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1. Understanding Spatiotemporal Human Mobility Patterns for Malaria Control Using a Multiagent Mobility Simulation Model

   https://doi.org/10.1093/cid/ciac568  

   Li, Yao ; Stewart, Kathleen ; Han, Kay Thwe ; Han, Zay Yar ; Aung, Poe P. ; Thein, Zaw W. ; Htay, Thura ; Chen, Dong ; Nyunt, Myaing M. ; Plowe, Christopher V. ( July 2022 , Clinical Infectious Diseases)

   More details about human movement patterns are needed to evaluate relationships between daily travel and malaria risk at finer scales. A multiagent mobility simulation model was built to simulate the movements of villagers between home and their workplaces in 2 townships in Myanmar.

   An agent-based model (ABM) was built to simulate daily travel to and from work based on responses to a travel survey. Key elements for the ABM were land cover, travel time, travel mode, occupation, malaria prevalence, and a detailed road network. Most visited network segments for different occupations and for malaria-positive cases were extracted and compared. Data from a separate survey were used to validate the simulation.

   Mobility characteristics for different occupation groups showed that while certain patterns were shared among some groups, there were also patterns that were unique to an occupation group. Forest workers were estimated to be the most mobile occupation group, and also had the highest potential malaria exposure associated with their daily travel in Ann Township. In Singu Township, forest workers were not the most mobile group; however, they were estimated to visit regions that had higher prevalence of malaria infection over other occupation groups.

   Using an ABM to simulate daily travel generated mobility patterns for different occupation groups. These spatial patterns varied by occupation. Our simulation identified occupations at a higher risk of being exposed to malaria and where these exposures were more likely to occur.

    

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2. Mapping current and future thermal limits to suitability for malaria transmission by the invasive mosquito Anopheles stephensi

   https://doi.org/10.1186/s12936-023-04531-4  

   Ryan, Sadie J. ; Lippi, Catherine A. ; Villena, Oswaldo C. ; Singh, Aspen ; Murdock, Courtney C. ; Johnson, Leah R. ( March 2023 , Malaria Journal)

   Anopheles stephensiis a malaria-transmitting mosquito that has recently expanded from its primary range in Asia and the Middle East, to locations in Africa. This species is a competent vector of bothPlasmodium falciparumandPlasmodium vivaxmalaria. Perhaps most alarming, the characteristics ofAn.stephensi, such as container breeding and anthropophily, make it particularly adept at exploiting built environments in areas with no prior history of malaria risk.

   In this paper, global maps of thermal transmission suitability and people at risk (PAR) for malaria transmission byAn.stephensiwere created, under current and future climate. Temperature-dependent transmission suitability thresholds derived from recently published species-specific thermal curves were used to threshold gridded, monthly mean temperatures under current and future climatic conditions. These temperature driven transmission models were coupled with gridded population data for 2020 and 2050, under climate-matched scenarios for future outcomes, to compare with baseline predictions for 2020 populations.

   Using the Global Burden of Disease regions approach revealed that heterogenous regional increases and decreases in risk did not mask the overall pattern of massive increases of PAR for malaria transmission suitability withAn.stephensipresence. General patterns of poleward expansion for thermal suitability were seen for bothP.falciparumandP.vivaxtransmission potential.

   Understanding the potential suitability forAn.stephensitransmission in a changing climate provides a key tool for planning, given an ongoing invasion and expansion of the vector. Anticipating the potential impact of onward expansion to transmission suitable areas, and the size of population at risk under future climate scenarios, and where they occur, can serve as a large-scale call for attention, planning, and monitoring.

    

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3. Deep time extinction of largest insular ant predators and the first fossil Neoponera (Formicidae: Ponerinae) from Miocene age Dominican amber

   https://doi.org/10.1186/s12915-022-01488-9  

   Fiorentino, Gianpiero ; Lattke, John ; Troya, Adrian ; Sosiak, Christine ; Dong, Minsoo ; Barden, Phillip ( February 2023 , BMC Biology)

   Ponerine ants are almost exclusively predatory and comprise many of the largest known ant species. Within this clade, the genusNeoponerais among the most conspicuous Neotropical predators. We describe the first fossil member of this lineage: a worker preserved in Miocene-age Dominican amber from Hispaniola.

   Neoponera vejestoriasp. nov. demonstrates a clear case of local extinction—there are no known extantNeoponeraspecies in the Greater Antilles. The species is attributable to an extant and well-defined species group in the genus, which suggests the group is older than previously estimated. Through CT scan reconstruction and linear morphometrics, we reconstruct the morphospace of extant and fossil ants to evaluate the history and evolution of predatory taxa in this island system.

   The fossil attests to a shift in insular ecological community structure since the Miocene. The largest predatory taxa have undergone extinction on the island, but their extant relatives persist throughout the Neotropics.Neoponera vejestoriasp. nov. is larger than all other predatory ant workers known from Hispaniola, extant or extinct. Our results empirically demonstrate the loss of a functional niche associated with body size, which is a trait long hypothesized to be related to extinction risk.

