Working across space and time: nonstationarity in ecological research and application

Rollinson, Christine R.; Finley, Andrew O.; Alexánder, Mario; Banerjee, Sudipto; Hamil, Kelly-Ann Dixon; Koenig, Lauren E.; Locke, Dexter H.; DeMarche, Megan L.; Tingley, Morgan W.; Wheeler, Kathryn I.; Youngflesh, Casey; Zipkin, Elise F.

doi:10.1002/fee.2298

Cited by 94 publications

(99 citation statements)

References 40 publications

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“…Statistical approaches that are insensitive to nonstationarity are important to macrosystems biologists as well as to the broader community of ecologists faced with similar statistical problems, and as such, Rollinson et al . (2021) summarize challenges and solutions relevant to all ecologists. Similarly, Zipkin et al .…”

Section: Macrosystems Is Developing As a Scientific Disciplinementioning

confidence: 99%

“…The articles included in this Special Issue also discuss how macrosystems research can be integrated with and enriches other fields of study, for example by addressing the challenges associated with nonstationarity (a stochastic process whose unconditional joint probability distribution is a function of time), combining data across scales, and training large collaborative groups. Nonstationarity is a common feature of ecological systems as they change over time, violating statistical assumptions, and is particularly a problem in analyses of large spatial scale data (Rollinson et al 2021). Statistical approaches that are insensitive to nonstationarity are important to macrosystems biologists as well as to the broader community of ecologists faced with similar statistical problems, and as such, Rollinson et al (2021) summarize challenges and solutions relevant to all ecologists.…”

Section: Macrosystems Is Developing As a Scientific Disciplinementioning

confidence: 99%

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Macrosystems revisited: challenges and successes in a new subdiscipline of ecology

Dodds

Rose

Fei

et al. 2021

Frontiers in Ecol & Environ

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Section: Macrosystems Is Developing As a Scientific Disciplinementioning

confidence: 99%

Section: Macrosystems Is Developing As a Scientific Disciplinementioning

confidence: 99%

Macrosystems revisited: challenges and successes in a new subdiscipline of ecology

Dodds

Rose

Fei

et al. 2021

Frontiers in Ecol & Environ

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“…In this time of rapid environmental change, forecasts respond to the imperative need to provide society with the best-available information to inform environmental decision making (Clark, 2001). The nonstationary, no-analog nature of many environmental changes makes the need for forecasts particularly important as traditional management approaches rely on historical norms that may no longer be relevant (Milly et al, 2008;Rollinson et al, 2021). Iterative forecasts, which can be tested and updated on decision relevant timescales, are a particularly pressing need, made possible in many domains by increases in data volume, openness, and speed (i.e.…”

Section: Introductionmentioning

confidence: 99%

A Community Convention for Ecological Forecasting: Output Files and Metadata

Dietze¹,

Thomas²,

Peters³

et al. 2021

Preprint

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This document summarizes the open community standards developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. Such open standards are intended to promote interoperability and facilitate forecast adoption, distribution, validation, and synthesis. For output files EFI has adopted a three-tiered approach reflecting trade-offs in forecast data volume and technical expertise. The preferred output file format is netCDF following the Climate and Forecast Convention for dimensions and variable naming, including an ensemble dimension where appropriate. The second-tier option is a semi-long CSV format, with state variables as columns and each row representing a unique issue datetime, prediction datetime, location, ensemble member, etc. The third-tier option is similar to option 2, but each row represents a specific summary statistic (mean, upper/lower CI) rather than individual ensemble members. For metadata, EFI expands upon the Ecological Metadata Language (EML), using additionalMetadata tags to store information designed to facilitate cross-forecast synthesis (e.g. uncertainty propagation, data assimilation, model complexity) and setting a subset of base EML tags (e.g. temporal resolution, output variables) to be required. To facilitate community adoption we also provides a R package containing a number of vignettes on how to both write and read in the EFI standard, as well as a metadata validator tool.

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“…Either way, these models rarely test the sensitivity of their results to these assumptions. Although uncertainty about the rate of ecological acclimation has been recognized [20][21][22][23] , to our knowledge its magnitude, and its consequences for ecosystem services, remains unknown.…”

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confidence: 99%

“…First, we need better information about the rate of ecological acclimation. Other recent studies have also emphasized the need for a better understanding of rates of ecological change 23,46 . We do not need precision; even estimates of such rates to the nearest order of magnitude could greatly reduce the uncertainty of long-term projections such as ours by identifying the appropriate climate reference period, or by informing a model weighting approach 22 .…”

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confidence: 99%

The rate of ecosystem acclimation is the dominant uncertainty in long-term projections of an ecosystem service

Felton

Shriver

Stemkovski

et al. 2021

Preprint

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The potential for ecosystems to continue providing society with essential services may depend on their ability to acclimate to climate change through multiple processes operating from cells to landscapes. While models to predict climate change impacts on ecosystem services often consider uncertainty among greenhouse gas emission scenarios or global circulation models (GCMs), they rarely consider the rate of ecological acclimation, which depends on decadal-scale processes such as species turnover. Here we show that uncertainty due to the unknown rate of ecological acclimation is larger than other sources of uncertainty in late-century projections of forage production in US rangelands. Combining statistical models fit to historical climate data and remotely-sensed estimates of herbaceous productivity with an ensemble of GCMs, we projected changes in forage production using two approaches. The time-series approach, which assumes minimal acclimation, projects widespread decreases in forage production. The spatial gradient approach, which assumes ecological acclimation keeps pace with climate, predicts widespread increases in forage production. This first attempt to quantify the magnitude of a critical uncertainty emphasizes that better understanding of ecological acclimation is essential to improve long-term forecasts of ecosystem services, and shows that management to facilitate ecological acclimation may be necessary to maintain ecosystem services at historical baselines.

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Working across space and time: nonstationarity in ecological research and application

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Cited by 94 publications

References 40 publications

Macrosystems revisited: challenges and successes in a new subdiscipline of ecology

Macrosystems revisited: challenges and successes in a new subdiscipline of ecology

A Community Convention for Ecological Forecasting: Output Files and Metadata

The rate of ecosystem acclimation is the dominant uncertainty in long-term projections of an ecosystem service

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