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Introduction: The emergence of voluntary carbon markets is creating new opportunities to sustainably finance Natural Climate Solution (NCS) projects. In Australia, the federal government recently enacted the Tidal Restoration of Blue Carbon Ecosystems Methodology Determination 2022 (Tidal Reconnection Method), whereby restoration activities that reintroduce tidal flows to allow the re-establishment of coastal wetland (blue carbon) ecosystems, through the removal or modification of a tidal restriction, can be used to gain and sell Australian carbon credit units. Australia has the highest net blue carbon wealth in the world, with 5%-11% of global carbon stocks, yet there is currently a lack of large-scale feasibility assessments and supporting methodologies to identify and prioritise sites with the greatest potential for NCS project implementation to help inform investment decisions. Methods: In this study, we applied a spatial Multi-Criteria Decision Analysis (MCDA) to identify, map, and prioritise potential sites for blue carbon coastal wetland restoration in South Australia that meet criteria outlined in the Tidal Reconnection Method. This study compared information on 1) predicted flooding extent following tidal reconnection and under sea level rise (SLR; present-day, 2050 and 2,100); 2) project implementation complexity (e.g. who possesses land tenure); and 3) carbon sequestration potential through predicted area of vegetation change under the above SLR scenarios. Results: Our results identified 64 sites of interest, of which 32 received an overall "high" prioritisation score of 3 or more out of 5. This equates to approximately 21,114 ha of high priority potential blue carbon restoration sites. Discussion: The MCDA enables development of a portfolio of viable restoration projects through a rapid "desktop" prioritisation of sites of interest, which can then guide investment in further detailed cost/benefit feasibility assessments. This study demonstrates an adaptable MCDA approach to map potential NCS projects at meaningful spatial scales and in-line with carbon market-based opportunities. [ABSTRACT FROM AUTHOR]

AbstractGeographically weighted regression (GWR) and its extensions are important local modelling techniques for exploring spatial heterogeneity in regression relationships. However, when dealing with spatial data of overlapping samples – for example, when precise locational information is aggregated to a shared neighbourhood to avoid revealing the addresses of individual survey respondents – GWR-based models can encounter several problems, including obtaining reliable bandwidths. Because data with this characteristic exhibit spatial hierarchical structures, we propose combining hierarchical linear modelling (HLM) with GWR to give a hierarchical and geographically weighted regression (HGWR) model that divides coefficients into sample-level fixed effects, group-level fixed effects, sample-level random effects, and group-level spatially weighted effects. This paper presents a back-fitting likelihood estimator to fit the model, a simulation experiment that suggests that HGWR is better able to capture these effects and the spatial heterogeneity within them than are traditional HLM or GWR models, and a case study looking at predictors of housing price in Beijing, China. The ability of HGWR to tackle both spatial and group-level heterogeneity simultaneously suggests its potential as a promising data modelling tool for handling spatio-temporal big data with spatially hierarchical structures. [ABSTRACT FROM AUTHOR]

Biomass estimates of fish resources by the daily egg production method (DEPM) are sensitive to the high variability of the daily egg production (P0) and egg mortality (Z) in space. This work presents a Bayesian approach to estimate these parameters. A prior distribution of Z based on literature serves to overcome the biologically implausible Z estimates that can result from frequentist approaches. In addition to the classical estimation of a single P0 over the spawning area, the Bayesian framework allows also the modelling of egg densities in space, by including either spatial random effects, smoothing functions, or kriging like models, providing insights into the spatial variability of P0. The Bayesian approach was applied to the Bay of Biscay anchovy DEPM surveys. Results showed that this Bayesian approximation solved the implausible Z problem resulting in tighter credible intervals of both P0 and Z. Overall, spatial models outperformed the non-spatial model in terms of goodness of fit and resulted in slightly different total production estimates across models for each year, with a moderate decrease on uncertainty estimates. [ABSTRACT FROM AUTHOR]

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Groundwater as a scarce freshwater resource requires extensive quality assessment, control and protection through innovative methods and efficient strategies in light of growing human population, pollution, and over–extraction. Groundwater is critical in arid and semi–arid regions of the world such as the Beaufort West area in the Karoo region of South Africa. Thus, this study investigated the key processes affecting groundwater quality using factor analysis and geospatial models to determine the spatial variability of groundwater quality. To achieve this objective, groundwater samples collected from 49 boreholes located in and around the town of Beaufort West and analysed for electrical conductivity (EC), pH, total dissolved solids (TDS), calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), bicarbonates (HCO3–), chlorides (Cl–), nitrates (NO3–) and sulphates (SO42–) using recommended standard methods. Factor analysis produced three factors explaining 81.4% of groundwater quality variation. Factor 1 (hardness) was characterised by high concentrations of Cl–, SO42–, Ca2+, Mg2+ and Na+; factor 2 (alkalinity) with HCO3– and K+, and factor 3 (anthropogenic) was characterised by high NO3– concentrations. Derived groundwater quality thematic maps using selected hydrochemical variables and factor scores showed the delineation of areas with hardness, alkalinity and anthropogenic influence on groundwater quality. These findings underscore the relevance of factor analysis and geospatial models in assessing groundwater quality at a catchment scale. [ABSTRACT FROM AUTHOR]

