Search Results (22)

The relict tree Cercidiphyllum japonicum, a Tertiary paleoendemic with significant ecological and timber value, prefers warm–cool humid climates and acidic soils. Using MaxEnt and ArcGIS, we modeled its distribution under current and future climate scenarios (SSP, Shared Socioeconomic Pathways). High-suitability areas (>0.6 probability) under current conditions are mainly concentrated in the Sichuan Basin and the Yellow–Yangtze transition zones. By 2050, projections show northwestward expansions (14.32–18.76% increase in area) and eastward movement toward Central China under both SSP1-2.6 and SSP5-8.5 scenarios. However, by 2090, habitat loss could exceed 22% under SSP5-8.5. The main environmental drivers of its distribution are minimum coldest-month temperature (bio6, 38.7%), annual precipitation (bio12, 29.1%), and temperature range (bio7, 18.5%). Precipitation seasonality and thermal extremes are expected to become more significant constraints in the future. Conservation strategies should focus on the following: (1) protecting refugia in the Daba–Wushan mountains, (2) facilitating assisted migration to northwestern high-latitude regions, and (3) preserving microclimates. This study offers a framework for evidence-based conservation of paleoendemic species under climate change. Full article

Histia rhodope is a defoliating pest that feeds mainly on the ornamental garden plant Bischofia polycarpa. Recently, frequent outbreaks of H. rhodope in Southern China have severely affected cityscapes and people’s lives. To provide a predictive early-warning program for the spread risk of H. rhodope in China and reduce damage to B. polycarpa, we used the MaxEnt model to investigate the potentially suitable spread characteristics of H. rhodope and its host B. polycarpa under different climate scenarios for the years 2050 and 2070. The results showed that the potentially suitable habitat of H. rhodope under the SSP5-8.5 scenario will reach an area of 3174.55 × 10³ km² in the 2070s, an increase of 1010 × 10³ km² from the current distribution. The potentially suitable habitat of B. polycarpa under the SSP5-8.5 scenario will reach 2618.01 × 10³ km² in the 2070s (an increase of 464 × 10³ km²). The potentially suitable habitats of H. rhodope and B. polycarpa are expected to shift to higher elevations under future climate scenarios. We also identified ten key environmental factors, of which Precipitation of Warmest Quarter (bio18) had the greatest influence on the probability of moth and host plant occurrence. Our results highlight the risk of further expansion of the potentially suitable area for H. rhodope and the important role of the host plant in this process, and provide a firm scientific basis for the monitoring and management of H. rhodope and B. polycarpa. Full article

In recent years, many spruce (Picea abies (L.) H. Karst., Pinaceae) forests have been severely affected by bark beetle (Ips typographus L., Coleoptera: Curculionidae) outbreaks in the Southern Alps, but their ecological impacts remain poorly studied. We analyzed the distribution, ecological, and floristic–vegetational characteristics of forests recently affected by the bark beetle in the upper basin of the Oglio River (Northern Italy) and developed a MaxEnt model to map forests with a bioclimate more prone to severe insect attacks in the coming decades. The results showed that the spruce forests affected by the bark beetle are located exclusively in the submountain and mountain belts (below 1600 m a.s.l.) and that 85% of them are found in areas with high annual solar radiation (>3500 MJ m⁻²). The predictive model for areas susceptible to severe bark beetle attacks proved highly accurate (AUC = 0.91) and was primarily defined by the mean temperature of the dry winter quarter (contribution: 80.1%), with values between −2.5 and 2.5 °C being particularly suitable for the pest. According to the model, more than 58% of the current spruce forests in the study area will exhibit high susceptibility (probability > 0.7) to severe bark beetle attacks by 2080. The floristic–vegetational and ecological analysis of plant communities of 11 bark beetle-affected areas indicated that more thermophilic and significantly different forest communities (in both floristic and physiognomic terms) are expected to develop compared to those of pre-disturbance. Furthermore, the high coverage of spruce snags/standing dead trees appears to accelerate plant succession, enabling the establishment of mature forest communities in a shorter time frame. Full article

