Communicating ecological data to land managers and management entities represents a different challenge than communicating with the general public. Land managers need to not only understand results but also the inherent limits and assumptions in order to make effective and legally defensible decisions. However, many face barriers to producing or interpreting results of data analysis including time, resources, and technical skills. Our group, a partnership between the USDA Agricultural Research Service (ARS), the National Aquatic Monitoring Center (NAMC), and the Bureau of Land Management (BLM), has been working on the BLM Assessment, Inventory, and Monitoring Strategy (AIM) since 2010. The purpose of AIM is to provide land managers with robust, high quality monitoring data gathered with consistent methods and statistically-sound sampling techniques. Through this effort, we have experienced both successes and failures in communicating data to land managers.
As a result, our data communication has necessarily evolved. We refined the amount of data, their format, and the kinds of results and delivery mechanisms. This process has required working iteratively with land managers to identify what analyses and data visualizations are useful and how to make them as accessible as possible. Results/Conclusions: Based on feedback from users and observing how data have been applied, we have arrived at general principles for communicating data to land managers. We find that starting with conversation about users’ analysis objectives and existing decision-making workflows that use the results often shapes data requests. High density, high complexity figures are less likely to be impactful; we try to consistently produce simple, direct figures which display only one kind of result. Land managers often find graphical figures easier to draw general conclusions from, but may need tabular results for documentation; we provide equivalent data in multiple formats which can be integrated into different analyses. We aim to present statistics in ways that users can interrogate and discuss as part of interpretation; this is especially important for statistics like uncertainty that users may have intuitive rather than formal understandings of.Our conclusions are shaped by direct feedback from land managers regarding what helps and hinders their use of AIM data. We will continue refining our approaches through workshops with the community of practice of land managers who are using the data.