Climate change is posing new challenges to forestry management practices. Thinning reduces competitive pressure in the forest by repeatedly reducing the tree density of forest stands, thereby increasing the productivity of plantations. Considering the impact of thinning on vegetation and physiological and ecological traits, for this study, we used Norway spruce (Picea abies) data from three sites in the PROFOUND dataset to parameterize the 3-PG model in stages. The calibrated 3-PG model was used to simulate the stand diameter at breast height and the stem, root, and leaf biomass data on a monthly scale. The 3PG-MT-LSTM model uses 3-PG simulation data as the input variable. The model uses a long short-term memory neural network (LSTM) as a shared layer and introduces multi-task learning (MTL). Based on the compatibility rules, the interpretability of the model was further improved. The models were trained using single-site and multi-site data, respectively, and multiple indicators were used to evaluate the model accuracy and generalization ability. Our preliminary results show that, compared with the process model and LSTM algorithm without MTL and compatibility rules, the hybrid model has higher biomass simulation accuracy and shows a more realistic biomass response to environmental driving factors. To illustrate the potential applicability of the model, we applied light (10%), moderate (20%), and heavy thinning (30%) at intervals of 10, 15, 20, 25, 30 years. Then, we used three climate scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—to simulate the growth of Norway spruce. The hybrid model can effectively capture the impact of climate change and artificial management on stand growth. In terms of climate, temperature and solar radiation are the most important factors affecting forest growth, and under warm conditions, the positive significance of forest management is more obvious. In terms of forest management practices, less frequent light-to-moderate thinning can contribute more to the increase in forest carbon sink potential; high-intensity thinning can support large-diameter timber production. In summary, moderate thinning should be carried out every 10 years in the young-aged forest stage. It is also advisable to perform light thinning procedures after the forest has progressed into a middle-aged forest stage. This allows for a better trade-off of the growth relationship between stand yield and diameter at breast height (DBH). The physical constraint-based hybrid modeling approach is a practical and effective tool. It can be used to measure long-term dynamic changes in forest production and then guide management activities such as thinning to achieve sustainable forest management.

Wild animals are an essential component of natural ecosystems, and the accurate identification of wildlife targets plays a critical role in ecological conservation and species monitoring. However, the effectiveness of conventional object detection algorithms is often limited by the challenges posed by complex outdoor environments, small target sizes, and group occlusions. To address these issues, this study constructs a dataset comprising over 8000 images of 10 protected wildlife species and investigates effective detection methods for wildlife in natural habitats. We propose a novel deep learning-based detection framework, YOLO-WildASM, which incorporates three key improvements to the YOLOv8 architecture: a P2 detection layer for small objects, a multi-head self-attention (MHSA) mechanism, and a bidirectional feature pyramid network (BiFPN). Experimental results demonstrate that YOLO-WildASM significantly outperforms YOLOv8 and other state-of-the-art models on the custom wildlife dataset, achieving a mAP50 of 94.1%, which is 2.8% higher than the baseline model and superior to the latest YOLOv12 model (92.2%). Furthermore, ablation and generalization experiments validate the model’s enhanced performance and adaptability in multi-scale wildlife detection tasks. The proposed deep learning-based detection framework provides an efficient and robust solution for wildlife monitoring and ecological conservation in complex natural ecosystems.