Sterculia foetida L., commonly known as the Java olive, is a tropical tree species native to regions of East Africa, tropical Asia, and northern Australia. This study employs species distribution modeling (SDM) to predict the potential geographic distribution of S. foetida under current and future climate scenarios. Using 1425 occurrence data and 19 environmental variables, we applied an ensemble modelling approach of three algorithms: Boosting Regression Trees (BRT), Generalized Linear Model (GLM), and Random Forests (RF), to generate distribution maps. Our models showed high accuracy (mean AUC = 0.98) to indicate that S. foetida has a broad ecological niche, with high suitability in tropical and subtropical regions of north Australia (New Guinea and Papua), Southeast Asia (India, Thailand, Myanmar, Taiwan, Philippines, Malaysia, Sri Lanka), Oman and Yemen in the southwest of Asia, Central Africa (Guinea, Ghana, Nigeria, Congo, Kenya and Tanzania), the Greater and Lesser Antilles, Mesoamerica, and the north of South America (Colombia, Panama, Venezuela, Ecuador and Brazil). Indeed, the probability of occurrence of S. foetida positively correlates with the Maximum temperature of warmest month (bio5), Mean temperature of wettest quarter (bio8) and Precipitation of wettest month (bio13). The model results showed a suitability area of 4,744,653 km², representing 37.86% of the total study area, classified into Low (14.12%), Moderate (8.71%), and High suitability (15.02%). Furthermore, the study found that habitat suitability for S. foetida showed similar trends under both near future climate scenarios (SSP1-2.6 and SSP5-8.5 for 2041–2060), with a slight loss in potential distribution (0.24% and 0.25%, respectively) and moderate gains (1.98% and 2.12%). In the far future (2061–2080), the low scenario (SSP1-2.6) indicated a 0.29% loss and a 2.52% gain, while the high scenario (SSP5-8.5) showed a more dramatic increase in both loss (0.6%) and gain areas (3.79%). These findings are crucial for conservation planning and management, particularly in regions where S. foetida is considered invasive and could become problematic. The study underscores the importance of incorporating climate change projections in SDM to better understand species invasiveness dynamics and inform biodiversity conservation strategies. Full article

Sect. Tuberculata, as one of the endemic plant groups in China, belongs to the genus Camellia of the Theaceae family and possesses significant economic and ecological value. Nevertheless, the characteristics of habitat distribution and the major eco-environmental variables affecting its suitability are poorly understood. In this study, using 65 occurrence records, along with 60 environmental factors, historical, present and future suitable habitats were estimated using MaxEnt modeling, and the important environmental variables affecting the geographical distribution of sect. Tuberculata were analyzed. The results indicate that the size of the its potential habitat area in the current climate was 1.05 × 10⁵ km², and the highly suitable habitats were located in Guizhou, central-southern Sichuan, the Wuling Mountains in Chongqing, the Panjiang Basin, and southwestern Hunan. The highest probability of presence for it occurs at mean diurnal range (bio2) ≤ 7.83 °C, basic saturation (s_bs) ≤ 53.36%, temperature annual range (bio7) ≤ 27.49 °C, −7.75 °C < mean temperature of driest quarter (bio9) < 7.75 °C, annual UV-B seasonality (uvb2) ≤ 1.31 × 10⁵ W/m², and mean UV-B of highest month (uvb3) ≤ 5089.61 W/m². In particular, bio2 is its most important environmental factor. During the historical period, the potential habitat area for sect. Tuberculata was severely fragmented; in contrast, the current period has a more concentrated habitat area. In the three future periods, the potential habitat area will change by varying degrees, depending on the aggressiveness of emissions reductions, and the increase in the potential habitat area was the largest in the SSP2.6 (Low-concentration greenhouse gas emissions) scenario. Although the SSP8.5 (High-concentration greenhouse gas emissions) scenario indicated an expansion in its habitat in the short term, its growth and development would be adversely affected in the long term. In the centroid analysis, the centroid of its potential habitat will shift from lower to higher latitudes in the northwest direction. The findings of our study will aid efforts to uncover its originsand geographic differentiation, conservation of unique germplasms, and forestry development and utilization. Full article

