Balancing resource protection and fishing activity: The case of the European hake in the northern Iberian Peninsula

One of the fundamental aims of the Ecosystem Approach to Fisheries Management is to ensure the long‐term sustainability of the fishery by protecting key life‐cycle habitats, such as recruitment areas. In this study, we apply a hurdle Bayesian hierarchical spatio‐temporal model that describes the abundance of European hake (Merluccius merluccius) recruits in the northern continental shelf of the Iberian Peninsula. Our findings clearly show four persistent nurseries, the main one being located along the continental shelf of the Artabrian gulf (off La Coruña). The preferential habitats identified for the hake recruits are areas within a bathymetric range of 120–200 m, with 15–16°C of Sea Surface Temperature, a Chl‐a concentration of 0.8–1.2 mg/m³ and low values of seabed rugosity (unconsolidated substrates). Searching for a compromise with fisheries, we also assess the degree of overlap of the main nursery areas with two fisheries footprints, a local one using Vessel Monitoring System data of trawl fishery of Marín, and a global one using Automatic Identification System data of different type of trawlers (bottom otter, beam and midwater trawls). The two fisheries footprints present different degree of overlapping in distinct areas, highlight the need of specific fleet‐adapted management rules in order to protect juvenile stages. We discuss that understanding the distributional patterns associated with key life stages, such as recruitment, and their interaction with fishing activities, is essential for applying appropriate spatial management measures and improving fishery sustainability.     

Difficulties associated with modeling growth in the Atlantic angel shark (Squatina dumeril)

Several methods were used in an attempt to develop an age and growth model for the Atlantic angel shark (Squatina dumeril). Band counts from vertebral sections, which were fit to the traditional von Bertalanffy growth equation, the Gompertz growth equation, and the two-parameter von Bertalanffy growth equation, did not produce realistic parameter estimates. Additionally, a length-based Bayesian model was applied to fishery-independent length–frequency data, and a full Bayesian model was fitted to length-at-age data to estimate parameters for von Bertalanffy growth equation. Both the length-based and full Bayesian models failed to converge; the length–frequency data showed high bimodality unrelated to season, year, or other factors, and band counts were not predictable by length. Vertebral band counts were not valid for ageing Atlantic angel sharks, and length-based methods, which require normally distributed length–frequencies, were not appropriate for this data set. This study represents the first attempt at modeling age and growth for this species and provides research guidelines for future research initiatives.     

基于贝叶斯分析的干热河谷区橙子林冠层蒸腾耗水模拟

需耗水机制是进行农田/果园水分管理和调控的基础。本文聚焦蒸腾耗水机制,基于贝叶斯参数估计方法对比了不同Jarvis-Stewart模型配置对干热河谷区橙子林蒸腾耗水量的模拟效果,探索了Jarvis-Stewart模型在影响因子交互效应较强条件下蒸腾耗水模拟中的适用性。结果表明,考虑不同影响因子及其限制函数会对模拟效果产生较大影响,其中考虑土壤含水率和叶面积指数对模拟效果改善作用明显,而引入饱和水汽压差和气温则不同程度地降低模拟精度;考虑的影响因子越多,模型结构越复杂,模拟效果不一定越好;筛选出的最佳模型结构基本实现了橙子林蒸腾耗水的可靠模拟,但模拟效果仍有明显改进空间,因此,应综合考虑模型复杂程度、模拟精度及不确定性等,进一步探究适宜的模型结构。研究可为果园节水灌溉技术体系建立和水分管理优化提供科学依据,也能为耗水模型的进一步发展和完善提供理论支撑。     

Modelling individual variability in growth of bull trout in the Walla Walla River Basin using a hierarchical von Bertalanffy growth model

