1. The Midwest Avian Data Center Bird conservation through data, science, and partnerships Katie Koch, Tom Will – US Fish and Wildlife Service Migratory Bird Program Gareth Rowell – National Park Service Inventory and Monitoring Network Leo Salas – Point Blue Conservation Science Analysis, Decision-support (DST) Data collection Adaptive Management Data management

2. Outline AKN 101 History and Evolving Philosophy Data Life Cycle Midwest Avian Data Center (MWADC) Entering and Managing Data Through MWADC Analyzing Data Through MWADC Accessing Data Through MWADC Developing Decision Support Tools Sustaining MWADC: Our Foundation in Partnerships Helpful Resources and Trainings

3. How to Develop a Decision Support Tool in MWADC Combining knowledge, the latest science and data to advance bird conservation

4. Outline The basic ingredients for a DST Designing your tool: Who What How What to avoid Completing the adaptive management cycle

5. Decision Support Tools: basic ingredients Science, data, people & technology Science provides the framework for informed conservation planning Monitoring data allow people to evaluate conservation plans People bring knowledge + expertise to the conservation planning process MWADC and other AKN nodes provide the technology to integrate science, data and expert knowledge

6. Who has data? (Maybe not all in the node) Who knows the ecology well? (Biologists, ecologists) Who knows how to use the data? (Statisticians, data scientists) Who knows what decisions to be made? (Managers) Possibly too: who has the web development know-how? (IT, web development, design) Designing a Decision Support Tool Who needs to be involved?

7. Designing a Decision Support Tool: What needs to be delivered? Define the conservation issue and define the metrics How are decisions made? (You got the DST result, now what?) Can birds be indicators of the desired metric? How often are questions asked of the DST? At what spatial scales?

8. Goal: The DST should reduce uncertainty Uncertainty matters! (Be sure to report it in your metric) Help reduce uncertainty: provide report on data gaps/limits/biases, parameter assumptions and sensitivities Spatial/temporal scales Supporting decision-making: (structured decision-making, scenario planning, all-evidence approaches) Designing a Decision Support Tool: Uncertainty

9. Zombie tools Untestable tools Swiss Army Knife tools Tools at the wrong scale? One-mistake dismissals Designing a Decision Support Tool What to avoid!

10. Completing the Adaptive Management Cycle How can users verify the success of their decisions? Collecting the same data the tool used Using the node to store the monitoring data and rerun the analyses The same metric delivered by the tool should provide evidence Evidence must account for uncertainties Statistical significance (a matter of time and $$?)

11. Completing the Adaptive Management Cycle How can your tool learn and be improved? Be specific about model assumptions that should be tested (i.e., model parameters and their uncertainties and sensitivities) Be specific about how management decisions can be evaluated for success/failure Be specific about monitoring after decision-making (must be in the budget of the proposed conservation work!) Be specific about data gaps

12. In Summary… DSTs involve people, data, science & technology. (AKN provides the latter) Who: data contributors, subject experts, decision-makers, IT What: appropriate metric, appropriate scales, specific, testable How: best science, transparent, repeatable, can incorporate new knowledge Adaptive management considerations: easy to update, test result with same metrics of tool, assumptions, sensitivities, data gaps Other considerations: shelf life, on-the-fly vs. once-a-year vs..., using Ravian, how to deliver