Abstract Species distribution models (SDMs) are important tools to explore the effects of future global changes in biodiversity. Previous studies show that variability is introduced into projected ...distributions through alternative datasets and modelling procedures. However, a multi-model approach to assess biogeographic shifts at the global scale is still rarely applied, particularly in the marine environment. Here, we apply three commonly used SDMs (AquaMaps, Maxent, and the Dynamic Bioclimate Envelope Model) to assess the global patterns of change in species richness, invasion, and extinction intensity in the world oceans. We make species-specific projections of distribution shift using each SDM, subsequently aggregating them to calculate indices of change across a set of 802 species of exploited marine fish and invertebrates. Results indicate an average poleward latitudinal shift across species and SDMs at a rate of 15.5 and 25.6 km decade−1 for a low and high emissions climate change scenario, respectively. Predicted distribution shifts resulted in hotspots of local invasion intensity in high latitude regions, while local extinctions were concentrated near the equator. Specifically, between 10°N and 10°S, we predicted that, on average, 6.5 species would become locally extinct per 0.5° latitude under the climate change emissions scenario Representative Concentration Pathway 8.5. Average invasions were predicted to be 2.0 species per 0.5° latitude in the Arctic Ocean and 1.5 species per 0.5° latitude in the Southern Ocean. These averaged global hotspots of invasion and local extinction intensity are robust to the different SDM used and coincide with high levels of agreement.
ABSTRACT
Changes in frequency, duration and/or intensity of extreme precipitation events, such as heavy precipitation or drought, profoundly impact both society and the natural environment. Regional ...climate models are valuable tools to assess any future progress of such events, and to complement the development of regional and local adaptation and mitigation strategies – here for the model region Dresden within the REGKLAM project. Multi‐model approaches may alleviate some of the problems related to uncertainties of projected changes.
The bandwidth of future climate conditions in Central Eastern Germany has been estimated, using 12 regional climate projections in daily resolution as simulated within the EU‐project ENSEMBLES (emission scenario A1B). Validation of the model runs against an observation data set displayed significant difficulties of the models to describe the regional precipitation characteristics. Shortcomings are particularly obvious in realistically simulating dry period characteristics, likely due to an overestimation of precipitation totals. Most models agree in the projection of more frequent and longer‐lasting drought events during summer, while wet phase frequency and persistence is projected to increase in winter. Extreme precipitation events (99th percentile) are likely to increase by the end of the 21st century in most seasons – even in summer, despite projected decreasing average precipitation.
The suggested display of all individual model results allows comparing the characteristics and the trend behaviour of the individual regional climate projections. This supports selecting the suitable model(s) for specific impact modelling demands.
•Sowing date can minimize the risks associated to soybean-maize succession in Brazil.•The optimal sowing date varied according to the Brazilian regions.•To maximize the net revenue in Central Brazil, ...the soybean sowing should be done in late September.•Drought, frosts and low solar radiation are the main causes of yield losses for soybean-maize succession in Brazil.
The soybean-maize succession is an important production system used in Brazil. The greatest challenge related to this kind of system is to define the best sowing dates for the producing regions with different climatic characteristics, improving farmer´s economic profitability. Thus, the aim of this study was to determine the best sowing dates for the above-mentioned crop system considering simulations with three crop simulation models (FAO-AZM, DSSAT and APSIM) in a multi-model approach, and to determine the economic profitability of this system at national scale. Previously calibrated and validated models were used to simulate soybean yields for 29 locations in 12 states, with sowing dates ranging from end of September to beginning of January for a period of 34 years (1980–2013). The maize off-season sowing was done just after the soybean harvest, ranging from end of January to beginning of May. The yield data was converted to gross revenue according to the prices commonly practiced in Brazil and then to net revenue by subtracting the production costs for each assessed region. The optimal sowing date varied according to the Brazilian region. For Central Brazil, the highest net revenue was obtained when soybean was sown between the end of September and beginning of October. This period is also recommended in Southern Brazil, because sowing delay can reduce maize yield due to risks of frosts and low solar radiation availability. In the Northern Brazil, mainly in Pará state, the soybean sowing should start in November, when net revenue is maximized.
•FAO‐AZM, DSSAT/CANEGRO, APSIM‐Sugarcane models were evaluated to estimate sugarcane yield in commercially managed fields.•The models were calibrated and validated for a wide range of environments ...under rainfed and irrigated conditions.•An empirical management factor was adopted to describe decline of sugarcane yield along successive ratoons.•A multi-model approach was used and models ensemble proved to be valuable for sugarcane yield estimation.•The integration of yield decline factor and multi-model approach was viable to estimate cane yield in commercial fields.
The sugarcane production system is very complex, involving a large number of variables, namely genotype, environmental conditions and crop management, which define yield level. Thus, estimation of sugarcane yield is also complex, nevertheless, highly important for planning and decision-making in the sugarcane industry. Crop simulation models calibrated to local conditions can be useful to estimate yield, since they can capture the effect of the crop management. On the other hand, recent studies have shown that the use of at least three simulation models in an ensemble can reduce simulation uncertainties, resulting in more reliable estimates than using a single model. Thus, the aims of this study were: i) to evaluate the performance of three sugarcane simulation models (FAO‐AZM, DSSAT/CANEGRO and APSIM‐Sugarcane), separately and in a multi-model approach, for commercially managed fields in Brazil; and ii) to propose a management factor (kdec) associated with the yield decline along successive crop cycles to improve performance of these models. Sugarcane yield and meteorological data were obtained for seven Brazilian states. The FAO‐AZM model was calibrated by changing the crop water deficit sensitivity coefficient values. For the DSSAT/CANEGRO and APSIM‐Sugarcane models, small adjustments were made to coefficients previously calibrated for Brazilian cultivars to improve their performances. The three models presented a weak performance, with high mean absolute error (MAE>29t ha‐1) and low precision (R2<0.54), which were caused by the lack of coefficients accounting for crop management. The introduction of kdec, which reflects the crop management level, improved yield estimates for all models. When kdec was applied, the mean absolute error decreased to≤12.9t ha‐1 for the calibration phase, and between 13.0 and 14.9t ha‐1 for the validation with independent data. Precision was improved, with R2 ranging between 0.70 and 0.72 for calibration phase and between 0.58 and 0.64 for validation. The multi‐model approach also allowed an improvement in modelling performance, in both phases, reducing errors (MAE between 11.7 and 12.9t ha‐1) and increasing precision and accuracy. The use of kdec associated with the multi-model approach improved the performance of sugarcane yield estimates, representing more effectively the distinct commercial field conditions of sugarcane cultivated under different cropping systems and Brazilian regions.
By using a multi-model co-localization approach of DFM, LSPR scattering spectra and SEM, the novel LSPR effect of plasmon line shape with two distinct peaks is observed on single no. 3 (110.32 ± ...14.63 nm) silver nanocube, and no. 2 (75.70 ± 9.05 nm) silver nanocubes are found to be more suitable as the light scattering probes due to the strong regularity and higher sensitivity.
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Insightful understanding of size-dependent optical signatures and precise regularity of nanosensors is critical for developing applications of plasmonic sensing. This work presents a systematic study on localized surface plasmon resonance (LSPR)-based nanosensors of plasmonic silver nanocubes (AgNCs) with the edge lengths of 59.84 ± 7.97 nm (no. 1 AgNCs), 75.70 ± 9.05 nm (no. 2 AgNCs) and 110.32 ± 14.63 nm (no. 3 AgNCs), respectively. The effects of different sizes on the scattering signatures and refractive index (RI) sensitivities of AgNCs were in situ determined using the multi-model co-localization approach of single AgNC by dark-field microscope (DFM), LSPR spectroscopy and scanning electron microscopy (SEM). The scattering light colour of single AgNC took place bathochromic shift from monocolour to multicolour with the growth of edge length of single AgNC. The LSPR scattering spectra of no. 1 and 2 AgNCs exhibited singlet and singlet with the shoulder peak from quadrupolar resonance mode, respectively. Compared with the scattering signatures of no. 1 and 2 AgNCs, the interesting LSPR effect of plasmon line shape with two distinct peaks was observed on single no. 3 AgNC. In situ studies on the scattering spectral response of single AgNC to the ambient solvents and probing the small-molecule adsorbates on the surface of single silver nanocube reveal that no. 2 AgNC is more suitable as nanosensor due to strong regularity and higher sensitivity. The mechanism involved in optical signatures was elaborated clearly by combining with the experiments and theoretical simulation.
The extensive use of plastics leads to the release and diffusion of microplastics. Household plastic products occupy a large part and are closely related to daily life. Due to the small size and ...complex composition of microplastics, it is challenging to identify and quantify microplastics. Therefore,a multi-model machine learning approach was developed for classification of household microplastics based on Raman spectroscopy. In this study, Raman spectroscopy and machine learning algorithm are combined to realize the accurate identification of seven standard microplastic samples, real microplastics samples and real microplastic samples post-exposure to environmental stresses. Four single-model machine learning methods were used in this study, including Support vector machine (SVM), K-nearest neighbor (KNN), Linear discriminant analysis (LDA), and Multi-layer perceptron (MLP) model. The principal components analysis (PCA) was utilized before SVM, KNN and LDA. The classification effect of four models on standard plastic samples is over 88%, and reliefF algorithm was used to distinguish HDPE and LDPE samples. A multi-model is proposed based on four single models including PCA-LDA, PCA-KNN and MLP. The recognition accuracy of multi-model for standard microplastic samples, real microplastic samples and microplastic samples post-exposure to environmental stresses is over 98%. Our study demonstrates that the multi-model coupled with Raman spectroscopy is a valuable tool for microplastic classification.
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•A multi-model approach was developed for classification of household microplastics.•Compared with four single models, the performance of the multi-model is the best.•The reliefF algorithm improved the identification accuracy of HDPE and LDPE samples.•The recognition accuracy of all samples is over 98%.•Raman spectrum change of samples post-exposure to environmental stresses.
El Niño Southern Oscillation (ENSO) is one of the most important atmospheric-oceanic phenomena, responsible for climate variability in several Brazilian regions, which affects agriculture, mainly ...soybean – maize off-season succession. Therefore, the ENSO impacts on soybean – maize off-season double crop system can affect global food security, since Brazil is a major player as producer of these two crops, with a total production that represents 27% and 6% of world's soybean and maize production, respectively. In order to understand the risks associated to this crop system, the aim of this study was to assess the influence of ENSO phenomenon on the spatial and temporal soybean and maize off-season yield variabilities, considering simulations with three different crop models (FAO-AZM, DSSAT and APSIM) in a multi-model approach, and to determine the best sowing windows for this production system for each ENSO phase (El Niño, La Niña and Neutral) in different Brazilian producing regions. Previously calibrated and validated models were used to simulate soybean yields for 29 locations in 12 states, with sowing dates ranging from late September to early January of each growing season for a period of 34 years (1980–2013). The maize off-season sowing was done just after the soybean harvest, ranging from late January to early May. ENSO phases affected soybean and maize yields across the country, which can be minimized by choosing the best sowing window for soybean. In northern Brazil, El Niño negatively impacts soybean and maize off-season yields, making the succession of these crops risk, with the best sowing window being very short. Similar result was found for southern and central Brazil during La Niña years. On the contrary, cropping soybean and maize off-season in succession during El Niño years in center-south of and during La Niña years up north have higher chances of success.
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•The ENSO impacts varied according to the crop (soybean and maize), sowing date, location and ENSO phase.•The ENSO impacts can be minimized by choosing the best sowing window.•El Niño events negatively impacted soybean and maize yields in northern Brazil.•The soybean and maize yields were positively impacted under El Niño years in Central-South Brazil.
A multi-scale modelling system was developed to provide hourly NOx concentration fields at a building-resolving scale in the urban area of Modena, a city in the middle of the Po Valley (Italy), one ...of the most polluted areas in Europe. The WRF-Chem model was applied over three nested domains and employed with the aim of reproducing local background concentrations, taking into account meteorological and chemical transformation at the regional scale with nested resolutions of 15 km, 3 km and 1 km. Conversely, the PMSS modelling system was applied to simulate 3D air pollutant dispersion, due to traffic emissions, with a very high-resolution (4 m) on a 6 km × 6 km domain covering the city of Modena.
The methodology employed to account for anthropogenic emissions relies on two different strategies. Traffic emissions were based on a bottom-up approach using emission factors suggested by the European Environmental Agency with traffic fluxes estimated by the PTV VISUM model in the urban area of Modena, combined with direct traffic flow measurements performed between October 28 and November 8, 2016 which was used for the hourly vehicle modulation. Other anthropogenic emissions were taken from the TNO-MACC III inventory at the scales resolved by the WRF-Chem model. Simulations were performed for the same period whereby the traffic measurement campaign was carried out.
2 m temperature and 10 m wind speed were captured quite well by the WRF-Chem model with statistical metrics in line with similar case studies related to the Northern Italy. The NOx concentrations reproduced in the Po Valley area by WRF-Chem were on average simulated reasonably well with a general negative bias in almost all the examined rural background monitoring stations. Additionally, the deployment of an emission inventory at the original resolution (7 km) highlighted that increasing resolution from 3 km to 1 km does not generally improve the model performance.
Nevertheless, simulated and observed NOx hourly concentrations in the urban area of Modena exhibit a large agreement in particular for urban traffic site where detailed traffic emission estimations proved to be very successful in reproducing the observed NOx trend. At urban background stations, despite a general underestimation of the observed concentrations, the combination of WRF-Chem with PMSS provided daily pattern in line with observations. The analysis of the modelled NOx daily cycle pointed out also that at both traffic and background urban stations the morning NOx peak concentration was on average underestimated. This could be explained with an overestimation of mixing phenomena between 07:30 a.m. and 10:00 a.m. by WRF-Chem which leads to a greater dispersion of NOx along the vertical and thus a morning underestimation.
The statistical analysis showed finally that PMSS combined with WRF-Chem at both the resolutions (3 km and 1 km) and at both traffic and background sites fulfilled standard acceptance criteria for urban dispersion model evaluation, confirming that the proposed multi-modelling system can be employed as a tool to support environmental policies, epidemiological studies and urban mobility planning.
A hybrid modeling system composed by the WRF-Chem model and the Parallel Micro SWIFT and SPRAY modelling suite was employed to estimate urban NOx concentrations at very high resolution (4m) in a real case study. Results show that the modeling system provides good accuracy in reproducing observations, particularly at the traffic reference site. Model performances are also compliant with validation criteria at both the urban traffic and the urban background reference stations. Display omitted
•Application of a multi-scale approach to estimate urban NOx level.•Tailored traffic emissions by the combination of direct measurements and simulation.•Model performances for NOx fulfill the acceptance criteria for urban environments.•Better description of NOx in urban traffic than in urban background conditions.
In the decade passed, considerable affords were made to develop effective trading systems based on different assumptions concerned with the market nature, methods for data processing and uncertainty ...modeling. Such systems are often so sophisticated that they can be applied only by their authors. Another limitation of them is concerned with the focus on the development of a universal single best model. Besides, any model works well only in limited time periods and fails when noticeable changes in the market behavior occur. Then a major revision or the development of a new model is inevitable. Unfortunately, usually this needs too much time. Therefore, in this paper, to avoid the above problems, the simple multi-model approach to the development of trading systems in the Forex market is proposed. It is based on some working hypotheses, which are justified in this paper. The first of them is based on the observation that the Forex is the aggregation of numerous streams (strategies) provided by the broad trades community. Therefore, we can expect that even a very simple model based on the particular trading idea or ideas may catch such a string to be profitable, at least during a small period. If we have developed a set of such simple models optimized for different currency pairs, in each trading period we can use the model providing maximal profit for a certain currency pair. The profitability of the proposed approach is illustrated by the trading results obtained on the symbols EURUSD,GBPUSD, AUDUSD and USDJPY for the timeframes H1 and H4 with the use of the Meta Trader 4 platform.
•Technical analysis indicators and trading rules.•Positive and Negative Overfitting effects.•Algorithmic trading systems and their open access software.•Multi-model based trading strategy and the leader correction method.•Forex market and trading platform.
•Bat ray females have a larger median size at maturity than males.•Logistic models are redundant and estimate the same median size at maturity.•Gompertz’s model is useful for estimating median size ...at maturity.•The multi-model inference must be used to estimate median size at maturity.•The northwest coast of Baja California Sur could be a nursery area for bat ray.
The median disc width at maturity DW^50) of males (n=91, 32.2–92.0 cm) and females (n=157, 31.0–130.0 cm) of the bat ray (Myliobatis californica) was estimated through a multi-model inference in northwestern Mexico. Gompertz’s model and four common logistics models (Lysack, Bakhayokho, White, and Brouwer and Griffiths) were compared and all fit the data well (Akaike’s differences ≤2). Logistics models estimated the same DW^50 for M. californica, and had similar Akaike’s weight suggesting that they are redundant models. Therefore, multi-model inference was performed individually with Gompertz’s model and each one of the logistic models to estimate an average model DW¯50, resulting in 64.6 cm DW for males and 99.0 cm DW for females in all analyzes. Multi-model inference is a useful tool to estimate the DW^50 of a species with greater reliability, but redundant models must not be combined in this analysis. Thus, in this case it is advisable to perform the multi-model inference with Gompertz’s model and any of logistic models. The information obtained could contribute to the fishing management of the species, which becomes more relevant considering the high percentage of immature individuals of M. californica (53% of males and 90% of females) observed in the landings from Bahía Tortugas zone, Mexico.
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