3种人工草地不同植被覆盖度实地测量方法比较

植被覆盖度是植被垂直投影面积占统计区域面积百分比;理想覆盖度实地测量方法耗时短,工具简单,结果准确,受人为因素影响小.以结缕草(Zoysia japonica)、白三叶(Trifolium repens)和雀稗(Paspalum thunbergii)人工草地为研究对象,以照相法测量值为参考,比较样线法、目估法、样针法...     

Spatial heterogeneity in distribution and ecology of Western Palearctic birds

Species vary in abundance and heterogeneity of spatial distribution, and the ecological and evolutionary consequences of such variability are poorly known. Evolutionary adaptation to heterogeneously distributed resources may arise from local adaptation with individuals of such locally adapted populations rarely dispersing long distances and hence having small populations and small overall ranges. We quantified mean population density and spatial heterogeneity in population density of 197 bird species across 12 similarly sized regions in the Western Palearctic. Variance in population density among regions differed significantly from a Poisson distribution, suggesting that random processes cannot explain the observed patterns. National estimates of means and variances in population density were positively correlated with continental estimates, suggesting that means and variances were maintained across spatial scales. We used Morisita's index of population abundance as an estimate of heterogeneity in distribution among regions to test a number of predictions. Heterogeneously distributed passerine bird species as reflected by Morisita's index had small populations, low population densities, and small breeding ranges. Their breeding populations had been consistently maintained at low levels for considerable periods of time, because the degree of genetic variation in a subsample of non-passerines and passerines was significantly negatively related to heterogeneity in distribution. Heterogeneously distributed passerine species were not more often habitat specialists than homogeneously distributed species. Furthermore, heterogeneously distributed passerine species had high annual adult survival rates but did not differ in annual fecundity from homogeneously distributed species. Heterogeneously distributed passerine species rarely colonized urban habitats. Finally, homogeneously distributed bird species were hosts to a greater diversity of blood parasite species than heterogeneously distributed species. In conclusion, small breeding ranges, population sizes, and population densities of heterogeneously distributed passerine bird species, combined with their low degree of genetic variability, and their inability to colonize urban areas may render such species particularly susceptible to human-influenced global climatic changes.     

Power evaluation of focused cluster tests

Many statistical tests have been developed to assess the significance of clusters of disease located around known sources of environmental contaminants, also known as focused disease clusters. The majority of focused-cluster tests were designed to detect a particular spatial pattern of clustering, one in which the disease cluster centers around the pollution source and declines in a radial fashion with distance. However, other spatial patterns of environmentally related disease clusters are likely given that the spatial dispersion patterns of environmental contaminants, and thus human exposure, depend on a number of factors (i.e., meteorology and topography). For this study, data were simulated with five different spatial patterns of disease clusters, reflecting potential pollutant dispersion scenarios: (1) a radial effect decreasing with increasing distance, (2) a radial effect with a defined peak and decreasing with distance, (3) a simple angular effect, (4) an angular effect decreasing with increasing distance and (5) an angular effect with a defined peak and decreasing with distance. The power to detect each type of spatially distributed disease cluster was evaluated using Stone’s Maximum Likelihood Ratio Test, Tango’s Focused Test, Bithell’s Linear Risk Score Test, and variations of the Lawson–Waller Score Test. Study findings underscore the importance of considering environmental contaminant dispersion patterns, particularly directional effects, with respect to focused-cluster test selection in cluster investigations. The effect of extra variation in risk also is considered, although its effect is not substantial in terms of the power of tests.     

