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The influence of anthropogenic pressure on the structure of communities of parasitoids of the Pimplinae subfamily (Hymenoptera, Ichneumonidae) inhabiting urban green areas
Urban green areas have favourable conditions for the development of phytophagous species, which feed on plants and thus reduce their aesthetic value. The population of plant pests can be limited by parasitoids of the Pimplinae subfamily. The aim of this study was to determine the influence of low, medium, and high anthropogenic pressure on the qualitative and quantitative structure of Pimplinae communities inhabiting urban green areas and to determine the influence of this pressure on the catchability of insects. The research was conducted between 2014 and 2016 in allotment gardens, Botanical Garden and Zoological Garden in the city of Poznań, using the method of catching adult insects in yellow Moericke traps. The research showed that the richness of plant species had the greatestinfluence on the qualitative and quantitative structure of Pimplinae communities, whereas the size of the area, distance from the city centre, and the intensity of air pollution exerted lesser influence.
The parasitisation of the rose tortrix [Archips rosana (L.)] Population in the apple orchard in the Wielkopolska region, Poland
The rose tortrix is a polyphagous species, which is dominant in the leafroller complex (Tortricidae) in orchards, including apple orchards and fruit-bearing shrubs in western and southeastern Poland. Rose tortrixes massively feed on leaf rosettes, developing leaves, buds, flowers and fruit buds in orchards. The population of this phytophage is regulated by environmental factors such as its natural enemies, e.g. parasitoids. The aim of the study was to determine the degree of parasitisation of rose tortrix pupae and to determine the role of individual families of parasitoids in reducing their population. The study showed that parasitoids from the Ichneumonidae, Chalcididae, Tachinidae, Euliphidae and Pteromalidae families reduced the rose tortrix population. The parasitoids of the Ichneumonidae family were the most effective entomophages. The degree of total parasitisation of rose tortrixes in individual years of the research was similar. The effectiveness of these entomophagous parasitoids is influenced by environmental factors – above all by weather conditions, the number of crop protection treatments and the ecological infrastructure of orchards.
Parasitoids of the subfamilies Pimplinae (Hymenoptera, Ichneumonidae) and Aphidiinae (Hymenoptera, Braconidae) occurring in urban greenery of the Poznań city
The study aimed to determine the species composition and abundance of parasitoids from the subfamilies Pimplinae (Hymenoptera, Ichneumonidae) and Aphidiinae (Hymenoptera, Braconidae) found in various habitats in urban greenery and to demonstrate the rela¬tionship between plant species diversity and the species structure of parasitoid assemblages. It was found that only for the subfamily Aphidiinae was there a significant effect of habitats on the abundance of individual assemblages and the effect of years on the total abundance of assemblages. A positive relationship was observed between vegetation diversity and the abundance and species diversity of parasitoids of the subfamily Aphidiinae, as well as similarity in the qualitative-quantitative structure of Pimplinae assemblages present in a given habitat. Positive effects of trees: Betula pendula, Tilia tomentosa, Salix alba and Salix caprea, shrubs: Berberis julianae, Ber¬beris vulgaris and Spiraea japonica, and herbaceous plants: Elymus repens, Festuca rubra and Hypochoeris radicata on the qualitative¬-quantitative structure of parasitoid assemblages of the subfamily Pimplinae by creating a suitable food base for these entomophagous imagines were noted.
Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications
Modelling plays an important role in identifying and solving problems that arise in a number of scientific issues including agriculture. Research in the natural environment is often costly, labour demanding, and, in some cases, impossible to carry out. Hence, there is a need to create and use specific “substitutes” for originals, known in a broad sense as models. Owing to the dynamic development of computer techniques, simulation models, in the form of information technology (IT) systems that support cognitive processes (of various types), are acquiring significant importance. Models primarily serve to provide a better understanding of studied empirical systems, and for efficient design of new systems as well as their rapid (and also inexpensive) improvement. Empirical mathematical models that are based on artificial neural networks and mathematical statistical methods have many similarities. In practice, scientific methodologies all use different terminology, which is mainly due to historical factors. Unfortunately, this distorts an overview of their mutual correlations, and therefore, fundamentally hinders an adequate comparative analysis of the methods. Using neural modelling terminology, statisticians are primarily concerned with the process of generalisation that involves analysing previously acquired noisy empirical data. Indeed, the objects of analyses, whether statistical or neural, are generally the results of experiments that, by their nature, are subject to various types of errors, including measurement errors. In this overview, we identify and highlight areas of correlation and interfacing between several selected neural network models and relevant, commonly used statistical methods that are frequently applied in agriculture. Examples are provided on the assessment of the quality of plant and animal production, pest risks, and the quality of agricultural environments.
Dimension Reduction of Digital Image Descriptors in Neural Identification of Damaged Malting Barley Grains
The paper covers the problem of determination of defects and contamination in malting barley grains. The analysis of the problem indicated that although several attempts have been made, there are still no effective methods of identification of the quality of barley grains, such as the use of information technology, including intelligent sensors (currently, quality assessment of grain is performed manually). The aim of the study was the construction of a reduced set of the most important graphic descriptors from machine-collected digital images, important in the process of neural evaluation of the quality of BOJOS variety malting barley. Grains were sorted into three size fractions and seed images were collected. As a large number of graphic descriptors implied difficulties in the development and operation of neural classifiers, a PCA (Principal Component Analysis) statistical method of reducing empirical data contained in the analyzed set was applied. The grain quality expressed by an optimal set of transformed descriptors was modelled using artificial neural networks (ANN). The input layer consisted of eight neurons with a linear Postsynaptic Function (PSP) and a linear activation function. The one hidden layer was composed of sigmoid neurons having a linear PSP function and a logistic activation function. One sigmoid neuron was the output of the network. The results obtained show that neural identification of digital images with application of Principal Component Analysis (PCA) combined with neural classification is an effective tool supporting the process of rapid and reliable quality assessment of BOJOS malting barley grains.
Urban greenery of Gdynia as a habitat for parasitoids of the subfamily Pimplinae (Hymenoptera, Ichneumonidae)
Urban greenery is defined by the high density and species diversity of plants. As such, it is a habitat for various groups of insects, including pests and insectivores. This study aims to determine the species composition and abundance of parasitoids of the subfamily Pimplinae in urban green spaces. The study was conducted between 2009 and 2016 in Gdynia. Sweep-net sampling and collecting dead insects from the beach were used. A total of 442 specimens belonging to 45 species of Pimplinae were collected. They represented 66.18% of the species listed in this habitat and 32.61% of the species found in Poland. The dominant species included Tromatobia lineatoria (14.26%), Scambus inanis (10.41%), Zaglyptus varipes (7.92%), Tromatobia ovivora (7.70%), Acropimpla pictipes (6.57%), Scambus calobatus (5.43%) and Gregopimpla inquisitor (5.21%). Three trophic groups were distinguished among the parasitoids, including parasitoids of phytophagous insects, i.e. Arachnida and Aculeata. Parasitoids of phytophagous insects were the most abundant [34 species (75.55%)].