\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[T2A]{fontenc}
\usepackage[ngerman,polish,english]{babel}



\usepackage{float}








\begin{document}

\title{Addressing uncertainty when projecting marine species distributions
under climate change}



\author[1]{Sarah Davies}%
\author[2]{Patrick Thompson}%
\author[3]{Catalina Gomez}%
\author[2]{Jessica Nephin}%
\author[4]{Anders Knudby}%
\author[2]{Ashley Park}%
\author[2]{Sarah Friesen}%
\author[5]{Laura Pollock}%
\author[2]{Emily Rubidge}%
\author[1]{Sean Anderson}%
\author[6]{Josephine Iacarella}%
\author[3]{Devin Lyons}%
\author[7]{A. Andrew MacDonald}%
\author[1]{Andrew McMillan}%
\author[8]{Eric Ward}%
\author[2]{Amber Holdsworth}%
\author[9]{Neil Swart}%
\author[10]{Jeff Price}%
\author[1]{Karen Hunter}%
\affil[1]{Fisheries and Oceans Canada - Pacific Biological Station}%
\affil[2]{Pacific Region Institute of Ocean Sciences}%
\affil[3]{Bedford Institute of Oceanography}%
\affil[4]{University of Ottawa}%
\affil[5]{McGill University}%
\affil[6]{Fisheries and Oceans Canada Pacific Region}%
\affil[7]{Université de Sherbrooke}%
\affil[8]{NOAA Fisheries Northwest Fisheries Science Center}%
\affil[9]{Canadian Centre for Climate Modelling and Analysis}%
\affil[10]{University of East Anglia}%


\vspace{-1em}



  
  \date{June 18, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Species distribution models (SDMs) have been widely used to project
terrestrial species' responses to climate change and are increasingly
being used for similar objectives in the marine realm. These projections
are critically needed to develop strategies for resource management and
the conservation of marine ecosystems. SDMs are a powerful and necessary
tool; however, they are subject to many sources of uncertainty. To
ensure that SDM projections are informative for management and
conservation decisions, sources of uncertainty must be considered and
properly addressed. Here we provide ten overarching guidelines that will
aid researchers to identify, minimize, and account for uncertainty
through the entire model development process, from the formation of a
study question to the presentation of results. These guidelines were
developed at an international workshop attended by over 50 researchers
and practitioners. Although our guidelines are broadly applicable across
biological realms, we provide particular focus to the challenges and
uncertainties associated with projecting the impacts of climate change
on marine species and ecosystems.%
\end{abstract}%



\sloppy


\textbf{Addressing uncertainty when projecting marine species
distributions under climate change}

\textbf{Abstract}

Species distribution models (SDMs) have been widely used to project
terrestrial species' responses to climate change and are increasingly
being used for similar objectives in the marine realm. These projections
are critically needed to develop strategies for resource management and
the conservation of marine ecosystems. SDMs are a powerful and necessary
tool; however, they are subject to many sources of uncertainty. To
ensure that SDM projections are informative for management and
conservation decisions, sources of uncertainty must be considered and
properly addressed. Here we provide ten overarching guidelines that will
aid researchers to identify, minimize, and account for uncertainty
through the entire model development process, from the formation of a
study question to the presentation of results. These guidelines were
developed at an international workshop attended by over 50 researchers
and practitioners. Although our guidelines are broadly applicable across
biological realms, we provide particular focus to the challenges and
uncertainties associated with projecting the impacts of climate change
on marine species and ecosystems.



\textbf{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, P\selectlanguage{ngerman}örtner 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.

\textbf{Guidelines for using SDMs to project marine species}

We break down the SDM analysis process into six main steps: goal
setting, data selection, model building, model evaluation and
validation, interpretation of results, and communication of results. We
propose guidelines that support a logical workflow starting from
articulating the goals of the study, through the modeling process, and
finally communicating results to other scientists, resource managers,
and policy makers (Figure 1). For each step in the SDM process, we have
identified key questions for analysts to consider and linked them to the
guidelines that will help to answer those questions. At each step, we
outline best practices with a focus on how to identify and minimize
uncertainty, when possible, and how to transparently communicate the
uncertainty that cannot be avoided.

\textbf{1.} \textbf{Frame the research question}

Clearly stating the research questions (i.e., the problem, the
objectives, and the hypotheses) is essential to ensure that objectives
are considered throughout the analysis and support transparent and
reproducible SDM results (Araújo et al. 2019, Zurell et al. 2020). A
research outline (Table 1) can communicate the intention of the
research, explicitly state the scope of the study, and help identify any
assumptions that may impact the outcome of the study. This understanding
can support qualitative identification of the tolerance for uncertainty.
For example, if projections of occurrence, rather than biomass or
abundance, are suitable for the objectives of the study, it may be
possible to combine data collected using different surveys because
presence-absence data are less sensitive than biomass data to
differences in gear type and methodology. Laying out the study plan
provides a clear communication tool for all parties involved in the
research and its outcomes.

\textbf{2.} \textbf{Ensure the scope of study is relevant, both in space
and in time}

The choice of extent and resolution in both space and time can impact
the accuracy of SDM projections and affect their utility to support
management decisions. It is assumed when projecting distributions into
future climates that species distributions across spatial climate
gradients will match species responses to temporal changes in climate.

Applications of SDMs to marine species have often involved fitting
models with observations from a subset of the species' range within
geopolitical boundaries (e.g., Thorson et al. 2015). While these types
of SDMs may be appropriate for questions related to specific
assessments, they are ill-suited to climate change applications. Using
only a subset of data in space or time will usually lead to truncated
species-environment relationships and introduce uncertainty in the
fitted SDM parameters. When projecting into future climates, these
truncated models are likely to have reduced transferability as they are
extrapolating beyond the range of observed conditions where they are not
calibrated or validated, and therefore generate poor distribution
projections (Charney et al. 2021, Muhling et al. 2020, Thuiller et al.
2004). To characterize the species' full niche, species observations
should be sourced from the widest spatial and temporal extent available
that best addresses the research question (Barbet-Massin et al. 2010,
Thuiller et al. 2004).

