The 3MRA Risk Assessment Framework—A Flexible Approach for Performing Multimedia,Multipathway, and Multireceptor Risk Assessments Under Uncertainty

A flexible framework for conducting nationwide multimedia, multipathway and multireceptor risk assessments (3MRA) under uncertainty was developed to estimate protective chemical concentration limits in a source area. The framework consists of two components: risk assessment and uncertainty analysis. The risk component utilizes linked source, fate/transport, exposure and risk assessment models to estimate the risk exposures for the receptors of concern. Both human and ecological receptors are included in the risk assessment framework. The flexibility of the framework is based on its ability to address problems varying in spatial scales from site-specific to regional and even national levels; and its ability to accommodate varying types of source, fate/transport, exposure and risk assessment models. The uncertainty component of the 3MRA framework is based on a two-stage Monte Carlo methodology. It allows the calculation of uncertainty in risk estimates, and the incorporation of the effects of uncertainty on the determination of regulatory concentration limits as a function of variability and uncertainty in input data, as well as potential errors in fate and transport and risk and exposure models. The framework can be adapted to handle a wide range of multimedia risk assessment problems. Two examples are presented to illustrate its use, and to demonstrate how regulatory decisions can be structured to incorporate the uncertainty in risk estimates.     

Calibrating the Tree of Life: fossils, molecules and evolutionary timescales

Background

Molecular dating has gained ever-increasing interest since the molecular clock hypothesis was proposed in the 1960s. Molecular dating provides detailed temporal frameworks for divergence events in phylogenetic trees, allowing diverse evolutionary questions to be addressed. The key aspect of the molecular clock hypothesis, namely that differences in DNA or protein sequence between two species are proportional to the time elapsed since they diverged, was soon shown to be untenable. Other approaches were proposed to take into account rate heterogeneity among lineages, but the calibration process, by which relative times are transformed into absolute ages, has received little attention until recently. New methods have now been proposed to resolve potential sources of error associated with the calibration of phylogenetic trees, particularly those involving use of the fossil record.

Scope and Conclusions

The use of the fossil record as a source of independent information in the calibration process is the main focus of this paper; other sources of calibration information are also discussed. Particularly error-prone aspects of fossil calibration are identified, such as fossil dating, the phylogenetic placement of the fossil and the incompleteness of the fossil record. Methods proposed to tackle one or more of these potential error sources are discussed (e.g. fossil cross-validation, prior distribution of calibration points and confidence intervals on the fossil record). In conclusion, the fossil record remains the most reliable source of information for the calibration of phylogenetic trees, although associated assumptions and potential bias must be taken into account.     

Estimating ancestral distributions of lineages with uncertain sister groups： a statistical approach to Dispersal-Vicariance Analysis and a case using Aesculus L. （Sapindaceae） including fossils

We propose a simple statistical approach for using Dispersal-Vicariance Analysis （DIVA） software to infer biogeographic histories without fully bifurcating trees. In this approach, ancestral ranges are first optimized for a sample of Bayesian trees. The probability P of an ancestral range r at a node is then calculated as P（rY） = ∑t^n=1 F（rY）t Pt where Y is a node, and F（rY） is the frequency of range r among all the optimal solutions resulting from DIVA optimization at node Y, t is one of n topologies optimized, and Pt is the probability of topology t. Node Y is a hypothesized ancestor shared by a specific crown lineage and the sister of that lineage ＂x＂, where x may vary due to phylogenetic uncertainty （polytomies and nodes with posterior probability 〈 100%）. Using this method, the ancestral distribution at Y can be estimated to provide inference of the geographic origins of the specific crown group of interest. This approach takes into account phylogenetic uncertainty as well as uncertainty from DIVA optimization. It is an extension of the previously described method called Bayes-DIVA, which pairs Bayesian phylogenetic analysis with biogeographic analysis using DIVA. Further, we show that the probability P of an ancestral range at Y calculated using this method does not equate to pp＊F（rY） on the Bayesian consensus tree when both variables are 〈 100%, where pp is the posterior probability and F（rY） is the frequency of range r for the node containing the specific crown group. We tested our DIVA-Bayes approach using Aesculus L., which has major lineages unresolved as a polytomy. We inferred the most probable geographic origins of the five traditional sections of Aesculus and ofAesculus californica Nutt. and examined range subdivisions at parental nodes of these lineages. Additionally, we used the DIVA-Bayes data from Aesculus to quantify the effects on biogeographic inference of including two wildcard fossil taxa in phylogenetic analysis. Our analysis resolved the geographic     

