Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by the rodent-transmitted orthohantaviruses (HVs), with China possessing the most cases globally. The virus hosts in China are Apodemus agrarius and Rattus norvegicus , and the disease spread is strongly influenced by global climate dynamics. To assess and predict the spatiotemporal trends of HFRS from 2005 to 2098, we collected historical HFRS data in mainland China (2005–2020), historical and projected climate and population data (2005–2098), and spatial variables including biotic, environmental, topographical, and socioeconomic. Spatiotemporal predictions and mapping were conducted under 27 scenarios incorporating multiple integrated representative concentration pathway models and population scenarios. We identify the type of magistral HVs host species as the best spatial division, including four region categories. Seven extreme climate indices associated with temperature and precipitation have been pinpointed as key factors affecting the trends of HFRS. Our predictions indicate that annual HFRS cases will increase significantly in 62 of 356 cities in mainland China. Rattus regions are predicted to be the most active, surpassing Apodemus and Mixed regions. Eighty cities are identified as at severe risk level for HFRS, each with over 50 reported cases annually, including 22 new cities primarily located in East China and Rattus regions after 2020, while 6 others develop new risk. Our results suggest that the risk of HFRS will remain high through the end of this century, with Rattus norvegicus being the most active host, and that extreme climate indices are significant risk factors. Our findings can inform evidence-based policymaking regarding future risk of HFRS.

Caused by Yersinia pestis , plague ravaged the world through three known pandemics: the First or the Justinianic (6th–8th century); the Second (beginning with the Black Death during c.1338–1353 and lasting until the 19th century); and the Third (which became global in 1894). It is debatable whether Y. pestis persisted in European wildlife reservoirs or was repeatedly introduced from outside Europe (as covered by European Union and the British Isles). Here, we analyze environmental data (soil characteristics and climate) from active Chinese plague reservoirs to assess whether such environmental conditions in Europe had ever supported “natural plague reservoirs”. We have used new statistical methods which are validated through predicting the presence of modern plague reservoirs in the western United States. We find no support for persistent natural plague reservoirs in either historical or modern Europe. Two factors make Europe unfavorable for long-term plague reservoirs: 1) Soil texture and biochemistry and 2) low rodent diversity. By comparing rodent communities in Europe with those in China and the United States, we conclude that a lack of suitable host species might be the main reason for the absence of plague reservoirs in Europe today. These findings support the hypothesis that long-term plague reservoirs did not exist in Europe and therefore question the importance of wildlife rodent species as the primary plague hosts in Europe.

The accurate habitat suitability evaluation of forest species is vital for forest resource management and conservation. Therefore, the previously published thresholds of soil organic carbon (SOC) contents for the six main forest species were used to screen sample points in this study; the maximum entropy modeling (MaxEnt) was applied to predict the potential distribution of those species in Lvliang Mountain, Shanxi Province, China. The following results were derived: (1) the area under the curve (AUC) value of the MaxEnt model was 0.905, indicating the model results had high accuracy; (2) the main environmental factors affecting the woodlands were mean diurnal temperature range, solar radiation, population density and slope; (3) the model accurately depicted the most suitable areas for those species, namely Populus davidiana Dode (Malpighiales: Salicaceae), Betula platyphylla Sukaczev (Fagales: Betulaceae), Quercus wutaishanica Mayr (Fagales: Fagaceae), Platycladus orientalis (L.) Franco (Pinales: Cupressaceae), Larix gmelinii (Rupr.) Kuzen. (Pinales: Pinaceae) and Pinus tabuliformis Carrière (Pinales: Pinaceae). This study has improved the representativeness of the samples based on prior knowledge to enhance the biological meaning and accuracy of the prediction results. Its findings provide a theoretical basis for the forest resource protection, management measures alongside the reconstruction of low-yield and low-efficiency forests.

