Are trait-growth models transferable? Predicting multi-species growth trajectories between ecosystems using plant functional traits
AuthorThomas, FM; Vesk, PA
Source TitlePLoS One
PublisherPUBLIC LIBRARY SCIENCE
University of Melbourne Author/sVesk, Peter
AffiliationSchool of BioSciences
Document TypeJournal Article
CitationsThomas, F. M. & Vesk, P. A. (2017). Are trait-growth models transferable? Predicting multi-species growth trajectories between ecosystems using plant functional traits. PLOS ONE, 12 (5), https://doi.org/10.1371/journal.pone.0176959.
Access StatusOpen Access
Plant functional traits are increasingly used to generalize across species, however few examples exist of predictions from trait-based models being evaluated in new species or new places. Can we use functional traits to predict growth of unknown species in different areas? We used three independently collected datasets, each containing data on heights of individuals from non-resprouting species over a chronosquence of time-since-fire sites from three ecosystems in south-eastern Australia. We examined the influence of specific leaf area, woody density, seed size and leaf nitrogen content on three aspects of plant growth; maximum relative growth rate, age at maximum growth and asymptotic height. We tested our capacity to perform out-of-sample prediction of growth trajectories between ecosystems using species functional traits. We found strong trait-growth relationships in one of the datasets; whereby species with low SLA achieved the greatest asymptotic heights, species with high leaf-nitrogen content achieved relatively fast growth rates, and species with low seed mass reached their time of maximum growth early. However these same growth-trait relationships did not hold across the two other datasets, making accurate prediction from one dataset to another unachievable. We believe there is evidence to suggest that growth trajectories themselves may be fundamentally different between ecosystems and that trait-height-growth relationships may change over environmental gradients.
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