The country wide lockdown implemented during 27th April to 14th June 2021 in order to prevent the spread of COVID-19 during the second wave in India. Effect of the restricted lockdown resulted in improved air quality. This study focuses on analyzing the spatio-temporal distribution analysis of major air pollutant concentration over Bangalore city in India. The inverse distance weighting (IDW) method is implemented for the spatial analysis in order to quantify the distribution of the pollutant concentrations at each location in the Urban city of Bangalore. The research considers the distinct periods of pre-lockdown and lockdown during the second wave of COVID-19 pandemic in 2021 to investigate the impact of reduced human activities on air quality over the city. The study mainly utilizes the air pollution data collected from Central Pollution Control Board (CPCB) monitoring stations across Bangalore, including measurements of pollutants such as PM2.5, PM10, O3, NO2, SO2, and CO. The IDW method is implemented to create the high-resolution pollution concentration maps for both the pre-lockdown and lockdown periods. This spatial distribution provides valuable insights into the variations in the pollution levels though out the Bangalore city. The comparative analysis of the concentration maps reveals significant changes in air pollution levels between the two periods; similarly, the temporal weekly average analysis also witnessed negative anomalies during the lockdown weeks. The results indicate substantial reductions in pollutant concentrations during the second wave COVID-19 lockdown, attributed to decreased vehicular emissions, industrial activities, and construction operations. The pre-lockdown period serves as a baseline for assessing the improvements in air quality during the lockdown. The spatio-temporal modeling approach enhances our understanding of the distribution patterns of air pollutants across the Bangalore metropolitan city. The findings underscore the potential benefits of implementing sustainable strategies to maintain improved air quality even after the pandemic subsides.

Abstract Climate variability significantly affects rainfall, air temperature, relative humidity, and solar radiation. In subtropical regions like India, intense monsoon rainfall primarily causes floods. This study aims to analyze the climatic factors contributing to flood occurrence in the Middle Ganga Plain (MGP) by examining meteorological parameters through various analyses, including monthly and yearly trends, spatial variability, exceedance probability, historical highest intensity rainfall, and wind trajectory analysis. By establishing a strong link between climatic variables and flood events, this research provides valuable insights into the impact of climate variability on flood patterns in the MGP. Results indicate that monsoon rainfall peaks from July to October, correlating with major flood events. During these months, high relative humidity increases the likelihood of rainfall. Spatial analysis shows higher rainfall in the northern Middle Ganga Plain (MGP), which aligns with observed flood patterns. Trend detection tests reveal increasing annual rainfall and rising average temperatures. Geostatistical analysis identifies the western and eastern MGP as areas prone to extreme rainfall. Exceedance probability analysis confirms the occurrence of substantial monsoon rainfall events. Wind trajectory analysis uncovers seasonal shifts that lead to low-pressure systems and heavy monsoon rainfall. The study establishes a strong link between climatic variables and flood events in the MGP region.

Gully erosion poses a threat to the sustainability of cultivated regions worldwide, is environmentally problematic, and causes significant rates of soil erosion. Understanding the formation process and gully development requires mapping gully erosion. An innovative modeling approach was used in this work using a case study in northern Iran. 14 gully-erosion parameter measurements from 1042 gully erosion locations were combined to produce a geographic dataset. For the purposes of modeling and validation, four training data sets (each in the standard 70:30 ratio) that represented 100 %, 75 %, 50 %, and 25 % of the total database were employed. The four machine learning models that were used to assess the utility of the four training situations were Bloom Filter Trees, Random Forest, (RF), Kernel Logistic Regression (KLR), and ensemble of KLR-RF. The most important effective factors, as determined by the RF research, were distance from the stream (72.61), vertical distance of the channel network (49.34), distance from the road (48.81), and elevation (47.68). The findings were validated using the receiver operating characteristic. Based on our findings, the combination of KLR and RF is the most optimum combination for both improved accuracy (0.928) and predictive capability (0.911). Out of them, the KLR-RF 50 % training scenario is the most optimal when compared to the other scenarios. [ABSTRACT FROM AUTHOR]

Abstract The control of arthropod disease vectors using chemical insecticides is vital in combating malaria, however the increasing insecticide resistance (IR) poses a challenge. Furthermore, climate variability affects mosquito population dynamics and subsequently IR propagation. We present a mathematical model to decipher the relationship between IR in Anopheles gambiae populations and climate variability. By adapting the susceptible-infected-resistant (SIR) framework and integrating temperature and rainfall data, our model examines the connection between mosquito dynamics, IR, and climate. Model validation using field data achieved 92% accuracy, and the sensitivity of model parameters on the transmission potential of IR was elucidated (e.g. μPRCC = 0.85958, p-value

Abstract Key message The dataset includes data from forest soil surveys conducted in the period 2000–2020. It provides soil and site variables from 8269 locations. Data are aggregated in three basic soil layers: upper organic soil horizon (FH, 6875 locations), upper mineral layer 0–30 cm (M03, 8051 locations) and deeper mineral soil layer 30–80 cm (M38, 2260 locations). The dataset is available at https://doi.org/10.5281/zenodo.10608814 , and access to the metadata is at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/38f24573-3c0d-469a-a66a-7060ce082155 .

Research on the growing support of radical right parties cannot ignore the role of spatial, contextual effects and geographical proximity. One important spatial factor is the halo effect, which suggests that a radical right party is more likely to perform well in a homogeneous electoral district surrounded by ethnically diverse areas. Voter behaviour is influenced by certain neighbourhood effects, which can be spatially distant, originating from neighbouring areas. In this way, the presence of groups more inclined to support the radical right in an area can, through interaction and observations of signs or norms, modify an individual's party preference. In this paper, I model the 2018 election results of the Sweden Democrats at the level of the 6004 Swedish electoral districts using the Spatial Durbin Model specification. The spatial model shows a better fit and indicates a spillover effect of the level of education and median wealth in addition to the contextual role of ethnic diversity and its change. Changes in the proportion of non‐Europeans in neighbouring areas have had a particularly strong spillover effect, which is associated with the growing support for the Sweden Democrats. [ABSTRACT FROM AUTHOR]

