Search Results (758)

With the increasing severity of forest pest problems, breeding insect-resistant varieties has become a crucial task for the sustainable development of forestry. The highly insect-resistant triploid Populus line Pb29, genetically modified with BtCry1Ac, served as the maternal parent in controlled hybridization with three paternal Populus cultivars. Hybrid progenies were obtained through embryo rescue and tissue culture. Results showed that 4 °C storage was favorable for pollen preservation, with 84K poplar exhibiting superior pollen viability and embryo germination rates. All progenies displayed significantly lower seedling height and ground diameter growth than the maternal parent (p < 0.05), with some showing leaf shape and branching variations. Among the three crosses, the 84K-sired progeny exhibited the best growth performance but the highest variability. PCR analysis confirmed stable inheritance of the BtCry1Ac and Kan genes from Pb29, showing tight linkage. Progenies carrying BtCry1Ac exhibited detectable gene transcription and toxic protein accumulation, though expression levels varied due to copy number, insertion sites, and potential co-suppression effects. Ploidy analysis suggested all hybrids were aneuploid, with lower survival rates than the maternal parent. Insect-feeding assays confirmed high resistance in all BtCry1Ac-inheriting progenies, with an average larval mortality rate of 97.03%. Mortality rates and death indices significantly correlated with transcript abundance and toxin protein levels. These results demonstrate that BtCry1Ac insect resistance is stably inherited through hybridization. Transgene expression appears co-modulated by copy number, insertion sites, and ploidy status. Simultaneously, it was found that the aneuploid progeny derived from triploid hybridization exhibited growth disadvantages. This provides an important basis for subsequent poplar improvement breeding. Full article

Tomicus yunnanensis, a notorious forest pest in southwest China, primarily employs infochemicals to coordinate mass attacks that overcome host tree defenses. However, secondary visual cues, particularly detection of host color changes, also aid host location. This study characterized the compound eye structure and vision of T. yunnanensis using electron microscopy and phototaxis tests. The apposition eye contains 224–266 ommatidia, with asymmetry between left and right. Quadrilateral facets occupy the dorsal third, while hexagonal facets dominate elsewhere. Each ommatidium comprises a large corneal lens, an acone-type crystalline cone from four cone cells, and an open-type rhabdom formed by eight retinular cells (R7–R8 centrally, R1–R6 peripherally), surrounded by two primary and at least seventeen secondary pigment cells. Dark/light adaptation alters cone size/shape and rhabdom cross-sectional area/outline (without pigment granule movement) to regulate light reaching the photoreceptors. Behavioral observations showed peak flight activity occurs between 7:00–11:00 AM, with no nighttime activity. Phototaxis tests revealed females are highly sensitive to 360 nm, 380 nm, and 700 nm wavelengths, while males exhibit high sensitivity to 360 nm and 400 nm. This work enhances knowledge on the integration of visual and olfactory sensory information in beetles for host location and non-host avoidance. Full article

Sirex noctilio is an invasive pest that contributes to pine tree decline, leading to visual symptoms such as needle discoloration, crown thinning, and eventual tree death. Detecting these visible phenotypic signs from drone imagery is challenging due to elongated or irregular crown shapes, weak color differences, and occlusion within dense forests. This study introduces YOLO-PTHD, a lightweight deep learning model designed for detecting visible signs of pine decline in UAV images. The model integrates three customized components: Strip-based convolution to capture elongated tree structures, Channel-Aware Attention to enhance weak visual cues, and a scale-sensitive dynamic loss function to improve detection of minority classes and small targets. A UAV-based dataset, the Sirex Woodwasp dataset, was constructed with annotated images of weakened, and dead pine trees. YOLO-PTHD achieved an mAP of 0.923 and an F1-score of 0.866 on this dataset. To evaluate the model’s generalization capability, it was further tested on the Real Pine Wilt Disease dataset from South Korea. Despite differences in tree symptoms and imaging conditions, the model maintained strong performance, demonstrating its robustness across different forest health scenarios. Field investigations targeting Sirex woodwasp in outbreak areas confirmed that the model could reliably detect damaged trees in real-world forest environments. This work demonstrates the potential of UAV-based visual analysis for large-scale phenotypic surveillance of pine health in forest management. Full article

