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

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

Water level regime variation is a crucial driver for taxonomic, functional, and phylogenetic diversity in seasonally flooded tropical forests

Floodplains contribute significantly to terrestrial ecosystem service provision but are also among the most vulnerable and degraded ecosystems worldwide. Heterogeneity in floodplain properties arises from variations in river-specific flood regimes, watershed characteristics, and valley morphology, influencing seasonally flooded forests' taxonomic, functional, and phylogenetic diversity. This study addresses persisting knowledge gaps in floodplain ecology, focusing on the seasonally dry tropics. We explore the relationships between flood regime, environmental conditions, vegetation composition, functional and phylogenetic diversity, and the impact of environmental variables on above-ground biomass (AGB) and ecological strategies. The study spans six rivers in southeastern Brazil's main river basins: Rio Grande and São Francisco. We identified five eco-units in each floodplain based on flooding regimes and surveyed six plots per eco-unit. We measured trees with DBH > 5 cm and collected functional traits, along with detailed soil, climate, and water level data. We calculated plot-level floristic composition, taxonomic, functional, and phylogenetic diversity, wood density, and AGB. Functional and phylogenetic dissimilarity were analyzed, and the effects of climate, soil, and hydrological variables were quantified using generalized linear mixed models. We show how flood frequency and duration affect floristic composition across the floodplains. Taxonomic and phylogenetic diversity responded to climate, soil, and hydrological variables, while functional diversity responded primarily to hydrological variables, emphasizing the role of environmental filtering. Hydrological seasonality, soil fertility, and flood regime emerged as key factors shaping community structure and ecological strategies in the studied seasonally flooded tropical forests. Plot-level AGB responded to phosphorus but not to climate or hydrological variables. The study also highlights functional and phylogenetic dissimilarities among eco-units and basins, indicating potential climate change impacts