Testing spatial cluster occurrence in maps equipped with environmentally defined structures

We propose a novel tool for testing hypotheses concerning the adequacy of environmentally defined factors for local clustering of diseases, through the comparative evaluation of the significance of the most likely clusters detected under maps whose neighborhood structures were modified according to those factors. A multi-objective genetic algorithm scan statistic is employed for finding spatial clusters in a map divided in a finite number of regions, whose adjacency is defined by a graph structure. This cluster finder maximizes two objectives, the spatial scan statistic and the regularity of cluster shape. Instead of specifying locations for the possible clusters a priori, as is currently done for cluster finders based on focused algorithms, we alter the usual adjacency induced by the common geographical boundary between regions. In our approach, the connectivity between regions is reinforced or weakened, according to certain environmental features of interest associated with the map. We build various plausible scenarios, each time modifying the adjacency structure on specific geographic areas in the map, and run the multi-objective genetic algorithm for selecting the best cluster solutions for each one of the selected scenarios. The statistical significances of the most likely clusters are estimated through Monte Carlo simulations. The clusters with the lowest estimated p-values, along with their corresponding maps of enhanced environmental features, are displayed for comparative analysis. Therefore the probability of cluster detection is increased or decreased, according to changes made in the adjacency graph structure, related to the selection of environmental features. The eventual identification of the specific environmental conditions which induce the most significant clusters enables the practitioner to accept or reject different hypotheses concerning the relevance of geographical factors. Numerical simulation studies and an application for malaria clusters in Brazil are presented.     

Land-use planning via enhanced multi-objective evolutionary algorithms: optimizing the land value of major Greenfield initiatives

The successful implementation of major development initiatives relies on the sound allocation of land uses against critical design criteria and constraints. The discovery of optimum development plans introduces severe complexities in formulating and solving the underlying multi-objective optimization problem. Moreover, in the presence of conflicting planning criteria decision-makers should be provided with a set of alterative-yet-optimum solutions that uniformly cover the spectrum of feasible maps. The introduction of sophisticated optimization algorithms addresses this challenge by pursuing a complete approximation of the Pareto front containing all prominent spatial allocations. This study demonstrates the effectiveness of a new evolutionary algorithm (UDT-MOEA) against the results of an established multi-objective genetic algorithm (NSGA-II) when applied on a major greenfield initiative against the optimum location(s), size and shape of three new land uses. Each algorithm performs best in different areas of the feasible objective space, providing planning alternatives with distinct characteristics.     

用遗传算法优化工业污染源布局

工业污染源的优化布局是复杂的以偏微分方程为约束条件的控制问题。遗传算法是一种近年发展起来并广泛应用于多个领域优化问题的最优化方法。引入罚函数来处理环境不等式约束，建立了适当的编码方式和适应度函数，应用遗传算法以求解优化布局问题，并进行了数值试验。数值试验结果表明，遗传算法可以有效地用于求解工业污染源的优化布局问题，而且该方法鲁棒性好，适用于任意的目标函数和空气污染模式，天然适合于并行计算，有很好的应用前景。     

编码输入法在PCB分配参数预测中的应用

根据Free-Wilson法中化合物结构表达的思想，采用两种简单的编码输入方法对58个多氯联苯(PCB)的结构进行表征，并基于模型简单性原则对多元线性回归(MLR)与误差反向传递(BP)人工神经网络、模拟退火(SA)人工神经网络和遗传算法(GA)人工神经网络PCB分配参数预测模型的预测能力进行了比较，试验证实，粗略考虑PCB结构对称性的简单编码输入规则可以简化PCB分配参数预测模型的数字形式，所获得的MLR模型具备广泛的应用前景。     

Genetic diversity and structure in Pelvetia fastigiata (Phaeophyta: Fucales): does a small effective neighborhood size explain fine-scale genetic structure?

