Change in the Russian boreal forest has the capacity to alter global carbon and climate dynamics. Fire disturbance is an integral determinant of the forest's composition and structure, and changing climate conditions are expected to create more frequent and severe fires. Using the individual tree-based forest gap model UVAFME, along with an updated fire disturbance module that tracks mortality based on tree-species and -size level effects, biomass and species dynamics are simulated across Russia for multiple scenarios: with and without fire, and with and without altered climate. Historical fire return intervals and percent of forest stand mortality are calculated for the Russian eco-regions and applied to 31 010 simulation points across Russia. Simulation results from the scenarios are compared to assess changes in biomass, composition, and stand structure after 600 years of successional change following bare-ground initiation. Simulations that include fire disturbance show an increase in biomass across the region compared to equivalent simulations without fire. Fire disturbance allows the deciduous needle-leaved conifer larch to maintain dominance across much of the region due to their high growth rate and fire tolerance relative to other species. Larch remain dominant under the scenario of altered climate conditions with fire disturbance. The distribution of age cohorts shifts for the scenario of altered climate with fire disturbance, displaying a bimodal distribution with a peak of 280-year-old trees and another of 100-year-old cohorts. In these simulations, fire disturbance acts to increase the turnover rate and patterns of biomass accumulation, though species and tree size are also important factors in determining mortality and competitive success. These results reinforce the importance of the inclusion of complex competition at the species level in evaluating forest response to fire and climate.

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Despite progress toward malaria elimination in the Greater Mekong Subregion, challenges remain owing to the emergence of drug resistance and the persistence of focal transmission reservoirs. Malaria transmission foci in Myanmar are heterogeneous and complex, and many remaining infections are clinically silent, rendering them invisible to routine monitoring. The goal of this research is to define criteria for easy-to-implement methodologies, not reliant on routine monitoring, that can increase the efficiency of targeted malaria elimination strategies. Studies have shown relationships between malaria risk and land cover and land use (LCLU), which can be mapped using remote sensing methodologies. Here we aim to explain malaria risk as a function of LCLU for five rural villages in Myanmar's Rakhine State. Malaria prevalence and incidence data were analyzed through logistic regression with a land use survey of 1,000 participants and a 30-m land cover map. Malaria prevalence per village ranged from 5% to 20% with the overwhelming majority of cases being subclinical. Villages with high forest cover were associated with increased risk of malaria, even for villagers who did not report visits to forests. Villagers living near croplands experienced decreased malaria risk unless they were directly engaged in farm work. Finally, land cover change (specifically, natural forest loss) appeared to be a substantial contributor to malaria risk in the region, although this was not confirmed through sensitivity analyses. Overall, this study demonstrates that remotely sensed data contextualized with field survey data can be used to inform critical targeting strategies in support of malaria elimination. Plain Language Summary While much progress has been made in recent years toward eliminating malaria in Myanmar, full elimination remains a challenge that is amplified by the emerging of drug-resistant malaria parasites. The lack of clinical symptoms in many people infected with malaria makes it extremely challenging to find the remaining reservoirs of the disease in the country. Previous studies identified some linkages between the prevalence of malaria and land cover and land use (LCLU) patterns. Satellite monitoring of LCLU could thus help identify potential areas where malaria elimination activities should be deployed. In this study, blood samples and surveys on land use activities (farming, visiting forests, etc.), collected in five villages in Myanmar's Rakhine State, were used to establish and describe the relationship between satellite-observed LCLU patterns surrounding the village and malaria prevalence. Results indicate that villages surrounded by lands with high amounts of forest cover were strongly associated with increased risk of malaria, even for villagers who did not report frequent visits to forested lands. Overall, this study demonstrates that satellite imagery data can be an important tool in support of targeted malaria elimination.

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Malaria is a serious infectious disease that leads to massive casualties globally. Myanmar is a key battleground for the global fight against malaria because it is where the emergence of drug-resistant malaria parasites has been documented. Controlling the spread of malaria in Myanmar thus carries global significance, because the failure to do so would lead to devastating consequences in vast areas where malaria is prevalent in tropical/subtropical regions around the world. Thanks to its wide and consistent spatial coverage, remote sensing has become increasingly used in the public health domain. Specifically, remote sensing-based land cover/land use (LCLU) maps present a powerful tool that provides critical information on population distribution and on the potential human-vector interactions interfaces on a large spatial scale. Here, we present a 30-meter LCLU map that was created specifically for the malaria control and eradication efforts in Myanmar. This bottom-up approach can be modified and customized to other vector-borne infectious diseases in Myanmar or other Southeastern Asian countries.