A general model for analyzing Taylor's spatial scaling laws

Taylor's spatial scaling law concerns the relation between the variance and the mean population counts within areas of a given size. For a range of area sizes, the log of the variance often is an approximately linear function of the mean with a slope between 1 and 2, depending on the range of areas considered. In this paper, we investigate this relationship theoretically for random quadrat samples within a large area. The model makes a distinction between the local point process determining the position of each individual and the population density described by a spatial covariance function. The local point process and the spatial covariance of population density both contribute to the general relationship between the mean and the variance in which the slope may begin at 1, increase to 2, and decrease to 1 again. It is demonstrated by an example that the slope theoretically may exceed 2 by a small amount for very regular patterns that generate spatial covariance functions that increase in certain intervals. We also show how properties of population dynamics in space and time determine this relationship.     

Bayesian nonlinear meta regression for benefit transfer

In recent years numerous meta-regression models for benefit transfer in the context of environmental quality changes have been proposed by the academic literature and used by government agencies for policy making. We examine a set of popular specifications in terms of consistency with some basic utility-theoretic considerations, including the adding-up condition that is currently under much scrutiny by benefit transfer practitioners. We also compare these models based on econometric fit with underlying data, and ability to generate meaningful and efficient benefit transfer distributions. We find that our preferred Bayesian Nonlinear Meta-Regression Model (BNL-MRM) satisfies all theoretical requirements. Using a built-in nonlinear model search algorithm we show that it produces benefit estimates that are comparable or superior in efficiency to those flowing from better fitting, but theoretically flawed linear models that do not satisfy adding-up.     

Sparse regression for analyzing the development of foliar nutrient concentrations in coniferous trees

Analyzing and predicting the development of foliar nutrient concentrations are important and challenging tasks in environmental monitoring. This article presents how linear sparse regression models can be used to represent the relations between different foliar nutrient concentration measurements of coniferous trees in consecutive years. In the experiments the models proved to be capable of providing relatively good and reliable predictions of the development of foliage with a considerably small number of regressors. Two methods for estimating sparse models were compared to more conventional linear regression models. Differences in the prediction accuracies between the sparse and full models were minor, but the sparse models were found to highlight important dependencies between the nutrient measurements better than the other regression models. The use of sparse models is, therefore, advantageous in the analysis and interpretation of the development of foliar nutrient concentrations.     

A regression strategy for analyzing environmental data generated by spatio-temporal processes

Highly complex spatio-temporal environmental data sets are becoming common in ecology because of the increasing use of large-scale simulation models and automated data collection devices. The spatial and temporal dimensions present real and difficult challenges for the interpretation of these data. A particularly difficult problem is that the relationship among variables can vary in dramatically in response to environmental variation; consequently, a single model may not provide adequate fit. The temporal dimension presents both opportunities for improved prediction because explanatory variables sometimes exert delayed effects on response variables, and problems because variables are often serially correlated. This article presents a regression strategy for accommodating these problems and exploiting serial correlation. The strategy is illustrated by a case study of simulated net primary production (SNPP) that compares ocean-atmosphere indices to terrestrial climate variables as predictors of SNPP across the conterminous United States, and describes spatial variation in the relative importance of terrestrial climate variables towards predicting SNPP. We found that the relationship between ocean-atmosphere indices and SNPP varies substantially over the United States, and that there is evidence of a substantive link only in the western portions of the United States. Evidence of multi-year delays in the effect of terrestrial climate effects on SNPP were also found.     

A nonlinear regression approach to test for size-dependence of competitive ability

An individual's competitive ability is often dependent on its size, but the methods commonly used to analyze plant competition experiments generally assume that the outcome of interactions are size independent. A method for the analysis of experiments with paired competition treatments based on nonlinear regression with a power function is presented. This method allows straightforward tests of whether a competitive interaction is size dependent, and for the significance of experimental treatments. The method is applied to three example data sets: (1) an experiment where pairs of plants were grown with and without competition at five fertilization levels, (2) an experiment where the fecundity of two snail species were compared between environments at two densities, and (3) an addition series experiment where two plant species were grown in proportional mixtures at several densities. Competitive ability was size-dependent in two of these examples, which demonstrates that a wide range of ecologically important information can be lost when the assumption of size-dependence is ignored. Regression with a power curve should always be used to test whether competitive interactions are size independent, and for the further analysis of size-dependent interactions.     

