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Simple Features for R: Standardized Support for Spatial Vector Data

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Abstract

Simple features are a standardized way of encoding spatial vector data (points, lines, polygons) in computers. The sf package implements simple features in R, and has roughly the same capacity for spatial vector data as packages sp, rgeos, and rgdal. We describe the need for this package, its place in the R package ecosystem, and its potential to connect R to other computer systems. We illustrate this with examples of its use.

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... However, some methods were not applicable due to the relatively low precision of GPS locations of households in the candidate surveys. For such cases, buffers were created around the approximated GPS locations of households using the 'sf ' package [44,45] in R [46]. The buffer radii were set at 5 km for LSMS households in rural areas, 2 km for LSMS households in urban areas, and 1.1 km for all RHoMIS households. ...
... The inset at the upper right corner shows central and southeastern parts of Africa, with the study area highlighted in red. This figure was made in R [46] with packages 'ggplot2' , 'raster' , 'sf ' , and 'rgdal' [44,45,51,55,56]. The polygon shapefiles were obtained from 'GADM' , the Database of Global Administrative Areas (https://gadm.org/). ...
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Livestock brucellosis is an endemic disease in many low-resource settings. Despite its widespread distribution, little is known about the scale of economic impacts caused by the disease. This study aimed to develop an integrated epidemiological-economic modelling framework to estimate production losses attributable to livestock brucellosis, using Tanzania as a case study. Data on livestock production and prevalence of exposure to Brucella spp. were obtained from surveys conducted in northern and central Tanzania between 2013 and 2019. A clustering algorithm was applied to classify households into pastoral and non-pastoral production systems. A Bayesian latent-class analysis model was applied to derive livestock brucellosis prevalence estimates. A herd-growth model was used to estimate production losses attributable to brucellosis. A total of 1,541 households (384 classified as pastoral and 1,157 as non-pastoral) contributed data on livestock production or prevalence of exposure to Brucella spp. The median (95% uncertainty interval, UI) individual-level brucellosis prevalence in cattle, sheep, and goats was 5.1% (3.4–6.9), 1.3% (0.1–3.0), and 2.5% (0.3–4.8) in the pastoral system, and 0.7% (0.1–1.6), 1.6% (0.2–3.8), and 2.5% (0.3–4.9) in the non-pastoral system, respectively. The median (95% UI) annual losses attributable to brucellosis in cattle, sheep, and goats, per infected animal, were 74.4 (26.2–211.7), 9.7 (3.4–23.1) and 10.6 (3.7–25.0) international dollars (int. )inthepastoralsystem,and62.3(16.8228.6),6.3(1.817.1)and7.0(2.217.9)int.) in the pastoral system, and 62.3 (16.8–228.6), 6.3 (1.8–17.1) and 7.0 (2.2–17.9) int. in the non-pastoral system, respectively. Household-level losses were equivalent to 4.4% (2.1–8.8) and 0.6% (0.2–1.6) of the median (95% UI) livestock-derived income in the pastoral and non-pastoral systems, respectively. This study did not capture the system-wide impacts of brucellosis, including on human health. The estimated losses are only a part of the full societal economic impact of the disease. These results can be used to inform cost-benefit analyses of potential interventions and guide policy development for brucellosis control.
... Spatial uncertainty highlights areas where simulated values vary significantly across realizations, potentially indicating regions that are poorly characterized by the sampling data or the variogram model used (Goovaerts, 1997). To assess spatial uncertainty, we computed the pointwise standard deviation (σ) and interquartile range (IQR) from the set of 100 realizations using the R packages sf (Pebesma 2018;) and terra (Hijmans, 2023), as follows: ...
... The EC e thresholds were selected to align with recent largescale mapping initiatives at national and continental levels (Schillaci et al., 2024;Omuto et al., 2020), while the ESP thresholds were deemed most appropriate for addressing soil sodification challenges specific to the country. All calculations were done in R, using the package sf (Pebesma 2018;) and terra (Hijmans, 2023). ...
... Once we had harmonized a data set and joined with its spatial data (hunting ground perimeters), we validated the structure of each harmonized data source using ShinyIVT 25 . We use tidyverse 2.0.0 26 and sf 1.0-16 27 packages of R 4.3.3 software 28 for data management. ...
... The R script files has been written in R 4.3.3 computing language and utilized the packages tidyverse 2.0.0 26 , sf 1.0-16 27 , terra 1.8-15 48 , dismo 1.3-16 49 , and deldir 2.0-4 50 . ...
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The data sets provide long-term information (2013–2022) of the presence-only of eight wild ungulates and red fox derived from harvest data in a grid of 5 × 5 km of Spain (21,836 cells). The collected data has been processed and reported yearly, as well as in two monitoring periods in accordance with Habitats Directive from the European Union to facilitate data reporting about the State of nature, and the sum of the whole period. Data sets are structured following the Darwin Core biological standard. The data set was published in the Spanish node of the Global Biodiversity Information Facility (GBIF), which are the most updated publicly available information for these species’ presence in Spain.
... The sf library for the R programming language was used to determine the "30" component [69,70]. The tree canopy cover was generated from the point cloud by transforming points of class 5 according to the LAS specification [62] into a raster with a 0.5 m resolution. ...
... First, the building and green space layers were downloaded from the Open-StreetMap database using the osmdata library [71]. Then, the polygons were processed using the sf library [69,70]. Only green areas larger than one hectare were considered. ...
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This study examines the applicability of the 3-30-300 rule in five medium-sized Polish municipalities. The rule states that residents should be able to see at least three trees from their homes, neighborhoods should have at least 30% tree canopy coverage, and public green spaces should be within 300 m. The method proposed in this study shows that the tree visibility component of the 3-30-300 concept is the most fluctuating index, and it strongly depends on the settings of the algorithm parameter, as well as on the placement of artificially generated observers. This demonstrates the complexity of the issue and the need to further specify the nuances of the 3-30-300 rule. The work shows that all variables of the 3-30-300 rule can be calculated based on publicly available data, such as point clouds, which are increasingly being made available for free for research and implementation purposes. The study concludes that the proposed solution is effective in assessing the availability of green spaces and emphasizes the need for qualitative improvements in the management of urban green spaces. While the 3-30-300 rule can serve as the foundation for future urban planning, complementary strategies are needed to ensure long-term sustainability and better access to green spaces.
... We used the same approach to characterize the oceanic conditions in the breeding and feeding grounds. For each two breeding grounds and the feeding ground, we built a polygon overlapping the study area and cropped out the land areas using the functions st_difference and st_intersection in the sf package 64 . We then drew 4,000 random locations within the resulting polygon using the st_sample function in the sf package 64 ( Figure S6). ...
... The star symbol in panels A, B and C indicate the location of the tagging. Base maps were created in R 59 , using the sf64 and rnaturalearth 70 packages. ...
