Numerical quantification of the significance of aquatic vegetation affecting spatial distribution of Japanese medaka (Oryzias latipes) in an agricultural canal

Aquatic vegetation plays a very important role in providing food, shelter, and nursery habitat and is also regarded as hydraulic resistance in the stream environment. To achieve better ecological restoration, this trade-off should be solved both hydraulically and ecologically. This study quantifies the effect of aquatic vegetation on the spatial distribution of Japanese medaka (Oryzias latipes) to evaluate its importance to fish habitat preference. The preference for aquatic vegetation index is calculated using a fuzzy preference intensity model (FPIM) with interactions among water depth, current velocity and cover ratio in an agricultural canal. In this model, simplified fuzzy reasoning is introduced to explicitly take the essential vagueness of fish behavior into consideration, and a simple genetic algorithm is used to search for an optimum model representation. Uncertainties in measurement errors and dispersions of the physical environment are positively taken into the model using symmetric triangular fuzzy numbers. To overcome the difficulty in model construction with insufficient data observed in an agricultural canal, this model was conjugated with a model developed in a laboratory experiment. The model obtained was then assessed using the AIC (Akaikes Information Criterion) to evaluate the significance of vegetation index with a statistical approach. The results suggest the significance of vegetation index to habitat selection by Japanese medaka and that utilization of the AIC enables us to grasp the validity of an additional factor contributing to habitat prediction with a view to a definite scale     

胸部双源性疾病外科治疗的临床决策

胸部双源性疾病随人口的老龄化而逐年增加,而老年人的心肺及各种脏器功能随年龄的增长逐渐衰退,并发症多,手术风险大,对此类病人的外科治疗如何做出正确的临床医疗决策呢？我们以循证医学理论为基础,努力做到临床医疗决策的科学性,治疗方案的合理性,大胆尝试,逐步从分期手术向一期手术过渡,既达到了解决病人疾苦,救治病人的目的,又解决了医疗需求的增加和卫生资源短缺的矛盾,取得了满意的效果,降低了医疗成本,节约了医疗资源,产生了明显的社会和经济效益。     

Independence and interdependence in the nest-site choice by honeybee swarms: Agent-based models, analytical approaches and pattern formation

In a recent paper List, Elsholtz and Seeley (List et al., 2009) have devised an agent-based model of the nest-choice dynamics in swarms of honeybees, and have concluded that both interdependence and independence are needed for the bees to reach a consensus on the best nest site. We here present a simplified version of the model which can be treated analytically with the tools of statistical physics and which largely has the same features as the original dynamics. Based on our analytical approaches it is possible to characterize the co-ordination outcome exactly on the deterministic level, and to a good approximation if stochastic effects are taken into account, reducing the need for computer simulations on the agent-based level. In the second part of the paper we present a spatial extension, and show that transient non-trivial patterns emerge, before consensus is reached. Approaches in terms of Langevin equations for continuous field variables are discussed.     

Fuzzy-Sets Approach to Noise Impact Assessment (7 pp)

Background, Aims and Scope Noise impacts are rarely assessed in Life Cycle Assessment (LCA), probably due to lack of data, to the difficulty of setting up an appropriate assessment method including relevant uncertainties and vagueness and to their site-dependent nature. The evaluation, as well as for odour, cultural and aesthetic impacts, seems to be closely related to human judgements and perception based. Although fuzzy-sets have been developed for this purpose since the late '60s and their usefulness has been proven by successful applications, noise impact assessment approaches have been essentially crisp so far. The aim of this paper is to present a method for noise impact assessment based on fuzzy sets with an application to a simple example. Methods The fuzzy noise impact assessment involves: 1) the quality assessment of the site concerned by the noise impact before the occurrence of noise emissions; quality is expressed by a crisp (i.e. non-fuzzy) function depending on variables (the so-called 'primitives'), which are relevant for the evaluation (e.g. the population density, the type of land use,...); 2) the fuzzy representation of the primitives, e.g. their evaluation by means of linguistic variables (such as 'the population density is high') and by fuzzy numbers; 3) the fuzzy representation of the quality, by fuzzifying the crisp function defined in 1) and 4) the fuzzy representation of the noise impact. In the example, the noise impacts of three processes of coal mining and combustion are assessed. Results and Discussion The application example proved the operationability of the method. Primitives and noise impact assessment results are represented by fuzzy numbers and intervals that are more informative than crisp numbers for the interpretation of results The quality and impact assessment results obtained seem to be coherent with the nature of the processes involved and of the variables characterizing them. Conclusion and Outlook Fuzzy intervals and numbers could be more informative and closer to human judgements and perceptions than crisp numbers are, thus improving the pertinence and the interpretation of the results. Despite the increase in sophistication and the fact that the representation of the variables involved in calculations should be developed further (e.g. on the basis of consensus gained in an expert panel), the fuzzy approach seems to be promising for the assessment of noise impacts in LCA.     

