Piotr Tryjanowski, Joanna T. Białas, Łukasz Jankowiak, Leszek Jerzak, Paweł Mielczarek, Marta K. Nowak, Piotr Profus, Joachim Siekiera, Marcin Tobółka, Kazimierz Walasz, Andrzej Wuczyński, Adam Zbyryt
Polish Journal of Ecology 72 (1-2), 45-64, (25 December 2024) https://doi.org/10.3161/15052249PJE2024.72.1.004
KEYWORDS: artificial intelligence, Bird study, future scenarios, Limitations, prognosis, Resource allocations
The white stork, Ciconia ciconia, is both a species familiar and charismatic to the public and a subject of extensive scientific research. Poland harbours a substantial breeding population of this species, characterized by behaviours typical of long-distance migrants. To chart a future course for research on Poland's white stork population, to deepen our understanding of its biology as well as advance conservation efforts, consultations were held with 41 Polish researchers engaged with this species. Collectively, these experts proposed 208 research queries, which were subsequently refined and condensed to 60, and each was categorized into one of 12 thematic groups identified throughout the process. An examination of the barriers to realizing these research topics (n = 60) was also undertaken. Identified impediments encompass e.g. financial limitations (12%), labour intensity (13%), and the lack of clarity in methodological directives (20%). Notably, a significant portion of these issues (42%) were deemed less appealing for scientific exploration, particularly when the anticipated impact of publication—gauged by the prestige of the scientific journal—does not align proportionately with the required time and financial investment. Nonetheless, we anticipate shifts in the priorities and practicability of these research topics, owing to substantial technological advancements in both field data acquisition and subsequent analysis. Consequently, we advocate for a continuous review process, such as a re-evaluation every 5 to 10 years, to reassess the relevancy of these topics, incorporating new ideas or potentially discarding some. Additionally, AI Chat GPT 4.0 was employed to perform similar analyses as those conducted by the authorial team. These data, which are challenging to interpret unambiguously, may also be utilized in the future for comparisons between the usefulness of topics proposed by experts and the language model.