Pedoenvironments driving the monodominance of Peltogyne gracilipes (Leguminosae) in the Northern Amazon, Brazil

Monodominance is unusual in the tropics when compared to the high diversity of tropical forests. Peltogyne gracilipes (Leguminosae) is a deciduous tree species that forms monodominant forests in the Brazilian Northern Amazon region. Initial research confirmed that P. gracilipes monodominance was associated with higher soil magnesium content, while recent studies have indicated a larger number of variables, suggesting a more complex hydro-edaphic habitat. As such, the present study aimed to describe the hydro-edaphic habitat (pedoenvironment) where P. gracilipes is monodominant. Edaphic and topographic variables (drainage proxy) were used in a forest inventory conducted in 129 sampling plots. Trees with a stem diameter greater than 10 cm were analyzed. Aboveground biomass was used as a descriptive variable of the different habitats in the study area. A total of 3041 individuals were sampled (298 P. gracilipes). Multivariate analysis demonstrated that the highest P. gracilipes abundance occurred in poorly drained (seasonal flooding) low-altitude habitats (<66 m), with high soil Fe+2 and Mg+2 concentrations. P. gracilipes monodominance in the study area is best described in habitats with more restricted hydro-edaphic conditions, where drainage is the primary factor and Fe+2 and Mg+2 are secondary agents resulting from the effect of seasonally drained soils. This study contributes to better understanding the environmental filters that characterize areas where P. gracilipes is more abundant, indicating that this species might potentially become monodominant in more restricted hydro-edaphic habitats in the Northern Amazon

Modelos alométricos para estimar altura de árvores em florestas ecotonais do norte da Amazônia

Allometric models defining the relationship between stem diameter and total tree height in the Amazon basin are important because they refine the estimates of tree carbon stocks and flow in the region. This study tests different allometric models to estimate the total tree height from the stem diameter in an ecotone zone between ombrophilous and seasonal forests in the Brazilian state of Roraima, in northern Amazonia. Stem diameter and total height were measured directly in 65 recently fallen trees (live or dead). Linear and nonlinear regressions were tested to represent the D:H relation in this specific ecotone zone. Criteria for model selection were the standard error of the estimate (Syx) and the adjusted coefficient of determination (R²adj), complemented by the Akaike Information Criterion (AIC). Analysis of residuals of the most parsimonious nonlinear models showed a tendency to overestimate the total tree height for trees in the 20-40 cm diameter range. Application of our best fitted model (Michaelis-Menten) indicated that previously published general equations for the tropics that use diameter as the independent variable can either overestimate tree height in the study area by 10-29% (Weibull models) or underestimate it by 8% (climate-based models). We concluded that our site-specific model can be used in the ecotone forests studied in Roraima because it realistically reflects the local biometric relationships between stem diameter and total tree height. Studies need to be expanded in peripheral areas of northern Amazonia in order to reduce uncertainties in biomass and carbon estimates that use the tree height as a variable in general models.Modelos alométricos que definem o relacionamento entre diâmetro do tronco e a altura total da árvore na bacia amazônica são importantes porque refinam as estimativas de fluxo e estoques de carbono arbóreo na região. Este estudo testou diferentes modelos alométricos para estimar a altura total de árvores a partir do diâmetro do tronco em uma zona de ecótono entre florestas ombrófilas e sazonais no estado de Roraima, norte da Amazônia. Diâmetro do tronco e altura total foram medidos de forma direta em 65 árvores tombadas recentemente (vivas e mortas). Regressões linear e não-linear foram testadas para representar a relação D:H nesta zona específica de ecótono. Os critérios de seleção dos modelos foram o erro padrão da estimativa (Syx), o coeficiente de determinação ajustado (R²adj) e o Critério de Informação de Akaike (AIC). A análise dos resíduos dos modelos não-lineares mais parcimoniosos mostrou uma tendência de superestimar a altura total para árvores entre 20-40 cm de diâmetro do tronco. A aplicação do modelo melhor ajustado (Michaelis-Menten) indicou que equações gerais publicadas previamente para os trópicos que usam diâmetro como variável independente podem superestimar em 10-29% (modelos Weibull) ou subestimar em 8% (modelos baseados no clima) a altura das árvores na área de estudo. Nós concluímos que o modelo de melhor ajuste pode ser usado nas florestas ecotonais estudadas em Roraima, porque ele reflete realisticamente o relacionamento biométrico local entre diâmetro do tronco e altura total da árvore. É necessário expandir os estudos para outras áreas periféricas do norte da Amazônia, com o intuito de reduzir as incertezas em estimativas de biomassa e carbono arbóreo que adotem altura da árvore como uma variável em modelos gerais

Inundation dynamics in seasonally dry floodplain forests in southeastern Brazil

Floodplains are one of the most threatened ecosystems. Even though the vegetation composition in floodplain forests is expected to reflect the variation in groundwater levels and flood duration and frequency, there is little field data on the inundation dynamics (e.g., the variability in flood duration and flood frequency), especially for the understudied seasonally dry tropics. This limits our understanding of these ecosystems and the mechanisms that cause the flooding. We, therefore, investigated six floodplain forests in the state of Minas Gerais in Brazil for 1.5 years (two wet seasons): Capivari, Jacaré, and Aiuruoca in the Rio Grande basin, and Jequitaí, Verde Grande, and Carinhanha in the São Francisco basin. These locations span a range of climates (humid subtropical to seasonal tropical) and biomes (Atlantic forest to Caatinga). At each location, we continuously measured water levels in five geomorphologically distinct eco‐units: marginal levee, lower terrace, higher terrace, lower plain, and higher plain, providing a unique hydrological dataset for these understudied regions. The levees and terraces were flooded for longer periods than the plains. Inundation of the terraces lasted around 40 days per year. The levees in the Rio Grande basin were flooded for shorter durations. In the São Francisco basin, the flooding of the levees lasted longer and the water level regime of the levees was more similar to that of the terraces. In the Rio Grande basin, flooding was most likely caused by rising groundwater levels (i.e., “flow pulse”) and flood pulses that caused overbank flooding. In the São Francisco basin, inundation was most likely caused by overbank flooding (i.e., “flood pulse”). These findings highlight the large variation in inundation dynamics across floodplain forests and are relevant to predict the impacts of changes in the flood regime due to climate change and other anthropogenic changes on floodplain forest functioning

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost