The effect of wind direction on cross-pollination in wind-pollinated GM crops.

In Europe, regulatory thresholds restrict adventitious GM (genetically modified) presence in conventional crops. Minimum distances for the spatial separation of fields are often recommended to reduce field-to-field cross-pollination to an acceptable level. Field trials are typically the basis for setting separation distances. However, using records of wind direction and speed from weather stations across Europe, we predict theoretically that field-to-field windborne cross-pollination in maize, oilseed rape, sugar beet, and rice varies greatly according to the relative orientation of the GM and non-GM fields. Furthermore, at a given site and orientation from a GM field, we predict that the cross-pollination rate varies substantially from year to year. Consequently, even replicated field trials may inaccurately estimate typical levels of cross-pollination and therefore distort our perception of the separation distances required to achieve sub-threshold adventitious GM presence. We propose methods to predict the likely range in levels of cross-pollination based on the limited data typically available from field trials. Additionally, we suggest suitable time lags between peak flowering in adjacent fields that could be introduced to reduce cross-pollination to a specified level.     

Modeling carcass removal time for avian mortality assessment in wind farms using survival analysis

In monitoring studies at wind farms, the estimation of bird and bat mortality caused by collision must take into account carcass removal by scavengers or decomposition. In this paper we propose the use of survival analysis techniques to model the time of carcass removal. The proposed method is applied to data collected in ten Portuguese wind farms. We present and compare results obtained from semiparametric and parametric models assuming four main competing lifetime distributions (exponential, Weibull, log-logistic and log-normal). Both homogeneous parametric models and accelerated failure time models were used. The fitted models enabled the estimation of the carcass persistence rates and the calculation of a scavenging correction factor for avian mortality estimation. Additionally, we discuss the impact that the distributional assumption can have on parameter estimation. The proposed methodology integrates the survival probability estimation problem with the analysis of covariate effects. Estimation is based on the most suitable model while simultaneously accounting for censored observations, diminishing scavenging rate estimation bias. Additionally, the method establishes a standardized statistical procedure for the analysis of carcass removal time in subsequent studies.     

Effects of sand movement by wind on nematodes and soil-borne fungi in coastal foredunes

In stabilized dunesAmmophila arenaria (marram grass) degenerates due to a process involving soil-borne pathogens and parasites. This leads to exposure of the sand surface so that wind erosion may create blowouts.Ammophila rejuvenates on the edges of the blowouts, where the sand has accumulated. We tested the hypothesis that such rejuvenation of plants may be related to a reduction of the plant-parasitic nematodes and fungal propagules during the process of wind-driven transport. Field measurements in blowouts during storm events indicated that the drifted sand contained relatively low numbers of plant pathogenic fungi and plant-parasitic nematodes. A wind tunnel experiment showed that drifting sand may indeed reduce the numbers of fungi and nematodes. Although most fungi were attached to sand particles, they were also affected by the wind-borne sand movement. Sand that had been deposited by wind was made up of a larger proportion of large-sized particles. In our experiment the relatively small particles were lost during transport. Stirring the soil (part of the forces of natural winds) by mixing for 15 min. with a propeller mixer at 1500 rpm significantly reduced the number of nematodes and fungi. Both sand movement in the wind tunnel and intensive stirring of the sand enhanced the growth ofAmmophila test plants in a bioassay. It was concluded that in wind-blown sand the pathogen inoculum is reduced. Therefore, serious consideration should be given to allowing controlled reactivation of blowouts to rejuvenate decliningAmmophila in stabilized foredunes. Implications for dune management are briefly discussed. Nomenclature: van der Meijden (1990) for vascular plants. Nematodes were identified to the genus level according to Bongers (1988). The allocation of nematodes to feeding groups was according to Yeates et al. (1993). Nomenclature of fungi according to Domsch et al. (1980) was used throughout this study except forFusarium. Species of the latter genus were identified according to Nelson et al. (1983).     

Modeling and inference of animal movement using artificial neural networks

Movement of animals in relation to objects in their environment is important in many areas of ecology and wildlife conservation. Tools for analysis of movement data, however, still remain rather limited. In previous work, we developed nonlinear regression models for movement in relation to a single landscape feature. Here we greatly expand these previous models by using artificial neural networks. The new models add substantial flexibility and capabilities, including the ability to incorporate multiple factors and covariates. We devise a likelihood-based model fitting procedure that utilizes genetic algorithms and demonstrate the approach with movement data for red diamond rattlesnakes. The proposed methodology can be useful for global positioning system tracking data that are becoming more common in studies of animal movement behavior.     

