完全均匀混合质量平衡水质模型在滇池中的应用

介绍了完全均匀混合假设下以质量平衡为基础的湖泊水质模型,运用滇池的实测数据对模型进行了参数率定、验证,给出了模型在滇池水质预测中的应用实测,最后讨论了模型的几个假设条件对滇池的适应性。实例研究表明,该模型可适用于滇池水质有机污染长期浓度预测。      

Approaches and terminology for causal analysis in land systems science

Research into land and social-ecological systems science could benefit from improved clarity in the terminology used for causal analysis and a structured way to make causal inferences. Here I identify two aspects of causality, i.e. causal effects and causal mechanisms, and discuss explanation in historical sciences. I then propose definitions for the major terms used for causal relations, including driver, (spatial) determinant, location and contextual factor, proximate and underlying factors. Finally, I discuss the contribution of various operational approaches, including time series and counterfactual approaches for assessing causal effects and process-tracing approaches for establishing causal mechanisms. Having a coherent concept of causality, agreeing on a precise vocabulary and harnessing our tools with the clear purpose of establishing both causal effects and causal mechanisms should strengthen causal explanations for single cases, for drawing policy-relevant lessons and for theoretical development in relation to land and, more broadly, social-ecological systems processes.     

Simulation of long-term nutrient removal in a full-scale closed-loop bioreactor for sewage treatment: an example of Bayesian inference

In this study, the performance of nitrogen and phosphorus removal in a full-scale closed-loop bioreactor (oxidation ditch) system was simulated using the ASM2d model. Routine data describing the process for two years were compiled for calibration and validation. To overcome the identifiability problem, the classic Bayesian inference approach was utilized for parameter estimation. The calibrated model could describe the long-term trend of nutrient removal and short-term variations of the process performance, showing that the Bayesian method was a reliable and useful tool for the parameter estimation of the activated sludge models. The anoxic phosphate uptake by polyphosphate accumulating organisms (PAO) contributed 71.2% of the total Poly-P storage, which reveals the dominance of denitrifying phosphorus removal process under the oxygen limiting conditions. It was found that 58.7% of the anoxic Poly-P storage and denitrification by PAO in the reactor was achieved in the aerated compartment, implying that the PAO’s anoxic activity was significantly stimulated by the low dissolved oxygen (DO) level in this compartment due to the oxygen gradient caused by brush aerator.     

Inverse calculation approaches for source determination in hazardous chemical releases

Source determination is vital in decision making and emergency planning involving hazardous chemical releases. This work was concentrated on inverse calculation approaches for source determination as well as current trends and future perspectives. In this paper, these different approaches are reviewed by dividing them into two categories: probability modeling methods and optimization modeling methods. The traits of these approaches are comparatively analyzed. Then it is shown how these approaches behave when applied to practical cases, and their feasibility, applicability, stability, and limitation in determining the location and strength are presented. It is argued that when experimenting with potential terrorist attacks involving hazardous chemical releases, observation points should be around the main line of the downwind direction when the source is known; while the uniform distribution of observation points is an efficient solution for unknown incidents. Probability modeling methods are demonstrated to be insufficient during emergency responses due to their lacking of enough prior information of unknown parameters, while optimization modeling methods are efficient and become a new trend in source determination. Findings reflect an urgent need for the development of high-accuracy detectors and further research of data transmission techniques in order to ensure the validity of these approaches.     

Impact of bias in predicted height on tree volume estimation: A case-study of intrinsic nonlinearity

Bias originating from intrinsic nonlinearity in nonlinear models is caused by excess curvature in the solution locus of parameter estimates derived from least squares procedures. Bias due to intrinsic nonlinearity varies according to sample size as well as model specification. This paper analyses consequences of fractionising data into smaller sub-samples. Based on measurements of stem diameter and total tree height from the first Danish national forest inventory, it is demonstrated how data splitting at random may cause the intrinsic nonlinear curvature to exceed the critical F-value. Application of a Taylor-series expansion shows that, for all practical purposes, the bias in predictions of individual tree volume (based on stem diameter and tree height) is negligible. To minimize residual variance, intrinsic curvature and, in turn, prediction bias, it is recommended that data be stratified according to site conditions, stand characteristics or other relevant criteria. Finally, the preferred model should exhibit close-to-linear behaviour.     

Studying ecological communities from a neutral standpoint: A review of models’ structure and parameter estimation

Neutral models provide an alternative to niche-based assembly rules of ecological communities by assuming that communities’ properties are shaped by the stochastic interplay between ecological drift, migration and speciation. The recent and ongoing interest about neutral assumptions has produced many developments on the theoretical side, with nevertheless limited echoes in terms of analyses of real-world data. The present review paper aims to help bridge the widening gap between modellers and field ecologists through two objectives. First, to provide a multi-criteria typology of the main neutral models, including those from population genetics that have not yet been transposed to ecology, by considering how the fundamental processes of ecological drift, speciation and migration are modelled and, specifically, how space is taken into account. Second, to review methods recently proposed to estimate models parameters from field data, a point that should be mastered to allow for broader applications.     

