9. Communicate the results and uncertainties
For SDM projections to be used appropriately in science-based decision-making, it is imperative that the results and associated uncertainty are communicated effectively to both technical and non-technical audiences (Baron 2010, Corner et al. 2018, Raimi et al. 2017). In the context of the changing ocean, where ideal marine management decisions achieve objectives both now and in the future, the clear communication of results aids in reducing misinterpretation or dismissal of important findings (Brodie et al. 2022). Involving end users throughout the development of SDM projections, from developing the study objectives to communication of SDM outputs, will enhance mutual understanding (Dietz 2013, Guillera-Arroita et al. 2015, Villero et al. 2017). Such collaborations ensure that researchers are aware of the values held by end users in the decision context, while end users understand the scope, proper interpretation, usage, and limitations of model outputs (Dietz 2013, Villero et al. 2017).
Communication with the end users should consider their knowledge, expertise, and values. Use of common and non-technical language to state the intent and spatial and temporal context of the SDM will clarify to end users how the SDM can support operational needs. Where possible, it is important to communicate results for time scales relevant to management. Managers often seek advice for operational needs over the next five years, while climate change models project over a 50-100-year time scale. This time scale mismatch and its implications for decision-making must be clearly stated and understood.
The narrative should lead first with all the information that is known or more certain, followed by the process of discussing uncertainties and strategies to address them (Corner et al. 2018). It is important to acknowledge that uncertainties exist in the modeling process and cannot be fully eliminated. Study caveats, and the potential for major assumption violations during the analytical process, should be transparently communicated (US National Research Council 2008). Communication strategies could include using standardized descriptions for statements of uncertainty (Budescu et al. 2012, IPCC 2021), as well as carefully crafted analogies comparing climate change to other familiar decision making scenarios, such as disaster preparedness (Raimi et al. 2017). Model outputs should be presented in the context of their certainty, and effort should be directed to identifying and targeting advice using a no regrets strategy (Box 1). A certainty-focused approach could help reduce uncertainty paralysis and improve objectives-based risk management associated with climate-mediated change (Duplisea et al. 2021, Roux et al. 2022).
10. Build a collaborative community for SDMs in future climates
Teams with multidisciplinary expertise (e.g., biology, oceanography, climate science, statistics, data management, computer science) are essential to properly develop SDM projections and address the associated uncertainty. Each step of the SDM analysis process (goal setting, data selection, model building, model evaluation and validation, interpretation of results, and communication of results) may require a unique set of experts to guide decisions. For example, data selection for a single species SDM projection would not only involve species experts with a strong statistical background but would also require collaboration with oceanographers and climatologists. Modeling steps in the analysis could involve additional support from statisticians and computer scientists that include both biological and climate modeler expertise. Connections amongst communities of practice working on common objectives and building complimentary tools can increase efficiency, reduce duplication of effort, and boost outcomes of research findings (e.g., Gomez et al. 2021). Collaborative efforts can both facilitate, and be facilitated by, improved accessibility of all predictors, species data, and model results; Bio-ORACLE is an example initiative aggregating geophysical, biotic, and climate layers with common spatial resolution (Assis et al. 2018, Tyberghein et al. 2012). It is important to make all input data, modeling methodology (including code), and decisions made during the analysis process publicly available to facilitate reproducible research and greater collaboration (Nature Editorials 2022, Nephin et al. 2020, Zurell et al. 2020).