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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Ecol. Evol. | doi: 10.3389/fevo.2019.00407

Regime shift imaging and screening

 Daniel R. Uden1*,  Dirac Twidwell1,  Craig R. Allen1, Matthew O. Jones2, David E. Naugle2, Jeremy D. Maestas3 and  Brady W. Allred2
  • 1University of Nebraska-Lincoln, United States
  • 2University of Montana, United States
  • 3Natural Resources Conservation Service, United States Department of Agriculture (NRCS-USDA), United States

Screening is a strategy for detecting undesirable change prior to manifestation of symptoms or adverse effects. Although the well-recognized utility of screening makes it commonplace in medicine, it has yet to be implemented in ecosystem management. Ecosystem management is in an era of diagnosis and treatment of undesirable change, and as a result, remains more reactive than proactive and unable to effectively deal with today’s plethora of non-stationary conditions. In this paper, we introduce spatial imaging-based screening to ecology. We link advancements in spatial resilience theory, data, and technological and computational capabilities and power to detect regime shifts (i.e., vegetation state transitions) that are known to be detrimental to human well-being and ecosystem service delivery. With a state-of-the-art landcover dataset and freely-available, cloud-based, geospatial computing platform, we screen for spatial signals of the three most iconic vegetation transitions studied in western USA rangelands: (1) erosion and desertification; (2) woody encroachment; and (3) annual exotic grass invasion. For a series of locations that differ in ecological complexity and geographic extent, we answer the following questions: (1) Which regime shift is expected or of greatest concern? (2) Can we detect a transition signal associated with the expected regime shift? (3) If detected, is the transition signal transient or persistent over time? (4) If detected and persistent, is the transition signal stationary or non-stationary over time? (5) What other signals do we detect? Our approach reveals a powerful and flexible methodology, whereby professionals can use spatial imaging to verify the occurrence of alternative vegetation regimes, image the spatial boundaries separating regimes, track the magnitude and direction of regime shift signals, differentiate persistent and stationary transition signals that warrant continued screening from the more concerning persistent and non-stationary transition signals, and then leverage disciplinary strength and resources for more targeted diagnostic testing (e.g., inventory and monitoring) and treatment (e.g., management) of regime shifts. While the rapid screening approach used here can continue to be implemented and refined for rangelands, it has broader implications and can be adapted to other ecological systems to revolutionize the information space needed to better manage critical transitions in nature.

Keywords: diagnosis, Early warning indicator, Google Earth Engine, Proactive management, Rangeland Analysis Platform, resilience, Spatial resilience, Treatment

Received: 15 Mar 2019; Accepted: 09 Oct 2019.

Copyright: © 2019 Uden, Twidwell, Allen, Jones, Naugle, Maestas and Allred. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Daniel R. Uden, University of Nebraska-Lincoln, Lincoln, United States, duden2@unl.edu