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Abies nephrolepis is a climate-vulnerable species that inhabits high mountains in the Baekdu–Daegan range and is distributed along the southern limit line in South Korea, making it suitable for climate change research. This study aimed to observe spatial distribution changes according to scenarios using species distribution models for Abies nephrolepis, analyze the relationship between various environmental factors and Abies nephrolepis density, and contribute to the future conservation and management of subalpine coniferous forests. We conducted a field survey to identify the growth environment of Abies nephrolepis and observed potentially suitable habitats for Abies nephrolepis based on location information obtained through the survey. We also analyzed the relationship between the density of Abies nephrolepis and various environmental factors using multiple linear regression models. Based on the field survey results, most Abies nephrolepis natural habitats in South Korea showed an unstable form. Vulnerability analysis examining the influence of climate change showed that most of these habitats would be affected. We found that various biological factors were significantly related to the density of Abies nephrolepis (diameter at breast height, DBH ≥ 6 cm) and young tree density (stems/ha). We confirmed that species diversity and rock exposure variables had a relatively high impact. Clarifying the relationship between the density of Abies nephrolepis and various environmental factors can provide new insights for setting future restoration directions.

Background: Understanding the response of vector habitats to climate change is essential for vector management. Increasingly, there is fear that climate change may cause vectors to be more important for animal husbandry in the future. Therefore, knowledge about the current and future spatial distribution of vectors, including ticks (Ixodida), is progressively becoming more critical to animal disease control. Methods: Our study produced present (2018) and future (2050) bont tick (Amblyomma hebraeum) niche models for Mashonaland Central Province, Zimbabwe. Specifically, our approach used the Ensemble algorithm in Biomod2 package in R 3.4.4 with a suite of physical and anthropogenic covariates against the tick's presence-only location data obtained from cattle dipping facilities. Results: Our models showed that currently (the year 2018) the bont tick potentially occurs in 17,008 km2, which is 60% of Mashonaland Central Province. However, the models showed that in the future (the year 2050), the bont tick will occur in 13,323 km2, which is 47% of Mashonaland Central Province. Thus, the models predicted an ~ 13% reduction in the potential habitat, about 3685 km2 of the study area. Temperature, elevation and rainfall were the most important variables explaining the present and future potential habitat of the bont tick. Conclusion: Results of our study are essential in informing programmes that seek to control the bont tick in Mashonaland Central Province, Zimbabwe and similar environments. [ABSTRACT FROM AUTHOR]

Understanding change at the base of the marine foodwebs is fundamental to understanding how climate change can impact fisheries. However, there is a shortage of empirical measurements of primary productivity, and models estimates often disagree with each other by an order of magnitude or more. In this study we incorporate information from empirical studies and a suite of Earth system models statistically downscaled using an ensemble model to produce estimates of North Sea primary production with robust quantification of uncertainties under two different climate scenarios. The results give a synthesised estimate of primary production that can feed into regional fisheries models. We found that Earth system models describe the dynamics of primary production in the North Sea poorly, and therefore the effects of climate change on future primary production are uncertain. The methods demonstrated here can be applied to other geographical locations and are not limited in application to primary production.

Abstract Background Lymphatic Filariasis (LF), a parasitic nematode infection, poses a huge economic burden to affected countries. LF endemicity is localized and its prevalence is spatially heterogeneous. In Ghana, there exists differences in LF prevalence and multiplicity of symptoms in the country’s northern and southern parts. Species distribution models (SDMs) have been utilized to explore the suite of risk factors that influence the transmission of LF in these geographically distinct regions. Methods Presence-absence records of microfilaria (mf) cases were stratified into northern and southern zones and used to run SDMs, while climate, socioeconomic, and land cover variables provided explanatory information. Generalized Linear Model (GLM), Generalized Boosted Model (GBM), Artificial Neural Network (ANN), Surface Range Envelope (SRE), Multivariate Adaptive Regression Splines (MARS), and Random Forests (RF) algorithms were run for both study zones and also for the entire country for comparison. Results Best model quality was obtained with RF and GBM algorithms with the highest Area under the Curve (AUC) of 0.98 and 0.95, respectively. The models predicted high suitable environments for LF transmission in the short grass savanna (northern) and coastal (southern) areas of Ghana. Mainly, land cover and socioeconomic variables such as proximity to inland water bodies and population density uniquely influenced LF transmission in the south. At the same time, poor housing was a distinctive risk factor in the north. Precipitation, temperature, slope, and poverty were common risk factors but with subtle variations in response values, which were confirmed by the countrywide model. Conclusions This study has demonstrated that different variable combinations influence the occurrence of lymphatic filariasis in northern and southern Ghana. Thus, an understanding of the geographic distinctness in risk factors is required to inform on the development of area-specific transmission control systems towards LF elimination in Ghana and internationally.

Approximately half of generalised anxiety disorder (GAD) patients do not recover from first-line treatments, and no validated prediction models exist to inform individuals or clinicians of potential treatment benefits. This study aimed to develop and validate an accurate and explainable prediction model of post-treatment GAD symptom severity. Data from adults receiving treatment for GAD in eight Improving Access to Psychological Therapies (IAPT) services (n =15,859) were separated into training, validation and holdout datasets. Thirteen machine learning algorithms were compared using 10-fold cross-validation, against two simple clinically relevant comparison models. The best-performing model was tested on the holdout dataset and model-specific explainability measures identified the most important predictors. A Bayesian Additive Regression Trees model out-performed all comparison models (MSE=16.54 [95 % CI=15.58; 17.51]; MAE=3.19; R²=0.33, including a single predictor linear regression model: MSE=20.70 [95 % CI=19.58; 21.82]; MAE=3.94; R²=0.14). The five most important predictors were: PHQ-9 anhedonia, GAD-7 annoyance/irritability, restlessness and fear items, then the referral-assessment waiting time. The best-performing model accurately predicted post-treatment GAD symptom severity using only pre-treatment data, outperforming comparison models that approximated clinical judgement and remaining within the GAD-7 error of measurement and minimal clinically important differences. This model could inform treatment decision-making and provide desired information to clinicians and patients receiving treatment for GAD. [ABSTRACT FROM AUTHOR]

