Herbicide Transport in Goodwater Creek Experimental Watershed: II. Long‐Term Research on Acetochlor,Alachlor, Metolachlor,and Metribuzin1

Lerch, R.N., E.J. Sadler, C. Baffaut, N.R. Kitchen, and K.A. Sudduth, 2010. Herbicide Transport in Goodwater Creek Experimental Watershed: II. Long‐Term Research on Acetochlor, Alachlor, Metolachlor, and Metribuzin. Journal of the American Water Resources Association (JAWRA) 1‐15. DOI: 10.1111/j.1752‐1688.2010.00504.x Abstract: Farmers in the Midwestern United States continue to be reliant on soil‐applied herbicides for weed control in crop production, and herbicide contamination of streams remains an environmental problem. The main objective of this study was to analyze trends in concentration and load of acetochlor, alachlor, metolachlor, and metribuzin in Goodwater Creek Experimental Watershed (GCEW) from 1992 to 2006. A secondary objective was to document the effects of best management practices (BMPs) implemented within GCEW on herbicide transport trends. Median relative herbicide loads, as a percent of applied, were 3.7% for metolachlor, 1.3% for metribuzin, 0.36% for acetochlor, and 0.18% for alachlor. The major decrease in alachlor use and increase in acetochlor use caused shifts in flow‐weighted concentrations that were observed over the entire concentration range. The smaller decrease in metolachlor use led to a consistent decreasing time trend only for the upper end of the concentration distribution. Metribuzin also showed moderate decreases in concentration with time since 1998. Annual loads were generally correlated to second quarter discharge. Despite extensive education efforts in the watershed, conservation BMPs within GCEW were mainly implemented to control erosion, and therefore had no discernable impact on reducing herbicide transport. Overall, changes in herbicide use and second quarter discharge had the greatest effect on trends in flow‐weighted concentration and annual load.     

The Regionalization of National‐Scale SPARROW Models for Stream Nutrients1

Schwarz, Gregory E., Richard B. Alexander, Richard A. Smith, and Stephen D. Preston, 2011. The Regionalization of National‐Scale SPARROW Models for Stream Nutrients. Journal of the American Water Resources Association (JAWRA) 47(5):1151‐1172. DOI: 10.1111/j.1752‐1688.2011.00581.x Abstract: This analysis modifies the parsimonious specification of recently published total nitrogen (TN) and total phosphorus (TP) national‐scale SPAtially Referenced Regressions On Watershed attributes models to allow each model coefficient to vary geographically among three major river basins of the conterminous United States. Regionalization of the national models reduces the standard errors in the prediction of TN and TP loads, expressed as a percentage of the predicted load, by about 6 and 7%. We develop and apply a method for combining national‐scale and regional‐scale information to estimate a hybrid model that imposes cross‐region constraints that limit regional variation in model coefficients, effectively reducing the number of free model parameters as compared to a collection of independent regional models. The hybrid TN and TP regional models have improved model fit relative to the respective national models, reducing the standard error in the prediction of loads, expressed as a percentage of load, by about 5 and 4%. Only 19% of the TN hybrid model coefficients and just 2% of the TP hybrid model coefficients show evidence of substantial regional specificity (more than ±100% deviation from the national model estimate). The hybrid models have much greater precision in the estimated coefficients than do the unconstrained regional models, demonstrating the efficacy of pooling information across regions to improve regional models.     

Addressing onsite sampling in recreation site choice models

Independent experts and politicians have criticized statistical analyses of recreation behavior, which rely upon onsite samples due to their potential for biased inference. The use of onsite sampling usually reflects data or budgetary constraints, but can lead to two primary forms of bias in site choice models. First, the strategy entails sampling site choices rather than sampling individuals—a form of bias called endogenous stratification. Under these conditions, sample choices may not reflect the site choices of the true population. Second, exogenous attributes of the individuals sampled onsite may differ from the attributes of individuals in the population—the most common form in recreation demand is avidity bias. We propose addressing these biases by combining two the existing methods: Weighted Exogenous Stratification Maximum Likelihood estimation and propensity score estimation. We use the National Marine Fisheries Service's Marine Recreational Fishing Statistics Survey to illustrate methods of bias reduction, employing both simulated and empirical applications. We find that propensity score based weights can significantly reduce bias in estimation. Our results indicate that failure to account for these biases can overstate anglers' willingness to pay for improvements in fishing catch, but weighted models exhibit higher variance of parameter estimates and willingness to pay.     

