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Aim: Seamounts are conspicuous geological features with an important ecological role and can be considered vulnerable marine ecosystems (VMEs). Since many deep‐sea regions remain largely unexplored, investigating the occurrence of VME taxa on seamounts is challenging. Our study aimed to predict the distribution of four cold‐water coral (CWC) taxa, indicators for VMEs, in a region where occurrence data are scarce. Location: Seamounts around the Cabo Verde archipelago (NW Africa). Methods: We used species presence–absence data obtained from remotely operated vehicle (ROV) footage collected during two research expeditions. Terrain variables calculated using a multiscale approach from a 100‐m‐resolution bathymetry grid, as well as physical oceanographical data from the VIKING20X model, at a native resolution of 1/20°, were used as environmental predictors. Two modelling techniques (generalized additive model and random forest) were employed and single‐model predictions were combined into a final weighted‐average ensemble model. Model performance was validated using different metrics through cross‐validation. Results: Terrain orientation, at broad scale, presented one of the highest relative variable contributions to the distribution models of all CWC taxa, suggesting that hydrodynamic–topographic interactions on the seamounts could benefit CWCs by maximizing food supply. However, changes at finer scales in terrain morphology and bottom salinity were important for driving differences in the distribution of specific CWCs. The ensemble model predicted the presence of VME taxa on all seamounts and consistently achieved the highest performance metrics, outperforming individual models. Nonetheless, model extrapolation and uncertainty, measured as the coefficient of variation, were high, particularly, in least surveyed areas across seamounts, highlighting the need to collect more data in future surveys. Main Conclusions: Our study shows how data‐poor areas may be assessed for the likelihood of VMEs and provides important information to guide future research in Cabo Verde, which is fundamental to advise ongoing conservation planning. [ABSTRACT FROM AUTHOR]

This paper proposes a novel ensemble approach, integrating Artificial Neural Networks (ANNs), Symbolic Regression (SR), and Decision Trees (DTs), to predict fish mortality caused by infectious diseases. The intensifying global burden of fish diseases threatens the sustainability of aquatic ecosystems and the aquaculture industry, necessitating sophisticated modelling strategies for effective disease management and control. The proposed approach capitalizes on the non-linear data modelling strength of ANNs, the explanatory power of SR, and the decision-making efficiency of DTs, offering both predictive accuracy and interpretable insights. The architecture of the proposed ensemble method is developed in two stages. In the intermediate stage, an ANN is employed to learn the complex, non-linear interactions between various biological and environmental factors impacting fish health. Additionally, SR is applied to produce a symbolic equation that effectively maps the input variables to fish mortality rates. In the final stage, a DT model is included to enhance prediction performance by capturing decision rules from the data. This hybrid approach offers superior prediction performance while also revealing meaningful biological/environmental relationships that can guide preventive and reactive interventions in the management of fish health. We evaluate the developed models using extensive real-world datasets acquired from two large Greek fish-farming units, which encompass representative disease types. The results demonstrate that our ensemble approach significantly outperforms traditional standalone models developed in our recent previous work, achieving enhanced predictive accuracy, robustness, and interpretability. Overall, this research has far-reaching implications for improving disease predictions, facilitating optimal decision-making in aquaculture management, and contributing to the sustainability of global fish stocks. [ABSTRACT FROM AUTHOR]

Climate change is expected to impact cacao cultivation in Ecuador, the fifth largest cacao producing country in the world and largest exporter of fine flavour cacao. The objective of this study was to evaluate the future impact of climate change on the suitable distribution of cultivated and wild cacao and identify areas where climate change tolerant genotypes may occur in Ecuador. Using 26,152 presence points for cultivated cacao and 95 presence points for wild cacao, we modelled the present suitability distribution of cultivated and wild cacao and performed future climate projections under two greenhouse gas emission scenarios (SSP2-4.5 and SSP3-7.0) and two time periods (2050s and 2070s). For both cultivated and wild cacao, we constructed six different ensemble models employing different filtering methods for presence points, we projected each ensemble model to future climatic conditions, and we then built the final maps of present distribution and future projections based on the majority-vote criterion. Our future projections predict a 8–16% contraction and 19–21% expansion of the currently suitable area of cultivated cacao, while wild cacao is expected to maintain most of its suitable area and experience a further 7–12% expansion in the future. Ecogeographical zones are predicted to change in 23-33% of the combined distributions of cultivated and wild cacao. We identified the areas in Ecuador where populations of climate change tolerant genotypes are expected to occur. Interventions to promote adaptation to climate change will be required in cacao cultivation areas that are expected to be impacted by climate change in Ecuador, including the use of tolerant genotypes. [ABSTRACT FROM AUTHOR]

Honey bees play a vital role in providing essential ecosystem services and contributing to global agriculture. However, the potential effect of climate change on honey bee distribution is still not well understood. This study aims to identify the most influential bioclimatic and environmental variables, assess their impact on honey bee distribution, and predict future distribution. An ensemble modelling approach using the biomod2 package in R was employed to develop three models: a climate‐only model, an environment‐only model, and a combined climate and environment model. By utilising bioclimatic data (radiation of the wettest and driest quarters and temperature seasonality) from 1990 to 2009, combined with observed honey bee presence and pseudo absence data, this model predicted suitable locations for honey bee apiaries for two future time spans: 2020–2039 and 2060–2079. The climate‐only model exhibited a true skill statistic (TSS) value of 0.85, underscoring the pivotal role of radiation and temperature seasonality in shaping honey bee distribution. The environment‐only model, incorporating proximity to floral resources, foliage projective cover, and elevation, demonstrated strong predictive performance, with a TSS of 0.88, emphasising the significance of environmental variables in determining habitat suitability for honey bees. The combined model had a higher TSS of 0.96, indicating that the combination of climate and environmental variables enhances the model's performance. By the 2020–2039 period, approximately 88% of highly suitable habitats for honey bees are projected to transition from their current state to become moderate (14.84%) to marginally suitable (13.46%) areas. Predictions for the 2060–2079 period reveal a concerning trend: 100% of highly suitable land transitions into moderately (0.54%), marginally (17.56%), or not suitable areas (81.9%) for honey bees. These results emphasise the critical need for targeted conservation efforts and the implementation of policies aimed at safeguarding honey bees and the vital apiary industry. [ABSTRACT FROM AUTHOR]

Mapping tree species diversity is essential for monitoring and managing forest ecosystems. Automating ecoforestry mapping using remote sensing images remains an important challenge due to the tremendous variability in forest covers and the conditions under which images used for classification are acquired. Deep learning algorithms have been increasingly used over the last few years to analyse remote sensing data for mapping tree species. However, most of these studies focus only on a small number of species or a limited area and avoid providing a spatially explicit representation of the uncertainty related to the predictions, rendering them unsuitable for operational use.In this study, we used an ensemble of convolutional neural networks to map forest species and land cover types across a 10,000 km2 area in Quebec, Canada, spanning mixed and boreal forests. We built a georeferenced label database to train and test nine models, which resulted from the combination of three training datasets and three-commonly used convolutional neural network architectures (VGG16, ResNet50v2, Densenet121). These models were trained on multiband aerial photographs and a canopy height model derived from airborne lidar point clouds and used to map the diversity and distribution of tree species and land cover types. The level of agreement among models was used to generate uncertainty maps. The performance of the super-ensemble using 1311 independent forest inventory plots and assessed the extent to which uncertainty maps could serve as a spatially explicit indicator of model performance.The super-ensemble achieved 90% global accuracy. Our results indicated that the performance of the super-ensemble surpassed that of all individual architectures while also showing a positive effect of the canopy height model on performance. The comparison of the super-ensemble map with the proportion of basal area measured in forest inventory plots confirmed the reliability of the model over the study area. Our results also indicated that mapping the inter-model agreement provides a reliable spatially explicit estimate of model performance. The robustness and reliability of the proposed approach support its use in an operational context while also providing a conceptual framework to evaluate the reliability of uncertainty maps.

The effective reproduction number $ R $ was widely accepted as a key indicator during the early stages of the COVID-19 pandemic. In the UK, the $ R $ value published on the UK Government Dashboard has been generated as a combined value from an ensemble of epidemiological models via a collaborative initiative between academia and government. In this paper, we outline this collaborative modelling approach and illustrate how, by using an established combination method, a combined $ R $ estimate can be generated from an ensemble of epidemiological models. We analyse the $ R $ values calculated for the period between April 2021 and December 2021, to show that this $ R $ is robust to different model weighting methods and ensemble sizes and that using heterogeneous data sources for validation increases its robustness and reduces the biases and limitations associated with a single source of data. We discuss how $ R $ can be generated from different data sources and show that it is a good summary indicator of the current dynamics in an epidemic. [ABSTRACT FROM AUTHOR]

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.

Abstract Honey bees play a vital role in providing essential ecosystem services and contributing to global agriculture. However, the potential effect of climate change on honey bee distribution is still not well understood. This study aims to identify the most influential bioclimatic and environmental variables, assess their impact on honey bee distribution, and predict future distribution. An ensemble modelling approach using the biomod2 package in R was employed to develop three models: a climate‐only model, an environment‐only model, and a combined climate and environment model. By utilising bioclimatic data (radiation of the wettest and driest quarters and temperature seasonality) from 1990 to 2009, combined with observed honey bee presence and pseudo absence data, this model predicted suitable locations for honey bee apiaries for two future time spans: 2020–2039 and 2060–2079. The climate‐only model exhibited a true skill statistic (TSS) value of 0.85, underscoring the pivotal role of radiation and temperature seasonality in shaping honey bee distribution. The environment‐only model, incorporating proximity to floral resources, foliage projective cover, and elevation, demonstrated strong predictive performance, with a TSS of 0.88, emphasising the significance of environmental variables in determining habitat suitability for honey bees. The combined model had a higher TSS of 0.96, indicating that the combination of climate and environmental variables enhances the model's performance. By the 2020–2039 period, approximately 88% of highly suitable habitats for honey bees are projected to transition from their current state to become moderate (14.84%) to marginally suitable (13.46%) areas. Predictions for the 2060–2079 period reveal a concerning trend: 100% of highly suitable land transitions into moderately (0.54%), marginally (17.56%), or not suitable areas (81.9%) for honey bees. These results emphasise the critical need for targeted conservation efforts and the implementation of policies aimed at safeguarding honey bees and the vital apiary industry.

An effective method for identifying species and evaluating the effects of changes caused by humans on specific species is the application of species distribution modelling (SDM) in desert environments. The fact that many dry lands and deserts throughout the world are situated in inhospitable regions may be the reason why such applications are still infrequently used on plant species in Egypt's Mediterranean region. Henceforth, the current study aims to map species richness and weighted endemism of Mediterranean endemics in the Mareotis subsector in Egypt and determine the environmental variables influencing distribution of these taxa. We produced a map of species distribution range using Ensemble SDMs. Further, stacked machine learning ensemble models derived from Random Forest (RF) and MaxEnt models were applied on 382 Mediterranean endemics distribution data to estimate and map diversity and endemism using two indices: species richness (SR) and weighted endemism index (WEI). The best models for ensemble modelling were chosen based on Kappa values and the Area Under the Receiver Operator Curve (AUC). The results showed that the models had a good predictive ability (Area Under the Curve (AUC) for all SDMs was > 0.75), indicating high accuracy in forecasting the potential geographic distribution of Mediterranean endemics. The main bioclimatic variables that impacted potential distributions of most species were wind speed, elevation and minimum temperature of coldest month. According to our models, six hotspots were determined for Mediterranean endemics in the present study. The highest species richness was recorded in Sallum, Matrouh wadis and Omayed, followed by Burg El-Arab, Ras El-Hekma and Lake Mariut. Indeed, species richness and endemism hotspots are promising areas for conservation planning. This study can help shape policy and mitigation efforts to protect and preserve Mediterranean endemics in the coastal desert of Egypt. These hotspots should be focused on by policy makers and stakeholders and declared as protectorates in the region. The largest number of species per area would be protected by focusing primarily on the hotspots with high species richness. [ABSTRACT FROM AUTHOR]

Body condition scoring is an objective scoring method used to evaluate the health of a cow by determining the amount of subcutaneous fat in a cow. Automated body condition scoring is becoming vital to large commercial dairy farms as it helps farmers score their cows more often and more consistently compared to manual scoring. A common approach to automated body condition scoring is to utilise a CNN-based model trained with data from a depth camera. The approaches presented in this paper make use of three depth cameras placed at different positions near the rear of a cow to train three independent CNNs. Ensemble modelling is used to combine the estimations of the three individual CNN models. The paper aims to test the performance impact of using ensemble modelling with the data from three separate depth cameras. The paper also looks at which of these three cameras and combinations thereof provide a good balance between computational cost and performance. The results of this study show that utilising the data from three depth cameras to train three separate models merged through ensemble modelling yields significantly improved automated body condition scoring accuracy compared to a single-depth camera and CNN model approach. This paper also explored the real-world performance of these models on embedded platforms by comparing the computational cost to the performance of the various models. [ABSTRACT FROM AUTHOR]

This study focused on assessing the impact of climate change on Cupressus funebris, one of the important endemic conifer species in China. The impact of fluctuations in temperature on the potential distribution and extinction risk of the C. funebris was evaluated using species distribution modelling. The outcomes of this current study revealed that the species was mainly distributed in mountainous forest areas, and climate variables played a major role in the distribution of this conifer tree. Under climate change, the threatened conifer will be mainly concentrated in mountainous forests and is projected to undergo contraction in distribution and shift northward. The conservation status of C. funebris is projected to be uplisted to "Near Threatened" status under the SSP5_8.5 scenario by 2040 and to the "Least Concern" status under all the other investigated climate and dispersal scenarios. Despite the high capacity of the species for adaptation to environmental changes, part of its AOO might be lost under severe climate change conditions. Key conservation areas were identified, and suggestions for redesigning some of the current natural reserves in the forested area where the species is found were proposed for the conservation of this key coniferous species. The stable area in the northwestern part of Yunnan in the Hengduan Mountain Forest can serve as a promising area for C. funebris reintroduction through afforestation programs. [ABSTRACT FROM AUTHOR]

Abstract Often there are several complex ecosystem models available to address a specific question. However, structural differences, systematic discrepancies and uncertainties mean that they typically produce different outputs. Rather than selecting a single ‘best’ model, it is desirable to combine them to give a coherent answer to the question at hand. Many methods of combining ecosystem models assume that one of the models is exactly correct, which is unlikely to be the case. Furthermore, models may not be fitted to the same data, have the same outputs, nor be run for the same time period, making many common methods difficult to implement. In this paper, we use a statistical model to describe the relationship between the ecosystem models, prior beliefs and observations to make coherent predictions of the true state of the ecosystem with robust quantification of uncertainty. We introduce EcoEnsemble, an R package that takes advantage of the statistical model's structure to efficiently fit the ensemble model, either sampling from the posterior distribution or maximising the posterior density. We demonstrate EcoEnsemble by investigating what would happen to four fish species in the North Sea under future management scenarios. Although developed for applications in ecology, EcoEnsemble can be used to combine any group of mechanistic models, for example in climate modelling, epidemiology or biology.

Plastic asphalt mixtures (PAMs) have garnered attention recently, but their field application has been limited due to a lack of understanding of asphalt mix behavior following modification. A modelling tool that can calculate the plastic influence on the characteristics of asphalt mixtures is required to close this gap. Hence, this study offers a performance analysis of various machine learning (ML) models in predicting the performance of PAMs through its various properties. These models include three methods, decision tree (DT) as an individual technique, adaboost regressor (AR), and bagging regressor (BR), as ensemble techniques for prediction of fundamental properties of PAMs i.e. air voids (Va), marshall flow (MF), marshall stability (MS), tensile strength ratio (TSR), and indirect tensile strength (ITS). A series of experimental works and their results on the PAMs properties were collected through literature, to develop ML models and compare their accuracy. The comparative findings demonstrated that the BR model had the largest coefficient of determination (R2) and the lowest statistical errors, making it the model with the best predictive ability. According to the study's findings, ensemble approaches can be effectively utilized to forecast different properties of PAMs. Comparing ensemble models to the single model that served as their base learner, ensemble models were able to increase prediction accuracy. Furthermore, cross validation was performed to check the accuracy of developed models. SHAP analysis was conducted to examine the effects of input parameters on the fundamental properties of PAMs.

Remote sensing and modelling of land use/land cover (LULC) change is useful to reveal the extent and spatial patterns of landscape changes at various environments and scales. Predicting susceptibility to LULC change is crucial for policy formulation and land management. However, the use of machine learning (ML) for modelling LULC change is limited. This study modelled LULC change susceptibility in the Okavango basin using ML techniques. Areas with high LULC change susceptibility are termed priority management areas (PMAs) in this study. Trajectories of LULC change between 1996 and 2020 are derived from existing LULC change maps of the Okavango basin. Overlay analysis is then used to detect patches of LULC change transitions. Three LULC transitional categories are adopted for modelling PMAs, namely 1) from natural to anthropogenic classes (Category A); 2) from anthropogenic to natural classes (Category B); and 3) from natural to another natural class (Category C). An ensemble of ML algorithms is calibrated with categories of LULC change and social-ecological drivers of change to produce maps showing the susceptibility of LULC change in the basin. Thereafter, thresholding is done on probability maps of susceptibility to LULC change based on the maximum sum of sensitivity and specificity (max SSS) to delineate PMAs. Results for trajectories of LULC change indicate that anthropogenic activities (croplands, built-up areas, and barelands) generally expanded, displacing natural areas (wetlands, woodlands, water, and shrubland) from 1996 to 2020. Regarding PMAs, anthropogenic-related PMAs (Category A ∼34 560 km2) covered a larger area compared to the natural ones (Categories B∼33 407 km2) and (Categories C∼15 040 km2). The findings of this study emphasize the value of ensemble ML modelling in identifying PMAs and guiding transboundary land use planning. Overall, this study highlights the role of anthropogenic activities in driving land use changes in Transboundary Drainage Basins (TDBs) and suggests a need to promote sustainable practices in predicted PMAs through comprehensive planning to ensure water availability in the Okavango basin.

Predictions of the unemployment duration of the economically active population play a crucial assisting role for policymakers and employment agencies in the well-organised allocation of resources (tied to solving problems of the unemployed, whether on the labour supply or demand side) and providing targeted support to jobseekers in their job search. This study aimed to develop an ensemble model that can serve as a reliable tool for predicting unemployment duration among jobseekers in Slovakia. The ensemble model was developed using real data from the database of jobseekers (those registered as unemployed and actively searching for a job through the Local Labour Office, Social Affairs, and Family) using the stacking method, incorporating predictions from three individual models: CART, CHAID, and discriminant analysis. The final meta-model was created using logistic regression and indicates an overall accuracy of the prediction of unemployment duration of almost 78%. This model demonstrated high accuracy and precision in identifying jobseekers at risk of long-term unemployment exceeding 12 months. The presented model, working with real data of a robust nature, represents an operational tool that can be used to check the functionality of the current labour market policy and to solve the problem of long-term unemployed individuals in Slovakia, as well as in the creation of future government measures aimed at solving the problem of unemployment. The measures from the state are financed from budget funds, and by applying the appropriate model, it is possible to arrive at the rationalization of the financing of these measures, or to specifically determine the means intended to solve the problem of long-term unemployment in Slovakia (this, together with the regional disproportion of unemployment, is considered one of the most prominent problems in the labour market in Slovakia). The model also has the potential to be adapted in other economies, taking into account country-specific conditions and variables, which is possible due to the data-mining approach used. [ABSTRACT FROM AUTHOR]

