Bet hedging in a guild of desert annuals

Evolutionary bet hedging encapsulates the counterintuitive idea that organisms evolve traits that reduce short-term reproductive success in favor of longer-term risk reduction. It has been widely investigated theoretically, and many putative examples have been cited including practical ones such as the dormancy involved in microbe and weed persistence. However, long-term data on demographic variation from the actual evolutionarily relevant environments have been unavailable to test for its mechanistic relationship to alleged bet hedging traits. I report an association between delayed germination (a bet hedging trait) and risk using a 22-year data set on demographic variation for 10 species of desert annual plants. Species with greater variation in reproductive success (per capita survival from germination to reproduction x per capita fecundity of survivors) were found to have lower average germination fractions. This provides a definitive test using realistic data on demographic variance that confirms the life history prediction for bet hedging. I also showed that the species with greater long-term demographic variation tended to be the ones with greater sensitivity of reproductive success to variation among years in growing-season precipitation.     

The development and application of remote sensing to monitor sand dune habitats

Sand dunes are complex systems that contain several habitats, often as mosaics or transitions between types. Several of these habitats are afforded protection under European Legislation and in the UK nationally within Special Areas of Conservation (SAC) and Sites of Special Scientific Interest (SSSI). Natural England has a statutory duty to report to Europe on the conservation status and condition of sand dunes; and is required to report to the UK Government on designated sites. To achieve this we have sought ways of capturing, analysing and interpreting data on the extent and location of sand dune habitats. This requires an ability to be able to obtain data over large areas of coastline in an efficient way. Natural England and Environment Agency Geomatics have worked collaboratively for over 16 years, sharing data and ecological knowledge. In 2012 work started to evaluate the use of remote sensing to map UK BAP and Annex I sand dune habitats. A methodology has now been developed and tested to map sand dune habitats. The key objective was to provide an operational tool that will help to map these habitats and understand change on sites around England. This has been achieved through analysis of LIDAR and Compact Airborne Spectrographic Imager (CASI) data using Object Orientated Image Analysis. Quality Control (QC) and accuracy assessments have shown this approach to be successful and 11 sites have been mapped to date. These techniques are providing a new approach to monitoring change in coastal vegetation communities and informing management of protected sites.     

农药对土壤微生物群落的副作用的研究方法(Methods for Detecting Side-Effect of Pesticides to Soil Microbial Communities)

微生物群落在保持土壤肥力和对外界适应能力方面起着十分重要的作用.随着农药的广泛应用和农药评估体系的建立与完善,人们越来越关注农药对土壤微生物群落的负面影响,并且尝试用多种方法进行研究.虽然对其中任何一种方法的使用,均有助于提高人们对农药副作用的认识,但是使用少数几种方法所获得的信息,已能够满足风险评估的最低要求.论文以农药风险评估的"资料要求"为依据,对"资料要求"中涉及的"碳转化"和"氮转化"两种室内试验的特点和设计思路进行了分析.此外还简要介绍了国内外农药管理部门在土壤微生物风险评估中常用的几种模型,并探讨了这些模型与具体试验方法之间的关联.从文中可以看出,对于农药的土壤微生物影响,现已建立起了比较规范化的室内测定方法,目前缺乏的是与风险评估模型相配套的田间试验方法或准则.     

Futurecoast: Predicting the future coastal evolution of England and Wales

Analysis of shoreline change is often based exclusively upon the littoral cell concept and modelling of hydrodynamic processes. The Futurecoast study has considered fresh approaches to assessing shoreline evolution, which have been used to provide an analysis of future long-term evolution for the entire shoreline of England and Wales. This has been based upon an improved understanding of coastal systems and their behavioural characteristics. The study has included a range of supporting studies, focussing upon maximizing use of existing information and experience. A number of additional data sets have also been produced. The integration of leading expertise from different areas of coastal research to collectively consider this information has been the foundation for the study. The key outputs from this research are: (1) Improved understanding of coastal behaviour; (2) Assessment of future shoreline evolution; (3) Supporting information and data; (4) Delivery of results on an interactive CD-ROM.     