[Display omitted] During the development of sustained-release pellets, the physical characteristics of the pellet cores can affect drug release in the preparation. The method based on near-infrared (NIR) spectroscopy and ensemble learning was proposed to swiftly assess the physical properties of the pellet cores. In the research, the potential of three algorithms, direct standardization (DS), partial least squares regression (PLSR) and generalized regression neural network (GRNN), was investigated and compared. The performance of the DS, PLSR and GRNN models were improved after applying bootstrap aggregating (Bagging) ensemble learning. And the Bagging-GRNN model showed the best predictive capacity. Except for inter-particle porosity, the mean absolute deviations of other 11 physical parameters were less than 1.0. Furthermore, the cosine coefficient values between the actual and predicted physical fingerprints was higher than 0.98 for 15 out of the 16 validation samples when using the Bagging-GRNN model. To reduce the model complexity, the 60 variables significantly correlated with angle of repose, particle size (D50) and roundness were utilized to develop the simplified Bagging-GRNN model. And the simplified model showed satisfactory predictive capacity. In summary, the developed ensemble modelling strategy based NIR spectra is a promising approach to rapidly characterize the physical properties of the pellet cores. [ABSTRACT FROM AUTHOR]

Food insecurity in sub-Saharan Africa is partly due to low staple crop yields, resulting from poor soil fertility and low nutrient inputs. Integrated soil fertility management (ISFM), which includes the combined use of mineral and organic fertilizers, can contribute to increasing yields and sustaining soil organic carbon (SOC) in the long term. Soil-crop simulation models can help assess the performance and trade-offs of a range of crop management practices including ISFM, under current and future climate. Yet, uncertainty in model simulations can be high, resulting from poor model calibration and/or inadequate model structure. Multi-model simulations have been shown to be more robust than those with single models and help understand and reduce modelling uncertainty. In this study, we aim to perform the first multi-model comparison for long-term simulations of crop yield and SOC and their feedbacks in SSA. We evaluated the performance of 16 soil-crop models using data from four long-term maize experiments at sites in SSA with contrasting climates and soils. Each experiment had four treatments: i) no exogenous inputs, ii) addition of mineral nitrogen (N) fertilizer, iii) use of organic amendments, and iv) combined use of mineral and organic inputs. We assessed model performance in two steps: through blind calibration involving a minimum level of experimental data provided to the modeling teams, and subsequently through full calibration, which included a more extensive set of observational data. Model ensemble accuracy was greater with full calibration than blind calibration. Improvement in model accuracy was larger for maize yields (nRMSE 48 vs 18%) than for topsoil SOC (nRMSE 22 vs 14%). Model ensemble uncertainty (defined as the coefficient of variation across the 16 models) increased over the duration of the long-term experiments. Uncertainty of SOC simulations increased when organic amendments were used, whilst uncertainty of yield predictions was largest when no inputs were applied. Our study revealed large discrepancies among the models in simulating i) crop-to-soil feedbacks due to uncertainties in simulated carbon coming from roots, and ii) soil-to-crop feedbacks due to large uncertainties in simulated crop N supply from soil organic matter decomposition. These discrepancies were largest when organic amendments were applied. The results highlight the need for long-term experiments in which root and soil N dynamics are monitored. This will provide the corresponding data to improve and calibrate soil-crop models, which will lead to more robust and reliable simulations of SOC and crop productivity, and their interactions. • Multi-model assessment for long-term simulations of crop yield and soil carbon • Model ensemble uncertainty in simulating soil carbon increase with organic amendment • Model ensemble uncertainty in simulating yield increase in no-input treatments • Models largely differed in simulating root biomass and nitrogen mineralisation • More detailed experiments on soil crop feedback are needed to improve models [ABSTRACT FROM AUTHOR]

Rapidly changing environmental conditions (bioclimatic, anthropogenic, topographic, and vegetation-related variables) are likely to alter the spatial distribution of flora and fauna. To understand the influence of environmental variables on the Blue bull’s distribution and to identify potential conflict zones, the habitat suitability analysis of the Blue bull was performed using ensemble modeling. We modelled the distribution of the Blue bull using an extensive database on the current distribution of the Blue bull and selected 15 ecologically significant environmental variables. We used ten species distribution modeling algorithms available in the BIOMOD2 R package. Among the ten algorithms, the Random Forest, Maxent, and Generalized linear model had the highest mean true skill statistics scores, ensuring better model performance, and were considered for further analysis. We found that 22,462.57 km2 (15.26%) of Nepal is suitable for the Blue bull. Slope, precipitation seasonality, and distance to the road are the environmental variables contributing the most to the distribution of Blue bull. Of the total predicted suitable habitats, 86% lies outside protected areas and 55% overlaps with agricultural land. Thus, we recommend that the future conservation initiatives including appropriate conflict mitigation measures should be prioritized equally in both protected areas and outside protected areas to ensure the species’ survival in the region.

We aimed to characterise the geographical distribution of Sørensen-based multi-site dissimilarity (βsor) and its underlying true turnover (βsim) and nestedness (βsne) components for Chinese Lauraceae and to analyse their relationships to current climate and past climate change. Methods We used ensembles of small models (ESMs) to map the current distributions of 353 Lauraceae species in China and calculated βsor and its βsim and βsne components. We tested the relationship between βsor, βsne and βsim with current climate and past climate change related predictors using a series of simultaneous autoregressive (SARerr) models. Results Spatial distribution of βsor of Lauraceae is positively correlated with latitude, showing an inverse relationship to the latitudinal α-diversity (species richness) gradient. High βsor occurs at the boundaries of the warm temperate and subtropical zones and at the Qinghai-Tibet Plateau due to high βsne. The optimized SARerr model explains βsor and βsne well, but not βsim. Current mean annual temperature determines βsor and βsne of Lauraceae more than anomalies and velocities of temperature or precipitation since the Last Glacial Maximum. Conclusions Current low temperatures and high climatic heterogeneity are the main factors explaining the high multi-site β-diversity of Lauraceae. In contrast to analyses of the β-diversity of entire species assemblages, studies of single plant families can provide complementary insights into the drivers of β-diversity of evolutionarily more narrowly defined entities. [ABSTRACT FROM AUTHOR]

