All Is Not Loss: Plant Biodiversity in the Anthropocene

Anthropogenic global changes in biodiversity are generally portrayed in terms of massive native species losses or invasions caused by recent human disturbance. Yet these biodiversity changes and others caused directly by human populations and their use of land tend to co-occur as long-term biodiversity change processes in the Anthropocene. Here we explore contemporary anthropogenic global patterns in vascular plant species richness at regional landscape scales by combining spatially explicit models and estimates for native species loss together with gains in exotics caused by species invasions and the introduction of agricultural domesticates and ornamental exotic plants. The patterns thus derived confirm that while native losses are likely significant across at least half of Earth's ice-free land, model predictions indicate that plant species richness has increased overall in most regional landscapes, mostly because species invasions tend to exceed native losses. While global observing systems and models that integrate anthropogenic species loss, introduction and invasion at regional landscape scales remain at an early stage of development, integrating predictions from existing models within a single assessment confirms their vast global extent and significance while revealing novel patterns and their potential drivers. Effective global stewardship of plant biodiversity in the Anthropocene will require integrated frameworks for observing, modeling and forecasting the different forms of anthropogenic biodiversity change processes at regional landscape scales, towards conserving biodiversity within the novel plant communities created and sustained by human systems

Relação da vegetação de caatinga com a condição geomorfométrica local

Objetivou-se, com o presente trabalho, avaliar o potencial das variáveis geomorfométricas extraídas de dados SRTM (Shuttle Radar Topographic Mission) para identificação de tipos vegetacionais da Reserva Particular do Patrimônio Natural de Serra das Almas, CE. Em estudo conduzido na escala de 1:100.000, as variáveis geomorfométricas (elevação, declividade, orientação de vertente, curvatura vertical e curvatura horizontal) foram confrontadas com o mapa de vegetação referência, através de análises de histogramas e análises discriminantes. As variáveis mais importantes na distinção entre os tipos vegetacionais, foram a elevação, a declividade e a curvatura vertical, embora se pudesse observar preferências de tipos mapeados em relação às demais variáveis. Apesar dos dados geomorfométricos mostrarem potencial indicativo das classes de vegetação pela interpretação dos padrões, as análises sob abordagem numérica resultaram em discriminação em um nível aquém do detalhamento temático do mapa referência. Concluiu-se que os dados geomorfométricos representaram significativos insumos para o mapeamento fitogeográfico, devendo ser explorados de forma integrada, em complementaridade às demais variáveis já utilizadas.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

Using Spatial Autocorrelation Techniques and Multi-temporal Satellite Data for Analyzing Urban Sprawl

Satellite time series offer great potential for a quantitative assessment of urban expansion, urban sprawl and for monitoring of land use changes and soil consumption. This study deals with the spatial characterization of expansion of urban areas by using spatial autocorrelation techniques applied to multi-date Thematic Mapper (TM) satellite images. The investigation focused on several very small towns close to Bari. Urban areas were extracted from NASA Landsat images acquired in 1976, 1999 and 2009, respectively. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and spatial autocorrelation techniques to reveal spatial patterns. Urban areas were analyzed using both global and local autocorrelation indexes. This approach enables the characterization of pattern features of urban area expansion and it improves land use change estimation. The obtained results showed a significant urban expansion coupled with an increase of irregularity degree of border modifications from 1976 to 2009