Combining state and transition models with dynamic Bayesian networks

Bashari et al. (2009) propose combining state and transition models (STMs) with Bayesian networks for decision support tools where the focus is on modelling the system dynamics. There is already an extension of Bayesian networks - so-called dynamic Bayesian networks (DBNs) - for explicitly modelling systems that change over time, that has also been applied in ecological modelling. In this paper we propose a combination of STMs and DBNs that overcome some of the limitations of Bashari et al.’s approach including providing an explicit representation of the next state, while retaining its advantages, such an the explicit representation of transitions. We then show that the new model can be applied iteratively to predict into the future consistently with different time frames. We use Bashari et al.’s rangeland management problem as an illustrative case study. We present a comparative complexity analysis of the different approaches, based on the structure inherent in the problem being modelled. This analysis showed that any models that explicitly represent all the transitions only remain tractable when there are natural constraints in the domain. Thus we recommend modellers should analyse these aspects of their problem before deciding whether to use the framework.     

Assessing the effects of disease and bleaching on Florida Keys corals by fitting population models to data

Coral diseases have increased in frequency over the past few decades and have important influences on the structure and composition of coral reef communities. However, there is limited information on the etiologies of many coral diseases, and pathways through which coral diseases are acquired and transmitted are still in question. Furthermore, it is difficult to assess the impacts of disease on coral populations because outbreaks often co-occur with temperature-induced bleaching and anthropogenic stressors. We developed spatially explicit population models of coral disease and bleaching dynamics to quantify the impact of six common diseases on Florida Keys corals, including aspergillosis, dark spots, white band, white plague, white patch, and Caribbean yellow band. Models were fit to an 8-year data set of coral abundance, disease prevalence, and bleaching prevalence. Model selection was used to assess alternative pathways for disease transmission, and the influence of environmental stressors, including sea temperature and human population density, on disease prevalence and coral mortality. Classic disease transmission from contagious to susceptible colonies provided the best-fit model only for aspergillosis. For other diseases, external disease forcing, such as through a vector or directly from pathogens in the environment, provided the best fit to observed data. Estimates of disease reproductive ratio values (R₀) were less than one for each disease, indicating coral colonies were below densities required for diseases to become established through contagious spread alone. Incidences of white band and white patch disease were associated with greater susceptibility or slower recovery of bleached colonies, and no disease outbreaks were associated with periods of elevated sea temperatures alone. Projections of best-fit models indicated that, atleast during the period of this study, disease and bleaching did not have substantial impacts on populations and impaired rates of population growth appeared to be attributable to other stressors. By applying epidemiological models to field data, our study gives qualitative insights into the dynamics of coral diseases, relative stressor impacts, and directions for future research.     

Modeling an exploited rocky coastal ecosystem: Bahia Tortugas, Mexico

A trophic structure model of the rocky coastal ecosystem in Bahia Tortugas, Mexico was constructed using Ecopath software to represent the main biomass flows in the system. Data for the model came from field observations (biomass estimates, stomach contents, and ecological observations for sea snails, abalones, lobster, some demersal finfishes, and macroalgae) carried out through ten field trips from 2006 to 2008. The results provide a snapshot of how the ecosystem operates. The model considers 23 functional groups. The total system throughput was 553 t/km²/year, 57% corresponds to internal consumption, 28% to respiration, 14% becomes detritus, and only 1% is removed through commercial fishing. The model suggests that even for exploited populations, predation and competition are heavier stresses than current fishing effort; however, because spiny lobster showed the second highest keystoneness’ index value, increasing fishing pressure on this group could strongly impact the entire ecosystem. We believe that this model has the potential to support management by allowing the exploration of the potential impacts of different fishing decisions at ecosystem level.     

