Robust trend tests with application to toxicology

In most real data situations in the one-way design both the underlying distribution and the shape of the dose-response curve are a priori unknown. The power of a trend test strongly depends on both. However, tests which are routinely used to analyze toxicological assays must be robust. We use nonparametric tests with different scores—powerful for different distributions—and different contrasts—powerful for different shapes—and use the maximum of all test statistics as a new test statistic. Simulation results indicate that this maximum test, which is a nonparametric multiple contrast test, stabilizes the power for various shapes and distributions. The investigated tests are applied to the data of a toxicological assay.     

Empirical evaluation of neutral theory

We describe a general framework for testing neutral theory. We summarize similarities and differences between ten different versions of neutral theory. Two central predictions of neutral theory are that species abundance distributions will follow a zero-sum multinomial distribution and that community composition will change over space due to dispersal limitation. We review all published empirical tests of neutral theory. With the exception of one type of test, all tests fail to support neutral theory. We identify and perform several new tests. Specifically, we develop a set of best practices for testing the fit of the zero-sum multinomial (ZSM) vs. a lognormal null hypothesis and apply this to a data set, concluding that the lognormal outperforms neutral theory on robust tests. We explore whether a priori parameterization of neutral theory is possible, and we conclude that it is not. We show that non-curve-fitting predictions readily derived from neutral theory are easily falsifiable. In toto, there is a current overwhelming weight of evidence against neutral theory. We suggest some next steps for neutral theory.     

Qualitative and quantitative assessment of the risk from the exposure to fetotoxic chemical compounds

A new mathematical dose-response model for the expected probability of toxic response and also for the expected measure of the overdispersion parameter for the reproductive and developmental risk assessment is proposed. The model for the expected probability of toxic response is an improvised Weibull dose-response model incorporating the litter-size effect while the model for the overdispersion parameter is a polynomial function of the dose level. A beta-binomial distribution for the number of offspring showing toxic responses in a litter satisfactorily accounts for the extra-binomial variation and the intralitter correlation of responses of these pups. Confidence limits for low-dose extrapolation are based on the asymptotic distribution of the likelihood ratio. The safe dose for human exposure is then calculated by simple linear extrapolation. The model for overdispersion allows us to obtain the estimates of the overdispersion parameter at these dosages. This was not possible in the earlier models. The proposed model is illustrated by an application to a study on the effect of exposure to diethylhexylphthalate in mice. The results are compared with those obtained by Chen and Kodell (1989) who have applied the simple Weibull dose-response model to the same data set.This paper was prepared with partial support from the United States Environmental Protection Agency under a Cooperative Agreement Number CR-815273. The contents have not been subject to Agency review and therefore do not necessarily reflect the views or policies of the Agency and no official endorsement should be inferred.     

内分泌干扰物的非单一剂量-效应研究进展

传统毒理学理论认为,有毒物质产生毒害作用的剂量-效应关系是线性单调的,线性剂量-效应关系也是目前管理毒理学中计算化学品安全剂量的理论基础。但越来越多的研究显示,内分泌干扰物的剂量-效应关系通常不是线性的,而是U型、倒U型或更复杂的非单调剂量-效应曲线形式。以典型案例的形式介绍了近年来生理激素和内分泌干扰物非单调剂量-效应的研究进展,总结了目前认为形成非单调剂量-效应关系的机制。在此基础上,讨论了内分泌干扰物的非单一剂量-效应给目前管理毒理学中的化学品风险评价带来的挑战,并提出该领域未来的研究方向。     

Bayes methods for combining disease and exposure data in assessing environmental justice

Environmental justice reflects the equitable distribution of the burden of environmental hazards across various sociodemographic groups. The issue is important in environmental regulation, siting of hazardous waste repositories and prioritizing remediation of existing sources of exposure. We propose a statistical framework for assessing environmental justice. The framework includes a quantitative assessment of environmental equity based on the cumulative distribution of exposure within population subgroups linked to disease incidence through a dose-response function. This approach avoids arbitrary binary classifications of individuals solely as 'exposed' or 'unexposed'. We present a Bayesian inferential approach, implemented using Markov chain Monte Carlo methods, that accounts for uncertainty in both exposure and response. We illustrate our method using data on leukaemia deaths and exposure to toxic chemical releases in Allegheny County, Pennsylvania.     

