Ecosystem dynamics and management after forest die-off: a global synthesis with conceptual state-and-transition models
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Cobb_et_al-2017-Ecosphere.pdf
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Author
Cobb, Richard C.Ruthrof, Katinka X.
Breshears, David D.
Lloret, Francisco
Aakala, Tuomas
Adams, Henry D.
Anderegg, William R. L.
Ewers, Brent E.
Galiano, Lucía
Grünzweig, José M.
Hartmann, Henrik
Huang, Cho-ying
Klein, Tamir
Kunert, Norbert
Kitzberger, Thomas
Landhäusser, Simon M.
Levick, Shaun
Preisler, Yakir
Suarez, Maria L.
Trotsiuk, Volodymyr
Zeppel, Melanie J. B.
Affiliation
Univ Arizona, Sch Nat Resources & EnvironmUniv Arizona, Dept Ecol & Evolutionary Biol
Issue Date
2017-12Keywords
climate changeconceptual state-and-transition models
drought
fire
forest management
pests and pathogens
tree die-off
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WILEYCitation
Ecosystem dynamics and management after forest die-off: a global synthesis with conceptual state-and-transition models 2017, 8 (12):e02034 EcosphereJournal
EcosphereRights
© 2017 Cobb et al. This is an open access article under the terms of the Creative Commons Attribution License.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Broad-scale forest die-off associated with drought and heat has now been reported from every forested continent, posing a global-scale challenge to forest management. Climate-driven die-off is frequently compounded with other drivers of tree mortality, such as altered land use, wildfire, and invasive species, making forest management increasingly complex. Facing similar challenges, rangeland managers have widely adopted the approach of developing conceptual models that identify key ecosystem states and major types of transitions between those states, known as "state-and-transition models" (S&T models). Using expert opinion and available research, the development of such conceptual S&T models has proven useful in anticipating ecosystem changes and identifying management actions to undertake or to avoid. In cases where detailed data are available, S&T models can be developed into probabilistic predictions, but even where data are insufficient to predict transition probabilities, conceptual S&T models can provide valuable insights for managing a given ecosystem and for comparing and contrasting different ecosystem dynamics. We assembled a synthesis of 14 forest die-off case studies from around the globe, each with sufficient information to infer impacts on forest dynamics and to inform management options following a forest die-off event. For each, we developed a conceptual S&T model to identify alternative ecosystem states, pathways of ecosystem change, and points where management interventions have been, or may be, successful in arresting or reversing undesirable changes. We found that our diverse set of mortality case studies fit into three broad classes of ecosystem trajectories: (1) single-state transition shifts, (2) ecological cascading responses and feedbacks, and (3) complex dynamics where multiple interactions, mortality drivers, and impacts create a range of possible state transition responses. We integrate monitoring and management goals in a framework aimed to facilitate development of conceptual S&T models for other forest die-off events. Our results highlight that although forest die-off events across the globe encompass many different underlying drivers and pathways of ecosystem change, there are commonalities in opportunities for successful management intervention.Note
Open access journal.ISSN
21508925Version
Final published versionSponsors
Mcintire Stennis funds; Sir Walter Murdoch Distinguished Collaborator Award through Murdoch University; NSF [DEB EF-0622770, EF-1340624, EF-1550756, EAR-1331408]; US Department of Energy, Office of Science, Biological and Environmental Research; Wyoming Water Development Commission; United States Geological Service; UA-CONAYCT Initiative; Arizona Agriculture Experiment Station; Spanish MEC [CGL2015-67419-R]; Ministry of Science and Technology; National Taiwan University; GACR [15-14840S]; CIGA [20154316]; Gordon and Betty Moore Foundation; USDA-FS Pacific Southwest Research Station; AGAUR [2014-SGR-00453]Additional Links
http://doi.wiley.com/10.1002/ecs2.2034ae974a485f413a2113503eed53cd6c53
10.1002/ecs2.2034
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Except where otherwise noted, this item's license is described as © 2017 Cobb et al. This is an open access article under the terms of the Creative Commons Attribution License.