Formalizing Darwinism and inclusive fitness theory

Inclusive fitness maximization is a basic building block for biological contributions to any theory of the evolution of society. There is a view in mathematical population genetics that nothing is caused to be maximized in the process of natural selection, but this is explained as arising from a misunderstanding about the meaning of fitness maximization. Current theoretical work on inclusive fitness is discussed, with emphasis on the author''s ‘formal Darwinism project’. Generally, favourable conclusions are drawn about the validity of assuming fitness maximization, but the need for continuing work is emphasized, along with the possibility that substantive exceptions may be uncovered. The formal Darwinism project aims more ambitiously to represent in a formal mathematical framework the central point of Darwin''s Origin of Species, that the mechanical processes of inheritance and reproduction can give rise to the appearance of design, and it is a fitting ambition in Darwin''s bicentenary year to capture his most profound discovery in the lingua franca of science.     

Reduced modeling and state observation of an activated sludge process

This article first proposes a reduction strategy of the activated sludge process model with alternated aeration. Initiated with the standard activated sludge model (ASM1), the reduction is based on some biochemical considerations followed by linear approximations of nonlinear terms. Two submodels are then obtained, one for the aerobic phase and one for the anoxic phase, using four state variables related to the organic substrate concentration, the ammonium and nitrate‐nitrite nitrogen, and the oxygen concentration. Then, a two‐step robust estimation strategy is used to estimate both the unmeasured state variables and the unknown inflow ammonium nitrogen concentration. Parameter uncertainty is considered in the dynamics and input matrices of the system. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

基于模型数据融合的长白山阔叶红松林碳循环模拟

 充分、有效地利用各种陆地生态系统碳观测数据改善陆地生态系统模型, 是当前我国陆地生态系统碳循环研究领域亟待解决的重要问题之一。该研究以2003~2005年长白山阔叶红松林的6组生物计量观测数据和涡度相关技术测定的碳通量数据为基础, 利用马尔可夫链-蒙特卡罗方法对陆地生态系统模型的关键参数(即碳滞留时间)进行了反演, 进而预测了长白山阔叶红松林生态系统碳库、碳通量及其不确定性。反演结果表明, 长白山阔叶红松林叶凋落物和微生物碳的平均滞留时间最短, 为2~6个月; 其次是叶和细根生物量碳, 二者的平均滞留时间为1~2 a; 慢性土壤有机碳的平均滞留时间为8~16 a; 碳在木质生物量和惰性土壤有机质库中的滞留时间最长, 平均滞留时间分别为77~109 a和409~1 879 a。模拟结果显示, 碳库和累积碳通量模拟值的不确定性将随着模拟时间的延长而增大。当气温升高10%和20%时, 长白山阔叶红松林总初级生产力年总量将分别增加6.5%和9.9%, 净生态系统生产力(NEP)年总量的变化取决于土壤温度的变化。若土壤温度保持不变, NEP年总量将分别增加11.4%~21.9%和17.6%~33.1%; 若土壤温度也相应升高10%和20%, NEP年总量的增幅反而下降甚至低于原来的水平。假设气候和植被保持在2003~2005年的状态, 2020年长白山阔叶红松林NEP年总量为(163±12) g C·m^–2·a^–1, 土壤呼吸年总量为(721±14) g C·m^–2·a^–1。马尔可夫链-蒙特卡罗方法是反演模型参数、优化模拟结果和评估模拟结果不确定性的有效方法, 但今后仍需在惰性土壤碳滞留时间的估计、驱动数据和模型结构的不确定性分析、模型数据融合方法方面进行深入研究, 以进一步提高碳循环模拟的准确性。     

植物对气候变化生理生态响应的不确定性分析

生态系统对全球气候变化的响应模式有利于人类预测与适应未来生态环境变化,植物作为陆地生态系统的重要组成部分,对全球气候变化的响应具有重要作用.本文通过对近年来植物对气候变化的生理生态响应研究中(包括定点控制实验,空间代替时间样带),植物响应模式的复杂性、多样性及可变性等诸多不确定性进行分析.以探讨预测未来气候情景下植物的动态变化及生理生态响应过程.分析结果认为.造成这些不确定性的主要原因包括:(1)利用空间代替时间的样带研究中,往往忽略了植物的非线性响应,存在明显的阈值;(2)样带及定点研究中,由于各种气候因子的耦合.很难确定各种气候因子对植物生理生态学特性影响的权重;(3)定点控制实验中往往忽略了植物对气候变化的适应性,使实验结果很难代表更长时间尺度上的反映模式;(4)在相同的气候变化条件下,不同植物的响应有可能存在明显差异.提出了今后植物对气候变化生理生态响应研究的建议.     

