The dietary patterns of water deer recently rediscovered in Northeast China exhibit remarkable similarities to those observed in other regions.

Climate change, human activities, and habitat fragmentation-or even loss-pose ongoing threats to the survival of wildlife. Understanding the dietary habits of endangered species is a critical component of their conservation. In this study, we investigated the diet of water deer (Hydropotes inermis) by integrating traditional fecal microhistological analysis with a deep learning algorithm. Fecal samples were collected from water deer in northeastern China, with microscopic slides prepared for both the warm season (203 samples) and cold season (451 samples). A deep learning model was trained and tested using labeled images of 130 known plant species, achieving an accuracy of 99.83%. The results revealed that water deer consume 110 plant species from 86 genera and 40 families annually. The dietary patterns observed in this study align closely with those reported in other regions, reflecting species-specific foraging characteristics and further validating the reliability of deep learning algorithms in ecological research. Notably, significant seasonal variations were identified, highlighting the adaptability of water deer to changing environmental conditions. By examining the feeding ecology and seasonal dietary shifts of water deer in northeastern China, this study provides valuable insights for the development of targeted conservation strategies to support their populations in this region and beyond.