Occurrence Data Research Articles

Habitat suitability and niche interaction between the invasive snail Achatina fulica and its biocontrol flatworm Platydemus manokwari in Southeast Asia

Bioinvasions are increasingly altering community structures around the world, especially with the changing climate. In invaded areas, biocontrol agents are introduced to regulate the invasive species. However, without thorough evaluation, these agents can proliferate and threaten other organisms. We assessed the niche dynamics between invasive Achatina fulica (Giant African Snail) and its biocontrol agent, the Platydemus manokwari (New Guinea Flatworm), in Southeast Asia. Species occurrence and environmental data were used to model the habitat suitability of both species in the present and future climate scenarios using ecological niche modeling with the MaxEnt algorithm. These models predicted 25.9% and 42.0% of the current conditions to be suitable and 73.8% and 57.8% to be unsuitable for A. fulica and P. manokwari, respectively. There was a predicted steady increase in suitable areas and a gradual decrease in A. fulica’s unsuitable areas as the carbon emissions are predicted to increase. Moderate to high niche overlap of 61.2% to 83.4% was expected between the species under different climate scenarios. Predicting the suitable areas for invasive species and their niche interaction with other species in future scenarios will aid in identifying vulnerable areas for conservation.

Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States, 2019.

In 2018, the authors reported estimates of the number and proportion of cancers attributable to potentially modifiable risk factors in 2014 in the United States. These data are useful for advocating for and informing cancer prevention and control. Herein, based on up-to-date relative risk and cancer occurrence data, the authors estimated the proportion and number of invasive cancer cases (excluding nonmelanoma skin cancers) and deaths, overall and for 30 cancer types among adults who were aged 30 years and older in 2019 in the United States, that were attributable to potentially modifiable risk factors. These included cigarette smoking; second-hand smoke; excess body weight; alcohol consumption; consumption of red and processed meat; low consumption of fruits and vegetables, dietary fiber, and dietary calcium; physical inactivity; ultraviolet radiation; and seven carcinogenic infections. Numbers of cancer cases and deaths were obtained from data sources with complete national coverage, risk factor prevalence estimates from nationally representative surveys, and associated relative risks of cancer from published large-scale pooled or meta-analyses. In 2019, an estimated 40.0% (713,340 of 1,781,649) of all incident cancers (excluding nonmelanoma skin cancers) and 44.0% (262,120 of 595,737) of all cancer deaths in adults aged 30 years and older in the United States were attributable to the evaluated risk factors. Cigarette smoking was the leading risk factor contributing to cancer cases and deaths overall (19.3% and 28.5%, respectively), followed by excess body weight (7.6% and 7.3%, respectively), and alcohol consumption (5.4% and 4.1%, respectively). For 19 of 30 evaluated cancer types, more than one half of the cancer cases and deaths were attributable to the potentially modifiable risk factors considered in this study. Lung cancer had the highest number of cancer cases (201,660) and deaths (122,740) attributable to evaluated risk factors, followed by female breast cancer (83,840 cases), skin melanoma (82,710), and colorectal cancer (78,440) for attributable cases and by colorectal (25,800 deaths), liver (14,720), and esophageal (13,600) cancer for attributable deaths. Large numbers of cancer cases and deaths in the United States are attributable to potentially modifiable risk factors, underscoring the potential to substantially reduce the cancer burden through broad and equitable implementation of preventive initiatives.

Why did the Hornbill not cross the river? Upland habitats rather than a physical barrier limit the distribution of the Brown Hornbill

