Polyanna da Conceição Bispo

An estimate of the number of tropical tree species

Significance People are fascinated by the amazing diversity of tropical forests and will be surprised to learn that robust estimates of the number of tropical tree species are lacking. We show that there are at least 40,000, but possibly more than 53,000, tree species in the tropics, in contrast to only 124 across temperate Europe. Almost all tropical tree species are restricted to their respective continents, and the Indo-Pacific region appears to be as species-rich as tropical America, with each of these two regions being almost five times as rich in tree species as African tropical forests. Our study shows that most tree species are extremely rare, meaning that they may be under serious risk of extinction at current deforestation rates. The high species richness of tropical forests has long been recognized, yet there remains substantial uncertainty regarding the actual number of tropical tree species. Using a pantropical tree inventory database from closed canopy forests, consisting of 657,630 trees belonging to 11,371 species, we use a fitted value of Fisher’s alpha and an approximate pantropical stem total to estimate the minimum number of tropical forest tree species to fall between ∼40,000 and ∼53,000, i.e., at the high end of previous estimates. Contrary to common assumption, the Indo-Pacific region was found to be as species-rich as the Neotropics, with both regions having a minimum of ∼19,000–25,000 tree species. Continental Africa is relatively depauperate with a minimum of ∼4,500–6,000 tree species. Very few species are shared among the African, American, and the Indo-Pacific regions. We provide a methodological framework for estimating species richness in trees that may help refine species richness estimates of tree-dependent taxa.

Variáveis geomorfométricas locais e sua relação com a vegetação da região do interflúvio Madeira-Purus (AM-RO)

The objective of this work was to assess the potential of geomorphometric variables, derived from SRTM (Shuttle Radar Topographic Mission) data, to help identify vegetation types in the Amazonian Madeira-Purus interfluvio region. A RADAMBRASIL project vegetation map was used as a reference and the geomorphometric variables (elevation, slope, aspect and profile and plan curvatures) were compared to the mapped units. Analyses indicated vegetation types easily discriminated, depending on the topographic position. The variables of elevation, slope and aspect were the most important for their high discrimination power of the vegetation types. Although geomorphometric data are recognized as having strong potential for characterizing vegetation, this was not shown in the results, due to the mismatching of variability scales between the two sources of data; large units tend to exhibit similar distribution patterns of geomorphometry, and comprise classes with different responses for geomorphometric constraints. Discriminant analyses of geomorphometric variables permitted vegetation mapping up to the sub-physiognomy levels.

Relação entre as variáveis morfométricas extraídas de dados SRTM (Shuttle Radar Topography Mission) e a vegetação do Parque Nacional de Brasília

Este trabalho visa ao estudo da relacao entre a distribuicao de fitofisionomias do Parque Nacional de Brasilia (PNB) e variaveis topograficas, para avaliar o potencial de dados SRTM isoladamente, como complemento aos dados tradicionalmente aplicados no sensoriamento remoto da vegetacao. Esta relacao foi verificada atraves de analises discriminantes entre o mapa de vegetacao referencia do PNB e as seguintes variaveis morfometricas: elevacao, declividade, orientacao de vertente, curvatura vertical e curvatura horizontal. Tais analises indicaram as classes de vegetacao que podem ser separadas com base nas condicoes topograficas do terreno. As variaveis morfometricas mais importantes na distincao entre os tipos vegetacionais foram a elevacao, a declividade e a orientacao de vertente. Apesar de os dados morfometricos mostrarem potencial indicativo das classes de vegetacao, as analises resultaram em discriminacao em um nivel aquem do detalhamento tematico do mapa referencia. Tal desempenho pode ser explicado pela incompatibilidade das escalas de variacao exibidas entre os dados morfometricos em relacao ao tamanho das unidades de mapeamento da vegetacao. Alem disso, a variacao de tipos de vegetacao do cerrado pode ser explicada por uma serie de outros fatores alem da topografia. Com base nas analises discriminantes das variaveis morfometricas, foi possivel o mapeamento experimental da vegetacao ao nivel de subfisionomias.

