This study applies the Contingent Valuation Method (CVM) to estimate the non-use value of Laojun Mountain National Park, a culturally and ecologically significant site within the Three Parallel Rivers World Heritage region of northwestern Yunnan, China. Based on 219 valid survey responses, the analysis identifies education, income, occupation, cultural identity, and recognition of legacy values as significant determinants of willingness to pay (WTP) for conservation. Interaction effect logistic regression shows that the influence of cultural identity on WTP is moderated by income level and ecological awareness. Valuation results indicate that the park’s annual non-use value ranges from 79.697 to 260.841 billion yuan, based on median and mean estimates. Motivational analysis highlights aesthetic appreciation, cultural meaning, and intergenerational ethics as key drivers of conservation support, while refusal to pay is primarily attributed to expectations of governmental responsibility, especially among low-income and less-educated respondents. These findings advance theoretical understanding of bio-cultural valuation, offer practical guidance for the design of Payment for Ecosystem Services (PES) schemes, and underscore the importance of integrating socio-cultural dimensions into sustainable conservation finance and policy strategies.

BACKGROUND: Semanotus bifasciatus Motschulsky (Coleoptera: Cerambycidae) is one of the most destructive wood-boring pests of Platycladus trees in East Asia, threatening the protection of ancient cypress species and urban ecological safety. Acoustic detection technology has the advantages of high sensitivity, single wood diagnosis and anti-interference, which can be useful for early identification of cryptic wood boring damage. However, there has been limited research on detection time window and acoustics features that suitable for early detection of forest wood borers. METHODS: In this study, we carried out a manipulated insect infestation experiment by inoculating S. bifasciatus into fresh logs, and the feeding sound signals of S. bifasciatus larvae were recorded in timeseries. Then, nine feature variables were selected to characterize the sounds of larval feeding activity. The best time window for acoustic detection during a single day and the whole larval growth stage was determined. And the optimal models for predicting larval instar and population were established using the stepwise regression (SR) and partial least squares regression (PLSR) approach.RESULTS: (1) The single pulse duration of S. bifasciatus was less than 15 ms, and the peak frequency was approximately 8 kHz; (2) Within a 24-hour day, the feeding sound signals were strongest during 13:00 and 20:00; (3) The feeding activity of larvae was greatest during the 1st to the 3rd instar, declined from the 4th instar, and was lowest at the 5th instar; (4) Weak correlations were found between larval instar and feature variables, r ranging from 0.3 to 0.6. By contrast, the larval population has a strong linear correlation with all variables (r>0.7). Except for Average pulse duration and Peak frequency, there indicated high or severe multicollinearity among other variables (the variance inflation factor, VIF >10); (5) The SR model was optimal for predicting larval instar; its prediction accuracy was R² = 0.71, RMSEp = 0.42, and RPD = 3.38. Average entropy, Peak frequency, and Average pulse duration had the largest influence on the model. (6) The optimal model for predicting population was the PLSR model, and its prediction accuracy was R² = 0.97, RMSEp = 61.96, and RPD = 28.87. Except for Peak Freq, the other eight variables had a great impact on the model. CONCLUSION: This study highlighted the suitable detection time window and acoustic feature variables for early identification of S. bifasciatus larvae, and optimal models for predicting its larval instar and population were provided. This work will promote further improvements in the efficiency and accuracy of acoustic detection technology for practical applications, providing a reference for evaluating the early damage of wood-boring pest.

Acoustic detection technology has emerged as a promising, non-destructive and continuous monitoring method for pest early detection at the single tree level. However, field application still encounters problems, especially under complex infestation scenarios, i.e., co-infestations by multiple pest species. This study aims to develop a novel acoustic-based recognition model for detecting forest wood-boring pests, specially designed to enhance monitoring accuracy under complex infestation scenarios. We collected feeding vibration signals from four wood-boring pests: Semanotus bifasciatus, Phloeosinus aubei, Agrilus planipennis, and Streltzoviella insularis. Three infestation scenarios were designed: single-species, co-infestation without mixed signals, and co-infestation with mixed signals. Three machine learning (ML) models (Random Forest, Support Vector Machine, and Artificial Neural Network) based on seven acoustic feature variables, and three deep learning (DL) models (AlexNet, ResNet, and VGG) using spectrograms were employed to classify the signals. Results showed that ML models achieved perfect accuracy (OA: 100%, Kappa: 1) in single-species scenarios but declined significantly under co-infestation scenarios with mixed signals. In contrast, DL models, particularly ResNet, maintained high accuracy (OA: 85.0–88.75%) and effectively discriminated mixed signals. In conclusion, this study demonstrates the superiority of spectrogram-based DL models for acoustic detection under complex infestation scenarios and provides a foundation for developing a general, real-time detection model for integrated pest management in forest ecosystems.

Climate change may alter grassland pest distributions, yet province-wide comparisons based on extensive field surveys remain limited in Yunnan, China. We analysed 10,342 census and special-survey records, retaining 4281 species–climate-cell records for nine pests. Five WorldClim predictors entered ten-algorithm ensembles weighted by the true skill statistic (TSS), evaluated using nested spatial cross-validation. Three climate models projected SSP1-2.6, SSP2-4.5, and SSP5-8.5 across four 20-year periods spanning 2021–2100. Predictive performance varied among species: area under the receiver operating characteristic curve (AUC) ranged from 0.591 to 0.778, and TSS from 0.198 to 0.365. Pseudoxya diminuta had the highest AUC (0.778), Patanga japonica the highest TSS (0.365), and the 27-cell Holotrichia parallela model the lowest AUC (0.591). Warm-quarter precipitation ranked first for six species, with mean permutation-induced AUC decreases of 0.054–0.145. Baseline threshold-defined suitable area was largest for Oxya chinensis (235.3 × 10³ km²). By 2081–2100 under SSP5-8.5, consensus suitable area declined by 63.4% for P. japonica and 54.6% for P. diminuta. These species-specific climatic associations and projected changes provide a basis for coordinated provincial surveillance, supported by survey-derived county–township revisit priorities.