The spatial resolution of environmental covariates should also be at a
biologically relevant scale for the taxa being modeled (Austin and Van
Niel 2011). For example, the relevant scale for the relationship between
bathymetry and a highly migratory pelagic fish species (e.g., tuna) is
likely coarser than that for an intertidal invertebrate (e.g., oyster).
One challenge with modeling at an appropriate scale is that the
available spatial resolution of environmental covariates may not match
the resolution of the species observations. In these cases,
environmental covariates should be up- or down-scaled (Araújo et al.
2019, Hijmans et al. 2005). Future climatic variables are necessarily
coarse since they are typically modeled at a global scale. Downscaling
methods can be applied to match the desired scale in an attempt to
capture the variability at the scale relevant to the organism; however,
this process may introduce additional uncertainty. Modeling at coarser
spatial resolutions than is biologically appropriate can increase
uncertainty in projections by over- or under-predicting habitat
(Franklin et al. 2013, Gottschalk et al. 2011, Randin et al. 2009, Seo
et al. 2008, Willis and Bhagwat 2009). Importantly, the spatial scale at
which species projections are generated should be considered when making
management decisions. Coarser resolution models (e.g.,100 km) that do
not resolve local topographic features, for example, may not be well
suited to support local management decisions (e.g., within a 10 km
squared coastal protected area).

The temporal resolution of environmental covariates is another important
consideration for building models that characterize the full species
niche. Ideally, the temporal resolution of the environmental covariates
should match the scale of the species data to reduce uncertainty in the
species-environment relationship (Araújo et al. 2019, Batalden et al.
2007). Many SDMs are static and are built using environmental covariates
derived from climatologies (i.e., long-term means) (Bateman et al.
2012). These models ignore interannual variability and exclude extreme
weather events and thus will not be well calibrated to the full range of
conditions experienced by the species over time (Bateman et al. 2012).
Comparison between models built with different temporal data may be
necessary.

Errors in the observation and environmental data, as well as spatial and
temporal sampling biases can impact the extent of available data and
create uncertainty in projections (Fernandes et al. 2019, Naimi et al.
2014, Osborne and Leitão 2009). Although it may not be feasible to
resolve these issues, mapping both observations and environmental data
can illustrate where these gaps occur and may be important information
to share with end users.

\textbf{3.} \textbf{Identify appropriate species data}

While consistent and standardized datasets of presence/absence or
abundance are ideal for minimizing uncertainty when building SDMs, they
may not be readily available or logistically feasible. For example,
marine species of commercial importance may have standardized stock
assessment or catch monitoring data available, whereas non-commercial
species may only have sporadic presence-only data. Existing data may
also come from a biased subset of a species range or be biased to a
certain time of year due to logistical constraints or data collection
priorities.

Alternative information sources may confirm or expand species
observation data. For example, environmental DNA (eDNA) is becoming
increasingly viable, particularly for bony fishes (Muha et al. 2017).
Advancements in imagery analysis also allow for biological surveys of
coastal habitats with remotely piloted aircraft (e.g., drones; (Monteiro
et al. 2021). Citizen science platforms and global databases can provide
observational data, trading sample size for potential inaccuracy and
spatial bias (Beck et al. 2014, Johnston et al. 2020). Expert and
Indigenous Knowledge can also be used in conjunction with survey data to
capture the extent of a species' distribution (Merow et al. 2017,
Skroblin et al. 2021). Although they each have limitations, these data
sources are increasing the availability of species data.

Combining data sources can fill in gaps in any individual dataset. For
example, this approach has been used to define the spatio-temporal
distribution of Killer Whales (Watson et al. 2019). However, analysts
must consider the biases that may result from differences across data
sources. For example, catchability often varies by fishing gear type,
and data collected from fisheries may be non-random and preferentially
sampled (Fletcher et al. 2019). Hybrid models using more complex
statistical structures to combine datasets from different sources can
increase the power of a model while still accounting for biases and
variances of the individual datasets (Rufener et al. 2021, Thorson et
al. 2021).

Information on a species' ecology can be used to improve the uncertainty
regarding the accuracy of model predictions. For instance, dispersal
barriers, ontogenetic shifts, and biotic influences on aggregations
(e.g., spawning) affect model accuracy and performance (Robinson et al.
2011). Dispersal barriers are less common in marine systems (Carr et al.
2003), but may be important to incorporate as post-hoc constraints to
SDM predictions for species with lower dispersal capacities (Robinson et
al. 2011). Uncertainty may be reduced by splitting observation data
between adults and juveniles if a species occupies habitats with
different environmental conditions across its life stages (Petitgas et
al. 2013). Experimentally derived responses can be applied to compare
the fundamental niche of a species relative to the realized niche
modeled by SDMs (Franco et al. 2018, Martínez et al. 2015) or
incorporated as priors in Bayesian SDMs (Gamliel et al. 2020). Though
physiological limits are unknown for many marine species, this
information is particularly valuable for SDM projections as
distributions will be underestimated when observed locations are
constrained by non-climatic factors (Araújo and Peterson 2012).

\textbf{4.} \textbf{Determine relevant climatic and non-climatic
environmental variables}

There are two key considerations when identifying relevant environmental
variables: 1) their ability to describe species responses to current
environmental conditions; and 2) the uncertainties that exist in how
those responses may change in future climates (guideline \#8). Many
studies have shown temperature-related variables to be among the most
powerful predictors of species distributions (Bosch et al. 2018, Bradie
and Leung 2017). A variety of mechanisms have been identified through
experiments, models, and observations of extreme thermal events whereby
temperature affects biological processes such as development, dispersal,
growth, and species interactions (Boyd et al. 2013, Kordas et al. 2011,
O'Connor et al. 2007, Sunday et al. 2012). Understanding these
mechanisms can help to determine the most suitable temporal values
(e.g., average daily maximum temperature, warmest month, or cumulative
values such as growing degree days). However, data availability and
realism must also be considered when selecting climatic variables. If
biological knowledge suggests that extreme temperature events contribute
to limiting the local-scale distribution of a species, it is necessary
to determine whether the spatial and temporal resolution of the data
(both from observations and climate models) are sufficient to resolve
such events. Global climate models are most suited to projecting changes
in the statistics of a climate phenomenon (e.g., mean temperature or the
frequency of an event), rather than the magnitude of an extreme event,
and the confidence in those extreme event projections can depend on the
variable and region (Seneviratne et al. 2012).