Numerical and experimental analysis of wind erosion on a sinusoidal pile

Erosion is a common phenomenon in nature, and it may cause a wide range of problems such as air pollution, and destruction of agricultural land, shelters and stockpiles. The present work deals with stockpiles, which have their profiles described by a sinus function or by similar trigonometric functions. Analysis of the erosion by air flow over these piles will provide for further understanding of the erosion underlying mechanisms and, moreover, how to control them and eventually to prevent them. To this purpose, different experimental tests are conducted for a pile with a sinuous profile, and particular attention is given to the time development of the profile due to the erosion process; the effect of the air velocity is also studied by selecting different velocities. In addition, the flow over several deformed piles is numerically predicted using the CFX software; the results clearly show the erosion process is strongly dependent upon time, velocity field and surface disturbances. A correlation between the erosion rate and the velocity is proposed.     

Predicting tree diversity across the United States as a function of modeled gross primary production.

At the regional and continental scale, ecologists have theorized that spatial variation in biodiversity can be interpreted as a response to differences in climate. To test this theory we assumed that ecological constraints associated with current climatic conditions (2000-2004) might best be correlated with tree richness if expressed through satellite-derived measures of gross primary production (GPP), rather than the more commonly used, but less consistently derived, net primary production. To evaluate current patterns in tree diversity across the contiguous United States we acquired information on tree composition from the USDA Forest Service's Forest Inventory and Analysis program that represented more than 17,4000 survey plots. We selected 2693 cells of 1000 km2 within which a sufficient number of plots were available to estimate tree richness per hectare. Our estimates of forest productivity varied from simple vegetation indices indicative of the fraction of light intercepted by canopies at 16-d intervals, a product from the MODIS (Moderate Resolution Imaging Spectro-radiometer), to 8- and 10-d GPP products derived with minimal climatic data (MODIS) and SPOT-Vegetation (Systeme Pour l'Observation de la Terre), to 3-PGS (Physiological Principles Predicting Growth with Satellites), which requires both climate and soil data. Across the contiguous United States, modeled predictions of gross productivity accounted for between 51% and 77% of the recorded spatial variation in tree diversity, which ranged from 2 to 67 species per hectare. When the analyses were concentrated within nine broadly defined ecoregions, predictive relations largely disappeared. Only 3-PGS predictions fit a theorized unimodal function by being able to distinguish highly productive forests in the Pacific Northwest that support lower than expected tree diversity. Other models predicted a continuous steep rise in tree diversity with increasing productivity, and did so with generally better or nearly equal precision with fewer data requirements.     

Modelling dependence structures of extreme wind speed using bivariate distribution: a Bayesian approach

When investigating extremes of weather variables, it is seldom that a single weather station determines the damage, and extremes may be caused from the combined behaviour of several weather stations. To investigate joint dependence of extreme wind speed, a bivariate generalised extreme value distribution (BGEVD) was considered from frequentist and Bayesian approaches to analyse the extremes of component-wise monthly maximum wind speed at selected weather stations in South Africa. In the frequentist approach, the parameters of extreme value distributions (EVDs) were estimated with maximum likelihood, whereas in the Bayesian approach the Markov Chain Monte Carlo (MCMC) technique was used with the Metropolis–Hastings algorithm. The results showed that when fitted to component-wise maxima of extreme weather variables, the BGEVD provided apparent benefits over the univariate method, which allowed information to be pooled across stations and resulted in improved precision of the estimates for the parameters and return levels of the distributions. The paper also discusses a method to construct informative priors empirically using historical data of the underlying process from weather characteristics of four pairs of surrounding weather stations at various distances. The results from the Bayesian analysis showed that posterior inference might be affected by the choice of priors that were used to formulate the informative priors. From the results, it could be inferred that the Bayesian approach provides a satisfactory estimation strategy in terms of precision, compared with the frequentist approach, because it accounts for uncertainty in parameters and return level estimations.     

