pamflow : An open‐source workflow for simplified passive acoustic monitoring data analysis

Abstract Passive acoustic monitoring (PAM) is becoming increasingly popular but has not yet achieved its full potential due to the lack of cohesive workflows for processing large volumes of data. Although several tools support specific components of PAM analysis and some platforms integrate multiple functionalities, researchers often still rely on combinations of software and custom scripts to move from raw recordings to standardized and reproducible outputs. We present pamflow, an open‐source workflow for PAM built on Python and the robust Kedro framework, which provides a command‐line interface that enables execution of complete pipelines through standardized configuration files. It prioritizes broad usability through three key traits: open access, low computational requirements and streamlined workflow configuration. These features are intended to facilitate adoption in biodiverse but geographically isolated contexts, where limited budgets, constrained computational resources and scarce programming training are common. The tool is designed for users with varying levels of technical experience. While its use requires basic familiarity with command‐line tools and configuration files, its modular design also enables experienced programmers to extend, adapt, and customize workflows for research‐specific purposes. To illustrate our framework, we present the outputs obtained from a sampling conducted in Colombian Amazonia to support community forestry initiatives. The project was co‐led by the Humboldt Institute and the Foundation for Conservation and Sustainable Development (FCDS). The sampling was carried out through a participatory approach. Fourteen families and a local school participated in the installation of 38 recorders, capturing over 720 h of the local soundscape. Framed within open‐science principles, pamflow promotes data and technological sovereignty. In particular, we expect pamflow to (a) increase the adoption of PAM by providing an inclusive and open‐source solution that supports complete data processing workflows; (b) increase the number of biodiversity records reported from PAM projects to global biodiversity platforms such as GBIF; and (c) improve collaboration and data sharing among research groups and institutions by transforming input data into a globally accepted data standard.