A distributed-delay model to predict ageing and survival rates of adults of Cryptolestes ferrugineus (Stephens) (Coleoptera: Laemophloeidae) in granaries filled with wheat

A time-varying distributed-delay model simulating effects of multifactors was developed. Prediction of the ageing rate and survival distribution of adults of the rusty grain beetle, Cryptolestes ferrugineus (Stephens) (Coleoptera: Laemophloeidae) in various environments found in wheat-filled granaries was conducted as an example to illustrate the application of this developed model. Published adult mortalities, determined at different temperatures, relative humidities, and food sources, were directly used to find the average ageing rate and family of cumulative function of adult mortality. The developed model could predict the adult survival rate at constant or transient temperatures with different relative humidities. This model could also simulate the effect of adult acclimation to their environment when they experience temperature and moisture fluctuations inside granaries. To validate the developed model, the simulation results were compared with available experimental data from the literature. There was no difference between predicted and measured mortalities in two granaries in which the mortalities were determined in a 4-month experiment.     

Examination of model predictions at different horizontal grid resolutions

While fluctuations in meteorological and air quality variables occur on a continuum of spatial scales, the horizontal grid spacing of coupled meteorological and photochemical models sets a lower limit on the spatial scales that they can resolve. However, both computational costs and data requirements increase significantly with increasing grid resolution. Therefore, it is important to examine, for any given application, whether the expected benefit of increased grid resolution justifies the extra costs. In this study, we examine temperature and ozone observations and model predictions for three high ozone episodes that occurred over the northeastern United States during the summer of 1995. In the first set of simulations, the meteorological model RAMS4a was run with three two-way nested grids of 108/36/12 km grid spacing covering the United States and the photochemical model UAM-V was run with two grids of 36/12 km grid spacing covering the eastern United States. In the second set of simulations, RAMS4a was run with four two-way nested grids of 108/36/12/4 km grid spacing and UAM-V was run with three grids of 36/12/4 km grid spacing with the finest resolution covering the northeastern United States. Our analysis focuses on the comparison of model predictions for the finest grid domain of the simulations, namely, the region overlapping the 12 km and 4 km domains. A comparison of 12 km versus 4 km fields shows that the increased grid resolution leads to finer texture in the model predictions; however, comparisons of model predictions with observations do not reveal the expected improvement in the predictions. While high-resolution modeling has scientific merit and potential uses, the currently available monitoring networks, in conjunction with the scarceness of highly resolved spatial input data and the limitations of model formulation, do not allow confirmation of the expected superiority of the high-resolution model predictions.The U.S. Governments right to retain a non-exclusive royalty-free licence in and to any copyright is acknowledged.     

The performance of RAMS in representing the convective boundary layer structure in a very steep valley

Data from a comprehensive field study in the Riviera Valley of Southern Switzerland are used to investigate convective boundary layer structure in a steep valley and to evaluate wind and temperature fields, convective boundary layer height, and surface sensible heat fluxes as predicted by the mesoscale model RAMS. Current parameterizations of surface and boundary layer processes in RAMS, as well as in other mesoscale models, are based on scaling laws strictly valid only for flat topography and uniform land cover. Model evaluation is required to investigate whether this limits the applicability of RAMS in steep, inhomogeneous terrain. One clear-sky day with light synoptic winds is selected from the field study. Observed temperature structure across and along the valley is nearly homogeneous while wind structure is complex with a wind speed maximum on one side of the valley. Upvalley flows are not purely thermally driven and mechanical effects near the valley entrance also affect the wind structure. RAMS captured many of the observed boundary layer characteristics within the steep valley. The wind field, temperature structure, and convective boundary layer height in the valley are qualitatively simulated by RAMS, but the horizontal temperature structure across and along the valley is less homogeneous in the model than in the observations. The model reproduced the observed net radiation, except around sunset and sunrise when RAMS does not take into account the shadows cast by the surrounding topography. The observed sensible heat fluxes fall within the range of simulated values at grid points surrounding the measurement sites. Some of the scatter between observed and simulated turbulent sensible heat fluxes are due to sub-grid scale effects related to local topography.     