Construction is one of the economic sectors with the greatest influence on climate change. In addition to working procedures, the primary carbon footprint is attributed to the choice of materials and the energy required for their manufacturing. The underlying idea of this study is to minimize the effects and offer new solutions to emerging problems in the quest for materials that can be deemed as natural, such as gypsum (calcium sulphate dihydrate) and rice straw (Oryza sativa). The acquisition of these materials involves a lower carbon footprint compared to the conventional materials. It is well known since ancient times that gypsum and cereal straw can be used in construction, with numerous examples still available. Cereal straw is one of the oldest construction materials, traditionally combined with earth and occasionally with certain binders, with it continuing to be employed in construction in many countries to this day. This work showcases the feasibility of producing stable prefabricated elements from straw waste with construction gypsum, addressing a significant environmental concern posed by the alternative of having to burn such materials. In this study, for the proposed bio-based material, specific tests, such as thermal conductivity, flexural and compressive strength, and fire resistance, were carried out to evaluate the principal physical and mechanical characteristics for different compositions of water, gypsum, and straw fiber samples. The results highlighted the good performance of the proposed materials in order to spread their use in the green building industry. The addition of straw fibers improved, in different ways, some important physical characteristics of these components so as to diminish environmental pollution and to obtain better material performance. The tests highlighted the different behaviors of the proposed material with respect to the different cuts of the straw and as well as the water/gypsum ratio; this is not very well understood and probably depends on the micro structure of the straw fibers. The blocks with raw straw showed a significant improvement in the breaking mechanism (1775.42 N) compared to the blocks with cut straw (712.26 N) when subjected to bending tests, and their performance in compression tests was also acceptable. Additionally, a very interesting reduction in thermal conductivity was achieved by incorporating rice straw (0.233 W/mK), and high fire exposure times were obtained, with gypsum preventing the spread of ignition in any type of fiber. Full article

During the large-scale vegetation restoration on the Loess Plateau, the introduction of exotic species with high water consumption, such as Robinia pseudoacacia L., led to widespread soil desiccation, and resulted in severe drought stress and increasing risk of forest degradation and mortality. Accurate assessment of drought-induced mortality risk in plantation forests is essential for evaluating and enhancing the sustainability of ecological restoration, yet quantitative research at the regional scale on the Loess Plateau is lacking. With a focus on Robinia pseudoacacia L. plantations, we utilized a coupled model of the Biome BioGeochemical Cycles model and plant supply–demand hydraulic model (BBGC-SPERRY model) to simulate the dynamics of the annual average percentage loss of whole-plant hydraulic conductance (APLK) at 124 meteorological stations over an extended period (1961–2020) to examine changes in plant hydraulic safety in Robinia pseudoacacia L. plantations. Based on the probability distribution of APLK at each site, the drought-induced mortality risk probability (DMRP) in Robinia pseudoacacia L. was determined. The results indicate the BBGC-SPERRY model could effectively simulate the spatiotemporal variations in transpiration and evapotranspiration in Robinia pseudoacacia L. stands on the Loess Plateau. The mean APLK and DMRP exhibited increasing trends from southeast to northwest along a precipitation gradient, with their spatial patterns on the Loess Plateau mainly driven by mean annual precipitation and also significantly influenced by other climatic and soil factors. The low-risk (DMRP < 2%), moderate-risk (2% ≤ DMRP ≤ 5%), and high-risk (DMRP > 5%) zones for drought-induced mortality in Robinia pseudoacacia L. accounted for 60.0%, 30.7%, and 9.3% of the study area, respectively. These quantitative findings can provide an important basis for rational forestation and sustainable vegetation management on the Loess Plateau. Full article