Static, non-climatic variables are essential to reduce uncertainty when
projecting species distributions (Willis and Bhagwat 2009). Ignoring
non-climatic variables that limit species distributions increases the
risk of overfitting the climatic variables, and over- or
under-estimating changes in a species' distribution and extinction risk
under climate change (Beaumont et al. 2005, Hof et al. 2012, Virkkala et
al. 2010, Zangiabadi et al. 2021). In the marine realm, excluding
physical habitat variables such as bathymetry can be problematic as they
are often correlated with climatic variables that are difficult to
measure or model, such as food availability, but integral to predicting
habitat (Luoto and Heikkinen 2008). Unlike climatic variables, static
variables can either be used as predictors in a model or used as a
filter to constrain the model domain depending on the question and
research objective. For example, when projecting kelp distribution,
which requires hard substrate for attachment, substrate type can be
included as a model covariate, or the model projections can be
restricted to areas with hard substrate.

Highly complex and overfit models tend to perform well within the
environmental space the model was trained with but may perform poorly
when projecting into future conditions (Bell and Schlaepfer 2016,
Moreno-Amat et al. 2015). To limit model complexity, biological
knowledge should be relied on to select the relevant environmental
variables (Austin and Van Niel 2011). Preference should be to include
the most proximate variables, those that have a direct physiological
effect on the species being modeled, over more distal or indirect
variables that are often used as proxies when proximal variables are
missing (Anderson 2013, Gardner et al. 2019). Some commonly used static
variables (e.g., depth and distance from shore; (Bosch et al. 2018,
Johnson et al. 2019)) are considered proxies for other variables, such
as pressure and exposure. When proxy variables are needed to represent
important processes, practitioners should note that an assumption of
stationarity between the proxy variable and the more direct variable it
aims to represent is implicit when projecting species distributions.

Variable selection can simplify complex models by seeking subsets of
predictor variables that still allow good predictive accuracy (Piironen
and Vehtari 2017). Nevertheless, careful consideration of the causal
link between each environmental variable and the focal species is needed
to prevent the removal of an environmental variable that may be
influential in a different set of conditions. In addition, collinearity
between variables can make their independent influence on a species
range hard to distinguish. This can be particularly problematic for
temperature and depth in marine systems; although they are often highly
correlated at regional scales, temperature is projected to warm while
depth remains constant (e.g., Thompson et al. 2022a). Projections
require that SDMs have accurately estimated how these two variables
shape species ranges. A solution is to include species data from across
a broader spatial extent where latitudinal temperature gradients can
break down the collinearity between temperature and depth (Thompson et
al. 2022b).

\textbf{5.} \textbf{Select the SDM model}

SDM models range from parametric, to semiparametric (e.g., Shelton et
al. 2014), to various forms of non-parametric approaches including
MaxEnt (Phillips et al. 2006) and machine- or deep-learning models
(e.g., Christin et al. 2019, Elith et al. 2008). Furthermore, SDMs can
be purely phenomenological (e.g., correlative, Jarnevich et al. 2015) or
built on assumed mechanisms and calibrated to data (e.g.,Essington et
al. 2022, Kearney and Porter 2009). Correlative models may perform well
on existing data but not extrapolate well if those correlations break
down (e.g., Davis et al. 1998). Mechanistic models are grounded in
physiological and biological principles, and may outperform correlative
models in future conditions, but are often challenging to construct
(Kearney and Porter 2009, Urban 2019). Hybrid models incorporate known
mechanisms in addition to phenomenological correlations, and have the
potential to borrow advantages from both kinds of models (Kearney and
Porter 2009). Creating ensembles by combining the outputs from several
individual models utilizing different algorithms can improve predictive
ability (Araújo and New 2007, but see Hao et al. 2020) and can be as
simple as unweighted or weighted averages (Araújo and New 2007) or as
complex as super-ensembles tuned to simulated or trusted data (Anderson
et al. 2017). However, an ensemble is only as good as the individual
models used to build it, therefore some effort is required to choose a
high quality candidate set; using models with different covariates or
structure may help identify misspecification of any single model.

A recent advance in SDMs is the move from single-species models to
multi-species models known as Joint Species Distribution Models (JSDMs;
Warton et al. 2015). For example, JSDMs have been used to understand the
joint influence of ongoing environmental change and fishing pressure on
groundfish species richness in Canada's Pacific waters (Thompson et al.
2022a). The flexible hierarchical structure makes it possible to account
for correlation among species and provide more robust uncertainty
estimates, and allows relevant biological information (e.g., functional
trait and phylogenetic information) to be added to the model. While
species correlations from JSDMs do not necessarily represent species
interactions (Dormann et al. 2018, Pollock et al. 2014), they can be
used to understand when there is substantial statistical correlation
between species in their shared response to the environment (as
represented in the model) or residual correlation (not explained by the
model). Finally, there are models for different taxonomic and spatial
scales (e.g., for alpha, beta, and gamma diversity; (summarized in
Pollock et al. 2020)) that can be appropriate depending on the specific
objectives. For example, if the objective can be evaluated with species
diversity or biomass rather than information from individual species,
then macroecological models could provide sufficient results with fewer
input data.

Model choice can influence uncertainty and should therefore be guided by
the objectives of the analysis, the model fit, and model evaluation. For
this reason, it is critical to start with a set of candidate models that
can support the objectives of the analysis. These candidate models may
include different variables or differing parameterization of these
variables. Second, it is necessary to evaluate candidate models for any
problems in the fit itself (e.g., failure to converge, non-sensible
response curves) as well as violations of their assumptions (e.g.,
residual analysis, (Rufener et al. 2021); posterior predictive checks,
(Gelman et al. 1996)). Several approaches are available to compare among
candidate models meeting the above criteria. Information theoretic
approaches such as AIC (Akaike 1973) or predictive model selection tools
such as the Leave One Out Cross-Validation Information Criterion (LOOIC)
(Vehtari et al. 2017) can help evaluate model parsimony; a more
parsimonious model should in theory make better predictions (e.g., Aho
et al. 2014). However, these approaches are not typically designed to
evaluate projections and are generally limited to parametric models.
Finally, practitioners should compare the predictive accuracy of all
candidate models using hold-out data, such as in cross-validation.
Threshold-independent statistics (e.g., receiver operator curve plots)
can be used to assess overall model performance and the models'
discriminatory ability across species and locations; while
threshold-dependent statistics (e.g., sensitivity, specificity, true
skill statistic) can support accuracy assessment (Freeman and Moisen
2008, Liu et al. 2011).