酚类化合物在矿化垃圾中吸附性能的研究

研究了苯酚、2-氯酚、4-氯酚、2,4-二氯酚在矿化垃圾中的吸附性能.酚在矿化垃圾中的吸附是一个比较迅速的过程,经过6h的吸附即可达到平衡.pH值对酚吸附容量的影响较大,低pH值有利于矿化垃圾对酚的吸附.四种酚在矿化垃圾中的吸附容量大小顺序为:4-氯酚＞2,4-二氯酚＞2-氯酚＞苯酚.在实验浓度范围内,酚在矿化垃圾中的吸附符合Freundlish等温吸附方程.四种酚在矿化垃圾中的吸附是酚在矿化垃圾有机质的分配作用和化学吸附作用共同作用的结果.     

溧阳地区森林景观的生态系统多样性研究

根据历史数据计算了溧阳地区森林景观变化过程中的生态系统多样性指数值,分析了其发展趋势,并结合其他相关资料分析区域森林生态系统多样性的变化,及其与森林经营的经济效果间的关系。     

Umweltindikatoren für Immissionswirkungen

“In contrast to other fields of politics, a few, minor measurements (indicators) are still lacking in the environmental policies which enable one to evaluate the environmental situation and to consequently take over a major role in political discussions” (BMU 1998). There are a multitude of suggestions for such environmental indicators on both a national and an international level. To date, however, there are still no indicators through which the ecological effects of biosphere pollution can be evaluated. This work provides two examples how biomonitoring data can be connected to environmental indicators. Based on the background concentrations of a multitude of heavy metals and dioxins/furans in two individual bioindicators (standardized grass and kale cultures), which have been combined to act as indicators for the topic in question, indices have been calculated through the determination of reference values. The results are indicators for the pollution effects of traffic-related emissions, the pollution effects from the emissions of fossil fuels and the pollution effects of dioxins and furanes. These indicators may still be combined further through the methods of calculation which are described. The further, methodical development of bioindicators, however, turned out to be problematic. Only data whose survey methods have been standardized to such an extent that the results are comparable with each other over longer periods of time can be combined to represent indicators: an inalienable requirement for the presentation of trends.     

Impulsive waves caused by subaerial landslides

This paper presents the experimental results of impulsive waves caused by subaerial landslides. A wide range of effective parameters are considered and studied by performing 120 laboratory tests. Considered slide masses are both rigid and deformable. The effects of bed slope angle, water depth, slide impact velocity, geometry, shape and deformation on impulse wave characteristics have been inspected. The impulse wave features such as amplitude, period and also energy conversation are studied. The effects of slide Froude number and deformation on energy conversation from slide into wave are also investigated. Based on laboratory measured data an empirical equation for impulse wave amplitude and period have been presented and successfully verified using available data of previous laboratory works.     

Turbulence-Based Models for Gas Transfer Analysis with Channel Shape Factor Influence

Gas transfer through surface water of streams is an effective process for the environmental quality of the aquatic ecosystem. Several theoretical approaches have been proposed to estimate gas transfer rate. This paper is devoted to present a turbulence-based model and to compare it with other 3 turbulence-based modeling frameworks that provide an estimation of gas-transfer coefficient K_L at the air-water interface. These models were derived for the reaeration process. In this paper, they have been verified both for reaeration and volatilization using experimental data collected in a laboratory rectangular flume and in a circular sewer reach. These data refer to oxygen absorption and cyclohexane volatilization, respectively. Comparison of results for oxygen shows that the tested models exhibit an average absolute difference between their results and the experimental data ranging from 12.5% and 25.6%. Also, the scaling analysis of the experimental data support both small-eddy based models and the model proposed by the authors. Moreover, volatilization results show that the process is also affected by a channel shape factor, which was, finally, quantified.     