The potential distribution of the pulse blue butterfly, Lampides boeticus (Lepidoptera: Lycaenidae), was determined using MaxEnt, random forest, and ensemble models. The results indicate that most tropical, subtropical, and some temperate regions are suitable habitats. Climate change is projected to expand the species’ habitat northward in the Northern Hemisphere. Predicted distributions aligned well with the known occurrence records for the species. The minimum temperature of the coldest month was the climatic variable that most strongly influenced the distribution of L. boeticus. As a tropical and subtropical species, it is assumed that cold temperatures are the main factor limiting its habitat range. Because the potential distribution of this pest covers major pulse cultivation areas under both current and future climate scenarios, these findings highlight the urgent need for developing a sustainable pest management strategy. Full article

As agricultural land continues to expand, the conversion of forests to farmland has intensified, significantly altering the structure and function of agroecosystems. However, the dynamic ecological responses and their interactions with economic outcomes remain insufficiently modeled. This study proposes an integrated framework that combines a dynamic food web model with the Eco-Economic Benefit and Sustainability (EEBS) model, utilizing empirical data from Brazil and Ghana. A system of ordinary differential equations solved using the fourth-order Runge–Kutta method was employed to simulate species interactions and energy flows under various land management strategies. Reintroducing key species (e.g., the seven-spot ladybird and ragweed) improved ecosystem stability to over 90%, with soil fertility recovery reaching 95%. In herbicide-free scenarios, introducing natural predators such as bats and birds mitigated disturbances and promoted ecological balance. Using XGBoost (Extreme Gradient Boosting) to analyze 200-day community dynamics, pest control, resource allocation, and chemical disturbance were identified as dominant drivers. EEBS-based multi-scenario optimization revealed that organic farming achieves the highest alignment between ecological restoration and economic benefits. The model demonstrated strong predictive power ($R^2$ = 0.9619, RMSE = 0.0330), offering a quantitative basis for green agricultural transitions and sustainable agroecosystem management. Full article

Entomopathogenic fungi (EPF) are one of the most environmentally friendly ways to control a plethora of chewing insects such as T. pityocampa, G. mellonella, and A. grisella. Bioassay of EPF on these highly damaging pests is considered important in the face of climate change in order to research alternative solutions that are capable of limiting chemical control, the overuse of which increases insects’ resistance to chemical compounds. In this study, the insecticidal virulence of Metarhizium robertsii isolates, retrieved from forest ecosystems, was tested on second-instar larvae of T. pityocampa, G. mellonella, and A. grisella. Bioassays were carried out in the laboratory, where experimental larvae were sprayed with 2 mL of a six-conidial suspension from each isolate. Mortality was recorded for 144 h after exposure. Mean mortality, lethal concentrations, sporulation percentage, and sporulation time were estimated for each isolate. Metarhizium isolates resulted in the highest mortality (89.2% for G. mellonella and 90.2% for A. grisella). Based on the LC₅₀ estimates determined by the concentration–mortality relationships for the tested fungal isolates, we demonstrated significant virulence on larvae of G. mellonella, A. grisella, and T. pityocampa. Our results indicate that entomopathogenic fungi have the potential to become a very useful tool in reducing chemical applications. Full article

The groundwater inverse problem has several challenges such as instability, non-uniqueness, and complexity, especially for heterogeneous aquifers. Solving the inverse problem is the traditional way to calibrate models, but it is both time-consuming and sensitive to errors in the measurements. This study explores the use of machine learning (ML) surrogate models, namely Random Forest (RF) and Extreme Gradient Boosting (XGBoost), to solve the inverse problem for the groundwater model calibration. Datasets for 20 hydraulic conductivity fields were created randomly based on statistics of hydraulic conductivity from the available data of the Northern Aquifer of Qatar, which was used as a case study. The corresponding hydraulic head values were obtained using MODFLOW simulations, and the data were used to train and validate the ML models. The trained surrogate models were used to estimate the hydraulic conductivity based on field observations. The results show that both RF and XGBoost have considerable predictive skill, with RF having better R² and RMSE values (R² = 0.99 for training, 0.93 for testing) than XGBoost (R² = 0.86 for training, 0.85 for testing). The ML-based method lowered the computational effort greatly compared to the classical solution of the inverse problem (i.e., using PEST) and still produced strong and reliable spatial patterns of hydraulic conductivity. This demonstrates the potential of machine learning models for calibrating complex groundwater systems. Full article

