中国降水量区域变化稳定性研究

利用中国气象中心160站点的实际观察资料,对中国半个世纪的降水变化进行了系统分析,发现中国的夏季降水变化稳定性有显著的纬度与海拔效应:纬度越高,方差值越小,降水变化的幅度也越小,稳定性越大;从海拔来看,海拔越高,降水稳定性越好,降水量的变化稳定性与海拔是成反相关关系.除过受区域水汽来源稳定性的影响,主要受全球降水稳定性的分布规律所制约;在海拔上,降水量稳定性的分布,除过受水汽来源影响外,还与水汽凝结的高度效应有一定的关系.     

Spread and potential impact of the recently introduced European green crab,Carcinus maenas,in central California

Our study examines the potential impact of the European green crab Carcinus maenas on communities of coastal embayments of western North America. We document the current distribution and range expansion of this species beyond San Francisco Bay, where C. maenas first became established along this coast in 1989–1990, and we test the effect of C. maenas predation on different species and sizes of infaunal invertebrates in field and laboratory experiments. In our samples from eight coastal locations in central California collected between June 1993 and May 1994, we found no green crabs at the two closest embayments south of San Francisco Bay and found the crabs in all four embayments sampled within 120 km north of San Francisco Bay, up to and including Bodega Harbor. C. maenas was not present in samples from sites farther north. This northward range expansion is apparently the result of larval recruitment by a single cohort, corresponding to the predominant northern transport of surface waters and the approximate distance water moves during larval green crab development. At Bodega Harbor, the current northern range limit, the C. maenas population is now well established and reproducing. Females and males became sexually mature within their first year at 40 mm carapace width, molting approximately monthly from summer through fall, and females were ovigerous in late fall of their first year at 50 mm. We expect larvae from this population to recruit locally and to the north, promoting episodic range extensions as new populations are established and reproduce. Enclosure experiments conducted during the summer of 1993 at the intertidal sandflats of Bodega Harbor showed that C. maenas significantly reduced densities of the most abundant taxa, including the bivalves Transennella confusa and T. tantilla, the cumacean Cumella vulgaris, and the amphipod Corophium sp. Furthermore, Carcinus maenas selectively removed larger (>3 mm) rather than smaller (<1 mm) Transennella spp. in both field and laboratory experiments. Based on the available data from this and other studies of green crabs, and our 10 yr study of community dynamics at Bodega Harbor, we predict C. maenas will significantly alter community structure, ecological interactions, and evolutionary processes in embayments of western North America.     

Multivariate bootstrap confidence regions for abundance vector using

Abundance vector estimation is a well investigated problem in statistical ecology. The use of simple random sampling with replacement or replicated sampling ensures good asymptotic properties of the abundance vector estimators. However, real surveys are based on small sample sizes, and assuming any specific distribution of the abundance vector estimator may be hazardous.In this paper we focus our attention on situations where the population is not too large and the sample size is small. We propose bootstrap multivariate confidence regions based on data depth. Data depth is a geometrical concept of ordering data from the center outwardly in higher dimensions. The Simplicial depth, the Tukey's depth and the Mahalanobis depth are presented. In order to build confidence regions in the presence of a skewed distribution of the abundance vector estimator, the use of Tukey's depth is suggested. The proposed method has been applied to the benthic community of Lake Lesina. A comparison with Mahalanobis depth and standard existing methods is reported.     

Analyzing longitudinal circular data by projected normal models: a semi-parametric approach based on finite mixture models

The analysis of circular data has been recently the focus of a wide range of literature, with the general objective of providing reliable parameter estimates in the presence of heterogeneity and/or dependence among observations under a longitudinal setting. In this paper, we extend the variance component model approach to the analysis of longitudinal circular data, defining a mixed effects model for radial projections onto the circle and introducing dependence between projections through a set of correlated random coefficients. Estimation is carried out by numerical integration through an expectation-maximization algorithm without parametric assumptions upon the random coefficients distribution. The resulting model is a finite mixture of projected normal distributions. A simulation study has been carried out to investigate the behavior of the proposed model in a series of empirical situations. The proposed model is computationally parsimonious and, when applied to a real dataset on animal orientation, produces novel results.     

