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1. Theory and previous studies have shown that commercial fishers with a diversified catch across multiple species may experience benefits such as increased revenue and reduced variability in revenue. However, fishers can only increase the species diversity of their catch if they own fishing permits that allow multiple species to be targeted, or if they own multiple single-species permits. Individuals holding a single permit can only increase catch diversity within the confines of their permit (e.g. by fishing longer or over a broader spatial area). 2. Using a large dataset of individual salmon fishers in Alaska, we build a Bayesian variance function regression model to understand how diversification impacts revenue and revenue variability, and how these effects have evolved since the 1970s. 3. Applying these models to six salmon fisheries that encompass a broad geographic range and a variety of harvesting methods and species, we find that the majority of these fisheries have experienced reduced catch diversity through time and increasing benefits of specialization on mean individual revenues. 4. One factor that has been hypothesized to reduce catch diversity in salmon fisheries is large-scale hatchery production. While our results suggest negative correlations between hatchery returns and catch diversity for some fisheries, we find little evidence for a change in variability of annual catches associated with increased hatchery production. 5. Synthesis and applications. Despite general trends towards more specialization among commercial fishers in Alaska, and more fishers exclusively targeting salmon, we find that catching fewer species can have positive effects on revenue. With increasing specialization, it is important to understand how individuals buffer against risk, as well as any barriers that prevent diversification. In addition to being affected by environmental variability, fishers are also affected by economic factors including demand and prices offered by processors. Life-history variation in the species targeted may also play a role. Individuals participating in Alaskan fisheries with high contributions of pink salmon — which have the shortest life cycles of all Pacific salmon — also have the highest variability in year-to-year revenue.

ABSTRACT Background Respiratory syncytial virus (RSV) morbidity and mortality in adults are often underestimated due to nonspecific symptoms, limited standard‐of‐care testing and lower diagnostic testing sensitivity compared with children. To accurately evaluate the RSV disease burden among adults in Australia, we conducted a model‐based study to estimate RSV‐attributable cardiorespiratory hospitalisation incidence and mortality rate. Methods A quasi‐Poisson regression model was used to estimate RSV‐attributable cardiorespiratory, respiratory and cardiovascular events, using weekly hospitalisation and mortality data from 2010 to 2019, accounting for periodic and aperiodic time trends and viral activity and allowing for potential overdispersion. The time‐series model compared the variability in confirmed RSV events alongside variability in all‐cause cardiorespiratory events identified from ICD‐10‐AM codes to estimate the number of RSV‐attributable events, including undiagnosed RSV‐related events. Results RSV‐attributable incidence of cardiorespiratory hospitalisations increased with age and was highest among adults ≥ 65 years (329.5–386.6 cases per 100,000 person‐years), nine times higher than in adults 18–64 years. The estimated incidence of RSV‐attributable respiratory hospitalisations in adults ≥65 years (219.7–247.8 cases per 100,000 person‐years) was 35‐fold higher than in adults 18–64 years. RSV‐attributable deaths accounted for 4% to 6% of cardiorespiratory deaths in adults ≥ 65 years, with RSV‐attributable mortality rates ranging from 65.6 to 77.6 deaths per 100,000 person‐years and respiratory mortality rates ranging from 20.3 to 24.0 deaths per 100,000 person‐years, both 70‐fold higher than in adults 18–64 years. Conclusions This study identified substantial RSV‐associated morbidity and mortality among Australian adults and is the first study to report RSV‐attributable mortality rates for Australia that account for untested events.

1. Ecosystems at high altitudes and latitudes are expected to be particularly vulnerable to the effects of global change. We assessed the responses of littoral invertebrate communities to changing abiotic conditions in subarctic Swedish lakes with long-term data (1988–2010) and compared the responses of subarctic lakes with those of more southern, hemiboreal lakes. 2. We used a complex systems approach, based on multivariate time-series modelling, and identified dominant and distinct temporal frequencies in the data; that is, we tracked community change at distinct temporal scales. We determined the distribution of functional feeding groups of invertebrates within and across temporal scales. Within and cross-scale distributions of functions have been considered to confer resilience to ecosystems, despite changing environmental conditions. 3. Two patterns of temporal change within the invertebrate communities were identified that were consistent across the lakes. The first pattern was one of monotonic change associated with changing abiotic lake conditions. The second was one of showing fluctuation patterns largely unrelated to gradual environmental change. Thus, two dominant and distinct temporal frequencies (temporal scales) were present in all lakes analysed. 4. Although the contribution of individual feeding groups varied between subarctic and hemiboreal lakes, they shared overall similar functional attributes (richness, evenness, diversity) and redundancies of functions within and between the observed temporal scales. This highlights similar resilience characteristics in subarctic and hemiboreal lakes. 5. Synthesis and applications. The effects of global change can be particularly strong at a single scale in ecosystems. Over time, this can cause monotonic change in communities and eventually lead to a loss of important ecosystem services upon reaching a critical threshold. Dynamics at other spatial or temporal scales can be unrelated to environmental change. The relative 'intactness' of these scales that are unaffected by global change and the persistence of functions at those scales may safeguard the whole system from the potential loss of functions at the scale at which global change impacts can be substantial. Thus, an understanding of scale-specific processes provides managers with a realistic assessment of vulnerabilities and the relative resilience of ecosystems to environmental change. Explicit consideration of 'intact' and 'affected' scales in analyses of global change impacts provides opportunities to tailor more specific management plans.

Estimating groundwater level (GWL) fluctuations is a vital requirement in hydrology and hydraulic engineering, and is commonly addressed through artificial intelligence (AI) models. The purpose of this research was to estimate groundwater levels using new modelling methods. The implementation of two separate soft computing techniques, a multilayer perceptron neural network (MLPNN) and an M5 model tree (M5-MT), was examined. The models are used in the estimation of monthly GWLs observed in a shallow unconfined coastal aquifer. Data for the water level were collected from observation wells located near Ganjimatta, India, and used to estimate GWL fluctuation. To do this, two scenarios were provided to achieve optimal input variables for modelling the GWL at the present time. The input parameters applied for developing the proposed models were a monthly time-series of summed rainfall, the mean temperature (within its lag times that have an efect on groundwater), and historical GWL observations throughout the period 1996-2006. The eficiency of each proposed model for Ganjimatt was investigated in stages of trial and error. A performance evaluation showed that the M5-MT outperformed the MLPNN model in estimating the GWL in the aquifer case study. Based on the M5-MT approach, the development of this model gives acceptable results for the Indian coastal aquifers. It is recommended that water managers and decision makers apply these new methods to monitor groundwater conditions and inform future planning.

ABSTRACT Background RSV incidence in adults is frequently underestimated due to non‐specific symptomatology, limited standard‐of‐care testing, and lower test sensitivity compared to infants. We conducted a retrospective observational study to estimate RSV‐attributable incidence of specific types of cardiorespiratory hospitalizations among adults in Germany between 2015 and 2019. Methods Information on hospitalizations and the number of people at risk of hospitalization (denominator) was gathered from a Statutory Health Insurance database. A quasi‐Poisson regression model accounting for periodic and aperiodic time trends and virus activity was fitted to estimate the RSV‐attributable incidence rate (IR) of four specific cardiovascular hospitalizations (arrhythmia, ischemic heart diseases, chronic heart failure exacerbations, and cerebrovascular diseases) and four specific respiratory hospitalizations (influenza/pneumonia, bronchitis/bronchiolitis, chronic lower respiratory tract diseases, and upper respiratory tract diseases). Results The estimated RSV‐attributable IRs of hospitalizations generally increased with age. Among estimated cardiovascular hospitalizations in adults aged ≥ 60 years, arrhythmia and ischemic heart diseases accounted for the highest incidence of RSV‐attributable events, followed by chronic heart failure exacerbation, with annual IR ranges of 157–260, 133–214, and 105–169 per 100,000 person‐years, respectively. The most frequent RSV‐attributable respiratory hospitalizations in adults aged ≥ 60 years were estimated for chronic lower respiratory tract diseases and bronchitis/bronchiolitis, with annual IR ranges of 103–168 and 77–122 per 100,000 person‐years, respectively. Conclusions RSV causes a considerable burden of respiratory and cardiovascular hospitalizations in adults in Germany, similar to other respiratory viruses (e.g., influenza and SARS‐CoV‐2). This highlights the need to implement effective prevention strategies, especially for older adults.

To characterize and explore the short-term climatic patterns over the last decade (Jan. 2009 to Dec. 2018), the present research has been carried out, involving time series analysis of precipitation pattern in three cities of Tamil Nadu, namely, Thanjavur, Nagapattinam, and Chennai, referring to deltaic, coastal and highly urbanized cities of Tamil Nadu, respectively. The study involves time series empirical analysis, decomposition, exponential smoothing, and various stochastic modeling. Herein, the location-specific suitable models are obtained and specific predictions are being carried out, as well.