The influence of productivity on the species richness of plants: a critical assessment

Despite much scrutiny the relationship between productivity and species richness remains controversial, and there is little agreement about causal processes. We present the results of a survey of 159 productivity-plant species richness (P-PSR) relationships from 131 published studies. We critically assessed each study with respect to experimental design and for the appropriateness of the surrogates used for productivity. We were able to accept only 60 of the reported relationships as robust tests of the P-PSR relationship and a further 18 as robust tests of the biomass species richness relationship. Previous analyses have found that unimodal P-PSR relationships predominate. In contrast, we found that, in studies that used data of continental to global extent, all P-PSR relationships were positive regardless of grain, that almost all were also positive in data sets of regional extent, and that unimodal relationships were not dominant even in studies of fine grain or small spatial extent. Our results differ substantially from previous meta-analyses because previous studies have included a large number of studies that do not meet basic experimental design criteria for objectively testing P-PSR relationships. These results have important implications for theory that attempts to explain species richness patterns. We critically review four dominant theories in light of our results and develop new falsifiable predictions of relationship from these theories at both small and large spatial scales.     

AIC model selection and multimodel inference in behavioral ecology: some background,observations, and comparisons

We briefly outline the information-theoretic (I-T) approaches to valid inference including a review of some simple methods for making formal inference from all the hypotheses in the model set (multimodel inference). The I-T approaches can replace the usual t tests and ANOVA tables that are so inferentially limited, but still commonly used. The I-T methods are easy to compute and understand and provide formal measures of the strength of evidence for both the null and alternative hypotheses, given the data. We give an example to highlight the importance of deriving alternative hypotheses and representing these as probability models. Fifteen technical issues are addressed to clarify various points that have appeared incorrectly in the recent literature. We offer several remarks regarding the future of empirical science and data analysis under an I-T framework.     

Exploratory techniques for the determination of potential dose-response relationships between human health and air pollution

This paper seeks to verify the usefulness of selected multivariate statistical techniques for exploring new dose-response relationships between human health and air pollution. We do so by comparing our results with those already established in the literature through hypothesis testing procedures or laboratory work. This use of multivariate techniques is pretheoretical and should be interpreted as suggesting relationships which warrant further investigation with more traditional methodologies. Our results conform very well with those existing in the literature and lend credence to the use of such pretheoretical statistical methods.     

Do we need to examine the quantitative data obtained from toxicity studies for both normality and homogeneity of variance?

Most of the statistical techniques used to evaluate the data obtained from toxicity studies are based on the assumption that the data show a normal distribution and homogeneity of variance. Literature review on toxicity studies on laboratory animals reveals that in most of the cases homogeneity of variance alone is examined for the data obtained from these studies. But the data that show homogeneity of variance need not always show a normal distribution. In fact, most of the data derived from toxicity studies, including hematological and biochemical parameters show a non-normal distribution. On examining normality of data obtained from various toxicity studies using different normality tests, we observed that Shapiro-Wilk test is more appropriate than Kolmogorov-Smimov test, Lilliefors test, the normal probability paper analysis and Chi square test. But there are situations, especially in the long-term toxicity studies, where normality is not shown by one or more than one of the dosage groups. In this situation, we propose that the data maybe analyzed using Dunnett multiple comparison test after excluding the data of the groups that do not show normality However, the biological relevance of the excluded data has to be carefully scrutinized. We also observed that the tendency of the data to show a normal distribution seems to be related to the age of the animals. Present paper describes various tests commonly used to test normality and their power, and also emphasizes the need of subjecting the data obtained from toxicity studies to both normality and homogeneity tests. A flow chart suggesting the statistical techniques that maybe used for both the types of data showing a normal or non-normal distribution is also proposed.     

Swarm cognition in honey bees

We synthesize findings from neuroscience, psychology, and behavioral biology to show that some key features of cognition in the neuron-based brains of vertebrates are also present in the insect-based swarm of honey bees. We present our ideas in the context of the cognitive task of nest-site selection by honey bee swarms. After reviewing the mechanisms of distributed evidence gathering and processing that are the basis of decision making in bee swarms, we point out numerous similarities in the functional organization of vertebrate brains and honey bee swarms. These include the existence of interconnected subunits, parallel processing of information, a spatially distributed memory, layered processing of information, lateral inhibition, and mechanisms of focusing attention on critical stimuli. We also review the performance of simulated swarms in standard psychological tests of decision making: tests of discrimination ability and assessments of distractor effects.     

Assessment of risk from long term exposure to waterborne pathogens

Disease due to waterborne pathogens, whether in outbreak or endemic form, continues to be a problem in both the developing and the developed world. Control of waterborne disease requires accurate assessment of the pathogen dose-response relation and of likely patterns of exposure. Heretofore, risk assessment of pathogen exposure has been done on the basis of several standard biologically plausible dose-response models. In this paper, the problem of estimating the long-term risk from waterborne pathogens is put into a rigorous mathematical and statistical framework. The implications of the biologic assumptions embedded in the dose-response models (e.g., heterogeneity in susceptibility) are fully considered, as are the likely patterns of long-term exposure (e.g., temporal correlations within individuals and heterogeneity of mean exposures). Two types of long-term risk are described, risk per person-time and risk per individual where the latter is the risk of infection at least once. The effects on these risks of heterogeneity in individuals susceptibilities and mean exposures and of temporal correlations of exposures are described, both theoretically and empirically using a sample of experimental data sets. Because different models with equal plausibility may give very different results in the low-dose range but fit the experimental data equally well, we apply the model uncertainty algorithm of Buckland et al. (1997) on example data sets. Finally, the computational aspects of the general problem, which are often challenging, are discussed along with the conditions under which simplifying approximations may be utilized.     

