Introduction
Managing natural ecosystems in this era of global change requires accounting for the ongoing and anticipated impacts of climate change. In general, species are tracking climates poleward (sensu IPCC 2022, Iverson et al. 2019), but the rate, extent, and direction of movement for any individual species is highly uncertain. While the primary application of species distribution models (SDMs) has been to predict the contemporary distribution of a species based on the spatial variation of environmental covariates, they are becoming a valuable tool to project the potential future distribution of those same species.
In the marine environment, increasing temperatures and other effects of climate change on ecosystems are already impacting species, with changes in physiology and range shifts being among the most recognized (Fredston-Hermann et al. 2020, Pecl Gretta et al. 2017, Pörtner and Peck 2010, Weiskopf et al. 2020). Species will either shift their distribution and attempt to track changing environments, acclimate or evolve in response to changing conditions, or become extirpated or possibly extinct (English et al. 2021, Holt 1990, Tittensor et al. 2021, Wiens et al. 2009). The three-dimensional marine realm presents some unique challenges to adaptation. For example, the stratification of the water column and the strong correlation between depth and dissolved oxygen can limit the ability of species to track colder water as it moves to deeper depths (English et al. 2021, Wiens 2016). In addition, while marine species are better at tracking climate shifts poleward than terrestrial species (Lenoir et al. 2020), human extractive activities (i.e., fishing) are also shifting poleward, making it difficult to disentangle the different pressures (Pinsky and Fogarty 2012). In light of these challenges, SDM predictions have been successfully used to support various marine resource management initiatives including conservation planning, fisheries management, risk assessments, marine spatial planning, and emergency response initiatives (Baker et al. 2021, Sofaer et al. 2019, Young and Carr 2015), and are a valuable tool to project the distributions of marine species (Brodie et al. 2022).
While there are many sources of uncertainty inherent to SDM predictions (Araújo et al. 2019, Zurell et al. 2020), the additional uncertainty associated with projections of species distributions into the future is the focus of this paper. When SDMs are used to project how species will respond to environmental change in the future, they rely on a space-for-time substitution (Elith and Leathwick 2009); in other words, they assume that the current associations between species and environmental gradients across space will be predictive of the way those species respond as the climate changes through time. Projecting SDMs into new time periods, with potentially new climate conditions, introduces three additional sources of uncertainty: (1) climate model uncertainty; (2) emissions scenario uncertainty; and (3) eco-evolutionary uncertainty. These additional sources of uncertainty stem from the underlying biological and environmental data, the climate projections, as well as the complexity and context dependency of natural ecological systems (Urban 2019). This uncertainty can hamper confidence in model results or interpretation and can include both parametric (uncertainty in model parameters or quantities of interest), and structural uncertainty (model misspecification) (Elith et al. 2002).
SDMs can provide critical information to fisheries and conservation managers, such as the identification of areas where species are projected to persist, increase, or decline under climate change. However, if uncertainty is not accounted for and addressed, there is a risk that species projections will, at best, fail to be informative for making management decisions, and at worst, lead to poor management decisions by presenting overconfident or inaccurate results. We argue that to produce rigorous SDM projections that meaningfully inform management decisions, uncertainty must be identified, minimized when possible, and communicated to end users. The themes of this paper were discussed by over 50 researchers and practitioners at an international workshop hosted by Fisheries and Oceans Canada in March 2021. Here, we propose a set of ten guidelines for addressing uncertainties when projecting marine species distributions under climate change, including identifying the sources of uncertainty, their impacts on the analytical process and results, approaches to manage these uncertainties, and how to appropriately communicate them to end users.