Mountains create steep environmental gradients that are sensitive barometers of climate change. We calibrated 10 statistical models to formulate ensemble ecological niche models for 12 predominantly alpine, flightless grasshopper species in Aotearoa New Zealand, using their current distributions and current conditions. Niche models were then projected for two future global climate scenarios: representative concentration pathway (RCP) 2.6 (1.0°C rise) and RCP8.5 (3.7°C rise). Results were species specific, with two-thirds of our models suggesting a reduction in potential range for nine species by 2070, but surprisingly, for six species, we predict an increase in potential suitable habitat under mild (+1.0°C) or severe global warming (+3.7°C). However, when the limited dispersal ability of these flightless grasshoppers is taken into account, all 12 species studied are predicted to suffer extreme reductions in range, with a quarter likely to go extinct due to a 96–100% reduction in suitable habitat. Habitat loss is associated with habitat fragmentation that is likely to escalate stochastic vulnerability of remaining populations. Here, we present the predicted outcomes for an endemic radiation of alpine taxa as an exemplar of the challenges that alpine species, both in New Zealand and internationally, are subject to by anthropogenic climate change.

This study quantifies the uncertainties in the projected changes in potential longshore sediment transport (LST) rates along a non-straight coastline. Four main sources of uncertainty, including the choice of emission scenarios, Global Circulation Model-driven offshore wave datasets (GCM-Ws), LST models, and their non-linear interactions were addressed through two ensemble modelling frameworks. The first ensemble consisted of the offshore wave forcing conditions without any bias correction (i.e., wave parameters extracted from eight datasets of GCM-Ws for baseline period 1979–2005, and future period 2081–2100 under two emission scenarios), a hybrid wave transformation method, and eight LST models (i.e., four bulk formulae, four process-based models). The differentiating factor of the second ensemble was the application of bias correction to the GCM-Ws, using a hindcast dataset as the reference. All ensemble members were weighted according to their performance to reproduce the reference LST patterns for the baseline period. Additionally, the total uncertainty of the LST projections was decomposed into the main sources and their interactions using the ANOVA method. Finally, the robustness of the LST projections was checked. Comparison of the projected changes in LST rates obtained from two ensembles indicated that the bias correction could relatively reduce the ranges of the uncertainty in the LST projections. On the annual scale, the contribution of emission scenarios, GCM-Ws, LST models and non-linear interactions to the total uncertainty was about 10–20, 35–50, 5–15, and 30–35%, respectively. Overall, the weighted means of the ensembles reported a decrease in net annual mean LST rates (less than 10% under RCP 4.5, a 10–20% under RCP 8.5). However, no robust projected changes in LST rates on annual and seasonal scales were found, questioning any ultimate decision being made using the means of the projected changes.

Anoplophora glabripennis (Motschulsky) (Coleoptera: Cerambycidae) is a forest pest that damages a wide range of trees in areas where it has recently been introduced, demanding a proactive evaluation of its possible future distribution.This study aimed to project the potential distribution of A. glabripennis using species distribution modelling and constructed an ensemble map for evaluating global risk areas.We used CLIMEX and MaxEnt to evaluate the potential distribution of A. glabripennis as a function of current and future climates.The results showed that the models predicted a high probability of A. glabripennis distribution where this species is currently found, and the suitable climate was shifted northward due to climate change.The projected area differed between the models because of different modelling algorithm and climate change scenario; thus, an ensemble map projecting the consensus areas from two models was constructed to identify the risk areas that corresponded to the eastern United States, Europe, and native countries, Korea and China, and nearby Japan.From the perspective of ensemble modelling for evaluating species distributions with reduced uncertainties, this study will enhance the model reliability for defining areas at risk of A. glabripennis occurrence. [ABSTRACT FROM AUTHOR]

Context: Climate change is an important driver of habitat contraction and the loss of biodiversity. Species distribution models (SDMs) are used to assess the impacts of climate change and to identify priority conservation areas. Conservation assessment of endemic keystone species can foster the conservation of forest ecosystems. Objectives: The objectives of this study are: (1) to evaluate the potential impacts of future climate scenarios (RCP 4.5 and RCP 8.5) on the extents of the habitat of eight endemic montane conifer species under dispersal scenarios (full and limited) and (2) to estimate the percentage loss in the area of occupancy (AOO) of the target species based on projected habitat suitability in order to assess their extinction risks and identify priority conservation areas. Methods: Southwestern China is a global hotspot of conifer diversity and endemism. We used three ensemble-SDMs along with the International Union for Conservation of Nature's Red List criteria to evaluate the impacts of climate change. Results: Abies fabri, Abies fargesii var. faxoniana, Abies recurvata var. ernestii, and Picea neoveitchii are predicted to lose more than 90% of their AOOs under both the climate and dispersal scenarios. All of these species are predicted to become extinct or critically endangered except for Picea retroflexa and Abies squamata. It should be noted that while the changes in the AOOs changes were filtered for the current unsuitable man-made areas these predictions do not account for future land use changes. Conclusions: Stable and suitable habitats are promising tools for in-situ conservation planning. Moreover, future conservation actions should give full consideration to the pattern of climate and land use. [ABSTRACT FROM AUTHOR]