Possibility Theory: A New Approach to Uncertainty Analysis? (3 pp)

Background, Aims and Scope The problem of the evaluation of practitioner's belief and belief-related uncertainties on LCA results obtained from different methodological choices has been addressed so far by scenario modeling, Cultural Theory perspectives and probabilistic simulation. The direct evaluation of belief and related uncertainties could be of interest, e.g. when the information available (resulting from classical uncertainty analysis or the application of the precautionary principle) do not allow one to choose between methodological alternatives leading to different LCA results and conclusions. The difficulty of modeling belief arises from the additive nature of classical measures, e.g. probabilities. Since the 1960s, non-additive measures (e.g. possibilities) have been developed and applied to model belief in real world problems. The aim of this paper is to discuss the application of possibility measures in LCA for uncertainty analysis in complement to classical approaches. Methods The nature and the meaning of possibilities are briefly introduced by comparison with probabilities (subjective or not) in order to enlighten strengths, drawbacks and complementarities. A tentative possibilistic approach based on the evaluation of a posteriori possibilities of final LCA results depending on a priori possibilities of the methodological choices behind the calculations is described, also by means of an application example. Results and Outlook. A new approach for the modeling of practitioner's belief and belief-related uncertainties in complement of classical methods of uncertainty analysis has been proposed for discussion. Uncertainty can be characterized by confidence intervals and indexes that could help practitioners in making methodological choices and could improve the interpretation and reliability of LCA results, still increasing its sophistication.     

The behavioral context of common dolphin (Delphinus sp.) vocalizations

Correlations between surface behavior and concurrent underwater vocalizations were modeled for common dolphins (Delphinus spp.) in the Southern California Bight (SCB) over multiple field seasons. Clicks, pulsed calls, and whistles were examined, with a total of 50 call features identified. Call features were used to classify behavior using random forest decision trees, with rates of correct classification reaching 80.6% for fast travel, 84.6% for moderate travel, 59.8% for slow travel, and 58% for foraging behavior. Common dolphins spent most of their time traveling. The highest number of clicks, pulsed calls, and complex whistles were produced during fast travel. In contrast, during foraging there were few pulsed calls and whistles produced, and the whistles were simple with narrow bandwidths and few harmonics. Behavior and vocalization patterns suggest nocturnal foraging in offshore waters as the primary feeding strategy. Group size and spacing were strongly correlated with behavior and rates of calling, with higher call rates in dispersed traveling groups and lower call rates in loosely aggregated foraging groups. These results demonstrate that surface behavior can be classified using vocalization data, which builds the framework for behavioral studies of common dolphins using passive acoustic monitoring techniques.     