The control of arthropod disease vectors using chemical insecticides is vital in combating malaria, however the increasing insecticide resistance (IR) poses a challenge. Furthermore, climate variability affects mosquito population dynamics and subsequently IR propagation. We present a mathematical model to decipher the relationship between IR in Anopheles gambiae populations and climate variability. By adapting the susceptible-infected-resistant (SIR) framework and integrating temperature and rainfall data, our model examines the connection between mosquito dynamics, IR, and climate. Model validation using field data achieved 92% accuracy, and the sensitivity of model parameters on the transmission potential of IR was elucidated (e.g. μPRCC = 0.85958, p-value < 0.001). In this study, the integration of high-resolution covariates with the SIR model had a significant impact on the spatial and temporal variation of IR among mosquito populations across Africa. Importantly, we demonstrated a clear association between climatic variability and increased IR (width = [0–3.78], α = 0.05). Regions with high IR variability, such as western Africa, also had high malaria incidences thereby corroborating the World Health Organization Malaria Report 2021. More importantly, this study seeks to bolster global malaria combat strategies by highlighting potential IR 'hotspots' for targeted intervention by National malria control programmes. [ABSTRACT FROM AUTHOR]

Key message: The dataset includes data from forest soil surveys conducted in the period 2000–2020. It provides soil and site variables from 8269 locations. Data are aggregated in three basic soil layers: upper organic soil horizon (FH, 6875 locations), upper mineral layer 0–30 cm (M03, 8051 locations) and deeper mineral soil layer 30–80 cm (M38, 2260 locations). The dataset is available at https://doi.org/10.5281/zenodo.10608814, and access to the metadata is at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/38f24573-3c0d-469a-a66a-7060ce082155. [ABSTRACT FROM AUTHOR]

Statistical stream temperature models predicting the fine-scale spatial distribution of water temperatures (i.e., "thermalscape") can guide aquatic species recovery and habitat restoration efforts. However, stream temperature modelling is complicated by spatial autocorrelation arising from non-independence of sampling sites within dendritic networks. We used August mean temperature data from miniature sensors deployed in Canadian Rocky Mountain streams to demonstrate two statistical stream temperature modelling techniques that account for spatial autocorrelation. The first was a spatial steam network (SSN) model specifically developed to account for spatial autocorrelation in dendritic stream networks. The second was an integrated nested Laplace approximation (INLA) model that accounts for spatial autocorrelation but was not designed to address anisotropic stream network data. We evaluated the best-fitting SSN and INLA models using leave-one-out cross-validation. Relative to INLA, SSN models had lower RMSE (1.23 vs. 1.45 C) and higher r2 (0.71 vs. 0.61); however, the SSN models required more preprocessing steps before incorporating spatially correlated random errors. We provide practical advice, an open-access r-script, and data to help non-experts develop statistical stream temperature models. [ABSTRACT FROM AUTHOR]

Increased use of electric vehicles (EVs) has the potential to reduce carbon emissions. Therefore, predicting the impact of governments' EV incentive policies on the future uptake of EVs is important. This study estimates the impact of incentive policies introduced in the Australian Capital Territory. This estimation is conducted through constructing a microsimulation model and using it to assess the impact of the incentive policies on the purchase of EVs for households in different income quintiles. In the model, the decision about purchasing an EV is based largely on the total cost of ownership of an EV compared to the vehicle already owned and the additional utility of having a new vehicle. The application of the model shows that, regardless of incentives, a drop in the price of EVs will play the most important role in uptake. Incentives will help lower to middle income households, although EV demand is dominated by those in the highest income quintile. Importantly, however, incentives can increase uptake in locations with previously low uptake. Future work needs to focus on the reliability of data on EV's, and how to incorporate the rapid change in the market (eg, rapid uptake of EV's in the ACT) seen in the last few years. • The assessment of government policies designed to increase EV uptake can be conducted using microsimulation modelling. • EV purchasing incentives are likely to be most effective if the average purchase price of EVs is close to the $50,000 price point. • Targeting EV purchasing incentives at low-income households may increase the spread of EV uptake but reduce the total number of purchases. • While the work showed some limitations of microsimulation modelling for estimating the impact of EV policy, overall the application provided a useful tool for the ACT Government in deciding on policies to implement to encourage EV usage in the ACT. [ABSTRACT FROM AUTHOR]

The occurrence of flood disasters is an annual unavoidable event in the Mejobo District, Kudus Regency. This study aims to map the vulnerable areas to floods in the Mejobo District using spatial modelling. The parameters used to determine the vulnerability areas include soil type, elevation, population density, river buffer, rainfall, and land use. The research location is situated in the Mejobo District. The research method employed is the models and multicriteria decision-making (MCDM) techniques, with the assistance of ArcGIS software for data processing and analysis. The research findings yield a final map depicting flood vulnerability, which indicates the distribution of flood-prone areas. Areas with a high level of flood vulnerability are dispersed across agricultural fields and areas adjacent to rivers, covering an area of 1,770 hectares. Meanwhile, areas with a moderate level of vulnerability are predominantly distributed in residential areas, covering an area of 1,907 hectares.

The application of Artificial Intelligence in various fields has witnessed tremendous progress in the recent years. The field of geosciences and natural hazard modelling has also benefitted immensely from the introduction of novel algorithms, the availability of large quantities of data, and the increase in computational capacity. The enhancement in algorithms can be largely attributed to the elevated complexity of the network architecture and the heightened level of abstraction found in the network's later layers. As a result, AI models lack transparency and accountability, often being dubbed as “black box” models. Explainable AI (XAI) is emerging as a solution to make AI models more transparent, especially in domains where transparency is essential. Much discussion surrounds the use of XAI for diverse purposes, as researchers explore its applications across various domains. With the growing body of research papers on XAI case studies, it has become increasingly important to address existing gaps in the literature. The current literature lacks a comprehensive understanding of the capabilities, limitations, and practical implications of XAI. This study provides a comprehensive overview of what constitutes XAI, how it is being used and potential applications in hydrometeorological natural hazards. It aims to serve as a useful reference for researchers, practitioners, and stakeholders who are currently using or intending to adopt XAI, thereby contributing to the advancements for wider acceptance of XAI in the future.

