Browsing by Autor "Nathan J. B. Kraft"

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Plant traits predict inter‐ and intraspecific variation in susceptibility to herbivory in a hyperdiverse Neotropical rain forest tree community
(Wiley, 2014) Rafael E. Cárdenas; Renato Valencia; Nathan J. B. Kraft; Adriana Argoti; Olivier Dangles
Summary A key issue in plant/herbivore interaction research is to understand which plant traits drive differences in herbivore damage. Variation in chemical, physical or phenological traits of plants may all modulate the degree of herbivore damage among species and individuals, yet the relative importance of these factors is still subject to debate, particularly in species‐rich systems such as tropical rain forests. To address this issue, we quantified leaf herbivore damage in 28 common tree species of the Yasuní forest dynamic plot ( YFDP ) in the Ecuadorian Amazon over 11 months. Census data from the YFDP allowed us to quantify several aspects of tree ecology potentially affecting herbivory including leaf turnover and spatial distribution patterns. We measured six chemical, eight physical and four ecological traits of the focal species. Using a combination of multivariate analyses and phylogenetic generalized linear regression model ( PGLS ), we assessed trade‐offs between physical and chemical traits and the relative effect of all these traits on leaf herbivore damage. Herbivore damage was highly variable among species and individuals, with leaves on average displaying damage over 13.4% (2.5–29.5%) of their area. We found no significant trade‐off between physical and chemical defences for the 28 studied tree species. Overall, leaf size, shearing resistance, cellulose, ash content and leaf size × ash were the best predictors of herbivore damage. Surprisingly, condensed tannins and latex did not significantly correlate with herbivore damage. In addition, we found no relationships between herbivory and local tree density. However, we did find a weak effect of tree clustering and strong effect of tree leaf turnover rates on herbivore damage. Synthesis . In the western Amazon, leaves are defended against herbivores through a combination of physical (toughness), chemical (toughness‐related elements), and phenological (rapid leaf replacement) characteristics that do not appear to be subject to obvious trade‐offs. Conventional strategies, such as condensed tannins or latex, do not seem to be strongly involved as a defence against herbivores in this community.
Use and misuse of trait imputation in ecology: the problem of using out‐of‐context imputed values
(Wiley, 2025) Lucas D. Gorné; Jesús Aguirre‐Gutiérrez; Fernanda C. Souza; Nathan G. Swenson; Nathan J. B. Kraft; Beatriz Schwantes Marimon; Timothy R. Baker; Renato A. Ferreira de Lima; Emilio Vilanova; Esteban Álvarez‐Dávila
Despite the progress in the measurement and accessibility of plant trait information, acquiring sufficiently complete data from enough species to answer broad‐scale questions in plant functional ecology and biogeography remains challenging. A common way to overcome this challenge is by imputation, or ‘gap‐filling' of trait values. This has proven appropriate when focusing on the overall patterns emerging from the database being imputed. However, some applications force the imputation procedure out of its original scope, using imputed values independently from the imputation context, and specific trait values for a given species are used as input for computing new variables. We tested the performance of three widely used imputation methods (Bayesian hierarchical probabilistic matrix factorization, multiple imputation by chained equations with predictive mean matching, and Rphylopars) on a database of tropical tree and shrub traits. By applying a leave‐one‐out procedure, we assessed the accuracy and precision of the imputed values and found that out‐of‐context use of imputed values may bias the estimation of different variables. We also found that low redundancy (i.e. low predictability of a new value on the basis of existing values) in the dataset, not uncommon for empirical datasets, is likely the main cause of low accuracy and precision in the imputed values. We therefore suggest the use of a leave‐one‐out procedure to test the quality of the imputed values before any out‐of‐context application of the imputed values, and make practical recommendations to avoid the misuse of imputation procedures. Furthermore, we recommend not publishing gap‐filled datasets, publishing instead only the empirical data, together with the imputation method applied and the corresponding script to reproduce the imputation. This will help avoid the spread of imputed data, whose accuracy, precision, and source are difficult to assess and track, into the public domain.