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Sensitivity of community-level trait–environment relationships to data representativeness: A test for functional biogeography

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Kattge,  Jens
Interdepartmental Max Planck Fellow Group Functional Biogeography, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Borgy, B., Violle, C., Choler, P., Garnier, E., Kattge, J., Loranger, J., et al. (2017). Sensitivity of community-level trait–environment relationships to data representativeness: A test for functional biogeography. Global Ecology and Biogeography, 26(6), 729-739. doi:10.1111/geb.12573.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002D-47AA-F
Abstract
Aim: The characterization of trait–environment relationships over broad-scale gradients is a critical goal for ecology and biogeography. This implies the merging of plot and trait databases to assess community-level trait-based statistics. Potential shortcomings and limitations of this approach are that: (i) species traits are not measured where the community is sampled and (ii) the availability of trait data varies considerably across species and plots. Here we address the effect of trait data representativeness [the sampling effort per species and per plot] on the accuracy of (i) species-level and (ii) community-level trait estimates and (iii) the consequences for the shape and strength of trait–environment relationships across communities. Innovation: We combined information existing in databases of vegetation plots and plant traits to estimate community-weighted means [CWMs] of four key traits [specific leaf area, plant height, seed mass and leaf nitrogen content per dry mass] in permanent grasslands at a country-wide scale. We propose a generic approach for systematic sensitivity analyses based on random subsampling and data reduction to address the representativeness of incomplete and heterogeneous trait information when exploring trait–environment relationships across communities. Main conclusions: The accuracy of the CWMs was little affected by the number of individual trait values per species [NIV] but strongly affected by the cover proportion of species with available trait values [PCover]. A PCover above 80% was required for all four traits studied to obtain an estimation bias below 5%. Our approach therefore provides more conservative criteria than previously proposed. Restrictive criteria on both NIV and PCover primarily excluded communities in harsh environments, and such reduction of the sampled gradient weakened trait–environment relationships. These findings advocate systematic measurement campaigns in natural environments to increase species coverage in global trait databases, with