Estimation of rare and clustered population mean using stratified adaptive cluster sampling

For many clustered populations, the prior information on an initial stratification exists but the exact pattern of the population concentration may not be predicted. Under this situation, the stratified adaptive cluster sampling (SACS) may provide more efficient estimates than the other conventional sampling designs for the estimation of rare and clustered population parameters. For practical interest, we propose a generalized ratio estimator with the single auxiliary variable under the SACS design. The expressions of approximate bias and mean squared error (MSE) for the proposed estimator are derived. Numerical studies are carried out to compare the performances of the proposed generalized estimator over the usual mean and combined ratio estimators under the conventional stratified random sampling (StRS) using a real population of redwood trees in California and generating an artificial population by the Poisson cluster process. Simulation results show that the proposed class of estimators may provide more efficient results than the other estimators considered in this article for the estimation of highly clumped population.

     

Cross-combined composite sampling designs for identification of elevated regions

Analyzing soils for contaminants can be costly. Generally, discrete samples are gathered from within a study area, analyzed by a laboratory and the results are used in a site-specific statistical analysis. Because of the heterogeneities that exist in soil samples within study areas, a large amount of variability and skewness may be present in the sample population. This necessitates collecting a large number of samples to obtain reliable inference on the mean contaminant concentration and to understand the spatial patterns for future remediation. Composite, or Incremental, sampling is a commonly applied method for gathering multiple discrete samples and physically combining them, such that each combination of discrete samples requires a single laboratory analysis, which reduces cost and can improve the estimates of the mean concentration. While incremental sampling can reduce cost and improve mean estimates, current implementations do not readily facilitate the characterization of spatial patterns or the detection of elevated constituent regions within study areas. The methods we present in this work provide efficient estimation and inference for the mean contaminant concentration over the entire spatial area and enable the identification of high contaminant regions within the area of interest. We develop sample design methodologies that explicitly define the characteristics of these designs (such as sample grid layout) and quantify the number of incremental samples that must be obtained under a design criteria to control false positive and false negative (Type I and II) decision errors. We present the sample design theory and specifications as well as results on simulated and real data.     

Respondent Experience and Contingent Valuation of Environmental Goods

Respondent experience (i.e., a respondent's information set) has long been suspected to influence contingent valuation estimates of environmental values. We assess the influence of experience by explicitly modeling the relationship between respondent experience and both fitted individual resource values and the conditional variance of these estimated values. Using three different joint specifications for experience and WTP—normal/censored-normal, Poisson/censored-normal, and zero-inflated Poisson/censored-normal—we find discrete jumps in resource values as experience increases from zero and that more-experienced respondents have smaller conditional variances. Simulation of arbitrary levels of experience allows standardization of the amount of information when developing welfare estimates.     

On spline-based approaches to spatial linear regression for geostatistical data

For spatial linear regression, the traditional approach to capture spatial dependence is to use a parametric linear mixed-effects model. Spline surfaces can be used as an alternative to capture spatial variability, giving rise to a semiparametric method that does not require the specification of a parametric covariance structure. The spline component in such a semiparametric method, however, impacts the estimation of the regression coefficients. In this paper, we investigate such an impact in spatial linear regression with spline-based spatial effects. Statistical properties of the regression coefficient estimators are established under the model assumptions of the traditional spatial linear regression. Further, we examine the empirical properties of the regression coefficient estimators under spatial confounding via a simulation study. A data example in precision agriculture research regarding soybean yield in relation to field conditions is presented for illustration.     

Semi-parametric statistical approaches for space-time process prediction

The problem of estimation and prediction of a spatial-temporal stochastic process, observed at regular times and irregularly in space, is considered. A mixed formulation involving a non- parametric component, accounting for a deterministic trend and the effect of exogenous variables, and a parametric component representing the purely spatio-temporal random variation is proposed. Correspondingly, a two-step procedure, first addressing the estimation of the non- parametric component, and then the estimation of the parametric component is developed from the residual series obtained, with spatial-temporal prediction being performed in terms of suitable spatial interpolation of the temporal variation structure. The proposed model formula-tion, together with the estimation and prediction procedure, are applied using a Gaussian ARMA structure for temporal modelling to space-time forecasting from real data of air pollution concentration levels in the region surrounding a power station in northwest Spain.     