Voluntary information programs and environmental regulation: Evidence from ‘Spare the Air’

This paper assesses whether individuals change their transportation choices in response to ‘Spare the Air’ (STA) advisories, a public voluntary information program in the San Francisco Bay Area that elicits reductions in ozone-producing activities. Since STAs are issued when ozone levels are predicted to exceed a particular threshold, we use a regression discontinuity design to identify the effect of STAs. We also use traffic conditions in Southern California, an area without STAs, to estimate difference-in-differences models. The results suggest that STAs reduce traffic volume and slightly increase the use of public transit, supporting a potential role for voluntary information programs that directly target individuals as a means for improving local air quality.     

Longevity can buffer plant and animal populations against changing climatic variability

Both means and year-to-year variances of climate variables such as temperature and precipitation are predicted to change. However, the potential impact of changing climatic variability on the fate of populations has been largely unexamined. We analyzed multiyear demographic data for 36 plant and animal species with a broad range of life histories and types of environment to ask how sensitive their long-term stochastic population growth rates are likely to be to changes in the means and standard deviations of vital rates (survival, reproduction, growth) in response to changing climate. We quantified responsiveness using elasticities of the long-term population growth rate predicted by stochastic projection matrix models. Short-lived species (insects and annual plants and algae) are predicted to be more strongly (and negatively) affected by increasing vital rate variability relative to longer-lived species (perennial plants, birds, ungulates). Taxonomic affiliation has little power to explain sensitivity to increasing variability once longevity has been taken into account. Our results highlight the potential vulnerability of short-lived species to an increasingly variable climate, but also suggest that problems associated with short-lived undesirable species (agricultural pests, disease vectors, invasive weedy plants) may be exacerbated in regions where climate variability decreases.     

Misspecification of within-area exposure distribution in ecological Poisson models

Ecological studies enable investigation of geographic variations in exposure to environmental variables, across groups, in relation to health outcomes measured on a geographic scale. Such studies are subject to ecological biases, including pure specification bias which arises when a nonlinear individual exposure-risk model is assumed to apply at the area level. Introduction of the within-area variance of exposure should induce a marked reduction in this source of ecological bias. Assuming several measurements per area of exposure and no confounding risk factors, we study the model including the within-area exposure variability when Gaussian within-area exposure distribution is assumed. The robustness is assessed when the within-area exposure distribution is misspecified. Two underlying exposure distributions are studied: the Gamma distribution and an unimodal mixture of two Gaussian distributions. In case of strong ecological association, this model can reduce the bias and improve the precision of the individual parameter estimates when the within-area exposure means and variances are correlated. These different models are applied to analyze the ecological association between radon concentration and childhood acute leukemia in France.

Estimation methods for nonlinear state-space models in ecology

The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance for all three methods was largely identical, however with BUGS providing overall wider credible intervals for parameters than HMM and ADMB confidence intervals.     

Strong response of an invasive plant species (Centaurea solstitialis L.) to global environmental changes

Global environmental changes are altering interactions among plant species, sometimes favoring invasive species. Here, we examine how a suite of five environmental factors, singly and in combination, can affect the success of a highly invasive plant. We introduced Centaurea solstitialis L. (yellow starthistle), which is considered by many to be California's most troublesome wildland weed, to grassland plots in the San Francisco Bay Area. These plots experienced ambient or elevated levels of warming, atmospheric CO2, precipitation, and nitrate deposition, and an accidental fire in the previous year created an additional treatment. Centaurea grew more than six times larger in response to elevated CO2, and, outside of the burned area, grew more than three times larger in response to nitrate deposition. In contrast, resident plants in the community responded less strongly (or did not respond) to these treatments. Interactive effects among treatments were rarely significant. Results from a parallel mesocosm experiment, while less dramatic, supported the pattern of results observed in the field. Taken together, our results suggest that ongoing environmental changes may dramatically increase Centaurea's prevalence in western North America.     