Abstract In building a robust knowledge base or validating existing models for use in ecological spatial modelling, having plentiful high‐quality data is paramount. Careful survey design helps attain that goal and, in part due to financial constraints, such design requires the balancing of hard monetary costs and the intangible benefit of improved ecological models. We propose a framework that quantifies a location's value to the modeller by accounting for both the probability of obtaining new samples and their expected contribution to the model. The approach is illustrated on a citizen science database of roadkills in Taiwan, modelled as a Poisson point process on a linear road network. Our method has revealed some valuable locations that were not self‐evident, for example, highlighting the possibility of sending volunteers to mountainous areas that despite being hard to reach, would provide valuable samples. We have also highlighted some ex situ sampling opportunities to avoid wasting resources by over‐sampling hard to access locations. Our technique is not restricted to presence‐only data, and in fact we present a general framework that can be applied to a wide range of settings by tuning its formulation. Our method is quite flexible and allows for more elaborate value functions, enabling managers to precisely quantify varied goals within the same framework.

ABSTRACTThis study focuses on determining the optimal distance metric in the K-Nearest Neighbors (KNN) algorithm for spatial modelling of floods. Four distance metrics of the KNN algorithm, namely KNN-Manhattan, KNN-Minkowski, KNN-Euclidean, and KNN-Chebyshev, were utilized for flood susceptibility mapping (FSM) in Estahban, Iran. A spatial database comprising 509 flood occurrence points extracted from satellite images and 12 factors influencing floods was created for analysis. The particle swarm optimization (PSO) algorithm was employed for hyperparameter optimization and feature selection, considering eight influential factors as modelling inputs. The modelling results revealed that the KNN-Manhattan algorithm exhibited superior accuracy (root mean squared error (RMSE) = 0.169, mean absolute error (MAE) = 0.051, coefficient of determination (R2) = 0.884, and area under the curve (AUC) = 0.94) compared with the other algorithms for identifying flood-prone areas. The KNN-Minkowski algorithm followed closely, with an RMSE of 0.175, MAE of 0.056, R2 of 0.876, and AUC of 0.939. The KNN-Euclidean algorithm achieved an RMSE of 0.183, MAE of 0.061, R2 of 0.842, and AUC of 0.929, whereas the KNN-Chebyshev algorithm achieved an RMSE of 0.198, MAE of 0.075, R2 of 0.842, and AUC of 0.924.

Climate‐driven environmental change is increasingly impacting global fisheries and marine resource use. Fisheries provide a broad range of economic, social and cultural benefits while delivering essential contributions to nutrient security and human health. Despite this, little is known about how climate change will impact the availability and quality of seafood‐derived nutrients. Here, we quantified spatial and temporal changes in the nutritional quality of the commercially harvested eastern school whiting, Sillago flindersi, sampled throughout the south‐east Australian ocean warming hotspot. Several nutrients measured in S. flindersi, including protein, ash, polyunsaturated fatty acids (PUFA) and the omega‐3 PUFA–docosahexaenoic acid (DHA, 22:6ɷ3), were related to one or more environmental factors (sea bottom temperature, depth and chlorophyll). We also detected seasonal variability in DHA and ash composition throughout the species' commercially harvested distribution. Historical and future spatial modelling predicted a decrease in DHA of up to 6% with increasing ocean temperature under future Representative Concentration Pathway (RCP) 4.5 and 8.5 emission scenarios. Overall, our results identified S. flindersi as a rich source of protein and essential PUFAs for human consumers and supported emerging evidence that reductions in seafood‐derived essential nutrients may occur under future ocean warming, specifically reductions in omega‐3 fatty acids. The development of nutritional quality forecasting tools for seafood holds the potential to inform fishers and managers of locations and times of the year to target species with optimal nutritional quality. [ABSTRACT FROM AUTHOR]

This study focuses on determining the optimal distance metric in the K-Nearest Neighbors (KNN) algorithm for spatial modelling of floods. Four distance metrics of the KNN algorithm, namely KNN-Manhattan, KNN-Minkowski, KNN-Euclidean, and KNN-Chebyshev, were utilized for flood susceptibility mapping (FSM) in Estahban, Iran. A spatial database comprising 509 flood occurrence points extracted from satellite images and 12 factors influencing floods was created for analysis. The particle swarm optimization (PSO) algorithm was employed for hyperparameter optimization and feature selection, considering eight influential factors as modelling inputs. The modelling results revealed that the KNN-Manhattan algorithm exhibited superior accuracy (root mean squared error (RMSE) = 0.169, mean absolute error (MAE) = 0.051, coefficient of determination (R²) = 0.884, and area under the curve (AUC) = 0.94) compared with the other algorithms for identifying flood-prone areas. The KNN-Minkowski algorithm followed closely, with an RMSE of 0.175, MAE of 0.056, R² of 0.876, and AUC of 0.939. The KNN-Euclidean algorithm achieved an RMSE of 0.183, MAE of 0.061, R² of 0.842, and AUC of 0.929, whereas the KNN-Chebyshev algorithm achieved an RMSE of 0.198, MAE of 0.075, R² of 0.842, and AUC of 0.924. [ABSTRACT FROM AUTHOR]

