Random measurement error and regression dilution bias.

Summary points: - The bias introduced by random measurement error will be different depending on whether the error is in an exposure variable (risk factor) or outcome variable (disease) - Random measurement error in an exposure variable will bias the estimates of regression slope coefficients towards the null - Random measurement error in an outcome variable will instead increase the standard error of the estimates and widen the corresponding confidence intervals, making results less likely to be statistically significant - Increasing sample size will help minimise the impact of measurement error in an outcome variable but will only make estimates more precisely wrong when the error is in an exposure variable

When rare is just a matter of sampling: unexpected dominance of clubtail dragonflies (Odonata, Gomphidae) through different collecting methods at Parque Nacional da Serra do Cipó, Minas Gerais State, Brazil

When rare is just a matter of sampling: Unexpected dominance of clubtail dragonflies (Odonata, Gomphidae) through different collecting methods at Parque Nacional da Serra do Cipó, Minas Gerais State, Brazil. Capture of dragonfly adults during two short expeditions to Parque Nacional da Serra do Cipó, Minas Gerais State, using three distinct collecting methodsaerial nets, Malaise and light sheet trapsis reported. The results are outstanding due the high number of species of Gomphidae (7 out of 26 Odonata species), including a new species of Cyanogomphus Selys, 1873, obtained by two non-traditional collecting methods. Because active collecting with aerial nets is the standard approach for dragonfly inventories, we discuss some aspects of the use of traps, comparing our results with those in the literature, suggesting they should be used as complementary methods in faunistic studies. Furthermore, Zonophora campanulata annulata Belle, 1983 is recorded for the first time from Minas Gerais State and taxonomic notes about Phyllogomphoides regularis (Selys, 1873) and Progomphus complicatus Selys, 1854 are also given.

Development of a system for 3D high-resolution seismic reflection profiling on lakes

A high-resolution three-dimensional (3D) seismic reflection system for small-scale targets in lacustrine settings has been developed. Its main characteristics include navigation and shot-triggering software that fires the seismic source at regular distance intervals (max. error of 0.25 m) with real-time control on navigation using differential GPS (Global Positioning System). Receiver positions are accurately calculated (error < 0.20 m) with the aid of GPS antennas attached to the end of each of three 24-channel streamers. Two telescopic booms hold the streamers at a distance of 7.5 m from each other. With a receiver spacing of 2.5 m, the bin dimension is 1.25 m in inline and 3.75 m in crossline direction. To test the system, we conducted a 3D survey of about 1 km(2) in Lake Geneva, Switzerland, over a complex fault zone. A 5-m shot spacing resulted in a nominal fold of 6. A double-chamber bubble-cancelling 15/15 in(3) air gun (40-650 Hz) operated at 80 bars and 1 m depth gave a signal penetration of 300 m below water bottom and a best vertical resolution of 1.1 m. Processing followed a conventional scheme, but had to be adapted to the high sampling rates, and our unconventional navigation data needed conversion to industry standards. The high-quality data enabled us to construct maps of seismic horizons and fault surfaces in three dimensions. The system proves to be well adapted to investigate complex structures by providing non-aliased images of reflectors with dips up to 30 degrees.

An unusual food plant for Cydia pomonella (Linnaeus) (Lepidoptera, Tortricidae) in Mexico

An unusual food plant for Cydia pomonella (Linnaeus) (Lepidoptera, Tortricidae) in Mexico. Larvae of Cydia pomonella (Linnaeus, 1758) were discovered on floral cones of Magnolia schiedeana (Schltdl, 1864) near the natural reserve of La Martinica, Veracruz, México. Magnolia represents an unusual host for this moth species, which is known throughout the world as the "codling moth", a serious pest of fruits of Rosaceae, especially apples. The larvae were identified using taxonomic keys, and identification was corroborated using molecular markers. Further sampling resulted in no additional larvae, hence, the observation was probably that of an ovipositional error by the female, and M. schiedeana is not at risk of attack by this important moth pest.