AbstractAimIn continuous environments, niche limits of species often determine their distribution limits. However, when these limits spatially coincide with a perceived dispersal barrier, the determinants of species' ranges may be confounded. We investigate the distribution pattern of the Brown Hornbill (Aves: Bucerotidae), which spans significant riverine barriers, but stops south of the Brahmaputra River. Considering its preference for low‐elevation evergreen forests, we posit that the lack of sufficient habitats north of the Brahmaputra prevents dispersal of the Brown Hornbill, and not the river itself.LocationThe Brahmaputra valley and the Indo‐Burma hotspot.TaxaAnorrhinus austeni, Aceros nipalensis.MethodsWe analysed citizen‐science occurrence data on hornbill presence against a suite of climatic, canopy‐specific and topographical predictors to model the environmental niche of the Brown Hornbill. We used presence‐only maximum entropy modelling in an information theoretic framework, in conjunction with constructing binary logistic regression models using presence and pseudoabsence data. We compared niche models of the Brown Hornbill and the Rufous‐necked hornbill, a close relative with a similar distribution, but which has spanned the Brahmaputra River.ResultsDespite the presence of suitable wet‐evergreen forests north of the Brahmaputra River, the hilly terrains in the region act as a biogeographic barrier for the Brown Hornbill, which prefers lowland evergreen forests. Further, highly suitable regions for the Brown and the Rufous‐necked Hornbills precisely delineate low and high‐elevation evergreen forests respectively, indicating that these birds are separated along an elevational axis.Main ConclusionsThe Brahmaputra River lies at the cusp of two subtly different environmental regimes. It may therefore serve as the niche limit for certain organisms and not as a physical obstacle to their dispersal. Our study implicitly predicts how widespread deforestation prevalent in the lowland evergreen forests of this region adversely impacts the distributions of organisms that depend on them, such as the Brown Hornbill. Further, our study proffers an approach to ascertain determinants of species distributions in a hypothesis testing framework.

A Review of Training Procedures for Simulated Engine Failure after Take-Off Exercises with Twin-Engine Aircraft under 5700 kg

Engine failure after take-off (or one engine being inoperative) is an exercise conducted as part of multi-engine flight training and on-going competency checking. To prepare pilots to manage a real in-flight emergency, this exercise has traditionally been conducted immediately after take-off. This has led to increased risks of fatal accidents due to the reduced height at which these exercises are typically conducted. Yet, there is variation in the heights stipulated in training procedures published by different stakeholders worldwide. Additionally, the conduct of the exercise has resulted in fatal accidents worldwide. This paper aims to review the previous literature on aviation training and aviation occurrence data to determine what empirical data exists to support the method of conducting simulated engine failures. Peer-reviewed academic publications on aviation training, aviation occurrence databases such as aviation investigation reports, and guidance materials published by aviation authorities on simulated training exercises will be included in this paper. It was found that the previous research on these exercises has focused on the transfer of motion cues or pilot responses to abnormal situations, but did not include specific data comparing pilot performance at different heights above ground level. A review of aviation occurrences found that actual engine failures occurred at higher heights that those used in simulated engine failures. A comparison of the guidance published by aviation authorities identified variations in the minimum altitude published and differing justifications for the minimum height chosen. Future research is needed to compare pilot performance during simulated engine failures to determine the ideal height to conduct the exercise to be representative of an actual engine failure while maintaining safety margins.

Sequential Behavior of Broiler Chickens in Enriched Environments under Varying Thermal Conditions Using the Generalized Sequential Pattern Algorithm: A Proof of Concept.

Behavior analysis is a widely used non-invasive tool in the practical production routine, as the animal acts as a biosensor capable of reflecting its degree of adaptation and discomfort to some environmental challenge. Conventional statistics use occurrence data for behavioral evaluation and well-being estimation, disregarding the temporal sequence of events. The Generalized Sequential Pattern (GSP) algorithm is a data mining method that identifies recurrent sequences that exceed a user-specified support threshold, the potential of which has not yet been investigated for broiler chickens in enriched environments. Enrichment aims to increase environmental complexity with promising effects on animal welfare, stimulating priority behaviors and potentially reducing the deleterious effects of heat stress. The objective here was to validate the application of the GSP algorithm to identify temporal correlations between heat stress and the behavior of broiler chickens in enriched environments through a proof of concept. Video image collection was carried out automatically for 48 continuous hours, analyzing a continuous period of seven hours, from 12:00 PM to 6:00 PM, during two consecutive days of tests for chickens housed in enriched and non-enriched environments under comfort and stress temperatures. Chickens at the comfort temperature showed high motivation to perform the behaviors of preening (P), foraging (F), lying down (Ld), eating (E), and walking (W); the sequences <{Ld,P}>; <{Ld,F}>; <{P,F,P}>; <{Ld,P,F}>; and <{E,W,F}> were the only ones observed in both treatments. All other sequential patterns (comfort and stress) were distinct, suggesting that environmental enrichment alters the behavioral pattern of broiler chickens. Heat stress drastically reduced the sequential patterns found at the 20% threshold level in the tested environments. The behavior of lying laterally "Ll" is a strong indicator of heat stress in broilers and was only frequent in the non-enriched environment, which may suggest that environmental enrichment provides the animal with better opportunities to adapt to stress-inducing challenges, such as heat.