Predators choosing between patches with standing crop: the influence of switching rules and input types

Where prey arriving in a patch are not consumed immediately, they will accumulate. Predators are then presented with a prey density or standing crop that increases through further input, and decreases through the consumption by predators. Firstly, I show that the switching rule of predators has a significant influence on the expected predator equilibrium distribution in such a dynamic system. Three rules are compared; for all rules, analytical solutions are calculated (where possible). To test their plausibility for natural situations, predator distributions are simulated given the assumption that each predator obtains individual patch profitability estimates by using a common learning rule. As long as prey arrive in the patches in constant numbers per time unit, the first rule leads to input matching because predators stop switching when consumption in the two patches is equal. The other two rules, where predators continue to sample both patches even in the equilibrium state, lead to predator distributions where the more profitable patch is underused. The final equilibrium depends on the exact assumptions of the switching rule; however, it is independent of interference. But if the input delivered into a patch is a function of the current prey standing crop (for example in a reproducing prey population), predator and prey distributions will not reach an equilibrium in most cases: either standing crops increase indefinitely, or they approach zero, with all predators concentrating on the better patch. Only a small number of parameter sets show intermediate crops that are reasonably stable. With this input type, only up to 54% of the simulations reach the expected distribution. In a system with competition for dynamic standing crop, it is therefore essential to know the type of input and the switching-rule used by predators to be able to predict equilibrium predator distributions. Received: 17 March 1995/Accepted after revision: 5 November 1995     

Composite likelihood approach to the regression analysis of spatial multivariate ordinal data and spatial compositional data with exact zero values

In many environmental and ecological studies, it is of interest to model compositional data. One approach is to consider positive random vectors that are subject to a unit-sum constraint. In landscape ecological studies, it is common that compositional data are also sampled in space with some elements of the composition absent at certain sampling sites. In this paper, we first propose a practical spatial multivariate ordered probit model for multivariate ordinal data, where the response variables can be viewed as the discretized non-negative compositions without the unit-sum constraint. We then propose a novel two-stage spatial mixture Dirichlet regression model. The first stage models the spatial dependence and the presence of exact zero values, and the second stage models all the non-zero compositional data. A maximum composite likelihood approach is developed for parameter estimation and inference in both the spatial multivariate ordered probit model and the two-stage spatial mixture Dirichlet regression model. The standard errors of the parameter estimates are computed by an estimate of the Godambe information matrix. A simulation study is conducted to evaluate the performance of the proposed models and methods. A land cover data example in landscape ecology further illustrates that accounting for spatial dependence can improve the accuracy in the prediction of presence/absence of different land covers as well as the magnitude of land cover compositions.     

Size-selective harvesting alters life histories of a temperate sex-changing fish.

Selective mortality, whether caused naturally by predation or through the influence of harvest practices, initiates changes within populations when individuals possessing certain heritable traits have increased fitness. Theory predicts that increased mortality rates will select for changes in a number of different life history characteristics. For example, fishing often targets larger individuals and has been shown repeatedly to alter population size structure and growth rates, and the timing of maturation. For sex-changing species, selective fishing practices can affect additional traits such as the mature population sex ratio and the timing of sexual transformation. Using historical comparisons, we examined the effects of exploitation on life history characteristics of California sheephead, Semicossyphus pulcher, a temperate protogynous (female-male sex changer) labrid that inhabits nearshore rocky environments from central California, USA, to southern Baja California, Mexico. Recreational fishing intensified and an unregulated commercial live-fish fishery developed rapidly in southern California between the historical and current studies. Collections of S. pulcher from three locations (Bahia Tortugas, Catalina Island, and San Nicolas Island) in 1998 were compared with data collected 20-30 years previously to ascertain fishery-induced changes in life history traits. At Bahia Tortugas, where fishing by the artisanal community remained light and annual survivorship stayed high, we observed no changes in size structure or shifts in the timing of maturation or the timing of sex change. In contrast, where recreational (Catalina) and commercial (San Nicolas) fishing intensified and annual survivorship correspondingly declined, males and females shifted significantly to smaller body sizes, females matured earlier and changed sex into males at both smaller sizes and younger ages and appeared to have a reduced maximum lifespan. Mature sex ratios (female:male) increased at San Nicolas, despite a twofold reduction in the mean time spent as a mature female. Proper fisheries management requires measures to prevent sex ratio skew, sperm limitation, and reproductive failure because populations of sequential hermaphrodites are more sensitive to size-selective harvest than separate-sex species. This is especially true for S. pulcher, where different segments of the fishery (commercial vs. recreational) selectively target distinct sizes and therefore sexes in different locations.     