The pepper weevil Anthonomus eugenii is a devastating pest native to Central America that can cause severe damage to over 35 pepper varieties. Global trade in peppers has significantly increased the risk of its spread and expansion. Moreover, future climate change may add more uncertainty to its distribution, resulting in considerable ecological and economic damage globally. Therefore, we employed an ensemble model combining Random Forests and CLIMEX to predict the potential global distribution of A. eugenii in historical and future climate scenarios. The results indicated that the maximum temperature of the warmest month is an important variable affecting global A. eugenii distribution. Under the historical climate scenario, the potential global distribution of A. eugenii is concentrated in the Midwestern and Southern United States, Central America, the La Plata Plain, parts of the Brazilian Plateau, the Mediterranean and Black Sea coasts, sub-Saharan Africa, Northern and Southern China, Southern India, Indochina Peninsula, and coastal area in Eastern Australia. Under future climate scenarios, suitable areas in the Northern Hemisphere, including North America, Europe, and China, are projected to expand toward higher latitudes. In China, the number of highly suitable areas is expected to increase significantly, mainly in the south and north. Contrastingly, suitable areas in Central America, northern South America, the Brazilian Plateau, India, and the Indochina Peninsula will become less suitable. The total land area suitable for A. eugenii under historical and future low- and high-emission climate scenarios accounted for 73.12, 66.82, and 75.97% of the global land area (except for Antarctica), respectively. The high-suitability areas identified by both models decreased by 19.05 and 35.02% under low- and high-emission scenarios, respectively. Building on these findings, we inferred the future expansion trends of A. eugenii globally. Furthermore, we provide early warning of A. eugenii invasion and a scientific basis for its spread and outbreak, facilitating the development of effective quarantine and control measures. Full article

Climate change is generally having a negative impact on forest health by inducing drought stress and favouring the spread of pest species, such as bark beetles. The terrestrial monitoring of bark beetle infestation is very time-consuming, especially in the early stages, and therefore not feasible for extensive areas, emphasising the need for a comprehensive approach based on remote sensing. Although numerous studies have researched the use of optical data for this task, radar data remains comparatively underexplored. Therefore, this study uses the weekly and cloud-free acquisitions of Sentinel-1 in the Bavarian Forest National Park. Time series analysis within a Multi-SAR framework using Random Forest enables the monitoring of moisture content loss and, consequently, the assessment of tree vitality, which is crucial for the detection of stress conditions conducive to bark beetle outbreaks. High accuracies are achieved in predicting future bark beetle infestation (R² of 0.83–0.89). These results demonstrate that forest vitality trends ranging from healthy to bark beetle-affected states can be mapped, supporting early intervention strategies. The standard deviation of 0.44 to 0.76 years indicates that the model deviates on average by half a year, mainly due to the uncertainty in the reference data. This temporal uncertainty is acceptable, as half a year provides a sufficient window to identify stressed forest areas and implement targeted management actions before bark beetle damage occurs. The successful application of this technique to extensive test sites in the state of North Rhine-Westphalia proves its transferability. For the first time, the results clearly demonstrate the expected relationship between radar backscatter expressed in the Kennaugh elements K₀ and K₁ and bark beetle infestation, thereby providing an opportunity for the continuous and cost-effective monitoring of forest health from space. Full article

True slime molds (Eumycetozoa) remain under-explored globally, particularly in water-logged forest habitats. Despite evidence suggesting a high biodiversity potential in the Knyszyn Forest of north-eastern Poland, no systematic effort had previously been undertaken there. In the present survey, plant substrates from eight swampy sub-compartments were incubated for over four months, resulting in the detection of fifteen slime mold species. Four of these taxa are newly reported for northern and north-eastern Poland, while several have been recorded only a handful of times in the global literature. These findings underscore how damp, nutrient-rich conditions foster Eumycetozoa and demonstrate the effectiveness of moist-chamber culturing in revealing rare or overlooked taxa. Current evidence shows that, although slime molds may occasionally colonize living plant or fungal tissues, their influence on crop productivity and tree vitality is negligible; they are therefore better regarded as biodiversity indicators than as pathogens or pests. By establishing a replicable framework for studying water-logged environments worldwide, this work highlights the ecological importance of swamp forests in sustaining microbial and slime mold diversity. Full article