Appropriate use of regression analysis in marine biology

The Model I linear regression theory is often used in the analysis of data under conditions when the Model II theory is clearly needed. Implications derived from the use of the two theories can differ greatly when there is not a high degree of correlation between the X and Y variables. The geometric mean Model II method is easy to use, and is probably needed in the analysis of most field data, since the X variable in field data is rarely under the control of the investigator.     

PCB disposition and different biological effects in rats following direct soil exposure vs. PCBs off-gassed from the soil

A comparison of PCB congener profiles and limited biological effects was made between direct exposure to PCB-contaminated soil and vapor phase PCBs from that soil to determine congener patterns useful for identifying exposure sources in humans and wildlife. Weanling female Sprague–Dawley rats were exposed to either control or PCB-contaminated soil (from a landfill in Southern Illinois) for 1 and 2 weeks. The exposures were via direct contact with the soil or via airborne exposure with the rats isolated from the soil by a wire screen. Total PCB of 25% contaminated soil used in the study was 13?500?ppm. No PCBs were detectable in control rats. In direct-exposed rats, total PCB residues in fat pad, ear skin, serum, liver, and inguinal lymph nodes after the 1-week exposure were 6256, 185, 3.2, 149, and 41?ppm, respectively, but decreased to 465, 72, 1.7, 106, and 32.4?ppm after the 2-week exposure. In airborne-exposed rats, total PCB residues were 7.8, 1.6, 0.03, 0.2, and 0.6 in the same manner and slightly increased in fat pad and ear skin to 11.6 and 2.14, respectively. Decreases in both the concentrations and percentages of “episodic” PCBs (those congeners rapidly metabolized) in the fat pad were apparent following the 2-week exposure compared to the 1-week exposure by both routes. Both EROD and BROD activities were significantly increased in the direct-exposed rats, whereas only BROD activity increased in airborne-exposed rats. Serum T ₄ levels were depleted in the direct-exposed rats regardless of time of exposure but were increased insignificantly after 1-week and significantly after 2 weeks in the airborne-exposed rats. No significant changes in serum insulin levels were apparent in any of the treated groups. The results suggested that exposure of animals to PCBs via different routes could result in different PCB profiles, which could cause different biological effects.     

稳定氢氧同位素在定量区分植物水分利用来源中的应用

全球气候变化下陆地生态系统的适应性是当前科学研究关注的主题之一,了解生态系统如何响应及影响全球气候变化有利于人类对未来生存环境的预测和适应。生态系统中不同来源水分对植物生长相对贡献决的大小一定程度上决定了生态系统对气候变化的响应方式、程度和响应结果,因此跟踪和分析植物利用水分的来源是制定全球气候变化对策的一个重要研究内容。本文介绍了稳定氢氧同位素技术研究历史及其在定量区分植物利用水分的来源研究中的应用原理与具体方法。由于土壤水分在被植物根系吸收及随后沿导管向上传输的过程中,与外界环境不发生水分交换,因此不存在同位素的分馏过程,所以植物茎木质部水分同位素组成能反映出植物利用的来源水分同位素信息。通过比较植物茎木质部水分与植物利用的不同来源水分同位素值,利用二项或三项分隔线性混合模型(two-orthree-compartment linear mixing model),可以估算出植物对不同来源水分的相对使用量。而由于植物叶片水分同位素组成受到周围环境的温度、湿度、降雨和土壤水分的异质性等许多因素的影响,通过比较分析植物茎木质部水分和叶片水分同位素组成的差异可以得到植物周围环境的气候信息。植物利用水分的来源存在显著的季节性差异,并且,不同生活型植物在利用水分来源上存在明显不同。植物根系的分布及根深是决定植物利用水分来源的一个重要的因素,表层和深层根系的相对分布及其活性影响着植物吸收水分的范围。当然,利用线型分隔混合模型定量区分植物利用水分的不同来源,还有许多值得改进的地方,而且,尽管稳定同位素技术在植物科学中的应用正迅速发展起来,但利用稳定氢氧同位素来分析环境因素对植物影响的研究还只是刚刚展开,还有许多方面值得去进一步探索。     