The Pacific sand lance (Ammodytes personatus) is a key forage species for many commercially important fish (e.g. salmon and groundfish), marine birds, and whales found in nearshore coastal waters of British Columbia, Canada.Sand lance lack a swim bladder and have a requirement for low‐silt, medium‐coarse sandy sea‐bed habitat for burying. Little information is available describing the distribution of burying habitat, partly because there are no commercial fisheries for A. personatus in British Columbia.This information is required by habitat and wildlife managers to identify and protect uncommon patches of burying habitats from detrimental activities, including dredging, infilling, and oil spills.In this study, habitat distribution results from five suitability modelling algorithms were evaluated: maximum entropy, generalized linear model, generalized additive model, random forest, and an ensemble model of the latter three.The maximum entropy model had the highest performance score (area under the receiver operator characteristic curve was 0.78) and was selected as the model that most accurately identified the presence of suitable A. personatus burying habitat.Model results indicate that suitable burying habitat is primarily influenced by derived sea‐bed substrate, distance to estuary, distance to sand‐gravel beaches, and bottom sea temperature.Overall, the spatial modelling identified only 105 km2 of highly suitable sand lance burying habitat, or 2.6% of the study area (0–150 m), primarily in Haro Strait, along the east coast of Vancouver Island, and in northern regions of the strait near Cortes, Savary, and Harwood islands.Identification of this uncommon and patchy burying habitat will contribute to the ongoing conservation of an important coastal prey species. [ABSTRACT FROM AUTHOR]

The integration of non-synchronous generation units and energy storage through power electronics is introducing new challenges in power system dynamics. Specifically, the rotor angle stability has been identified as one of the major obstacle with regards to power electronics dominated power systems. To date, conventional power system stabilizer (PSS) devices are used for damping electromechanical oscillations, which are only tuned sporadically leading to significant deterioration in performance against the ever-changing operating conditions. In this paper, an intelligent power oscillation damper (iPOD) is proposed for grid-forming converters to attenuate electromechanical inter-area power oscillation. In particular, the iPOD is applied to a synchronous power controller (SPC) based grid-forming power converter to increases gain of the active power control loop at the oscillatory frequency. Predictions regarding the mode frequency, corresponding to the current operating points, are given by an artificial intelligence ensemble model called Random Forests. The performance of the proposed controller is verified using the two area system considering symmetrical fault for random operating points. In addition, a comparison with PSS installed in each generator reveals the individual contribution with respect to the inter-area mode damping.

Mathematical modelling has played an important role in offering informed advice during the COVID-19 pandemic. In England, a cross government and academia collaboration generated medium-term projections (MTPs) of possible epidemic trajectories over the future 4-6 weeks from a collection of epidemiological models. In this article, we outline this collaborative modelling approach and evaluate the accuracy of the combined and individual model projections against the data over the period November 2021-December 2022 when various Omicron subvariants were spreading across England. Using a number of statistical methods, we quantify the predictive performance of the model projections for both the combined and individual MTPs, by evaluating the point and probabilistic accuracy. Our results illustrate that the combined MTPs, produced from an ensemble of heterogeneous epidemiological models, were a closer fit to the data than the individual models during the periods of epidemic growth or decline, with the 90% confidence intervals widest around the epidemic peaks. We also show that the combined MTPs increase the robustness and reduce the biases associated with a single model projection. Learning from our experience of ensemble modelling during the COVID-19 epidemic, our findings highlight the importance of developing cross-institutional multi-model infectious disease hubs for future outbreak control., Competing Interests: We declare we have no competing interests., (© 2024 The Authors.)

Accuracy in the prediction of the particulate matter (PM2.5 and PM10) concentration in the atmosphere is essential for both its monitoring and control. In this study, a novel neuro fuzzy ensemble (NF-E) model was proposed for prediction of hourly PM2.5 and PM10 concentration. The NF-E involves careful selection of relevant input parameters for base modelling and using an adaptive neuro fuzzy inference system (ANFIS) model as a nonlinear kernel for obtaining ensemble output. The four base models used include ANFIS, artificial neural network (ANN), support vector regression (SVR) and multilinear regression (MLR). The dominant input parameters for developing the base models were selected using two nonlinear approaches (mutual information and single-input single-output ANN-based sensitivity analysis) and a conventional Pearson correlation coefficient. The NF-E model was found to predict both PM2.5 and PM10 with higher generalization ability and least error. The NF-E model outperformed all the single base models and other linear ensemble techniques with a Nash-Sutcliffe efficiency (NSE) of 0.9594 and 0.9865, mean absolute error (MAE) of 1.63 μg/m3 and 1.66 μg/m3 and BIAS of 0.0760 and 0.0340 in the testing stage for PM2.5 and PM10, respectively. The NF-E could improve the efficiency of other models by 4–22% for PM2.5 and 3–20% for PM10 depending on the model. [ABSTRACT FROM AUTHOR]

Aim: Cacao (Theobroma cacao L.) is expected to be vulnerable to climate change. The objectives of this study were to (a) assess the future impact of climate change on cacao in Peru and (b) identify areas where climate change‐tolerant genotypes are potentially present. Location: Peru Methods: Drawing on 19,700 and 1,200 presence points of cultivated and wild cacao, respectively, we modelled their suitability distributions using multiple ensemble models constructed based on both random and target group selection of pseudo‐absence points and different resolutions of spatial filtering. To estimate the uncertainty of future predictions, we generated future projections for all the ensemble models. We investigated the potential emergence of novel climates, determined expected changes in ecogeographical zones (zones representative for particular sets of growth conditions) and carried out an outlier analysis based on the environmental variables most relevant for climate change adaptation to identify areas where climate change‐tolerant genotypes are potentially present. Results: We found that the best modelling approaches differed between cultivated and wild cacao and that the resolution of spatial filtering had a strong impact on future suitability predictions, calling for careful evaluation of the effect of model selection on modelling results. Overall, our models foresee a contraction of suitable area for cultivated cacao while predicting a more positive future for wild cacao in Peru. Ecogeographical zones are expected to change in 8%–16% of the distribution of cultivated and wild cacao. We identified several areas where climate change‐tolerant genotypes may be present in Peru. Main conclusions: Our results indicate that tolerant genotypes will be required to facilitate the adaptation of cacao cultivation under climate change. The identified cacao populations will be target of collection missions. [ABSTRACT FROM AUTHOR]