On the Estimation of Dispersal Kernels from Individual Mark-Recapture Data

We present a new method for estimating a distribution of dispersal displacements (a dispersal kernel) from mark-recapture data. One conventional method of calculating the dispersal kernel assumes that the distribution of displacements are Gaussian (e.g. resulting from a diffusion process) and that individuals remain within sampled areas. The first assumption prohibits an analysis of dispersal data that do not exhibit the Gaussian distribution (a common situation); the second assumption leads to underestimation of dispersal distance because individuals that disperse outside of sampling areas are never recaptured. Our method eliminates these two assumptions. In addition, the method can also accommodate mortality during a sampling period. This new method uses integrodifference equations to express the probability of spatial mark-recapture data; associated dispersal, survival, and recapture parameters are then estimated using a maximum likelihood method. We examined the accuracy of the estimators by applying the method to simulated data sets. Our method suggests designs for future mark-recapture experiments. Received: January 2004 / Revised: July 2005     

Obtaining species: sample size considerations

Suppose fish are to be sampled from a stream. A fisheries biologist might ask one of the following three questions: ‘How many fish do I need to catch in order to see all of the species?’, ‘How many fish do I need to catch in order to see all species whose relative frequency is more than 5%?’, or ‘How many fish do I need to catch in order to see a member from each of the species A, B, and C?’. This paper offers a practical solution to such questions by setting a target sample size designed to achieve desired results with known probability. We present three sample size methods, one we call ‘exact’ and the others approximate. Each method is derived under assumed multinomial sampling, and requires (at least approximate) independence of draws and (usually) a large population. The minimum information needed to compute one of the approximate methods is the estimated relative frequency of the rarest species of interest. Total number of species is not needed. Choice of a sample size method depends largely on available computer resources. One approximation (called the ‘Monte Carlo approximation’) gets within ±6 units of exact sample size, but usually requires 20–30 minutes of computer time to compute. The second approximation (called the ‘ratio approximation’) can be computed manually and has relative error under 5% when all species are desired, but can be as much as 50% or more too high when exact sample size is small. Statistically, this problem is an application of the ‘sequential occupancy problem’. Three examples are given which illustrate the calculations so that a reader not interested in technical details can apply our results.     

Market responses to hurricanes

This paper uses one of the strongest hurricanes to hit the US, Andrew in 1992, to define a quasi-random experiment that permits estimation of the responses of housing values to information about new hurricanes. Lee County, Florida did not experience damage from Andrew. The storm was a “near-miss.” We hypothesize that Andrew conveyed risk information to homeowners in the county. A difference-in-differences (DND) framework identifies the effect of this information on property values in areas likely to experience significant storm damage. The DND findings indicate at least a 19 percent decline in property values.     

Multistate models for monitoring individual trees in permanent observation plots

This interdisciplinary research on forest ecosystems begins with some characteristics of ecosystems which are the basis for the derivation of statistical models for the development and vitality of trees. Several ecological problems which could be solved by longitudinal studies are mentioned. Statistical methods for the evaluation of the crowns of spruce trees (Picea abies Karst) in three permanent observation plots in Switzerland are described. In particular, the time-dependent proportional odds model and a transitional model are used. Through application of these multistate models the data give information on the dependence of an ordered categorical response variable on covariates characterizing the ecosystem. The response variable is observed through infrared aerial photographs. This monitoring system gives insight into the dynamic behaviour of the forest ecosystem. The need for more eco-systematically motivated statistical research using longitudinal studies is identified.     

A simple approach to modeling microbial biomass in the rhizosphere

Microorganisms make an important contribution to the degradation of contaminants in bioremediation as well as in phytoremediation. An accurate estimation of microbial concentrations in the soil would be valuable in predicting contaminant dissipation during various bioremediation processes. A simple modeling approach to quantify the microbial biomass in the rhizosphere was developed in this study. Experiments were conducted using field column lysimeters planted with Eastern gamagrass. The microbial biomass concentrations from the rhizosphere soil, bulk soil, and unplanted soil were monitored for six months using an incubation–fumigation method. The proposed model was applied to the field microbial biomass data and good correlation between simulated and experimental data was achieved. The results indicate that plants increase microbial concentrations in the soil by providing root exudates as growth substrates for microorganisms. Since plant roots are initially small and do not produce large quantities of exudates when first seeded, the addition of exogenous substrates may be needed to increase initial microbial concentrations at the start of phytoremediation projects.     

