

Section: Why Our System Needs To Consider All Monitoring Types?mentioning

confidence: 99%

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Effective ecosystem monitoring requires a multi‐scaled approach

et al. 2020

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“…Here, we assess the performance of a real network of monitoring sites -the Terrestrial Ecosystem Research Network's (TERN) Ausplots -in terms of representativeness by comparing its environmental coverage with that of virtual plot networks based on alternative, 'retrospective' sampling schemes. TERN Ausplots are terrestrial ecosystem monitoring sites located across Australia that have been sampled using a consistent method involving quantitative measurements of vegetation and characterisation of soils (White et al 2012;Guerin et al 2017;Cleverly et al 2019;Sparrow et al 2019a). Ausplots sites form an ecosystem surveillance monitoring network (sensu Eyre et al 2011;Sparrow et al 2019b) that now has plots at over 700 locations.…”

Section: Introductionmentioning

confidence: 99%

Stocktaking the environmental coverage of a continental ecosystem observation network

Guerin

Williams

Sparrow

et al. 2020

Preprint

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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.

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The Landscape Data Commons: A system for standardizing, accessing, and applying large environmental datasets for agroecosystem research and management

McCord,

Webb,

Bestelmeyer

et al. 2023

Agricultural & Env Letters

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Understanding where, when, and why agroecosystems are changing requires quality information about ecosystems that span land tenure, ecological processes, and spatial scales. Over the past two decades, land management agencies and research groups have adopted a suite of standardized methods for monitoring rangelands, which have been implemented at over 85,000 monitoring locations globally. However, the ability to use these data to understand agroecosystem dynamics and change across scales and across land ownership has been limited because, until now, these data have not been available in a harmonized, accessible format for analyses, modeling, and decision‐support tools. We present the Landscape Data Commons, a cyberinfrastructure platform that harmonizes and aggregates standardized agroecosystem data, enables linkages to models, and facilitates analysis and interpretation of data within decision‐support tools. The Landscape Data Commons provides a community platform for users to contribute data and develop next‐generation tools to support agroecosystem management through the 21st century.

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TERN, Australia’s land observatory: addressing the global challenge of forecasting ecosystem responses to climate variability and change

Cited by 40 publications

References 147 publications

Effective ecosystem monitoring requires a multi‐scaled approach

Effective ecosystem monitoring requires a multi‐scaled approach

Stocktaking the environmental coverage of a continental ecosystem observation network

The Landscape Data Commons: A system for standardizing, accessing, and applying large environmental datasets for agroecosystem research and management

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