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This article summarizes data obtained during the 5 years nestboxing project aimed to multiply breeding population of the Ural Owl (Strix uralensis) in the basin of Izdrevaya river, Novosibirsk region, Russia in 2012–2016. From 2012 to 2015 a total of 38 nestboxes (NB) were installed. The distance between neighboring NBs vary from 0.58 to 1.80 km (n=24), 1.12±0.29 km average. From breeding season of 2013 to breeding season of 2015 the number of NB in project grows from 18 NB to 33 NB, however only 4–5 of them were occupied each year. Only in breeding season of 2016 we observed population growth when 8 out of 38 (21.05 %) NBs were found occupied by the Ural Owl. The average clutch size was 3.17±0.98 eggs (n=6; min 2, max 4 eggs). The average brood size was 2.00±0.58 nestlings (n=7; min 2, max 3 nestlings). The distance between neighboring pairs breed in nestboxes ranged from 0.92 to 6.25 km (n=15), 1.87±1.30 km at average. We used discriminant analysis to estimate the role of landscape on owl preferences when choosing NB and to make predictions on a future distribution of owls. The resulting discrimination function allows us to classify correctly between empty and occupied NBs with accuracy of 81.85%, Wilki’s Lambda = 0.67 (classification accuracy 81.58 %, р

Modeling of carbon nanostructures by means of classical molecular dynamics requires a lot of computations. One of the ways to improve the performance of basic algorithms is to transform them for running on SIMD-type computing systems such as systems with dedicated GPU. In this work we describe the development of algorithms for computation of many-body interaction based on Tersoff and embedded-atom potentials by means of OpenCL technology. OpenCL standard provides universality and portability of the algorithms and can be successfully used for development of the software for heterogeneous computing systems. The performance of algorithms is evaluated on CPU and GPU hardware platforms. It is shown that concurrent memory writes is effective for Tersoff bond order potential. The same approach for embedded-atom potential is shown to be slower than algorithm without concurrent memory access. Performance evaluation shows a significant GPU acceleration of energy-force evaluation algorithms for many-body potentials in comparison to the corresponding serial implementations.