The red imported fire ant (RIFA; Solenopsis invicta) is an invasive species that severely threatens ecology, agriculture, and public health in Taiwan. In this study, the feasibility of applying multispectral imagery captured by unmanned aerial vehicles (UAVs) to detect red fire ant mounds was evaluated in Fenlin Township, Hualien, Taiwan. A DJI Phantom 4 multispectral drone collected reflectance in five bands (blue, green, red, red-edge, and near-infrared), derived indices (normalized difference vegetation index, NDVI, soil-adjusted vegetation index, SAVI, and photochemical pigment reflectance index, PPR), and textural features. According to analysis of variance F-scores and random forest recursive feature elimination, vegetation indices and spectral features (e.g., NDVI, NIR, SAVI, and PPR) were the most significant predictors of ecological characteristics such as vegetation density and soil visibility. Texture features exhibited moderate importance and the potential to capture intricate spatial patterns in nonlinear models. Despite limitations in the analytics, including trade-offs related to flight height and environmental variability, the study findings suggest that UAVs are an inexpensive, high-precision means of obtaining multispectral data for RIFA monitoring. These findings can be used to develop efficient mass-detection protocols for integrated pest control, with broader implications for invasive species monitoring. Full article

With the objective of identifying superior Vernicia montana trees grounded in phenotypic and agronomic traits, this study sought to develop and implement a comprehensive evaluation method which would provide a practical foundation for future clonal breeding initiatives. Using the Vernicia montana propagated from seedling forests grown in the Suxian District of Chenzhou City in southern Hunan Province, we conducted pre-selection, primary selection, and re-selection of Vernicia montana forest stands and took the nine trait indices of single-plant fruiting quantity, single-plant fruit yield, disease and pest resistance, fruit ripening consistency, fruit aggregation, fresh fruit single-fruit weight, fresh fruit seed rate, dry seed kernel rate, and seed kernel oil content rate as the optimal evaluation indexes and carried out cluster analysis and a comprehensive evaluation in order to establish a comprehensive evaluation system for superior Vernicia montana trees. The results demonstrated that a three-stage selection process—consisting of pre-selection, primary selection, and re-selection—was conducted using a comprehensive analytical approach. The pre-selection phase relied primarily on sensory evaluation criteria, including fruit count per plant, tree size, tree morphology, and fruit clustering characteristics. Through this rigorous screening process, 60 elite plants were selected. The primary selection was based on phenotypic traits, including single-plant fruit yield, pest and disease resistance, and uniformity of fruit ripening. From this stage, 36 plants were selected. Twenty plants were then selected for re-selection based on key performance indicators, such as fresh fruit weight, fresh fruit seed yield, dry seed kernel yield, and oil content of the seed kernel. Then the re-selected optimal trees were clustered and analyzed into three classes, with 10 plants in class I, 7 plants in class II, and 3 plants in class III. In class I, the top three superior plants exhibited outstanding performance across key traits: their fresh fruit weight per fruit, fresh fruit seed yield, dry seed yield, and seed kernel oil content reached 41.61 g, 42.80%, 62.42%, and 57.72%, respectively. Compared with other groups, these figures showed significant advantages: 1.17, 1.09, 1.12, and 1.02 times the average values of the 20 reselected superior trees; 1.22, 1.19, 1.20, and 1.08 times those of the 36 primary-selected superior trees; and 1.24, 1.25, 1.26, and 1.19 times those of the 60 pre-selected trees. Fruits counts per plant and the number of fruits produced per plant of the best three plants in class I were 885 and 23.38 kg, respectively, which were 1.13 and 1.18 times higher than the average of 20 re-selected superior trees, 1.25 and 1.30 times higher than the average of 36 first-selected superior trees, and 1.51 and 1.58 times higher than the average of 60 pre-selected superior trees. Class I superior trees, especially the top three genotypes, are suitable for use as mother trees for scion collection in grafting. The findings of this study provide a crucial foundation for developing superior clonal varieties of Vernicia montana through selective breeding. Full article