By providing predicted protein structures from nearly all known protein sequences, the artificial intelligence program AlphaFold (AF) is having a major impact on structural biology. While a stunning accuracy has been achieved for many folding units, predicted unstructured regions and the arrangement of potentially flexible linkers connecting structured domains present challenges. Focusing on single‐chain structures without prosthetic groups, an earlier comparison of features derived from small‐angle X‐ray scattering (SAXS) data taken from the Small‐Angle Scattering Biological Data Bank (SASBDB) is extended to those calculated using the corresponding AF‐predicted structures. Selected SASBDB entries were carefully examined to ensure that they represented data from monodisperse protein solutions and had sufficient statistical precision and q resolution for reliable structural evaluation. Three examples were identified where there is clear evidence that the single AF‐predicted structure cannot account for the experimental SAXS data. Instead, excellent agreement is found with ensemble models generated by allowing for flexible linkers between high‐confidence predicted structured domains. A pool of representative structures was generated using a Monte Carlo method that adjusts backbone dihedral allowed angles along potentially flexible regions. A fast ensemble modelling method was employed that optimizes the fit of pair distance distribution functions [P(r) versus r] and intensity profiles [I(q) versus q] computed from the pool to their experimental counterparts. These results highlight the complementarity between AF prediction, solution SAXS and molecular dynamics/conformational sampling for structural modelling of proteins having both structured and flexible regions. [ABSTRACT FROM AUTHOR]

Often there are several complex ecosystem models available to address a specific question. However, structural differences, systematic discrepancies and uncertainties mean that they typically produce different outputs. Rather than selecting a single 'best' model, it is desirable to combine them to give a coherent answer to the question at hand.Many methods of combining ecosystem models assume that one of the models is exactly correct, which is unlikely to be the case. Furthermore, models may not be fitted to the same data, have the same outputs, nor be run for the same time period, making many common methods difficult to implement. In this paper, we use a statistical model to describe the relationship between the ecosystem models, prior beliefs and observations to make coherent predictions of the true state of the ecosystem with robust quantification of uncertainty.We introduce EcoEnsemble, an R package that takes advantage of the statistical model's structure to efficiently fit the ensemble model, either sampling from the posterior distribution or maximising the posterior density.We demonstrate EcoEnsemble by investigating what would happen to four fish species in the North Sea under future management scenarios. Although developed for applications in ecology, EcoEnsemble can be used to combine any group of mechanistic models, for example in climate modelling, epidemiology or biology. [ABSTRACT FROM AUTHOR]

Global change effects on species are most pronounced when there is a large mismatch between past climate conditions, and the present climate, and this chasm will grow as global change proceeds without mitigation. Global change encompasses the alteration of temperature and precipitation patterns worldwide and these drivers can both increase the risk of species extirpation, and extinction. Juniperus phoenicea is an endemic plant species in the Mediterranean region of high conservation concern. Ensemble distribution models and the potential impact of future climate scenarios revealed that temperature, isothermality, and precipitation are the only significant bioclimatic factors affecting the geographical distribution of J. phoenicea. To study the potential impact of global change, we constrained the SDMs with a combination of two shared socio-economic pathways (SSPs) climate scenarios in the near (2030) and far (2090) future, together with two dispersal scenarios (full and limited). After removing incompatible regions based on current land-use distribution, the comparison of the current and future areas of occupancy revealed strong declines in the distribution of J. phoenicea. Applying the IUCN criteria, the species is predicted in all scenarios to be up-listed from the currently ''least-concern'' status to the ''vulnerable'', and potentially to the ''critically endangered'' status under the highest emission scenario in 2090. The range shifts predicted by our analysis draws attention to regions with stable distribution, and others predicted to become favorable for the species establishment. This information is essential for future conservation planning, including afforestation and reforestation programs.

Climate change is altering marine ecosystems across the globe and is projected to do so for centuries to come. Marine conservation agencies can use short- and long-term projections of species-specific or ecosystem-level climate responses to inform marine conservation planning. Yet, integration of climate change adaptation, mitigation, and resilience into marine conservation planning is limited. We analysed future trajectories of climate change impacts on total consumer biomass and six key physical and biogeochemical drivers across the Northwest Atlantic Ocean to evaluate the consequences for Marine Protected Areas (MPAs) and Other Effective area-based Conservation Measures (OECMs) in Atlantic Canada. We identified climate change hotspots and refugia, where the environmental drivers are projected to change most or remain close to their current state, respectively, by mid- and end-century. We used standardized outputs from the Fisheries and Marine Ecosystem Model Intercomparison Project and the 6th Coupled Model Intercomparison Project. Our analysis revealed that, currently, no existing marine conservation areas in Atlantic Canada overlap with identified climate refugia. Most (75%) established MPAs and more than one-third (39%) of the established OECMs lie within cumulative climate hotspots. Our results provide important long-term context for adaptation and future-proofing spatial marine conservation planning in Canada and the Northwest Atlantic region.

Traders and investors are interested in accurately predicting cryptocurrency prices to increase returns and minimize risk. However, due to their uncertainty, volatility, and dynamism, forecasting crypto prices is a challenging time series analysis task. Researchers have proposed predictors based on statistical, machine learning (ML), and deep learning (DL) approaches, but the literature is limited. Indeed, it is narrow because it focuses on predicting only the prices of the few most famous cryptos. In addition, it is scattered because it compares different models on different cryptos inconsistently, and it lacks generality because solutions are overly complex and hard to reproduce in practice. The main goal of this paper is to provide a comparison framework that overcomes these limitations. We use this framework to run extensive experiments where we compare the performances of widely used statistical, ML, and DL approaches in the literature for predicting the price of five popular cryptocurrencies, i.e., XRP, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and Monero (XMR). To the best of our knowledge, we are also the first to propose using the temporal fusion transformer (TFT) on this task. Moreover, we extend our investigation to hybrid models and ensembles to assess whether combining single models boosts prediction accuracy. Our evaluation shows that DL approaches are the best predictors, particularly the LSTM, and this is consistently true across all the cryptos examined. LSTM reaches an average RMSE of 0.0222 and MAE of 0.0173, respectively, 2.7% and 1.7% better than the second-best model. To ensure reproducibility and stimulate future research contribution, we share the dataset and the code of the experiments.

In this study four experiments were conducted to investigate uncertainty in future longshore sediment transport (LST) projections due to: working with continuous time series of CSIRO CMIP6-driven waves (experiment #1) or sliced time series of waves from CSIRO-CMIP6-Ws and CSIRO-CMIP5-Ws (experiment #2); different wave-model-parametrization pairs to generate wave projections (experiment #3); and the inclusion/exclusion of sea level rise (SLR) for wave transformation (experiment #4). For each experiment, a weighted ensemble consisting of offshore wave forcing conditions, a surrogate model for nearshore wave transformation and eight LST models was used. The results of experiment # 1 indicated that the annual LST rates obtained from a continuous time series of waves were influenced by climate variability acting on timescales of 20-30 years. Uncertainty decomposition clearly reveals that for near-future coastal planning, a large part of the uncertainty arises from model selection and natural variability of the system (e.g., on average, 4% scenario, 57% model, and 39% internal variability). For the far future, the total uncertainty consists of 25% scenario, 54% model and 21% internal variability. Experiment #2 indicates that CMIP6 driven wave climatology yield similar outcomes to CMIP5 driven wave climatology in that LST rates decrease along the study area’s coast by less than 10%. The results of experiment #3 indicate that intra- and inter-annual variability of LST rates are influenced by the parameterization schemes of the wave simulations. This can increase the range of uncertainty in the LST projections and at the same time can limit the robustness of the projections. The inclusion of SLR (experiment #4) in wave transformation, under SSP1-2.6 and SSP5-8.5 scenarios, yields only meagre changes in the LST projections, compared to the case no SLR. However, it is noted that future research on SLR influence should include potential changes in nearshore profile shapes.