Soil salinization is one of the main threats to soils worldwide, which has serious impacts on soil functions. Our objective was to map and assess salt-affectedness on arable land (0.85 km2) in Hungary, with high spatial resolution, using a combination of ensemble machine learning and multivariate geostatistics on three salt-affected soil indicators (i.e., alkalinity, electrical conductivity, and sodium adsorption ratio (n = 85 soil samples)). Ensemble modelling with five base learners (i.e., random forest, extreme gradient boosting, support vector machine, neural network, and generalized linear model) was carried out and the results showed that ensemble modelling outperformed the base learners for alkalinity and sodium adsorption ratio with R2 values of 0.43 and 0.96, respectively, while only the random forest prediction was acceptable for electrical conductivity. Multivariate geostatistics was conducted on the stochastic residuals derived from machine learning modelling, as we could reasonably assume that there is spatial interdependence between the selected salt-affected soil indicators. We used 10-fold cross-validation to check the performance of the spatial predictions and uncertainty quantifications, which provided acceptable results for each selected salt-affected soil indicator (for pH value, electrical conductivity, and sodium adsorption ratio, the root mean square error values were 0.11, 0.86, and 0.22, respectively). Our results showed that the methodology applied in this study is efficient in mapping and assessing salt-affectedness on arable lands with high spatial resolution. A probability map for sodium adsorption ratio represents sodic soils exceeding a threshold value of 13, where they are more likely to have soil structure deterioration and water infiltration problems. This map can help the land user to select the appropriate agrotechnical operation for improving soil quality and yield.

Ischia is a densely inhabited and touristic volcanic island located in the northern sector of the Gulf of Naples (Italy). In 2017, the Mw 3.9 Casamicciola earthquake occurred after more than one century of seismic quiescence characterized only by minor seismicity, which followed a century with three destructive earthquakes (in 1828, 1881, and 1883). These events, despite their moderate magnitude (Mw < 5.5), lead to dreadful effects on buildings and population. However, an integrated catalogue systematically covering historical and instrumental seismicity of Ischia has been still lacking since many years. Here, we review and systematically re-analyse all the available data on the historical and instrumental seismicity, to build an integrated earthquake catalogue for Ischia with a robust characterization of existing uncertainties. Supported by new or updated macroseismic datasets, we significantly enriched existing catalogues, as the Italian Parametric Earthquake Catalogue (CPTI15) that, with this analysis, passed from 12 to 57 earthquakes with macroseismic parametrization. We also extended back by 6 years the coverage of the instrumental catalogue, homogenizing the estimated seismic parameters. The obtained catalogue will not only represent a solid base for future local hazard quantifications, but also it provides the unique opportunity of characterizing the evolution of the Ischia seismicity over centuries. To this end, we analyse the spatial, temporal, and magnitude distributions of Ischia seismicity, revealing for example that, also in the present long-lasting period of volcanic quiescence, is significantly non-stationary and characterized by a b-value larger than 1.

Aim: Climate is often the sole focus of global change research in mountain ecosystems although concomitant changes in land use might represent an equally important threat. As mountain species typically depend on fine‐scale environmental characteristics, integrating land use change in predictive models is crucial to properly assess their vulnerability. Here, we present a modelling framework that aims at providing more comprehensive projections of both species' distribution and abundance under realistic scenarios of land use and climate change, and at disentangling their relative effects. Location: Switzerland. Methods: We used the ring ouzel (Turdus torquatus), a red‐listed and declining mountain bird species, as a study model. Based on standardized monitoring data collected across the whole country, we fitted high‐resolution ensemble species distribution models to predict current occurrence probability, while spatially explicit density estimates were obtained from N‐mixture models. We then tested for the effects of realistic scenarios of land use (land abandonment versus farming intensification) and climate change on future species distribution and abundance. Results: Occurrence probability was mostly explained by climatic conditions, so that climate change was predicted to have larger impacts on distribution and abundance than any scenarios of land use change. In the mid‐term (2030–2050), predicted effects of environmental change show a high spatial heterogeneity due to regional differences in climate and dominant land use, with farming intensification identified as an important threat locally. In the long term (2080–2100), climate models forecast a marked upward range shift (up to +560 m) and further population decline (up to −35%). Main conclusions: Our innovative approach highlights the spatio‐temporal heterogeneity in the relative effects of different environmental drivers on species distribution and abundance. The proposed framework thus provides a useful tool not only for better assessing species' vulnerability in the face of global change, but also for identifying key areas for conservation interventions at a meaningful scale. [ABSTRACT FROM AUTHOR]

The explosive development of artificial intelligence (AI) has reshaped all aspects of life, including credit scoring. At the same time, the rapid expansion of the consumer finance industry has led to a huge demand. In this study, a new hybrid ensemble model with feature enhancement and soft voting weight optimization is proposed to achieve superior predictive power for credit scoring. For mining and characterizing the implicit information of the features, a new voting-based feature enhancement method is proposed to adaptively integrate the outlier detection and clustering capabilities through the weighted voting mechanism to form a feature-enhanced training set. To balance the feature-enhanced training set precisely and effectively, a new bagging-based undersampling method is proposed to obtain a balanced training set by undersampling from the negative binomial distribution through the bagging strategy. To maximize the performance of the model, a new weight-optimized soft voting method is proposed to optimize the soft voting weights of the base classifiers in the classifier ensemble using the COBYLA algorithm and then constructing the stacking-based ensemble model. Five datasets and five evaluation indicators were used for evaluation. The experimental results demonstrate the superior performance of the proposed model and prove its robustness and effectiveness. [ABSTRACT FROM AUTHOR]

[Display omitted] • Time-series ensemble model is proposed for real-time anaerobic digestion operation. • Simple/practical features i.e. waste composition, water and feeding volume are used. • Prediction accuracy is improved from 75% to 91% in comparison to benchmark models. • Proposed weekly operation could reduce 70% of required feeding day operation. This study presents a novel approach for real-time operation of anaerobic digestion using an ensemble decision-making framework composed of weak learner data mining models. The framework utilises simple but practical features such as waste composition, added water and feeding volume to predict biogas yield and to generate an optimised weekly operation pattern to maximise biogas production and minimise operational costs. The effectiveness of this framework is validated through a real-world case study conducted in the UK. Comparative analysis with benchmark models demonstrates a significant improvement in prediction accuracy, increasing from the range of 50–80% with benchmark models to 91% with the proposed framework. The results also show the efficacy of the weekly operation pattern, which leads to a substantial 78% increase in biogas generation during the testing period. Moreover, the pattern contributes to a reduction of 71% in total days required for feeding and 30% in total days required for pre-feeding. [ABSTRACT FROM AUTHOR]