Comparative abundance and diversity of Dryininae (Hymenoptera, Dryinidae) in three savannah phytophysiognomies in southeastern Brazil, under three sampling methods

Comparative abundance and diversity of Dryininae (Hymenoptera, Dryinidae) in three savannah phytophysiognomies in southeastern Brazil, under three sampling methods. This study aimed to assess the abundance and diversity of Dryininae in riparian vegetation, Brazilian savannah, and savannah woodland vegetation at the Estação Ecológica de Jataí, in Luiz Antônio, State of São Paulo, Brazil, by using Moericke, Malaise, and light traps. The sampling was carried out from December 2006 to November 2009, and 371 specimens of Dryininae were caught, with the highest frequencies in spring and summer. Fourteen species of Dryinus Latreille, 1804 and one of Thaumatodryinus Perkins, 1905 were identified. The highest frequencies of Dryinus in the riparian vegetation differed significantly from those obtained in the Brazilian savannah and savannah woodland vegetation. In the riparian vegetation, the highest number of Dryinus was collected using light traps and the interactions between abundance and the collection method used were significant. The number of specimens of Dryinus collected in the Brazilian savannah and savannah woodland vegetation using Malaise traps did not differ significantly from those obtained using Moericke traps. Males significantly outnumbered females in the sex ratio of Dryinus. The species diversity of Dryinus based on females collected using Malaise traps was high in the Brazilian savannah. Furthermore, high species richness of female Dryinus was observed in riparian vegetation (six species) and Brazilian savannah (five). The light trap was the most successful method for sampling diversity of Dryininae.

Sampling strategies for tropical forest nutrient cycling studies: a case study in São Paulo, Brazil

The precise sampling of soil, biological or micro climatic attributes in tropical forests, which are characterized by a high diversity of species and complex spatial variability, is a difficult task. We found few basic studies to guide sampling procedures. The objective of this study was to define a sampling strategy and data analysis for some parameters frequently used in nutrient cycling studies, i. e., litter amount, total nutrient amounts in litter and its composition (Ca, Mg, Κ, Ν and P), and soil attributes at three depths (organic matter, Ρ content, cation exchange capacity and base saturation). A natural remnant forest in the West of São Paulo State (Brazil) was selected as study area and samples were collected in July, 1989. The total amount of litter and its total nutrient amounts had a high spatial independent variance. Conversely, the variance of litter composition was lower and the spatial dependency was peculiar to each nutrient. The sampling strategy for the estimation of litter amounts and the amount of nutrient in litter should be different than the sampling strategy for nutrient composition. For the estimation of litter amounts and the amount of nutrients in litter (related to quantity) a large number of randomly distributed determinations are needed. Otherwise, for the estimation of litter nutrient composition (related to quality) a smaller amount of spatially located samples should be analyzed. The determination of sampling for soil attributes differed according to the depth. Overall, surface samples (0-5 cm) showed high short distance spatial dependent variance, whereas, subsurface samples exhibited spatial dependency in longer distances. Short transects with sampling interval of 5-10 m are recommended for surface sampling. Subsurface samples must also be spatially located, but with transects or grids with longer distances between sampling points over the entire area. Composite soil samples would not provide a complete understanding of the relation between soil properties and surface dynamic processes or landscape aspects. Precise distribution of Ρ was difficult to estimate.

Which is the optimal sampling strategy for habitat suitability modelling

Designing an efficient sampling strategy is of crucial importance for habitat suitability modelling. This paper compares four such strategies, namely, 'random', 'regular', 'proportional-stratified' and 'equal -stratified'- to investigate (1) how they affect prediction accuracy and (2) how sensitive they are to sample size. In order to compare them, a virtual species approach (Ecol. Model. 145 (2001) 111) in a real landscape, based on reliable data, was chosen. The distribution of the virtual species was sampled 300 times using each of the four strategies in four sample sizes. The sampled data were then fed into a GLM to make two types of prediction: (1) habitat suitability and (2) presence/ absence. Comparing the predictions to the known distribution of the virtual species allows model accuracy to be assessed. Habitat suitability predictions were assessed by Pearson's correlation coefficient and presence/absence predictions by Cohen's K agreement coefficient. The results show the 'regular' and 'equal-stratified' sampling strategies to be the most accurate and most robust. We propose the following characteristics to improve sample design: (1) increase sample size, (2) prefer systematic to random sampling and (3) include environmental information in the design'