A retrospective analysis of pollen host plant use by stable and declining bumble bee species

Understanding population declines has been the objective of a wide range of ecological studies. When species have become rare such studies are complicated because particular behavior or life history traits may be the cause but also the result of the decline of a species. We approached this problem by studying species' characteristics on specimens that were collected before the onset of their decline and preserved in natural history museums. In northwestern Europe, some bumble bee species declined dramatically during the 20th century whereas other, ecologically similar, species maintained stable populations. A long-standing debate focuses on whether this is caused by declining species having stricter host plant preferences. We compared the composition of pollen loads of five bumble bee species with stable populations and five with declining populations using museum specimens collected before 1950 in Belgium, England, and The Netherlands. Prior to 1950, the number of plant taxa in pollen loads of declining species was almost one-third lower than that in stable species even though individuals of stable and declining species generally originated from the same areas. There were no systematic differences in the composition of pollen loads between stable and declining species, but the plant taxa preferred by declining species before 1950 had experienced a stronger decline in the 20th century than those preferred by stable species. In 2004 and 2005, we surveyed the areas where bumble bees had been caught in the past and compared the composition of past and present pollen loads of the stable, but not of the by now locally extinct declining species. The number of collected pollen taxa was similar, but the composition differed significantly between the two periods. Differences in composition reflected the major changes in land use in northwestern Europe but also the spread of the invasive plant species Impatiens glandulifera. The main question now is why declining species apparently were not able to switch to less preferred food plants when stable species were. This study shows that natural history collections can play an important role in improving our understanding of the ecological mechanisms driving species population change.     

Evaluation of wind environment around a residential complex using a PIV velocity field measurement technique

Wind-tunnel simulations were employed to evaluate the wind environment around a tested residential area located near industrial complexes. The scaled-down geomorphological model of the test area was placed in the test section of a boundary layer wind tunnel. Particle image velocimetry (PIV) measurements were made in five vertical planes and one horizontal plane around the test area for two prevailing wind directions. The results showed that the wind speed decreased in the near surface layer and the velocity fluctuations increased in the upper region due to the presence of hills and high-rise buildings around the test area. Regions of flow separation and low-speed flow were found inside the test area for both the wind directions. The result suggests that the high-rise buildings should be well arranged with respect to the main wind directions to increase the natural ventilation inside the residential complex at the initial design stage.     

Inferring ecological and behavioral drivers of African elephant movement using a linear filtering approach

Understanding the environmental factors influencing animal movements is fundamental to theoretical and applied research in the field of movement ecology. Studies relating fine-scale movement paths to spatiotemporally structured landscape data, such as vegetation productivity or human activity, are particularly lacking despite the obvious importance of such information to understanding drivers of animal movement. In part, this may be because few approaches provide the sophistication to characterize the complexity of movement behavior and relate it to diverse, varying environmental stimuli. We overcame this hurdle by applying, for the first time to an ecological question, a finite impulse-response signal-filtering approach to identify human and natural environmental drivers of movements of 13 free-ranging African elephants (Loxodonta africana) from distinct social groups collected over seven years. A minimum mean-square error (MMSE) estimation criterion allowed comparison of the predictive power of landscape and ecological model inputs. We showed that a filter combining vegetation dynamics, human and physical landscape features, and previous movement outperformed simpler filter structures, indicating the importance of both dynamic and static landscape features, as well as habit, on movement decisions taken by elephants. Elephant responses to vegetation productivity indices were not uniform in time or space, indicating that elephant foraging strategies are more complex than simply gravitation toward areas of high productivity. Predictions were most frequently inaccurate outside protected area boundaries near human settlements, suggesting that human activity disrupts typical elephant movement behavior. Successful management strategies at the human-elephant interface, therefore, are likely to be context specific and dynamic. Signal processing provides a promising approach for elucidating environmental factors that drive animal movements over large time and spatial scales.     

Sensitivity analysis of the asymptotic behavior of a model for the environmental movement of radionuclides

The asymptotic behavior of a linear compartment model for the environmental movement of radionuclides is investigated. Here, the expression asymptotic behavior is used to designate the behavior of q(t) as t → ∞, where q is the solution of a vector differential equation of the form dq/dt = h + Kq. The asymptotic behavior of such equations is described. For the model and conditions under consideration, each element of q converges monotonically to a steady-state value. A hydrologic system is defined and used to illustrate this behavior. An approach to sensitivity analysis employing Latin hypercube sampling, rank transformations and stepwise regression is presented and then applied to this system. A total of 20 independent variables is introduced and the following dependent variables are investigated for the various components of the system: amount of radionuclide present at steady state. concentration of radionuclide at steady state, and time required to reach 90% of steady state. Finally, an application of asymptotic behavior in the analysis of a hypothetical site for the geologic isolation of high-level radioactive waste is described and a brief discussion of differential sensitivity analysis is given.     

