Multiple paternity in meadow voles (Microtus pennsylvanicus): investigating the role of the female

Multiple paternity in single litters conceived in the wild was recently demonstrated in meadow voles (Microtuspennsylvanicus). In this study, we used an experimental approach (males tethered and females allowed to mate freely with one or several males) to investigate the role of female meadow voles in multiple paternity. We found that among 29 (of 39) females that copulated during our experiment, 79.3% chose to mate with more than one male. Female behavior in meadow voles thus clearly promotes multiple paternity and their role is an active one. Some of the hypotheses explaining promiscuity in meadow voles should be reconsidered in light of this result. We do not know the primary determinant of female mate choice, but male body mass played a secondary role in driving female preferences. The partial dependence between male body mass and female choice, coupled with the active role played by females, indicates that intersexual selection has the potential for reinforcing the effects of intrasexual selection (male-male dominance relationships) in this species. Finally, we demonstrate that the time period over which tests are conducted is an important part of the design of experiments aimed at understanding the role of females in multiple paternity. Received: 14 April 1998 / Accepted after revision: 12 September 1998     

People matter: The importance of social capital in the co-management of natural resources

Co‐management involves the shared administration of natural resources by two or more parties. This study examines the role of social capital in the process of developing co‐management in three river corridors in Canada. Qualitative analysis reveals that social capital acts as a catalyst helping groups to progress through the stages of the co‐management process. Forms of social capital (bridging and bonding) are identified that advance and/or inhibit the development of co‐management. The article reaffirms the need to expand the institutional basis for natural resource management and provides empirical evidence that social capital plays a fundamental role in developing co‐management. In conclusion, the article suggests that resource agencies need to recognize the value of social capital and the necessity for government representatives to be informed of and practiced in these skills, if they are to engage meaningfully with the civilian population.     

Fish assemblages composition in a natural, then regulated, stream: A quantitative long-term study

This paper tests the hypothesis how effectively fish assemblage composition was shaped by local climate changes and by engineering impact in 1989. This was possible due to the monitoring protocols of the present study, which allowed estimation of the magnitude of anthropogenic changes from changes naturally occurring in nature. Fish were sampled at the end of every growing season (October) for 23 years at five contiguous sites in a stream, before (1979-1988) and after (1989-2001) regulation. In each sample, six successive electrofishing passes were used to calculate the density and mean biomass for assemblage analysis using the Zippin model. During the study, the natural, meandering stream with pools, riffles, and a moderate canopy was modified into a straight stream of uniform width and depth, stripped of all vegetation. The output layer of a self-organizing map (SOM, the artificial neural network algorithm) applied in this study for site similarity analysis was partitioned into six subclusters placed in two main clusters. Subclusters in the upper part of the SOM were occupied chiefly by regulated stream samples and those in the lower part of the SOM by natural stream samples. Subclusters in the middle position, contained both natural (19) and regulated (20) samples in nearly equal proportion. In addition, the SOM contained one subcluster with sites only from the regulated period and another with only natural sites. Differences between subclusters were attributed to differences in climate, with some differences profound. Warming of the local weather, which became most evident in the 1990s, may have resulted in changes in fish assemblages. This is shown in the SOM, in which samples from the 1980s with cold years dominate the bottom of the SOM, whereas those from the 1990s and later are at the top. Subclusters dominated by regulated or natural sites were not always significantly different when the number of species and diversity indices were considered. Clear differences between the regulated and natural samples involved qualitative characteristics and mainly concerned assemblage composition. They were also confirmed by significant IndVal values (indicator species) and neither mixed subcluster contained important species in their assemblages.     

Group decision-making for leakage management strategy of water network

The problem of leakage is complex and requires actions drawn from different aspects of water network management. Inadequate maintenance has serious consequences, both financial and environmental. This paper proposes a group decision-making model based on PROMETHEE V method to aim a leakage management strategy, which takes into account the points of view of four stakeholders, selecting feasible options, and considering the available budget as constraint. Thus, this strategy is the combination of options that will efficiently meet technical, socio-economic and environmental criteria to achieve sustainable development.     

Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests

Forestry science has a long tradition of studying the relationship between stand productivity and abiotic and biotic site characteristics, such as climate, topography, soil and vegetation. Many of the early site quality modelling studies related site index to environmental variables using basic statistical methods such as linear regression. Because most ecological variables show a typical non-linear course and a non-constant variance distribution, a large fraction of the variation remained unexplained by these linear models. More recently, the development of more advanced non-parametric and machine learning methods provided opportunities to overcome these limitations. Nevertheless, these methods also have drawbacks. Due to their increasing complexity they are not only more difficult to implement and interpret, but also more vulnerable to overfitting. Especially in a context of regionalisation, this may prove to be problematic. Although many non-parametric and machine learning methods are increasingly used in applications related to forest site quality assessment, their predictive performance has only been assessed for a limited number of methods and ecosystems.In this study, five different modelling techniques are compared and evaluated, i.e. multiple linear regression (MLR), classification and regression trees (CART), boosted regression trees (BRT), generalized additive models (GAM), and artificial neural networks (ANN). Each method is used to model site index of homogeneous stands of three important tree species of the Taurus Mountains (Turkey): Pinus brutia, Pinus nigra and Cedrus libani. Site index is related to soil, vegetation and topographical variables, which are available for 167 sample plots covering all important environmental gradients in the research area. The five techniques are compared in a multi-criteria decision analysis in which different model performance measures, ecological interpretability and user-friendliness are considered as criteria.When combining these criteria, in most cases GAM is found to outperform all other techniques for modelling site index for the three species. BRT is a good alternative in case the ecological interpretability of the technique is of higher importance. When user-friendliness is more important MLR and CART are the preferred alternatives. Despite its good predictive performance, ANN is penalized for its complex, non-transparent models and big training effort.     