Predicting contemporary and future species distributions is relevant for science and decision making, yet the development of high‐resolution spatial predictions for numerous taxonomic groups and regions is limited by the scalability of available modelling tools. Uniting species distribution modelling (SDM) techniques into one high‐performance computing (HPC) pipeline, we developed N‐SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments. N‐SDM was built around a spatially‐nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales. N‐SDM allows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state‐of‐the‐art SDM features embodied in N‐SDM includes a newly devised covariate selection procedure, five modelling algorithms, an algorithm‐specific hyperparameter grid search, and the ensemble of small‐models approach. N‐SDM is designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time. All the information required for installing and running N‐SDM is openly available on the GitHub repository https://github.com/N‐SDM/N‐SDM. [ABSTRACT FROM AUTHOR]

Climate change is one of the primary causes of species redistribution and biodiversity loss, especially for threatened and endemic important plant species. Therefore, it is vital to comprehend "how" and "where" priority medicinal and aromatic plants (MAPs) might be effectively used to address conservation-related issues under rapid climate change. In the present study, an ensemble modelling approach was used to investigate the present and future distribution patterns of Aquilegia fragrans Benth. under climate change in the entire spectrum of Himalayan biodiversity hotspot. The results of the current study revealed that, under current climatic conditions, the northwest states of India (Jammu and Kashmir, Himachal Pradesh and the northern part of Uttarakhand), the eastern and southern parts of Pakistan Himalaya have highly suitable climatic conditions for the growth of A. fragrans. The ensemble model exhibited high forecast accuracy, with temperature seasonality and precipitation seasonality as the main climatic variables responsible for the distribution of the A. fragrans in the biodiversity hotspot. Furthermore, the study predicted that future climate change scenarios will diminish habitat suitability for the species by −46.9% under RCP4.5 2050 and −55.0% under RCP4.5 2070. Likewise, under RCP8.5, the habitat suitability will decrease by −51.7% in 2050 and −94.3% in 2070. The current study also revealed that the western Himalayan area will show the most habitat loss. Some currently unsuitable regions, such as the northern Himalayan regions of Pakistan, will become more suitable under climate change scenarios. Hopefully, the current approach may provide a robust technique and showcases a model with learnings for predicting cultivation hotspots and developing scientifically sound conservation plans for this endangered medicinal plant in the Himalayan biodiversity hotspot. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) has been linked to a number of adverse health effects, hence its prediction for epidemiological studies has become very crucial. In this study, a novel ensemble technique was proposed for the prediction of PM2.5 concentration in cities with high traffic noise using traffic noise as an input parameter. Air pollutants concentration (P), meteorological parameters (M) and traffic data (T) simultaneously collected from seven sampling points in North Cyprus were used for conducting the study. The modelling was done in 2 scenarios. In scenario I, PM2.5 was modelled using 4 different input combination without traffic noise as input parameter while in scenario II, traffic noise was added as an input variable for 4 input combinations. The models were evaluated using 4 performance criteria including Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), Correlation Coefficient (CC) and Bias (BIAS). Modelling PM2.5 with combined relevant input parameters of P, M and T could improve the performance of the model developed with only one set of the parameters by up to 12, 17 and 29% for models containing only P, M and T respectively. All the models in scenario II have demonstrated high prediction accuracy than the corresponding model in scenario I by up to 12% in the verification stage. The Support Vector Regression-based Ensemble model (SVR-E) could improve the performance accuracy of single models by up to 17% in the verification stage.

A robust short-term significant wave height (Hs) modelling framework based on an ensemble local mean decomposition method integrated with random forest (i.e. , En-RLMD-RF) is developed. The robust local mean decomposition (RLMD) decomposed the Hs data series into three subseries; amplitude modulation, frequency modulation and the low-frequency product function (PFs). The partial autocorrelation function was employed to determine the correlation-based significant predictor signals between the PFs at t 0 and t 1. Then the statistically significant PFs were incorporated into the random forest (RF) to construct the RLMD-RF model. The RLMD-RF based forecasted PFs were used again in the RF model as input predictors resulting in an ensemble-based RLMD-RF (i.e. , En-RLMD-RF) model for forecasting short-term Hs. The En-RLMD-RF model is validated and compared with RF, extreme learning machine (ELM) and multiple linear regression (MLR) models and their hybrids RLMD-RF, RLMD-ELM, RLMD-MLR, En-RLMD-ELM and En-RLMD-MLR counterparts using a set of performance metrics. The results demonstrated that the En-RLMD-RF model generates better forecasting accuracy against the benchmarking models. This study is beneficial for the application and optimization of more clean energy resources worldwide for sustained energy generation. [ABSTRACT FROM AUTHOR]

Simple Summary: Rapidly changing environmental conditions can alter the spatial distribution of flora and fauna. This study aimed to understand the influence of environmental variables on the Blue bull's distribution and identify potential conflict zones in Nepal. Using ensemble modeling, the habitat suitability analysis of the Blue bull was performed by selecting 15 ecologically significant environmental variables and employing ten species distribution modeling algorithms. Random Forest, Maxent, and Generalized linear models showed the highest mean true skill statistics scores and were further analyzed. The study found that 15.26% of Nepal, or 22,462.57 km2, is suitable for the Blue bull, and the environmental variables contributing to the distribution of the Blue bull were slope, precipitation seasonality, and distance to the road. Furthermore, 45% of the predicted suitable habitats overlap with agricultural land, highlighting the potential for human–Blue bull conflicts. Therefore, this study recommends implementing appropriate conflict mitigation measures, such as cooperatively guarding crops, changing cropping patterns, using repellents, fencing, translocation, physical barriers, and sterilization. This study establishes a baseline for suitable habitats for the Blue bull and identifies potential conflict zones in Nepal, emphasizing the need for conservation initiatives inside and outside protected areas. 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. [ABSTRACT FROM AUTHOR]