On the relative contributions of wind vs. animals to seed dispersal of four Sierra Nevada pines

Selective pressures that influence the form of seed dispersal syndromes are poorly understood. Morphology of plant propagules is often used to infer the means of dispersal, but morphology can be misleading. Several species of pines, for example, have winged seeds adapted for wind dispersal but owe much of their establishment to scatter-hoarding animals. Here the relative importance of wind vs. animal dispersal is assessed for four species of pines of the eastern Sierra Nevada that have winged seeds but differed in seed size: lodgepole pine (Pinus contorta murrayana, 8 mg); ponderosa pine (Pinus ponderosa ponderosa, 56 mg); Jeffrey pine (Pinus jeffreyi, 160 mg); and sugar pine (Pinus lambertiana, 231 mg). Pre-dispersal seed mortality eliminated much of the ponderosa pine seed crop (66%), but had much less effect on Jeffrey pine (32% of seeds destroyed), lodgepole pine (29%), and sugar pine (7%). When cones opened most filled seeds were dispersed by wind. Animals removed > 99% of wind-dispersed Jeffrey and sugar pine seeds from the ground within 60 days, but animals gathered only 93% of lodgepole pine seeds and 38% of ponderosa pine seeds during the same period. Animals gathered and scatter hoarded radioactively labeled ponderosa, Jeffrey, and sugar pine seeds, making a total of 2103 caches over three years of study. Only three lodgepole pine caches were found. Caches typically contained 1-4 seeds buried 5-20 mm deep, depths suitable for seedling emergence. Although Jeffrey and sugar pine seeds are initially wind dispersed, nearly all seedlings arise from animal caches. Lodgepole pine is almost exclusively wind dispersed, with animals acting as seed predators. Animals treated ponderosa pine in an intermediate fashion. Two-phased dispersal of large, winged pine seeds appears adaptive; initial wind dispersal helps to minimize pre-dispersal seed mortality whereas scatter hoarding by animals places seeds in sites with a higher probability of seedling establishment.     

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.     

The use of fly ash as conditioner for dewatering of biological sludges in drying beds

In this paper the conditioning of the secondary sludge by adding fly ash from thermal power plant of Ptolemaida (Greece) for dewatering in drying beds is examined.

The results of the experimental procedure that has been followed in a pilot unit are very encouraging and show that the addition of the fly ash in concentration of 6 % of the weight of the total suspended solids of the sludge in the entrance of the drying bed increases the filterability at the level of 95.4 % and the retention of the solids at the level of 110.5 %. Consequently the method employed allows the decrease of the area of the drying bed at the level of 100 % approximately.     

Endogenous vs. exogenous regulations in the commons

It is widely believed that there is strong experimental evidence to support the idea that exogenously imposed regulations crowd out the intrinsic motivations of common pool resource (CPR) users to refrain from over-harvesting. We introduce a novel experimental design that attempts to disentangle potential confounds in previous experiments. A key feature of our experimental design is to have the exact same regulations chosen endogenously as those that are imposed exogenously. When we compare the same regulations chosen endogenously to those externally imposed, we observe no differences in extraction levels among CPR users in a laboratory experiment. We also observe no differences between weak external regulations and no regulations, after controlling for a potential confound. However, when we add communication to our endogenous treatment, we observe significant behavioral differences between endogenous regulations with communication and exogenous regulations without communication. Our results suggest that externally imposed regulations do not crowd out intrinsic motivations in the lab and they confirm that communication facilitates cooperation to reduce extraction.     