A comparative study of four well established surrogate models used to predict the non-linear entrainment performance of a dual-phase fluid driving jet pump (JP) apparatus is performed. A JP design flow configuration comprising a dual-phase (air and water) flow driving a secondary gas-air flow, for which no one has ever provided a unique set of design solutions, is described. For the construction of the global approximations (GA), the response surface methodology (RSM), Kriging and the radial basis function artificial neural network (RBFANN), were primarily used. The stacked/ensemble models methodology was integrated in this study, to improve the predictive model results, thus providing accurate GA that facilitate the multi-variable non-linear response design optimisation. An error analysis of all four models along with a multiple model accuracy analysis of each case study were performed. The RSM, Kriging, RBFANN and stacked models formed part of the surrogate-based optimisation, having the entrainment ratio as the main objective function. Optimisation problems were solved by the interior-point algorithm and the genetic algorithm and incurred a hybrid formulation of both algorithms. A total of 60 optimisation problems were formulated and solved with all three approximation models. Results showed that the hybrid formulation having the level-2 ensemble Kriging model performed best, predicting the experimental performance results for all JP models within an error margin of less than 10 % in 90 % of the cases. [ABSTRACT FROM AUTHOR]

Smallholder farmers in sub‐Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low‐input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi‐arid Rwanda, hot subhumid Ghana and hot semi‐arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in‐season soil water content from 2‐year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low‐input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management. [ABSTRACT FROM AUTHOR]

Aim: Creating a network of marine protected areas in the Southern Ocean requires extensive knowledge on species' abundances, distributions and population trends especially in the Weddell Sea where year‐round pack ice makes most of the Weddell Sea inaccessible. We combine satellite images and citizen science to model habitat suitability for crabeater seals (Lobodon carcinophaga) throughout the Weddell Sea. Location: Weddell Sea, Antarctica. Methods: High‐resolution satellite images covering 18,219 km2 of the Weddell Sea during crabeater seal breeding season (October—November) were hosted on the crowd‐sourcing platform Tomnod (DigitalGlobe). Citizen scientists marked "maps" where seals were present/absent and these votes were compared with the votes of an experienced observer. Correction factors were used to correct votes to either a continuous probability of seal presence, or a binary seal presence/absence value. We modelled probability of seal presence using ensemble models of Random Forests (RF), Boosted Regression Trees (BRT) and Support Vector Machines (SVM), and used fitted Maxent models to model seal presence/absence data. Results: Model predictive power was low (RF: R2 = 0.076 ± 0.002: BRT: R2 = 0.086 ± 0.0008; SVM: R2 = 0.082 ± 0.003) to average (Maxent: AUC = 0.71 ± 0.004). Distance to the ice edge and bathymetry were the most important variables that influenced crabeater seal distribution. Main conclusions: Crabeater seals were more likely to be present over abyssal water, which coincides with typical adult Antarctic krill habitat — crabeater seal preferred prey. Where ice concentrations were more variable, that is more accessible, crabeater seals were also more likely to occur. Results agreed with the known ecology of crabeaters seals and the abundance, distribution and ecology of Antarctic krill. We were able to survey the largest area ever surveyed in the Weddell Sea and provide a model to assist furthering policy around the proposed protected area. [ABSTRACT FROM AUTHOR]

Aim: Central part of Iran harbours populations of wild ungulates that are threatened or extinct over large parts of the region, and are likely to be impacted by climate change. In this study, we predicted the impact of climate change on the distribution of three vulnerable ungulates in central Iran. We then evaluated future suitability of corridors connecting the protected areas for movement of the ungulates in response to climate change. Location: Central Iran. Methods: Impact of climate change on distribution of goitered gazelle (Gazella subgutturosa), wild sheep (Ovis spp) and wild goat (Capra aegagrus) was predicted adapting an ensemble modelling approach and under the RCP 8.5 emission scenario. We then used CIRCUITSCAPE software with current and future distribution maps to identify corridors for movement of the three ungulates, and evaluate likely changes in their suitability under climate change. Results: Our results revealed that climate change might result in loss of 55%, 69% and 76% of suitable habitats for goitered gazelle, wild sheep and wild goat by 2070, respectively. These losses also resulted in some protected areas to become unsuitable for the ungulates. However, we identified key protected areas with the potential for future protection of these ungulates. For the three species, we also identified corridors which would persist into the future, allowing the impacted populations to move in response to climate change. Main conclusions: Conservation of ungulate populations in Iran mainly depends on the protected areas. To maintain the role of the protected areas in conserving these mammals under climate change, we recommend the incorporation of their potential future distribution into conservation plans, increasing protection status of the key protected areas, and maintain critical corridors. In this regard, combining results of distribution and connectivity models provides useful information for effective management of these ungulates in the future. [ABSTRACT FROM AUTHOR]

The group of langur species in the Himalayan range, comprising Semnopithecus ajax, Semnopithecus hector and Semnopithecus schistaceus, now proved to be one single species, were earlier thought to be different species. Their taxonomic confusion has led to deficiency in the understanding of their geographic range and their vulnerability to future climate has not been studied. This study attempts to map their distribution in the face of changing climate. The Western Indian Himalayas and Nepal were considered as the study area for this work. An ensemble model approach for Species Distribution Modelling (SDM), for estimating the habitat loss risks of the Himalayan Langur (HL) in the event of climate change was adopted in this study. Patch metrics and corridor analysis were used to understand fragmentation of suitable habitat. The suitable habitat area for HL was predicted to be 24,240 km2 in the present scenario. It is predicted to decline by 64.6% in 2050; 64.1% 2070 (RCP 4.5); and 63.6% in 2050, 20.3% in 2070 (RCP 8.5). A minimum shrinkage by 58% in the mean habitat patch size is predicted. A list of protected areas important for conservation of the species from the habitat connectivity perspective was extracted. The HL was predicted to shift both longitudinally as well as along the latitude, in the landscape, confirming effects of climate change on the species. The HL was also predicted to find refuge under climate change in areas that are not presently protected forests, suggesting management of the refugia would be needed in future.