Leaf loss caused by pest infestations poses a serious threat to forest health. The leaf loss rate (LLR) refers to the percentage of the overall tree-crown leaf loss per unit area and is an important indicator for evaluating forest health. Therefore, rapid and accurate acquisition of the LLR via remote sensing monitoring is crucial. This study is based on drone hyperspectral and LiDAR data as well as ground survey data, calculating hyperspectral indices (HSI), multispectral indices (MSI), and LiDAR indices (LI). It employs Savitzky–Golay (S–G) smoothing with different window sizes (W) and polynomial orders (P) combined with recursive feature elimination (RFE) to select sensitive features. Using Random Forest Regression (RFR) and Convolutional Neural Network Regression (CNNR) to construct a multidimensional (horizontal and vertical) estimation model for LLR, combined with LiDAR point cloud data, achieved a three-dimensional visualization of the leaf loss rate of trees. The results of the study showed: (1) The optimal combination of HSI and MSI was determined to be W11P3, and the LI was W5P2. (2) The optimal combination of the number of sensitive features extracted by the RFE algorithm was 13 HSI, 16 MSI, and hierarchical LI (2 in layer I, 9 in layer II, and 11 in layer III). (3) In terms of the horizontal estimation of the defoliation rate, the model performance index of the CNNR_HSI model (MPI = 0.9383) was significantly better than that of RFR_MSI (MPI = 0.8817), indicating that the continuous bands of hyperspectral could better monitor the subtle changes of LLR. (4) The I-CNNR_HSI+LI, II-CNNR_HSI+LI, and III-CNNR_HSI+LI vertical estimation models were constructed by combining the CNNR_HSI model with the best accuracy and the LI sensitive to different vertical levels, respectively, and their MPIs reached more than 0.8, indicating that the LLR estimation of different vertical levels had high accuracy. According to the model, the pixel-level LLR of the sample tree was estimated, and the three-dimensional display of the LLR for forest trees under the pest stress of larch caterpillars was generated, providing a high-precision research scheme for LLR estimation under pest stress. Full article

Anoplophora glabripennis (Asian longhorned beetle, ALB) (Motschulsky, 1854) is a local forest pest in China. Although the suitable area for this pest has some research history, it does not accurately predict the future distribution area of ALB. Accurate prediction of its suitable area can help control the harm caused by ALB more effectively. In this study, we applied the maximum entropy model to predict the suitable area for ALB. Moreover, the prediction results revealed that ALB is distributed mainly in northern, eastern, central, southern, southwestern, and northwestern China, and its high-fit areas are located mainly in northern, northwestern, and southwestern China. The average minimum temperature in September, precipitation seasonality (coefficient of variation), the average maximum temperature in April, and average precipitation in October had the greatest influence on ALB. The greatest distribution probabilities were observed at the September average minimum temperature of 16 °C, the precipitation seasonality (coefficient of variation) of 130%, the April average maximum temperature of 14 °C, and the October average precipitation of 30 mm. Furthermore, with climate change, the non-suitability area for the ALB will show a decreasing trend in the future. The intermediate suitability area will increase, while the low and high suitability areas will first increase and then decrease. Taken together, the potentially suitable areas for ALB in China include the Beijing–Tianjin–Hebei region and the Shanghai region in North China and East China, providing a deeper understanding of ALB control. Full article

Dutch elm disease is one of the most devastating plant diseases, primarily spread through bark beetles. Scolytus scolytus is a key vector of this disease. In this study, distribution data of S. scolytus were collected and filtered. Combined with environmental and climatic variables, an ensemble model was developed using the Biomod2 platform to predict its potential geographical distribution in China. The selection of climate variables was critical for accurate prediction. Eight bioclimatic factors with high importance were selected from 19 candidate variables. Among these, the three most important factors are the minimum temperature of the coldest month (bio6), precipitation seasonality (bio15), and precipitation in the driest quarter (bio17). Under current climate conditions, suitable habitats for S. scolytus are mainly located in the temperate regions between 30° and 60° N latitude. These include parts of Europe, East Asia, eastern and northwestern North America, and southern and northeastern South America. In China, the low-suitability area was estimated at 37,883.39 km², and the medium-suitability area at 251.14 km². No high-suitability regions were identified. However, low-suitability zones were widespread across multiple provinces. Under future climate scenarios, low-suitability areas are still projected across China. Medium-suitability areas are expected to increase under SSP370 and SSP585, particularly along the eastern coastal regions, peaking between 2041 and 2060. High-suitability zones may also emerge under these two scenarios, again concentrated in coastal areas. These findings provide a theoretical basis for entry quarantine measures and early warning systems aimed at controlling the spread of S. scolytus in China. Full article