Origins and scales of hypoxia on the Louisiana shelf: Importance of seasonal plankton dynamics and river nutrients and discharge

Management plans for the Mississippi River Basin call for reductions in nutrient concentrations up to 40% or more to reduce hypoxia in the Gulf of Mexico (GOM), while at the same time the government is considering new farm subsidies to promote development of biofuels from corn. Thus there are possibilities of both increasing and decreasing river nutrients depending on national priorities. River flow rates which also influence the extent of hypoxia on the shelf may be altered by global climate change. We have therefore developed a series of simulations to forecast ecosystem response to alterations in nutrient loading and river flow. We simulate ecosystem response and hypoxia events using a linked model consisting of multiple phytoplankton groups competing for nitrogen, phosphorus and light, zooplankton grazing that is influenced by prey edibility and stoichiometry, sub-pycnocline water-column metabolism that is influenced by sinking fecal pellets and algal cells, and multi-element sediment diagenesis. This model formulation depicts four areas of increasing salinity moving westward away from the Mississippi River point of discharge, where the surface mixed layer, four bottom layers and underlying sediments are represented in each area. The model supports the contention that a 40% decrease in river nutrient will substantially reduce the duration and areal extent of hypoxia on the shelf. But it also suggests that in low and middle salinity areas the hypoxia response is saturated with respect to nutrients, and that in high salinity regions small increases in nutrient and river flow will have disproportionally large effects on GOM hypoxia. The model simulations also suggest that river discharge is a stronger factor influencing hypoxia than river nutrients in the Mississippi River plume. Finally, the model simulations suggest that primary production in the low salinity regions is light limited while primary production in the higher salinity zones is phosphate limited during the May to October period when hypoxia is prevalent in the Mississippi River plume.     

A herbivore specific grazing capacity model accounting for spatio-temporal environmental variation: A tool for a more sustainable nature conservation and rangeland management

Grazing with livestock is a common feature of nature and rangeland management. Although both aim at different, seemingly opposing goals, i.e. maintenance of biodiversity values versus maximization of animal production, they nonetheless have a common interest in maintaining the rangeland or natural environment in a state that ensures either the first or the second goal. In order to accomplish an effective and efficient grazing management, in terms of grazer density, grazer composition, grazing seasonality, and to prevent under- and overgrazing, a grazing capacity model (GCM) was developed, that should be applicable in both rangeland and nature conservation management conditions and that takes spatio-temporal environmental variation into account. This spatio-temporally dynamic model considers crucial variables at both the terrain and the grazer level, such as (seasonally) fluctuating forage yield, forage quality, plant palatability, accessibility of the area, soil erosion vulnerability, animal nutritive requirements, animal behaviour and general habitat condition. It predicts the optimal grazer species and density, taking into account the seasonal variation in animal needs and fluctuating terrain characteristics. A sensitivity analysis was conducted to define each parameter's relative impact on the final outcome of the model.We present the GCM outline and illustrate the functionality of this model for Shetland ponies and Highland cattle, grazing in a temperate coastal dune environment. According to the model, seasonal fluctuations in optimal grazer densities occur: the area can support higher densities in summer and autumn than it can during winter and spring. With the current density of grazing animals and the choice for year-round grazing at non-fluctuating animal densities, the model consequently predicts overgrazing in winter and undergrazing in summer and autumn. Both undergrazing and overgrazing scenarios might lead to non-sustainable situations in the future.     

Identifying fishing trip behaviour and estimating fishing effort from VMS data using Bayesian Hidden Markov Models

Recent advances in technologies have lead to a vast influx of data on movements, based on discrete recorded position of animals or fishing boats, opening new horizons for future analyses. However, most of the potential interest of tracking data depends on the ability to develop suitable modelling strategies to analyze trajectories from discrete recorded positions. A serious modelling challenge is to infer the evolution of the true position and the associated spatio-temporal distribution of behavioural states using discrete, error-prone and incomplete observations. In this paper, a Bayesian Hierarchical Model (HBM) using Hidden Markov Process (HMP) is proposed as a template for analyzing fishing boats trajectories based on data available from satellite-based vessel monitoring systems (VMS). The analysis seeks to enhance the definition of the fishing pressure exerted on fish stocks, by discriminating between the different behavioural states of a fishing trip, and also by quantifying the relative importance of each of these states during a fishing trip. The HBM approach is tested to analyse the behaviour of pelagic trawlers in the Bay of Biscay. A hidden Markov chain with a regular discrete time step is used to model transitions between successive behavioural states (e.g., fishing, steaming, stopping (at Port or at sea)) of each vessel. The parameters of the movement process (speed and turning angles) are defined conditionally upon the behavioural states. Bayesian methods are used to integrate the available data (typically VMS position recorded at discrete time) and to draw inferences on any unknown parameters of the model. The model is first tested on simulated data with different parameters structures. Results provide insights on the potential of HBM with HMP to analyze VMS data. They show that if VMS positions are recorded synchronously with the instants at which the process switch from one behavioural state to another, the estimation method provides unbiased and precise inferences on behavioural states and on associated movement parameters. However, if the observations are not gathered with a sufficiently high frequency, the performance of the estimation method could be drastically impacted when the discrete observations are not synchronous with the switching instants. The model is then applied to real pathways to estimate variables of interest such as the number of operations per trip, time and distance spent fishing or travelling.     