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Humpback whales, a species of baleen whale occurring in all oceans globally, undergo seasonal migration between their breeding grounds in tropical warm waters and high latitude feeding grounds. Using multiple years of satellite tracking data, we modeled the effect of oceanic conditions on the movement behaviour of 42 humpback whales belonging to the Southeastern Pacific population (also known as Breeding Stock G) during their migration from breeding grounds in Costa Rica, Panama and Ecuador to feeding grounds in waters around the Antarctic Peninsula. We report evidence that during their migration, humpback whales engage in a movement behaviour frequently associated with feeding, and that this behaviour was more likely to occur in relatively more productive waters. We show that whales partly rely on cues they perceive in their immediate environment to initiate their southward migration, but also on their memory of oceanic conditions on their feeding grounds, timing their arrival with the complete melting of sea ice which triggers a bloom of krill in the Antarctic Ocean. Overall, our findings suggest that humpback whales integrate information they gather from their immediate environment to predict the oceanic conditions at distant locations and adjust the timing of their migration, maximizing their interaction with their preys. However, it is unclear if humpback whales will fully succeed in tracking their preys in a rapidly changing climate and ensure the long-term persistence of the species.
... In the process of data preprocessing and corpus construction, packages such as dplyr [28], quanteda [29], and ldatuning [20] were used. The visualization of results primarily relied on the ggplot2 [30] and sf [31] packages. Subsequent graphic enhancements have been made using the Inkscape tool, with world map data sourced from the open data of the Natural Earth network. ...
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Background: Infertility is a significant negative factor affecting societal population growth and economic stability, with male infertility being a major cause of infertility. In recent years, with the development and advancement of next-generation sequencing technologies and high-resolution mass spectrometry, the volume of male infertility-related literature in scientific databases such as Scopus and PubMed has rapidly increased, and its topics have undergone complex changes over the past 50 years. Additionally, the advent of large language models (LLMs) has provided new tools for enhancing traditional literature analysis and topic modeling. Objective: This study aims to investigate the changes and trends in research hotspots on male infertility over the past 50 years. Furthermore, to explore the potential of large language models (LLMs) in decision support systems for the clinical translation of male infertility research, we also evaluated the information enhancement capabilities of LLMs in the context of research hotspots on male infertility. Methods: Various methods, including bibliometrics, topic modeling, Gemini' and ChatGPT's question-answer approach, were employed to compare male infertility hotspots between real-world and virtual world data. Additionally, the study investigated LLMs's ability to enhance information in summarizing male infertility hotspots. Conclusions: Under the literature evidence of 14,852 male infertility-related publications (12,884 article-type publications and 1,968 review-type publications), traditional bibliometric analyses such as annual analysis, country analysis, and high-impact author analysis show that countries like the United States, China, and Italy are major publishers in infertility research, with the United States being the leading technical influencer in male infertility research. Subsequently, results from topic modeling analysis have effectively mapped out the research themes in male infertility over the past 50 years, this analysis highlights key subjects such as "the impact of gene expression on male infertility", "the effect of age on sperm parameters", and "pathogenic genes of male infertility", marking them as recent research hotspots. However, this method falls short in clearly presenting the latest hotspots in male infertility research. Lastly, the integration of LLMs information enhancement offers a new dimension in this research. This approach successfully presents the recent hotspots in male infertility, encompassing not only the impact of risk factors like "Environmental Exposures", "Genetics", "Immunological Factors", "Hormonal Imbalances" on sperm count and quality but also highlighting emerging areas such as "Precision Medicine" and "Artificial Intelligence (AI)" in male infertility research. Therefore, combining real-world literature evidence with the capabilities of LLMs is crucial for understanding and mapping future trends in this field.
... We visually inspected trace plots to evaluate how well the chains were mixing and used effective sample size and an R threshold of 1.01 for convergence diagnostics (Vehtari et al., 2021). We used packages sf (Pebesma, 2018) and terra (Hijmans, 2023) to create spatially explicit population simulations, package nimbleDistance (Scroggie & Ramsey, 2023) to fit the hazard rate function to the dispersal distances, and package Nimble (de Valpine et al., 2017) to implement the MCMC sampling in the R programming environment version 4.1.2 (R Core Team, 2022). ...
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Close‐kin mark–recapture (CKMR) methods use information on genetic relatedness among individuals to estimate demographic parameters. An individual's genotype can be considered a ‘recapture’ of each of its parent's genotype, and the frequency of kin‐pair matches detected in a population sample can directly inform estimates of abundance. CKMR inference procedures require analysts to define kinship probabilities in functional forms, which inevitably involve simplifying assumptions. Among others, population structure can have a strong influence on how kinship probabilities are formulated. Many terrestrial species are philopatric or face barriers to dispersal, and not accounting for dispersal limitation in kinship probabilities, can create substantial bias if sampling is also spatially structured (e.g. via harvest). We present a spatially explicit formulation of CKMR that corrects for incomplete mixing by incorporating natal dispersal distances and spatial distribution of individuals into the kinship probabilities. We used individual‐based simulations to evaluate the accuracy of abundance estimates obtained with one spatially naïve and two spatially explicit CKMR models across six scenarios with distinct spatial patterns of relative abundance and sampling probability. Estimates of abundance obtained with a CKMR model naïve to spatial structure were negatively biased when sampling was spatially biased. Incorporating patterns of natal dispersal in the kinship probabilities helped address this bias, but estimates were not always accurate depending on the model used and the scenario considered. Incorporating natal dispersal into spatially structured CKMR models can address the bias created by population structure and heterogeneous sampling but will often require additional assumptions and auxiliary data (e.g. relative abundance indices). The models shown here were designed for terrestrial species with continuous patterns of natal dispersal and high year‐to‐year site fidelity but could be extended to other species.
... We used the data products described above to estimate the distribution of forest-obligate species and changes to their ranges from overall forest loss and forest loss attributable to each driver country in 2001-2015. We completed raster manipulation primarily with the terra and sf packages in R (v.4.2.1), with occasional use of the packages fasterize, raster, rasterVis, remotes, rgdal and rgeos, and calculated pixel area using a correction for latitudinal variation in pixel area with the cellSize function in the terra package [58][59][60][61][62][63][64][65][66] . We completed all analysis and visualizations using the tidyverse package and affiliated packages in R (refs. ...
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Globalization increasingly allows countries to externalize the environmental costs of land use, including biodiversity loss¹. So far, we have a very incomplete understanding of how countries cause biodiversity loss outside their own borders through their demand for agricultural and forestry products grown in other countries². Here we quantify the global range losses to forest vertebrates from 2001 to 2015 caused by deforestation attributable to 24 developed countries by means of their consumption of products obtained through global supply chains. We show that these driver countries are responsible for much greater cumulative range loss to species outside their own borders than within them. These international impacts were concentrated geographically, allowing us to map global hotspots of outsourced losses of biodiversity. Countries had the greatest external impacts on species occurring in nearby regions. However, in a few cases, developed countries also inflicted disproportionate harm on vertebrates in distant countries.
... The plots of all data were generated using ggplot2 (ref. 69) and maps were generated using Natural Earth data (free vector and raster map data at https://naturalearthdata.com) [70][71][72] . ...
