MaxEnt algorithm applying to moss species distribution analysis

Bioclimatic modeling method MaxEnt is tested on objects occupying micro-habitats on the example ofmosses. Data organizing options and the results are discussed.

Bioclimatic modeling of moss distribution: MaxEnt interpretation for test species

Bioclimatic modeling method MaxEnt is tested to micro-habitats occupying objects on the example of five moss species. Modeling is done out on a planetary and regional scale. Results are discussed.

Predicting Microbial Species in a River Based on Physicochemical Properties by Bio-Inspired Metaheuristic Optimized Machine Learning

The main goal of the analysis of microbial ecology is to understand the relationship between Earth’s microbial community and their functions in the environment. This paper presents a proof-of-concept research to develop a bioclimatic modeling approach that leverages artificial intelligence techniques to identify the microbial species in a river as a function of physicochemical parameters. Feature reduction and selection are both utilized in the data preprocessing owing to the scarce of available data points collected and missing values of physicochemical attributes from a river in Southeast China. A bio-inspired metaheuristic optimized machine learner, which supports the adjustment to the multiple-output prediction form, is used in bioclimatic modeling. The accuracy of prediction and applicability of the model can help microbiologists and ecologists in quantifying the predicted microbial species for further experimental planning with minimal expenditure, which is become one of the most serious issues when facing dramatic changes of environmental conditions caused by global warming. This work demonstrates a neoteric approach for potential use in predicting preliminary microbial structures in the environment.

Range of Pterostichus oblongopunctatus (Coleoptera, Carabidae) in conditions of global climate change

Using geodata technology, we conducted a bioclimatic modeling of the spatial distribution of the common palearctic ground beetle – Pterostichus oblongopunctatus (Fabricius, 1787). The range of comfort of the territories included in this species’ range was obtained. We used the data on 510 sampling points, obtained as a result of the authors’ field surveys and the data base of the GBIF global fund of biodiversity and 19 climatic parameters from the WorldClim open base and MaxEnt program. The results determined the factors which have the greatest impact on the current distribution of P. oblongopunctatus. The main climatic factors affecting the distribution of P. oblongopunctatus are average annual temperature, average 24-hour amplitude of temperature over each month, average temperature over the driest quarter, average temperature over the warmest quarter of the year, total of precipitations in the driest month of the year. We performed a prediction of possible change in the range by two scenarios (RCP 2.6 and RCP 8.5) for 2050 and 2070. Using QGIS program, we estimated the areas of the species’ range, and compared them. According to the scenario RCP 2.6, by 2050, the range of the species will contract due to decrease in the territories with moderately continental climate, and by 2070, a restoration of the range would take place, for according to this scenario, the average annual temperature stabilizes. According to the scenario RCP 8.5, the range will contract by 2050 and will continue to decrease by 2070, for the concentration of CO2 continues to increase along with increase in average annual temperature. Climate changes can affect the life cycle of the beetle, its life expectancy and activity over the season. With changes in temperature, eggs and larvae of P. oblongopunctatus can be more vulnerable.