Imposed and inherent scales in cellular automata models of habitat

Both observational and modelling studies of the natural environment are characterised by their ‘grain’ and ‘extent’, the smallest and largest scales represented in time and space. These are imposed scales that should be chosen to ensure that the natural scales of the system are captured in the study. A simple cellular automata model of habitat represents only the presence or absence of vegetation, with global and local interactions described by four empirical parameters. Such a model can be formulated as a nonlinear Markov equation for the habitat probability. The equation produces inherent space and time scales that may be considered as transition scales or the scales for recovery from disturbance. However, if the resolution of the model is changed, the empirical parameters must be changed to preserve the properties of the system. Further, changes in the spatial resolution lead to different interpretations of the spatial structure. In particular, as the resolution is reduced, the apparent dominance of one habitat type over the other increases. The model provides an ability to compare both field and model investigations conducted at different resolutions in time and space.     

Updating coarse-scale species distribution models using small fine-scale samples

Large, fine-grained samples are ideal for predictive species distribution models used for management purposes, but such datasets are not available for most species and conducting such surveys is costly. We attempted to overcome this obstacle by updating previously available coarse-grained logistic regression models with small fine-grained samples using a recalibration approach. Recalibration involves re-estimation of the intercept or slope of the linear predictor and may improve calibration (level of agreement between predicted and actual probabilities). If reliable estimates of occurrence likelihood are required (e.g., for species selection in ecological restoration) calibration should be preferred to other model performance measures. This updating approach is not expected to improve discrimination (the ability of the model to rank sites according to species suitability), because the rank order of predictions is not altered. We tested different updating methods and sample sizes with tree distribution data from Spain. Updated models were compared to models fitted using only fine-grained data (refitted models). Updated models performed reasonably well at fine scales and outperformed refitted models with small samples (10-100 occurrences). If a coarse-grained model is available (or could be easily developed) and fine-grained predictions are to be generated from a limited sample size, updating previous models may be a more accurate option than fitting a new model. Our results encourage further studies on model updating in other situations where species distribution models are used under different conditions from their training (e.g., different time periods, different regions).     

农业区氨氮流失模型

定量分析了农田降水、产流,下渗,排水,蒸散发,灌溉之间的关系并提出相应的数学方程式,采用Ｌａｎｇｍｕｉｒ等温线动态描述土壤中氨氮吸附与两相平衡状态,在此基础上,建立了一个模拟随农业排水和产沙的充失数学模型,并以江苏省锡澄农业区为例说明了这一模型的应用。     

以粉煤灰和电石渣为催化剂的聚丙烯裂解模型研究

对粉煤灰和电石渣为催化剂的聚丙烯裂解进行模型研究.通过模型计算了裂解反应的表观活化能、频率因子、反应级数等.模型计算表明随着反应温度的升高反应速率增加,达到相同转化率所需反应时间减少.当剂塑比从0增加到30%时,聚丙烯裂解的活化能呈现降低的趋势,特别当剂塑比为30%时,其活化能降低约50%.说明粉煤灰和电石渣对聚丙烯裂解具有一定的催化效果.     

土壤污染物溶质运移模型研究进展

土壤中污染物溶质的运移已经成为了近年来研究的热点。本文回顾了关于土壤中溶质运移模型的研究成果,讨论了各个模型的优势及其不足之处,介绍了应用最为广泛的传统微分方程模型。对应用于非均匀介质中的随机运移模型进行了描述,并将其和传统微分方程模型进行了比较。介绍了可动-不可动水体模型并讨论了其相关参数的影响因素。列举了几种和其他学科或研究方向结合的溶质运移模型。最后提出了今后模型开发的发展方向。     

事故致因“2-4”模型中“事件”与“动作”的关系探讨

为进一步完善和简化事故致因"2-4"模型,对模型中的"事件"和"动作"之间的关系进行探讨。首先,通过文献分析,确定了事件、动作和状态的定义及属性,提出了动作是事件的观点并加以图示说明。然后,以北京交通大学"12. 26"较大实验室爆炸事故为案例,对图示进行解释,详细分析了上述观点的合理性。结果表明:动作和状态是人和物都具备的,并且状态包含在动作过程中;动作、状态、事故都是事件,事件是人或物的动作,或者动作过程中的某一瞬时状态,故"2-4"模型中动作和事件是等价的,可以相互替代或混用。