PyLUR: Efficient software for land use regression modeling the spatial distribution of air pollutants using GDAL/OGR library in Python

• PyLUR comprises four modules for developing and applying a LUR model. • It considers both conventional and novel potential predictor variables. • GDAL/OGR libraries are used to do spatial analysis in the modeling and prediction. • Developed on Python platform, PyLUR is rather efficient in data processing. Land use regression (LUR) models have been widely used in air pollution modeling. This regression-based approach estimates the ambient pollutant concentrations at un-sampled points of interest by considering the relationship between ambient concentrations and several predictor variables selected from the surrounding environment. Although conceptually quite simple, its successful implementation requires detailed knowledge of the area, expertise in GIS, statistics, and programming skills, which makes this modeling approach relatively inaccessible to novice users. In this contribution, we present a LUR modeling and pollution-mapping software named PyLUR. It uses GDAL/OGR libraries based on the Python platform and can build a LUR model and generate pollutant concentration maps efficiently. This self-developed software comprises four modules: a potential predictor variable generation module, a regression modeling module, a model validation module, and a prediction and mapping module. The performance of the newly developed PyLUR is compared to an existing LUR modeling software called RLUR (with similar functions implemented on R language platform) in terms of model accuracy, processing efficiency and software stability. The results show that PyLUR out-performs RLUR for modeling in the Bradford and Auckland case studies examined. Furthermore, PyLUR is much more efficient in data processing and it has a capability to handle detailed GIS input data.     

On the reasons of hyperbolic growth in the biological and human world systems

Macroevolution of the biological and human world systems in the aspect of time-dependence of their sizes is studied. These systems are considered as ‘civilizations’, which are defined here in a generalized sense as the systems having memory and producing knowledge (vital information) necessary for survival. Sizes of three types of memory – genetic, neural, and external – are estimated. Dominating one of them leads to the development of an appropriate type of civilization. The rise and development of the genetic memory was accompanied with the formation of the biota (which can be tractable as a biological civilization) and a hyperbolic growth of its biodiversity. The prevailing development of the neural memory in one of the taxa of biota led to the rise of the human civilization and to a hyperbolic growth of its population. The development of the external memory will probably lead to the extraction of a taxon (probably, a pool of countries) from the human world community, with a hyperbolic growth of the taxon's memory and fund of knowledge but without a pronounced growth of its population.     

Enhancement of the polynomial functions response surface model for real-time analyzing ozone sensitivity

• The calculation process and algorithm of response surface model (RSM) were enhanced. • The prediction errors of RSM in the margin and transition areas were greatly reduced. • The enhanced RSM was able to analyze O₃-NO_x-VOC sensitivity in real-time. • The O₃ formations were mainly sensitive to VOC, for the two case study regions. Quantification of the nonlinearities between ambient ozone (O₃) and the emissions of nitrogen oxides (NO_x) and volatile organic compound (VOC) is a prerequisite for an effective O₃ control strategy. An Enhanced polynomial functions Response Surface Model (Epf-RSM) with the capability to analyze O₃-NO_x-VOC sensitivities in real time was developed by integrating the hill-climbing adaptive method into the optimized Extended Response Surface Model (ERSM) system. The Epf-RSM could single out the best suited polynomial function for each grid cell to quantify the responses of O₃ concentrations to precursor emission changes. Several comparisons between Epf-RSM and pf-ERSM (polynomial functions based ERSM) were performed using out-of-sample validation, together with comparisons of the spatial distribution and the Empirical Kinetic Modeling Approach diagrams. The comparison results showed that Epf-RSM effectively addressed the drawbacks of pf-ERSM with respect to over-fitting in the margin areas and high biases in the transition areas. The O₃ concentrations predicted by Epf-RSM agreed well with Community Multi-scale Air Quality simulation results. The case study results in the Pearl River Delta and the north-western area of the Shandong province indicated that the O₃ formations in the central areas of both the regions were more sensitive to anthropogenic VOC in January, April, and October, while more NO_x-sensitive in July.     