Macrophenological dynamics from citizen science plant occurrence data

Abstract Phenological shifts across plant species is a powerful indicator to quantify the effects of climate change. Today, mobile applications with automated species identification open new possibilities for phenological monitoring across space and time. Here, we introduce an innovative spatio‐temporal machine learning methodology that harnesses such crowd‐sourced data to quantify phenological dynamics across taxa, space and time. Our algorithm links individual phenological responses across thousands of species and geographical locations, using a similarity measure. The analysis draws on nearly ten million plant observations collected through the AI‐based plant identification app Flora Incognita in Germany from 2018 to 2021. Our method quantifies changes in synchronisation across the annual cycle. During the growing season, synchronised behaviour can be encoded by a few characteristic macrophenological patterns. Nonlinear spatio‐temporal changes of these patterns can be efficiently quantified using a data compressibility measure. Outside the growing season, the phenological synchronisation diminishes introducing noise into the patterns. Despite biases and uncertainties associated with crowd‐sourced data, for example due to human data collection behaviour, our study demonstrates the feasibility of deriving meaningful indicators for monitoring plant macrophenology from individual plant observations. As crowd‐sourced databases continue to expand, our approach holds promise to study climate‐induced phenological shifts and feedback loops.

A Systematic Review of the Distribution and Prevalence of Viruses Detected in the Peromyscus maniculatus Species Complex (Rodentia: Cricetidae).

The North American Deermouse, Peromyscus maniculatus, is one of the most widespread and abundant mammals on the continent. It is of public health interest as a known host of several viruses that are transmissible to humans and can cause illness, including the acute respiratory disease Hantavirus Pulmonary Syndrome (HPS). However, recent taxonomic studies indicate that P. maniculatus is a complex of multiple species, raising questions about how to identify and interpret three decades of hantavirus monitoring data. We conducted a systematic review investigating the prevalence and spatial distribution of viral taxa detected in wild populations allocated to P. maniculatus. From the 46 relevant studies published from 2000 to 2022, we extracted and analyzed spatial occurrence data to calculate weighted populational prevalences for hantaviruses. We found that detection efforts have been concentrated in the Western United States and Mexico with a focus on the spread of Sin Nombre virus, the primary causative agent of HPS. There are significant gaps in the existing literature both geographically and in regard to the types of hantaviruses being sampled. These results are significantly impacted by a recent taxonomic split of P. maniculatus into four species, which results in the relabeling of 92% of hantavirus observations. Considering the uncertain, and likely multiple, phylogenetic histories of these viral hosts should be a key emphasis of future modeling efforts.

Future trends of marine fish biomass distributions from the North Sea to the Barents Sea

Climate warming is one of the facets of anthropogenic global change predicted to increase in the future, its magnitude depending on present-day decisions. The north Atlantic and Arctic Oceans are already undergoing community changes, with warmer-water species expanding northwards, and colder-water species retracting. However, the future extent and implications of these shifts remain unclear. Here, we fitted a joint species distribution model to occurrence data of 107, and biomass data of 61 marine fish species from 16,345 fishery independent trawls sampled between 2004 and 2022 in the northeast Atlantic Ocean, including the Barents Sea. We project overall increases in richness and declines in relative dominance in the community, and generalised increases in species’ ranges and biomass across three different future scenarios in 2050 and 2100. The projected decline of capelin and the practical extirpation of polar cod from the system, the two most abundant species in the Barents Sea, drove an overall reduction in fish biomass at Arctic latitudes that is not replaced by expanding species. Furthermore, our projections suggest that Arctic demersal fish will be at high risk of extinction by the end of the century if no climate refugia is available at eastern latitudes.

Evidence of environmental niche separation between threatened mobulid rays in Aotearoa New Zealand: Insights from species distribution modelling

