Environmental and Ecological Statistics最新文献

The fish species N. chitala is a freshwater fish that is widely distributed in African and Asian countries, including India, Pakistan, Bangladesh, Sri Lanka, Nepal, Thailand, and Indonesia. This species has been categorized as endangered (EN) in the Conservation Assessment and Management Plan. The study aims to investigate the cause of the species’ decline in their natural habitat. Using mathematical models supported by empirical data analysis, we explore the interaction of the species with other tropic levels and discover important parameters that may be attributed to the rapid decline. Based on the literature, we considered an intraguild predation (IGP) system consisting of three species, namely Chitala (IG predator), Mugil (IG prey), and shrimp (resource). Two variants of IGP models governed by three coupled differential equations are considered for data modeling purposes. Chitala depends only on Mugil and shrimp in one model. An alternative food source is available to Chitala in the second model. The models are estimated using the Bayesian modeling framework. Posterior estimates of the parameters for each model were obtained using the Gibbs algorithm, and the reversible-jump Markov chain Monte Carlo method has been utilized for posterior model inference. Our findings suggest that the primary reason for the decline in Chitala is due to the reduced nutritional gain from the Mugil and reduced predation efficiency in acquiring shrimp as a food source in the unavailability of Mugil. This study may be useful to develop management strategies for Chitala conservation by emphasizing the regeneration of Mugil populations.

Transportation noise is a widespread environmental problem in today’s society. The continuous movement of different vehicles on urban roads is the primary cause of such pollution. The review paper attempted to investigate numerous health issues caused by traffic noise exposure and how these health consequences were predicted using machine learning approaches such as structural equation modelling and artificial neural networks. Urban residents are exposed to such pollution during the day and night and have experienced its psychophysiological effects, whether knowingly or unknowingly. Furthermore, by reviewing numerous articles, this study attempted to investigate the relationship between socio-demographic factors and the effect of traffic noise, such as annoyance. The study also attempted to assess the relationships between various traffic noise-induced health issues such as headache, depression, sleeping problems, annoyance, blood pressure, and tiredness. Besides, evaluation and prediction play a key role to resolve any issue. Machine learning techniques such as structural equation modelling and artificial neural networks are useful tools that are rarely used in acoustic science and can be used to find associations as well as predict the effect of noise. The methodology and application of these two approaches are discussed in this study to provide a clear understanding of this application to the researchers working in this field.

Rare cancers affect millions of people worldwide each year. However, estimating incidence or mortality rates associated with rare cancers presents important difficulties and poses new statistical methodological challenges. In this paper, we expand the collection of multivariate spatio-temporal models by introducing adaptable shared spatio-temporal components to enable a comprehensive analysis of both incidence and cancer mortality in rare cancer cases. These models allow the modulation of spatio-temporal effects between incidence and mortality, allowing for changes in their relationship over time. The new models have been implemented in INLA using r-generic constructions. We conduct a simulation study to evaluate the performance of the new spatio-temporal models. Our results show that multivariate spatio-temporal models incorporating a flexible shared spatio-temporal term outperform conventional multivariate spatio-temporal models that include specific spatio-temporal effects for each health outcome. We use these models to analyze incidence and mortality data for pancreatic cancer and leukaemia among males across 142 administrative health care districts of Great Britain over a span of nine biennial periods (2002–2019).

A new hybrid approach for the river discharge prediction is proposed by integrating the Hampel filter (HF) with an autoregressive distributed lag (ARDL) model and multi-model error correction method. This study applied the HF to detect and correct outliers present in the data. Then, the HF-treated data variables were employed in the ARDL model to obtain discharge predictions and errors were obtained. Next, a multi-model approach (named ASR) was used based on a combination of artificial neural networks (ANN), support vector machines (SVM), and random forest (RF) models to predict errors. The ASR-predicted errors were aggregated with HF-ARDL prediction to determine the final HF-ARDL-ASR hybrid model predictions. The effectiveness of this approach was explored and compared with different models on the discharge data of four rivers of the Indus River basin of Pakistan. The root mean squared error (RMSE) of the HF-ARDL-ASR hybrid model in Jhelum River (Domel station) is 96.88 m³/s in the testing phase that is 53.92%, 50.0%, 48.7%, 50.0%, 13.4%, 53.2%, 50.3%, 46.4%, and 49.1% lower than the RMSE of the multiple linear regression (MLR), SVM, ANN, RF, ARDL, HF-MLR, HF-SVM, HF-ANN, and HF-RF models respectively. On test data, the Nash–Sutcliffe Efficiency (NSE) values of the suggested HF-ARDL-ASR hybrid model in Jhelum River (Chattar Kallas station) is 0.8571, Jhelum River (Domel) is 0.8294, Kabul River (Nowshera) is 0.8291 and Kunhar River (Talhata) is 0.8506. Therefore, the proposed HF-ARDL-ASR model has shown superior performance, lower errors, and higher prediction accuracy than all comparative models in the study.