On the importance of life history and age structure in biological invasions

The age specific patterns of reproduction and mortality dictated by the life history of an organism apply to potential invaders as well as resident species of an area, but whether certain life history traits are more invasive than others is an unresolved issue. We analyze a two-population system of an invading and a resident species and test the effects of age on the probability to invade when the organisms are iteroparous or semelparous. The life history characteristics of the populations are projected in Leslie matrices, and the probability that the invader exceeds different population sizes is calculated by Monte Carlo analysis. The simulations show that (a) the invasion probability of an iteroparous organism increases with age until the individuals introduced are mature for first reproduction, and then becomes independent of age; (b) the invasion probability is more age sensitive for iteroparous organisms with high juvenile mortality (Type III organisms) than for those with a lower (Type I); (c) invading semelparous organisms are most affected by competition from resident organisms; (d) variations in vital rates of semelparous residents have greater influence on the invasion probability of an iteroparous organism than variations in traits of the invader.     

On the use of growth rate parameters for projecting population sizes: Application to aphids

This paper extends the application of the cumulative size based mechanistic model, which has previously been shown to describe diverse aphid population size data well. The mechanistic model is reviewed with a focus on the explanatory role of the birth and death rate formulation. An analysis of two data sets, one on the mustard aphid and the other on the pecan aphid, indicates that multiple linear regression equations based on the estimated birth and death rate parameters alone account for nearly all (R² > 0.95) of the variability in two key population attributes, namely the peak count and the cumulative density. This indicates that population size variables may be projected directly from the growth rate parameters using linear equations. Such linear relationships based on the birth and death rate parameters are shown to hold also for certain generalized mechanistic models for which the analytical solution is not available. The birth and death rate coefficients, therefore, constitute a new succinct set of variables that could be included in the predictive modeling of aphid populations, as well as other insect and animal populations with local collapse which follow similar growth dynamics.     

Control targets for the management of biological systems

Attention is focused on biological systems which are describable in terms of ordinary differential equations subject to human control inputs. The concept of an isochronal system is introduced in order to include systems for which the differential equations are valid only over regularly reoccurring time intervals.It is assumed that the control inputs are to be chosen so that an integral cost function of the state of the system, the control used, current time, and the time interval of the control program is minimized. Problems associated with minimizing this cost function over an infinitely long time interval is then considered. Difficulties inherent with minimizing a cost integral on an infinite time interval are shown to be avoided by minimizing an average of the cost function over an unknown but periodic time interval. Under proper circumstance, the optimal control program for the average cost function is either identical to or a good approximation to the optimal control program for the original cost function over an infinitely long time interval.Necessary conditions are obtained for minimizing an average cost function over an unspecified time interval subject to the system equations. For a given problem the necessary conditions will yield but a single system trajectory in the state space. For management purposes this trajectory may be thought of as a target to which the system should be driven and maintained.A number of examples illustrate the use of the necessary conditions to obtain control targets. Certain problems associated with the stability of the target solutions are illustrated with the examples.     

A nonparametric procedure for analyzing repeated measures of spatially correlated data

Many agricultural, biological, and environmental studies involve detecting temporal changes of a response variable, based on data observed at sampling sites in a spatial region and repeatedly over several time points. That is, data are repeated measures over time and are potentially correlated across space. The traditional repeated-measures analysis allows for time dependence but assumes that the observations at different sampling sites are mutually independent, which may not be suitable for field data that are correlated across space. In this paper, a nonparametric large-sample inference procedure is developed to assess the time effects while accounting for the spatial dependence using a block bootstrap. For illustration, the methodology is applied to describe the population changes of root-lesion nematodes over time in a production field in Wisconsin.