AbstractAimMobulid rays are a group of threatened batoid fishes susceptible to population decline from targeted fisheries and accidental capture. Spatial distributions of mobulid rays remain poorly known. Prior studies found commonalities between favourable environments and prey among various mobulid species, yet most were conducted in tropical waters. To explore the habitat use and distribution of mobulid rays in a temperate environment we model the habitat suitability of two mobulid rays (Mobula mobular and Mobula birostris) in Aotearoa New Zealand using fisheries and citizen science occurrence data spanning almost two decades.LocationNortheastern coast of Aotearoa, New Zealand.MethodsBoosted Regression Tree models were used to predict the annual habitat suitability and favourable environmental conditions of the two species based on available sightings records in conjunction with high resolution (1 km2) environmental data.ResultsThe sympatric study species had contrasting habitat requirements. We found a separation in their spatial distribution defined by the 200 m isobath – the onshore extent for M. birostris and the offshore extent for M. mobular. While there were only subtle variations in relative habitat suitability for M. mobular over the study period, M. birostris exhibited greater interannual variability. Despite differing interannual patterns, spatial separation, as a function of environmental properties, persisted regardless of the year.Main ConclusionsOur results suggest that associations between mobulid species may differ from tropical regions due to regional adaptations to prey availability or local environmental conditions unique to colder and more productive temperate waters. Our findings highlight the importance of multi‐species surveys and the inclusion of temporal variability in support of separate species‐specific management plans to account for differing stressors impacting each species.

Phenotypic plasticity rather than ecotypic differentiation explains the broad realized niche of a Neotropical orchid species.

Local adaptation is common in plant species, and knowing whether a population is locally adapted has fundamental and applied relevance. However, local adaptation in tropical plants remains largely less studied, and covering this gap is not simple since reciprocal transplantation - the gold standard for detecting local adaptation - is not feasible for most species. Here, we combined genetic, climatic and phenotypic data to investigate ecotypic differentiation, an important aspect of local adaptation, in coastal and inland populations of the orchid Epidendrum fulgens Brongn., a long-lived tropical plant for which reciprocal transplantation would not be feasible. We used nine microsatellite markers to estimate genetic divergence between inland and coastal populations. Moreover, occurrence data and climate data were used to test for differences in the realized niche of those populations. Finally, we assessed saturated water content, leaf specific area, height, and stomatal density in common garden and in situ to investigate the effects of ecotypic differentiation and plasticity on the phenotype. Coastal and inland groups' niches do not overlap, the former occupying a wetter and warmer area. However, this differentiation does not seem to be driven by ecotypic differentiation since there was no positive correlation between genetic structure and climate dissimilarity. Moreover, specific leaf area and leaf saturated water content, which are important phenotypic traits related to soil fertility and drought stress, were rather plastic. We conclude that ecotypic differentiation is absent, since phenotypic plasticity is an important mechanism explaining the niche broadness of this species.

Modelling wild boar abundance at high resolution

Abstract By using the latest available data, we provide estimates of wild boar (Sus scrofa) distribution and abundance pre‐African Swine Fever (ASF) based on occurrence data in Europe. Secondly, as a basis for the calibration model output into densities, we used the predictions of relative abundance, and hunting yield‐based model (hunted individuals per km2), at 2x2 km for wild boar (by ENETWILD Consortium) and local wild boar densities (individuals per km2) considered reliable and obtained in the framework of the European Observatory of Wildlife (EOW), as well as some from recent literature (2015 onwards). Hunting yield predictions were considered at different spatial scales namely 5, 10 and 15 km radii buffer around localities with density estimations. The calibration of hunting yield‐based model into densities are a better fit for 15 km radius buffer and a significant relationship between model predictions of hunting yield and reliable density values at European level. This calibration of wild boar hunting yield‐based model into densities will offer the possibility to predict density values of wild boar. This will be useful to incorporate into risk factor analyses for African Swine Fever at the selected spatial range. This is the first time that absolute density estimates have been made using these two approaches for Europe, which demonstrates the added value of the observatory approach (a number of study areas where reliable density values are obtained, such as from the EOW) to generate novel information of high value for epidemiological assessment. During an ASF outbreak hunting effort will change dramatically and will take a few years to return to similar pre‐ASF levels, so post‐ASF estimates of density would be limited to areas where ASF has been present for a while. However, there will be relatively limited effect on sighting data as these rely on a number of different actors, many of whom may be expected to return to normal activities relatively soon after ASF arrives. Thus, relative post‐ASF wild boar density may be more reliable in the short term. These relative post‐ASF densities were calculated but with the limited sighting data available at the chosen locations the uncertainty was high. We advocate for the developing this nework of wildlife monitoring across Europe, and in general, harmioized wildlife monitoring programs, ensuring standardisation and consistency in the data generated and collected, which is essential for assessing management and risks related not only to ASF but other wildlife diseases.