沉积物毒性鉴别评估技术在一个有着多重污染物输入地点的应用

沉积物毒性鉴别评估用于确定造成沉积物整体毒性测试中观察到有害效果的原因。然而在多污染物的情况下,将总体毒性划分为为不同个体污染物单独造成的毒性的方法存在很多问题。利用一个有着多重输入与相关污染物的站点数据,我们为毒性鉴别评估提出一种通过追踪毒性单位（TUs）来确定是否囊括全部毒性的方法。首先确定出沉积物整体的毒性水平,然后将毒性来源划分至几种污染物大类,例如氨类,金属,非极性有机物等。本文中毒性很大程度上来源于非极性有机物,所以在萃取沉积物之后,将萃取物重新加回稀释水中以检测毒性的复原。每一部分污染物的毒性占比由溶剂梯度测量,并进行毒性检测。目标占比由气相色谱-质谱分析得到,从而鉴定出与特定成分相关的毒性。之后对这些成分相关的毒性单位进行测定以确定可能的原因组合和是否已囊括全部毒性。数据表明毒性既与已有化合物相关,又与新兴污染物相关,而两种污染物对于总体毒性的具体贡献则随实验地点改变。
精选自Howard C. Bailey, Catherine A. Curran, Peter Arth, Bonnie P. Lo, Rich Gossett. Application of Sediment Toxicity Identification Evaluation Techniques to a Site with Multiple-Contaminants. Environmental Toxicology and Chemistry: Volume 35, Issue 10, pages 2456–2465, October 2016. DOI: 10.1002/etc.3488
详情请见http://onlinelibrary.wiley.com/doi/10.1002/etc.3488/full
     

Complementarity,impatience, and the resilience of natural-resource-dependent economies

We study how society's preferences affect the resilience of economies that depend on more than one type of natural resource. In particular, we analyze whether the degree of complementarity of natural resources in consumer preferences may give rise to multiple steady states and path dependence even when resources are managed optimally. We find that, for a given social discount rate, society tends to be less willing to buffer exogenous shocks if resource good are complements in consumption than if they are substitutes. The stronger the complementarity between the various types of natural resources, the less resilient the economy is, and even more so the higher is the social discount rate.     

Optimum parameters for fault detection and diagnosis system of batch reaction using multiple neural networks

Batch process usually differs from the continuous process because of its time-varying variables and the process parameters. An early detection and isolation of faults in the process will help to reduce the process upsets and keep it safe and reliable. This paper discusses on the application of multi-layer perceptron neural network in detecting various faults in batch chemical reactor based on an esterification process that involves the reaction of ethanol and acetic acid catalyzed by sulfuric acid. A multi-layer feed forward neural network with double hidden layers has been used in the neural network architecture. The detection was based on the different patterns generated between normal and faulty conditions. An optimum network configuration was found when the network produced the minimal error with respect to the training, testing and data validation.     

FREEDOM II: An improved methodology to assess domino effect frequency using simulation techniques

The uncertainty and the complexity associated with the domino effect is a barrier to assessing the frequency of such accidents analytically. The use of simulation techniques, such as Monte Carlo, to examine the domino effect instead of analytical techniques has shown great promise. In this paper, a new method to assess the frequency of domino accidents is proposed—FREEDOM II—which is an improvement on the recent algorithm proposed by the authors (FREEDOM). The modifications on FREEDOM were carried out to overcome a limitation of the method and to extend its capabilities. A key shortcoming of the earlier method was its inability to handle multiple failure scenarios. This shortcoming has been overcome in FREEDOM II. A new and improved algorithm has been developed that carries out the simulation in a significantly shorter run time. The applicability of the new model is shown by performing a multi-scenario case study.     

Using Ecological Risk Assessment to Identify the Major Anthropogenic Stressor in the Waquoit Bay Watershed,Cape Cod,Massachusetts

The Waquoit Bay Watershed ecological risk assessment was performed by an interdisciplinary and interagency workgroup. This paper focuses on the steps taken to formulate the analysis plan for this watershed assessment. The workgroup initially conducted a series of meetings with the general public and local and state managers to determine environmental management objectives for the watershed. The workgroup then decided that more information was needed on the impacts of six stressors: nutrient enrichment, physical alteration of habitat, altered freshwater flow, toxic chemicals, pathogens, and fisheries harvesting. Assessment endpoints were selected to establish the link between environmental management objectives, impacts of stressors, and scientifically measurable endpoints. The following assessment endpoints were selected: estuarine eelgrass cover, scallop abundance, finfish diversity and abundance, wetland bird distribution and abundance, piping plover distribution and abundance, tissue contaminant levels, and brook trout distribution and abundance in streams. A conceptual model was developed to show the pathways between human activities, stressors, and ecological effects. The workgroup analyzed comparative risks, by first ranking stressors in terms of their potential risk to biotic resources in the watershed. Then stressors were evaluated by considering the components of stressors (e.g., the stressor chemical pollution included both heavy metals and chlorinated solvents components) in terms of intensity and extensiveness. The workgroup identified nutrient enrichment as the major stressor. Nutrient enrichment comprised both phosphorus enrichment in freshwater ponds and nitrogen enrichment within estuaries. Because phosphorus impacts were being analyzed and mitigated by the Air Force Center for Environmental Excellence, this assessment focused on nitrogen. The process followed to identify the predominant stressor and focus the analyses on nitrogen impacts on eelgrass and scallops will serve as an example of how to increase the use of the findings of a watershed assessment in decision making.