Procedures for outlier detection in circular time series models

It is known that the occurrence of outliers in linear or non-linear time series models may have adverse effects on the modelling and statistical inference of the data. Consequently, extensive research has been conducted on developing outlier detection procedures so that outliers may be properly managed. However, no work has been done on the problem of outliers in circular time series data. This problem is the focus of this paper. The main objective is to develop novel numerical and graphical procedures for detecting these outliers in circular time series data.A number of circular time series models have been proposed including the circular autoregressive model. We extend the iterative outlier detection procedure which has been successfully used in linear time series models to the circular autoregressive model. The proposed procedure shows a good performance when investigated via simulation for the circular autoregressive model of order one. At the same time, several statistical techniques have been used to detect the change of preferred trend in time series data using SLIME and CUSUM plots. While the methods fail to indicate directly the outliers in circular time series data, we use the ideas employed to develop three novel graphical procedures for identifying the outliers. For illustration, we apply the procedures to a particular set of wind direction data. An agreement between the results of the graphical and iterative detection procedures is observed. These procedures could be very useful in improving the modelling and inferential processes for circular time series data.     

Estimating nonlinear interaction strengths: an observation-based method for species-rich food webs

Efforts to estimate the strength of species interactions in species-rich, reticulate food webs have been hampered by the multitude of direct and indirect interactions such systems exhibit and have been limited by an assumption that pairwise interactions display linear functional forms. Here we present a new method for directly measuring, on a per capita basis, the nonlinear strength of trophic species interactions within such food webs. This is an observation-based method, requiring three pieces of information: (1) species abundances, (2) predator and prey-specific handling times, and (3) data from predator-specific feeding surveys in which the number of individuals observed feeding on each of the predator's prey species has been tallied. The method offers a straightforward way to assess the completeness of one's sampling effort in accurately estimating interaction strengths through the construction of predator-specific prey accumulation curves. The method should be applicable to a variety of systems in which empirical estimates of direct interaction strengths have thus far remained elusive.     

Sensitivity of Marine-Reserve Design to the Spatial Resolution of Socioeconomic Data

Abstract:  Socioeconomic considerations should have an important place in reserve design. Systematic reserve-selection tools allow simultaneous optimization for ecological objectives while minimizing costs but are seldom used to incorporate socioeconomic costs in the reserve-design process. The sensitivity of this process to biodiversity data resolution has been studied widely but the issue of socioeconomic data resolution has not previously been considered. We therefore designed marine reserves for biodiversity conservation with the constraint of minimizing commercial fishing revenue losses and investigated how economic data resolution affected the results. Incorporating coarse-resolution economic data from official statistics generated reserves that were only marginally less costly to the fishery than those designed with no attempt to minimize economic impacts. An intensive survey yielded fine-resolution data that, when incorporated in the design process, substantially reduced predicted fishery losses. Such an approach could help minimize fisher displacement because the least profitable grounds are selected for the reserve. Other work has shown that low-resolution biodiversity data can lead to underestimation of the conservation value of some sites, and a risk of overlooking the most valuable areas, and we have similarly shown that low-resolution economic data can cause underestimation of the profitability of some sites and a risk of inadvertently including these in the reserve. Detailed socioeconomic data are therefore an essential input for the design of cost-effective reserve networks.     

A Markov random field spatio-temporal analysis of ocean temperature

The National Oceanic Data Center (NODC) contains historical records from approximately 144,000 hydrographic stations in the North Atlantic. This data has been used by oceanographers to construct maps of point estimates of pressure, temperature, salinity and oxygen in the North Atlantic (Levitus (1994); Lozier et al. (1995)). Because data from any particular year are scarce, the previous maps have been for time-averaged values only. In addition, the maps have been reported without uncertainty estimates. This paper presents a Markov random field (MRF) analysis that can generate maps for specific time periods along with associated uncertainties. To estimate changes in oceanic properties over time previous oceanographic work has focused on differences between a few time periods each having many observations. Due to data scarcity this poses a severe restriction for both spatial and temporal coverage of climatic change. The MRF analysis provides a means for temporal modeling that does not require high data density at each time period. To demonstrate the usefulness of a MRF analysis of oceanic data we investigate the temporal variability along 24.5°N in the North Atlantic. Our results are compared to an earlier analysis (Parrilla et al. (1994)) where data from only three time periods was used. We obtain a more thorough understanding of the temperature change found by this previous study.     

