Browsing by Autor "Emilio Vilanova"

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A Multi-Scale Ecological Approach for the Conservation and Restoration of Venezuelan Andean Cloud Forests
(2025) C. García‐Núñez; Fermín Rada; Ana Quevedo-Rojas; Mauricio Jeréz; Luis D. Llambí; C. Pacheco; Luis E. Gámez; Emilio Vilanova
Assessing the extent of “conflict of use” in multipurpose tropical forest trees: A regional view
(Elsevier BV, 2013) Cristina Herrero‐Jáuregui; Manuel R. Guariguata; Dairón Cárdenas; Emilio Vilanova; M.C. Rubio Robles; Juan Carlos Licona; W. Nalvarte
Guiding Principles for Small-Scale Forestry in a Watershed of the Venezuelan Andes: Constraints and Opportunities
(Springer Science+Business Media, 2008) Armando Torres‐Lezama; Emilio Vilanova; Hirma Ramírez‐Angulo
Making forest data fair and open
(Nature Portfolio, 2022) Renato A. Ferreira de Lima; Oliver L. Phillips; Álvaro Duque; J. Sebastián Tello; Stuart J. Davies; Alexandre A. Oliveira; Sandra Cristina Müller; Eurídice N. Honorio Coronado; Emilio Vilanova; Aida Cuní‐Sanchez
Planning and Policy Issues in Small-scale Forestry Development in Southern Aragua State, Venezuela
(Springer Science+Business Media, 2010) Armando Torres‐Lezama; Emilio Vilanova; Hirma Ramírez‐Angulo
Tropical forests in the Americas are changing too slowly to track climate change
(American Association for the Advancement of Science, 2025) Jesús Aguirre‐Gutiérrez; Sandra Dı́az; Sami W. Rifai; José Javier Corral‐Rivas; María Guadalupe Nava‐Miranda; Roy González‐M.; Ana Belén Hurtado‐M; Norma Salinas; Emilio Vilanova; Everton Cristo de Almeida
Understanding the capacity of forests to adapt to climate change is of pivotal importance for conservation science, yet this is still widely unknown. This knowledge gap is particularly acute in high-biodiversity tropical forests. Here, we examined how tropical forests of the Americas have shifted community trait composition in recent decades as a response to changes in climate. Based on historical trait-climate relationships, we found that, overall, the studied functional traits show shifts of less than 8% of what would be expected given the observed changes in climate. However, the recruit assemblage shows shifts of 21% relative to climate change expectation. The most diverse forests on Earth are changing in functional trait composition but at a rate that is fundamentally insufficient to track climate change.
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.