Using hidden Markov chains and empirical Bayes change-point estimation for transect data

Consider a lattice of locations in one dimension at which data are observed. We model the data as a random hierarchical process. The hidden process is assumed to have a (prior) distribution that is derived from a two-state Markov chain. The states correspond to the mean values (high and low) of the observed data. Conditional on the states, the observations are modelled, for example, as independent Gaussian random variables with identical variances. In this model, there are four free parameters: the Gaussian variance, the high and low mean values, and the transition probability in the Markov chain. A parametric empirical Bayes approach requires estimation of these four parameters from the marginal (unconditional) distribution of the data and we use the EM-algorithm to do this. From the posterior of the hidden process, we use simulated annealing to find the maximum a posteriori (MAP) estimate. Using a Gibbs sampler, we also obtain the maximum marginal posterior probability (MMPP) estimate of the hidden process. We use these methods to determine where change-points occur in spatial transects through grassland vegetation, a problem of considerable interest to plant ecologists.     

Zooplankton proportion estimates from non-uniform sample volumes

The relative abundance of organisms from different taxa provides information about ecosystem health and diversity. When the numbers of sampled organisms are modelled as Poisson counts, and the sample volumes are not uniform, variance for the proportion attributable to each taxon is difficult to compute. We present a method for computing approximate variances for this situation. The point estimates and their standard errors reduce to the standard multinomial maximum likelihood results when sample volumes are uniform. Further, given initial estimates of population densities for the taxa of interest, optimal sample volumes can be computed. The methods are illustrated for zooplankton counts from Andrus Lake, Michigan.     

Coregionalization analysis with a drift for multi-scale assessment of spatial relationships between ecological variables 1. Estimation of drift and random components

In two articles, we present ‘coregionalization analysis with a drift’ (CRAD), a method to assess the multi-scale variability of and relationships between ecological variables from a multivariate spatial data set. In phase I of CRAD (the first article), a deterministic drift component representing the large-scale pattern and a random component modeled as a second-order stationary process are estimated for each variable separately. In phase II (this article), a linear model of coregionalization (LMC) is fitted by estimated generalized least squares to the direct and cross experimental variograms of residuals (i.e., after the removal of estimated drifts). Structural correlations and coefficients of determination at smaller scales are then computed from the estimated coregionalization matrices, while the estimated drifts are used to calculate pseudo coefficients at large scale. The performance of five procedures in estimating correlations and coefficients of determination was compared using a Monte Carlo study. In four CRAD procedures, drift estimation was based on local polynomials of order 0, 1, 2 (L₀, L₁, L₂) or a global polynomial with forward selection of the basis functions; the fifth procedure was coregionalization analysis (CRA), in which large-scale patterns were modeled as a supplemental component in the LMC. In bivariate and multivariate analyses, the uncertainty in the estimation of correlations and coefficients of determination could be related to the interference between spatial components within a bounded sampling domain. In the bivariate case, most procedures provided acceptable estimates of correlations. In regionalized redundancy analysis, uncertainty was highest for CRA, while L₁ provided the best results overall. In a forest ecology example, the identification of scale-specific correlations between plant species diversity and soil and topographical variables illustrated the potential of CRAD to provide unique insight into the functioning of complex ecosystems.     

The maximum likelihood based intensity estimate for circular point processes

Identifying the geometrical nature of spatial point patterns plays an important role in many areas of scientific research. Common types of spatial point processes involve random, regular, and cluster patterns. However, some point patterns suggest identifiable geometrical shapes such as a circular or other conic patterns. These patterns may be recognized as either a specific clustered shape or an inhomogeneous point pattern. Less noisy conic shapes, including circular patterns, are heavily discussed in the pattern recognition literature, but the goodness-of-fit of conic-fitting algorithms is rarely discussed for very noisy data. This study addresses a parameter estimation technique for noisy circular point patterns using the maximum likelihood principle. Additionally, a spatial statistical tool known as the L-function is used to investigate whether the fitted location pattern is reasonably attributable to a circular shape. A novel quantity named ‘relative log-error’ (\(\gamma \)) is introduced to quantify the goodness-of-fit for circular model fits. An iteratively re-weighted least squares procedure is introduced and robustness is evaluated under several error structures. Computational efficiency of the current and novel circle-fitting methods is also discussed. The findings are applied to two environmental science data sets.     