Visual extrapolation under risk: human observers estimate and compensate for exogenous uncertainty

Humans commonly face choices between multiple options with uncertain outcomes. Such situations occur in many contexts, from purely financial decisions (which shares should I buy?) to perceptuo-motor decisions between different actions (where should I aim my shot at goal?). Regardless of context, successful decision-making requires that the uncertainty at the heart of the decision-making problem is taken into account. Here, we ask whether humans can recover an estimate of exogenous uncertainty and then use it to make good decisions. Observers viewed a small dot that moved erratically until it disappeared behind an occluder. We varied the size of the occluder and the unpredictability of the dot''s path. The observer attempted to capture the dot as it emerged from behind the occluded region by setting the location and extent of a ‘catcher’ along the edge of the occluder. The reward for successfully catching the dot was reduced as the size of the catcher increased. We compared human performance with that of an agent maximizing expected gain and found that observers consistently selected catcher size close to this theoretical solution. These results suggest that humans are finely tuned to exogenous uncertainty information and can exploit it to guide action.     

Monitoring in the Context of Structured Decision-Making and Adaptive Management

Abstract: In a natural resource management setting, monitoring is a crucial component of an informed process for making decisions, and monitoring design should be driven by the decision context and associated uncertainties. Monitoring itself can play ≥3 roles. First, it is important for state-dependent decision-making, as when managers need to know the system state before deciding on the appropriate course of action during the ensuing management cycle. Second, monitoring is critical for evaluating the effectiveness of management actions relative to objectives. Third, in an adaptive management setting, monitoring provides the feedback loop for learning about the system; learning is sought not for its own sake but primarily to better achieve management objectives. In this case, monitoring should be designed to reduce the critical uncertainties in models of the managed system. The United States Geological Survey and United States Fish and Wildlife Service are conducting a large-scale management experiment on 23 National Wildlife Refuges across the Northeast and Midwest Regions. The primary management objective is to provide habitat for migratory waterbirds, particularly during migration, using water-level manipulations in managed wetlands. Key uncertainties are related to the potential trade-offs created by management for a specific waterbird guild (e.g., migratory shorebirds) and the response of waterbirds, plant communities, and invertebrates to specific experimental hydroperiods. We reviewed the monitoring program associated with this study, and the ways that specific observations fill ≥1 of the roles identified above. We used observations from our monitoring to improve state-dependent decisions to control undesired plants, to evaluate management performance relative to shallow-water habitat objectives, and to evaluate potential trade-offs between waterfowl and shorebird habitat management. With limited staff and budgets, management agencies need efficient monitoring programs that are used for decision-making, not comprehensive studies that elucidate all manner of ecological relationships.     

Associating Seasonal Range Characteristics With Survival of Female White-Tailed Deer

ABSTRACT Delineating populations is critical for understanding population dynamics and managing habitats. Our objective was to delineate subpopulations of migratory female white-tailed deer (Odocoileus virginianus) in the central Black Hills, South Dakota and Wyoming, USA, on summer and winter ranges. We used fuzzy classification to assign radiocollared deer to subpopulations based on spatial location, characterized subpopulations by trapping sites, and explored relationships among survival of subpopulations and habitat variables. In winter, Kaplan-Meier estimates for subpopulations indicated 2 groups: high (S = 0.991 ± 0.005 [x̄ ± SE]) and low (S = 0.968 ± 0.007) weekly survivorship. Survivorship increased with basal area per hectare of trees, average diameter at breast height of trees, percent cover of slash, and total point-center quarter distance of trees. Cover of grass and forbs were less for the high survivorship than the lower survivorship group. In summer, deer were spaced apart with mixed associations among subpopulations. Habitat manipulations that promote or maintain large trees (i.e., basal area = 14.8 m²/ha and average dbh of trees = 8.3 cm) would seem to improve adult survival of deer in winter.     

系统理论指导下辩证思维模式与抗休克决策

休克是由于有效循环血量不足引发的微循环障碍而表现一系列症候群,作为一种系统性疾病,病因十分复杂多样,诊疗过程中的临床决策应全面而富有针对性,并充分考虑到救治措施的协调一致和互相配合。系统论主张整体大于部分之和强调的就是措施之间的整体化效果,充分考虑到患者个体间差异,辩证分析不同类型休克、不同个体间不同表现,在系统理论指导下的富有针对性措施间协调一致的决策模式才是较为完整的休克救治临床决策模式。