Tree species occurring in Amazonian wetland forests consistently show broader range sizes and niche breadths than trees in upland forests.

Generally, species with broad niches also show large range sizes. We investigated the relationship between hydrological niche breadth and geographic range size for Amazonian tree species seeking to understand the role of habitat specialization to Amazonian wetlands and upland forests on the current distribution of tree species. We obtained 571,092 valid occurrence points from GBIF and SpeciesLink to estimate the range size and the niche breadth of 76% of all known Amazonian tree species (5150 tree species). Hydrological niche breadth was measured on different unidimensional axes defined by (1) total annual precipitation; (2) precipitation seasonality; (3) actual evapotranspiration; and (4) water table depth. Geographic range sizes were estimated using alpha-hull adjustments. General linear models were used to relate niche breadth to range size while contrasting tree species occurring and not occurring in wetlands. The hydrological niche breadth of Amazonian tree species varied mostly along the water table depth axis. The average range size for an Amazonian tree species was 751,000 km2 (median of 154,000 km2 and standard deviation of 1,550,000 km2). Niche breadth-range size relationships for Amazonian tree species were positive for all models, and the explanatory power of the models improved when including whether a species occurred in wetlands or in terrestrial uplands. Wetland species had steeper positive slopes for the niche breadth-range size relationship, and consistently larger range sizes for a given niche breadth. Amazonian tree species varied strongly in hydrological niche breadth and range size, but most species had narrow niche breadths and range sizes. Our results suggest that the South American riverscape may have been acting as a corridor for species dispersal in the Neotropical lowlands.

One sixth of Amazonian tree diversity is dependent on river floodplains.

Amazonia's floodplain system is the largest and most biodiverse on Earth. Although forests are crucial to the ecological integrity of floodplains, our understanding of their species composition and how this may differ from surrounding forest types is still far too limited, particularly as changing inundation regimes begin to reshape floodplain tree communities and the critical ecosystem functions they underpin. Here we address this gap by taking a spatially explicit look at Amazonia-wide patterns of tree-species turnover and ecological specialization of the region's floodplain forests. We show that the majority of Amazonian tree species can inhabit floodplains, and about a sixth of Amazonian tree diversity is ecologically specialized on floodplains. The degree of specialization in floodplain communities is driven by regional flood patterns, with the most compositionally differentiated floodplain forests located centrally within the fluvial network and contingent on the most extraordinary flood magnitudes regionally. Our results provide a spatially explicit view of ecological specialization of floodplain forest communities and expose the need for whole-basin hydrological integrity to protect the Amazon's tree diversity and its function.

Mapping density, diversity and species-richness of the Amazon tree flora.

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution.

Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology.

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics.

Biased-corrected richness estimates for the Amazonian tree flora.

Amazonian forests are extraordinarily diverse, but the estimated species richness is very much debated. Here, we apply an ensemble of parametric estimators and a novel technique that includes conspecific spatial aggregation to an extended database of forest plots with up-to-date taxonomy. We show that the species abundance distribution of Amazonia is best approximated by a logseries with aggregated individuals, where aggregation increases with rarity. By averaging several methods to estimate total richness, we confirm that over 15,000 tree species are expected to occur in Amazonia. We also show that using ten times the number of plots would result in an increase to just ~50% of those 15,000 estimated species. To get a more complete sample of all tree species, rigorous field campaigns may be needed but the number of trees in Amazonia will remain an estimate for years to come.

Rarity of monodominance in hyperdiverse Amazonian forests.

Tropical forests are known for their high diversity. Yet, forest patches do occur in the tropics where a single tree species is dominant. Such "monodominant" forests are known from all of the main tropical regions. For Amazonia, we sampled the occurrence of monodominance in a massive, basin-wide database of forest-inventory plots from the Amazon Tree Diversity Network (ATDN). Utilizing a simple defining metric of at least half of the trees ≥ 10 cm diameter belonging to one species, we found only a few occurrences of monodominance in Amazonia, and the phenomenon was not significantly linked to previously hypothesized life history traits such wood density, seed mass, ectomycorrhizal associations, or Rhizobium nodulation. In our analysis, coppicing (the formation of sprouts at the base of the tree or on roots) was the only trait significantly linked to monodominance. While at specific locales coppicing or ectomycorrhizal associations may confer a considerable advantage to a tree species and lead to its monodominance, very few species have these traits. Mining of the ATDN dataset suggests that monodominance is quite rare in Amazonia, and may be linked primarily to edaphic factors.

Massive tree mortality from flood pulse disturbances in Amazonian floodplain forests: The collateral effects of hydropower production.

