To demonstrate how a simple decision support model can be developed and applied to decision-making under climate change, we developed a Bayesian network (BN) to predict the occupancy of bull trout (Salvelinus confluentus) in the Boise River, Idaho, USA.  The paper by Peterson et al (2013) describes the conceptual basis for the model, details on model parameterization, and a representative worked example for spatially-explicit prioritization.  In brief, the BN predicts occurrence of bull trout as a function of habitat suitability, occurrence of non-native brook trout (Salvelinus fontinalis), and their interactions mediated by climate—in this case stream flows and temperature.

The model can be implemented in real time, to explore the effect of changed conditions in a particular stream segment, or used to process a large number of cases based on climate inputs across a larger area, like a river basin (see example in Peterson et al. 2013).  The BN model, a user guide, and example data input files can be downloaded below.

To obtain the model and documentation: please download the zip folder: [BullTrout_climate_2012]

To implement the model:  if you are not already using it, you will also need to download a demo version of the software Netica.  Note: The use of trade or firm names (i.e., Netica modeling software) is for reader information and demonstration purposes only and does not imply endorsement by the US Department of Interior or USDA Forest Service of any product or service.

Proper use of the model “Boise bull trout BN 2012”:  This Bayesian network (BN) was developed for bull trout in the Boise River Basin, Idaho, USA and its application was meant to demonstrate the process by which such a model can be used to inform conservation planning and management under climate change.  The model is NOT meant for use in vulnerability analysis across the range of bull trout; users interested in applying this model beyond the Boise River Basin can use it as a template to be refined, revised or updated based on their management question(s) and parameters (probabilities) derived from justifiable criteria.

Related literature:
Peterson, D.P., S.J. Wenger, B.E. Rieman, and D. J. Isaak. 2013. Linking climate change and fish conservation efforts using spatially explicit decision support tools. Fisheries 38(3):111-125. DOI:10.1080/03632415.2013.769157

User Guide