\textbf{6.} \textbf{Identify climate model uncertainty}

Global Climate Models (GCMs) are process-based models that include
coupled atmosphere, ocean and land models, representing the fundamental
components of the climate system (Flato 2011). When coupled to models of
biogeochemical cycling, they are known as Earth System Models (ESMs) and
are the primary scientific tools for estimating~future climate
states.~ESMs from major climate modeling centres participate in
coordinated experiments, including the Coupled Model Intercomparison
Project (CMIP), which has evolved through six discrete phases of
activity over the past 30 years. The future trajectory of human activity
and the associated greenhouse gas emissions are unknown, so future
socio-economically based emissions scenarios are developed to illustrate
the range of possible pathways. Climate models driven by these emissions
scenarios produce projections of the future climate state. Each phase of
CMIP contains new scenarios and updated models, and concludes with the
release of open data for downstream climate change studies (Eyring et
al. 2016).

Global climate projections have three sources of uncertainty: 1)
internal variability; 2) model uncertainty; and 3) scenario uncertainty
(Hawkins and Sutton 2009). Internal variability arises from fluctuations
in climate (such as El Niño), and within a single year this fluctuation
can be larger than the climate signal itself. The precise evolution of
internal variability in future decades cannot be predicted. However, the
range of possible outcomes resulting from internal variability can be
quantified by the spread across an ensemble of realizations from the
same model and scenario. Each realization starts from different initial
conditions, and while they will differ in their variability, they will
each experience the same overall climate change.

Climate model uncertainty results from an imperfect understanding of the
climate system, and from assumptions and compromises made in
representing this understanding in software-based numerical models. For
example, the global scale and process complexity in ESMs and limited
supercomputing capacity constrains the feasible resolution to about 100
km. Processes that are not resolved at this scale (e.g., mesoscale ocean
eddies) are approximately represented by parameterizations that are
imperfect and often differ between models. Climate model uncertainty can
be quantified by the spread obtained when multiple independent climate
models are run using the same climate scenario. Summary reports such as
the IPCC Assessments normally report on the multi-model mean result
(IPCC 2021), which is generally more accurate than the projections from
any one model.

Regional SDMs often require information at finer spatial scales
than~ESMs can resolve, so the ESM outputs must be downscaled to a finer
spatial resolution. Dynamical downscaling uses a nested modeling
approach in which regional models are forced at their boundaries by ESMs
to generate finer resolution projections (e.g., Holdsworth et al. 2021,
Peña et al. 2019). These models directly solve the equations of motion
at regional scales and are particularly effective in regions where
topographic effects on wind, temperature, and precipitation are
important. Regional model uncertainty can be quantified by the spread
obtained when an ensemble of independent regional models is run using
the same driving ESMs and climate scenario. Statistical downscaling can
be used to downscale ensembles of climate models. They rely on the
assumption that regional climates are driven by large-scale influences
and often require a target fine-resolution simulation to train on. Both
downscaling techniques inherit all the uncertainties from their parent
ESMs and also introduce their own sources of uncertainty (e.g., Giorgi
and Gutowski 2015). To minimize model uncertainty, bias correction
methods can be applied prior to using global or regionally downscaled
climate variables in SDMs, though depending on the research question,
this may add additional uncertainty to the analysis process (Maraun
2016, Xu et al. 2021).

Finally, scenario uncertainty arises because the future of human
behavior, and the resulting emissions and land use changes, are unknown.
Scenario uncertainty is quantified by comparing different scenarios run
by the same model (or ensemble of models). CMIP6 created an ensemble of
projections for a discrete range of climate scenarios. Broadly, the
uncertainty is given by the range between the highest and lowest
emissions scenarios (SSP585 and SSP119 in CMIP6). Though, it has been
argued that the extreme high and low scenarios are less plausible and
unnecessarily inflate uncertainty (Hausfather and Peters 2020).
Communities of practice are forming to help inform relevant scenario
selection by users (Stammer et al. 2021).

The relative magnitude of each source of uncertainty (internal, model,
and scenario) largely depends on the spatial and temporal scales and
variables of interest (Hawkins and Sutton 2009). At global averaging
scales, scenario uncertainty tends to dominate, and internal variability
is typically the least important, particularly in the distant future.
However, at regional scales and for nearer-term time horizons
(\textless{}20 years), model variability and internal variability can be
significantly larger (Frölicher et al. 2016).

Propagation of climate projection uncertainties into downstream SDM
models presents a challenge. Ideally, SDM projections would be generated
from all possible regional models, which had downscaled all possible
ESMs, for all possible scenarios. While this approach is not practically
possible, it conceptually illustrates the full cascade of uncertainty,
which increases at each step of the process in moving from ESM climate
projections to end-use impact studies such as species distributions
(Falloon et al. 2014). A more feasible approach to estimating these
uncertainties is to generate several SDM projections from a
representative range of regional models, which themselves are driven by
a representative ensemble of ESMs and scenarios. Unfortunately, the
necessary data for these robust uncertainty estimates are often not
available. While there is some coordination under projects like the
Coordinated Regional Downscaling Experiment (CORDEX; Giorgi and Gutowski
2015), there is no equivalent to the CMIP ensemble, particularly for the
ocean. Hence, users are forced to construct these representative
downscaled ensembles themselves, and to be explicit about the
uncertainties that cannot be represented in their SDM projections.