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Growing evidence indicates that migratory animals exploit the magnetic field of the Earth for navigation, both as a compass to determine direction and as a map to determine geographical position¹. It has long been proposed that, to navigate using a magnetic map, animals must learn the magnetic coordinates of the destination2,3, yet the pivotal hypothesis that animals can learn magnetic signatures of geographical areas has, to our knowledge, yet to be tested. Here we report that an iconic navigating species, the loggerhead turtle (Caretta caretta), can learn such information. When fed repeatedly in magnetic fields replicating those that exist in particular oceanic locations, juvenile turtles learned to distinguish magnetic fields in which they encountered food from magnetic fields that exist elsewhere, an ability that might underlie foraging site fidelity. Conditioned responses in this new magnetic map assay were unaffected by radiofrequency oscillating magnetic fields, a treatment expected to disrupt radical-pair-based chemical magnetoreception4, 5–6, suggesting that the magnetic map sense of the turtle does not rely on this mechanism. By contrast, orientation behaviour that required use of the magnetic compass was disrupted by radiofrequency oscillating magnetic fields. The findings provide evidence that two different mechanisms of magnetoreception underlie the magnetic map and magnetic compass in sea turtles.
... Following the removal of synonyms, erroneous data points were removed from occurrence records using the CoordinateCleaner R package (Zizka et al., 2019). After this, a sampling polygon was selected on a map of Regions A, B, and C ( Fig. 1) using a series of R packages including rgbif (Chamber-lain et al., 2024), sf (Pebesma, 2018), and rnaturalearth (Massicotte & South, 2023). Polygons were delineated using the Watershed tool from the Hydrology Toolset in ArcGIS Pro (ESRI, 2024). ...
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We compiled a list of the fern and lycophyte species that occur in Mesoamerica and extend into Colombia and Ecuador, where they are restricted to the western side of the Andes; that is, they occur only west of the crest of the easternmost cordillera and are absent from that cordillera’s eastern slope and in adjacent Amazonia. We found 131 species with this Mesoamerican and west-of-the-eastern-cordillera distribution. Those 131 species constitute 7% of the total 1805 fern and lycophyte species that have been recorded west of the crest of the easternmost cordillera in Colombia and Ecuador. All 131 species have elevation ranges with midpoints at low (0–900 m) or middle (900–3000 m) elevations, and none occur above 3000 m. This suggests the cordilleras have acted as elevational barriers. We also investigated the blockage of these 131 species by each of the Andean cordilleras. We found that 75 (57%) of the 131 species were limited eastward by the western cordillera, 15 (11%) by the central cordillera (this cordillera in Colombia only), and 40 (31%) by the eastern cordillera. If estimates of endemics to the western Andean region are considered, then at least 20%–22% of all fern and lycophyte species in the region are restricted west of the crest of the easternmost cordillera. Although usually exhibiting larger geographic distributions compared to angiosperms, fern and lycophyte species may show significant geographic restriction by mountain ranges.
... This study was conducted in R statistical software v. 4.1.1 (R Core Team, 2024) using the following packages: tidyverse (Wickham et al., 2019) and sf (Pebesma, 2018). ...
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The introduction of foot-and-mouth disease (FMD) leads to substantial economic impacts through animal loss, decreased livestock and meat production, increased government and private spending on control and eradication measures, and trade restrictions. This study evaluates the direct cost-effectiveness of four control and eradication scenarios of hypothetical FMD outbreaks in Rio Grande do Sul, Brazil. Our model simulation considered scenarios with depopulation of detected farms and emergency vaccination and two enhanced scenarios featuring increased capacity for emergency vaccination and depopulation. FMD outbreaks were simulated using a multi-host, single-pathogen Susceptible-Exposed-Infectious-Recovered model incorporating species-specific transmission probabilities, within-farm dynamics, and spatial transmission factors. The economic cost evaluation encompassed animal elimination (a.k.a. depopulation), carcass disposal, visits by animal health officials, laboratory testing, emergency vaccination, and sanitary barriers (a.k.a. traffic-control points), and movement restrictions due to control zones. Our results provided a range of predicted costs for a potential reintroduction of FMD ranging from 977,128to977,128 to 52,275,811. Depopulation was the most expensive, followed by local traffic control points and emergency vaccination. Our results demonstrated that higher rates of depopulation, or depopulation combined with vaccination, were the most effective strategies to reduce long-term economic impacts despite higher initial costs. Allocating more resources early in the outbreak was cost-effective in minimizing the overall effect and achieving faster eradication.
... Simultaneously, SDoH metrics such as single parent, federal financial assistance, poverty, language barrier, unemployment, education, and access to a vehicle were measured at the household scale. The Nebraska state and county boundary shapefiles were obtained from the U.S. Census Bureau's TIGER/Line database using the "tigris" R package (Walker, 2016) and the "sf" R package (Pebesma, 2018). ...
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Nebraska's age‐adjusted incidence rates for childhood cancers are among the highest in the US. Previous studies indicated associations between agrichemical exposures (atrazine and nitrates) and pediatric cancer rate, assuming single pollutant exposure. We evaluated the joint association between the agricultural mixture and pediatric cancer. Agrichemical exposures at a county scale were quantified using the USGS Pesticide National Synthesis Project for frequently applied pesticides from 1992 to 2014 in 93 Nebraska counties. Outcomes were quantified using pediatric cancer diagnosed among children <20 years of age (1992–2014) from the Nebraska cancer registry. We adjusted for social vulnerability factors such as race, income, employment, and access to care. The associations between 32 agrichemicals and cancer subtypes were assessed using the Generalized Weighted Quantile Sum Regression (gWQS) model. The model was fit assuming a Poisson distribution and using the pediatric population as an offset‐term and social vulnerability factors as covariates. We observed a statistically significant positive association between the 32 agrichemicals and overall pediatric cancer and subtypes. The strength of associations was slightly stronger among brain and CNS cancers (β = 0.36, CI = 0.14, 0.57) compared to overall cancer (β = 0.30, CI = 0.16, 0.44) and leukemia (β = 0.23, CI = 0.09, 0.38). Dicamba, glyphosate, paraquat, quizalofop, triasulfuron, and tefluthrin largely contributed to the joint association. These findings may explain the joint associations of the agrichemical mixture on childhood cancer. Alternative biomarker‐based approaches to measuring human exposure are worth investigating for chemicals of concern, particularly in counties with high agrichemical and cancer rates.
... For comparison, we also generated a matching number of reference locations and buffers where DOR squirrels were not observed during our surveys. The reference locations were sampled randomly using the st_sample() function from the sf R package (Pebesma, 2018). We then quanti ed road and landscape features for each DOR and reference location. ...