ImproRisk model, an open access risk assessment tool

Abstract ImproRisk is an open‐access Risk Assessment model, developed in Cyprus, which contributes to the harmonization, standardization and transparency of the Dietary Exposure Assessment methodologies across Europe. It is a simple and transparent tool, built in R, for conducting accurate dietary exposure assessments to chemical substances. Aiming to be established as a standardized Risk Assessment tool at European level, the model has been designed to accept occurrence and consumption data, coded according to the EFSA's food classification and description system version 2 (FoodEx2). ImproRisk users are able to estimate dietary exposure to a chemical substance under study at any level of the EFSA's FoodEx2, considering the FoodEx2 base term and the Process facet (F28). Dietary exposure is estimated at individual level for different population groups and exposure estimates are stratified by different demographic characteristics. ImproRisk is considered as a useful tool both for risk assessors and risk managers, as it facilitates decision making, by providing information regarding the percentage of the population exceeding the relevant health‐based guidance value or reference points for a specific chemical substance.

Bivariate flood frequency analysis of dates of occurrence and maximum flow through Copula functions

En el centro y sur de la república mexicana cada año los huracanes del mar Caribe y del océano Pacífico originan crecientes que definen una estación húmeda, y que en general aumentan en magnitud y peligrosidad conforme transcurre la temporada de ciclones. Ambas condiciones permiten el análisis de frecuencias bivariado de sus fechas de ocurrencia y sus gastos máximos (Qm). En este estudio, la distribución conjunta se formó con base en la función Cópula de Gumbel-Hougaard, que satisface la condición de dependencia ("λ" _U^CFG) observada y que combina como distribuciones marginales la de von Mises para las fechas de ocurrencia en el año y para los Qm una función probabilística idónea. La teoría expuesta se aplica a las crecientes anuales registradas en la estación de aforos Guamúchil de la Región Hidrológica No. 10 (Sinaloa), México, en el periodo de 1940 a 1971. La distribución de von Mises se ajusta vía optimización numérica con el algoritmo de Rosenbrock y la distribución idónea de los Qm fue la Kappa. Se formó la gráfica de periodos de retorno conjuntos de tipo AND de 50, 100 y 500 años. Además, se estimaron periodos de retorno conjuntos condicionales de fechas de ocurrencia, dado que el Qm tiene los periodos de retorno citados. Lo anterior permite estimaciones de la probabilidad de excedencia del Qm en lapsos definidos. Las conclusiones destacan la simplicidad de estos análisis de frecuencias bivariados por medio de las funciones Cópula y la importancia práctica de sus predicciones, según las fechas de ocurrencia.

Wildlife habitat suitability analysis and mapping the former dhidhessa wildlife sanctuary using GIS-based analytical hierarchal process and weighted linear combination methods

Management of wildlife populations and the creation of conservation programs depend on the evaluation of wildlife habitats. Habitat suitability mapping is a technique typically used to map appropriate environmental factors and assess species existence in different areas. This study aims to map wildlife habitat suitability sites in Former Dhidhessa wildlife sanctuary, Ethiopia, using GIS-based Analytical Hierarchal Process and Weighted Linear Combination Methods. This study used both primary and secondary data sources. Datasets used to collect data include Digital Elevation Model (DEM), Landsat 9 (OLI/TIRS) and population data. Beside, large mammalian species occurrence data obtained from field survey was used. To map wildlife habitat suitability sites in Former Dhidhessa wildlife sanctuary, environmental factors such as proximity of road network, distance to surface water, land use land cover types, slope, population density and topography were used with the integration of species occurrence data recorded from the study area. These environmental factors scaled to common ranges, and assigned appropriate weights. The quantile classification method was utilized to classify suitability index into five zones (unsuitable, less suitable, moderately suitable, suitable, and highly suitable) to produce the map. Accordingly, the model revealed that 18.9 % of the study area is highly suitable, 19.5 % is suitable, 19.9 % is moderately suitable, 19.5 % is less suitable, and 22.2 % is unsuitable for wildlife. About 58.3 % of the study area is currently identified as suitable for wildlife whereas 41.7 % is unsuitable. This showed that the former Dhidhessa wildlife sanctuary is still having large suitable habitats that can support wide ranges of wildlife. Hence, based on the developed preliminary habitat suitability indices and maps, the federal and local governments shall reevaluate the status of former Dhidhessa wildlife sanctuary and develop future conservation and management plans to enhance the conservation of wildlife and their habitats in the area.