Sophora alopecuroides L., a perennial herb in the arid and semi-arid regions of northwest China, has the ecological functions of windbreaking and sand fixation and high medicinal value. In recent years, global warming and human activities have led to changes in suitable habitats for S. alopecuroides, which may affect the accumulation of natural products. In this study, MaxEnt 3.4 and ArcGIS 10.4 software were used to predict the distribution of potentially suitable habitats for S. alopecuroides in China under climate change. Furthermore, the geographical distribution of S. alopecuroides as affected by human activities, the differences in the content of natural products of S. alopecuroides between different suitable habitats, and the correlation between natural products and environmental factors were analyzed. The results showed that suitable habitats for S. alopecuroides were projected to expand in the future, and the major environmental factors were temperature (Bio1), rainfall (Bio18), and soil pH (pH). When Bio1, Bio18, and pH were 8.4283 °C, 7.1968 mm, and 9.9331, respectively, the distribution probability (P) of S. alopecuroides was the highest. After adding a human activity factor, the accuracy of the model prediction results was improved, and the area of suitable habitats was greatly reduced, showing a fragmented pattern. Meanwhile, habitat suitability had a specific effect on the content of natural products in S. alopecuroides. Specifically, the content of natural products in S. alopecuroides in wild habitats was higher than that in artificial cultivation, and highly suitable habitats showed higher contents than those in non-highly suitable habitats. The contents of total alkaloids and total flavonoids were positively correlated with human activities and negatively correlated with land use types. Among them, total alkaloids were negatively correlated with aspect, and total flavonoids were positively correlated with aspect. In addition, it is suggested that Xinjiang should be the priority planting area for S. alopecuroides in China, and priority should be given to protection measures in the Alashan area. Overall, this study provides an important foundation for the determination of priority planting areas and resource protection for S. alopecuroides. Full article

Human-induced climate change modifies plant species distribution, reorganizing ecologically suitable habitats for invasive species. In this study, we identified the environmental factors that are important for the spread of Calyptocarpus vialis, an emerging invasive weed in the northwestern Indian Himalayan Region (IHR), along with possible habitats of the weed under current climatic scenarios and potential range expansion under several representative concentration pathways (RCPs) using MaxEnt niche modeling. The prediction had a high AUC (area under the curve) value of 0.894 ± 0.010 and a remarkable correlation between the test and expected omission rates. BIO15 (precipitation seasonality; 38.8%) and BIO1 (annual mean temperature; 35.7%) had the greatest impact on the probable distribution of C. vialis, followed by elevation (11.7%) and landcover (6.3%). The findings show that, unlike the current situation, “high” and “very high” suitability areas would rise while less-suited habitats would disappear. All RCPs (2.6, 4.5, 6.0, and 8.5) indicate the expansion of C. vialis in “high” suitability areas, but RCP 4.5 predicts contraction, and RCPs 2.6, 6.0, and 8.5 predict expansion in “very high” probability areas. The current distribution of C. vialis is 21.59% of the total area of the state, with “medium” to “high” invasion suitability, but under the RCP 8.5 scenario, it might grow by 10% by 2070. The study also reveals that C. vialis may expand its niche at both lower and higher elevations. This study clarifies how bioclimatic and topographic factors affect the dispersion of invasive species in the biodiverse IHR. Policymakers and land-use managers can utilize the data to monitor C. vialis hotspots and develop scientifically sound management methods. Full article

Sichuan Province preserves numerous rare and ancient species of plants and animals, making it an important bio-genetic repository in China and even the world. However, this region is also vulnerable to fire disturbance due to the rich forest resources, complex topography, and dry climate, and thus has become one of main regions in China needing wildfire prevention. Analyzing the main driving factors influencing wildfire incidence can provide data and policy guidance for wildfire management in Sichuan Province. Here we analyzed the spatial and temporal distribution characteristics of wildfires in Sichuan Province based on the wildfire spot data during 2010–2019. Based on 14 input variables, including climate, vegetation, human factors, and topography, we applied the Pearson correlation analysis and Random Forest methods to investigate the most important factors in driving wildfire occurrence. Then, the Logistic model was further applied to predict wildfire occurrences. The results showed that: (1) The southwestern Sichuan Province is a high-incidence area for wildfires, and most fires occurred from January to June. (2) The most important factor affecting wildfire occurrence is monthly average temperature, followed by elevation, monthly precipitation, population density, Normalized Difference Vegetation Index (NDVI), NDVI in the previous month, and Road kernel density. (3) The Logistic wildfire prediction model yielded good performance, with the area under curve (AUC) values higher than 0.94, overall accuracy (OA) higher than 86%, true positive rate (TPR) values higher than 0.82, and threat score (TS) values higher than 0.71. The final selected prediction model has an AUC of 0.944, an OA of 87.28%, a TPR of 0.829, and a TS of 0.723. (4) The results of the prediction indicate that extremely high danger of wildfires (probability of fire occurrence higher than 0.8) is concentrated in the southwest, which accounted for about 1% of the area of the study region, specifically in Panzhihua and Liangshan. These findings demonstrated the effectiveness of the Logistic model in predicting forest fires in Sichuan Province, providing valuable insights regarding forest fire management and prevention efforts in this region. Full article