Forecasting solar radiation has recently become the focus of numerous researchers due to the growing interest in green energy. This study aims to develop a seasonal auto-regressive integrated moving average (SARIMA) model to predict the daily and monthly solar radiation in Seoul, South Korea based on the hourly solar radiation data obtained from the Korean Meteorological Administration over 37 years (1981–2017). The goodness of fit of the model was tested against standardized residuals, the autocorrelation function, and the partial autocorrelation function for residuals. Then, model performance was compared with Monte Carlo simulations by using root mean square errors and coefficient of determination (R2) for evaluation. In addition, forecasting was conducted by using the best models with historical data on average monthly and daily solar radiation. The contributions of this study can be summarized as follows: (i) a time series SARIMA model is implemented to forecast the daily and monthly solar radiation of Seoul, South Korea in consideration of the accuracy, suitability, adequacy, and timeliness of the collected data; (ii) the reliability, accuracy, suitability, and performance of the model are investigated relative to those of established tests, standardized residual, autocorrelation function (ACF), and partial autocorrelation function (PACF), and the results are compared with those forecasted by the Monte Carlo method; and (iii) the trend of monthly solar radiation in Seoul for the coming years is analyzed and compared on the basis of the solar radiation data obtained from KMS over 37 years. The results indicate that (1,1,2) the ARIMA model can be used to represent daily solar radiation, while the seasonal ARIMA (4,1,1) of 12 lags for both auto-regressive and moving average parts can be used to represent monthly solar radiation. According to the findings, the expected average monthly solar radiation ranges from 176 to 377 Wh/m2.

One of the most challenging tasks in rainfall prediction is designing a reliable computational methodology owing the random and stochastic characteristics of time-series. In this study, the potential of five different data-driven models including Multilayer Perceptron (MLP), Least Square Support Vector Machine (LSSVM), Neuro-fuzzy, Hammerstein-Weiner (HW) and Autoregressive Integrated Moving Average (ARIMA) were employed for multi-station (Hien, Thank My, Hoi Khanh, Ai Nghia and Cai Lau) prediction of daily rainfall in the Vu Gia-Thu Bon River basin in Central Vietnam. Subsequently, hybrid ARIMA-MLP, ARIMA-LSSVM, ARIMA-NF and ARIMA-HW models were also utilized to predict the daily rainfall at these stations. The results were evaluated in terms of widely used performance criteria, viz.: determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (CC). Besides, the Taylor diagram is also used to examine and compare the similarity between the observed and predicted rainfall. The quantitative analysis indicated that the HW model increased the prediction accuracy by 5%, 3% and 2% at Hien, Ai Nghia and Cau Lau stations, respectively, compared to the other models. Likewise, the NF model increased the prediction accuracy at Thanh My and Hoi Khanh stations in contrast to the other models in terms of the mean absolute error. Also, the results of hybrid ARIMA-NF and ARIMA-HW models showed the best performance in terms of predictive skills and verified to increase the prediction accuracy in comparison to the single models.

This paper presents the results of the 2022 Groundwater Time Series Modelling Challenge, where 15 teams from different institutes applied various data-driven models to simulate hydraulic-head time series at four monitoring wells. Three of the wells were located in Europe and one was located in the USA in different hydrogeological settings in temperate, continental, or subarctic climates. Participants were provided with approximately 15 years of measured heads at (almost) regular time intervals and daily measurements of weather data starting some 10 years prior to the first head measurements and extending around 5 years after the last head measurement. The participants were asked to simulate the measured heads (the calibration period), to provide a prediction for around 5 years after the last measurement (the validation period for which weather data were provided but not head measurements), and to include an uncertainty estimate. Three different groups of models were identified among the submissions: lumped-parameter models (three teams), machine learning models (four teams), and deep learning models (eight teams). Lumped-parameter models apply relatively simple response functions with few parameters, while the artificial intelligence models used models of varying complexity, generally with more parameters and more input, including input engineered from the provided data (e.g. multi-day averages). The models were evaluated on their performance in simulating the heads in the calibration period and in predicting the heads in the validation period. Different metrics were used to assess performance, including metrics for average relative fit, average absolute fit, fit of extreme (high or low) heads, and the coverage of the uncertainty interval. For all wells, reasonable performance was obtained by at least one team from each of the three groups. However, the performance was not consistent across submissions within each group, which implies that the application of each method to individual sites requires significant effort and experience. In particular, estimates of the uncertainty interval varied widely between teams, although some teams submitted confidence intervals rather than prediction intervals. There was not one team, let alone one method, that performed best for all wells and all performance metrics. Four of the main takeaways from the model comparison are as follows: (1) lumped-parameter models generally performed as well as artificial intelligence models, which means they capture the fundamental behaviour of the system with only a few parameters. (2) Artificial intelligence models were able to simulate extremes beyond the observed conditions, which is contrary to some persistent beliefs about these methods. (3) No overfitting was observed in any of the models, including in the models with many parameters, as performance in the validation period was generally only a bit lower than in the calibration period, which is evidence of appropriate application of the different models. (4) The presented simulations are the combined results of the applied method and the choices made by the modeller(s), which was especially visible in the performance range of the deep learning methods; underperformance does not necessarily reflect deficiencies of any of the models. In conclusion, the challenge was a successful initiative to compare different models and learn from each other. Future challenges are needed to investigate, for example, the performance of models in more variable climatic settings to simulate head series with significant gaps or to estimate the effect of drought periods.