Changes in overwintering ground of small yellow croaker (Larimichthys polyactis) based on MaxEnt and GARP models: A case study of the southern Yellow Sea stock

Predicting the global potential distribution of Bursaphelenchus xylophilus using an ecological niche model: expansion trend and the main driving factors

Bursaphelenchus xylophilus (Steiner&Buhrer) Nickle is a global quarantine pest that causes devastating mortality in pine species. The rapid and uncontrollable parasitic spread of this organism results in substantial economic losses to pine forests annually. In this study, we used the MaxEnt model and GIS software ArcGIS10.8 to predict the distribution of B. xylophilus based on collected distribution points and 19 environmental variables (with a correlation coefficient of|R| > 0.8) for the contemporary period (1970-2000), 2041-2060 (2050s), 2061-2080 (2070s), and 2081-2100 (2090s) under four shared socioeconomic pathways (SSPs). We conducted a comprehensive analysis of the key environmental factors affecting the geographical distribution of B. xylophilus and suitable distribution areas. Our results indicate that in current prediction maps B. xylophilus had potential suitable habitats in all continents except Antarctica, with East Asia being the region with the most highly suitable areas and the most serious epidemic area currently. Precipitation of the warmest quarter, temperature seasonality, precipitation of the wettest month, and maximum temperature of the warmest month were identified as key environmental variables that determine the distribution of B. xylophilus. Under future climatic conditions, the potential geographic distribution of B. xylophilus will expand relative to current conditions. In particular, under the SSP5-8.5 scenario in 2081-2100, suitable areas will expand to higher latitudes, and there will be significant changes in suitable areas in Europe, East Asia, and North America. These findings are crucial for future prevention and control management and monitoring.

Predicting the potential distribution of 12 threatened medicinal plants on the Qinghai-Tibet Plateau, with a maximum entropy model

Climate change is a vital driver of biodiversity patterns and species distributions, understanding how organisms respond to climate change will shed light on the conservation of endangered species. In this study, the MaxEnt model was used to predict the potential suitable area of 12 threatened medicinal plants in the QTP (Qinghai-Tibet Plateau) under the current and future (2050s, 2070s) three climate scenarios (RCP2.6, RCP4.5, RCP8.5). The results showed that the climatically suitable habitats for the threatened medicinal plants were primarily found in the eastern, southeast, southern, and some parts of the central regions on the QTP. Moreover, 25% of the threatened medicinal plants would have reduced suitable habitat areas within the next 30-50 years in the different future global warming scenarios. Among these medicinal plants, RT (Rheum tanguticum) would miss the most habitat (98.97%), while the RAN (Rhododendron anthopogonoides) would miss the least habitat (10.15%). Nevertheless, 33.3% of the threatened medicinal plants showed an increase in their future habitat area because of their physiological characteristics which are more adaptable to a wide range of climates. The climatic suitable habitat for 50% of the threatened medicinal plants would migrate to higher altitudes or higher latitudes regions. This study provides a data foundation for the conservation of biodiversity and wild medicinal plants on the QTP.

Detection and prediction of pathogenic microorganisms in aquaculture (Zhejiang Province, China)

The detection and prediction of pathogenic microorganisms play a crucial role in the sustainable development of the aquaculture industry. Currently, researchers mainly focus on the prediction of water quality parameters such as dissolved oxygen for early warning. To provide early warning directly from the pathogenic source, this study proposes an innovative approach for the detection and prediction of pathogenic microorganisms based on yellow croaker aquaculture. Specifically, a method based on quantitative polymerase chain reaction (qPCR) is designed to detect the Cryptocaryon irritans (Cri) pathogenic microorganisms. Furthermore, we design a predictive combination model for small samples and high noise data to achieve early warning. After performing wavelet analysis to denoise the data, two data augmentation strategies are used to expand the dataset and then combined with the BP neural network (BPNN) to build the fusion prediction model. To ensure the stability of the detection method, we conduct repeatability and sensitivity tests on the designed qPCR detection technique. To verify the validity of the model, we compare the combined BPNN to long short-term memory (LSTM). The experimental results show that the qPCR method provides accurate quantitative measurement of Cri pathogenic microorganisms, and the combined model achieves a good level. The prediction model demonstrates higher accuracy in predicting Cri pathogenic microorganisms compared to the LSTM method, with evaluation indicators including mean absolute error (MAE), recall rate, and accuracy rate. Especially, the accuracy of early warning is increased by 54.02%.