\textbf{7.} \textbf{Identify SDM uncertainty}

Species distribution models can have at least three main sources of
uncertainty (sensu Hilborn 1987). The first is from regular
environmental and biological variation (`noise') that influences a
species' distribution but is well observed and can be accounted for in a
model and contributes to parameter uncertainty and observation error.
The second source of uncertainty is the impact of extreme and
unpredictable events, and their effect on species distributions, which
can be dramatic (Anderson and Ward 2019). Unanticipated events (e.g.,
tsunamis, disease outbreaks, extreme heat waves) not captured in the
observations used to fit the SDM may only be partially accounted for in
the SDM projections. For example, it may be unknown how a species will
respond to extreme temperatures that are beyond observed values used to
build the projections and beyond the documented temperature range for
the species. Finally, there is the uncertainty stemming from ecological
patterns and processes that are only partially understood, or what
Hilborn (1987) calls uncertain states of nature. This can include
uncertainty related to climate model outputs (guideline \#6), the
suitability of one environmental variable as a proxy for another, and
the influence of eco-evolutionary processes (e.g., species interactions,
dispersal limitation, local adaptation; guideline \#8).

A variety of approaches are available to account for uncertainty across
possible states of nature. Multiple models can be used to evaluate the
influence of different combinations of covariates, or to characterize
the effect of a given covariate via linear or non-linear relationships.
For example, Brodie et al. (2020) applied three model types with
different covariate configurations (spatiotemporal only, environmental
only, and both spatiotemporal and environmental) to estimate responses
of fish species in the eastern Bering Sea. Predictions from multiple SDM
models or modeling assumptions can also be used to characterize the
range of such uncertainty (e.g., Nephin et al. 2020, Thuiller et al.
2019).

It is also critical to evaluate model accuracy and whether uncertainty
intervals encompass true values. Cross-validation provides a general
tool to characterize how well an SDM may be accounting for uncertainty.
Central to effective cross-validation is choosing an appropriate
blocking scheme to characterize the uncertainty of interest (e.g.,
Roberts et al. 2017). For example, spatial blocking can assess how well
an SDM can predict into areas that are omitted from the training data,
and temporal blocks can assess how well an SDM can forecast periods of
time that are omitted from the training data. Despite the importance of
cross-validation, it is important to consider that no cross-validation
strategy will fully encompass the uncertainty introduced by predicting
under new climate change conditions.

In addition, to accurately project uncertainty from SDMs, the model
needs to be statistically valid, accounting for major sources of
residual correlation caused by sampling schemes or spatial correlation
from unmodeled covariates (Legendre and Fortin 1989). Whenever possible,
SDM model uncertainty should be included in projections through error
propagation methods (e.g., via hierarchical modeling or
simulation--extrapolation; Stoklosa et al. 2015). Random effects can
provide a unified framework with which to integrate over uncertainty
from latent variables and residual correlation (Anderson et al. 2022,
Shelton et al. 2014, Thorson and Minto 2014). However, the omission of
relevant climate variables may cause spatial or spatiotemporal random
effects to absorb climate-driven variation and thereby underestimate
projected impacts of climate change (guideline \#4).

\textbf{8.} \textbf{Identify eco-evolutionary uncertainty}

SDM modeling assumes that a species' environmental niche can be
estimated by correlating occurrences or abundances with environmental
variation across space. However, environmental conditions are only one
determinant of species distributions. Distributions are also influenced
by interactions with other species, spatial patterns of dispersal, and
stochasticity (i.e., random events; Thompson et al. 2020, Vellend 2016).
Furthermore, SDMs also assume that all individuals of a species share
the same environmental response curves (Zurell et al. 2020) but this may
not be true if subpopulations are locally adapted to the conditions they
experience (Aitken et al. 2008) or if environmental responses differ
across life stages in an organism (Kingsolver et al. 2011). Together,
eco-evolutionary processes make the relationship between species
distributions and environmental conditions context-dependent (Urban et
al. 2016) which introduces three types of uncertainty when SDMs are used
to project responses to future conditions: 1) uncertainty in the model
parameters, 2) uncertainty in the assumption that all individuals within
a species will share the same environmental responses, and 3)
uncertainty in how well current species-environment relationships will
reflect future species-environment relationships.

While parameter uncertainty may be partially captured in that of the
fitted model (guideline \#7), uncertainty regarding how eco-evolutionary
processes will alter species-environment relationships will not be. This
uncertainty stems from eco-evolutionary processes influencing whether or
not a species will shift its distribution at the same rate as the
climate changes (Urban et al. 2016). If species are dispersal limited or
if habitat connectivity is low, they may not be able to shift their
distributions fast enough to keep pace with the changing climate
(Schloss et al. 2012). Species will also only be able to establish in
new habitats if there is sufficient food, if obligate mutualists are
also present, and if predators, competitors, parasites, and diseases are
not too abundant or prevalent (Alexander et al. 2015, Brown and Vellend
2014, Thompson and Gonzalez 2017, Zarnetske et al. 2012). The northward
movement of the predatory whelk \emph{Mexacanthina lugubris} into new
habitats is an example of range expansion that is mediated by a trophic
interaction (Wallingford and Sorte 2022). Alternatively, the loss of a
competitor or predator may allow a species to expand its distribution to
a wider range of environmental conditions than it historically occupied
(Urli et al. 2016). Additionally, species that adapt---either
evolutionarily or behaviorally---quickly to changing environmental
conditions will not need to shift their distributions as quickly, if at
all (Bell and Gonzalez 2009, Carlson et al. 2014, Thompson and Fronhofer
2019). These complex eco-evolutionary processes mean that species
distributions under future climates will inevitably differ from what
SDMs project based on current species environmental associations, and
thus should be communicated as hypotheses (Urban et al. 2016). Such
deviations may be due to the emergence of extreme and unpredictable
events (Anderson et al. 2017) such as disease outbreaks, species
interactions, invasive species, or simply from the fact that species
ranges may not perfectly track changes in climate (Wiens 2016).

