The implications for potential marginal land resources of cassava across worldwide under climate change challenges.

The demand for energy plants is foreseen to grow as worldwide energy and climate policies promote the use of bioenergy for climate change mitigation. To avoid competing with food production, it's critical to assess future changes in marginal land availability for energy plant development. Using a machine learning method, boosted regression tree, this study modeled potential marginal land resources suitable for cassava under current and different climate change scenarios, based on cassava occurrence records and environmental covariates. The findings revealed that, currently, over 80% of the 1357.24 Mha of available marginal land for cassava cultivation is distributed in Africa and South America. Under three climate change scenarios, by 2030, worldwide suitable marginal land resources were predicted to grow by 39.71Mha, 66.21 Mha, and 39.31Mha for the RCP4.5, RCP6.0, and RCP8.5 scenarios, respectively; by 2050, the potential marginal land suitable for cassava will increase by 38.98Mha, 83.02 Mha, and 55.43Mha, respectively; by 2080, the global marginal land resources were estimated to rise by 40.82 Mha, 99.74 Mha, and 21.87 Mha from now, respectively. Our results highlight the impacts of climate change on potential marginal land resources of cassava across worldwide, which provide the basis for assessing bioenergy potential in the future.

An integrated deep-learning and multi-level framework for understanding the behavior of terrorist groups.

Human security is threatened by terrorism in the 21st century. A rapidly growing field of study aims to understand terrorist attack patterns for counter-terrorism policies. Existing research aimed at predicting terrorism from a single perspective, typically employing only background contextual information or past attacks of terrorist groups, has reached its limits. Here, we propose an integrated deep-learning framework that incorporates the background context of past attacked locations, social networks, and past actions of individual terrorist groups to discover the behavior patterns of terrorist groups. The results show that our framework outperforms the conventional base model at different spatio-temporal resolutions. Further, our model can project future targets of active terrorist groups to identify high-risk areas and offer other attack-related information in sequence for a specific terrorist group. Our findings highlight that the combination of a deep-learning approach and multi-scalar data can provide groundbreaking insights into terrorism and other organized violent crimes.

Exploring the worldwide impact of COVID-19 on conflict risk under climate change.

Objectives: Understand whether and how the COVID-19 pandemic affects the risk of different types of conflict worldwide in the context of climate change. Methodology: Based on the database of armed conflict, COVID-19, detailed climate, and non-climate data covering the period 2020-2021, we applied Structural Equation Modeling specifically to reorganize the links between climate, COVID-19, and conflict risk. Moreover, we used the Boosted Regression Tree method to simulate conflict risk under the influence of multiple factors. Findings: The transmission risk of COVID-19 seems to decrease as the temperature rises. Additionally, COVID-19 has a substantial worldwide impact on conflict risk, albeit regional and conflict risk variations exist. Moreover, when testing a one-month lagged effect, we find consistency across regions, indicating a positive influence of COVID-19 on demonstrations (protests and riots) and a negative relationship with non-state and violent conflict risk. Conclusion: COVID-19 has a complex effect on conflict risk worldwide under climate change. Implications: Laying the theoretical foundation of how COVID-19 affects conflict risk and providing some inspiration for the implementation of relevant policies.

Quantifying risk factors and potential geographic extent of African swine fever across the world.

African swine fever (ASF) has spread to many countries in Africa, Europe and Asia in the past decades. However, the potential geographic extent of ASF infection is unknown. Here we combined a modeling framework with the assembled contemporary records of ASF cases and multiple covariates to predict the risk distribution of ASF at a global scale. Local spatial variations in ASF risk derived from domestic pigs is influenced strongly by livestock factors, while the risk of having ASF in wild boars is mainly associated with natural habitat covariates. The risk maps show that ASF is to be ubiquitous in many areas, with a higher risk in areas in the northern hemisphere. Nearly half of the world's domestic pigs (1.388 billion) are in the high-risk zones. Our results provide a better understanding of the potential distribution beyond the current geographical scope of the disease.

Prediction of global marginal land resources for Pistacia chinensis Bunge by a machine learning method.

Biofuel has attracted worldwide attention due to its potential to combat climate change and meet emission reduction targets. Pistacia chinensis Bunge (P. chinensis) is a prospective plant for producing biodiesel. Estimating the global potential marginal land resources for cultivating this species would be conducive to exploiting bioenergy yielded from it. In this study, we applied a machine learning method, boosted regression tree, to estimate the suitable marginal land for growing P. chinensis worldwide. The result indicated that most of the qualified marginal land is found in Southern Africa, the southern part of North America, the western part of South America, Southeast Asia, Southern Europe, and eastern and southwest coasts of Oceania, for a grand total of 1311.85 million hectares. Besides, we evaluated the relative importance of the environmental variables, revealing the major environmental factors that determine the suitability for growing P. chinensis, which include mean annual water vapor pressure, mean annual temperature, mean solar radiation, and annual cumulative precipitation. The potential global distribution of P. chinensis could provide a valuable basis to guide the formulation of P. chinensis-based biodiesel policies.

Spatiotemporal patterns and spatial risk factors for visceral leishmaniasis from 2007 to 2017 in Western and Central China: A modelling analysis.

Visceral leishmaniasis (VL) is a neglected disease caused by trypanosomatid protozoa in the genus Leishmania, which is transmitted by phlebotomine sandflies. Although this vector-borne disease has been eliminated in several regions of China during the last century, the reported human VL cases have rebounded in Western and Central China in recent decades. However, understanding of the spatial epidemiology of the disease remains vague, as the spatial risk factors driving the spatial heterogeneity of VL. In this study, we analyzed the spatiotemporal patterns of annual human VL cases in Western and Central China from 2007 to 2017. Based on the related spatial maps, the boosted regression tree (BRT) model was adopted to explore the relationships between VL and spatial correlates as well as predicting both the existing and potential infection risk zones of VL in Western and Central China. The mined links reveal that elevation, minimum temperature, relative humidity, and annual accumulated precipitation make great contributions to the spatial heterogeneity of VL. The maps show that Xinjiang Uygur Autonomous Region, Gansu, western Inner Mongolia Autonomous Region, and Sichuan are predicted to fall in the highest infection risk zones of VL. Approximately 61.60 million resident populations lived in the high-risk regions of VL in Western and Central China. Our results provide a better understanding of how spatial risk factors driving VL spread as well as identifying the potential endemic risk region of VL, thereby enhancing the biosurveillance capacity of public health authorities.