In building a robust knowledge base or validating existing models for use in ecological spatial modelling, having plentiful high‐quality data is paramount. Careful survey design helps attain that goal and, in part due to financial constraints, such design requires the balancing of hard monetary costs and the intangible benefit of improved ecological models.We propose a framework that quantifies a location's value to the modeller by accounting for both the probability of obtaining new samples and their expected contribution to the model. The approach is illustrated on a citizen science database of roadkills in Taiwan, modelled as a Poisson point process on a linear road network.Our method has revealed some valuable locations that were not self‐evident, for example, highlighting the possibility of sending volunteers to mountainous areas that despite being hard to reach, would provide valuable samples. We have also highlighted some ex situ sampling opportunities to avoid wasting resources by over‐sampling hard to access locations.Our technique is not restricted to presence‐only data, and in fact we present a general framework that can be applied to a wide range of settings by tuning its formulation. Our method is quite flexible and allows for more elaborate value functions, enabling managers to precisely quantify varied goals within the same framework. [ABSTRACT FROM AUTHOR]

Reintroducing endangered species into their restored habitats is a significant aspect of conservation biology. The scimitar-horned oryx (Oryx dammah), one of the most critically endangered antelopes worldwide, was reintroduced into several protected areas in Tunisia. Understanding their habitat requirements within micro-level ecosystems is crucial for developing effective management plans to aid in species recovery. Using information-theoretic and multimodel inference (MMI) techniques, we evaluated the impact of habitat and management characteristics on the distribution of the scimitar-horned oryx in Tunisia's Sidi Toui National Park during different seasons, from June 2020 – May 2022. Our analyses, using regression coefficients and selection probabilities, revealed that factors influencing habitat suitability varied seasonally for the scimitar-horned oryx. However, the presence of grasses emerged as the most consistent indicator of their occurrence throughout the year. The strong fit of models to the data was confirmed by Receiver Operating Characteristic (ROC) plots, which indicated an Area Under the Curve (AUC) > 0.9. The study has significant implications for developing practical habitat management plans for the scimitar-horned oryx in Sidi Toui National Park.

Abstract Background Geostatistical analysis of health data is increasingly used to model spatial variation in malaria prevalence, burden, and other metrics. Traditional inference methods for geostatistical modelling are notoriously computationally intensive, motivating the development of newer, approximate methods for geostatistical analysis or, more broadly, computational modelling of spatial processes. The appeal of faster methods is particularly great as the size of the region and number of spatial locations being modelled increases. Methods This work presents an applied comparison of four proposed ‘fast’ computational methods for spatial modelling and the software provided to implement them—Integrated Nested Laplace Approximation (INLA), tree boosting with Gaussian processes and mixed effect models (GPBoost), Fixed Rank Kriging (FRK) and Spatial Random Forests (SpRF). The four methods are illustrated by estimating malaria prevalence on two different spatial scales—country and continent. The performance of the four methods is compared on these data in terms of accuracy, computation time, and ease of implementation. Results Two of these methods—SpRF and GPBoost—do not scale well as the data size increases, and so are likely to be infeasible for larger-scale analysis problems. The two remaining methods—INLA and FRK—do scale well computationally, however the resulting model fits are very sensitive to the user’s modelling assumptions and parameter choices. The binomial observation distribution commonly used for disease prevalence mapping with INLA fails to account for small-scale overdispersion present in the malaria prevalence data, which can lead to poor predictions. Selection of an appropriate alternative such as the Beta-binomial distribution is required to produce a reliable model fit. The small-scale random effect term in FRK overcomes this pitfall, but FRK model estimates are very reliant on providing a sufficient number and appropriate configuration of basis functions. Unfortunately the computation time for FRK increases rapidly with increasing basis resolution. Conclusions INLA and FRK both enable scalable geostatistical modelling of malaria prevalence data. However care must be taken when using both methods to assess the fit of the model to data and plausibility of predictions, in order to select appropriate model assumptions and parameters.

Soil erosion is a natural process that refers to the wearing top of the soil layer and transport to another place. This process has turned into an increasing spatial problem as human activities cause changes in the land cover. The increase in soil erosion not only affects soil fertility, but also threatens food security. Studies within the scope of soil erosion focus on sediment transport and modeling of erosion-sensitive areas in order to understand the dynamics of the process and to minimize the negative effects. The increase in modeling applications on this subject in the last 30 years shows that the interest on the soil erosion problem has increased. The aim of this study is to examine the changes in the most used models in soil erosion studies within the scope of the literature. For this, the data properties and algorithm structures of the models were examined in detail and it was determined that among dozens of models, USLE/RUSLE, SWAT and WEPP models and machine learning approaches came to the fore in terms of the number of applications. In conclusion, some models are more preferred since they are compatible with different environmental conditions and simulate process with high accuracy.

Species Distribution Models are pivotal for fisheries management. There has been an increasing number of fishery data sources available, making data integration an attractive way to improve model predictions. A wide range of methods have been applied to integrate different datasets in different disciplines. We focus on the use of Integrated Species Distribution Models (ISDMs) due to their capacity to formally accommodate different types of data and scale proportional gear efficiencies. ISDMs use joint modelling to integrate information from different data sources to improve parameter estimation by fitting shared environmental, temporal and spatial effects. We illustrate this method first using a simulated example, and then apply it to a case study that combines data coming from a fishery-independent trawl survey and a fishery-dependent trammel net observations on Solea solea. We explore the sensitivity of model outputs to several weightings for the commercial data and also compare integrated model results with ensemble modelling to combine population trends in the case study. We obtain similar results but discuss that ensemble modelling requires both response variables and link functions to be the same across models. We conclude by discussing the flexibility and requirements of ISDMs to formally combine different fishery datasets. [ABSTRACT FROM AUTHOR]