Spectral tests of randomness for spatial point patterns

We develop a spectral framework for testing the hypothesis of complete spatial randomness (CSR) for a spatial point pattern. Five formal tests based on the periodogram (sample spectrum) proposed in Mugglestone (1990) are considered. A simulation study is used to evaluate and compare the power of the tests against clustered, regular and inhomogeneous alternatives to CSR. A subset of the tests is shown to be uniformly more powerful than the others against the alternatives considered. The spectral tests are also compared with three widely used space-domain tests that are based on the mean nearest-neighbor distance, the reduced second-order moment function (K-function), and a bivariate Cramér-von Mises statistic. The test based on the scaled cumulative R-spectrum is more powerful than the space-domain tests for detecting clustered alternatives to CSR, especially when the number of events is small.     

Nearest neighbor methods for testing reflexivity

Nearest neighbor (NN) methods are widely employed for drawing inferences about spatial point patterns of two or more classes. We introduce a method for testing reflexivity in the NN structure (i.e., NN reflexivity) based on a contingency table which will be called reflexivity contingency table (RCT) henceforth. The RCT is based on the NN relationships among the data points and was used for testing niche specificity in literature, but we demonstrate that it is actually more appropriate for testing the NN reflexivity pattern. We derive the asymptotic distribution of the entries of the RCT under random labeling and introduce tests of reflexivity based on these entries. We also consider Pielou’s approach on RCT and show that it is not appropriate for completely mapped spatial data. We determine the appropriate null hypotheses and the underlying conditions/assumptions required for all tests considered. We investigate the finite sample performance of the tests in terms of empirical size and power by extensive Monte Carlo simulations and illustrate the methods on two real-life ecological data sets.     

Testing for a change in diet using fatty acid signatures

Determining whether the diet of predators has changed is an important ecological problem and appropriate methodology is needed in order to test for differences or changes in diet. It is known that the fatty acid (FA) signature in a predator’s adipose tissue predictably reflects the prey consumed and that, consequently, a change in the FA signatures can be largely attributed to changes in the predator’s diet composition. The use of FA signatures as a means of detecting change in diet presents some statistical challenges however, since the FA signatures are compositional and sample sizes relative to the dimension of a signature are often small due to biological constraints. Furthermore, the FA signatures often contain zeros precluding the direct use of traditional compositional data analysis methods. In this paper, we provide the methodology to carry out valid statistical tests for detecting changes in FA signatures and we illustrate both independent and paired cases using simulation studies and real life seabird and seal data. We conclude that the statistical challenges using FA data are overcome through the use of nonparametric tests applied to the multivariate setting with suitable test statistics capable of handling the zeros that are present in the data.     

Statistical models of health risk due to microbial contamination of foods

Between 6 million and 33 million cases of food-related illness are estimated to occur in the United States each year, with about 5000 episodes resulting in death. Growing concerns about the safety of food prompted the National Food Safety Initiative of 1997, the goal of which is to reduce the incidence of illness caused by food-borne pathogens. A key component of the food safety initiative is the improvement of farm-to-table risk assessment capabilities, including the development of improved dose-response models for estimating risk. When sufficient data are available, allowable contamination levels of specific micro-organisms in food are established using dose-response models to predict risk at very low doses based on experimental data at much higher doses. This necessitates having reliable models for setting allowable exposures to food-borne pathogens. While only limited data on relatively few micro-organisms that occur in food are available at present for dose-response modeling and risk estimation, still none of the two-parameter models proposed so far, including the popular Beta-Poisson (BP) model, appears to be completely satisfactory for describing and fitting all of the present data (Holcomb et al., 1999). The Weibull–Gamma (WG) model is the only three-parameter model that has been proposed to date. In this paper, new competitive three-parameter models are derived, using a formulation that can be parameterized to represent statistical variation with respect to the dose of micro-organism received by the host and the hosts susceptibility to infection. Parameters of the models are estimated using the maximum likelihood method. Experimental data on several common microbial contaminants in food are used to illustrate the methodology.     