Eco-evolutionary uncertainty is distinct from uncertainty associated
with statistical model fitting (guideline \#7) and from climate model
uncertainty (guideline \#6). In cases where evidence of local adaptation
or phenotypic plasticity to climate variation is available, this
information can be incorporated into SDMs (e.g., Benito Garzón et al.
2011, Homburg et al. 2014, Lowen et al. 2019, Valladares et al. 2014);
however, for most species, this information is lacking. One signal of
local adaptation is that SDM parameter coefficients may vary across the
species range. This uncertainty can be assessed using spatial block
cross-validation or spatially varying coefficients. In addition,
practitioners can account for eco-evolutionary uncertainty in the
interpretation and communication of the results (guideline \#9). Much of
the uncertainty associated with eco-evolutionary processes stems from
whether species will successfully establish in new locations, and
whether they will be lost in areas where conditions are projected to
become unsuitable. Researchers can be reasonably certain of areas where
species are projected to persist in future climates, but less certain of
areas where species are projected to shift, and this can be highlighted
when communicating SDM results (see Box 1).~Where species are expected
to shift, either as a range retraction or an expansion, monitoring
programs can help to understand species' range dynamics and provide data
to refine model(s) over time.\selectlanguage{english}
\begin{longtable}[]{@{}l@{}}
\toprule
\tabularnewline
Box 1: No Regrets Strategy No regrets strategies for climate change
adaptation are based on present day actions that can be undertaken
without understanding all dimensions and impacts of climate change
(Circles of Social Life 1996, IPCC 1996). Focusing spatial planning
effort on areas that are projected to remain suitable for a focal
species offers a no regrets (sensu Heltberg et al. 2009, Hoegh-Guldberg
and Bruno John 2010) strategy for dealing with much of the uncertainty
associated with SDM projections. While practitioners can aim to reduce
uncertainty in the modeling process, they can also present model outputs
in ways that have the highest confidence. Where predicted and projected
probabilities of suitable habitat overlap, there is greater certainty
that the species presence will not change, and a greater likelihood that
such space will meet resource management operational goals (Kujala et
al. 2013a, Kujala et al. 2013b). In these areas, conservation will
benefit the species now, and focuses attention on areas of the species'
projected range where SDM models have the highest certainty. An
evidence-based no regrets strategy can inform risk management and
decision making processes (Makino et al. 2015).\tabularnewline
\bottomrule
\end{longtable}

\textbf{9.} \textbf{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).

\textbf{10.} \textbf{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).

\textbf{Conclusion}

Based on recommendations of an international workshop of SDM experts, we
have outlined potential sources of uncertainty linked to the various
stages of analysis needed to complete an SDM projection into future
climates (Table 2). This begins with the need to identify sources of
uncertainty during goal setting and at the onset of an analysis
(guidelines \#1 and 2), while selecting relevant data sources
(guidelines \#3 and 4), throughout model building and evaluation
(guideline \#5), right through uncertainty estimation and the
interpretation of results (guidelines \#6-8), and finally, during the
communication of results (guidelines \#9 and 10).

Through the application of SDM outputs, researchers and end users may
identify important data gaps or other elements that need to be
reassessed for clarity; this feedback can lead to iterative improvement
of both the analytical process and resulting outputs. The need to build
a community of practice that includes a diversity of perspectives and
skills for projecting marine species distributions is a challenge and a
gap. Partnerships between scientists, practitioners, and managers are
necessary to evaluate approaches that can lead to clear and consistent
standards and science advice to support a variety of marine spatial
planning decisions now and in the years to come.

Many ecosystems have species and environmental data shortfalls that will
limit a modeler's ability to minimize some sources of uncertainty in SDM
projections. For example, there are currently few datasets available of
downscaled, high-resolution climate variables for marine regions and no
coordinated global effort to develop them. However, even in the absence
of such data, these guidelines provide practical steps for identifying
the relevant sources of uncertainty, quantifying their magnitude, and
communicating their effects. Following these guidelines will help
practitioners to identify areas of higher confidence where species
distributions are not expected to change. SDM projections may represent
the best available knowledge to inform management strategies; thus, it
is essential to acknowledge and report on uncertainty to avoid poor
management decisions. By following the guidelines laid out in this
review and communicating the decisions that were made throughout the
analysis process, SDM projections can be informative to researchers,
managers, and policy makers interested in planning for a changing and
uncertain future climate.

\textbf{Literature Cited}

Aho, K., Derryberry, D. and Peterson, T. 2014. Model selection for
ecologists: the worldviews of AIC and BIC. Ecology \textbf{95} (3):
631-636.

Aitken, S.N., Yeaman, S., Holliday, J.A., Wang, T. and Curtis-McLane, S.
2008. Adaptation, migration or extirpation: climate change outcomes for
tree populations. Evolutionary applications \textbf{1} (1): 95-111.

Akaike, H. 1973. Information theory and an extension of the maximum
likelihood principle. \emph{In} 2nd International Symposium on
Information Theory. Akad\selectlanguage{ngerman}émiai Kiadó, Budapest, Hungary. pp. 267-281.

Alexander, J.M., Diez, J.M. and Levine, J.M. 2015. Novel competitors
shape species' responses to climate change. Nature \textbf{525} (7570):
515-518.

Anderson, R.P. 2013. A framework for using niche models to estimate
impacts of climate change on species distributions. Annals of the New
York Academy of Sciences \textbf{1297} (1): 8-28.
https://doi.org/10.1111/nyas.12264.

Anderson, S.C. and Ward, E.J. 2019. Black swans in space: modeling
spatiotemporal processes with extremes. Ecology \textbf{100} (1):
e02403. https://doi.org/10.1002/ecy.2403.

Anderson, S.C., Ward, E.J., English, P.A. and Barnett, L.A.K. 2022.
sdmTMB: an R package for fast, flexible, and user-friendly generalized
linear mixed effects models with spatial and spatiotemporal random
fields. bioRxiv: 2022.2003.2024.485545. 10.1101/2022.03.24.485545.

Anderson, S.C., Cooper, A.B., Jensen, O.P., Minto, C., Thorson, J.T.,
Walsh, J.C., Afflerbach, J., Dickey\selectlanguage{english}-Collas, M., Kleisner, K.M. and
Longo, C. 2017. Improving estimates of population status and trend with
superensemble models. Fish and Fisheries \textbf{18} (4): 732-741.