Aim: To assess whether flexible species distribution models that perform well at nearby testing locations still perform strongly when evaluated on spatially separated testing data. Location: Australian Wet Tropics (AWT), Ontario, Canada (CAN), north‐east New South Wales, Australia (NSW), New Zealand (NZ), five countries of South America (SA), and Switzerland (SWI). Time period: Most species data were collected between 1950 and 2000. Major taxa studied: Birds, mammals, plants and reptiles. Methods: We compared 10 species distribution modelling methods with varying flexibility in terms of the allowed complexity of their fitted functions [boosted regression trees (BRT), generalized additive model (GAM), multivariate adaptive regression splines (MARS), maximum entropy (MaxEnt), support vector machine (SVM), variants of generalized linear model (GLM) and random forest (RF), and an Ensemble model]. We used established practices for model selection to avoid overfitting, including parameter tuning in learning methods. Models were trained on presence–background data for 171 species and tested on presence–absence data. Training and testing data were separated using both random and spatial partitioning, the latter based on 75‐km blocks. We calculated the average performance and mean rank of the methods (focussing on the area under the receiver operating characteristic and precision‐recall gain curves, and correlation) and assessed the statistical significance of the differences between them. Results: The ranking of methods did not change when evaluated on spatially separated testing data. Methods with the strongest predictive performance were nonparametric methods known to be flexible. An ensemble formed by averaging predictions of five pre‐selected modelling methods was the best model in both random and spatial partitioning, followed by MaxEnt and a variant of random forest. Main conclusions Whilst some modellers expect methods limited to simple smooth functions to predict better spatially separated data, we found no evidence of that using blocks of 75 km. We conclude that flexible models that are tuned well enough to avoid overfitting are effective at predicting to spatially distinct areas. [ABSTRACT FROM AUTHOR]

Traders and investors are interested in accurately predicting cryptocurrency prices to increase returns and minimize risk. However, due to their uncertainty, volatility, and dynamism, forecasting crypto prices is a challenging time series analysis task. Researchers have proposed predictors based on statistical, machine learning (ML), and deep learning (DL) approaches, but the literature is limited. Indeed, it is narrow because it focuses on predicting only the prices of the few most famous cryptos. In addition, it is scattered because it compares different models on different cryptos inconsistently, and it lacks generality because solutions are overly complex and hard to reproduce in practice. The main goal of this paper is to provide a comparison framework that overcomes these limitations. We use this framework to run extensive experiments where we compare the performances of widely used statistical, ML, and DL approaches in the literature for predicting the price of five popular cryptocurrencies, i.e., XRP, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and Monero (XMR). To the best of our knowledge, we are also the first to propose using the temporal fusion transformer (TFT) on this task. Moreover, we extend our investigation to hybrid models and ensembles to assess whether combining single models boosts prediction accuracy. Our evaluation shows that DL approaches are the best predictors, particularly the LSTM, and this is consistently true across all the cryptos examined. LSTM reaches an average RMSE of 0.0222 and MAE of 0.0173 , respectively, 2.7 % and 1.7 % better than the second-best model. To ensure reproducibility and stimulate future research contribution, we share the dataset and the code of the experiments. [ABSTRACT FROM AUTHOR]

Maps of forest resources and other ecosystem services are needed for decision making at different levels. However, such maps are typically presented without addressing the uncertainties. Thus, the users of the maps have vague or no understanding of the uncertainties and can easily make wrong conclusions. Attempts to visualize the uncertainties are also rare, even though the visualization would be highly likely to improve understanding. One complication is that it has been difficult to address the predictions and their uncertainties simultaneously. In this article, the methods for addressing the map uncertainty and visualize them are first reviewed. Then, the methods are tested using laser scanning data with simulated response variable values to illustrate their possibilities. Analytical kriging approach captured the uncertainty of predictions at pixel level in our test case, where the estimated models had similar log-linear shape than the true model. Ensemble modelling with random forest led to slight underestimation of the uncertainties. Simulation is needed when uncertainty estimates are required for landscape level features more complicated than small areas.

This study focused on assessing the impact of climate change on Cupressus funebris, one of the important endemic conifer species in China. The impact of fluctuations in temperature on the potential distribution and extinction risk of the C. funebris was evaluated using species distribution modelling. The outcomes of this current study revealed that the species was mainly distributed in mountainous forest areas, and climate variables played a major role in the distribution of this conifer tree. Under climate change, the threatened conifer will be mainly concentrated in mountainous forests and is projected to undergo contraction in distribution and shift northward. The conservation status of C. funebris is projected to be uplisted to “Near Threatened” status under the SSP5_8.5 scenario by 2040 and to the “Least Concern” status under all the other investigated climate and dispersal scenarios. Despite the high capacity of the species for adaptation to environmental changes, part of its AOO might be lost under severe climate change conditions. Key conservation areas were identified, and suggestions for redesigning some of the current natural reserves in the forested area where the species is found were proposed for the conservation of this key coniferous species. The stable area in the northwestern part of Yunnan in the Hengduan Mountain Forest can serve as a promising area for C. funebris reintroduction through afforestation programs.

Body condition scoring is an objective scoring method used to evaluate the health of a cow by determining the amount of subcutaneous fat in a cow. Automated body condition scoring is becoming vital to large commercial dairy farms as it helps farmers score their cows more often and more consistently compared to manual scoring. A common approach to automated body condition scoring is to utilise a CNN-based model trained with data from a depth camera. The approaches presented in this paper make use of three depth cameras placed at different positions near the rear of a cow to train three independent CNNs. Ensemble modelling is used to combine the estimations of the three individual CNN models. The paper aims to test the performance impact of using ensemble modelling with the data from three separate depth cameras. The paper also looks at which of these three cameras and combinations thereof provide a good balance between computational cost and performance. The results of this study show that utilising the data from three depth cameras to train three separate models merged through ensemble modelling yields significantly improved automated body condition scoring accuracy compared to a single-depth camera and CNN model approach. This paper also explored the real-world performance of these models on embedded platforms by comparing the computational cost to the performance of the various models.

Abstract 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. Graphical Abstract

Her Majesty's Revenue and Customs (HMRC) has the ambitious target of making tax digital for all its customers and collecting tax in a more efficient, effective and accurate manner for both the government and UK taxpayers. Self-assessment tax returns, the biggest key business event for HMRC, is also one of the most popular digital services with over 90% of the approximately 12 million taxpayers in self assessment filing their return online each year. The majority of returns are filed in January immediately prior to the self-assessment deadline (31st January), putting significant pressure not only on the self-assessment digital service but also on all other HMRC digital services. Hence, understanding and predicting demand for the system is vital to provide a robust and responsive service. We therefore developed mathematical models with Bayesian inference techniques to forecast volumes of Self-assessment (SA) returns submitted online during January, providing accurate hourly predictions of traffic on the digital system in the run up to the SA deadline. Because none of the models being considered is believed to be the true model, we use an ensemble modelling technique that combines forecasts from different models to develop a less risky demand forecast. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) has been linked to a number of adverse health effects, hence its prediction for epidemiological studies has become very crucial. In this study, a novel ensemble technique was proposed for the prediction of PM2.5 concentration in cities with high traffic noise using traffic noise as an input parameter. Air pollutants concentration (P), meteorological parameters (M) and traffic data (T) simultaneously collected from seven sampling points in North Cyprus were used for conducting the study. The modelling was done in 2 scenarios. In scenario I, PM2.5 was modelled using 4 different input combination without traffic noise as input parameter while in scenario II, traffic noise was added as an input variable for 4 input combinations. The models were evaluated using 4 performance criteria including Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), Correlation Coefficient (CC) and Bias (BIAS). Modelling PM2.5 with combined relevant input parameters of P, M and T could improve the performance of the model developed with only one set of the parameters by up to 12, 17 and 29% for models containing only P, M and T respectively. All the models in scenario II have demonstrated high prediction accuracy than the corresponding model in scenario I by up to 12% in the verification stage. The Support Vector Regression-based Ensemble model (SVR-E) could improve the performance accuracy of single models by up to 17% in the verification stage. [ABSTRACT FROM AUTHOR]