We investigated the genetic diversity and genetic structure of southern California populations of the common intertidal fucoid seaweed Pelvetia fastigiata, (J. Ag.) De Toni by means of allozyme electrophoresis and estimates of genetic neighborhood area and size, which are the first for seaweeds. We predicted that P. fastigiata populations would exhibit relatively low genetic diversity and high genetic structure because the seaweed is monoecious and has limited dispersal of gametes and zygotes. This prediction was supported; genetic diversity indices were all low compared to other seaweeds studied, but high genetic structure was evident particularly within individual reefs. Geospatial statistical analyses (second-order analyses) revealed clustered distribution of glucose-6-phosphate isomerase (GPI) alleles at the scale of 1 to 6 m within three reefs. The rare alleles were distributed only at the landward third of the reefs. Genetic neighborhood area (2.3 m²) and size (133 individuals) were estimated from parent-offspring dispersal distributions of gametes and zygotes from attached thalli and also detached reproductive fragments, which contributed very little to the effective neighborhood size. The neighborhood size was in the small theoretical range in which genetic drift could be responsible for the within-reef genetic structure. This result was equivocal, because the stereotyped distribution of rare alleles on the tips of each reef was highly unlikely to be due to random events (6.9×10^-24). These results emphasize (1) the importance of allele mapping in addition to spatial statistics to elucidate genetic structure, and (2) that interpretation of genetic-structure statistics as evidence for gene flow can be complicated, even when supported with independent estimates of gene flow, if data are lacking on selection and sporadic migration events. The emerging pattern of low levels of polymorphisms in brown seaweeds will limit the use of Wright's F-statistics and will require alternative, more direct techniques for the analysis of mechanisms responsible for population genetic structure.     

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.     

Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov chain Monte Carlo algorithm

● A hydrodynamic-Bayesian inference model was developed for water pollution tracking. ● Model is not stuck in local optimal solutions for high-dimensional problem. ● Model can estimate source parameters accurately with known river water levels. ● Both sudden spill incident and normal sewage inputs into the river can be tracked. ● Model is superior to the traditional approaches based on the test cases. Water quality restoration in rivers requires identification of the locations and discharges of pollution sources, and a reliable mathematical model to accomplish this identification is essential. In this paper, an innovative framework is presented to inversely estimate pollution sources for both accident preparedness and normal management of the allowable pollutant discharge. The proposed model integrates the concepts of the hydrodynamic diffusion wave equation and an improved Bayesian-Markov chain Monte Carlo method (MCMC). The methodological framework is tested using a designed case of a sudden wastewater spill incident (i.e., source location, flow rate, and starting and ending times of the discharge) and a real case of multiple sewage inputs into a river (i.e., locations and daily flows of sewage sources). The proposed modeling based on the improved Bayesian-MCMC method can effectively solve high-dimensional search and optimization problems according to known river water levels at pre-set monitoring sites. It can adequately provide accurate source estimation parameters using only one simulation through exploration of the full parameter space. In comparison, the inverse models based on the popular random walk Metropolis (RWM) algorithm and microbial genetic algorithm (MGA) do not produce reliable estimates for the two scenarios even after multiple simulation runs, and they fall into locally optimal solutions. Since much more water level data are available than water quality data, the proposed approach also provides a cost-effective solution for identifying pollution sources in rivers with the support of high-frequency water level data, especially for rivers receiving significant sewage discharges.     

Finding all optimal solutions to the reserve site selection problem: formulation and computational analysis

The problem of selecting nature reserves has received increased attention in the literature during the past decade, and a variety of approaches have been promoted for selecting those sites to include in a reserve network. One set of techniques employs heuristic algorithms and thus provides possibly sub-optimal solutions. Another set of models and accompanying algorithms uses an integer programming formulation of the problem, resulting in an optimization problem known as the Maximal Covering Problem, or MCP. Solution of the MCP provides an optimal solution to the reserve site selection problem, and while various algorithms can be employed for solving the MCP they all suffer from the disadvantage of providing a single optimal solution dictating the selection of areas for conservation. In order to provide complete information to decision makers, the determination of all alternate optimal solutions is necessary. This paper explores two procedures for finding all such solutions. We describe the formulation and motivation of each method. A computational analysis on a data set describing native terrestrial vertebrates in the state of Oregon illustrates the effectiveness of each approach.     