Lack-of-fit tests for assessing mean structures for continuous dose-response data

We review a range of lack-of-fit tests suitable for assessing the appropriateness of the mean function in dose-response models. The review encompasses both well-known tests and new tests based on recent developments in statistics, which we have extended to the dose-response case. We argue that the classical methods are inadequate in certain situations, where the new tests may be applied. Power comparisons are carried out by means of extensive simulation studies, covering both designs with and without replicates at small and large sample sizes. Three datasets from dose-response applications illustrate differences and similarities between the tests. The results suggest that the new tests perform better and exhibit a wider applicability.     

Spatio-temporal modeling of striped-bass egg, larval movement, and fate in the San Francisco Bay-Delta

Most models developed for the movement and fate of eggs and larvae of aquatic species are based on a particle tracking approach. Although this method has many advantages due to its high flexibility, particle tracking may become computationally intensive for complex geometries and when large numbers of particles are needed to simulate the population properly. In continuous models based on advection and dispersion mechanisms, the computational burden is independent of the size of the population. We developed a continuous fate and transport model for striped bass eggs and larvae in the San Francisco Bay-Delta. The model predicts the concentration of eggs and larvae at any location over time. The method of moments was used to account for the effect of temperature and age on the transition of eggs to larvae and larvae to juveniles. Egg and larval mortality were represented as functions of temperature, and eggs also experienced settling mortality. The fate and transport model used the same one-dimensional spatial grid as the existing Delta Simulation Model II (DSM2) hydrodynamics model. DSM2 output of flow rates, water depths, and cross-sectional areas were inputted into the fate and transport model to determine transport. The model was applied to striped bass eggs and larvae data collected during years 1990-1994; agreement between the modeled and the measured data was acceptable in most cases. Exploratory simulations were performed to demonstrate how the model could be used to evaluate the effects on egg and larval survival and total juvenile production of water diversions for supply and agricultural use and changes in the long-term mean water temperature. The model can be further used to examine the impact of various operation strategies in the San Francisco Bay-Delta, where diversion losses of early life stages of fishes remain a major management issue.     

Historical trends of trace metals in a sediment core from a contaminated tidal salt marsh in San Francisco Bay

Sedimentation of metals preserves historical records of contaminant input from local and regional sources, and measurement of metals in sediment cores can provide information for reconstruction of historical changes in regional water and sediment quality. Sediment core was collected from Stege Marsh located in central San Francisco Bay (California, USA) to investigate the historical input of trace metals. Aluminum-normalized enrichment factors indicate that inputs from anthropogenic sources were predominant over natural input for Ag, Cu, Pb, and Zn. Among these, lead was the most anthropogenically impacted metal with enrichment factors ranging from 32 to 108. Depth profiles and coefficients of variation show that As, Cd, and Se were also influenced by anthropogenic input. The levels of these anthropogenically impacted metals decline gradually towards the surface due to regulation of the use of leaded gasoline, municipal and industrial wastewater discharge control, and closure of point sources on the upland of Stege Marsh. Although trace metal contamination is expected to be continuously declining, the rates of decline have slowed down. For lead, it is estimated to take 44, 82, and 153 years to decrease to probable effects level (112 μg/g), the San Francisco Bay ambient surface sediment level (43.2 μg/g), and the local baseline levels (5 μg/g), respectively. Some metals in surface sediments (0–6 cm) are still higher than sediment quality guidelines such as the probable effects level. To further facilitate the recovery of sediment quality, more efficient management plans need to be developed and implemented to control trace metals from non-point sources such as stormwater runoff.     

Using the bootstrap with two-phase adaptive stratified samples from multiple populations at multiple locations

Recently the two-phase adaptive stratified sampling design proposed by Francis (1984) has been extended by Manly et al. (2002) for situations where several biological populations are sampled simultaneously, and where this is done at several different geographical locations in order to estimate population totals or means. The method uses the results from a first phase sample to decide how best to allocate a second phase sample to locations and strata, in order to maximise a criterion (based on estimated coefficients of variation) that measures the accuracy of estimation for population totals, for all variables at all locations. One potential problem with this method is bias in the estimators of the population totals and means. In this paper bootstrapping is considered as a means of overcoming these biases. It is shown using model populations of Pacific walrus and shellfish, based on real data, that bootstrapping is a useful tool for removing about half of the bias. This is also confirmed from some simulations using artificial data.     