Background: Voluntary non‐remunerated blood donors (VNRBDs) are essential to sustain national blood supplies. Expanding testing capacity for the major transfusion‐transmitted infections (TTI) is crucial to ensure safe blood products. Understanding trends in TTIs can inform prioritisation of resources. Methods: We conducted a retrospective cohort data analysis of routine blood donation data collected from VNRBDs by the Malawi Blood Transfusion Service from January 2015 to October 2021. Variables included age, occupation; and screening results of TTIs (HIV, Hepatitis B and C, and syphilis). We estimated both prevalence and incidence per person‐year for each TTI using longitudinal and spatial logistic regression models. Results: Of the 213 626 donors, 204 920 (95.8%) donors were included in the final analysis. Most donors (77.4%) were males, baseline median age was 19.9 (IQR 18.0, 24.1), 70.9% were students, and over 80.0% were single at first donation. Overall TTI prevalence among donors was 10.7%, with HBV having the highest prevalence (3.4%), followed by syphilis (3.3%), then HIV (2.4%) and HCV (2.4%). Incidence per 1000 person‐years for syphilis was 20.1 (19.0, 21.3), HCV was 18.4 (17.3, 19.5), HBV was 13.7 (12.8, 14.7), and HIV was 11.4 (10.6, 12.3). We noted geographical variations with the northern region having lower rates of both prevalence and incidence compared to central and southern regions. Conclusion: The individual TTI prevalence and incidence rates from this study are consistent with Southern African regional estimates. By identifying geographical variations of TTI prevalence and incidence, these findings could potentially inform prioritisation of blood collection efforts to optimise blood collection processes. [ABSTRACT FROM AUTHOR]

The groundwater vulnerability assessment is an effective measure to analyse potential quality of available water in increasingly populated and industrial areas in plain topography. The objective of this research was to evaluate contamination susceptibility and its spatial modelling in district Sheikhupura, using fuzzy logic and Bayesian interpolation method for adjusted DRASTIC model and HVF (hazard vulnerability factor) model. The present research espoused a specific factor-based approach to model the existing contamination rate of heavy metals with underplaying DRASTIC parameters for estimated pollution rate and related vulnerability. The modifications were also applied in ranks and weights by analysing the association of parameters with respect to heavy metals (Cr, Cd, Pb). The heavy metals concentrations in groundwater were evaluated at the 54 different random sites of water pumps. The heavy metals concentration was used to identify risk of pollution associated with aquifer and soil type. The adjusted DRASTIC model was analysed for groundwater vulnerability by using different parameters including, aquifer media, net recharge, soil type, hydraulic conductivity, topography and depth of water table. It was assessed by using EC, TDS, hardness, heavy metals and COD, BOD parameters. The results showed the vulnerability areas and modelled efficiently at spatial scale than conventional DRASTIC model. The very high vulnerability zone is 10.75% with > 200 drastic values; however, main study area is covered with moderate vulnerability class (42.49%). Most of the effluent-irrigated and neighbouring agricultural sites were located around the high and relatively high-risk areas 11.56% to 20.29% areas, which may raise risk of chemicals and pesticides seepage in the groundwater. [ABSTRACT FROM AUTHOR]

Spatial models jointly simulating population and land-use change provide support for policy-making, by allowing to explore territorial developments under alternative scenarios and resulting impacts in the environment, economy and society. However, their ability to reproduce observed spatial patterns is rarely evaluated through model validation. This lack of insight prevents researchers and policy-makers of fully grasping the ability of existing models to provide sensible projections of future land use and population density. In this article, we address this gap by performing a model validation of the LUISA Territorial Modelling Platform, a spatial model jointly simulating population and land use at a fine resolution (100 m) in the European Union and United Kingdom. In particular, we compare observed and simulated patterns of population and urban residential land-use change for the period of 1990–2015, and evaluate the model performance according to different degrees of urbanisation. The results show that model performance can vary depending on the context, even when the same data and methods are uniformly applied. The model performed consistently well in urban areas characterized by compact urban growth, but poorly where residential development occurred predominantly in scattered patterns across rural areas. Overall, the model tends to favour the formation of densely populated, highly accessible urban conglomerations, which often do not entirely correspond to the observed patterns. Based on the validation results, we propose directions for further model improvement and development. Model validation should be regarded as a critical step, and an integral part, in the process of developing models for policy support. [ABSTRACT FROM AUTHOR]

Background: Geostatistical analysis of health data is increasingly used to model spatial variation in malaria prevalence, burden, and other metrics. Traditional inference methods for geostatistical modelling are notoriously computationally intensive, motivating the development of newer, approximate methods for geostatistical analysis or, more broadly, computational modelling of spatial processes. The appeal of faster methods is particularly great as the size of the region and number of spatial locations being modelled increases. Methods: This work presents an applied comparison of four proposed 'fast' computational methods for spatial modelling and the software provided to implement them—Integrated Nested Laplace Approximation (INLA), tree boosting with Gaussian processes and mixed effect models (GPBoost), Fixed Rank Kriging (FRK) and Spatial Random Forests (SpRF). The four methods are illustrated by estimating malaria prevalence on two different spatial scales—country and continent. The performance of the four methods is compared on these data in terms of accuracy, computation time, and ease of implementation. Results: Two of these methods—SpRF and GPBoost—do not scale well as the data size increases, and so are likely to be infeasible for larger-scale analysis problems. The two remaining methods—INLA and FRK—do scale well computationally, however the resulting model fits are very sensitive to the user's modelling assumptions and parameter choices. The binomial observation distribution commonly used for disease prevalence mapping with INLA fails to account for small-scale overdispersion present in the malaria prevalence data, which can lead to poor predictions. Selection of an appropriate alternative such as the Beta-binomial distribution is required to produce a reliable model fit. The small-scale random effect term in FRK overcomes this pitfall, but FRK model estimates are very reliant on providing a sufficient number and appropriate configuration of basis functions. Unfortunately the computation time for FRK increases rapidly with increasing basis resolution. Conclusions: INLA and FRK both enable scalable geostatistical modelling of malaria prevalence data. However care must be taken when using both methods to assess the fit of the model to data and plausibility of predictions, in order to select appropriate model assumptions and parameters. [ABSTRACT FROM AUTHOR]