Vegetation competition model for water and light limitation. II: Spatial dynamics of groundwater and vegetation

In temperate climates groundwater can have a profound effect on vegetation, because it strongly influences the spatio-temporal distribution of soil moisture in the rootzone and therefore the occurrence of water and oxygen stress of vegetation. This article focuses on vegetation and groundwater dynamics along a hill slope by developing and evaluating a fully coupled hydrological-vegetation model for a temperate forest ecosystem. The vegetation model is described in part 1 of this series of two papers. To simulate the hydrology an extended version of the saturated-unsaturated hydrological model STARWARS has been used. The coupled model is used to investigate both the short and long-term dynamics for a system of two species. Both compete for light and water where one is adapted to wet conditions and the other to dry conditions. The daily dynamics show that the influence of groundwater is particularly strong in spring when waterlogging occurs due to decreased evapotranspiration in winter. Long simulation runs of 1000 years were performed to study the equilibrium state for the two species. Comparison of simulation results with observations of groundwater depth and vegetation types along a dry-wet gradient in a natural forest shows that a reductionist approach is able to capture these patterns well. Sensitivity analysis shows that the border between wet- and dry-adapted species moves upslope with increased rainfall, decreased slope angle and decreased aquifer thickness. These results are similar to previous findings which were based on global maximization of ecosystem evaporation or minimizing ecosystem stress. Comparison of runs with a fixed and a dynamic groundwater table shows that a dynamic groundwater table facilitates a wider transition zone between vegetation types along the hill slope. In this transition the biomass of vegetation is higher in the case of a dynamic groundwater than in case of a static groundwater table. This underlines the importance of incorporating spatial groundwater dynamics in models of groundwater influenced ecosystems.     

Effects of Rock Climbing on Populations of Presettlement Eastern White Cedar (Thuja occidentalis) on Cliffs of the Niagara Escarpment, Canada

Cliffs of the Niagara Escarpment support a self-sustaining presettlement forest of eastern white cedar (  Thuja occidentalis L.) in which some trees are over 1000 years old. Many of the cliffs are also popular locations for recreational rock climbing. Our study employed a stratified random sampling design to assess the impact of rock climbing on populations of cliff-face and cliff-edge trees. Tree density and age structure were compared between four climbed and three unclimbed sites in the vicinity of Milton, Ontario. Signs of physical damage were also recorded for the trees sampled at each site. The results showed that living tree density on the cliff face was lower in climbed areas. The age structures of these forests showed that the numbers of older and younger age classes have been reduced on climbed cliff faces compared with unclimbed areas. A high percentage of trees on climbed cliff faces showed evidence of damage by humans. These trends were not as apparent on cliff edges where other disturbances have affected age structure. We recommend that cliff faces be explored for the presence of presettlement forest and that recreation managers of lands with exposed cliff faces incorporate rock climbing considerations into their management plans. Monitoring programs would help track levels of disturbance. Education of the climbing community would be the most effective long-term solution to limiting disturbance in sensitive areas.     

Integrating models to predict regional haze from wildland fire

Visibility impairment from regional haze is a significant problem throughout the continental United States. A substantial portion of regional haze is produced by smoke from prescribed and wildland fires. Here we describe the integration of four simulation models, an array of GIS raster layers, and a set of algorithms for fire-danger calculations into a modeling framework for simulating regional-scale smoke dispersion. We focus on a representative fire season (2003) in the northwestern USA, on a 12 km domain, and track the simulated dispersion and concentration of PM_2.5 over the course of the season. Simulated visibility reductions over national parks and wilderness areas are within the ranges of measured values at selected monitoring sites, although the magnitudes of peak events are underestimated because these include inputs other than fire. By linking the spatial and temporal patterns of haze-producing emissions to climatic variability, particularly synoptic weather patterns, and the stochastic nature of fire occurrence across the region, we can provide a robust method for estimating the quantity and distribution of fire-caused regional haze under climate-warming scenarios.     