Abstract Background Lymphatic filariasis (LF) is a mosquito-borne parasitic disease and a major cause of disability worldwide. It is one of the neglected tropical diseases identified by the World Health Organization for elimination as a public health problem by 2020. Maps displaying disease distribution are helpful tools to identify high-risk areas and target scarce control resources. Methods We used pre-intervention site-level occurrence data from 1192 survey sites collected during extensive mapping surveys by the Nigeria Ministry of Health. Using an ensemble of machine learning modelling algorithms (generalised boosted models and random forest), we mapped the ecological niche of LF at a spatial resolution of 1 km2. By overlaying gridded estimates of population density, we estimated the human population living in LF risk areas on a 100 × 100 m scale. Results Our maps demonstrate that there is a heterogeneous distribution of LF risk areas across Nigeria, with large portions of northern Nigeria having more environmentally suitable conditions for the occurrence of LF. Here we estimated that approximately 110 million individuals live in areas at risk of LF transmission. Conclusions Machine learning and ensemble modelling are powerful tools to map disease risk and are known to yield more accurate predictive models with less uncertainty than single models. The resulting map provides a geographical framework to target control efforts and assess its potential impacts.

Statistical landslide susceptibility mapping is a topic in complete and constant evolution, especially since the introduction of machine learning (ML) methods. A new methodological approach is here presented, based on the ensemble of artificial neural network, generalized boosting model and maximum entropy ML algorithms. Such approach has been tested in the Monterosso al Mare area, Cinque Terre National Park (Northern Italy), severely hit by landslides in October 2011, following an extraordinary precipitation event, which caused extensive damage at this World Heritage site. Thirteen predisposing factors were selected and assessed according to the main characteristics of the territory and through variance inflation factor, whilst a database made of 260 landslides was adopted. Four different Ensemble techniques were applied, after the averaging of 300 stand-alone methods, each one providing validation scores such as ROC (receiver operating characteristics)/AUC (area under curve) and true skill statistics (TSS). A further model performance evaluation was achieved by assessing the uncertainty through the computation of the coefficient of variation (CV). Ensemble modelling thus showed improved reliability, testified by the higher scores, by the low values of CV and finally by a general consistency between the four Ensemble models adopted. Therefore, the improved reliability of Ensemble modelling confirms the efficacy and suitability of the proposed approach for decision-makers in land management at local and regional scales. [ABSTRACT FROM AUTHOR]

Aim: Forested regions are of global importance for a multitude of ecosystem functions and services and are critical for biodiversity. Anthropogenic climate‐change compounds negative effects of land‐use change on forest persistence and forest‐dependent biodiversity. Habitat loss and climate change have an additive effect and drive species' extinctions in similar ways, resulting in a homogenization of biodiversity. Connectivity is key in conservation planning for mitigating climate change effects and facilitating species' abilities to disperse throughout remnant habitat and track their climate niches. We used three forest‐specialized and habitat‐specific bird species as focal species to understand avian connectivity and conservation of each of South Africa's three threatened forest classes, as each species is range‐restricted to its respective forest type. Location: South Africa. Methods: We created ensemble models of species' distributions and combined core home‐ and breeding‐range patches with a hybrid of least‐cost pathways and ecological circuit theory linkages to assess the success of corridors in facilitating connectivity of each of the three forest types. We then predicted the likelihood of niche persistence for each species under future climate‐change scenarios, and the efficacy of our connectivity modelling to facilitate range expansion or climate‐niche tracking. Results: The projected habitat loss under climate‐change scenarios impacted core‐habitat patch distribution, size and connectivity, exacerbated habitat fragmentation and increased resistance and the severity of pinch points and barriers along dispersal corridors. Forest systems and associated focal species projected to experience the highest levels of habitat loss/contraction occurred at mid‐ to high elevations. Climate‐change resilience across ecosystems, and persistence of species therein, was dependent on connectivity, facilitating species' ability to track specific climate niches. Main conclusions: Climate‐change resilience of ecosystems, and persistence of biodiversity therein, is most likely to be a product of high functional biodiversity, connectedness and the ability of species to track specific climate niches. [ABSTRACT FROM AUTHOR]

The fragmented nature of arable landscapes and diverse cropping patterns often thwart the precise mapping of crop types. Recent advances in remote-sensing technologies and data mining approaches offer a viable solution to this mapping problem. We demonstrated the potential of using hyperspectral imaging and an ensemble classification approach that combines five machine-learning classifiers to map crop types in the Anand District of Gujarat, India. We derived a set of narrow/broad-band indices from the Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery to represent spectral variations and identify target classes and their distribution patterns. The results showed that Maximum Entropy (MaxEnt) and Generalised Linear Model (GLM) had strong discriminatory image classification abilities with Area Under the Curve (AUC) values ranging between 0.75 and 0.93 for MaxEnt and between 0.73 and 0.92 for GLM. The ensemble model resulted in improved accuracy scores compared to individual models. We found the Photochemical Reflectance Index (PRI) and Moment Distance Ratio Right/Left (MDRRL) to be important predictors for target classes such as wheat, legumes, and eggplant. Results from the study revealed the potential of using one-class ensemble modelling approach and hyperspectral images with limited field dataset to map agricultural systems that are fragmented in nature. [ABSTRACT FROM AUTHOR]