Predictive Models of Primary Tropical Forest Structure from Geomorphometric Variables Based on SRTM in the Tapajós Region, Brazilian Amazon

Surveying primary tropical forest over large regions is challenging. Indirect methods of relating terrain information or other external spatial datasets to forest biophysical parameters can provide forest structural maps at large scales but the inherent uncertainties need to be evaluated fully. The goal of the present study was to evaluate relief characteristics, measured through geomorphometric variables, as predictors of forest structural characteristics such as average tree basal area (BA) and height (H) and average percentage canopy openness (CO). Our hypothesis is that geomorphometric variables are good predictors of the structure of primary tropical forest, even in areas, with low altitude variation. The study was performed at the Tapajós National Forest, located in the Western State of Pará, Brazil. Forty-three plots were sampled. Predictive models for BA, H and CO were parameterized based on geomorphometric variables using multiple linear regression. Validation of the models with nine independent sample plots revealed a Root Mean Square Error (RMSE) of 3.73 m2/ha (20%) for BA, 1.70 m (12%) for H, and 1.78% (21%) for CO. The coefficient of determination between observed and predicted values were r2 = 0.32 for CO, r2 = 0.26 for H and r2 = 0.52 for BA. The models obtained were able to adequately estimate BA and CO. In summary, it can be concluded that relief variables are good predictors of vegetation structure and enable the creation of forest structure maps in primary tropical rainforest with an acceptable uncertainty.

Relação da vegetação de caatinga com a condição geomorfométrica local

The objective of this work was to assess the potential of geomorphometric variables, derived from SRTM (Shuttle Radar Topographic Mission) data, to help in identifying vegetation types in the Serra das Almas National Park (CE). A 1:100.000 survey vegetation map was used as reference and the geomorphometric variables (elevation, slope, aspect and profile and plan curvatures) were compared to the mapped units. The variables elevation, slope and profile curvature were shown as the most important for their high discrimination power of the vegetation types. Although geomorphometric data had strong potential for characterizing vegetation through map comparisons, the achieved thematic detail levels were under those of the reference map when data was analyzed under a numerical approach. It was concluded that geomorphometric data were important input for vegetation mapping, and should be employed together with currently used data.

Tropical forest structure observation with TanDEM-X data

TanDEM-X forms together with TerraSAR-X the first single-pass polarimetric interferometer in space. This allows for the first time the acquisition and analysis of Single-, Dual-, and Quad-Pol-InSAR data without the disturbing effect of temporal decorrelation globally. For this reason, the exploration of TanDEM-X data for forestry is constantly increasing especially concerning forest height estimation, biomass classification and structure characterization. This paper reports the results of recent experiments aimed at investigating the potentials of TanDEM-X in characterizing quantitatively the spatial variability of the canopy top and phase center height, which is a proxy to horizontal structure. It is shown that such characterization can allow to differentiate among e.g. different successional and / disturbance stages in tropical forests.

Drivers of metacommunity structure diverge for common and rare Amazonian tree species

We analysed the flora of 46 forest inventory plots (25 m x 100 m) in old growth forests from the Amazonian region to identify the role of environmental (topographic) and spatial variables (obtained using PCNM, Principal Coordinates of Neighbourhood Matrix analysis) for common and rare species. For the analyses, we used multiple partial regression to partition the specific effects of the topographic and spatial variables on the univariate data (standardised richness, total abundance and total biomass) and partial RDA (Redundancy Analysis) to partition these effects on composition (multivariate data) based on incidence, abundance and biomass. The different attributes (richness, abundance, biomass and composition based on incidence, abundance and biomass) used to study this metacommunity responded differently to environmental and spatial processes. Considering standardised richness, total abundance (univariate) and composition based on biomass, the results for common species differed from those obtained for all species. On the other hand, for total biomass (univariate) and for compositions based on incidence and abundance, there was a correspondence between the data obtained for the total community and for common species. Our data also show that in general, environmental and/or spatial components are important to explain the variability in tree communities for total and common species. However, with the exception of the total abundance, the environmental and spatial variables measured were insufficient to explain the attributes of the communities of rare species. These results indicate that predicting the attributes of rare tree species communities based on environmental and spatial variables is a substantial challenge. As the spatial component was relevant for several community attributes, our results demonstrate the importance of using a metacommunities approach when attempting to understand the main ecological processes underlying the diversity of tropical forest communities.