The use of open source GIS algorithms, big geographic data, and cluster computing techniques to compile a geospatial database that can be used to evaluate upstream bathing and sanitation behaviours on downstream health outcomes in Indonesia, 2000-2008.

BACKGROUND: Waterborne diseases are one of the leading causes of mortality in developing countries, and diarrhea alone is responsible for over 1.5 million deaths annually. Such waterborne illnesses most often affect those in impoverished rural communities who rely on rivers for their supply of drinking water. Deaths are most common among infants and the elderly. Without knowledge of which communities are upstream of a community, upstream sanitary and bathing behaviors can never be directly linked to downstream health outcomes including disease outbreaks. Although current GIS technologies can answer the upstream question for a limited number of downstream communities, no systematic way existed of labeling each downstream village with all its upstream contributing villages along river networks or within basins at the large national scale, such as in Indonesia. This limitation prohibits macro analyses of waterborne illness across developing world communities globally. RESULTS: This novel method approach combines parallel computing, big data, community data, and open source GIS to create a database of upstream communities for 50,000-70,0000 villages in Indonesia across four differing periods. The resultant village database provides information that can be tied to the Indonesian PODES health and behavior surveys in each village to connect upstream sanitary behaviors to downstream health outcomes. We find that the approximately 250,000 communities analyzed across the four periods in Indonesia have a combined total of 13.7 million upstream villages. The average number of upstream villages per village was almost 55, the maximum number of upstream villages for any single village was over 5300. CONCLUSIONS: Advances in big-data availability, particularly high-resolution elevation data, the lowering of the cost of parallel computing options, mass survey data, and open source GIS algorithms that can utilize parallel processing and big-data, open new opportunities for the study of human health at micro granularities but across entire nations. The database generated has already been used by health researchers to compute the influence of upstream behaviors on downstream diarrhea outbreaks and to monitor avoidance behaviors to upstream water behaviors across all downstream 250,000 Indonesian villages over 4 years, and further waterborne health analyses are underway.

Establishing the relationship between non-human primates and mangrove forests at the global, national, and local scales.

Global and spatially explicit information about the interaction between habitat and wildlife species is critical to enhancing conservation efforts. Despite the recognized importance of mangrove forests to non-human primates, the relationship between the two lacks understanding. To counter this, we created the MangPrim-21 database to map and measure the locations of interactions between all non-human primates and all mangrove forests globally. We report our findings across the global, national, and local scales for all inventoried non-human primates and all inventoried mangrove forests. Globally, we find that half of all non-primates potentially use mangrove forests, and more than half of the global mangrove forest falls within the delineated range of at least one non-human primate species. Nationally, we find that Indonesia, Madagascar, Brazil, Cameroon, and Malaysia likely have the most non-human primate and mangrove forest interactions. At the subnational level, we find that several discrete locations in Kalimantan are critical to both mangrove forests and non-human primates. The MangPrim-21 database provides a globally consistent and locally applicable database of non-human primate and mangrove forest interactions. The results presented have broader implications for non-human primate and mangrove conservation and global actions to protect both. Additionally, our results raise questions about the idea that non-human primates primarily use mangrove forests as a refuge from human encroachment and habitat degradation.

High-resolution bathymetries and shorelines for the Great Lakes of the White Nile basin.

HRBS-GLWNB 2020 presents the first open-source and high-resolution bathymetry, shoreline, and water level data for Lakes Victoria, Albert, Edward, and George in East Africa. For each Lake, these data have three primary products collected for this project. The bathymetric datasets were created from approximately 18 million acoustic soundings. Over 8,200 km of shorelines are delineated across the three lakes from high-resolution satellite systems and uncrewed aerial vehicles. Finally, these data are tied together by creating lake surface elevation models collected from GPS and altimeter measures. The data repository includes additional derived products, including surface areas, water volumes, shoreline lengths, lake elevation levels, and geodetic information. These data can be used to make allocation decisions regarding the freshwater resources within Africa, manage food resources on which many tens of millions of people rely, and help preserve the region's endemic biodiversity. Finally, as these data are tied to globally consistent geodetic models, they can be used in future global and regional climate change models.

Mangroves and coastal topography create economic "safe havens" from tropical storms.

Evidence suggests that mangroves protect economic activity in coastal areas. We estimate this protection from mangroves and coastal elevation globally, examining both "direct" and "indirect" exposure events (< 100 km vs. ≥ 100 km distance from a cyclone's "eye", respectively). We find that higher elevation (≥ 50 m) or wide mangroves (≥ 10 m seaward width) alone shelter economic activity from indirect cyclone exposure, whereas protection from direct cyclone exposure occurs only in high elevation communities with wide mangroves. Our results reveal that the majority of these "safe havens" are in upper middle-income countries but provide significant benefits to populations in lower middle-income countries.

Ecuador's mangrove forest carbon stocks: a spatiotemporal analysis of living carbon holdings and their depletion since the advent of commercial aquaculture.

In this paper we estimate the living carbon lost from Ecuador's mangrove forests since the advent of export-focused shrimp aquaculture. We use remote sensing techniques to delineate the extent of mangroves and aquaculture at approximately decadal periods since the arrival of aquaculture in each Ecuadorian estuary. We then spatiotemporally calculate the carbon values of the mangrove forests and estimate the amount of carbon lost due to direct displacement by aquaculture. Additionally, we calculate the new carbon stocks generated due to mangrove reforestation or afforestation. This research introduces time and LUCC (land use / land cover change) into the tropical forest carbon literature and examines forest carbon loss at a higher spatiotemporal resolution than in many earlier analyses. We find that 80 percent, or 7,014,517 t of the living carbon lost in Ecuadorian mangrove forests can be attributed to direct displacement of mangrove forests by shrimp aquaculture. We also find that IPCC (Intergovernmental Panel on Climate Change) compliant carbon grids within Ecuador's estuaries overestimate living carbon levels in estuaries where substantial LUCC has occurred. By approaching the mangrove forest carbon loss question from a LUCC perspective, these findings allow for tropical nations and other intervention agents to prioritize and target a limited set of land transitions that likely drive the majority of carbon losses. This singular cause of transition has implications for programs that attempt to offset or limit future forest carbon losses and place value on forest carbon or other forest good and services.