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Cities impose unique selection pressures on wildlife and generate clines in phenotypic traits along urban-rural gradients. Roads, as a ubiquitous feature of human dominated landscapes, are a significant source of direct mortality for wildlife, but their role as a selective force in producing phenotypic trait variation along urban-rural clines is not known. This study tested the hypothesis that roads influence natural selection of coat color in the eastern gray squirrel ( Sciurus carolinensis ), a species for which vehicular collisions is a significant source of mortality. This species has two distinct coat colors: a gray morph that is common in all areas, and a melanic morph more prevalent in urban areas than in rural ones. We conducted standardized road cruise surveys to compare the proportion of each color morph among road-killed and living squirrels along an urbanization gradient in Syracuse, New York, USA. We also examined the prevalence of each color morph on and off road surfaces in a range-wide compilation of > 100,000 photographs of S. carolinensis . The prevalence of melanism among living squirrels in Syracuse was negatively related to distance from the city center, whereas there was no urban-rural cline in melanism among road-killed squirrels. The melanic morph was underrepresented among road-killed squirrels by up to 30% along the urbanization gradient. We also found the melanic morph was underrepresented among road-killed squirrels in the image database. Our study highlights vehicular collisions as an important cause of natural selection on phenotypic traits that can contribute to the maintenance of urban-rural clines.
... We used scaled Schoenfeld residuals to test the assumption that our hazards were proportional with time. All analyses were conducted in R v. 3.6.1 [51] using the packages: tidyverse [52], here [53], lubridate [54], wesanderson [55], patchwork [56], survival [57], sf [58], broom [59], survminer [60], lme4 [61], broom [59] and boot [62]. ...
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Mutualisms can increase the ability of foundation species to resist individual stressors, but it remains unclear whether mutualisms can also ameliorate co-occurring stressors for habitat-forming species. To examine whether a suspected mutualist could improve foundation species’ resistance to multiple stressors, we tested how a common coral-dwelling crab affected corals exposed to macroalgal contact and physical wounding during a widespread heat stress event using flow-through tanks supplied with seawater from a nearby reef flat. High temperatures on the reef flat, which raised the temperature in our tanks, appeared to trigger rapid tissue loss in experimental corals, but the amount of tissue lost by corals was strongly determined by treatment. Macroalgal contact increased, while the presence of a crab decreased, the amount of tissue lost. Although the effect of wounding was not strong in isolation, when wounding occurred in the presence of a crab, coral tissue loss unexpectedly decreased below that of all other treatments. We propose that wounding increased coral resistance to stress by attracting crabs—a result that appeared supported in a field experiment. These results highlight that mutualisms can interact with stressors in unexpected ways, buffering the effects of both local and global stressors on foundation species.
... The manuscript authoring, data analysis and visualizations were conducted using RStudio [31] and the R statistical language [28] on Windows 10 x 64 (build 19045). An important group of R-packages were used which included: FSA [32], ggthemes [33], gridExtra [34], lubridate [35], Hmisc [36], rms [37], doBy [38], neatRanges [39], ggpubr [40], ggside [41], fmesher [42], report [43], tibble [44], rgl [45], RGraphics [46], RColorBrewer [47], gifski [48], sf [49,50], GGally [51], tmaptools [52], gtools [53], reshape2 [54] , plyr [55], ggplot2 [56], forcats [57], stringr [58], tidyverse [59], readxl [60], usethis [61], dplyr [62], purrr [63] , readr [64], devtools [65], scales [66], tidyr [67], cowplot [68], bookdown [69], knitr [70] and zoo [71]. ...
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The wastewater viral activity level (WVAL) was developed by the United States Centers for Disease Control and Prevention (US CDC) as a standardized metric to aggregate SARS-CoV-2 wastewater data, enabling the assessment of infection levels and trends at state/territorial, regional and national scales. This approach also facilitates comparative analysis of SARS-CoV-2 prevalence across regions. In this study, we developed and evaluated graphical methods to integrate the WVAL metric into interpretable visualizations for public health decision-making. Preliminary analysis demonstrated that WVAL values correlated strongly with clinical case counts, supporting its role as a confirmatory epidemiological measure. The WVAL framework provided a linear quantification method, allowing for the comparison of regional variations in infection patterns. This study leveraged data from the Ontario Wastewater Surveillance Initiative (Ontario WSI), which included over 100 sampling sites across seven geographical regions. Weekly mean WVAL values were computed for each site and aggregated at regional and provincial levels. In total, 59 sites contributed to the provincial WVAL calculation. The computational aggregation method followed the US CDC WVAL approach and was generally comparable to the Public Health Ontario (PHO) aggregation method, with the notable improvement of incorporating a linear level scale. Overall, this study demonstrated that WVAL effectively quantified SARS-CoV-2 differences at a public health regional scale. The WVAL metric proved to be a robust epidemiological tool, complementing other surveillance measures to support public health decision-making.
... The package tidyverse 2.0.0 [39], ggpubr 0.6.0 [40], spdep 1.2-8 [41], corrplot 0.92 [42] and sf 1.0-13 [43] used for data analysis and visualization. Table 2 presents a descriptive analysis of the HFMD incidence rates and meteorological data outlined in Table 1. ...
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Hand, Foot, and Mouth Disease (HFMD) predominantly affects children under the age of five and remains a significant public health concern in the Asia-Pacific region. HFMD outbreaks are closely linked to seasonal changes and meteorological factors, particularly in tropical and subtropical areas. In Thailand, a total of 657,570 HFMD cases were reported between 2011 and 2022 (12 years). This study aimed to identify the high- and low-risk HFMD outbreak areas using machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forests (RF), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). Our findings showed that the XGBoost model outperformed the other models in predicting unseen data and defining the best model. The best model can be used to detect high-risk outbreak areas and to explore the relationship between meteorological factors and HFMD outbreaks. The results highlighted the seasonal distribution of high-risk HFMD outbreak months across different provinces in Thailand, with average maximum temperature, average rainfall, and average vapor pressure identified as the most influential factors. Furthermore, the best model was used to analyze HFMD outbreaks during the COVID-19 pandemic, showing a notable reduction in high-risk outbreak months and areas, likely due to the control measures implemented during this period. Overall, our model shows great potential as a tool for warnings, providing useful insights to help public health officials reduce the impact of HFMD outbreaks.
... All statistical analyses of the data were conducted in R Studio (R Core Team, 2024). To visually explore the spatial distribution of Z. spinimana across occurrence datasets and years, maps were programmed that display the cumulative observations by data source pear year, applying packages countrycode (Arel-Bundock et al., 2018), rnaturalearth (Massicotte & South, 2023), rnaturalearthdata (South, 2017), sf (Pebesma, 2018) and tidyverse (Wickham et al., 2019). In addition, north-eastern expansion in metres were calculated from the maximum latitude and longitude from the cumulative yearly observations using the distGeo function from the geosphere package (Hijmans, 2022) and displayed as crosshairs in the distribution maps. ...
... The final iid model was then compared to a BYM model based on the same untransformed spatial predictor variables selected during the forward model selection procedure. R software version 4.2.0 [31] and the sf [32,33], spdep [34] and R-INLA [35] packages were used for spatial data manipulation, analyses and to produce the figures and maps of this manuscript. The R code about the spatial smoothing of variables is available online (10. ...