Computing has revolutionised the study of complex nonlinear systems, both by allowing us to solve previously intractable models and through the ability to visualise solutions in different ways. Using ubiquitous computing infrastructure, we provide a means to go one step further in using computers to understand complex models through instantaneous and interactive exploration. This ubiquitous infrastructure has enormous potential in education, outreach and research. Here, we present VisualPDE, an online, interactive solver for a broad class of 1D and 2D partial differential equation (PDE) systems. Abstract dynamical systems concepts such as symmetry-breaking instabilities, subcritical bifurcations and the role of initial data in multistable nonlinear models become much more intuitive when you can play with these models yourself, and immediately answer questions about how the system responds to changes in parameters, initial conditions, boundary conditions or even spatiotemporal forcing. Importantly, VisualPDE is freely available, open source and highly customisable. We give several examples in teaching, research and knowledge exchange, providing high-level discussions of how it may be employed in different settings. This includes designing web-based course materials structured around interactive simulations, or easily crafting specific simulations that can be shared with students or collaborators via a simple URL. We envisage VisualPDE becoming an invaluable resource for teaching and research in mathematical biology and beyond. We also hope that it inspires other efforts to make mathematics more interactive and accessible. [ABSTRACT FROM AUTHOR]

This research aims to evaluate the dynamics of soil loss through soil erosion by water in agricultural lands in steppe areas using a modification of the RUSLE2 model from the 1980s to the 2010s. The calculation was performed using a raster model of data that included a model of the slope angle, slope length, soil erodibility, rainfall and snowmelt erosivity factors, types of land use, and cover management factor. All data were taken from open sources. The average soil erosion in the territory studied amounted to 1.48 t ha−1 year−1 in the 1980s and 1.72 t ha−1 year−1 in the 2010s. The discrepancy with other studies was 12% for the level of the 1980s and 2–7% for the level of the 2010s. The main factor leading to an increase in soil loss was soil erodibility due to the loss of soil organic matter, which affected about 52% of the studied lands. The increase in the amount of soil loss occurred against a background of compensating processes: reduction in precipitation and climate change (getting drier), as well as the overgrowth of agricultural lands with natural steppe vegetation. Modified model RUSLE2 has shown good results correlated with other studies for the research area. [ABSTRACT FROM AUTHOR]

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Cultural ecosystem services (CES) are non-material intangible benefits that humans derive from ecosystems, which are indispensable for the well-being of communities and directly influence the quality of life. CES are deeply interconnected to each other and to providing and regulating services, thus influencing everyday life. CES are among the most important values that people associate with nature, but understanding them may be challenging. The definition of CES is both self-evident and elusive, specifically because they consist of the interaction between two dynamic systems: human societies and natural ecosystems. This paper updates the state of the art about CES evaluation methods, underlining the gap between their economic values and their incorporation into planning and decision-making on different scales and in different sectors, and emphasizes their importance in conservation policies and sustainable development programs. This study reviewed 68 articles published between August 2019 and May 2023 from the SCOPUS database, and classified CES assessment into 15 evaluation methods. This review reveals that the choice of CES assessment methodologies has often depended on evaluation purposes. In addition, specific CES classifications are required, since different definitions and unstandardized economic concepts for assigning market values to the CES can lead to conflicting results. The combination of different methods, monetary and non-monetary, can aid better evaluation of CES by focusing on the interaction between different components, and can facilitate the mapping and quantification of social values of ecosystem services. This can help decision-makers to develop sustainable territorial planning and policies.

Simple Summary: Crimean–Congo haemorrhagic fever (CCHF) is an emerging disease that is spreading across the globe. We originally released a predictive map in 2015, which we now update and improve by extending its coverage to Europe and by incorporating the limiting distributions of its main confirmed or potential tick vectors in Europe: Hyalomma marginatum and Hyalomma lusitanicum. Our new models suggest that the disease risk is now predicted to cover substantial parts of southern and central Europe. These maps can be used to more effectively target surveillance and monitoring. Crimean–Congo haemorrhagic fever (CCHF) is considered to be spreading across the globe, with many countries reporting new human CCHF cases in recent decades including Georgia, Türkiye, Albania, and, most recently, Spain. We update a human CCHF distribution map produced in 2015 to include global disease occurrence records to June 2022, and we include the recent records for Europe. The predicted distributions are based on long-established spatial modelling methods and are extended to include all European countries and the surrounding areas. The map produced shows the environmental suitability for the disease, taking into account the distribution of the most important known and potential tick vectors Hyalomma marginatum and Hyalomma lusitanicum, without which the disease cannot occur. This limits the disease's predicted distribution to the Iberian Peninsula, the Mediterranean seaboard, along with Türkiye and the Caucasus, with a more patchy suitability predicted for inland Greece, the southern Balkans, and extending north to north-west France and central Europe. These updated CCHF maps can be used to identify the areas with the highest probability of disease and to therefore target areas where mitigation measures should currently be focused. [ABSTRACT FROM AUTHOR]

The application of a process model to investigate pine tree infestation caused by bark beetles is discussed. The analysis of this disease was carried out using spatial and spatio−temporal models of random point patterns. Spatial point patterns were constructed for remote sensing images of pine trees damaged by the apical bark beetle. The method of random point processes was used for their analysis. A number of known models of point pattern processes with pairwise interaction were fitted to actual data. The best model to describe the real data was chosen using the Akaike information index. The residual K−function was used to check the fit of the model to the real data. According to values of the Akaike information criterion and the residual K−function, two models were found to correspond best to the investigated data. These are the generalized Geyer model of the point process of saturation and the pair interaction process with the piecewise constant potential of a pair of points. For the first time, a spatio−temporal model of the contagious process was used for analysis of tree damage. [ABSTRACT FROM AUTHOR]