Adaptive design methods can be used to make changes to survey designs in ecosystem monitoring to ensure that informative data are collected in an ongoing, cost-effective, and flexible manner. Such methods are of particular benefit in environmental monitoring as such monitoring is often very costly and in many cases consists of only a few sampling sites from which inference about a larger geographical region is needed. In addition, ecological processes are continuously changing, and monitoring programs must account for both known and unknown drivers, so making changes to data collection plans over time may be needed based on the current state and understanding of the process of interest. Through considering a Long-term Monitoring Program of Australia’s Great Barrier Reef, this paper aims to develop adaptive design approaches to efficiently monitor coral health through the consideration of a statistical model that accounts for both spatial variability and time-varying disturbance patterns. In particular, to develop this model, we considered time-varying disturbance data that have been reproduced at a fine spatial resolution for uniform representation over the study region. By adopting our proposed approach, we show that adaptive designs are able to significantly reduce survey effort while still remaining effective in, for example, quantifying the effects of different environmental disturbances.

Widespread flooding in Africa has devastating repercussions on communities, and sometimes leading to loss of life, displacement of populations, and significant damage to infrastructure and agriculture. Despite this, there are limited studies that investigate the behaviour of high time frequency river flows for the major river systems of Africa to inform adaptation and mitigation strategies for improved resilience of society. This paper fills this gap by assessing the occurrence time of annual maximum daily discharge for five of the longest rivers of Africa using a statistical modelling approach. This is the first of such a study covering all of the five longest rivers of Africa in one paper. Annual maximum daily discharge time for each river was modeled by mixtures of von Mises distributions, fitted by a Markov chain Monte Carlo algorithm. Data on mean daily discharge was obtained from the Global Runoff Data Centre database for the Niger, Zambezi, Okavango, Limpopo and Orange rivers in Africa. Estimates were inferred for the location parameter of the major mode, location parameter of the minor mode, concentration parameter of the major mode, concentration parameter of the minor mode, mean time, mean resultant, circular variance, circular skewness, and circular kurtosis. The developed models reveal distinctive temporal patterns of peak discharge events in each river, which can have significant implications for flood management, water resource planning, hydrological modeling, risk assessment and infrastructure design.

For quantifying m-dimensional ((m ge 3)) niche regions and niche overlaps using a copula-based approach, commonly used copulas, including Archimedean and elliptical copula families, are unsatisfactory alternatives in characterizing a complex dependence structure among multiple variables, especially when bi-variate copulas characterizing dependency structures of two-dimensional sub-variables differ. To solve the problem, we improve the copula-based niche space modeling approach using simplified vine copulas, a powerful tool containing various bi-variate dependence structures in one multivariate copula. Using four simulated data sets, we then check the performance of simplified vine copula approximation when the simplifying assumption is invalid. Finally, we apply the improved copula-based approach to quantifying a three-dimensional niche space of a real case of Swanson et al. (Ecology 96(2):318–324, 2015. https://doi.org/10.1890/14-0235.1) and discover that among various simplified vine and other flexible multi-dimensional copulas, non-parametric simplified vine copula approximation performs best in fitting the data set. In the discussion, to analyze differences in calculating niche overlaps caused by using different copulas, we compare non-parametric simplified vine copula approximation with non-parametric and parametric simplified vine copula approximation, elliptical copula, Hierarchical Archimedean copula estimation, and empirical beta copula and give some comments on the results.

European anchovies and round sardinella play a crucial role, both ecological and commercial, in the Mediterranean Sea. In this paper, we investigate the distribution of their larval stages by analyzing a dataset collected over time (1998–2016) and spaced along the area of the Strait of Sicily. Environmental factors are also integrated. We employ a hierarchical spatio-temporal Bayesian model and approximate the spatial field by a Gaussian Markov Random Field to reduce the computation effort using the Stochastic Partial Differential Equation method. Furthermore, the Integrated Nested Laplace Approximation is used for the posterior distributions of model parameters. Moreover, we propose an index that enables the temporal evaluation of species abundance by using an abundance aggregation within a spatially confined area. This index is derived through Monte Carlo sampling from the approximate posterior distribution of the fitted models. Model results suggest a strong relationship between sea currents’ directions and the distribution of larval European anchovies. For round sardinella, the analysis indicates increased sensitivity to warmer ocean conditions. The index suggests no clear overall trend over the years.

Climate model selection stands as a critical process in climate science and research. It involves choosing the most appropriate climate models to address specific research questions, simulating climate behaviour, or making projections about future climate conditions. This paper proposes a new approach, using spatial functional data analysis, to asses which of the 18 EURO CORDEX simulation models work better for predicting average temperatures in the Campania region (Italy). The method involves two key steps: first, using functional data analysis to process climate variables and select optimal models by a hierarchical clustering procedure; second, validating the chosen models by proposing a new conformal prediction approach to the anomalies associated to each cluster.

Species diversity analysis of microbial communities is an important tool for assessing an ecosystem health. The advent of high-throughput genome sequencing techniques has made it possible to process an unprecedented number of RNA sequences. However, many studies report the presence of a significant number of fictitious rare species in datasets generated using these techniques. These species are the product of errors that can occur at any step of the sequence analysis pipeline. The overcount of rare species (especially singletons) affects the estimation of the total number of species, and of the diversity of the community as measured by Shannon’s index. To avoid overestimating these quantities, it is crucial to model the source of error. In this work, we present a new model that treats spurious singletons as false-negative record linkage errors, and compare it with another approach where spurious singletons are considered for deletion. We discuss the two inferential approaches both with an application to real data and on theoretical grounds. We demonstrate that, while Shannon’s index can differ significantly under the two models, the estimate of the total number of species is equivalent.