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In this study, we present a comprehensive analysis of the key spatial risk factors and predictive risk maps for HPAI infection in France, with a focus on the 2016–17 and 2020–21 epidemic waves. Our findings indicate that the most explanatory spatial predictor variables were related to fattening duck movements prior to the epidemic, which should be considered as indicators of farm operational status, e.g., whether they are active or not. Moreover, we found that considering the operational status of duck houses in nearby municipalities is essential for accurately predicting the risk of future HPAI infection. Our results also show that the density of fattening duck houses could be used as a valuable alternative predictor of the spatial distribution of outbreaks per municipality, as this data is generally more readily available than data on movements between houses. Accurate data regarding poultry farm densities and movements is critical for developing accurate mathematical models of HPAI virus spread and for designing effective prevention and control strategies for HPAI. Finally, our study identifies the highest risk areas for HPAI infection in southwest and northwest France, which is valuable for informing national risk-based strategies and guiding increased surveillance efforts in these regions.
... For generating maps, iMESc relies on leaflet (Cheng et al., 2019), ggplot2, plotly (Sievert et al., 2021), and sf (Pebesma, 2018). Leaflet enables interactive mapping and dynamic exploration of spatial data with features such as zooming and panning. ...
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As environmental sciences increasingly rely on complex datasets, machine learning (ML) has become crucial for identifying patterns and relationships. However, the integration of ML into workflows can pose challenges due to technical barriers or the time-intensive nature of coding. To address these issues, we developed iMESc, an interactive ML app designed to streamline and simplify ML workflows for environmental data. Developed in R and built on the Shiny platform, iMESc enables the integration of supervised and unsupervised ML methods, along with tools for data preprocessing, visualization, descriptive statistics, and spatial analysis. The Datalist system ensures seamless transitions between analytical workflows, while the “savepoints” feature enhances reproducibility by preserving the analysis state. We demonstrate iMESc’s flexibility with four workflows applied to a case study predicting nematode community structure based on environmental data. The classical statistical approaches, the Redundancy Analysis (RDA) and Piecewise RDA (pwRDA), explained 30.7% and 53%, respectively. The SuperSOM model achieved an R² of 0.60 for training and 0.291 for testing, identifying spatial patterns across depth zones. Finally, a hybrid model combining an unsupervised SOM and followed by the supervised Random Forest model returned an accuracy of 83.47% for the training and 80.77% for the test, with Bathymetry, Chlorophyll, and Coarse Sand as key predictive variables. IMESc permits the customization of plots and saving the workflows into “savepoints” guarantying reproducibility. iMESc bridges the gap between the complexity of machine learning algorithms and the need for user-friendly interfaces in environmental research. By reducing the technical burden of coding, iMESc allows researchers to focus on scientific inquiry, improving both the efficiency and depth of their analyses.
... Because distance to transplant center is a commonly studied SDOH, we used geographic distance between geocoded locations provided by each tool for each address as a supplementary metric to compare their performance [14][15][16][17]. We first calculated the straight-line geographic distances between geolocations provided by each tool for all addresses [18]. We then calculated median distances and performed non-parametric Kruskal-Wallis tests for differences in the distribution of distances by strata. ...
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Objectives Linking neighborhood- and patient-level data provides valuable information about the influence of upstream social determinants of health (SDOH). However, sharing of these data across health systems presents challenges. We set out to develop a pipeline to acquire, deidentify, and share neighborhood-level SDOH data across multiple health systems. Methods We created a pipeline centered around Decentralized Geomarker Assessment for Multi-Site Studies (DeGAUSS) that utilizes containerization to geocode patient addresses and obtain neighborhood-level SDOH variables. We compared DeGAUSS to a third-party vendor geocoding tool available at Duke Health using a cohort of adult patients referred for abdominal transplant from January 1, 2016, to December 31, 2022. We calculated Cohen’s Kappa and percent disagreement at census block group and tract levels, and by Area Deprivation Index, urbanicity, and year. Results The pipeline successfully generated SDOH data for 97.8% of addresses. There was high concordance between DeGAUSS and the vendor tool at the census block group (0.93) and tract levels (0.95). At the block group level, disagreement proportion differed by year and urbanicity, with larger disagreement in the rural category than in micropolitan and metropolitan categories (13%, 7%, 6.2%, respectively). Discussion and conclusion We describe a novel pipeline that can facilitate the secure acquisition and sharing of neighborhood-level SDOH without sharing PHI. The pipeline can be scaled to include additional social, climate, and environmental variables, and can be extended to an unlimited number of health systems.
... Once medians were calculated, we constructed a dissimilarity matrix calculated with Euclidian distances and the clustering was performed with Ward's minimum variance method. All analyses were performed in the R software version 4.2.3 (R Core Team, 2020) through the tidyverse (Wickham et al. 2019), vegan (Oksanen et al. 2025), cluster (Maechler et al. 2023), and sf (Pebesma 2018) packages. ...
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... All statistical analyses and visualizations were conducted using the R statistical language [35] using the packages ggstatsplot [36] and tidyverse [37]. The release map was created using packages sf [38], mapview [39], raster [40], and leaflet [41]. ...
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... In this section, we illustrate the usage of the Whittle-Matérn log-Gaussian Cox processes through an application to road accident data from Al-Ahsa, which is the largest governorate in Saudi Arabia's Eastern Province, with a population of over one million. All analyses were conducted using R [29] and the MetricGraph, and R-INLA packages [8,32] for modeling, while the packages ggplot2, sf, and excursions [36,28,6] supported visualization and exceedance probability calculations. ...
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The modeling of spatial point processes has advanced considerably, yet extending these models to non-Euclidean domains, such as road networks, remains a challenging problem. We propose a novel framework for log-Gaussian Cox processes on general compact metric graphs by leveraging the Gaussian Whittle-Mat\'ern fields, which are solutions to fractional-order stochastic differential equations on metric graphs. To achieve computationally efficient likelihood-based inference, we introduce a numerical approximation of the likelihood that eliminates the need to approximate the Gaussian process. This method, coupled with the exact evaluation of finite-dimensional distributions for Whittle-Mat\'ern fields with integer smoothness, ensures scalability and theoretical rigour, with derived convergence rates for posterior distributions. The framework is implemented in the open-source MetricGraph R package, which integrates seamlessly with R-INLA to support fully Bayesian inference. We demonstrate the applicability and scalability of this approach through an analysis of road accident data from Al-Ahsa, Saudi Arabia, consisting of over 150,000 road segments. By identifying high-risk road segments using exceedance probabilities and excursion sets, our framework provides localized insights into accident hotspots and offers a powerful tool for modeling spatial point processes directly on complex networks.
