Expertise in biodiversity research (taxonomy, faunistics, conservation with taxonomic background) appears to decline worldwide. While the “taxonomic impediment” is discussed extensively in the literature, much fewer papers focus on the identification crisis, i.e., the decreasing number of experts who can identify species, and the decline of species-based biodiversity research. As a test case to explore the gravity of the identification crisis, we chose Hungary, a Central European country with a strong history of comprehensive taxonomic expertise and research output. We set out to answer two main questions. (1) What proportion of the Hungarian fauna could currently be identified by Hungarian experts, and what factors determine which groups are covered; and (2) what are the trends of biodiversity research in Hungary, and what are the underlying reasons for these trends? We show that Hungary lacks active biodiversity experts for almost half of the nearly 36,000 animal species recorded in the country, and more than a quarter of the fauna have only one or two active experts available. We also show that faunistic research experienced a golden era between ca. 1990 and 2010. Since then, however, there has been a strong decline, with the number of active experts and published papers decreased to a level like that of the 1970s. Multiple factors are identified causing this trend, such as increased pressure to publish in high impact journals and increasing administrative duties of professional scientists. The next generation of biodiversity experts needs to be fluent in modern techniques and publication strategies but also maintain robust morphology-based knowledge to be equipped for identification tasks of difficult taxa. Despite being disadvantaged by exclusive application of citation-based evaluation, we do need more positions and focused grants for biodiversity researchers to maintain the country’s knowledge base and to avoid being increasingly dependent on—equally declining—foreign expertise.

Citizen science approaches in ecology have recently become increasingly popular. Although many advantages, such as the cost-effective collection of vast amounts of data, outweigh the disadvantages, most projects face difficulties, such as non-random sampling, pseudo-absences or various biases, such as detection/reporting biases or participant-related biases. To unravel some of the environmental and socio-economic factors underlying data provision occurring non-randomly, we analysed the geographically tractable record-level databases of 17 separate citizen science projects in ecology and conservation in Hungary. We matched the records to an independent administrative dataset to identify those environmental and socio-economic predictors that are expected to shape participant activity, which varies widely according to the purpose, subject and other characteristics of the projects. Despite the projects' variation, we were able to identify general patterns linking population density of a given municipality and the proportion of protected areas with participant activity. Both variables were significantly associated with the number of observations. If the most urbanised and densely populated capital was left out of the analysis, both the level of education and the proportion of elderly people were positively associated with the number of observations a project received. However, the relationship between a population's socio-economic status and participant activity varied greatly across particular citizen science projects. Our results highlight that citizen science participation is shaped by both environmental context and socio-economic characteristics, revealing systematic spatial biases in data provision. Our results thus provide new insights into the methodology and design of future citizen science projects. Read the free Plain Language Summary for this article on the Journal blog.