Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

No abstract

No abstract

Based on the integration of geological information, the pollen record, radiocarbon data, and modern mangrove distribution, this paper proposes a model of evolutionary development for mangroves on the Brazilian Braganc¸a Peninsula driven by relative sea-level changes from the middle to late Holocene. After a postglacial relative sea-level rise (RSL), the mangrove habitats on the Braganc¸a Peninsula began to develop at about 5100 years BP close to the current RSL, originating in the middle of the peninsula. Between 1800 and 1400 years BP, this littoral area underwent a maximum RSL fall of 1 m below the current one, followed by a gradual RSL rise until 1000 years BP, when the modern RSL was reached. Between 5100 and 1000 years BP, the RSL of the Braganc¸a coastline was probably never higher than 0.6 m above the present level. The first Holocene mangrove forest (5100 years BP) in this region disappeared from the plains due to RSL fall and was replaced by herbaceous vegetation (Cyperaceae and Poaceae).

No abstract

No abstract

Since the 1980s, mangrove cover mapping has become a common scientific task. However, the systematic and continuous identification of vegetation cover, whether on a global or regional scale, demands large storage and processing capacities. This manuscript presents a Google Earth Engine (GEE)-managed pipeline to compute the annual status of Brazilian mangroves from 1985 to 2018, along with a new spectral index, the Modular Mangrove Recognition Index (MMRI), which has been specifically designed to better discriminate mangrove forests from the surrounding vegetation. If compared separately, the periods from 1985 to 1998 and 1999 to 2018 show distinct mangrove area trends. The first period, from 1985 to 1998, shows an upward trend, which seems to be related more to the uneven distribution of Landsat data than to a regeneration of Brazilian mangroves. In the second period, from 1999 to 2018, a trend of mangrove area loss was registered, reaching up to 2% of the mangrove forest. On a regional scale,~85% of Brazil's mangrove cover is in the states of Maranhão, Pará, Amapá and Bahia. In terms of persistence,~75% of the Brazilian mangroves remained unchanged for two decades or more.Globally, mangrove forests are distributed in tropical and subtropical intertidal regions between approximately 30 • N and 30 • S [6]. In 2000, mangrove forests represented a total area of 137,760 km 2 , distributed in 118 countries and making up~1% of the tropical forests in the world [7]. Mangrove forests are an evergreen type of vegetation typically distributed from the mean sea level to the highest spring tide [8] and grow in extreme environmental conditions such as extreme tides, high salinity, high temperatures and muddy anaerobic soils [9].Mangrove systems play an essential role in human sustainability, providing a wide range of ecosystem services, including nutrient cycling, soil formation and wood production. They also provide fish spawning grounds and carbon (C) storage [10][11][12], being one of the most productive and biologically complex ecosystems on earth [13]. Mangroves and coastal wetlands sequester carbon at an annual rate two to four times greater than that of mature tropical forests and store three to five times more carbon per equivalent area than do tropical forests [10]. Despite its importance, this environment is still highly threatened due to population growth and urbanisation processes.Since the 1980s, mapping and change detection in mangrove areas at the global scale have been carried out [7,11,[14][15][16]. However, there are few studies in the current literature that include the systematic and continuous identification of mangroves and associated changes, whether on the global or regional scale. In Brazil, the first national mangrove map was published in 1991 [17], based on airborne real aperture radar data collected from 1972 to 1975. At that time, the national mangrove area was~13,800 km 2 . In the same period, Schaeffer-Novelli et al. [18] described the variability in the mangrove ecosystems along the Brazilian c...

Although genetic diversity ultimately determines the ability of organisms to adapt to environmental changes, conservation assessments like the widely used International Union for Conservation of Nature (IUCN) Red List Criteria do not explicitly consider genetic information. Including a genetic dimension into the IUCN Red List Criteria would greatly enhance conservation efforts, because the demographic parameters traditionally considered are poor predictors of the evolutionary resilience of natural populations to global change. Here we perform the first genomic assessment of genetic diversity, gene flow, and patterns of local adaptation in tropical plant species belonging to different IUCN Red List Categories. Employing RAD-sequencing we identified tens of thousands of single-nucleotide polymorphisms in an endangered narrow-endemic and a least concern widespread morning glory (Convolvulaceae) from Amazonian savannas, a highly threatened and under-protected tropical ecosystem. Our results reveal greater genetic diversity and less spatial genetic structure in the endangered species. Whereas terrain roughness affected gene flow in both species, forested and mining areas were found to hinder gene flow in the endangered plant. Finally we implemented environmental association tests and genome scans for selection, and identified a higher proportion of candidate adaptive loci in the widespread species. These mainly contained genes related to pathogen resistance and physiological adaptations to life in nutrient-limited environments. Our study emphasizes that IUCN Red List Criteria do not always prioritize species with low genetic diversity or whose genetic variation is being affected by habitat loss and fragmentation, and calls for the inclusion of genetic information into conservation assessments. More generally, our study exemplifies how landscape genomic tools can be employed to assess the status, threats and adaptive responses of imperiled biodiversity.