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Cold tolerance strategies in plants vary from structural to biochemical permitting many plants to survive and grow on sites that experience freezing conditions intermittently. Although tree ferns occur predominantly across the tropics, they also occur in temperate zones and occasionally in areas that experience sub‐zero temperatures, and how these large ferns survive freezing conditions is unknown. Many temperate tree fern taxa are marcescent – retaining whorls of dead fronds encircling the upper trunk – or develop short or prostrate trunks, possibly to insulate against frost damage to their trunks and growing crowns. We asked the following questions: 1) do global growth patterns and traits of tree ferns respond to freezing conditions associated with latitude and elevation, 2) do growth patterns of tree ferns in New Zealand vary along a temperature‐related gradient, and 3) do marcescent tree fern skirts insulate the growing crown from sub‐zero temperatures? To establish what morphological adaptations permitted the Cyatheales to occur in biomes that experience intermittent sub‐zero temperatures and frost, we 1) reviewed the global distributions of these structural and morphological traits within the tree ferns (Cyatheales); 2) assessed the patterns of tree fern marcescence, and other traits potentially associated with cold tolerance (no trunk, prostrate, short‐trunked) of nine taxa of the Cyatheales along environmental gradients across New Zealand; and 3) conducted a field experiment to assess the thermal insulation properties of tree fern marcescent skirts. We identified significant trends among growth forms, marcescence, and environmental gradients consistent with our hypothesis that these are adaptations to tolerate cold. Our field experiments provide quantitative evidence that marcescent skirts have a strong insulating effect on tree fern trunks. The Cyatheales have evolved several strategies to protect the pith cores of their trunks from extreme cold temperatures in temperate forests allowing them to capture niche space in environments beyond the tropics.
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Human-modified environments offer novel resources, but their exploitation can be a source of human-wildlife conflict. Residents of Sydney have reported increasing cases of bin-opening behavior by sulphur-crested cockatoos (Cacatua galerita), with evidence this behavior is socially learned between birds. Households protected their bins, yet cockatoos have learned to defeat these defences. In response, residents increase their defence-level, setting the stage for a behavioral "arms race". Here, we investigate this arms race by combining field observations with agent-based modeling. We systematically document protections in a suburban locality over two years, revealing spatial clustering of protections indicative of social learning among residents. We find that protections have decreased since 2019, and overly-costly protection is dis-preferred. With a controlled assay, we characterize the knowledge of local cockatoos, finding differential proficiency in defeating protections. Surprisingly, we identified several cockatoos that can defeat high-efficacy protections, such as locks. Finally, we simulate interactions between two populations of learning agents representing households and cockatoos. We find that social learning accelerates adaptation in both species, while coordination reduces costs associated with defensive escalation. However, policy interventions can have unintended consequences, accelerating cockatoos' skill acquisition and shifting conflict to neighboring areas. Our findings highlight the dynamic nature of human-wildlife conflict and the importance of considering behavioral feedback loops in urban wildlife management.
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For herpes simplex virus 1 (HSV-1) the skin is the primary site of infection. After the primary lytic infection, the virus enters the peripheral nervous system where it establishes latency. Spontaneous reactivation from the latently infected neurons leads to the typical HSV-1-induced diseases like cold sores. Modelling HSV-1-induced skin pathologies is challenging due to the variety of different cell types and structures in the skin and human-specific responses to the infection. Nevertheless, studies using monolayer cell lines, raft cultures, ex vivo skin and mouse models provided an immense contribution to our understanding of HSV-1 infection in the skin. However, the contribution of many skin-specific structures, especially hair follicles, to primary infection and reactivation remains unclear. In this study, we used complex human hair bearing skin organoids that were derived from induced pluripotent stem cell as a model for HSV-1 infection. We performed microscopy, bulk and spatial transcriptomics with single cell resolution to gain new insights into the cell-type specific viral life cycle and host responses. We show a restricted viral infection in keratinocytes of the epidermis and specific cell types of hair follicles. We show a cell type specific induction of interferon-stimulated genes and the TNF pathway. We can follow paracrine signaling through the tissue, showing that TNF response genes are upregulated in adjacent cells. Taken together, the skin organoids in combination with novel spatial transcriptomics techniques provide a physiologically highly relevant model system for HSV-1 infection in the skin.
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Species Distribution Models (SDMs) often suffer from spatial autocorrelation (SAC), leading to biased performance estimates. We tested cross-validation (CV) strategies - random splits, spatial blocking with varied distances, environmental (ENV) clustering, and a novel spatio-temporal method - under two proposed training schemes: LAST FOLD, widely used in spatial CV at the cost of data loss, and RETRAIN, which maximizes data usage but risks reintroducing SAC. LAST FOLD consistently yielded lower errors and stronger correlations. Spatial blocking at an optimal distance (SP 422) and ENV performed best, achieving Spearman and Pearson correlations of 0.485 and 0.548, respectively, although ENV may be unsuitable for long-term forecasts involving major environmental shifts. A spatio-temporal approach yielded modest benefits in our moderately variable dataset, but may excel with stronger temporal changes. These findings highlight the need to align CV approaches with the spatial and temporal structure of SDM data, ensuring rigorous validation and reliable predictive outcomes.
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This publication provides an overview of dengue fever, focusing on its epidemiological landscape in Florida. Dengue, caused by dengue virus and transmitted through the bites of infected Aedes mosquitoes, is a major global health concern. Florida has experienced local dengue transmissions, and the incidence of dengue is increasing in the state. The biology and ecology of the primary vectors, Aedes aegypti and Aedes albopictus, play important roles in the disease's transmission cycle. No vaccine and no specific antiviral treatments are yet available, and diagnosis is difficult because dengue symptoms overlap with those of other, similar febrile illnesses. Prevention and control strategies, such as mosquito management, are therefore crucial to mitigate disease risks. Alongside urbanization, climate change, and globalization, incidence of dengue is increasing in Florida. Continued vigilance and research will help to combat this public health threat.
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Ecosystem maps support a vast array of applications in conservation, land management and policy. The capacity of an ecosystem map to support these applications is determined by its ability to accurately represent ecosystem distributions, which is heavily influenced by the model used to produce them. Here, we evaluated the influence of key modelling decisions made whilst developing a new and comprehensive ecosystem map using a recently developed ecosystem typology for the remote Tiwi Islands, Australia. We collated a reference set of training points from diverse datasets and employed a pixel-based, random forest model to classify and predict ecosystem distributions. We tested decisions at three stages of the model formulation. First, we tested the number of classes by aggregating ecosystem types (finest scale, n = 11) into functional groups (n = 10) and biomes (coarsest, n = 8) according to the Global Ecosystem Typology. Second, we compared data acquired from the Sentinel-2 satellite using the MSI sensor and Landsat-9 with the OLI-2 sensor. Finally, we tested covariates from satellite image bands only or satellite imagery combined with additional covariates describing other ecological characteristics. We evaluated these decisions using a range of model performance metrics, including overall, by-class and spatially explicit estimates. Our study found that using covariates additional to those from satellite images improved all evaluation metrics for all model decisions. Acquisitions from Landsat-9 tended to improve model performance over Sentinel-2, although the effect was variable. Developing maps at the biome scale (coarsest resolution) slightly improved overall performance but hinders applications that need to differentiate between ecosystem types. Including additional relevant covariates or considering alternative satellites are better options for improving map performance than simplifying the classes. Producing spatially explicit evaluation of ecosystem maps is a rapid and achievable method to communicate limitations and support users to make informed decisions.
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Creating supportive food environments has been identified as one of the most effective strategies to promote healthy diets. In this context, the present work aimed to characterize the retail food environment in a low-income area of the city of Montevideo (Uruguay). The study relied on a mixed-methods approach. A survey of food retail outlets was conducted. All the outlets were geocoded, and service areas were created considering a 600 m threshold. Quantitative information was supplemented with qualitative information from semi-structured interviews with residents and grocery store owners. A total of 415 outlets selling a variety of foods were identified. Adequate physical access to the foods recommended by the Uruguayan dietary guidelines was found in most of the residential areas. Small behind-the-counter grocery stores were identified as the key source of healthy foods in the area. However, the accounts of residents highlighted problems related to the price, variety and quality of the foods sold by the local grocery stores. Therefore, strategies to ensure access to healthy food in the study area should go beyond physical access and tackle food affordability, variety and quality. The promotion of short food supply chains in the area should be a key element of the strategies to achieve sustainable urban food systems in Montevideo.
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Temperature regulates the location, frequency, and extent of irruptive forest insect herbivore outbreak cycles. Across the Pacific coastal temperate rainforest, recent outbreaks by a native defoliator, western blackheaded budworm, have impacted the greatest land area recorded since the advent of aerial detection programs and led to widespread losses of canopy leaf area and forest growth. Evidence suggests that the geographic distribution of budworm outbreaks has tracked a poleward shift in suitable temperature across the ecoregion. In this manuscript, we compile aerial observer estimates of insect defoliation, forest inventory data, and historical and projected climate data under three emissions scenarios to hind- and forecast the distribution of budworm outbreaks from 1901 to 2100. Climate data indicate that seasonal temperatures have warmed and are projected to warm further across the ecoregion, while seasonal precipitation has and will remain relatively constant. Models indicate that a range of spring and summer temperatures primarily constrain the biogeography of budworm outbreaks, while minimum host availability, autumn and winter temperatures, and seasonal precipitation further contribute. Projected warming will shift a substantial portion of regional forestland beyond the upper temperature threshold of historic outbreaks. Thus, our forecasts suggest that budworm outbreak distribution will narrow under all three future climatic scenarios tested. Across much of the ecoregion, the distribution of forestlands suitable for budworm outbreaks is projected to shift poleward and upslope, eventually eclipsing its host’s elevational distribution. The possible disruption of periodic defoliator outbreak disturbances in this system may have important ramifications for primary productivity, forest dynamics, and forest structure.
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Motivation Pollinators play a crucial role in maintaining Earth's terrestrial biodiversity. However, rapid human‐induced environmental changes are compromising the long‐term persistence of plant‐pollinator interactions. Unfortunately, we lack robust, generalisable data capturing how plant‐pollinator communities are structured across space and time. Here, we present the EuPPollNet (European Plant‐Pollinator Networks) database, a fully open European‐level database containing harmonised taxonomic data on plant‐pollinator interactions referenced in both space and time, along with other ecological variables of interest. In addition, we evaluate the taxonomic and sampling coverage of EuPPollNet, and summarise key structural properties in plant‐pollinator networks. We believe EuPPollNet will stimulate research to address data gaps in plant‐pollinator interactions and guide future efforts in conservation planning. Main Types of Variables Included EuPPollNet contains 1,162,109 interactions between plants and pollinators from 1864 distinct networks, which belong to 52 different studies distributed across 23 European countries. Information about sampling methodology, habitat type, biogeographic region and additional taxonomic rank information (i.e. order, family, genus and species) is also provided. Spatial Location and Grain The database contains 1214 different sampling locations from 13 different natural and anthropogenic habitats that fall in 7 different biogeographic regions. All records are geo‐referenced and presented in the World Geodetic System 1984 (WGS84). Time Period and Grain Species interaction data was collected between 2004 and 2021. Major Taxa and Level of Measurement The database contains interaction data at the species level for 94% of the records, including a total of 1411 plant and 2223 pollinator species. The database includes data on 6% of the European species of flowering plants, 34% of bees, 26% of butterflies and 33% of syrphid species at the European level. Software Format The database was built with R and is stored in ‘.rds’ and ‘.csv’ formats. Its construction is fully reproducible and can be accessed at: https://doi.org/10.5281/zenodo.14747448 .
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Microplastics (MP) are widely distributed environmental pollutants with the potential to impact terrestrial and aquatic ecosystems. MP produced in urban areas are transported through rivers to marine environments, interacting with water, sediments and organisms along the way. To date, most studies have characterized MP pollution associated with urban centers. This study quantified the MP abundance associated with a representative rural community of the Southern Caribbean Coast of Venezuela, Chichiriviche de la Costa (Chichi), and its influence on a neighboring riverine and marine ecosystem. MP pollution was assessed in the dry and rainy season by sampling riverine water and sediments upstream and downstream of the village. Additionally, marine water, sediments and organisms (fish and sponges) were sampled in the bay. Samples were processed according to standardized protocols with strict quality control procedures. MP were characterized through ATR-FT-IR. The riverine water and sediments downstream of the village had a MP abundance that was 2.3 and 3.8 times higher than the upstream sampling site, respectively. A higher MP abundance was found in the sediments of the river mouth and the waters of the inner bay of Chichi, suggesting that the river was the main source of MP to the bay. MP were found in all marine organisms. The MP abundance in the waters of the inner bay of Chichi was 1.7 to 1197.3 times higher than previous studies conducted in urban centers of Latin America. Our study highlights the role of rural centers as sources of MP pollution.
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Introdução: Um dos principais papéis da vigilância do câncer é fornecer ao gestor um cenário da situação do câncer, projetando expectativas sobre a incidência dos diferentes tipos de câncer nos territórios, possibilitando o planejamento racional das ações de prevenção, detecção precoce e tratamento direcionados aos principais tipos de câncer incidentes naquela Região. A elaboração de estimativas de câncer constitui uma das principais ferramentas nesse sentido, mas sua elaboração detalhada pelos diferentes tipos de câncer e regiões é complexa e trabalhosa. O software R é uma ferramenta poderosa de manipulação de dados e seu uso ainda é pouco difundido nas atividades de vigilância em saúde. Objetivo: Descrever a metodologia do cálculo das estimativas de incidência do câncer e disponibilizar o conjunto de scripts do software R utilizado, com o objetivo de difundir e fomentar o seu uso nas atividades de vigilância do câncer. Método: É apresentada a elaboração das Estimativas de Câncer em Minas Gerais, para o ano de 2024, por Macrorregiões de Saúde. Todo o processo de manipulação e cálculos foi estruturado em scripts do R, que são disponibilizados como um suplemento. Resultados: A elaboração de estimativas é potencializada pelo uso do software R, pela facilidade da manipulação da grande quantidade de dados e dos cálculos necessários, além da possibilidade de apresentação dos resultados em gráficos e mapas. Conclusão: Com a disponibilização dos scripts, espera-se que todo o processo possa ser adaptado por outros Estados, para a elaboração de suas próprias estimativas regionais.