Multiple source spatial cluster detection via multi-criteria analysis

Multiple data sources are essential to provide reliable information regarding the emergence of potential health threats, compared to single source methods. Spatial Scan Statistics have been adapted to analyze multivariate data sources, but only ad hoc procedures have been devised to address the problem of selecting the most likely cluster and computing its significance. In this work, information from multiple data sources of disease surveillance is incorporated to achieve more coherent spatial cluster detection using tools from multi-criteria analysis. The best cluster solutions are found by maximizing two objective functions simultaneously, based on the concept of dominance. To evaluate the statistical significance of solutions, a statistical approach based on the concept of attainment function is used. The multi-criteria approach has several advantages: the representation of the evaluation function for each data source is clear, and does not suffer from an artificial, and possibly confusing mixture with the other data source evaluations; it is possible to attribute, in a rigorous way, the statistical significance of each candidate cluster; and it is possible to analyze and pick-up the best cluster solutions, as given naturally by the non-dominated set. The methodology is illustrated with real datasets.     

Uncertainty analyses for calibrating a soil carbon balance model to agricultural field trial data in Sweden and Kenya

How do additional data of the same and/or different type contribute to reducing model parameter and predictive uncertainties? Most modeling applications of soil organic carbon (SOC) time series in agricultural field trial datasets have been conducted without accounting for model parameter uncertainty. There have been recent advances with Monte Carlo-based uncertainty analyses in the field of hydrological modeling that are applicable, relevant and potentially valuable in modeling the dynamics of SOC. Here we employed a Monte Carlo method with threshold screening known as Generalized Likelihood Uncertainty Estimation (GLUE) to calibrate the Introductory Carbon Balance Model (ICBM) to long-term field trail data from Ultuna, Sweden and Machang’a, Kenya. Calibration results are presented in terms of parameter distributions and credibility bands on time series simulations for a number of case studies. Using these methods, we demonstrate that widely uncertain model parameters, as well as strong covariance between inert pool size and rate constant parameters, exist when root mean square simulation errors were within uncertainties in input estimations and data observations. We show that even rough estimates of the inert pool (perhaps from chemical analysis) can be quite valuable to reduce uncertainties in model parameters. In fact, such estimates were more effective at reducing parameter and predictive uncertainty than an additional 16 years time series data at Ultuna. We also demonstrate an effective method to jointly, simultaneously and in principle more robustly calibrate model parameters to multiple datasets across different climatic regions within an uncertainty framework. These methods and approaches should have benefits for use with other SOC models and datasets as well.     

Ableitung ökotoxikologisch begründeter Bodenqualitätskriterien am Beispiel Arsen

Background

A vast amount of data on the concentration of substances in soils is being generated in environmental monitoring programmes. This data has to be assessed in terms of effects on terrestrial biota. Benchmarks for such an assessment have been missing up to now. This gap has been filled by the development of a concept for the derivation of ecotoxicologically based soil quality criteria (SQC) and the application of this concept to environmentally relevant substances.

Objectives

The derivation method was applied to arsenic. The derivation process was described in detail from the compilation of data regarding effects on microbial processes, plants, and soil invertebrates to the formulation of a recommendation for an SQC.

Methods

The conceptual framework for the derivation of ecotoxicologically based SQC was developed after reviewing national and international concepts for the assessment of the effects of substances on terrestrial communities. For the assessment of arsenic effects, all relevant biotic processes and trophic levels have been taken into account. Moreover, soil characteristics that may affect the toxicity to biota have been reviewed.