Rarefaction and nonrandom spatial dispersion patterns

Rarefaction estimates how many species are expected in a random sample of individuals from a larger collection and allows meaningful comparisons among collections of different sizes. It assumes random spatial dispersion. However, two common dispersion patterns, within-species clumping and segregation among species, can cause rarefaction to overestimate the species richness of a smaller continuous area. We use field studies and computer simulations to determine (1) how robust rarefaction is to nonrandom spatial dispersion and (2) whether simple measures of spatial autocorrelation can predict the bias in rarefaction estimates. Rarefaction does not estimate species richness accurately for many communities, especially at small sample sizes. Measures of spatial autocorrelation of the more abundant species do not reliably predict amount of bias. Survey sites should be standardized to equal-sized areas before sampling. When sites are of equal area but differ in number of individuals sampled, rarefaction can standardize collections. When communities are sampled from different-sized areas, the mean and confidence intervals of species accumulation curves allow more meaningful comparisons among sites. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.

Simulating Historical Variability in the Amount of Old Forests in the Oregon Coast Range

Abstract: We developed the landscape age-class demographics simulator ( LADS) to model historical variability in the amount of old-growth and late-successional forest in the Oregon Coast Range over the past 3,000 years. The model simulated temporal and spatial patterns of forest fires along with the resulting fluctuations in the distribution of forest age classes across the landscape. Parameters describing historical fire regimes were derived from data from a number of existing dendroecological and paleoecological studies. Our results indicated that the historical age-class distribution was highly variable and that variability increased with decreasing landscape size. Simulated old-growth percentages were generally between 25% and 75% at the province scale (2,250,000 ha) and never fell below 5%. In comparison, old-growth percentages varied from 0 to 100% at the late-successional reserve scale (40,000 ha). Province-scale estimates of current old-growth (5%) and late-successional forest (11%) in the Oregon Coast Range were lower than expected under the simulated historical fire regime, even when potential errors in our parameter estimates were considered. These uncertainties do, however, limit our ability to precisely define ranges of historical variability. Our results suggest that in areas where historical disturbance regimes were characterized by large, infrequent fires, management of forest age classes based on a range of historical variability may be feasible only at relatively large spatial scales. Comprehensive landscape management strategies will need to consider other factors besides the percentage of old forests on the landscape, including the spatial pattern of stands and the rates and pathways of landscape change.     

A practical look at the variable area transect

The variable area transect (VAT) is a plotless density estimator that has received little attention in the ecological literature despite having potentially robust estimation properties. VAT allows for density estimations without the lengthy search times associated with other plotless density estimators. In spite of this, little has been written about the effect of varying transect widths on its density estimation properties or on the practical implementation of the VAT in field settings. An artificial population sampler was used to examine the effect of transect width on density estimates obtained using the VAT. Three transect widths were chosen corresponding to the mean object size, the largest object size, and twice the size of the largest object. Transect width had a marked effect on the quality of the density estimation, with the largest transect width resulting in significant negative biases in estimation. For the narrowest width, most estimates were within 10% of the true value for a nonrandomly distributed population. The practical considerations of choosing a VAT transect width are enumerated.     

Efficient statistical mapping of avian count data

We develop a spatial modeling framework for count data that is efficient to implement in high-dimensional prediction problems. We consider spectral parameterizations for the spatially varying mean of a Poisson model. The spectral parameterization of the spatial process is very computationally efficient, enabling effective estimation and prediction in large problems using Markov chain Monte Carlo techniques. We apply this model to creating avian relative abundance maps from North American Breeding Bird Survey (BBS) data. Variation in the ability of observers to count birds is modeled as spatially independent noise, resulting in over-dispersion relative to the Poisson assumption. This approach represents an improvement over existing approaches used for spatial modeling of BBS data which are either inefficient for continental scale modeling and prediction or fail to accommodate important distributional features of count data thus leading to inaccurate accounting of prediction uncertainty.     

