Community science brings together natural history collections and historical survey data to understand changing ecological patterns

Abstract Museum specimens document the impacts of interacting and increasingly pervasive environmental stressors on global biodiversity. Environmental, sampling, and management records are needed to interpret the causes of these changes and address collection biases; however, such data are rarely available in museum catalogues. We outline a process to join collections and associated data using data science workflows in tandem with community science tools. Our approach is illustrated with a case study of lake survey data from Michigan that provide ecological context for catalogued fish specimens. Using the web-based community science platform Zooniverse, we engage stakeholders, public, and educational audiences. Our process includes five key steps: archive assessment and preparing images, workflow development, community engagement and data transcription, data curation, and data archiving. Integrating historical records with museum specimens can clarify environmental impacts on biodiversity over recent history and refine our predictions of future impacts.