Uncertainties in the response of a forest landscape to global climatic change

Many studies have been conducted to quantify the possible ecosystem/landscape response to the anticipated global warming. However, there is a large amount of uncertainty in the future climate predictions used for these studies. Specifically, the climate predictions can be very different based on a variety of global climate models and alternative greenhouse emission scenarios. In this study, we coupled a forest landscape model, LANDIS-II, and a forest process model, PnET-II, to examine the uncertainty (that results from the uncertainty in the future climate predictions) in the forest-type composition prediction for a transitional forest landscape [the Boundary Water Canoe Area]. Using an improved global-sensitivity analysis technique [Fourier amplitude sensitivity test], we also quantified the amount of uncertainty in the forest-type composition prediction contributed by different climate variables including temperature, CO₂, precipitation and photosynthetic active radiation (PAR). The forest landscape response was simulated for the period 2000–2400 ad based on the differential responses of 13 tree species under an ensemble of 27 possible climate prediction profiles (monthly time series of climate variables). Our simulations indicate that the uncertainty in the forest-type composition becomes very high after 2200 ad , which is close to the time when the current forests are largely removed by windthrow disturbances and natural mortality. The most important source of uncertainty in the forest-type composition prediction is from the uncertainty in temperature predictions. The second most important source is PAR, the third is CO₂ and the least important is precipitation. Our results also show that if the optimum photosynthetic temperature rises due to CO₂ enrichment, the forest landscape response to climatic change measured by forest-type composition may be substantially reduced.     

Robust filtering for stochastic genetic regulatory networks with time-varying delay

This paper addresses the robust filtering problem for a class of linear genetic regulatory networks (GRNs) with stochastic disturbances, parameter uncertainties and time delays. The parameter uncertainties are assumed to reside in a polytopic region, the stochastic disturbance is state-dependent described by a scalar Brownian motion, and the time-varying delays enter into both the translation process and the feedback regulation process. We aim to estimate the true concentrations of mRNA and protein by designing a linear filter such that, for all admissible time delays, stochastic disturbances as well as polytopic uncertainties, the augmented state estimation dynamics is exponentially mean square stable with an expected decay rate. A delay-dependent linear matrix inequality (LMI) approach is first developed to derive sufficient conditions that guarantee the exponential stability of the augmented dynamics, and then the filter gains are parameterized in terms of the solution to a set of LMIs. Note that LMIs can be easily solved by using standard software packages. A simulation example is exploited in order to illustrate the effectiveness of the proposed design procedures.     

Soil organic carbon stock change due to land use activity along the agricultural frontier of the southwestern Amazon,Brazil, between 1970 and 2002

The southwestern portion of the Brazilian Amazon arguably represents the largest agricultural frontier in the world, and within this region the states of Rondônia and Mato Grosso have about 24% and 32% of their respective areas under agricultural management, which is almost half of the total area deforested in the Brazilian Amazon biome. Consequently, it is assumed that deforestation in this region has caused substantial loss of soil organic carbon (SOC). In this study, the changes in SOC stocks due to the land use change and management in the southwestern Amazon were estimated for two time periods from 1970–1985 and 1985–2002. An uncertainty analysis was also conducted using a Monte Carlo approach. The results showed that mineral soils converted to agricultural management lost a total of 5.37 and 3.74 Tg C yr^?1 between 1970–1985 and 1985–2002, respectively, along the Brazilian Agricultural Frontier in the states of Mato Grosso and Rondônia. Uncertainties in these estimates were ±37.3% and ±38.6% during the first and second time periods, respectively. The largest sources of uncertainty were associated with reference carbon (C) stocks, expert knowledge surveys about grassland condition, and the management factors for nominal and degraded grasslands. These results showed that land use change and management created a net loss of C from soils, however, the change in SOC stocks decreased substantially from the first to the second time period due to the increase in land under no‐tillage.