The potential range and distribution of insects are greatly impacted by climate change. This study evaluates the potential global shifts in the range of Gryllus bimaculatus (Orthoptera: Gryllidae) under several climate change scenarios. The Global Biodiversity Information Facility provided the location data for G. bimaculatus, which included nineteen bioclimatic layers (bio01–bio19), elevation data from the WorldClim database, and land cover data. For the near future (2021–2040) and far future (2081–2100) under low (SSP1-2.6) and high (SSP5-8.5) emission scenarios, the Beijing Climate Center Climate System Model (BCC-CSM2-MR) and the Institute Pierre-Simon Laplace Coupled Model Intercomparison Project (IPSL-CM6A-LR) were used. Assessing habitat gain, loss, and stability for G. bimaculatus under potential scenarios was part of the evaluation analysis. The results showed that the main environmental parameters affecting the distribution of G. bimaculatus were mean temperature of the driest quarter, mean diurnal temperature range, isothermality, and seasonal precipitation. Since birds, small mammals, and other insectivorous insects rely on G. bimaculatus and other cricket species as their primary food supply, habitat loss necessitates management attention to the effects on the food web. The spread of G. bimaculatus as a sentinel species in the food chain and its use in animal feeds are both impacted by habitat loss and gain.

Culex pipiens (Diptera: Culicidae) is a disease vector for the West Nile virus (WNV). Climate change impacts the disease transmission carried by mosquitoes, and it is partly to blame for the resurgence of mosquitoes as important disease vector insects. This study assessed the geographic range of Culex pipiens in the United States under both present and projected climatic scenarios, identifying the primary environmental factors influencing its distribution. Employing species distribution modeling (MaxEnt, Version 3.4.1) and occurrence data from the Global Biodiversity Information Facility, we examined the effects of key variables, including altitude, temperature seasonality (bio4), and annual precipitation (bio12), on habitat suitability for C. pipiens. The analysis revealed that altitude accounted for 60.3% of the model’s explanatory power, followed by temperature seasonality (31%) and annual precipitation (8.7%). Areas having low elevation and moderate temperature fluctuations were the most favorable for C. pipiens, with a predicted range extending across the Midwest and southeastern United States under present variables. Future projections for 2050 and 2070 under Representative Concentration Pathway (climatic change) scenarios suggest possible northward expansion in response to rising temperatures and altered precipitation patterns. The study highlights some shifts in C. pipiens distribution and the potential for increased disease transmission into new areas. This study serves as a catalyst for decision-makers to coordinate their management reaction and create more resilient and comprehensive strategies to safeguard human health.

Danaus plexippus L. (Linnaeus, 1758) (Lepidoptera: Nymphalidae) is well-known and captivating migratory insect that cannot overwinter in temperate climates. Although common throughout North America, monarch butterfly populations appear to be declining widely in their geographically distinct areas. Rapid climatic change is posing a serious threat to monarch butterfly populations, especially the migratory groups in the east. This study assesses how several climate change scenarios will affect the global distribution of monarch butterflies. Using climate projections from the Coupled Model Intercomparison Project Phase 6 model, we conducted projections for the near future (2021–2040) and far future (2041–2060, 2081–2100) under high (SSP5_8.5) and low (SSP1_2.6) emission scenarios. We estimated habitat gain, loss, and stability for D. plexippus. True Skill Statistic (TSS) and the Area Under the Curve (AUC) were used to assess the model’s performance. The results indicated that annual precipitation, land cover, and altitude (Alt) were the most influential factors affecting D. plexippus distribution. Potential habitat shifts were observed in Central Asia, Africa, Europe, and North America, with both gains and losses. These findings highlight the interconnected relationship between the climatic factors and distribution of migratory insects, emphasizing the need for targeted conservation efforts to mitigate climate change impacts on D. plexippus populations. Management strategies should prioritize habitat restoration, focus on overwintering site preservation, and implement adaptive management approaches. This research provides an evidence base for adaptive management to minimize climate change impacts on migratory insect populations.

Cultural traits may replace human mobility data in forecasting COVID-19 mortality: a deep learning approach, Abbas, Saif, Oraby, Tamer, Tyshenko, Michael G, Bhattacharyya, Samit Cultural traits may replace human mobility data in forecasting COVID-19 mortality: a deep learning approach, Abbas, Saif, Oraby, Tamer, Tyshenko, Michael G, Bhattacharyya, Samit

A quantitative risk assessment of human loss of control over advanced AI used a Bowtie diagram extended with fault tree-like and event tree-like analysis. Six primary threats were identified (recursive self‑improvement, power seeking, deceptive alignment, loss of corrigibility, off‑switch subversion, malicious misuse) and six consequences (systemic infrastructure collapse, economic breakdown, resource shortages, non‑human value lock‑in, human marginalization, global supply cascade failures). Preventive and mitigative barriers were assigned per pathway from expert literature. Input probabilities (threat base rates and barrier failure‑on‑demand values) were sourced from experts and modeled with triangular uncertainty distributions. A 1,000‑iteration Monte Carlo simulation propagated epistemic uncertainty, yielding a median probability of the top event (loss of human control) of 12.8% (90% CI: 11.3%–14.4%), roughly 1 in 8. The distribution is approximately symmetric with slight positive skew, indicating modest tail risk if barrier failures interact. Conditional on the top event, Expected Severity is 1.85 on a 1–10 scale (90% CI: 1.75–1.96), suggesting mitigation is effective in most scenarios. Results align with expert estimates and demonstrate barrier effects; narrow CIs reflect model consistency. Remaining tail risks support precautionary governance, increased alignment research, iterative risk modeling, and investment in international coordination with robust safety measures to reduce the existential risk of AI loss of control.