We examined growth in length of fluvial bull trout (Salvelinus confluentus) in the Walla Walla River Basin, Washington and Oregon. Our objectives were to quantify individual variability in growth; examine growth within and among years, life history forms, life stages and sexes; and estimate von Bertalanffy growth parameters. Individual variability was evaluated by modelling asymptotic length (L_∞) and the growth coefficient (k) as random variables. All models were fit with Bayesian methods and were evaluated for fit by the deviance information criterion. By incorporating individual variability, population‐level estimates of L_∞ and k appeared appropriate and estimated growth trajectories for specific bull trout fit individual observed patterns in growth. Growth trajectories and positive correlation between individual estimates of L_∞ and k suggest that some individuals grow at a faster rate and reach a larger maximum size than other individuals and those differences are maintained throughout life. Selected models suggest that fluvial migrants have higher estimates of L_∞ and k than residents, but there were only slight differences in parameter estimates among migrants from two adjacent spawning populations in the Walla Walla River Basin, as well as between males and females. Growth rates increased for fluvial migrants after subadult emigration. Individual variability in growth is consistent with the life history diversity assumed essential for bull trout population persistence. Quantifying this variability is important for modelling population dynamics and viability to conserve this threatened species.     

Linking Northeast Pacific recruitment synchrony to environmental variability

We investigated the hypothesis that synchronous recruitment is due to a shared susceptibility to environmental processes using stock–recruitment residuals for 52 marine fish stocks within three Northeast Pacific large marine ecosystems: the Eastern Bering Sea and Aleutian Islands, Gulf of Alaska, and California Current. There was moderate coherence in exceptionally strong and weak year‐classes and correlations across stocks. Based on evidence of synchrony from these analyses, we used Bayesian hierarchical models to relate recruitment to environmental covariates for groups of stocks that may be similarly influenced by environmental processes based on their life histories. There were consistent relationships among stocks to the covariates, especially within the Gulf of Alaska and California Current. The best Gulf of Alaska model included Northeast Pacific sea surface height as a predictor of recruitment, and was particularly strong for stocks dependent on cross‐shelf transport during the larval phase for recruitment. In the California Current the best‐fit model included San Francisco coastal sea level height as a predictor, with higher recruitment for many stocks corresponding to anomalously high sea level the year before spawning and low sea level the year of spawning. The best Eastern Bering Sea and Aleutian Islands model included several environmental variables as covariates and there was some consistent response across stocks to these variables. Future research may be able to utilize these across‐stock environmental influences, in conjunction with an understanding of ecological processes important across early life history stages, to improve identification of environmental drivers of recruitment.     

安全屏障对储油罐区池火灾多米诺效应概率的影响

随着石油储库朝大型化、集约化方向发展,罐区工艺与设备日趋繁杂,一旦发生火灾事故,极易引发多米诺效应,导致灾难性后果.为了降低石油储库事故对周边人员、环境等造成的影响,基于主动、被动、应急与响应措施3种安全屏障的性能,采用事件树与贝叶斯网络方法针对不同安全屏障建模,进而对事故多米诺效应概率进行定量评估.结果表明:多层安全...     

Application of soft sensor in the on line measurement of theiron ore’s surface temperature

It is difficult to measure the surface temperature of iron ore directly. A method is put forward to handle this problem by using soft sensing technique. This on line measurement method is used to replace the Lagrange interpolation off line method to estimate surface temperature. The method used L M optimum algorithm to build up ANN soft sensor model combined with off line learning neural network to establish the correlation between input variables and target variables, to achieve the surface temperature on line detection. The results of simulation and experimentation indicate that the method is reasonable and feasible.     

基于贝叶斯分类的农作物产品质量挖掘研究

采用贝叶斯算法的基本原理,并结合农作物生长环境数据的特点,分析和预测了农作物的产品质量。采用随机选取的样本组进行试验预测,预测准确率高,表明该算法是有效的。     

基于贝叶斯分类的农作物产品质量挖掘研究

采用贝叶斯算法的基本原理,并结合农作物生长环境数据的特点,分析和预测了农作物的产品质量。采用随机选取的样本组进行试验预测,预测准确率高,表明该算法是有效的。     

Bayesian structural equation models for inferring relationships between phenotypes: a review of methodology, identifiability, and applications

Structural equation models provide a general statistical modelling technique for estimating and testing relationships among variables. Such relationships are often not revealed by standard linear models, but are of importance for understanding mechanisms underlying e.g., production-related diseases, such as mastitis. This paper gives a review of Bayesian structural equation models concerning methodology and identifiability, focused on animal breeding and genetics modelling. Applications of this type of methods in animal breeding are also reviewed critically, with discussion on advantages and disadvantages of these approaches.