Projecting the Potential Distribution Areas of Ixodes scapularis (Acari: Ixodidae) Driven by Climate Change

Ixodes scapularis is a vector of tick-borne diseases. Climate change is frequently invoked as an important cause of geographic expansions of tick-borne diseases. Environmental variables such as temperature and precipitation have an important impact on the geographical distribution of disease vectors. We used the maximum entropy model to project the potential geographic distribution and future trends of I. scapularis. The main climatic variables affecting the distribution of potential suitable areas were screened by the jackknife method. Arc Map 10.5 was used to visualize the projection results to better present the distribution of potential suitable areas. Under climate change scenarios, the potential suitable area of I. scapularis is dynamically changing. The largest suitable area of I. scapularis is under SSP3-7.0 from 2081 to 2100, while the smallest is under SSP5-8.5 from 2081 to 2100, even smaller than the current suitable area. Precipitation in May and September are the main contributing factors affecting the potential suitable areas of I. scapularis. With the opportunity to spread to more potential suitable areas, it is critical to strengthen surveillance to prevent the possible invasion of I. scapularis.

Inferring excitation-inhibition dynamics using a maximum entropy model unifying brain structure and function

Neural activity coordinated across different scales from neuronal circuits to large-scale brain networks gives rise to complex cognitive functions. Bridging the gap between micro- and macro-scale processes, we present a novel framework based on the maximum entropy model to infer a hybrid resting state structural connectome, representing functional interactions constrained by structural connectivity. We demonstrate that the structurally informed network outperforms the unconstrained model in simulating brain dynamics; wherein by constraining the inference model with the network structure we may improve the estimation of pairwise BOLD signal interactions. Further, we simulate brain network dynamics using Monte Carlo simulations with the new hybrid connectome to probe connectome-level differences in excitation-inhibition balance between apolipoprotein E (APOE)-ε4 carriers and noncarriers. Our results reveal sex differences among APOE-ε4 carriers in functional dynamics at criticality; specifically, female carriers appear to exhibit a lower tolerance to network disruptions resulting from increased excitatory interactions. In sum, the new multimodal network explored here enables analysis of brain dynamics through the integration of structure and function, providing insight into the complex interactions underlying neural activity such as the balance of excitation and inhibition.

Spatial-temporal habitat suitability for lemuru fish (Sardinella lemuru) using the Second-generation Global Imager (SGLI) and Maximum Entropy model in the Bali Strait, Indonesia

Lemuru fish (Sardinella lemuru), the most dominant fishery resource, has economic values for the fisherman fishing activities in the Bali Strait (between Jawa and Bali islands), Indonesia. Spatial and temporal prediction for the fishing location is essential information for effective fisheries management. The high spatial resolution of sea surface temperature (SST) and Chlorophyll-a (Chl-a) by the second-generation global imager (SGLI) on the global change observation mission (GCOM-C) satellite was employed for the input of the Maximum Entropy Model (MaxEnt) to predict the potential fishing area of lemuru fish in 2020. This study analyzed SST and Chl-a using the SGLI data and shows the variability of SST and Chl-a for lemuru fish-catching data. The MaxEnt model performance to predict the habitat suitability for lemuru fish in the Bali Strait has been shown in this study. As a result, the maximum average Chl-a estimated in August 2020 was around 1.62 mg m−3 and maximum SST in March 2020 around 28.12°C. The correlation between SST and Chl-a with total lemuru fish-catching were -0.209 and 0.375 for SST and Chl-a, respectively. The prediction of lemuru fishing areas using the MaxEnt model showed excellent model evaluations with a correlation value higher than 0.80.

Predicting the Potential Distribution of Perennial Plant Coptis chinensis Franch. in China under Multiple Climate Change Scenarios

Coptis chinensis Franch. (Ranales: Ranunculaceae) is a perennial species with high medicinal value. Predicting the potentially geographical distribution patterns of C. chinensis against the background of climate change can facilitate its protection and sustainable utilization. This study employed the optimized maximum entropy model to predict the distribution patterns and changes in potentially suitable C. chinensis’ regions in China under multiple climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) across different time periods (1970–2000, 2050s, 2070s, and 2090s). The results revealed that the currently potentially suitable regions of C. chinensis span an area of 120.47 × 104 km2, which accounts for 12.54% of China’s territory. Among these areas, the low, moderate, and highly suitable regions are 80.10 × 104 km2, 37.16 × 104 km2, and 3.21 × 104 km2, respectively. The highly suitable regions are primarily distributed in Chongqing, Guizhou, Zhejiang, Hubei, and Hunan Provinces. Over time, the potentially suitable regions of C. chinensis are predicted to shrink. Furthermore, our study revealed that the relatively low impact areas of C. chinensis were mainly distributed in Yunnan, Guizhou, Hubei, Chongqing, and other Provinces. Centroid transfer analysis indicated that except for SSP1-2.6, the center of the potentially suitable region of C. chinensis showed a trend of gradual transfer to the northwest and high-altitude areas.

Predict the Suitable Places to Run in the Urban Area of Beijing by Using the Maximum Entropy Model

Many people in the world do not have enough physical activities to maintain good health, which has recently become a threat to public health. In addition to individual genetic and social factors, we considered the geographical environment of the city as a factor that affects these healthy physical activities. We used the location-based data in social media combined with the open geographic data to explore the impact mechanism of urban environmental factors on human running behaviors. This study collected nine urban environmental variables and preference tracks in Beijing’s main urban area. We used the Maximum Entropy Model (MaxEnt) to analyze the relationship between running behaviors and environmental variables and identify suitable areas for running in Beijing. The results showed that: firstly, the variables of attractions, sports and sidewalk density contributed the most to running suitability. Secondly, 47.5% of the main urban areas in Beijing are suitable for running, mainly in the main urban areas with better economic development. Thirdly, the distribution of suitable places for running is unfair in that some places with large populations do not have a matching running environment.