Mark H. DeVisser

A dynamic species distribution model of Glossina subgenus Morsitans: The identification of tsetse reservoirs and refugia

Tsetse flies are the primary vector for African trypanosomiasis, a neglected tropical disease that affects both humans and livestock across the continent of Africa. In 1973 tsetse were estimated to inhabit 22% of Kenya; by 1996 that number had risen to roughly 34%. Efforts to control the disease are hampered by a lack of information and costs associated with the identification of infested areas. To aid control efforts we have constructed the Tsetse Ecological Distribution Model (TED Model). The TED Model is a raster based dynamic species distribution model that predicts tsetse distributions at 250 m spatial resolution, based on habitat suitability and fly movement rates, at 16-day intervals. Although the TED Model can be parameterized to any tsetse subgenus/species requirements, for the purpose of this study the TED Model was parameterized to identify suitable habitat for Glossina subgenus Morsitans. Using the TED Model we have identified where and when Glossina subgenus Morsitans populations should be co...

Optimum land cover products for use in a Glossina-morsitans habitat model of Kenya

BackgroundTsetse flies are the primary vector for African trypanosomiasis, a disease that affects both humans and livestock across the continent of Africa. In 1973 tsetse flies were estimated to inhabit 22% of Kenya; by 1996 that number had risen to roughly 34%. Efforts to control the disease were hampered by a lack of information and costs associated with the identification of infested areas. Given changing spatial and demographic factors, a model that can predict suitable tsetse fly habitat based on land cover and climate change is critical to efforts aimed at controlling the disease. In this paper we present a generalizable method, using a modified Mapcurves goodness of fit test, to evaluate the existing publicly available land cover products to determine which products perform the best at identifying suitable tsetse fly land cover.ResultsFor single date applications, Africover was determined to be the best land use land cover (LULC) product for tsetse modeling. However, for changing habitats, whether climatically or anthropogenically forced, the IGBP DISCover and MODIS type 1 products where determined to be most practical.ConclusionThe method can be used to differentiate between various LULC products and be applied to any such research when there is a known relationship between a species and land cover.

Climate Change and Risk Projection: Dynamic Spatial Models of Tsetse and African Trypanosomiasis in Kenya

African trypanosomiasis, otherwise known as sleeping sickness in humans and nagana in animals, is a parasitic protist passed cyclically by the tsetse fly. Despite more than a century of control and eradication efforts, the fly remains widely distributed across Africa and coextensive with other prevalent diseases. Control and planning are hampered by spatially and temporally variant vector distributions, ecologically irrelevant boundaries, and neglect. Tsetse are particularly well suited to move into previously disease-free areas under climate change scenarios, placing unprepared populations at risk. Here we present the modeling framework ATcast, which combines a dynamically downscaled regional climate model with a temporally and spatially dynamic species distribution model to predict tsetse populations over space and time. These modeled results are integrated with Kenyan population data to predict, for the period 2050 to 2059, exposure potential to tsetse and, by association, sleeping sickness and nagana across Kenya.

Exploration of sensor comparability: A case study of composite modis aqua and terra data

Comparability across sensors is a common goal of remotely sensed data products; however, sensor calibration and atmospheric conditions often limit implementation. Recently, a slight degradation in the blue band sensor on the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite was reported (Wang et al. 2012), and it was suggested at the 2011 fall NASA Carbon Cycle and Ecosystems/Biodiversity and Ecological Forecasting Team meeting that users switch to using MODIS Aqua data until the corrected collection 6 Terra data are released. Following this recommendation, a switch from Terra to Aqua data inputs used in the Tsetse Ecological Distribution (TED) model was implemented; however, significant variability between model outputs for Kenya was found. This letter considers the possible sources of incongruity between the MODIS Aqua and Terra sensors that might result in the observed differences between model outputs, including sensor degradation, temporal overlap, daily acquisition and data quality. While for data of Kenya it appears that differences in daily acquisition times and data quality result in differences between composite products, ultimately the selection of what data product and sensor to use should be based on reported data quality and biophysical conditions of the region under study.

Glaciers and Perennial Snowfields of the U.S. Cordillera

Of more than 8,000 glaciers and perennial snow-fields on 21 mountain ranges in the western U.S. (excluding Alaska), only 120 are larger than 1 km2, and just one exceeds 10 km2. Where changes in size are known, the overwhelming majority of glaciers are shrinking. There are a few that are growing. These changes, with a few exceptions that relate mainly to rock debris abundances on the glaciers, are due overwhelmingly to climate change, though it is a complex relationship. Analysis of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery has been used in special case studies, along with published field data, to track changes in debris loads of glaciers on Mt. Rainier, show the effects of a lahar from Mt. Rainier, and track the continued shrinkage of Grinnell Glacier in Glacier National Park. The response time of glaciers in the region varies from under a decade to over a century. Blue Glacier (Olympic Mountains) is a fast responder; its length, area, and volume fluctuation history indicates that it is responding to decadal climate fluctuations as well as local long-term warming in the 20th and 21st centuries, which is probably related to greenhouse gas-driven global warming.