Browsing by Subject "plant functional trait"

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    Do evergreen and deciduous tree differ in their effects on soil nitrogen availability
    (Ecological Society of America, 2012) Mueller, Kevin E; Hobbie, Sarah E; Oleksyn, Jacek; Reich, Peter B; Eissenstat, David M
    Evergreen and deciduous plants are widely expected to have different impacts on soil nitrogen (N) availability because of differences in leaf litter chemistry and ensuing effects on net N mineralization (Nmin). We evaluated this hypothesis by compiling published data on net Nmin rates beneath co-occurring stands of evergreen and deciduous trees. The compiled data included 35 sets of co-occurring stands in temperate and boreal forests. Evergreen and deciduous stands did not have consistently divergent effects on net Nmin rates; net Nmin beneath deciduous trees was higher when comparing natural stands (19 contrasts), but equivalent to evergreens in plantations (16 contrasts). We also compared net Nmin rates beneath pairs of co-occurring genera. Most pairs of genera did not differ consistently, i.e., tree species from one genus had higher net Nmin at some sites and lower net Nmin at other sites. Moreover, several common deciduous genera (Acer, Betula, Populus) and deciduous Quercus spp. did not typically have higher net Nmin rates than common evergreen genera (Pinus, Picea). There are several reasons why tree effects on net Nmin are poorly predicted by leaf habit and phylogeny. For example, the amount of N mineralized from decomposing leaves might be less than the amount of N mineralized from organic matter pools that are less affected by leaf litter traits, such as dead roots and soil organic matter. Also, effects of plant traits and plant groups on net Nmin probably depend on site-specific factors such as stand age and soil type.
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    Estimating themissing species bias in plant trait measurements
    (Wiley, 2015) Sandel, Brody; Gutiérrez, Alvaro G; Reich, Peter B; Schrodt, Franziska; Dickie, John; Kattge, Jens
    Aim 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.