Extreme value analysis of Munich air pollution data

We present three different approaches to modelling extreme values of daily air pollution data. We fitted a generalized extreme value distribution to the monthly maxima of daily concentration measures. For the exceedances of a high threshold depending on the data, the parameters of the generalized Pareto distribution were estimated. Accounting for autocorrelation, clusters of exceedances were used. To obtain information about the relationship of the exceedance of the air quality standard and possible predictors we applied logistic regression. Results and their interpretation are given for daily average concentrations of ozone and nitrogen dioxide at two monitoring sites within the city of Munich.     

Making literature reviews more reliable through application of lessons from systematic reviews

Review articles can provide valuable summaries of the ever‐increasing volume of primary research in conservation biology. Where findings may influence important resource‐allocation decisions in policy or practice, there is a need for a high degree of reliability when reviewing evidence. However, traditional literature reviews are susceptible to a number of biases during the identification, selection, and synthesis of included studies (e.g., publication bias, selection bias, and vote counting). Systematic reviews, pioneered in medicine and translated into conservation in 2006, address these issues through a strict methodology that aims to maximize transparency, objectivity, and repeatability. Systematic reviews will always be the gold standard for reliable synthesis of evidence. However, traditional literature reviews remain popular and will continue to be valuable where systematic reviews are not feasible. Where traditional reviews are used, lessons can be taken from systematic reviews and applied to traditional reviews in order to increase their reliability. Certain key aspects of systematic review methods that can be used in a context‐specific manner in traditional reviews include focusing on mitigating bias; increasing transparency, consistency, and objectivity, and critically appraising the evidence and avoiding vote counting. In situations where conducting a full systematic review is not feasible, the proposed approach to reviewing evidence in a more systematic way can substantially improve the reliability of review findings, providing a time‐ and resource‐efficient means of maximizing the value of traditional reviews. These methods are aimed particularly at those conducting literature reviews where systematic review is not feasible, for example, for graduate students, single reviewers, or small organizations.     

Evaluating predictive errors of a complex environmental model using a general linear model and least square means

A general linear model (GLM) was used to evaluate the deviation of predicted values from expected values for a complex environmental model. For this demonstration, we used the default level interface of the regional mercury cycling model (R-MCM) to simulate epilimnetic total mercury concentrations in Vermont and New Hampshire lakes based on data gathered through the EPAs Regional Environmental Monitoring and Assessment Program (REMAP). The response variable for the GLM was defined as R-MCMs predictive error: the difference between observed mercury concentrations and modeled mercury concentrations in each lake. Least square means of the response variable are used as an estimate of the magnitude and significance of bias, i.e., a statistically discernable trend in predictive errors for a given lake type, e.g., acidic, stratified, or oligotrophic. Using our approach, we determined lake types where significant over-prediction and under-prediction of epilimnetic total mercury concentration was occurring, i.e., regions in parameter space where the model demonstrated significant bias was distinguished from regions where no significant bias existed. This technique is most effective for finding regions of parameter space where bias is significant. Drawing conclusions concerning regions that show no significant bias can be misleading. The significant interaction terms in the GLM demonstrated that addressing this problem using univariate statistical techniques would lead to a loss of important information.     

Patch dynamics based on Prisoner's Dilemma game: superiority of golden rule

There has been much literature on ecological model of Prisoner's Dilemma (PD) game. This game illustrates that cooperation can evolve in situations where individuals tend to look after themselves. In order to explain some behaviors of altruism in animal societies, the strategy All Cooperate (AC), often called the Golden Rule, is more appropriate than other strategies. However, very little is known about the superiority of AC. In the present article, we study patch dynamics based on non-iterated PD game, applying two different methods: island and lattice models. Each patch is assumed to be either vacant or composed of a population of AC or All Defect (AD), where AD means a selfish strategy. Both models exhibit a phase transition between a phase where both AC and AD survive, and a phase where AD is extinct. The latter phase means that AC beats AD completely. In the case of lattice model, the extinction of AD easily occurs and the abundance of AC takes a larger value, compared with the island model. Our models can be also extended to general iterated PD game; we describe the reason why AC can outperform any other strategy.