An analysis of wind conditions at pedestrian level in the selected types of multi-family housing developments

Environmental Fluid Mechanics - The following article addresses the issue of wind conditions around urban building development at pedestrian level. Factors that depend on those issues include wind...     

Effect of pollinator abundance on self-fertilization and gene flow: application to GM Canola.

Cross-pollination from fields of transgenic crops is of great public concern. Although cross-pollination in commercial canola (Brassica napus) fields has been empirically measured, field trials are expensive and do not identify the causes of cross-pollination. Therefore, theoretical models can be valuable because they can provide estimates of cross-pollination at any given site and time. We present a general analytical model of field-to-field gene flow due to the following competing mechanisms: the wind, bees, and autonomous pollination. We parameterize the model for the particular case of field-to-field cross-pollination of genetically modified (GM) canola via the wind and via bumble bees (Bombus spp.) and honey bees (Apis mellifera). We make extensive use of the large data set of bee densities collected during the recent U.K. Farm Scale Evaluations. We predict that canola approaches almost full seed set without pollinators and that autonomous pollination is responsible for > or = 25% of seed set, irrespective of pollinator abundance. We do not predict the relative contribution of bees vs. the wind in landscape-scale gene flow in canola. However, under model assumptions, we predict that the maximum field-to-field gene flow due to bumble bees is 0.04% and 0.13% below the current EU limit for adventitious GM presence for winter- and spring-sown canola, respectively. We predict that gene flow due to bees is approximately 3.1 times higher at 20% compared to 100% male-fertility, and due to the wind, 1.3 times higher at 20% compared to 100% male-fertility, for both winter- and spring-sown canola. Bumble bee-mediated gene flow is approximately 2.7 times higher and wind-mediated gene flow approximately 1.7 times lower in spring-sown than in winter-sown canola, regardless of the degree of male-sterility. The model of cross-pollination due to the wind most closely predicted three previously published observations: field-to-field gene flow is low; gene flow increases with the proportion of plants that are male-sterile; and gene flow is higher in winter- than in spring-sown canola. Our results therefore suggest that the wind, not bees, is the main vector of long-distance gene flow in canola.     

应用模糊相似比作马尾松优良种源推广的适合度分析

应用欧氏距离模糊相似优先比分析外省马尾松３个优良种源在浙江省各地区的推广适合度。取水分、热量和高、径生长量等９个相似因子，以原产地为固定样品比较省内５个试验点的适合度。分析结果与１０年生试验林生庆观测结果基本吻合，贵港种源主要适于浙中、浙西地区，龙胜种源主要适于浙东、浙中地区，常宁种源主要适于浙中、浙南地区。     

Colony state and regulation of pollen foraging in the honey bee,Apis mellifera L.

Summary To place social insect foraging behavior within an evolutionary context, it is necessary to establish relationships between individual foraging decisions and parameters influencing colony fitness. To address this problem, we examined interactions between individual foraging behavior and pollen storage levels in the honey bee, Apis mellifera L. Colonies responded to low pollen storage conditions by increasing pollen intake rates 54% relative to high pollen storage conditions, demonstrating a direct relationship between pollen storage levels and foraging effort. Approximately 80% of the difference in pollen intake rates was accounted for by variation in individual foraging effort, via changes in foraging activity and individual pollen load size. An additional 20% resulted from changes in the proportion of the foraging population collecting pollen. Under both high and low pollen storage treatments, colonies returned pollen storage levels to pre-experimental levels within 16 days, suggesting that honey bees regulate pollen storage levels around a homeostatic set point. We also found a direct relationship between pollen storage levels and colony brood production, demonstrating the potential for cumulative changes in individual foraging decisions to affect colony fitness. Offprint requests to: J.H. Fewell at the current address     

