Are network indices robust indicators of food web functioning? A Monte Carlo approach

Indices based on network theory are often used to describe food web functioning. These indices take as input food web flows that are estimated based on merging of (scarce) data with linear inverse methods (LIMs). Due to under sampling, most food webs are highly uncertain and can only be quantified within a specific uncertainty range. The linear inverse method (LIM) can estimate food web flows using a variety of techniques, e.g. the parsimonious or minimum norm (MN) solution, which selects one food web, based on a quadratic minimization technique or the Monte Carlo solution where a finitely many random solutions are generated which are then averaged. We use the Monte Carlo approach (MCA) to estimate the values of several indices from four published food webs, the Gulf of Riga for the autumn, summer and spring seasons, and the Takapoto atoll system. We first show that network indices are much better constrained than the uncertain food webs from which they are calculated. Therefore, even in the face of food web uncertainty, they are robust estimators of food web functioning. We then use the MCA-derived network indices to generate cumulative density functions for each index. These serve to compute the probabilities of the MN indices estimates being an extreme solution as compared to the median values. Our findings show that 82% of the MN solutions are smaller than the MCA solutions, and 63% of the network indices are significantly under-estimated.     

Using accelerometer,high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model

The study described in this paper developed a model of animal movement, which explicitly recognised each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behaviour within several “stay” areas where cows remained for long periods. Model parameters were estimated for hidden behaviour states such as relocating, foraging and bedding. For cows’ movement between the “stay” areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behaviour data collected. This modelling methodology could easily be applied to interactions of other animal species.     

Bridging biogeochemical and food web models for an End-to-End representation of marine ecosystem dynamics: The Venice lagoon case study

The simultaneous accounting of effects of natural and anthropogenic changes within a common framework calls for the development of new comprehensive tools capable of integrating biological processes that span a huge range of scales, from viruses to fishes, in addition to their interactions with physical–chemical environmental properties, i.e. End-to-End models.     

A Bayesian hierarchical framework for calibrating aquatic biogeochemical models

Model practitioners increasingly place emphasis on rigorous quantitative error analysis in aquatic biogeochemical models and the existing initiatives range from the development of alternative metrics for goodness of fit, to data assimilation into operational models, to parameter estimation techniques. However, the treatment of error in many of these efforts is arguably selective and/or ad hoc. A Bayesian hierarchical framework enables the development of robust probabilistic analysis of error and uncertainty in model predictions by explicitly accommodating measurement error, parameter uncertainty, and model structure imperfection. This paper presents a Bayesian hierarchical formulation for simultaneously calibrating aquatic biogeochemical models at multiple systems (or sites of the same system) with differences in their trophic conditions, prior precisions of model parameters, available information, measurement error or inter-annual variability. Our statistical formulation also explicitly considers the uncertainty in model inputs (model parameters, initial conditions), the analytical/sampling error associated with the field data, and the discrepancy between model structure and the natural system dynamics (e.g., missing key ecological processes, erroneous formulations, misspecified forcing functions). The comparison between observations and posterior predictive monthly distributions indicates that the plankton models calibrated under the Bayesian hierarchical scheme provided accurate system representations for all the scenarios examined. Our results also suggest that the Bayesian hierarchical approach allows overcoming problems of insufficient local data by “borrowing strength” from well-studied sites and this feature will be highly relevant to conservation practices of regions with a high number of freshwater resources for which complete data could never be practically collected. Finally, we discuss the prospect of extending this framework to spatially explicit biogeochemical models (e.g., more effectively connect inshore with offshore areas) along with the benefits for environmental management, such as the optimization of the sampling design of monitoring programs and the alignment with the policy practice of adaptive management.     

Visualisation of the complexity of EUSES

The interdependencies of parameters applied in the models of EUSES are visualised in a directed connectivity graph. The parameters (inputs, defaults, state variables, outputs) are represented by boxes (nodes) and their relations by lines (edges). The visualisation, on the one hand, clarifies the complexity of the models in EUSES and, on the other hand, creates an overview and transparency. The parameters’ relations to each other can be recognised faster, and the models can be better understood. The complexity was quantified by the number (variety), kind (substance parameter, physico-chemical parameter, concentration, other parameters), and depth (dimension) of the parameter and the number of relations (connectivity). The variety of EUSES (without the modelsSimple Treat andSimple Box whose interior structure is not documented and without the effect and risk characterisation) amounts to 466, the connectivity to 961, and the maximal dimension is 21.     

高锰酸钾预氧化后地表水中三氯甲烷生成量的预测模型研究

在饮用水处理中,适当地采用高锰钾预氧化可以降低水中的三氯甲烷生成量。本文研究了影响三氯甲烷生成量的因素,如反应时间,反应温度等,并以这些影响因素为参数通过一元线性回归等数学方法,建立了各影响因素下三氯甲烷生成量折了模型和综合预测模型。     

玉溪市未来人口预测三种模型的分析

人口问题是一个国家或地区可持续发展的前提。人口规模是否合理,对一个国家或地区未来的经济、社会和生态环境可持续发展影响深远。运用马尔萨斯人口模型、Logistic增长模型和线性函数三种理论模型,利用《玉溪市统计年鉴》统计数据对玉溪市2010—2020年的人口发展规模做出预测,预测结果显示3种模型均取得了较好的模拟效果。Logistic增长模型的预测值最小,故采用其预测值作为预测结果。     

Application of air pollution dispersion modeling for source-contribution assessment and model performance evaluation at integrated industrial estate-Pantnagar

Source-contribution assessment of ambient NO₂ concentration was performed at Pantnagar, India through simulation of two urban mathematical dispersive models namely Gaussian Finite Line Source Model (GFLSM) and Industrial Source Complex Model (ISCST-3) and model performances were evaluated. Principal approaches were development of comprehensive emission inventory, monitoring of traffic density and regional air quality and conclusively simulation of urban dispersive models. Initially, 18 industries were found responsible for emission of 39.11 kg/h of NO₂ through 43 elevated stacks. Further, vehicular emission potential in terms of NO₂ was computed as 7.1 kg/h. Air quality monitoring delineates an annual average NO₂ concentration of 32.6 μg/m³. Finally, GFLSM and ISCST-3 were simulated in conjunction with developed emission inventories and existing meteorological conditions. Models simulation indicated that contribution of NO₂ from industrial and vehicular source was in a range of 45-70% and 9-39%, respectively. Further, statistical analysis revealed satisfactory model performance with an aggregate accuracy of 61.9%.     

Assessing exposure to allied ground troops in the Vietnam War: A quantitative evaluation of the Stellman Exposure Opportunity Index model

The Exposure Opportunity Index (EOI) is a proximity-based model developed to estimate relative exposure of ground troops in Vietnam to aerially applied herbicides. We conducted a detailed quantitative evaluation of the EOI model by using actual herbicide spray missions isolated in time and space. EOI scores were calculated for each of 36 hypothetical receptor location points associated with each spray mission for 30 herbicide missions for two time periods – day of herbicide application and day 2–3 post-application. Our analysis found an enormous range of EOI predictions with 500–1000-fold differences across missions directly under the flight path. This quantitative examination of the EOI suggests that extensive testing of the model’s code is warranted. Researchers undertaking development of a proximity-based exposure model for epidemiologic studies of either Vietnam veterans or the Vietnamese population should conduct a thorough and realistic analysis of how precise and accurate the model results are likely to be and then assess whether the model results provide a useful basis for their planned epidemiologic studies.