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).