Beta-diversity, defined as spatial replacement in species composition, is crucial to the understanding of how local communities assemble. These changes can be driven by environmental or geographic factors (such as geographic distance), or a combination of the two. Spiders have been shown to be good indicators of environmental quality. Accordingly, spiders are used in this work as model taxa to ...

Ecological regions are increasingly used as a spatial unit for planning and environmental management. It is important to define these regions in a scientifically defensible way to justify any decisions made on the basis that they are representative of broad environmental assets. The paper describes a methodology and tool to identify cohesive bioregions. The methodology applies an elicitation pr...

a r t i c l e i n f o Keywords: Beta diversity MODIS NDVI Mantel test Multivariate distance WWF ecoregion Biogeographical Evolutionary history Considerable amount of research on the relationships between species diversity and productivity at different spatial, ecological, and taxonomic scales has been conducted. However, the overall trend of the correlation at the global scale still remains ske...

The Atlantic Forest (AF) of South America houses 7% of the world’s species making it one of the most diverse ecosystems on the planet (Quintela, 1990 in Stevens & Husband, 1998). In addition to its high biodiversity, the AF is also characterized by high levels of endemism placing it among the world’s highest priorities for conservation (Myers et al., 2000; Galindo-Leal & Câmara, 2003). The AF i...

Aim To examine the trends of 1982–2003 satellite-derived normalized difference vegetation index (NDVI) values at several spatial scales within tundra and boreal forest areas of Alaska. Location Arctic and subarctic Alaska. Methods Annual maximum NDVI data from the twice monthly Global Inventory Modelling and Mapping Studies (GIMMS) NDVI 1982–2003 data set with 64-km 2 pixels were extracted from...

Recent increase in wildfires worldwide has led to the need for real-time fire nowcasting. Physics-driven models, such as cellular automata and computational fluid dynamics can provide high-fidelity spread simulations but they are computationally expensive time-consuming. Much effort been put into developing machine learning models prediction. However, these often region-specific require a subst...