NIMO: A graphical user interface‐based R package for species distribution modelling

Abstract Species distribution modelling (SDM) is essential for understanding and predicting biodiversity patterns globally. However, the complexities of data preparation, algorithm selection and model evaluation can present significant challenges. This paper introduces NIMO, an R package with a user‐friendly graphical interface aimed at streamlining the entire SDM workflow. In SDM, data preparation is a crucial and time‐consuming process, which requires the acquisition of spatiotemporal occurrence records. The Global Biodiversity Information Facility (GBIF) database plays a key role in addressing data scarcity by providing extensive community science‐based repositories. However, compiling, aligning and verifying spatial references can be laborious. NIMO addresses these challenges by offering a flexible workflow that allows users to more easily query biodiversity data. NIMO organizes its features into three main steps: pre‐modelling, modelling and post‐modelling. It empowers users to optimize data for modelling with a variety of algorithms. NIMO allows for default hyperparameter values or extensive tuning, providing users with flexibility in selecting models. Practical implication: To demonstrate the effectiveness of NIMO, we presented a case study focusing on the critically endangered white‐backed vulture (Gyps africanus) in Kruger National Park, South Africa. We used NIMO to collect GBIF occurrence data and integrated environmental predictors to map suitable habitat (probability of occurrence). The distribution modelling workflow included area calibration, collinearity check, generation of training and testing data sets, model fitting and model combination. The results showcase the discriminative ability of fitted models, especially the generalized linear model, support vector machine and random forest, that constituted the ensemble model. Spatial prediction using the ensemble model revealed that the species has a higher occurrence probability close to roads, indicating a possible increased risk of mortality for the vultures in Kruger National Park from motor vehicle collisions.

Leveraging Artificial Neural Networks for Robust Landslide Susceptibility Mapping: A Geospatial Modeling Approach in the Ecologically Sensitive Nilgiri District, Tamil Nadu

Landslides pose a significant threat to the lives and livelihoods of marginalised communities residing in rural areas and the delicate ecological balance of the environment. Implementing advanced technologies is crucial for improving hazard risk assessment and enhancing preparedness measures in regions characterised by diverse topography and complex geological formations. Geospatial applications and modelling techniques have emerged as indispensable in mitigating landslide risks, particularly in environmentally sensitive areas. This study presents a comprehensive approach to landslide susceptibility mapping in the Nilgiri District of Tamil Nadu, India, leveraging the power of Artificial Neural Networks (ANNs) and integrating multi-dimensional geospatial datasets. Integrating ANN-based modelling and geospatial techniques offers significant advantages in terms of statistical robustness, reproducibility, and the ability to analyse the complex interplay of factors influencing landslide hazards quantitatively. The methodology involves rigorous pre-processing and integrating spatial data, including landslide event occurrences as the dependent variable and ten independent parameters influencing landslide susceptibility. These parameters encompass elevation, slope aspect, slope degree, distance to roads, land use patterns, geomorphology, lithology, drainage density, lineament density, and rainfall distribution. Feature extraction and selection techniques are employed to effectively model the complex interactions between these factors and landslide occurrences. This process identifies the most relevant variables influencing landslide susceptibility, enhancing the model's predictive capabilities. The state-of-the-art ANNs are trained using historical landslide occurrence data and the selected influencing factors, enabling the development of a robust and accurate landslide susceptibility model. The performance of the developed model is rigorously evaluated using a comprehensive suite of metrics, including accuracy, precision, and the Area under the Receiver Operating Characteristic (ROC) curve. Preliminary results indicate that the ANN-based landslide susceptibility model outperforms traditional zonation methods, demonstrating higher accuracy and reliability in predicting landslide-prone areas. The resulting Landslide Susceptibility Map (LSM) categorises the study area into five distinct hazard zones, ranging from very high (664.1 km2), high (598.9 km2), moderate (639.7 km2), low (478.9 km2) and to very low (170.9 km2) . Notably, the eastern and central regions of the district emerge as particularly vulnerable to landslide occurrences. The study's findings have far-reaching implications for disaster risk reduction efforts, land-use planning, and sustainable development strategies in the ecologically sensitive Nilgiri District and beyond.