Null model analysis of species associations using abundance data

The influence of negative species interactions has dominated much of the literature on community assembly rules. Patterns of negative covariation among species are typically documented through null model analyses of binary presence/absence matrices in which rows designate species, columns designate sites, and the matrix entries indicate the presence (1) or absence (0) of a particular species in a particular site. However, the outcome of species interactions ultimately depends on population-level processes. Therefore, patterns of species segregation and aggregation might be more clearly expressed in abundance matrices, in which the matrix entries indicate the abundance or density of a species in a particular site. We conducted a series of benchmark tests to evaluate the performance of 14 candidate null model algorithms and six covariation metrics that can be used with abundance matrices. We first created a series of random test matrices by sampling a metacommunity from a lognormal species abundance distribution. We also created a series of structured matrices by altering the random matrices to incorporate patterns of pairwise species segregation and aggregation. We next screened each algorithm-index combination with the random and structured matrices to determine which tests had low Type I error rates and good power for detecting segregated and aggregated species distributions. In our benchmark tests, the best-performing null model does not constrain species richness, but assigns individuals to matrix cells proportional to the observed row and column marginal distributions until, for each row and column, total abundances are reached. Using this null model algorithm with a set of four covariance metrics, we tested for patterns of species segregation and aggregation in a collection of 149 empirical abundance matrices and 36 interaction matrices collated from published papers and posted data sets. More than 80% of the matrices were significantly segregated, which reinforces a previous meta-analysis of presence/absence matrices. However, using two of the metrics we detected a significant pattern of aggregation for plants and for the interaction matrices (which include plant-pollinator data sets). These results suggest that abundance matrices, analyzed with an appropriate null model, may be a powerful tool for quantifying patterns of species segregation and aggregation.     

Formal modelling of predator preferences using molecular gut-content analysis

The literature on modelling a predator’s prey selection describes many intuitive indices, few of which have both reasonable statistical justification and tractable asymptotic properties. Here, we provide a simple model that meets both of these criteria, while extending previous work to include an array of data from multiple species and time points. Further, we apply the expectation–maximisation algorithm to compute estimates if exact counts of the number of prey species eaten in a particular time period are not observed. We conduct a simulation study to demonstrate the accuracy of our method, and illustrate the utility of the approach for field analysis of predation using a real data set, collected on wolf spiders using molecular gut-content analysis.     

Compositional analysis of overdispersed counts using generalized estimating equations

Multivariate abundance data are commonly collected in ecology, and used to explore questions of “community composition”—how relative abundance of different taxa changes with environmental conditions. In this paper, we propose a log-linear marginal modeling approach for analyzing such compositional count data, via generalized estimating equations. This method exploits the multiplicative nature of log-linear models for counts, by reparameterizing models that describe marginal effects on mean abundance. This allows partitioning into “main effects” and compositional effects, which is appealing for interpretation. We apply the proposed approach to reanalyze compositional counts of benthic invertebrates from Delaware Bay, and data of invertebrate communities inhabiting Acacia plants in eastern Australia. In both cases we resort to a resampling approach to make inferences about regression parameters, because the number of clusters was not large compared to cluster size.     

Measurement error causes scale-dependent threshold erosion of biological signals in animal movement data.

Recent advances in telemetry technology have created a wealth of tracking data available for many animal species moving over spatial scales from tens of meters to tens of thousands of kilometers. Increasingly, such data sets are being used for quantitative movement analyses aimed at extracting fundamental biological signals such as optimal searching behavior and scale-dependent foraging decisions. We show here that the location error inherent in various tracking technologies reduces the ability to detect patterns of behavior within movements. Our analyses endeavored to set out a series of initial ground rules for ecologists to help ensure that sampling noise is not misinterpreted as a real biological signal. We simulated animal movement tracks using specialized random walks known as Lévy flights at three spatial scales of investigation: 100-km, 10-km, and 1-km maximum daily step lengths. The locations generated in the simulations were then blurred using known error distributions associated with commonly applied tracking methods: the Global Positioning System (GPS), Argos polar-orbiting satellites, and light-level geolocation. Deviations from the idealized Lévy flight pattern were assessed for each track after incrementing levels of location error were applied at each spatial scale, with additional assessments of the effect of error on scale-dependent movement patterns measured using fractal mean dimension and first-passage time (FPT) analyses. The accuracy of parameter estimation (Lévy mu, fractal mean D, and variance in FPT) declined precipitously at threshold errors relative to each spatial scale. At 100-km maximum daily step lengths, error standard deviations of > or = 10 km seriously eroded the biological patterns evident in the simulated tracks, with analogous thresholds at the 10-km and 1-km scales (error SD > or = 1.3 km and 0.07 km, respectively). Temporal subsampling of the simulated tracks maintained some elements of the biological signals depending on error level and spatial scale. Failure to account for large errors relative to the scale of movement can produce substantial biases in the interpretation of movement patterns. This study provides researchers with a framework for understanding the limitations of their data and identifies how temporal subsampling can help to reduce the influence of spatial error on their conclusions.