Results

Effects of arsenic on biota may occur at soil background concentrations or slightly above. There are only a few studies in which the influence of soil characteristics, such as soil pH, soil texture, and soil organic carbon, on the effects of arsenic has been investigated systematically. That is why the SQC was oriented on soil background concentrations for sandy soils (5 mg As/kg soil dw). A comparison of the SQC derived in this work with ecotoxicologically based soilquality values from Canada, Denmark, the Netherlands, and the United States shows an average value of 16 mg As/kg, with a range between 5 and 34 mg/kg. The variations in the soil quality values are mainly due to differences in the derivation approaches.

Conclusions and Outlook

The concept applied is considered suitable for the derivation of SQC designed to protect terrestrial communities against direct effects of substances in soils. Therefore, its use is planned to derive SQC for other environmentally relevant substances. In addition to compensation factors, statistical methods should be taken into account to compensate for risks which cannot be quantified. Furthermore, there is the need for an assessment of indirect effects of soil contaminants on birds and mammals.     

Ecological relevance of performance criteria for species distribution models

Species distribution models have often been developed based on ecological data. To develop reliable data-driven models, however, a sound model training and evaluation procedures are needed. A crucial step in these procedures is the assessment of the model performance, with as key component the applied performance criterion. Therefore, we reviewed seven performance criteria commonly applied in presence-absence modelling (the correctly classified instances, Kappa, sensitivity, specificity, the normalised mutual information statistic, the true skill statistic and the odds ratio) and analysed their application in both the model training and evaluation process. Although estimates of predictive performance have been used widely to assess final model quality, a systematic overview was missing because most analyses of performance criteria have been empirical and only focused on specific aspects of the performance criteria. This paper provides such an overview showing that different performance criteria evaluate a model differently and that this difference may be explained by the dependency of these criteria on the prevalence of the validation set. We showed theoretically that these prevalence effects only occur if the data are inseparable by an n-dimensional hyperplane, n being the number of input variables. Given this inseparability, different performance criteria focus on different aspects of model performance during model training, such as sensitivity, specificity or predictive accuracy. These findings have important consequences for ecological modelling because ecological data are mostly inseparable due to data noise and the complexity of the studied system. Consequently, it should be very clear which aspect of the model performance is evaluated, and models should be evaluated consistently, that is, independent of, or taking into account, species prevalence. The practical implications of these findings are clear. They provide further insight into the evaluation of ecological presence/absence models and attempt to assist modellers in their choice of suitable performance criteria.     

Hierarchical modeling of count data with application to nuclear fall-out

We propose a method for a Bayesian hierarchical analysis of count data that are observed at irregular locations in a bounded domain of R². We model the data as having been observed on a fine regular lattice, where we do not have observations at all the sites. The counts are assumed to be independent Poisson random variables whose means are given by a log Gaussian process. In this article, the Gaussian process is assumed to be either a Markov random field (MRF) or a geostatistical model, and we compare the two models on an environmental data set. To make the comparison, we calibrate priors for the parameters in the geostatistical model to priors for the parameters in the MRF. The calibration is obtained empirically. The main goal is to predict the hidden Poisson-mean process at all sites on the lattice, given the spatially irregular count data; to do this we use an efficient MCMC. The spatial Bayesian methods are illustrated on radioactivity counts analyzed by Diggle et al. (1998).     

Monitoring campaign as ICM tools for Tokyo Bay

On 26 March 2003, the Tokyo Bay Renaissance Promotion Council, composed of six central government agencies and eight regional government bodies endorsed an “Action Plan for Tokyo Bay Renaissance”. Under the action plan, the monitoring campaign of bay environment has been started in 2008. Dissolved oxygen (DO), salinity, and temperature were monitored in sea area, chemical oxygen demand, temperature, and discharge were monitored in river area. These data sets show a detailed snap shot of hypoxic water occurrence in the inner part of the bay. A series of environment maps for Tokyo Bay have been published for sharing the data. For example, in 2012, goby census has been implemented to show importance of biological – environmental relations. Participants of the census are 240, and the more than 8,000 data of fished gobies has been corrected. It clearly shows relation between growth and special environment variation. From just few trial of the campaigns are not enough to answer all questions, nevertheless, these trials shows its importance and efficiency on bay environment monitoring and management as one of the ICM tools.