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An ecoclimatic framework for evaluating the resilience of vegetation to water deficit. | LitMetric

AI Article Synopsis

  • - The text discusses a new ecoclimatic framework aimed at understanding how drought affects forest ecosystems beyond just tree mortality, focusing on water deficits and their cumulative impacts on various ecosystem processes.
  • - It is based on two main hypotheses: (i) that water deficit exposure can be predicted across different ecosystems and (ii) that the impact of water deficiency depends on the resistance and recovery traits of those ecosystems.
  • - Case studies on Pinus edulis and Eucalyptus globulus illustrate how this framework can be applied to assess resilience and define climatic thresholds related to vegetation responses under water stress.

Article Abstract

The surge in global efforts to understand the causes and consequences of drought on forest ecosystems has tended to focus on specific impacts such as mortality. We propose an ecoclimatic framework that takes a broader view of the ecological relevance of water deficits, linking elements of exposure and resilience to cumulative impacts on a range of ecosystem processes. This ecoclimatic framework is underpinned by two hypotheses: (i) exposure to water deficit can be represented probabilistically and used to estimate exposure thresholds across different vegetation types or ecosystems; and (ii) the cumulative impact of a series of water deficit events is defined by attributes governing the resistance and recovery of the affected processes. We present case studies comprising Pinus edulis and Eucalyptus globulus, tree species with contrasting ecological strategies, which demonstrate how links between exposure and resilience can be examined within our proposed framework. These examples reveal how climatic thresholds can be defined along a continuum of vegetation functional responses to water deficit regimes. The strength of this framework lies in identifying climatic thresholds on vegetation function in the absence of more complete mechanistic understanding, thereby guiding the formulation, application and benchmarking of more detailed modelling.

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Source
http://dx.doi.org/10.1111/gcb.13177DOI Listing

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