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1. The natal multimammate mouse (Mastomys natalensis) is the primary reservoir host of Lassa mammarenavirus (LASV), a zoonotic pathogen causing Lassa fever that is endemic to West Africa. The occurrence and abundance of this species is regulated by the human environment and biotic interactions with other small-mammal species, but these ecological drivers remain poorly understood in the regions where Lassa fever outbreaks are observed. 2. We developed a Bayesian multi-species occupancy model incorporating incomplete detection to assess habitat use from data obtained as part of a multi-year small-mammal trapping study (43,226 trap nights across four village sites in Sierra Leone, 2020-2023). We investigated the effects of land use gradients and small-mammal community dynamics on the spatial distribution of M. natalensis. 3. Mastomys natalensis occupancy increased along a gradient from forest to agriculture to village habitats but was reduced in peri-urban settings compared to rural settings. Invasive rodent species influenced this pattern, with Mus musculus presence associated with reduced M. natalensis occupancy in peri-urban settings. We did not observe a similar effect when considering the co-occurrence of invasive Rattus rattus with M. natalensis in rural settings. 4. These findings suggest that land use and species interactions drive spatial heterogeneity in M. natalensis populations, potentially explaining reduced Lassa fever incidence in urban areas. The results highlight the importance of considering community dynamics when predicting the risk of outbreaks of endemic zoonoses and the need to widen the context of studies of LASV transmission beyond the primary reservoir host species. 5. To better assess public health risk and improve allocation of limited resources, we recommend more precise characterisation of small-mammal communities in LASV endemic regions, particularly in areas undergoing rapid land use change which may alter community level small-mammal biodiversity.
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The Law No. 12,651/2012 brought changes to the widths of riparian Permanent Preservation Areas (PPAs) to be restored, which now also depend on the size of the rural property in fiscal modules, which in turn, varies depending on the municipality. Because of this, defining the environmental liabilities of these PPAs is complex, requiring geoprocessing procedures and information that are not always readily available. To automate the identification of these areas to be restored, the restauraRapp package was created, which presents a set of functions for automating four steps of the procedure: I) Data acquisition; II) Classification of property sizes; III) Processing information on land use and cover data in riparian areas; IV) Cartographical and tabular output generation. This package can support landscape planning focused on the restoration of riparian PPAs, potentially assisting NGOs, municipality governments and landowners.
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Background Regional differences in the Big Five personality domains have been observed in several countries at different geographical granularities, often correlating with regional political, economic, social, and health (PESH) indicators. Objective We examined the extent of regional personality differences in Estonia and whether these differences were meaningfully correlated with PESH indicators. Methods Using data from the Estonian Biobank ( N = 72,268; 7% of the adult population, providing unprecedented representativeness), we tested regional personality differences and their relations with PESH indicators with and without spatial smoothing. Results We found that regional Big Five scores varied by 1.19 (extraversion) to 2.78 (openness) T ‐score units across counties ( N = 15) and by 2.80 (extraversion) to 4.74 (openness) units across municipalities ( n = 74). Also, the correlations with the PESH indicators at the county and municipality levels persisted even after controlling for gender, age, and spatial dependency, and were moderately consistent with our predictions ( r = 0.23 to 0.30) and between the county and municipality levels ( r = 0.41). Conclusions Estonian residents tended to be similar in personality traits regardless of their location, replicating results from other countries. Yet, small regional personality domain differences could represent valid and possibly consequential psychological variation.
Article
Cryptic species are an important part of freshwater biodiversity, yet it remains unclear how these species integrate into communities from local to regional geographic scales. To protect biodiversity, particularly overlooked cryptic species, an accurate understanding of the underlying processes and adequate level of protection is needed. We analysed patterns of syntopies (local co‐occurrences) and sympatries (regional range overlap) to explore how the phylogenetic origin of cryptic species shapes biodiversity patterns. We hypothesised (i) that syntopies were more common among distantly than closely related cryptic species, and (ii) that the existing sympatries were an outcome of phylogenetic relatedness and dispersal. The hypotheses were tested on a polyphyletic species complex of subterranean amphipod species ( Niphargus rhenorhodanensis complex) by deploying molecular species delimitation, time‐calibrated phylogenies, and co‐occurrence analyses with probabilistic and generalised linear models (GLM). The studied complex comprised 37–48 molecular operational taxonomic units (MOTUs) from nine different clades, with syntopies occurring at random or less frequently than expected. GLM indicated age of divergence did not predict species sympatries, although they emerged more frequently among MOTUs from different clades. Sympatries, however, emerged through dispersal, in MOTUs with large geographic ranges. These mostly overlapped at the foothills of the Alps, the Jura and the Central Massif. We conclude that the observed spatial patterns are mainly driven by dispersal and presumably reflect the geographic circumstances of speciation. While species richness on a local scale may be an outcome of competition and dispersal, regional biodiversity patterns presumably arise through a clade‐level cascade of historical events, including orogeny and climatic shifts.
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OBJECTIVE Pediatric hydrocephalus is a common complex condition, in which late diagnosis can cause irreversible sequelae. The prevalence worldwide is estimated to be approximately 88/100,000, but the literature suggests it is higher in developing countries, with predominantly postinfectious etiologies. The incidence has been found to be inversely associated with a country’s income level. South America is among the regions considered most affected by this disease, but very few recent prevalence studies exist. This is the first prevalence study of pediatric hydrocephalus in Colombia, an upper-middle-income country. This study aimed to estimate the prevalence of pediatric hydrocephalus (ages 0 to 17 years) in Colombia between 2017 and 2022 and to determine its national distribution. METHODS A search of the Colombian System of Integrated Information of Social Protection was performed, using International Classification of Diseases, 10th Revision codes to extract the Individual Registries for Provision of Health Services. These data were compared to those in the population registries of the National Administrative Department of Statistics. Prevalence for each code was calculated, and distribution according to age group, sex, and department was made. Yearly and overall prevalence rates were graphed on nationwide maps throughout the study period years. RESULTS The authors found a nationwide prevalence of 57.2/100,000, with an underreporting rate for all cases of 31.3%. The adjusted prevalence for underreporting was 83.0 cases per 100,000. A total of 55% of cases were in male patients. The reported causes of hydrocephalus were as follows: 24.9% of cases were due to postinfectious etiologies, 9.9% were attributed to CNS malformations, 0.3% were posttraumatic, and 0.3% were neoplastic. In most cases, etiology was not reported. The maps created show a heterogeneous prevalence distribution through the years. The adjusted prevalence rate map shows a prevalence distribution with higher rates in lower-income regions. CONCLUSIONS In this study, the estimated prevalence of pediatric hydrocephalus in Colombia was lower than the prevalence estimated worldwide, and even lower than that estimated for high-income areas. This is explained partially by a significant rate of underreporting; however, even accounting for the underreporting, the prevalence remains considerably lower than that estimated for developing regions like South America. This may suggest a trend of decreasing prevalence in developing countries whose economies have grown in recent years. These findings can guide public policy for adequate surveillance and prevention of pediatric hydrocephalus in Colombia and highlight the importance of further updated research in the region.
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