Different escape tactics of two vole species affect the success of the hunting predator, the least weasel

In the ongoing evolutionary arms race between predators and their prey, successful escape from the predator leads to the evolution of improved escape tactics in prey, but also predators become more effective in following and attacking the prey. Antipredatory behavior of prey is considered to be the strongest towards their most dangerous predators. However, prey species can differ both in vulnerability and efficiency of escape to a shared predator. We studied escape reactions of two vole species, the bank vole (Myodes glareolus) and the field vole (Microtus agrestis), under a simulated predation risk of the least weasel (Mustela nivalis nivalis). We conducted a laboratory experiment where a vole was given a possibility to escape from a weasel by fleeing to a horizontal tunnel or climbing the tree. Subsequently to the vole escape decision, we released a weasel to the same tunnel system to test how the weasel succeeded in following the vole. Weasel presence changed the behavior of voles as especially bank voles escaped by climbing. Instead, the majority of field voles fled into the ground-layer tunnel. The different escape tactics of the voles affected the success of the weasel, because climbing voles were less often successfully followed. We suggest that the difference in escape tactics has evolved as an adaptation to different habitats; meadow-exploiting field voles using ground-level escape while bank voles living in three-dimensional forest habitat frequently use arboreal escape tactics. This is likely to lead to different habitat-dependent vulnerabilities to predation in Microtus and Myodes vole species.     

A new algorithm for estimating the parameters of the spatial generalized linear mixed models

Non-Gaussian spatial responses are usually modeled using a spatial generalized linear mixed model with location specific latent variables. The likelihood function of this model cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. So far, several numerical algorithms to solve the problem of calculating maximum likelihood estimates of this model have been presented. In this paper to estimate the parameters an approximate method is considered and a new algorithm is introduced that is much faster than existing algorithms but just as accurate. This is called the Approximate Expectation Maximization Gradient algorithm. The performance of the proposed algorithm and is illustrated with a simulation study and on a real data set.     

An EM algorithm for capture-recapture estimation

Analysis of capture—recapture data often involves maximizing a complex likelihood function with many unknown parameters. Statistical inference based on selection of a proper model depends on successful attainment of this maximum. An EM algorithm is developed for obtaining maximum likelihood estimates of capture and survival probabilities conditional on first capture from standard capture—recapture data. The algorithm does not require the use of numerical derivatives which may improve precision and stability relative to other estimation schemes. The asymptotic covariance matrix of the estimated parameters can be obtained using the supplemented EM algorithm. The EM algorithm is compared to a more traditional Newton-Raphson algorithm with both a simulated and a real dataset. The two algorithms result in the same parameter estimates, but Newton-Raphson variance estimates depend on a numerically estimated Hessian matrix that is sensitive to step size choice.     

How many animals really do the Lévy walk?

Lévy walks (LW) are superdiffusive and scale-free random walks that have recently emerged as a new conceptual tool for modeling animal search paths. They have been claimed to be more efficient than the "classical" random walks, and they also seem able to account for the actual search patterns of various species. This suggests that many animals may move using a LW process. LW patterns look like the actual search patterns displayed by animals foraging in a patchy environment, where extensive and intensive searching modes alternate, and which can be generated by a mixture of classical random walks. In this context, even elementary composite Brownian walks are more efficient than LW but may be confounded with them because they present apparent move-length-heavy tail distributions and superdiffusivity. The move-length "survival" distribution (i.e., the cumulative number of moves greater than any given threshold) appears to be a better means to highlight a LW pattern. Even once such a pattern has been clearly identified, it remains to determine how it was actually generated, because a LW pattern is not necessarily produced by a LW process but may emerge from the way the animal interacted with the environment structure through more classical movement processes. In any case, emergent movement patterns should not be confused with the processes that gave rise to them.     

