2. “Population objectives depend on opinions and technical capacity” – R. Dettmers. Enlist folks with the proper modeling expertise when necessary, and try to enlist species experts and folks who will be using the models early in the model development process. When in doubt go with the technique you/they are most familiar with. Modeling is as much art as science and experience with a modeling technique is valuable. This step will help make the model more useful.

3. Link models & data uses

4. It is important to keep your modeling objective clearly in mind during model development. This includes how you are going to use the model, but also how you are going to test the model. Careful selection of input variables will be important, especially if you plan to use the model for decision support (are the variables manageable?). If you have good-quality existing data, chose an approach appropriate to those data. If you are going to collect data, develop your modeling approach in parallel to your data collection design.

5. Evaluate models prior to using them for planning

6. Model evaluation increases credibility. Model evaluation techniques (e.g. cross-validation are plentiful), but assessment with independent data is best when possible (see recommendation #2). Even if your model evaluates well, you can still improve it and learn about your system by conducting a sensitivity analysis. It is important to implement this step early. In the SHC diagram, model evaluation occurs twice – during biological planning and during monitoring and evaluation of conservation actions. Focusing efforts on evaluation at the first point reduces the chance that you’ve wasted a lot of time and money when you get to the second point.

7. Think of modeling as an iterative endeavor

8. Models are formal statements of hypotheses. Assumptions inherent in the model should be stated explicitly and tested. These are the tenants of assumption-driven research. But there is a tension between having enough information to make a decision and perfecting knowledge & understanding. We’ve all heard the adage, “All models are wrong, some models are useful.” At what point does the model become “useful?” While the answer to this question is likely case-specific, it needs to be asked & answered. Having a useful model does not mean the iterations are over – a model may outlive its useful life. As new data and techniques come available consider increasing the sophistication of your modeling approach – go from moving in circles (e.g. SHC) to progressing forward in spirals.

9. Use multiple modeling approaches whenever possible

10. Several recent papers (2009) have shown the value assessing data with multiple statistical models simultaneously. Thuiller et al. developed an extension (BIOMOD) for the r statistical package that automates the process for some datasets. Jones-Farrand et al. unpublished are comparing statistical models to theoretical models, essentially comparing where the birds are compared to where we think is best for them. Concurrence and disagreement between models can yield valuable information about the system, even when the approaches are built on different datasets and assumptions.

11. Be careful converting model outputs to population estimates

12. Although statistical models can be a key component of the process of setting population objectives, population objectives are neither inputs nor outputs of statistical models (as they are for energetic models). Neither are habitat objectives. Statistical models can only provide information to support decisions on which tradeoffs between alternative management scenarios the partnerships want to select. Statistical models can be particularly helpful for helping set population objectives when they account for variability in habitat quality and predictive errors.

13. Other considerations

15. This question was asked recently. Basically, the questioner was asking, “if you can do an estimate quick and easy based on a lot of assumptions that then you have to go out and test, wouldn’t it be more efficient to just go out and collect the necessary data in the first place and build a better model on that?” Arguments can be made for both sides.

16. At what iteration is a model “useful”?

17. Who are the users of your model (the deciders) and what types of decisions can they make based on your model outputs?

18. Managers vs. administrators; site-level management vs. policy options

19. At what level can decisions be made?

20. Level = both administrative level & spatial scale

21. How do we make users & funders understand the need for validation?

22. Does your modeling approach:

23. Identify the set of conditions or actions that result in a sustainable population?

24. Identify if breeding habitat is limiting?

25. Estimate the probability of success of a management action?

26. Allow assessment of the costs of reaching the population goal?