The Western Paleartic species of Neocrepidodera Heikertinger (Coleoptera: Chrysomelidae: Galerucinae: Alticini) mostly occur in medium and high elevation ecosystems particularly sensitive to climate change.Here, using ensemble projections from state‐of‐the‐art habitat suitability modelling techniques, we investigated how climate change and associated changes in host availability may affect the persistence of three pairs of closely related Neocrepidodera taxa.Modelled niches and suitability patterns reflected the current distributions of the targeted taxa. Neocrepidodera ligurica occupies a small portion of the broader environmental niche of N. melanostoma, and its narrow geographical range makes this species particularly vulnerable to potential loss of suitable habitats in Western Alps. Neocrepidodera cyanescens cyanescens and N. cyanescens concolor were found to occupy separate niches, but the non‐significance of the niche similarity test suggested their divergence being probably due to allopatric processes. Neocrepidodera corpulenta and N. rhaetica showed partially overlapping niches, coherently with their co‐occurrence in Western Alps. Most of the targeted taxa were predicted to potentially lose large portions of currently suitable areas in the forthcoming decades.Notwithstanding the candidate host plants did not emerge as most important predictors, except Aconitum lycoctonum for N. cyanescens concolor, a clear reduction of potential insect‐plant co‐occurrence areas resulted for most future scenarios.Climate was confirmed to noticeably affect the distribution of the targeted taxa, among which N. ligurica, N. cyanescens concolor, N. corpulenta and N. rhaetica may need specific prioritisation measures in the future decades, claiming for further attention on mountainous entomofauna in a warming world. [ABSTRACT FROM AUTHOR]

The aim of this research was to evaluate the predictive performances of frequency ratio (FR), logistic regression (LR) and weight of evidence (WoE), in flood susceptibility mapping in China. In addition, the ensemble WoE and LR and ensemble FR and LR techniques were applied and used in the evaluation. The flood inventory map, consisting of 196 flood locations, was extracted from a number of sources. The flood inventory data were randomly divided into a testing data-set, allocating 70% for training, and the remaining 30% for validation. The 15 flood conditioning factors included in the spatial database were altitude, slope, aspect, geology, distance from river, distance from road, distance from fault, soil type, land use/cover, rainfall, Normalized Difference Vegetation Index, Stream Power Index, Topographic Wetness Index, Sediment Transport Index and curvature. For validation, success and prediction rate curves were developed using area under the curve (AUC) method. The results indicated that the highest prediction rate of 90.36% was achieved using the ensemble technique of WoE and LR. The standalone WoE produced the highest prediction rate among the individual methods. It can be concluded that WoE offers a more advanced method of mapping prone areas, compared with the FR and LR methods.

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• Robustness-based weighting techniques on changing climate conditions are studied. • Weighting hydrological models improved performances on contrasting conditions. • The Granger-Ramanathan type A showed generally more robust performances. • More complex hydrological models can add value in contrasting climate conditions. Reliable hydrological projections are of great importance for climate change impact studies, especially knowing that these analyses can allow identifying regional adaptation and mitigation strategies for the future. However, the literature has highlighted that hydrological models used under climate conditions that are contrasting to those used during their calibrations can lower their performance and reliability, an issue that lowers the confidence in hydrological projections and adds uncertainty to their analyses. More studies are needed to explore this issue and evaluate potential strategies that might improve hydrological models' reliability in climate change impact studies. Thus, the present study evaluates the robustness of hydrological models under contrasting climatic conditions and investigates the use of weighting techniques to improve their combined performance and reliability. The robustness of five lumped hydrological models is analysed using a Differential Split-Sample Testing (DSST) that evaluates their performance under cold, warm, humid and dry historical contrasting conditions over 77 basins covering different hydroclimatic conditions (two domains, one located in Quebec, Canada, and one in Mexico). Additionally, four basins were selected from the study area to evaluate the robustness of a more complex semi-distributed and more physically-based hydrological model and compare its simulations against the simpler lumped hydrological models. Based on the resulting performance of each hydrological model, five different weighting methods were applied to evaluate the potential improvements in the multi-model ensemble performance and quantify their effects on hydrological projections, particularly on future peak flows. For each basin, these streamflow projections were produced using two regional climate simulations (one per studied domain) issued from the Canadian Regional Climate Model version 5 (CRCM5) under the Representative Concentration Pathway (RCP) 8.5 for the 1976–2005, 2041–2070 and 2070–2099 periods. The results showed that weighting hydrological models, even with the most simplistic methods, showed better performances over historical contrasting conditions than the best-performing lumped hydrological model. Between the different weighting methods, the Granger-Ramanathan type A showed the overall best performance among the different basins and climate conditions, particularly in peak streamflows. Over the CRCM5-driven peak flow projections, the weighting methods Granger-Ramanathan types A and B produced the largest impacts on the projected floods magnitudes and climate change signals. On the other hand, the additional tests using the semi-distributed and more physically-based hydrological model revealed that this model showed more robust simulations than the weighted lumped hydrological models on low flows over the four selected basins. Additionally, more robust high-flow simulations were observed over a small snow-dominated basin, suggesting a potential added value in adding more complex hydrological models to simulate conditions under a changed climate. [ABSTRACT FROM AUTHOR]

Improved quantitative forecasts of volcanic ash are in great demand by the aviation industry to enable better risk management during disruptive volcanic eruption events. However, poor knowledge of volcanic source parameters and other dispersion and transport modelling uncertainties, such as those due to errors in numerical weather prediction fields, make this problem very challenging. Nonetheless, satellite-based algorithms that retrieve ash properties, such as mass load, effective radius, and cloud top height, combined with inverse modelling techniques, such as ensemble filtering, can significantly ameliorate these problems. The satellite-retrieved data can be used to better constrain the volcanic source parameters, but they can also be used to avoid the description of the volcanic source altogether by direct insertion into the forecasting model. In this study we investigate the utility of the direct insertion approach when employed within an ensemble filtering framework. Ensemble members are formed by initializing dispersion models with data from different timesteps, different values of cloud top height, thickness, and NWP ensemble members. This large ensemble is then filtered with respect to observations to produce a refined forecast. We apply this approach to 14 different eruption case studies in the tropical atmosphere. We demonstrate that the direct insertion of data improves model forecast skill, particularly when it is used in a hybrid ensemble in which some ensemble members are initialized from the volcanic source. Moreover, good forecast skill can be obtained even when detailed satellite retrievals are not available, which is frequently the case for volcanic eruptions in the tropics.