Araujo, M. and Peterson, A. 2012. Uses and misuses of bioclimatic
envelope models. Ecology \textbf{93} : 1527-1539. 10.2307/23225219.

Araujo, M.B. and New, M. 2007. Ensemble forecasting of species
distributions. Trends in ecology \& evolution \textbf{22} (1): 42-47.

Araujo, M.B., Anderson, R.P., Marcia Barbosa, A., Beale, C.M., Dormann,
C.F., Early, R., Garcia, R.A., Guisan, A., Maiorano, L., Naimi, B.,
O'Hara, R.B., Zimmermann, N.E. and Rahbek, C. 2019. Standards for
distribution models in biodiversity assessments. Science
Advances\textbf{5} (1): eaat4858. 10.1126/sciadv.aat4858.

Assis, J., Tyberghein, L., Bosch, S., Verbruggen, H., Serrao, E.A. and
De Clerck, O. 2018. Bio-ORACLE v2.0: Extending marine data layers for
bioclimatic modelling. Global Ecology and Biogeography \textbf{27} (3):
277-284. https://doi.org/10.1111/geb.12693.

Austin, M.P. and Van Niel, K.P. 2011. Improving species distribution
models for climate change studies: variable selection and scale. Journal
of Biogeography \textbf{38} (1): 1-8.
https://doi.org/10.1111/j.1365-2699.2010.02416.x.

Baker, D.J., Maclean, I.M.D., Goodall, M. and Gaston, K.J. 2021. Species
distribution modelling is needed to support ecological impact
assessments. Journal of Applied Ecology \textbf{58} (1): 21-26.
https://doi.org/10.1111/1365-2664.13782.

Barbet-Massin, M., Thuiller, W. and Jiguet, F. 2010. How much do we
overestimate future local extinction rates when restricting the range of
occurrence data in climate suitability models? Ecography \textbf{33}
(5): 878-886. https://doi.org/10.1111/j.1600-0587.2010.06181.x.

Baron, N. 2010. Escape from the ivory tower\selectlanguage{english} : a guide to making your
science matter. Island Press.

Batalden, R.V., Oberhauser, K. and Peterson, A.T. 2007. Ecological
Niches in Sequential Generations of Eastern North American Monarch
Butterflies (Lepidoptera: Danaidae): The Ecology of Migration and Likely
Climate Change Implications. Environmental Entomology \textbf{36} (6):
1365-1373. 10.1603/0046-225X(2007)36{[}1365:ENISGO{]}2.0.CO;2.

Bateman, B., Vanderwal, J. and Johnson, C. 2012. Nice weather for
bettongs: Using weather events, not climate means, in species
distribution models. Ecography \textbf{35} : 306-314. 10.2307/41418669.

Beaumont, L., Hughes, L. and Poulsen, M. 2005. Predicting species
distributions: Use of climatic parameters in BIOCLIM and its impact on
predictions of species' current and future distributions. Ecological
Modelling \textbf{186} : 251-270. 10.1016/j.ecolmodel.2005.01.030.

Beck, J., Boller, M., Erhardt, A. and Schwanghart, W. 2014. Spatial bias
in the GBIF database and its effect on modeling species' geographic
distributions. Ecological Informatics \textbf{19} : 10-15.
https://doi.org/10.1016/j.ecoinf.2013.11.002.

Bell, D. and Schlaepfer, D. 2016. On the dangers of model complexity
without ecological justification in species distribution modeling.
Ecological Modelling \textbf{330} : 50-59.
10.1016/j.ecolmodel.2016.03.012.

Bell, G. and Gonzalez, A. 2009. Evolutionary rescue can prevent
extinction following environmental change. Ecology letters\textbf{12}
(9): 942-948.

Benito Garzon, M., Alia, R., Robson, T.M. and Zavala, M.A. 2011.
Intra-specific variability and plasticity influence potential tree
species distributions under climate change. Global Ecology and
Biogeography \textbf{20} (5): 766-778.
https://doi.org/10.1111/j.1466-8238.2010.00646.x.

Bosch, S., Tyberghein, L., Deneudt, K., Hernandez, F. and De Clerck, O.
2018. In search of relevant predictors for marine species distribution
modelling using the MarineSPEED benchmark dataset. Diversity and
Distributions \textbf{24} (2): 144-157.
https://doi.org/10.1111/ddi.12668.

Boyd, P.W., Rynearson, T.A., Armstrong, E.A., Fu, F., Hayashi, K., Hu,
Z., Hutchins, D.A., Kudela, R.M., Litchman, E., Mulholland, M.R.,
Passow, U., Strzepek, R.F., Whittaker, K.A., Yu, E. and Thomas, M.K.
2013. Marine Phytoplankton Temperature versus Growth Responses from
Polar to Tropical Waters -- Outcome of a Scientific Community-Wide
Study. PLOS ONE \textbf{8} (5): e63091. 10.1371/journal.pone.0063091.

Bradie, J. and Leung, B. 2017. A quantitative synthesis of the
importance of variables used in MaxEnt species distribution models.
Journal of Biogeography \textbf{44} (6): 1344-1361.
https://doi.org/10.1111/jbi.12894.

Brodie, S., Smith, J.A., Muhling, B.A., Barnett, L.A.K., Carroll, G.,
Fiedler, P., Bograd, S.J., Hazen, E.L., Jacox, M.G., Andrews, K.S.,
Barnes, C.L., Crozier, L.G., Fiechter, J., Fredston, A., Haltuch, M.A.,
Harvey, C.J., Holmes, E., Karp, M.A., Liu, O.R., Malick, M.J., Pozo
Buil, M., Richerson, K., Rooper, C.N., Samhouri, J., Seary, R., Selden,
R.L., Thompson, A.R., Tommasi, D., Ward, E.J. and Kaplan, I.C. 2022.
Recommendations for quantifying and reducing uncertainty in climate
projections of species distributions. Global Change Biology\textbf{28}
(22): 6586-- 6601. https://doi.org/10.1111/gcb.16371.