微板吸光法测定9种农药对斜生栅藻的抑制毒性

以斜生栅藻(Scenedesmus obliquus)为指示生物,96微孔板为暴露反应载体,SpectraMax M5酶标仪为吸光度测试设备,建立了测定毒物对藻生长抑制毒性的微板吸光法.论文系统研究了微板吸光法中斜生栅藻的可见吸收光谱和生长曲线以及pH和暴露时间对藻生长的影响,同时应用该方法成功测定了环嗪酮、阿特拉津、西草净、扑灭通、苯嗪草酮、敌草快、草甘膦7种除草剂和磷胺、甲胺磷2种杀虫剂对斜生栅藻的剂量-效应曲线(DRC).通过对剂量-效应数据进行非线性最小二乘模拟,获得了这些农药的半数效应浓度EC50及置信区间.对比标准锥形瓶栅藻毒性试验,微板吸光法具有测试简便快速,所需样品体积少,便于多次平行毒性测试等优点.     

On the use of nearest neighbor contingency tables for testing spatial segregation

For two or more classes (or types) of points, nearest neighbor contingency tables (NNCTs) are constructed using nearest neighbor (NN) frequencies and are used in testing spatial segregation of the classes. Pielou’s test of independence, Dixon’s cell-specific, class-specific, and overall tests are the tests based on NNCTs (i.e., they are NNCT-tests). These tests are designed and intended for use under the null pattern of random labeling (RL) of completely mapped data. However, it has been shown that Pielou’s test is not appropriate for testing segregation against the RL pattern while Dixon’s tests are. In this article, we compare Pielou’s and Dixon’s NNCT-tests; introduce the one-sided versions of Pielou’s test; extend the use of NNCT-tests for testing complete spatial randomness (CSR) of points from two or more classes (which is called CSR independence, henceforth). We assess the finite sample performance of the tests by an extensive Monte Carlo simulation study and demonstrate that Dixon’s tests are also appropriate for testing CSR independence; but Pielou’s test and the corresponding one-sided versions are liberal for testing CSR independence or RL. Furthermore, we show that Pielou’s tests are only appropriate when the NNCT is based on a random sample of (base, NN) pairs. We also prove the consistency of the tests under their appropriate null hypotheses. Moreover, we investigate the edge (or boundary) effects on the NNCT-tests and compare the buffer zone and toroidal edge correction methods for these tests. We illustrate the tests on a real life and an artificial data set.     

Improving the Evaluation of Conservation Programs

Abstract: The evaluation of conservation programs is rare but increasingly important in improving their effectiveness. Regular evaluations of conservation programs and the implementation of recommendations resulting from such assessments are infrequent because of resistance by participants and lack of funding. Evaluations may be internal or external, depending on the purpose of the review and how broadly it is focused. We strongly recommend external peer review of long-term complex conservation programs every 5 years, supported by more frequent (annual) internal reviews. Criteria for success must encompass both biological and social measures and include learning and the application of new knowledge to management. Evaluations must also go beyond monitoring to assess the value of the program. We emphasize the need to include the organization and function of a conservation program (the process) in any evaluation in addition to substantive criteria for success, which usually involve biological measures (numbers). A dysfunctional program organization and process can as effectively cripple a conservation effort as can a major biological catastrophe. We provide examples of different types of conservation program evaluations, including moderated workshops and case-study analysis, and provide advice on the logistics and organization of the review, emphasizing the importance of the evaluation process itself to a successful outcome. One important aspect of an evaluation is having an individual with leadership ability and considerable expertise to organize the format and oversee the review process itself. Second, it is essential at the outset to ensure agreement among the program participants and the review committee on the goals and objectives of the conservation program, what is to be evaluated, and the criteria for defining success. Finally, the best evaluations are inclusive and involve all participants and stakeholders.     

Perspectives on area-based conservation and its meaning for future biodiversity policy

During 2021, Parties to the Convention on Biological Diversity (CBD) are expected to meet in Kunming, China, to agree on a new global biodiversity framework aimed at halting and reversing biodiversity loss, encouraging the sustainable use of biodiversity, and ensuring the equitable sharing of its benefits. As the post-2020 global biodiversity framework evolves, parties to the convention are being exposed to a range of perspectives on the conservation and sustainable use of biodiversity, relating to the future framework as a whole or to aspects of it. Area-based conservation measures are one such aspect, and there are diverse perspectives on how new targets might be framed in relation to these measures. These perspectives represent different outlooks on the relationship between human and nonhuman life on Earth. However, in most cases there is a lack of clarity on how they would be implemented in practice, the implications this would have for biodiversity and human well-being, and how they would contribute to achieving the 2050 Vision for Biodiversity of “living in harmony with nature.” We sought to clarify these issues by summarizing some of these perspectives in relation to the future of area-based biodiversity conservation. We identified these perspectives through a review of the literature and expert consultation workshops and compiled them into 4 main groups: Aichi+, ambitious area-based conservation perspectives, new conservation, and whole-earth conservation. We found that although the perspectives Aichi+ and whole earth are in some cases at odds with one another, they also have commonalities, and all perspectives have elements that can contribute to developing and implementing the post-2020 global biodiversity framework and achieving the longer term CBD 2050 Vision.