Predictions of the unemployment duration of the economically active population play a crucial assisting role for policymakers and employment agencies in the well-organised allocation of resources (tied to solving problems of the unemployed, whether on the labour supply or demand side) and providing targeted support to jobseekers in their job search. This study aimed to develop an ensemble model that can serve as a reliable tool for predicting unemployment duration among jobseekers in Slovakia. The ensemble model was developed using real data from the database of jobseekers (those registered as unemployed and actively searching for a job through the Local Labour Office, Social Affairs, and Family) using the stacking method, incorporating predictions from three individual models: CART, CHAID, and discriminant analysis. The final meta-model was created using logistic regression and indicates an overall accuracy of the prediction of unemployment duration of almost 78%. This model demonstrated high accuracy and precision in identifying jobseekers at risk of long-term unemployment exceeding 12 months. The presented model, working with real data of a robust nature, represents an operational tool that can be used to check the functionality of the current labour market policy and to solve the problem of long-term unemployed individuals in Slovakia, as well as in the creation of future government measures aimed at solving the problem of unemployment. The measures from the state are financed from budget funds, and by applying the appropriate model, it is possible to arrive at the rationalization of the financing of these measures, or to specifically determine the means intended to solve the problem of long-term unemployment in Slovakia (this, together with the regional disproportion of unemployment, is considered one of the most prominent problems in the labour market in Slovakia). The model also has the potential to be adapted in other economies, taking into account country-specific conditions and variables, which is possible due to the data-mining approach used.

This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration (ROP) of tunnel boring machine (TBM), which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnelling and underground projects in a rock environment. For this purpose, a sum of 185 datasets was collected from the literature and used to predict the ROP of TBM. Initially, the main dataset was utilised to construct and validate four conventional soft computing (CSC) models, i.e. minimax probability machine regression, relevance vector machine, extreme learning machine, and functional network. Consequently, the estimated outputs of CSC models were united and trained using an artificial neural network (ANN) to construct a hybrid ensemble model (HENSM). The outcomes of the proposed HENSM are superior to other CSC models employed in this study. Based on the experimental results (training RMSE = 0.0283 and testing RMSE = 0.0418), the newly proposed HENSM is potential to assist engineers in predicting ROP of TBM in the design phase of tunnelling and underground projects.

Coronavirus disease 2019 (COVID-19) forecasts from over 100 models are readily available. However, little published information exists regarding the performance of their uncertainty estimates (i.e. probabilistic performance). To evaluate their probabilistic performance, we employ the classical model (CM), an established method typically used to validate expert opinion. In this analysis, we assess both the predictive and probabilistic performance of COVID-19 forecasting models during 2021. We also compare the performance of aggregated forecasts (i.e. ensembles) based on equal and CM performance-based weights to an established ensemble from the Centers for Disease Control and Prevention (CDC). Our analysis of forecasts of COVID-19 mortality from 22 individual models and three ensembles across 49 states indicates that—(i) good predictive performance does not imply good probabilistic performance, and vice versa; (ii) models often provide tight but inaccurate uncertainty estimates; (iii) most models perform worse than a naive baseline model; (iv) both the CDC and CM performance-weighted ensembles perform well; but (v) while the CDC ensemble was more informative, the CM ensemble was more statistically accurate across states. This study presents a worthwhile method for appropriately assessing the performance of probabilistic forecasts and can potentially improve both public health decision-making and COVID-19 modelling.

Wildlife rescue and rehabilitation centers offer important insights regarding the threats and attributes associated with raptor casualties. We utilized a data set on raptors admitted to a single wildlife rescue center (WRC) in the Mid-Hills region of Nepal by applying two approaches. First, we explored the overview of species admitted and the cause of admission at WRC, and modeled the outcome (survival, mortality) of frequently admitted raptor species (FARS) by analyzing the cause of admittance, the season of admittance, and age category through binary logistic regression. Second, we identified the high-risk zone for raptors through an ensemble approach using multiple species distribution modeling algorithms in BIOMOD2. A total of 17 species of raptors with 830 cases of admittance; Black Kite (Milvus migrans) 47%, Barn Owl (Tyto alba) 24%, Eurasian Eagle-Owl (Bubo bubo) 12%, and other species (17%) were recorded at the WRC over the course of five years (2017–2021). Unknown causes (39%) comprised the largest proportion of admittance, followed by illegal captivity (17%), injured by dogs (10%), and injured by humans (10%). Among the three FARS, the mortality rate was highest in the Barn Owl (62%), followed by the Eurasian Eagle-Owl (55%) and the Black Kite (47%). The model with an additive effect of species, cause, and age category was the best fit model to predict the outcome for FARS. Out of the total study area (705 km2), 19% was under high-risk, 41% was under medium-risk, and 40% was under low-risk. Most of the high-risk zones were incorporated within agricultural areas (57%) and built-up areas (31%). The variation in mortality rate among the FARS indicates that species-specific conservation strategies need to be developed to reduce casualty risk. Identified potential anthropogenic threats and their impact on the survivability and mortality of raptors in this study could help to mitigate casualties. Moreover, the high-risk areas associated with raptor casualties in this study need to be prioritized to implement the conservation endeavors for the raptors.