This dataset presents global soil organic carbon stocks in mangrove forests at 30 m resolution, predicted for 2020. We used spatiotemporal ensemble machine learning to produce predictions of soil organic carbon content and bulk density (BD) to 1 m soil depth, which were then aggregated to calculate soil organic carbon stocks. This was done by using training data points of both SOC (%) and BD in mangroves from a global dataset and from recently published studies, and globally consistent predictive covariate layers. A total of 10,331 soil samples were validated to have SOC (%) measurements and were used for predictive soil mapping. We used time-series remote sensing data specific to time periods when the training data were sampled, as well as long-term (static) layers to train an ensemble of machine learning model. Ensemble models were used to improve performance, robustness and unbiasedness as opposed to just using one learner. In addition, we performed spatial cross-validation by using spatial blocking of training data points to assess model performance. We predicted SOC stocks for the 2020 time period and applied them to a 2020 mangrove extent map, presenting both mean predictions and prediction intervals to represent the uncertainty around our predictions. Predictions are available for download under CC-BY license from 10.5281/zenodo.7729491 and also as Cloud-Optimized GeoTIFFs (global mosaics).

This study utilised multi-year data from 5354 incidents to predict pedestrian–road traffic accidents (PTAs) based on twelve socioeconomic and built-environment factors. The research employed the logistic regression model (LRM) and the fuzzy-analytical hierarchy process (FAHP) techniques to evaluate and assign weights to each factor. The susceptibility map for PTAs is generated using the "Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)". Subsequently, the probability of accidents in 2020 was predicted using real multi-year accident data and the Markov chain (MC) and cellular automata Markov chain (CA-MC) models, with the prediction accuracy assessed using the Kappa index. Building upon promising results, the model was extrapolated to forecast the probability of accidents in 2023. The findings of the LRM demonstrated the significance of the selected variables as predictors of accident likelihood. The prediction approaches identified areas prone to high-risk accidents. Additionally, the Kappa for no information (KNO) statistical value was calculated for both the MC and CA-MC models, which yielded values of 0.94 and 0.88, respectively, signifying a high level of accuracy. The proposed methodology is generalizable, and the identification of high-risk locations can aid urban planners in devising appropriate preventive measures. [ABSTRACT FROM AUTHOR]

Marine sublittoral sandbanks are essential offshore feeding grounds for larger crustaceans, fish and seabirds. In the southern North Sea, sandbanks are characterized by considerable natural sediment dynamics and are subject to chronic bottom trawling. However, except for the Dogger Bank, sandbanks in the southeastern North Sea have been only poorly investigated until now. We used an extensive, multi-annual dataset covering ongoing national monitoring programmes, environmental impact assessments, and basic research studies to analyse benthic communities on sublittoral sandbanks, evaluating their ecological value against the backdrop of similar seafloor habitats in this region. The analysis revealed complex spatial structuring of sandy seafloor habitats of the southeastern North Sea. Different infauna clusters were identified and could be specified by their composition of characteristic species. The sandbanks shared common structural features in their infauna community composition although they were not necessarily characterized by particularly high biodiversity compared to other sandy habitats. A close association of one of the main bioturbators in the southern North Sea, the sea urchin Echinocardium cordatum, with sandbanks was detected, which may promote the sediment-bound biogeochemical activity in this particular seafloor habitat. This would corroborate the status of sandbanks as sites of high ecological value calling for consideration in marine conservation. [ABSTRACT FROM AUTHOR]

Points of Interest (POI) are an inherent element of the urban landscape, and their number and density reflect, among other things, the degree of urbanization and the city’s spatial structure. The very presence of POI in the closest vicinity of a residential property may indirectly or directly affect housing value. This paper aims to demonstrate the usefulness of POI density information of different categories in assessing the quality of a property’s immediate surroundings. While the mere presence of POIs in the nearest neighborhood may affect real estate prices, the influence of specific categories may not necessarily be positive. Therefore, the study attempted to classify POIs and determine their importance in the price formation process using spatial regression models. The results indicate that a high density of POIs in the immediate area is a stimulant for housing prices. The detailed analysis indicated that only some POI categories might be related to transaction prices, while in certain situations, some POI categories may negatively impact prices.

Abstract Statistical models use observations of animals to make inferences about the abundance and distribution of species. However, the spatial distribution of animals is a complex function of many factors, including landscape and environmental features, and intra‐ and interspecific interactions. Modelling approaches often have to make significant simplifying assumptions about these factors, which can result in poor model performance and inaccurate predictions. Here, we explore the implications of complex spatial structure for modelling the abundance of the Serengeti wildebeest, a gregarious migratory species. The social behaviour of wildebeest leads to a highly aggregated distribution, and we examine the consequences of omitting this spatial complexity when modelling species abundance. To account for this distribution, we introduce a multi‐latent framework that uses two random fields to capture the clustered distribution of wildebeest. Our results show that simplifying assumptions that are often made in spatial models can dramatically impair performance. However, by allowing for mixtures of spatial models accurate predictions can be made. Furthermore, there can be a non‐monotonic relationship between model complexity and model performance; complex, flexible models that rely on unfounded assumptions can potentially make highly inaccurate predictions, whereas simpler more traditional approaches involve fewer assumptions and are less sensitive to these issues. We demonstrate how to develop flexible spatial models that can accommodate the complex processes driving animal distributions. Our findings highlight the importance of robust model checking protocols, and we illustrate how realistic assumptions can be incorporated into models using random fields.