Accurate inventories of grasslands are important for studies of greenhouse gas (GHG) dynamics, as grasslands store about one-third of the global terrestrial carbon stocks. This paper develops a framework for large-area grassland mapping based on the probability of grassland occurrence and the interactive pathways of fractional vegetation and soil-related endmember nexuses. In this study, grassland occurrence probability maps were produced based on data on bio-climate factors obtained from MODIS/Terra Land Surface Temperature (MOD11A2), MODIS/Terra Vegetation Indices (MOD13A3), and Tropical Rainfall Measuring Mission (TRMM 3B43) using the random forests (RF) method. Time series of 8-day fractional vegetation-related endmembers (green vegetation, non-photosynthetic vegetation, sand land, saline land, and dark surfaces) were generated using linear spectral mixture analysis (LSMA) based on MODIS/Terra Surface Reflectance data (MOD09A1). Time-series endmember fraction maps and grassland occurrence probabilities were employed to map grassland distribution using an RF model. This approach improved the accuracy by 5% compared to using endmember fractions alone. Additionally, based on the grassland occurrence probability maps, we identified extensive ecologically sensitive regions, encompassing 1.54 (10⁴ km²) of desert-to-steppe (D-S) and 2.34 (10⁴ km²) of steppe-to-meadow (S-M) transition regions. Among these, the D-S area is located near the threshold of 310 mm/yr in precipitation, an annual temperature of 10.16 °C, and a surface comprehensive drought index (TVPDI) of 0.59. The S-M area is situated close to the line of 437 mm/yr in precipitation, an annual temperature of 5.49 °C, and a TVPDI of 0.83. Full article

In this paper we produced a bio-based polyether-polyurethane foam PU1 through the prepolymer method. The prepolymer was obtained by the reaction of PEG 400 with _L-Lysine ethyl ester diisocyanate (L-LDI). The freshly prepared prepolymer was extended with 2,5-bis(hydroxymethyl)furan (BHMF) to produce the final polyurethane. The renewable chemical BHMF was produced through the chemical reduction of HMF by sodium borohydride. HMF was produced by a previously reported procedure from fructose using choline chloride and ytterbium triflate. To evaluate the degradation rate of the foam PU1, we tested the chemical stability by soaking it in a 10% sodium hydroxide solution. The weight loss was only 12% after 30 days. After that, we proved that enzymatic hydrolysis after 30 days using cholesterol esterase was more favoured than hydrolysis with NaOH, with a weight loss of 24%, probably due to the hydrophobic character of the PU1 and a better adhesion of the enzyme on the surface with respect to water. BHMF was proved to be of crucial importance for the enzymatic degradation assay at 37 °C in phosphate buffer solution, because it represents the breaking point inside the polyurethane chain. Soil burial degradation test was monitored for three months to evaluate whether the joint activity of sunlight, climate changes and microorganisms, including bacteria and fungi, could further increase the biodegradation. The unexpected weight loss after soil burial degradation test was 45% after three months. This paper highlights the potential of using sustainable resources to produce new biodegradable materials. Full article

Numerous applications in agriculture, climate, ecology, hydrology, and the environment are severely constrained by the lack of detailed information on soil texture. The purpose of this study was to predict soil particle-size fractions (PSF) in the Ri-Bhoi district of Meghalaya state, India, using a random forest model (RF). For the modeling of soil particle-size fractions, we employed 95 soil profiles (456 depth-wise layers) gathered from a recent national land resource inventory as well as currently accessible environmental variables. Sand, silt, and clay content were predicted using the Random Forest model at varied depths of 0–5, 5–15, 30–60, 60–100, and 100–200 cm. Our results showed the R² for sand was found to be 0.30 (0–5 cm), 0.28 (5–15 cm), and 0.21 (15–30 cm). For the sand, silt, and clay fractions, respectively, the concordance correlation coefficient (CCC) was found to be greater in the 0–30 cm, 0–60 cm, and 0–15 cm depths. When there is a reasonably close monitoring of the coverage probability with a confidence level along the 1:1 line, prediction interval coverage probability (PICP) gives a decent indicator of what to anticipate. The most crucial variables for the prediction of sand and silt were channel network base level (CNBL) and LS-Factor, whereas Min Temperature of Coldest Month (°C) (BIO6) was discovered for clay prediction. For all three soil texture fractions, the range between the 5% lower and 95% higher prediction bounds was large, indicating that the existing spatial predictions may be improved. The maps of soil texture were significantly more precise, and they accurately depicted the spatial variations of particle-size fractions. Additionally, there is still a need to investigate novel methodologies for extensive digital soil mapping, which will be very advantageous for many international initiatives. Full article