Distribution of diatoms in the surficial sediments of the Mangyung-Dongjin tidal flat,west coast of Korea (Eastern Yellow Sea)

The relationship between the distribution of benthic diatoms and sediment characteristics of the Mangyung-Dongjin tidal flat on the west coast of Korea was investigated during June and July 1988. Diatoms were collected from the upper 5 mm of sediments at 60 sites along eight transect lines running perpendicular to the shore line. Of the 371 taxa encountered in the study area, 88% were pennate diatoms. Genera represented by the greatest number of species were Navicula, Nitzschia, Amphora, Cocconeis, Fragilaria and Achnanthes. The most abundant species were Paralia sulcata, Navicula sp. #1, N. arenaria and Cymatosira belgica; all were broadly distributed across the tidal flats. The 60 sites could be assigned to eight clusters with respect to similarity in species composition. Discriminant analysis showed that separation cluster was primarily related to the mean grain size of the sediment. The species could not be separated into groups based on similarities in occurrence; a high degree of spatial overlap was observed. The preferences of the more abundant species for grain size were, therefore, analysed by plotting numerical abundance against mean grain size. There were at least four patterns: species groups could be associated with finegrained sediments, those of intermediate size and coarser sediments and the last group showed no discernible pattern.     

Neighborhood effects on seed dispersal by frugivores: testing theory with a mistletoe-marsupial system in Patagonia

The outcome of the dispersal process in zoochorous plants is largely determined by the behavior of frugivorous animals. Recent simulation studies have found that fruit removal rates and mean dispersal distances are strongly affected by fruiting plant neighborhoods. We empirically tested the effects of conspecific fruiting plant neighborhoods, crop sizes, and plant accessibility on fruit removal rates and seed dispersal distances of a mistletoe species exclusively dispersed by an arboreal marsupial in Northern Patagonia. Moreover, in this study, we overcome technical limitations in the empirical estimation of seed dispersal by using a novel 15N stable isotope enrichment technique together with Bayesian mixing models that allowed us to identify dispersed seeds from focal plants without the need of extensive genotyping. We found that, as predicted by theory, plants in denser neighborhoods had greater fruit removal and shorter mean dispersal distances than more isolated plants. Furthermore, the probability of dispersing seeds farther away decreased with neighborhood density. Larger crop sizes resulted in larger fruit removal rates and smaller probabilities of longer distance dispersal. The interplay between frugivore behavioral decisions and the spatial distribution of plants could have important consequences for plant spatial dynamics.     

Semi-nonparametric Distribution-Free Dichotomous Choice Contingent Valuation

We apply a semi-nonparametric distribution-free estimator for binary discrete response data to the estimation of a dichotomous choice contingent valuation model. Using this estimator, mean and median compensating and equivalent variation can be consistently estimated without making nontheoretically motivated assumptions on consumer' preferences. The approach is illustrated using a contingent valuation survey of willingness to pay for reduction of risk of premature death due to exposure to hazardous waste. We find that a conventional parametric estimator and the proposed estimator give similar estimates of unconditional WTP, but that conditional on explanatory variables the estimates are quite different.     

A spatial zero-inflated poisson regression model for oak regeneration

Ecological counts data are often characterized by an excess of zeros and spatial dependence. Excess zeros can occur in regions outside the range of the distribution of a given species. A zero-inflated Poisson regression model is developed, under which the species range is determined by a spatial probit model, including physical variables as covariates. Within that range, species counts are independently drawn from a Poisson distribution whose mean depends on biotic variables. Bayesian inference for this model is illustrated using data on oak seedling counts. Received: May 2004 / Revised: December 2004