Part I: Improving Wildfire Occurrence Prediction for CONUS Using Deep Learning and Fire Weather Variables

Abstract The purpose of this research is to build an operational model for predicting wildfire occurrence for the contiguous United States (CONUS) in the 1–10-day range using the U-Net 3+ machine learning model. This paper illustrates the range of model performance resulting from choices made in the modeling process, such as how labels are defined for the model and how input variables are codified for the model. By combining the capabilities of the U-Net 3+ model with a neighborhood loss function, fractions skill score (FSS), we can quantify model success by predictions made both in and around the location of the original fire occurrence label. The model is trained on weather, weather-derived fuel, and topography observational inputs and labels representing fire occurrence. Observational weather, weather-derived fuel, and topography data are sourced from the gridded surface meteorological (gridMET) dataset, a daily, CONUS-wide, high-spatial-resolution dataset of surface meteorological variables. Fire occurrence labels are sourced from the U.S. Department of Agriculture’s Fire Program Analysis Fire-Occurrence Database (FPA-FOD), which contains spatial wildfire occurrence data for CONUS, combining data sourced from the reporting systems of federal, state, and local organizations. By exploring the many aspects of the modeling process with the added context of model performance, this work builds understanding around the use of deep learning to predict fire occurrence in CONUS. Significance Statement Our work seeks to explore the limits to which deep learning can predict wildfire occurrence in CONUS with the ultimate goal of providing decision support to those allocating fire resources during high fire seasons. By exploring with what accuracy and lead time we can provide insights to these persons, we hope to reduce loss of life, reduce damage to property, and improve future event preparedness. We compare two models, one trained on all fires in the continental United States and the other on only large lightning fires. We found that a model trained on all fires produced a higher probability of fire.

Animal dietary exposure in the risk assessment of contaminants in feed.

EFSA performs dietary exposure assessments for food-producing and non-food-producing animals to deliver risk assessment for mandates on the presence of contaminants in feed. The CONTAM and FEEDAP Panels identified the need to update the animal dietary exposure assessment model used in those assessments in CONTAM Scientific Opinions since 2011 in cases where insufficient occurrence data are available on species specific compound feeds. The Panels proposed in this statement a series of model diets based on groups of feed materials with the possibility to use different feed materials in their formulation. The Panels considered that the currently proposed model diets cover the need of the CONTAM Panel to assess the dietary exposure of animals to contaminants in feed.

IUFD (IUD) Among Different Gestational Age Groups

Purpose of the study: To compare the occurrence of Unexplained IUFD among different gestational age groups and to find if any possible association with seasonal variation in the occurrence of IUFD. Methodology: A retrospective multicentric study was performed. Secondary data regarding unexplained or uncomplicated IUFD that occurred between January 2008 and December 2019 was collected from hospital records. Gestational age, Date of occurrence and fetal birth weight were obtained and subjected to statistical analysis using SPSS (version.22) software. Results: During the study period a total of 318 IUFD occurred due to unexplained or uncomplicated reasons. The highest occurrence of IUFD (40.9%) was observed in 201 to 250 days gestational age group. The peak occurrence of IUFD was observed in the monsoon particularly in July. Conclusion: Most of the IUFDs were noted during 28 weeks to 35 weeks gestation. We also found an increased incidence of IUFD during monsoon season and in the third trimester of pregnancy.

Fault prioritisation for Air Handling units using fault modelling and actual fault occurrence data

Fault impact analysis is an important step in developing Fault Detection and Diagnosis (FDD) tools for Heating, Ventilation and Air-Conditioning (HVAC) systems. It provides an insight into how HVAC systems react in the presence of operational faults and helps to prioritise which faults to focus on. The research in the existing literature has mostly focused on modelling several faults occurring in various HVAC systems and components to evaluate the effect of the fault on energy performance and the predicted thermal comfort of occupants. However, the real frequency of fault occurrence, an important factor which affects the overall impact a fault has on the system’s performance, has barely been addressed in the reviewed literature. This paper addresses this gap by considering real fault occurrence frequency data in addition to the effect of the faults on the energy performance of the building to assess their total energy impact. A real office building in the Netherlands was modelled using DesignBuilder and its energy performance was simulated using EnergyPlus. Air Handling Unit (AHU) faults were introduced using the native fault object and Energy Management System (EMS) features available in EnergyPlus. The fault occurrence data was extracted from AHU work orders using text mining. The fault modelling and text mining results were combined to get the total energy impact of the fault and, subsequently, the faults were prioritised using the Pareto principle. The research identified that fan failure, Heat Recovery Wheel (HRW) failure, fan stuck at 50% and Heating Coil Valve (HCV) stuck at 0% are the priority faults for winter and fan failure is the priority fault for summer.