    

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4. Neglected tropical diseases risk correlates with poverty and early ecosystem destruction

   https://doi.org/10.1186/s40249-023-01084-1  

   Magalhães, Arthur Ramalho ; Codeço, Cláudia Torres ; Svenning, Jens-Christian ; Escobar, Luis E. ; Van de Vuurst, Paige ; Gonçalves-Souza, Thiago ( December 2023 , Infectious Diseases of Poverty)

   Neglected tropical diseases affect the most vulnerable populations and cause chronic and debilitating disorders. Socioeconomic vulnerability is a well-known and important determinant of neglected tropical diseases. For example, poverty and sanitation could influence parasite transmission. Nevertheless, the quantitative impact of socioeconomic conditions on disease transmission risk remains poorly explored.

   This study investigated the role of socioeconomic variables in the predictive capacity of risk models of neglected tropical zoonoses using a decade of epidemiological data (2007–2018) from Brazil. Vector-borne diseases investigated in this study included dengue, malaria, Chagas disease, leishmaniasis, and Brazilian spotted fever, while directly-transmitted zoonotic diseases included schistosomiasis, leptospirosis, and hantaviruses. Environmental and socioeconomic predictors were combined with infectious disease data to build environmental and socioenvironmental sets of ecological niche models and their performances were compared.

   Socioeconomic variables were found to be as important as environmental variables in influencing the estimated likelihood of disease transmission across large spatial scales. The combination of socioeconomic and environmental variables improved overall model accuracy (or predictive power) by 10% on average (P < 0.01), reaching a maximum of 18% in the case of dengue fever. Gross domestic product was the most important socioeconomic variable (37% relative variable importance, all individual models exhibitedP < 0.00), showing a decreasing relationship with disease indicating poverty as a major factor for disease transmission. Loss of natural vegetation cover between 2008 and 2018 was the most important environmental variable (42% relative variable importance,P < 0.05) among environmental models, exhibiting a decreasing relationship with disease probability, showing that these diseases are especially prevalent in areas where natural ecosystem destruction is on its initial stages and lower when ecosystem destruction is on more advanced stages.

   Destruction of natural ecosystems coupled with low income explain macro-scale neglected tropical and zoonotic disease probability in Brazil. Addition of socioeconomic variables improves transmission risk forecasts on tandem with environmental variables. Our results highlight that to efficiently address neglected tropical diseases, public health strategies must target both reduction of poverty and cessation of destruction of natural forests and savannas.

    

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5. ntbox : An r package with graphical user interface for modelling and evaluating multidimensional ecological niches

   https://doi.org/10.1111/2041-210X.13452  

   Osorio‐Olvera, Luis ; Lira‐Noriega, Andrés ; Soberón, Jorge ; Peterson, Andrew Townsend ; Falconi, Manuel ; Contreras‐Díaz, Rusby G. ; Martínez‐Meyer, Enrique ; Barve, Vijay ; Barve, Narayani ; Qiao, ed., Huijie ( August 2020 , Methods in Ecology and Evolution)

   Biodiversity studies rely heavily on estimates of species' distributions often obtained through ecological niche modelling. Numerous software packages exist that allow users to model ecological niches using machine learning and statistical methods. However, no existing package with a graphical user interface allows users to perform model calibration and selection based on convex forms such as ellipsoids, which may match fundamental ecological niche shapes better, incorporating tools for exploring, modelling, and evaluating niches and distributions that are intuitive for both novice and proficient users.

   Here we describe anrpackage, NicheToolBox(ntbox), that allows users to conduct all processing steps involved in ecological niche modelling: downloading and curating occurrence data, obtaining and transforming environmental data layers, selecting environmental variables, exploring relationships between geographic and environmental spaces, calibrating and selecting ellipsoid models, evaluating models using binomial and partial ROC tests, assessing extrapolation risk, and performing geographic information system operations via a graphical user interface. A summary of the entire workflow is produced for use as a stand‐alone algorithm or as part of research reports.

   The method is explained in detail and tested via modelling the threatened feline speciesLeopardus wiedii. Georeferenced occurrence data for this species are queried to display both point occurrences and the IUCN extent of occurrence polygon (IUCN, 2007). This information is used to illustrate tools available for accessing, processing and exploring biodiversity data (e.g. number of occurrences and chronology of collecting) and transforming environmental data (e.g. a summary PCA for 19 bioclimatic layers). Visualizations of three‐dimensional ecological niches modelled as minimum volume ellipsoids are developed with ancillary statistics. This niche model is then projected to geographic space, to represent a corresponding potential suitability map.

   Usingntboxallows a fast and straightforward means by which to retrieve and manipulate occurrence and environmental data, which can then be implemented in model calibration, projection and evaluation for assessing distributions of species in geographic space and their corresponding environmental combinations.

    

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