Large dams built for hydroelectric power generation alter the hydrology of rivers, attenuating the flood pulse downstream of the dam and impacting riparian and floodplain ecosystems. The present work mapped black-water floodplain forests (igapó) downstream of the Balbina Reservoir, which was created between 1983 and 1987 by damming the Uatumã River in the Central Amazon basin. We apply remote sensing methods to detect tree mortality resulting from hydrological changes, based on analysis of 56 ALOS/PALSAR synthetic aperture radar images acquired at different flood levels between 2006 and 2011. Our application of object-based image analysis (OBIA) methods and the random forests supervised classification algorithm yielded an overall accuracy of 87.2%. A total of 9800 km2 of igapó forests were mapped along the entire river downstream of the dam, but forest mortality was only observed below the first 49 km downstream, after the Morena rapids, along an 80-km river stretch. In total, 12% of the floodplain forest died within this stretch. We also detected that 29% of the remaining living igapó forest may be presently undergoing mortality. Furthermore, this large loss does not include the entirety of lost igapó forests downstream of the dam; areas which are now above current maximum flooding heights are no longer floodable and do not show on our mapping but will likely transition over time to upland forest species composition and dynamics, also characteristic of igapó loss. Our results show that floodplain forests are extremely sensitive to long-term downstream hydrological changes and disturbances resulting from the disruption of the natural flood pulse. Brazilian hydropower regulations should require that Amazon dam operations ensure the simulation of the natural flood-pulse, despite losses in energy production, to preserve the integrity of floodplain forest ecosystems and to mitigate impacts for the riverine populations.

The tree species pool of Amazonian wetland forests: Which species can assemble in periodically waterlogged habitats?

We determined the filtered tree species pool of Amazonian wetland forests, based on confirmed occurrence records, to better understand how tree diversity in wetland environments compares to tree diversity in the entire Amazon region. The tree species pool was determined using data from two main sources: 1) a compilation of published tree species lists plus one unpublished list of our own, derived from tree plot inventories and floristic surveys; 2) queries on botanical collections that include Amazonian flora, curated by herbaria and available through the SpeciesLink digital biodiversity database. We applied taxonomic name resolution and determined sample-based species accumulation curves for both datasets, to estimate sampling effort and predict the expected species richness using Chao's analytical estimators. We report a total of 3 615 valid tree species occurring in Amazonian wetland forests. After surveying almost 70 years of research efforts to inventory the diversity of Amazonian wetland trees, we found that 74% these records were registered in published species lists (2 688 tree species). Tree species richness estimates predicted from either single dataset underestimated the total pooled species richness recorded as occurring in Amazonian wetlands, with only 41% of the species shared by both datasets. The filtered tree species pool of Amazonian wetland forests comprises 53% of the 6 727 tree species taxonomically confirmed for the Amazonian tree flora to date. This large proportion is likely to be the result of significant species interchange among forest habitats within the Amazon region, as well as in situ speciation processes due to strong ecological filtering. The provided tree species pool raises the number of tree species previously reported as occurring in Amazonian wetlands by a factor of 3.2.

Management impacts on fire occurrence: A comparison of fire regimes of African and South American tropical savannas in different protected areas.

Humans can alter fire dynamics in grassland systems by changing fire frequency, fire seasonality and fuel conditions. These changes have effects on vegetation structure and recovery, species composition, and ecosystem function. Understanding how human management can affect fire regimes is vital to detect potential changes in the resilience of plant communities, and to predict vegetation responses to human interventions. We evaluated the fire regimes of two recently protected areas in Madagascar (Ibity and Itremo NPA) and one in Brazil (Serra do Cipó NP) before and after livestock exclusion and fire suppression policies. We compare the pre- and post-management fire history in these areas and analyze differences in terms of total annual burned area, density of ignitions, burn scar size distribution, fire return period and seasonal fire distribution. More than 90% of total park areas were burned at least once during the studied period, for all parks. We observed a significant reduction in the number of ignitions for Ibity NPA and Serra do Cipó NP after livestock exclusion and active fire suppression, but no significant change in total burned area for each protected area. We also observed a seasonal shift in burning, with fires happening later in the fire season (October-November) after management intervention. However, the protected areas in Madagascar had shorter fire return intervals (3.23 and 1.82 years) than those in Brazil (7.91 years). Our results demonstrate that fire exclusion is unattainable, and probably unwarranted in tropical grassland conservation areas, but show how human intervention in fire and vegetation patterns can alter various aspects of the fire regimes. This information can help with formulating realistic and effective fire management policies in these valuable conservation areas.

Species Distribution Modelling: Contrasting presence-only models with plot abundance data.

