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Data from: Plant functional indicators of vegetation response to climate change, past present and future: I. Trends, emerging hypotheses and plant functional modality

Data files

May 23, 2019 version files 7.29 MB

Abstract

Plant functional traits are widely applied in models that simulate the effects of climate change on biodiversity and resource management. Here the aim is to examine the potential role of specific plant functional traits and their whole-plant syndromes (Plant Functional Types or PFTs as specific ‘modal’ trait assemblages) as indicators of vegetation response to climate change, past, present and future. Because plant functional characteristics have evolved through time, it is widely argued that models of vegetation performance under future climates should benefit from a study of plant response under previous climates. This paper presents an overview first, of developmental concepts underlying the current use of PFTs as indicators of plant response to environmental change, second, implications arising from species acclimation and third, process-based models used in reconstruction of vegetation under mainly Holocene environments but also with respect to present and future climates. In this regard the role of individual functional traits in ‘biomization’ procedures in vegetation response models is briefly discussed. It is concluded that, while PFTs possess limited indicator value at biome scale, uncertainties in the delimitation of local paleohabitats greatly restrict their use as indicators for paleovegetation reconstruction at community level. Emerging hypotheses are: 1) A whole-plant system of modal PFTs based on a novel set of functional traits can provide an improved alternative to PFTs and traits used in models of vegetation response to climate change, 2) Modal PFTs are potentially more efficient indicators of vegetation response to climate change than individual traits, 3) Improved plant functional selection criteria can lead to more efficient parameterization of Earth System and Dynamic Global Vegetation Models.