Spatio-temporal dissolved oxygen dynamics in the Orbetello lagoon by fuzzy pattern recognition

This paper models the dissolved oxygen (DO) dynamics in the Orbetello lagoon as a function of the physico-chemical and ecological system variables, including the submerged vegetation, nutrients, and hydrodynamics. It should be viewed as the concluding sequel to a previous paper describing the dynamics of the lagoon ecosystem [Giusti, E., Marsili-Libelli, S., 2006. An integrated model for the Orbetello lagoon ecosystem, Ecol. Model. 196, 379–394] by introducing the missing DO dynamics. The model considers the oxygen demand originating from the decay of carbonaceous and nitrogenous compounds, as well as photosynthesis and natural reaeration by winds and currents as the oxygen producing processes. With a fixed-parameter set the model could accurately reproduce each single circadian DO cycle, but in the long run it failed to extend this fit and could not accommodate the large DO fluctuations induced by the seasonal variability. In order to enhance the model flexibility, a fuzzy pattern recognition algorithm was designed to classify the circadian DO patterns into four typical behaviours, related to the season, and estimate the corresponding parameters, with the overall model output being a fuzzy combination of these sets. The paper discusses several methods to patch the parameter sets and compares their performance in tracking long-term DO variations. A final assessment of the model validity is obtained by incorporating the whole DO dynamics (model, fuzzy pattern recognition and parameter combination) into the general lagoon model and producing a consistently correct series of DO daily distributions over a yearly cycle. Thus the paper contains both a practical and a methodological aspect. The practical one is the linking of all the lagoon dynamics to the dissolved oxygen kinetics in order to clarify to what extent macroalgae and macrophytes influence the oxygen balance. The methodological aspect consists of extending the validity of short-term models to long time-horizons through a patching technique supported by fuzzy pattern recognition.     

A Bayesian approach to multi-source forest area estimation

In efforts such as land use change monitoring, carbon budgeting, and forecasting ecological conditions and timber supply, there is increasing demand for regional and national data layers depicting forest cover. These data layers must permit small area estimates of forest area and, most importantly, provide associated error estimates. This paper presents a model-based approach for coupling mid-resolution satellite imagery with plot-based forest inventory data to produce estimates of probability of forest and associated error at the pixel-level. The proposed Bayesian hierarchical model provides access to each pixel’s posterior predictive distribution allowing for a highly flexible analysis of pixel and multi-pixel areas of interest. The paper presents a trial using multiple dates of Landsat imagery and USDA Forest Service Forest Inventory and Analysis plot data. The results describe the spatial dependence structure within the trial site, provide pixel and multi-pixel summaries of probability of forest land use, and explore discretization schemes of the posterior predictive distributions to forest and non-forest classes. Model prediction results of a holdout set analysis suggest the proposed model provides high classification accuracy, 88%, for the trial site.

Use of Scale Invariance in Evaluating Judgement Indicators

Indicators are used to draw conclusions about ecological endpoints when these endpoints cannot be measured directly. In many cases, inferences about an endpoint are only possible because assumptions have been made about the relationship between indicator and endpoint; we refer to such indicators as judgement indicators. The validity of inferences made using a judgement indicator can be gauged by examining the known or assumed form of the general relationship between indicator and endpoint. The rules for this kind of inference are a consequence of scale invariance, which originates from measurement theory. For simple indicators comprised of a single indicator measurement, the inferences allowed – equivalence, rank, equality of intervals, and equality of ratios – depend on whether the data are nominal, ordinal, interval, or ratio scaled. For composite indicators containing two or more simple indicators, inferences are also affected by the mathematical form of combination; e.g., whether the terms are summed or multiplied. Standardizing simple or composite indicators can allow inferences about the relative importance of observations, based on the natural range of occurrence. Scale invariance is a particularly important consideration in landscape assessments, since these often make use of judgement indicators.     

Extinction rate of discovered and undiscovered plants in Singapore

Extinction is a key issue in the assessment of global biodiversity. However, many extinction rate measures do not account for species that went extinct before they could be discovered. The highly developed island city–state of Singapore has one of the best-documented tropical floras in the world. This allowed us to estimate the total rate of floristic extinctions in Singapore since 1822 after accounting for sampling effort and crypto extinctions by collating herbaria records. Our database comprised 34,224 specimens from 2076 native species, of which 464 species (22%) were considered nationally extinct. We assumed that undiscovered species had the same annual per-species extinction rates as discovered species and that no undiscovered species remained extant. With classical and Bayesian algorithms, we estimated that 304 (95% confidence interval, 213–414) and 412 (95% credible interval, 313–534) additional species went extinct before they could be discovered, respectively; corresponding total extinction rate estimates were 32% and 35% (range 30–38%). We detected violations of our 2 assumptions that could cause our extinction estimates, particularly the absolute numbers, to be biased downward. Thus, our estimates should be treated as lower bounds. Our results illustrate the possible magnitudes of plant extirpations that can be expected in the tropics as development continues.