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Accurate monitoring of eider duck populations in Arctic Canada is essential for understanding ecosystem health and supporting conservation efforts in a rapidly changing climate. Traditional manual counting from aerial imagery is time-consuming, labor-intensive, and prone to observer bias. In this work, we present a human-in-the-loop wildlife counting system that integrates an open-vocabulary multi-species object detector to streamline and enhance the accuracy of eider duck surveys. The system leverages a pre-trained open-vocabulary model, enabling the identification of both target and incidental species without retraining, and employs human validation to correct and refine automated detections. This collaborative workflow combines the scalability of machine learning with expert ecological knowledge, reducing annotation effort while maintaining high accuracy. Field validation using aerial imagery from Arctic Canada demonstrates that our approach can significantly accelerate population assessments, improve consistency across surveys, and facilitate adaptive monitoring in remote environments.