Spatial Imaging and Screening for Regime Shifts
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-10-01
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| Series: | Frontiers in Ecology and Evolution |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fevo.2019.00407/full |
| _version_ | 1818031410797084672 |
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| author | Daniel R. Uden Daniel R. Uden Dirac Twidwell Dirac Twidwell Craig R. Allen Craig R. Allen Matthew O. Jones David E. Naugle Jeremy D. Maestas Brady W. Allred |
| author_facet | Daniel R. Uden Daniel R. Uden Dirac Twidwell Dirac Twidwell Craig R. Allen Craig R. Allen Matthew O. Jones David E. Naugle Jeremy D. Maestas Brady W. Allred |
| author_sort | Daniel R. Uden |
| collection | DOAJ |
| description | 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 signal associated with the expected regime shift? (3) If detected, is the 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 more concerning persistent and non-stationary transition signals, and 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. |
| first_indexed | 2024-12-10T05:51:02Z |
| format | Article |
| id | doaj.art-4da2db636a274f6a99e07d1eb4ba6026 |
| institution | Directory Open Access Journal |
| issn | 2296-701X |
| language | English |
| last_indexed | 2024-12-10T05:51:02Z |
| publishDate | 2019-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Ecology and Evolution |
| spelling | doaj.art-4da2db636a274f6a99e07d1eb4ba60262022-12-22T02:00:03ZengFrontiers Media S.A.Frontiers in Ecology and Evolution2296-701X2019-10-01710.3389/fevo.2019.00407460349Spatial Imaging and Screening for Regime ShiftsDaniel R. Uden0Daniel R. Uden1Dirac Twidwell2Dirac Twidwell3Craig R. Allen4Craig R. Allen5Matthew O. Jones6David E. Naugle7Jeremy D. Maestas8Brady W. Allred9Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE, United StatesSchool of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United StatesDepartment of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE, United StatesCenter for Resilience in Agricultural Working Landscapes, University of Nebraska–Lincoln, Lincoln, NE, United StatesSchool of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United StatesCenter for Resilience in Agricultural Working Landscapes, University of Nebraska–Lincoln, Lincoln, NE, United StatesW. A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, United StatesW. A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, United StatesUnited States Department of Agriculture, Natural Resources Conservation Service, Portland, OR, United StatesW. A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, United StatesScreening 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 signal associated with the expected regime shift? (3) If detected, is the 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 more concerning persistent and non-stationary transition signals, and 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.https://www.frontiersin.org/article/10.3389/fevo.2019.00407/fulldiagnosisearly warning indicatorGoogle Earth Engineproactive managementrangeland analysis platformresilience |
| spellingShingle | Daniel R. Uden Daniel R. Uden Dirac Twidwell Dirac Twidwell Craig R. Allen Craig R. Allen Matthew O. Jones David E. Naugle Jeremy D. Maestas Brady W. Allred Spatial Imaging and Screening for Regime Shifts Frontiers in Ecology and Evolution diagnosis early warning indicator Google Earth Engine proactive management rangeland analysis platform resilience |
| title | Spatial Imaging and Screening for Regime Shifts |
| title_full | Spatial Imaging and Screening for Regime Shifts |
| title_fullStr | Spatial Imaging and Screening for Regime Shifts |
| title_full_unstemmed | Spatial Imaging and Screening for Regime Shifts |
| title_short | Spatial Imaging and Screening for Regime Shifts |
| title_sort | spatial imaging and screening for regime shifts |
| topic | diagnosis early warning indicator Google Earth Engine proactive management rangeland analysis platform resilience |
| url | https://www.frontiersin.org/article/10.3389/fevo.2019.00407/full |
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