Ticks have medical and economic importance due to their ability to transmit pathogens to humans and animals. In tropical and sub‐tropical countries, tick‐borne diseases (TBD) are among the most important diseases affecting livestock and humans. The fast spread of ticks and TBD requires a quick development and application of efficient prevention and/or control programs. Therefore, prior investigations on TBD and related vectors epidemiology, for instance, through accurate epidemiological models, are mandatory. This study aims to develop models to forecast suitable habitat for Rhipicephalus microplus distribution in West Africa. Tick occurrences were assembled from 10 different studies carried out in six West African countries in the past decade. Six statistical models (maximum entropy in a single model and generalised linear model, generalised additive model, random forest, boosted regression tree and support vector machine model in an ensemble model) were applied and compared to predict the habitat suitability of R. microplus distribution in West Africa. Each model was evaluated with the area under the receiver operating characteristic curve (AUC), the true skill statistic (TSS) and the Boyce index (BI). The selected models had good performance according to their AUC (above.8), TSS (above.7) and BI (above.8). Temperature played a key role in MaxEnt model, whereas normalised difference vegetation index (NDVI) was the most important variable in the ensemble model. The model predictions showed coastal countries of West Africa as more suitable for R. microplus. However, some Sahelian areas seems also favourable. We stress the importance of vector surveillance and control in countries that have not yet detected R. microplus but are in the areas predicted to host suitable habitat. Indeed, awareness‐raising and training of different stakeholders must be reinforced for better prevention and control of this tick in these different countries according to their status. [ABSTRACT FROM AUTHOR]

Reliable predictions of future distribution ranges of ecologically important species in response to climate change are required for developing effective management strategies. Here we used an ensemble modelling approach to predict the distribution of three important species of Abies namely, Abies pindrow, Abies spectabilis and Abies densa in the Hindu Kush Himalayan region under the current and two shared socioeconomic pathways (SSP245 and SSP585) and time periods of 2050 and 2090s. A correlative ensemble model using presence/absence data of the three Abies species and 22 environmental variables, including 19 bioclimatic variables and 3 topographic variables, from known distributions was built to predict the potential current and future distribution of these species. The individual models used to build the final ensemble performed well and provided reliable results for both the current and future distribution of all three species. For A. pindrow, precipitation of the driest month (Bio14) was the most important environmental variable with 83.3% contribution to model output while temperature seasonality (Bio4) and annual mean diurnal range (Bio2) were the most important variables for A. spectabilis and A. densa with 48.4% and 46.1% contribution to final model output, respectively. Under current climatic conditions, the ensemble models projected a total suitable habitat of about 433,003 km2, 790,837 km2 and 676,918 km2 for A. pindrow, A. spectabilis and A. densa, respectively, which is approximately 10.36%, 18.91% and 16.91% of the total area of Hindu Kush Himalayan region. Projections of habitat suitability under future climate scenarios for all the shared socioeconomic pathways showed a reduction in potentially suitable habitats with a maximum overall loss of approximately 14% of the total suitable area of A. pindrow under SSP 8.5 by 2090. A decline in total suitable habitat is predicted to be 9.6% in A. spectabilis by 2090 under the SSP585 scenario while in A. densa 6.67% loss in the suitable area is expected by 2050 under the SSP585 scenario. Furthermore, there is no elevational change predicted in the case of A. pindrow while A. spectabilis is expected to show an upward shift by about 29 m per decade and A. densa is showing a downward shift at a rate of 11 m per decade. The results are interesting, and intriguing given the occurrence of these species across the Hindu Kush Himalayan region. Thus, our study underscores the need for consideration of unexpected responses of species to climate change and formulation of strategies for better forest management and conservation of important conifer species, such as A. pindrow, A. spectabilis and A. densa. [ABSTRACT FROM AUTHOR]

Ensembles of geomodels provide an opportunity to investigate a range of parameters and possible operational outcomes for a reservoir. Full-featured dynamic modelling of all ensemble members is often computationally unfeasible, however some form of modelling, allowing us to discriminate between ensemble members based on their flow characteristics, is required. Flow diagnostics (based on a single-phase, steady-state simulation) can provide tools for analysing flow patterns in reservoir models but can be calculated in a much shorter time than a full-physics simulation. Heterogeneity measures derived from flow diagnostics can be used as proxies for oil recovery. More advanced flow diagnostic techniques can also be used to estimate recovery. With these tools we can rank ensemble members and choose a subset of models, representing a range of possible outcomes, which can then be simulated further. We demonstrate two types of flow diagnostics. The first are based on volume-averaged travel times, calculated on a cell by cell basis from a given flow field. The second use residence time distributions, which take longer to calculate but are more accurate and allow for direct estimation of recovery volumes. Additionally we have developed new metrics which work better for situations where we have a non-uniform initial saturation, e.g., a reservoir with an oil cap. Three different ensembles are analysed: Egg, Norne, and Brugge. Very good correlation, in terms of model ranking and recovery estimates, is found between flow diagnostics and full simulations for all three ensembles using both the cell-averaged and residence time based diagnostics. [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. [ABSTRACT FROM AUTHOR]

Background and aim: Billfish are epipelagic marine predators facing increasing pressures such as overfishing and rising global temperatures. Overfishing is a major concern, as they are caught by industrial longline fishers targeting tuna. Billfish are targeted by multiple fishing sectors, which provides food, socio‐economic and cultural benefits. To support effective billfish management and conservation, it is essential to understand their spatial distribution and the environmental factors that may influence it. Location: The focus of this study is the Indian Ocean (IO), where there are gaps in understanding the interactions between fisheries and billfish distribution. Three of six billfish species are at risk from overfishing. Therefore, determining their distribution is crucial to their management and conservation. Methods: Using Ocean Biogeographic Information System (OBIS) occurrence data, Indian Ocean Tuna Commission (IOTC) catch data, and environmental covariates, we applied species distribution models to investigate the spatial extent of the realized niches of six billfish species in the IO. We also determined the role and relative importance of environmental drivers. Moreover, we evaluated the association between species' spatial distribution and the fishing effort distribution. Results: We found niche partitioning and overlap among the six species identified spatial distribution, with higher species richness in the northern region of the IO and off the East coast of Africa. Temperature, mixed layer depth and salinity were identified as the most important predictors of species distribution, with moderately warm and stable environments preferred by most billfish species. Areas with high species richness and high fishing effort overlap were primarily found in the Areas Beyond National Jurisdiction (ABNJ). In contrast, areas with high species diversity richness and low fishing effort were found mainly in the Exclusive Economic Zone (EEZ). Main conclusion: Spatial overlap between fishing effort and billfish projected distribution suggests inadvertent fishing pressure on billfish populations as they are caught together with targeted tuna. Spatial distribution transcends maritime zones, reinforcing a need to formulate effective management policies for marine areas beyond national jurisdictions. [ABSTRACT FROM AUTHOR]

In the changing climatic conditions, species distribution modelling is considered as a key strategy to estimate the probable influence of climatic variabilities on the habitat ranges of any species. The present study explores the potential distribution of Aconitum heterophyllum under current and future climatic scenarios. The results unfold that the distribution of this endemic species is governed significantly by bio12, i.e., Annual Precipitation. Ensemble modelling predicted that higher altitudes of Jammu, Kashmir and Ladakh are suitable habitats for A. heterophyllum. However, the future climatic modelling revealed that there will be a significant decrease in the suitable habitats for A. heterophyllum. Most of the shrinkage of habitats is predicted to occur within the time period of 2050, which seriously challenges their survival. The present study recommends an urgent need to frame a pertinent conservation and management policy for Aconitum heterophyllum and will act as a framework for planning such a policy. [ABSTRACT FROM AUTHOR]