Species distribution models (SDMs) are widely used in ecology and conservation. Presence-only SDMs such as MaxEnt frequently use natural history collections (NHCs) as occurrence data, given their huge numbers and accessibility. NHCs are often spatially biased which may generate inaccuracies in SDMs. Here, we test how the distribution of NHCs and MaxEnt predictions relates to a spatial abundance model, based on a large plot dataset for Amazonian tree species, using inverse distance weighting (IDW). We also propose a new pipeline to deal with inconsistencies in NHCs and to limit the area of occupancy of the species. We found a significant but weak positive relationship between the distribution of NHCs and IDW for 66% of the species. The relationship between SDMs and IDW was also significant but weakly positive for 95% of the species, and sensitivity for both analyses was high. Furthermore, the pipeline removed half of the NHCs records. Presence-only SDM applications should consider this limitation, especially for large biodiversity assessments projects, when they are automatically generated without subsequent checking. Our pipeline provides a conservative estimate of a species' area of occupancy, within an area slightly larger than its extent of occurrence, compatible to e.g. IUCN red list assessments.

Reconstructing historical forest cover change in the Lower Amazon floodplains using the LandTrendr algorithm / Reconstrução histórica de mudanças na cobertura florestal em várzeas do Baixo Amazonas utilizando o algoritmo LandTrendr

The Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzea forest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of "start year", "magnitude", and "duration" of the changes, as well as "NDVI at the end of series". Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain.

As várzeas amazônicas são um importante componente do bioma Amazônico, mas impactos antrópicos e climáticos têm levado à perda florestal e à interrupção de processos e serviços ecossistêmicos. O presente estudo teve como objetivos avaliar a aplicabilidade do algoritmo Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) na detecção de mudanças na cobertura florestal de várzea no Baixo Amazonas, e analisar o potencial de atributos espectrais e temporais na classificação das perdas florestais em antrópicas ou naturais. Utilizamos uma série temporal de 37 imagens Landsat TM e ETM+, adquiridas entre 1984 e 2009. Aplicamos o algoritmo LandTrendr para detectar mudanças na cobertura florestal e extrair os atributos de "duração", "magnitude" e "ano de início" das mudanças, além de "NDVI ao final da série". A detecção se restringiu a áreas identificadas como cobertura florestal no início e/ou final da série. Os atributos derivados da série temporal foram classificados pelo algoritmo Support Vector Machine (SVM), diferenciando as perdas florestais antrópicas e naturais. A confiabilidade da detecção dos eventos de mudança foi consistentemente alta ao longo do rio Amazonas, e mais variável no interior da várzea. As trajetórias espectrais-temporais representaram fielmente os eventos de mudança na cobertura florestal, com base em averiguações em campo. A perda da cobertura florestal por causas antrópicas foi maior (1.071 ha) do que por causas naturais (884 ha), com exatidão global de classificação de 94%. Concluímos que o algoritmo LandTrendr é uma ferramenta confiável para aplicação em estudos de dinâmica da cobertura florestal de várzea.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques / Classificação da cobertura da terra na planície de inundação do Lago Grande de Curuai (Amazônia, Brasil) utilizando dados multisensor e fusão de imagens

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Dadas as limitações de diferentes tipos de imagens de sensores remotos, classificações automáticas do uso e cobertura do solo na várzea Amazônica podem resultar em índices de acurácia insatisfatórios. Uma das maneiras de melhorar esses índices é através da combinação de dados de distintos sensores, por fusão de imagens ou através de classificações multi-sensores. Desta forma, o presente estudo teve o objetivo de determinar qual método de classificação é mais eficiente em melhorar os índices de acurácia das classificações do uso e cobertura do solo para a várzea Amazônica e áreas úmidas similares - (a) a fusão sintética de imagens SAR e ópticas ou (b) a classificação multi-sensor de imagens ópticas e SAR pareadas. Classificações da cobertura do solo com base em imagens de um único sensor (Landsat TM ou Radarsat-2) foram comparadas com as classificações multi-sensor e classificações baseadas em fusão de imagens. A análise de imagens baseada em objetos (OBIA) e o algoritmo de mineração de dados J.48 foram utilizados para realizar a classificação automática, cuja precisão foi avaliada com o índice kappa e com as medidas de discordância de alocação e de quantidade, recentemente propostas na literatura. Em geral, as classificações baseadas em imagens ópticas apresentaram melhor precisão do que as classificações baseadas em dados SAR. Uma vez que ambos os conjuntos de dados foram combinados em uma abordagem multi-sensores, houve uma redução de 2% no erro de alocação da classificação, uma vez que o método foi capaz de superar parte das limitações presentes em ambas as imagens. Contudo, a precisão diminuiu quando foram usados métodos de fusão de imagens. Concluiu-se que o método de classificação multi-sensor é mais apropriado para classificações de uso do solo na várzea amazônica.