Accurately estimating the above-ground biomass (AGB) of spruce forests and analyzing their spatial patterns are critical for quantifying forest carbon stocks and assessing regional climate conditions in China’s drylands, with significant implications for the sustainable management and conservation of forest ecosystems in the Tianshan Mountains. The K-Means clustering algorithm was used to divide 144 measured AGB samples into four AGB classes, combined with remote sensing data from Landsat products, 19 bioclimatic variables, 3 topographical variables, and 3 soil variables to generate probability distributions of four AGB classes using the MaxEnt model. Finally, the spatial distribution of AGB was mapped using the mathematical formulae available in the GIS software. Results indicate that (1) the area under the receiver operating characteristic curve (AUC-ROC) of the AGB models for all classes exceeded 0.8, indicating satisfactory model accuracy; (2) the dominant factors affecting the distribution of different AGB classes varied. The primary dominant factors for the first–fourth AGB classes model were altitude (20.4%), precipitation of warmest quarter (Bio18, 15.7%), annual mean temperature (Bio1, 50.5%), and red band (Band4, 26.7%), respectively, and the response curves indicated that the third AGB model was more tolerant of elevation than the first and second AGB classes; (3) the AGB has a spatial distribution pattern of being higher in the west and low in the east, with a “single-peaked” pattern in terms of latitude, and the average AGB of pixels was 680.92 t·hm⁻²; (4) the correlation coefficient between measured and predicted AGB is 0.613 (p < 0.05), with the average uncertainty of AGB estimation at 39.32%. This study provides valuable insights into the spatial patterns and drivers of AGB in spruce forests in the Tianshan Mountains, which can inform effective forest management and conservation strategies. Full article

Ecological agriculture (E.A.) protects soil, water, and the climate, ensuring nutritious food. It encourages biodiversity and prohibits chemical inputs or hybrids. Agricultural development strategy should prioritize the development of water, land, forests, biodiversity, agricultural infrastructure, research and extension, technology transfer, investment, and unified management to bring about significant changes in agriculture. Agricultural practices have resulted in deforestation, biodiversity loss, ecosystem extinction, genetic engineering, irrigation issues, pollution, degraded soils, and related waste. Food producers increasingly use artificial neural networks (ANN) at most agricultural production and farm management stages. A new EA-ANN method, including agriculture, has been widely employed to solve categorization and prediction tasks. In addition to maintaining natural resources, sustainable agriculture helps preserve soil quality, reduces erosion, and conserves water. Ecological farming uses ecological services, including water filtering, pollination, oxygen generation, and disease and insect management. ANN increases harvest quality and accuracy of evaluating the economy by enhancing productivity. Agriculture’s prediction and economic profitability are focused on the energy optimization afforded by ANN. Ecological knowledge is assessed in light of commercial markets’ inability to provide sufficient environmental goods. Future agriculture can include robotics, sensors, aerial photos, and global positioning systems. The proposed method uses supervised artificial learning to read the data and provide an output based on effectively classifying the natural and constructed environment. The probability distribution implemented in ANN is a function specifying all possible values and probabilities of a random variable within a specific range of values. The mathematical model assumes that EA-ANN utilizes machine learning on an internet of things platform with bio-sensor assistance to achieve ecological agriculture. Microbial biotechnology is activated, and the best option for EA-ANN is calculated for an effective data-driven model. This ensures profitability and limits the impacts of manufacturing, such as pollution and waste, on the environment. Various agricultural strategies can result in environmental concerns. The EA-ANN methodology is used to make accurate predictions using field data. Agricultural workers can use the results to plan for the future of water resources more effectively. Full article