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Are plant community responses to wildfire contingent upon historical disturbance regimes?
Author(s) -
Miller Jesse E. D.,
Safford Hugh D.
Publication year - 2020
Publication title -
global ecology and biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.164
H-Index - 152
eISSN - 1466-8238
pISSN - 1466-822X
DOI - 10.1111/geb.13115
Subject(s) - disturbance (geology) , ecology , intermediate disturbance hypothesis , ecosystem , plant community , geography , species richness , fire regime , environmental science , environmental resource management , biology , paleontology
Background Ecological disturbance is a major driver of ecosystem structure and evolutionary selection, and theory predicts that the frequency and/or intensity of disturbance should determine its effects on communities. However, adaptations of species pools to different historical disturbance regimes are rarely considered in the search for generalizable community responses to disturbance. To explore how the severity of disturbance affects plant diversity patterns, we review studies of understorey plant community responses to wildfire in conifer forests of western North America across a gradient of departure from historical fire regimes. Review findings We find that post‐fire plant species richness may generally be maximized at disturbance severities that match the predominant historical disturbance regime in a given ecosystem. Studies that examined multiple spatial scales indicate that plant community responses to fire are likely to be scale dependent, suggesting that post‐disturbance monitoring should consider community responses at multiple scales. Synthesis Our review highlights that consideration of historical disturbance regimes might improve the ability to predict the effects of disturbance on communities. We discuss future research needs; quantitative studies that compare community responses to fire at multiple scales across different historical fire regimes would be particularly useful. Ultimately, consideration of disturbance as a multivariate problem is likely to lead to greater inference than traditional bivariate approaches.