Design and validation of a spatially explicit simulation model for bottomland hardwood forests

Species composition in forests depends on the interaction of species traits and species availability. Yet many forest simulation models focus only on interactions of adult trees and saplings, ignoring how species become members of the community. We modify a published forest model for bottomland hardwood forests (program SWAMP [Phipps, R.L., 1979. Simulation of wetlands forest vegetation dynamics. Ecol. Modell. 7, 257–288]) to make it spatially explicit and incorporate explicit seed production and dispersal algorithms. The resulting individual-based, spatially explicit forest simulator (YAFSIM) combines mechanistic seed dispersal with growth and mortality of trees to track forest dynamics over time. We describe the structure of the model and test its validity for dynamics in small bottomland hardwood patches in the Mississippi Alluvial Valley. Dynamics of species composition and basal areas of trees predicted by Yazoo Forest Simulator (YAFSIM) were similar to those of natural second- and old-growth bottomland forests. However, diversity of simulated forest patches declined over time largely because of random dynamics acting on small, isolated populations.     

Benefits of Conservation of Plant Genetic Diversity to Arthropod Diversity

Abstract:  We argue that the genetic diversity of a dominant plant is important to the associated dependent community because dependent species such as herbivores are restricted to a subset of genotypes in the host-plant population. For plants that function as habitat, we predicted that greater genetic diversity in the plant population would be associated with greater diversity in the dependent arthropod community. Using naturally hybridizing cottonwoods (  Populus spp.) in western North America as a model system, we tested the general hypothesis that arthropod alpha (within cross-type richness) and beta (among cross-type composition) diversities are correlated with cottonwood cross types from local to regional scales. In common garden experiments and field surveys, leaf-modifying arthropod richness was significantly greater on either the F₁ (1.54 times) or backcross (1.46 times) hybrid cross types than on the pure broadleaf cross type (  P. deltoides Marshall or P. fremontii Watson). Composition was significantly different among three cross types of cottonwoods at all scales. Within a river system, cottonwood hybrid zones had 1.49 times greater richness than the broadleaf zone, and community composition was significantly different between each parental zone and the hybrid zone, demonstrating a hierarchical concentration of diversity. Overall, the habitats with the highest cottonwood cross-type diversity also had the highest arthropod diversity. These data show that the genetics of habitat is an important conservation concept and should be a component of conservation theory.     

Evaluation of the Contribution of Genetic Research to the Management of the Endangered Plant Zieria prostrata

Abstract: Zieria prostrata (Rutaceae) is known from only four headlands within a 3-km stretch of coastline in New South Wales, Australia. The species was presumed to have occurred at a headland 24 km south of its present range. We used random amplified polymorphic DNA analysis to assess patterns of genetic variation within and among the extant populations. The analysis also included an individual reputedly rescued from the now extinct population. A high level of population divergence was revealed by principal coordinate analysis and an analysis of molecular variance (AMOVA; 37% among populations). Our genetic findings provide implications for the conservation management of the species. First, the loss of any one population would lead to a severe loss of genetic variation. Second, an adequate ex situ collection must sample the full range of genetic diversity from all populations. Third, the consequences of mixing populations may be an important conservation consideration if further translocations proceed. Fourth, the individual apparently sampled prior to its population extinction is genetically similar to individuals from one of the extant sites. This degree of similarity was unexpected and, after further investigation, led to the conclusion that prior existence of the species at the site is doubtful. Subsequently, a planned reintroduction program was abandoned. So far, of these four management implications, only the last has had a direct management outcome. Those implications that failed to lead to practical management outcomes did so because the same management recommendations could be obtained without genetic research. Clearly, the challenge for more effective conservation is to identify those cases in which genetic studies are likely to produce practical outcomes for conservation managers. This may be best achieved by assessing the outcomes of genetic studies already conducted.     

River water quality management model using genetic algorithm

Conventional mathematical programming methods, such as linear programming, non linear programming, dynamic programming and integer programming have been used to solve the cost optimization problem for regional wastewater treatment systems. In this study, a river water quality management model was developed through the integration of a genetic algorithm (GA). This model was applied to a river system contaminated by three determined discharge sources to achieve the water quality goals and wastewater treatment cost optimization in the river basin. The genetic algorithm solution, described the treatment plant efficiency, such that the cost of wastewater treatment for the entire river basin is minimized while the water quality constraints in each reach are satisfied. This study showed that genetic algorithm can be applied for river water quality modeling studies as an alternative to the present methods.