Browsing by Author "Schrodt, Franziska"
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Item Estimating themissing species bias in plant trait measurements(Wiley, 2015) Sandel, Brody; Gutiérrez, Alvaro G; Reich, Peter B; Schrodt, Franziska; Dickie, John; Kattge, JensAim Do plant trait databases represent a biased sample of species, and if so, can that bias be corrected? Ecologists are increasingly collecting and analysing data on plant functional traits, and contributing them to large plant trait databases. Many applications of such databases involve merging trait measurements with other data such as species distributions in vegetation plots; a process that invariably produces matrices with incomplete trait and species data. Typically, missing data are simply ignored and it is assumed that the missing species are missing at random. Methods Here, we argue that this assumption is unlikely to be valid and propose an approach for estimating the strength of the bias regarding which species are represented in trait databases. The method leverages the fact that, within a given database, some species have many measurements of a trait and others have few (high vs low measurement intensity). In the absence of bias, there should be no relationship between measurement intensity and trait values. We demonstrate the method using five traits that are part of the TRY database, a global archive of plant traits. Our method also leads naturally to a correction for this bias, which we validate and apply to two examples. Results Specific leaf area and seed mass were strongly positively biased (frequently measured species had higher trait values than rarely measured species), leaf nitrogen per unit mass and maximum height were moderately negatively biased, and maximum photosynthetic capacity per unit leaf area was weakly negatively biased. The bias-correction method yielded greatly improved estimates in the validation tests for the two most biased traits. Further, in our two applications, ecological interpretations were shown to be sensitive to uncorrected bias in the data. Conclusions Species inclusion in trait databases appears to be strongly biased in some cases, and failure to correct this can lead to incorrect conclusions.Item The imprint of plants on ecosystem functioning: A data-driven approach(Elsevier, 2015) Musavi, Talie; Mahecha, Miguel D; Migliavacca, Mirco; Reichstein, Markus; van de Weg, Martine Janet; van Bodegom, Peter M; Bahn, Michael; Wirth, Christian; Reich, Peter B; Schrodt, Franziska; Kattge, JensTerrestrial ecosystems strongly determine the exchange of carbon, water and energy between the biosphere and atmosphere. These exchanges are influenced by environmental conditions (e.g., local meteorology, soils), but generally mediated by organisms. Often, mathematical descriptions of these processes are implemented in terrestrial biosphere models. Model implementations of this kind should be evaluated by empirical analyses of relationships between observed patterns of ecosystem functioning, vegetation structure, plant traits, and environmental conditions. However, the question of how to describe the imprint of plants on ecosystem functioning based on observations has not yet been systematically investigated. One approach might be to identify and quantify functional attributes or responsiveness of ecosystems (often very short-term in nature) that contribute to the long-term (i.e., annual but also seasonal or daily) metrics commonly in use. Here we define these patterns as “ecosystem functional properties”, or EFPs. Such as the ecosystem capacity of carbon assimilation or the maximum light use efficiency of an ecosystem. While EFPs should be directly derivable from flux measurements at the ecosystem level, we posit that these inherently include the influence of specific plant traits and their local heterogeneity. We present different options of upscaling in situ measured plant traits to the ecosystem level (ecosystem vegetation properties – EVPs) and provide examples of empirical analyses on plants’ imprint on ecosystem functioning by combining in situ measured plant traits and ecosystem flux measurements. Finally, we discuss how recent advances in remote sensing contribute to this framework.