土壤养分空间估测方法研究综述

土壤养分是土壤提供的植物生长发育所必需的营养元素。由于受到自然因素和人为因素的共同作用,土壤养分具有高度的空间变异性。土壤的这种特性不仅表现在区域尺度上,而且也表现在田块尺度上。在研究方法上经历了从传统统计学到地统计学,再到神经网络、地理信息技术以及高精度曲面建模等新方法的不断改进过程。文章从地统计学方法引入到土壤养分空间变异研究中为出发点,论述了国内外基于地统计学的土壤养分空间变异的研究现状,主要包括利用地统计学方法来确定合理的土壤采样数目,土壤养分空间变异的定量化研究,土壤养分空间变异的尺度效应;然后简述了神经网络、地理信息技术、高精度曲面建模等技术在土壤养分空间变异研究中的研究现状和应用。最后对比分析了各种研究方法在应用中存在的缺陷,同时指明了今后应加强作物生长的不同时期土壤养分的空间变异性、土壤在四维空间尺度上的演变机理以及环境信息获取的不确定性等方面的研究。     

宁波市农村生活污水处理模式研究

对现有农村污水治理方式进行了总结和效果评价。提出了适合宁波地区农村污水治理的4类模式。分别是污废合流的集中模式、分散模式,污废分流的分散模式、过渡模式。分析了模式选择的影响因素,分别是人口规模、集聚模式、地形、地质条件、功能区域,并对目标村庄进行亍相应分类,建立数学模型探讨了集中与分散模式的经济距离以及集中模式下户均投资的控制距离,最后根据研究成果对首批实施的14个行政村25个自然村的生活污水治理模式进行了选择。     

River environmental decision support system development for Suzhou Creek in Shanghai

The Suzhou Creek Rehabilitation Project (SCRP) is one of the largest water-related environmental rehabilitation schemes ever undertaken in the vicinity of Shanghai, China. This paper details the development and application of a River Environmental Decision Support System (REDSS) for scientific planning and decision-making on the Suzhou Creek project, and illustrates the flexibility of the REDSS framework. We developed the following components: (1) a GIS-based analysis employing Component technology; (2) a "data mart" for multi-dimensional, multi-level, integrated, dynamic, and flexible data querying; and (3) a set of hydrodynamic and water quality models which can simulate complex tidal river networks. In addition, we detail how a water quality assessment model is embedded into the REDSS by employing an Identification Index Method. With the REDSS, all GIS and non-GIS components are integrated seamlessly and data from different sources can be queried simultaneously. This allows for various scenarios to be simulated and analyzed in advance to predict and assess the effects of proposed engineering and management measures. Generated information can thus support effective decisions. All operations of the REDSS can be implemented conveniently through user-friendly interfaces. The function of the REDSS framework is demonstrated through an application to Suzhou Creek. Because the REDSS characteristics are quite general, it may be applied in different geographic regions.     

骆马湖浮游植物演替规律及驱动因子

江苏骆马湖作为南水北调工程的重要调蓄湖泊,水生态系统结构变化不容忽视.为研究骆马湖浮游植物群落结构演替规律及其与环境因子的关系,于2014~2018年进行了逐月监测.研究期间骆马湖总氮、高锰酸盐指数、电导率等参数呈逐年升高趋势,氟离子浓度呈逐年下降趋势.共鉴定有浮游植物7门71属,月均生物量变化范围0.16~5.51mg·L^-1.硅藻和绿藻为绝对优势门类,其次为甲藻及隐藻;主要优势属为针杆藻（Synedra sp.）、隐藻（Chroomonas spp.）、直链硅藻（Aulacoseira spp.）、锥囊藻（Dinobryon sp.）、栅藻（Scenedesmus spp.）、脆杆藻（Fragilaria spp.）、转板藻（Mougeotia sp.）、纤维藻（Ankistrodesmus sp.）和裸藻（Euglena spp.）.2014~2018年骆马湖浮游植物群落结构年际差异较大,其变化主要体现在浮游植物生物量的再分配,硅藻和绿藻继续保持优势外甲藻和蓝藻的优势度增加.非度量多维尺度分析显示,骆马湖浮游植物群落变化与总氮、氟离子、水温、总磷、溶解氧、pH、电导率和高锰酸盐指数等因素有关,广义可加模型同样显示总氮、氟离子浓度和水温是主导骆马湖浮游植物群落结构变化的主要因素.总氮、氟离子浓度是影响浮游植物群落年际变化的环境因子,而水温是引起浮游植物群落季节变化的主要因子.结合近几年管理部门采取的措施,浮游植物群落年际变化可能与骆马湖实行禁止采砂和拆除围网等管理修复措施有关.