Tillage and slope will influence soil surface roughness that changes during rainfall events. This study tests this effect under controlled conditions quantified by geostatistical and fractal indices. When four commonly adopted tillage practices, namely, artificial backhoe (AB), artificial digging (AD), contour tillage (CT), and linear slope (CK), were prepared on soil surfaces at 2 × 1 × 0.5 m soil pans at 5°, 10°, or 20° slope gradients, artificial rainfall with an intensity of 60 or 90 mm h⁻¹ was applied to it. Measurements of the difference in elevation points of the surface profiles were taken before rainfall and after rainfall events for sheet erosion. Tillage practices had a relationship with fractal indices that the surface treated with CT exhibited the biggest fractal dimension D value, followed by the surfaces AD, AB, and CK. Surfaces under a stronger rainfall tended to have a greater D value. Tillage treatments affected anisotropy differently and the surface CT had the strongest effect on anisotropy, followed by the surfaces AD, AB, and CK. A steeper surface would have less effect on anisotropy. Since the surface CT had the strongest effect on spatial variability or the weakest spatial autocorrelation, it had the smallest effect on runoff and sediment yield. Therefore, tillage CT could make a better tillage practice of conserving water and soil. Simultaneously, changes in semivariogram and fractal parameters for surface roughness were examined and evaluated. Fractal parameter – crossover length l – is more sensitive than fractal dimension D to rainfall action to describe vertical differences in soil surface roughness evolution.

Dengue fever, influenced by climate dynamics and human mobility in nonendemic regions, remains poorly understood. We assessed the effects of large-scale climate features on domestic dengue outbreaks using data from China (2013–2021) and projections for 2023–2028, incorporating climate, human mobility, and environmental factors. A significant positive correlation (r = 0.89, P < 0.01) was found between domestic dengue incidence and overseas imported cases. Significant associations were also noted with domestic (F2.96, 25.78 = 518.03, P < 0.01) and international human mobility (F2.77, 25.78 = 66.84, P < 0.01), the Indian Ocean Dipole (IOD) index (F2.98, 25.78 = 522.84), and vectorial capacity (F2.92, 25.78 = 338.74, P < 0.01). IOD is the most influential climate feature, with a trend of intensification over time. The Extreme Gradient Boosting (XGBoost) algorithm incorporating IOD (r2 = 0.56) predicted outbreaks in 2025, 2026, and 2028. Our findings reveal that large-scale climate phenomenon, notably the IOD, and human mobility significantly influence dengue domestic outbreaks in China through a “dislocated impact.”

Natural hazards significantly influence landscape patterns and ecological processes, yet their integration into land-use simulation remains limited. This study incorporates landslide susceptibility into the Patch-generating Land Use Simulation (PLUS) model to evaluate the effect of construction restrictions on future landslide exposure in Nanbu County, Sichuan Province, China. Landslide susceptibility was assessed using the certainty factor–logistic regression (CF-LR) model based on multi-period historical data, while future land-use changes (2020–2050) were projected with the PLUS model under two scenarios: a baseline scenario (without landslide constraints) and a susceptibility-constrained scenario (where high-susceptibility zones are restricted for construction). The results indicate that landslide-prone areas are spatially heterogeneous, with non-high-susceptibility zone concentrated in the west and northwest, and high-susceptibility zones clustered in the central area near the county seat. Under the baseline scenario, construction land within high-susceptibility zones increases by 107.47% over the 2020–2050 period. By contrast, the susceptibility-constrained scenario reduces construction land in these zones by 56.6% relative to the baseline 2050 projection, and by 10.01% relative to its own 2020 level. Ecosystem service value (ESV) assessment reveals that the two scenarios achieve nearly identical total ESV (with only a 0.09% difference), but the susceptibility-constrained scenario preserves 10.36 km² of high-value ecosystems within high-susceptibility zones compared to the baseline scenario. These findings demonstrate that incorporating landslide susceptibility as a spatial constraint reduces future landslide exposure without compromising ecosystem service value, providing a practical, science-based framework for decision makers to inform land-use planning in mountainous regions prone to landslides.