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Abstract Unveiling the intricate relationships between animal movement ecology, feeding behavior, and internal energy budgeting is crucial for a comprehensive understanding of ecosystem functioning, especially on coral reefs under significant anthropogenic stress. Here, herbivorous fishes play a vital role as mediators between algae growth and coral recruitment. Our research examines the feeding preferences, bite rates, inter‐bite distances, and foraging energy expenditure of the Brown surgeonfish (Acanthurus nigrofuscus) and the Yellowtail tang (Zebrasoma xanthurum) within the fish community on a Red Sea coral reef. To this end, we used advanced methods such as remote underwater stereo‐video, AI‐driven object recognition, species classification, and 3D tracking. Despite their comparatively low biomass, the two surgeonfish species significantly influence grazing pressure on the studied coral reef. A. nigrofuscus exhibits specialized feeding preferences and Z. xanthurum a more generalist approach, highlighting niche differentiation and their importance in maintaining reef ecosystem balance. Despite these differences in their foraging strategies, on a population level, both species achieve a similar level of energy efficiency. This study highlights the transformative potential of cutting‐edge technologies in revealing the functional feeding traits and energy utilization of keystone species. It facilitates the detailed mapping of energy seascapes, guiding targeted conservation efforts to enhance ecosystem health and biodiversity.

Environmental challenges are rarely confined to national, disciplinary, or linguistic domains. Convergent solutions require international collaboration and equitable access to new technologies and practices. The ability of international, multidisciplinary and multilingual research teams to work effectively can be challenging. A major impediment to innovation in diverse teams often stems from different understandings of the terminology used. These can vary greatly according to the cultural and disciplinary backgrounds of the team members. In this paper we take an empirical approach to examine sources of terminological confusion and their effect in a technically innovative, multidisciplinary, multinational, and multilingual research project, adhering to Open Science principles. We use guided reflection of participant experience in two contrasting teams-one applying Deep Learning (Artificial Intelligence) techniques, the other developing guidance for Open Science practices-to identify and classify the terminological obstacles encountered and reflect on their impact. Several types of terminological incongruities were identified, including fuzziness in language, disciplinary differences and multiple terms for a single meaning. A novel or technical term did not always exist in all domains, or if known, was not fully understood or adopted. Practical matters of international data collection and comparison included an unanticipated need to incorporate different types of data labels from country to country, authority to authority. Sometimes these incongruities could be solved quickly, sometimes they stopped the workflow. Active collaboration and mutual trust across the team enhanced workflows, as incompatibilities were resolved more speedily than otherwise. Based on the research experience described in this paper, we make six recommendations accompanied by suggestions for their implementation to improve the success of similar multinational, multilingual and multidisciplinary projects. These recommendations are conceptual drawing on a singular experience and remain to be sources for discussion and testing by others embarking on their research journey.