## 2 Improvements to the YOLO v8 Segmentation Algorithm  
The YOLO family is a very popular set of algorithmic frameworks in the field of target detection and has been widely used in various fields such as traffic ([PERSON] and [PERSON], 2018; [PERSON] et al., 2021) [22; 23], medicine ([PERSON] et al., 2018; [PERSON] et al., 2020; [PERSON], [PERSON], and [PERSON], 2015)[24; 25; 26], and water monitoring ([PERSON] et al.) [27]. In recent years, many researchers have tried to apply the YOLO algorithm to segmentation ([PERSON], [PERSON], and [PERSON], 2021; [PERSON] et al., 2021) [28; 29], and the YOLO v5 team integrated the segmentation module into the official project when they released version 7.0 and stated that YOLO v5 segmentation was fast and good. YOLO v8 is one of the latest versions of the YOLO family, developed by the v5 team, and it includes the image segmentation module. Therefore, this paper chooses to improve on YOLO v8 and apply the latest technology to the environmental protection of the Sanjiangyuan region. Recent modifications to YOLO have significantly enhanced its performance across various application scenarios. A notable example is YOLO-Facev2 ([PERSON] et al., 2024), which introduces the Receptive Field Enhancement (RFE) module and the Separation and Enhancement Attention Module (SEAM) [30]. These innovations address challenges such as scale variation and occlusion, improving face detection accuracy in complex environments. This approach could inspire similar improvements for rodent detection in ecological environments, especially in the presence of scale variation and occlusion. Additionally, YOLO-FD ([PERSON] et al., 2024) integrates a multi-task learning framework, combining object detection and semantic segmentation tasks, achieving excellent performance in fish disease detection, surpassing traditional models like YOLO v8 and Deeplabv3+ [31]. This method has significantly improved the performance in complex multi-task applications and incorporates techniques like PCGrad and weight uncertainty optimization, offering valuable insights for enhancing the multi-task framework for rodent detection and segmentation on the Qinghai Plateau. Furthermore, studies such as drone-based weed detection and Sunflower-YOLO illustrate how YOLO architectures can be further optimized for specific applications. For instance, [PERSON] et al. (2024) improved real-time weed detection accuracy through enriched feature extraction layers, such as Ghost Convolutions and BottleNeckCSP [32]. This innovation inspired us to integrate similar techniques into our YOLO v8 modifications to enhance the model's ability to detect small features, such as rodent burrows or plant damage, in large-scale ecological environments. Similarly, Sunflower-YOLO [PERSON] et al., 2024) addresses challenges posed by different plant growth stages and environmental conditions through the introduction of deformable convolutions and the SimAM attention mechanism [33]. While this method was not directly applied in our study, it provided valuable inspiration for optimizing our approach to detecting rodent-induced damage in various vegetation types across the Qinghai Plateau. These recent studies demonstrate how YOLO versions have been tailored to specific fields to achieve substantial performance improvements. For example, YOLO v8 itself includes several upgrades, such as replacing the C3 module with the gradient-rich C2f module and shifting from anchor-based detection to anchor-free detection. These changes collectively enhance the overall performance of the model. The YOLO series has used mosaic data augmentation since v4 ([PERSON], [PERSON], and [PERSON], 2020) to improve data diversity [34]. v8 also uses this data augmentation series and turns off mosaic data augmentation in the last 10 epochs, thus effectively improving the accuracy.