Statistical learning theory for geospatial data. Case study: Aral sea

In recent years there has been an explosive growth in the development of adaptive and data driven methods. One of the efficient and data-driven approaches is based on statistical learning theory (Vapnik 1998). The theory is based on Structural Risk Minimisation (SRM) principle and has a solid statistical background. When applying SRM we are trying not only to reduce training error ? to fit the available data with a model, but also to reduce the complexity of the model and to reduce generalisation error. Many nonlinear learning procedures recently developed in neural networks and statistics can be understood and interpreted in terms of the structural risk minimisation inductive principle. A recent methodology based on SRM is called Support Vector Machines (SVM). At present SLT is still under intensive development and SVM find new areas of application (www.kernel-machines.org). SVM develop robust and non linear data models with excellent generalisation abilities that is very important both for monitoring and forecasting. SVM are extremely good when input space is high dimensional and training data set i not big enough to develop corresponding nonlinear model. Moreover, SVM use only support vectors to derive decision boundaries. It opens a way to sampling optimization, estimation of noise in data, quantification of data redundancy etc. Presentation of SVM for spatially distributed data is given in (Kanevski and Maignan 2004).

Active learning methods for remote sensing image classification

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.

Sample size and statistical power in the hierarchical analysis of variance: applications in morphometry of the nervous system.

Analysis of variance is commonly used in morphometry in order to ascertain differences in parameters between several populations. Failure to detect significant differences between populations (type II error) may be due to suboptimal sampling and lead to erroneous conclusions; the concept of statistical power allows one to avoid such failures by means of an adequate sampling. Several examples are given in the morphometry of the nervous system, showing the use of the power of a hierarchical analysis of variance test for the choice of appropriate sample and subsample sizes. In the first case chosen, neuronal densities in the human visual cortex, we find the number of observations to be of little effect. For dendritic spine densities in the visual cortex of mice and humans, the effect is somewhat larger. A substantial effect is shown in our last example, dendritic segmental lengths in monkey lateral geniculate nucleus. It is in the nature of the hierarchical model that sample size is always more important than subsample size. The relative weight to be attributed to subsample size thus depends on the relative magnitude of the between observations variance compared to the between individuals variance.

Sampling and TDR probe insertion in the determination of the volumetric soil water content

Volumetric soil water content (theta) can be evaluated in the field by direct or indirect methods. Among the direct, the gravimetric method is regarded as highly reliable and thus often preferred. Its main disadvantages are that sampling and laboratory procedures are labor intensive, and that the method is destructive, which makes resampling of a same point impossible. Recently, the time domain reflectometry (TDR) technique has become a widely used indirect, non-destructive method to evaluate theta. In this study, evaluations of the apparent dielectric number of soils (epsilon) and samplings for the gravimetrical determination of the volumetric soil water content (thetaGrav) were carried out at four sites of a Xanthic Ferralsol in Manaus - Brazil. With the obtained epsilon values, theta was estimated using empirical equations (thetaTDR), and compared with thetaGrav derived from disturbed and undisturbed samples. The main objective of this study was the comparison of thetaTDR estimates of horizontally as well as vertically inserted probes with the thetaGrav values determined by disturbed and undisturbed samples. Results showed that thetaTDR estimates of vertically inserted probes and the average of horizontally measured layers were only slightly and insignificantly different. However, significant differences were found between the thetaTDR estimates of different equations and between disturbed and undisturbed samples in the thetaGrav determinations. The use of the theoretical Knight et al. model, which permits an evaluation of the soil volume assessed by TDR probes, is also discussed. It was concluded that the TDR technique, when properly calibrated, permits in situ, nondestructive measurements of q in Xanthic Ferralsols of similar accuracy as the gravimetric method.