Dynamics of soil organic matter in primary and secondary forest succession on sandy soils in The Netherlands: An application of the ROMUL model

We applied the simulation model ROMUL of soil organic matter dynamics in order to analyse and predict forest soil organic matter (SOM) changes following stand growth and also to identify gaps of data and modelling problems. SOM build-up was analysed (a) from bare sand to forest soil during a primary succession in Scots pine forest and (b) on mature forest soil under Douglas fir plantations as an example of secondary succession in The Netherlands. As some of the experimental data were unreliable we compiled a set of various scenarios with different soil moisture regime, initial SOM pools and amount and quality of above and below ground litter input. This allowed us to find the scenarios that reflect the SOM dynamics more realistically. In the Scots pine forest, total litter input was estimated as 0.50 kg m⁻² year⁻¹. Two scenarios were defined for the test runs: (a) forest floor moisture regimes—‘dry, mesic and hydric’ and (b) augmenting a root litter pool with three ratios of needles and branches to roots: 1:1, 1:1.5 and 1:2.0. The scenario finally compiled had the following characteristics: (a) climate for dry site with summer drought and high winter moisture of forest floor; (b) a litter input of 0.25 kg m⁻² year⁻¹ above ground and 0.50 kg m⁻² year⁻¹ below ground; (c) a low nitrogen and ash content in all litter fall fractions. The test runs for the estimation of the initial SOM pools and the amount and proportion of above and below ground litter fall were also performed in the Douglas fir plantation. The inputs of above ground litter tested in various combinations were 0.30 and 0.60 kg m⁻² year⁻¹, and below ground litter 0.30, 0.60 and 0.90 kg m⁻² year⁻¹. The scenario that fitted the experimental data had an SOM pool of 20–25 kg m⁻², an aboveground litter input of 0.6 kg m⁻² year⁻¹and a below ground litter input of 0.9 kg m⁻² year⁻¹. The long-term simulation corresponded well with the observed patterns of soil organic matter accumulation associated with the forest soil development in primary and secondary succession. During primary succession in Scots pine forest on dry sand there is a consistent accumulation of a raw humus forest floor. The soil dynamics in the Douglas fir plantation also coincide with the observed patterns of SOM changes during the secondary succession, with SOM decreasing significantly under young forest, and SOM being restored in the older stands.     

Using the expert model PERPEST to translate measured and predicted pesticide exposure data into ecological risks

An important topic in the registration of pesticides and the interpretation of monitoring data is the estimation of the consequences of a certain concentration of a pesticide for the ecology of aquatic ecosystems. Solving these problems requires predictions of the expected response of the ecosystem to chemical stress. Up until now, a dominant approach to come up with such a prediction is the use of simulation models or safety factors. The disadvantage of the use of safety factors is a crude method that does not provide any insight into the concentration–response relationships at the ecosystem level. On the other hand, simulation models also have serious drawbacks like that they are often very complex, lack transparency, their implementation is expensive and there may be a compilation of errors, due to uncertainties in parameters and processes. In this paper, we present the expert model prediction of the ecological risks of pesticides (PERPEST) that overcomes these problems. It predicts the effects of a given concentration of a pesticide based on the outcome of already performed experiments using experimental ecosystems. This has the great advantage that the outcome is more realistic. The paper especially discusses how this model can be used to translate measured and predicted concentrations of pesticides into ecological risks, by taking data on measured and predicted concentrations of atrazine as an example. It is argued that this model can be of great use to evaluate the outcome of chemical monitoring programmes (e.g. performed in the light of the Water Framework Directive) and can even be used to evaluate the effects of mixtures.