Browsing by Autor "Beatriz Schwantes Marimon"

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Long-term thermal sensitivity of Earth’s tropical forests
(American Association for the Advancement of Science, 2020) Martin J. P. Sullivan; Simon L. Lewis; Kofi Affum‐Baffoe; Carolina V. Castilho; Flávia R. C. Costa; Aida Cuní‐Sanchez; Corneille E. N. Ewango; Wannes Hubau; Beatriz Schwantes Marimon; Abel Monteagudo‐Mendoza
The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (-9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater impact per °C in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth's climate.
On the delineation of tropical vegetation types with an emphasis on forest/savanna transitions
(Taylor & Francis, 2013) Mireia Torello‐Raventos; Ted R. Feldpausch; Elmar Veenendaal; Franziska Schrodt; Gustavo Saiz; Tomas F. Domingues; Gloria Djagbletey; Andrew Ford; J. Kemp; Beatriz Schwantes Marimon
Background: There is no generally agreed classification scheme for the many different vegetation formation types occurring in the tropics. This hinders cross-continental comparisons and causes confusion as words such as ‘forest’ and ‘savanna’ have different meanings to different people. Tropical vegetation formations are therefore usually imprecisely and/or ambiguously defined in modelling, remote sensing and ecological studies. Aims: To integrate observed variations in tropical vegetation structure and floristic composition into a single classification scheme. Methods: Using structural and floristic measurements made on three continents, discrete tropical vegetation groupings were defined on the basis of overstorey and understorey structure and species compositions by using clustering techniques. Results: Twelve structural groupings were identified based on height and canopy cover of the dominant upper stratum and the extent of lower-strata woody shrub cover and grass cover. Structural classifications did not, however, always agree with those based on floristic composition, especially for plots located in the forest–savanna transition zone. This duality is incorporated into a new tropical vegetation classification scheme. Conclusions: Both floristics and stand structure are important criteria for the meaningful delineation of tropical vegetation formations, especially in the forest/savanna transition zone. A new tropical vegetation classification scheme incorporating this information has been developed.
Rarity of monodominance in hyperdiverse Amazonian forests
(Nature Portfolio, 2019) Hans ter Steege; Terry W. Henkel; Nora Helal; Beatriz Schwantes Marimon; Ben Hur Marimon; Andreas Huth; Jürgen Groeneveld; Daniel Sabatier; Luiz de Souza Coêlho; Diógenes de Andrade Lima Filho
Soil pyrogenic carbon in southern Amazonia: Interaction between soil, climate, and above-ground biomass
(Frontiers Media, 2022) Edmar Almeida de Oliveira; Ted R. Feldpausch; Beatriz Schwantes Marimon; Paulo S. Morandi; Oliver L. Phillips; Michael I. Bird; Alejandro Araujo Murakami; Luzmila Arroyo; Carlos A. Quesada; Ben Hur Marimon
The Amazon forest represents one of the world’s largest terrestrial carbon reservoirs. Here, we evaluated the role of soil texture, climate, vegetation, and distance to savanna on the distribution and stocks of soil pyrogenic carbon (PyC) in intact forests with no history of recent fire spanning the southern Amazonia forest-Cerrado Zone of Transition (ZOT). In 19 one hectare forest plots, including three Amazonian Dark Earth (ADE, terra preta) sites with high soil PyC, we measured all trees and lianas with diameter ≥ 10 cm and analyzed soil physicochemical properties, including texture and PyC stocks. We quantified PyC stocks as a proportion of total organic carbon using hydrogen pyrolysis. We used multiple linear regression and variance partitioning to determine which variables best explain soil PyC variation. For all forests combined, soil PyC stocks ranged between 0.9 and 6.8 Mg/ha to 30 cm depth (mean 2.3 ± 1.5 Mg/ha) and PyC, on average, represented 4.3% of the total soil organic carbon (SOC). The most parsimonious model (based on AICc) included soil clay content and above-ground biomass (AGB) as the main predictors, explaining 71% of soil PyC variation. After removal of the ADE plots, PyC stocks ranged between 0.9 and 3.8 Mg/ha (mean 1.9 ± 0.8 Mg/ha –1 ) and PyC continued to represent ∼4% of the total SOC. The most parsimonious models without ADE included AGB and sand as the best predictors, with sand and PyC having an inverse relationship, and sand explaining 65% of the soil PyC variation. Partial regression analysis did not identify any of the components (climatic, environmental, and edaphic), pure or shared, as important in explaining soil PyC variation with or without ADE plots. We observed a substantial amount of soil PyC, even excluding ADE forests; however, contrary to expectations, soil PyC stocks were not higher nearer to the fire-dependent Cerrado than more humid regions of Amazonia. Our findings that soil texture and AGB explain the distribution and amount of soil PyC in ZOT forests will help to improve model estimates of SOC change with further climatic warming.
Tree diversity and above-ground biomass in the South America Cerrado biome and their conservation implications
(Springer Science+Business Media, 2018) Paulo S. Morandi; Beatriz Schwantes Marimon; Ben Hur Marimon; J. A. Ratter; Ted R. Feldpausch; Guarino Rinaldi Colli; Cássia Beatriz Rodrigues Munhoz; Manoel Cláudio da Silva Júnior; Edson de Souza Lima; Ricardo Flores Haidar
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.