Brodie, S.J., Thorson, J.T., Carroll, G., Hazen, E.L., Bograd, S.,
Haltuch, M.A., Holsman, K.K., Kotwicki, S., Samhouri, J.F.,
Willis-Norton, E. and Selden, R.L. 2020. Trade-offs in covariate
selection for species distribution models: a methodological comparison.
Ecography \textbf{43} (1): 11-24. https://doi.org/10.1111/ecog.04707.

Brown, C.D. and Vellend, M. 2014. Non-climatic constraints on upper
elevational plant range expansion under climate change. Proceedings of
the Royal Society B: Biological Sciences \textbf{281} (1794): 20141779.

Budescu, D.V., Por, H.-H. and Broomell, S.B. 2012. Effective
communication of uncertainty in the IPCC reports. Climatic
Change\textbf{113} (2): 181-200. 10.1007/s10584-011-0330-3.

Carlson, S.M., Cunningham, C.J. and Westley, P.A. 2014. Evolutionary
rescue in a changing world. Trends in Ecology \& Evolution\textbf{29}
(9): 521-530.

Carr, M.H., Neigel, J.E., Estes, J.A., Andelman, S., Warner, R.R. and
Largier, J.L. 2003. Comparing Marine and Terrestrial Ecosystems:
Implications for the Design of Coastal Marine Reserves. Ecological
Applications \textbf{13} (1): S90-S107.

Charney, N.D., Record, S., Gerstner, B.E., Merow, C., Zarnetske, P.L.
and Enquist, B.J. 2021. A Test of Species Distribution Model
Transferability Across Environmental and Geographic Space for 108
Western North American Tree Species. Frontiers in Ecology and
Evolution\textbf{9} (393). 10.3389/fevo.2021.689295.

Christin, S., Hervet, E. and Lecomte, N. 2019. Applications for deep
learning in ecology. Methods in Ecology and Evolution \textbf{10} (10):
1632-1644.

Circles of Social Life. 1996. No Regrets: Circles of Climate Change
Adaptation. Available from https://www.circlesofclimate.org/ {[}accessed
April 27, 2022{]}.

Corner, A., Shaw, C. and Clarke, J. 2018. Principles for effective
communication and public engagement on climate change A Handbook for
IPCC authors outreach Project team Lead Authors Contributing Author
Editing \& Production.

Davis, A.J., Jenkinson, L.S., Lawton, J.H., Shorrocks, B. and Wood, S.
1998. Making mistakes when predicting shifts in species range in
response to global warming. Nature \textbf{391} (6669): 783-786.

Dietz, T. 2013. Bringing values and deliberation to science
communication. Proceedings of the National Academy of
Sciences\textbf{110} (Supplement 3): 14081. 10.1073/pnas.1212740110.

Dormann, C., Bobrowski, M., Dehling, M., Harris, D., Hartig, F.,
Lischke, H., Moretti, M., Pagel, J., Pinkert, S., Schleuning, M.,
Schmidt, S., Sheppard, C., Steinbauer, M., Zeuss, D. and Kraan, C. 2018.
Biotic interactions in species distribution modelling: 10 questions to
guide interpretation and avoid false conclusions. Global Ecology and
Biogeography \textbf{27} . 10.1111/geb.12759.

Duplisea, D.E., Roux, M.-J., Hunter, K.L. and Rice, J. 2021. Fish
harvesting advice under climate change: A risk-equivalent empirical
approach. PLOS ONE \textbf{16} (2): e0239503.
10.1371/journal.pone.0239503.

Elith, J. and Leathwick, J.R. 2009. Species Distribution Models:
Ecological Explanation and Prediction Across Space and Time. Annual
Review of Ecology, Evolution, and Systematics \textbf{40} (1): 677-697.
10.1146/annurev.ecolsys.110308.120159.

Elith, J., Burgman, M.A. and Regan, H.M. 2002. Mapping epistemic
uncertainties and vague concepts in predictions of species distribution.
Ecological Modelling \textbf{157} (2): 313-329.
https://doi.org/10.1016/S0304-3800(02)00202-8.

Elith, J., Leathwick, J.R. and Hastie, T. 2008. A working guide to
boosted regression trees. Journal of Animal Ecology \textbf{77} (4):
802-813. 10.1111/j.1365-2656.2008.01390.x.

English, P.A., Ward, E.J., Rooper, C.N., Forrest, R.E., Rogers, L.A.,
Hunter, K.L., Edwards, A.M., Connors, B.M. and Anderson, S.C. 2021.
Contrasting climate velocity impacts in warm and cool locations show
that effects of marine warming are worse in already warmer temperate
waters. Fish and Fisheries \textbf{23} (1): 239-- 255.
https://doi.org/10.1111/faf.12613.

Essington, T.E., Anderson, S.C., Barnett, L.A.K., Berger, H.M.,
Siedlecki, S.A. and Ward, E.J. 2022. Advancing statistical models to
reveal the effect of dissolved oxygen on the spatial distribution of
marine taxa using thresholds and a physiologically based index.
Ecography \textbf{2022} (8): e06249. https://doi.org/10.1111/ecog.06249.

Eyring, V., Bony, S., Meehl, G.A., Senior, C.A., Stevens, B., Stouffer,
R.J. and Taylor, K.E. 2016. Overview of the Coupled Model
Intercomparison Project Phase 6 (CMIP6) experimental design and
organization. Geosci. Model Dev. \textbf{9} (5): 1937-1958.
10.5194/gmd-9-1937-2016.

Falloon, P., Challinor, A., Dessai, S., Hoang, L., Johnson, J. and
Koehler, A.-K. 2014. Ensembles and uncertainty in climate change
impacts. Frontiers in Environmental Science \textbf{2} (33).
10.3389/fenvs.2014.00033.

Fernandes, R.F., Scherrer, D. and Guisan, A. 2019. Effects of simulated
observation errors on the performance of species distribution models.
Diversity and Distributions \textbf{25} (3): 400-413.
https://doi.org/10.1111/ddi.12868.

Flato, G.M. 2011. Earth system models: an overview. WIREs Climate
Change\textbf{2} (6): 783-800. https://doi.org/10.1002/wcc.148.

Fletcher, R.J., Hefley, T.J., Robertson, E.P., Zuckerberg, B., McCleery,
R.A. and Dorazio, R.M. 2019. A practical guide for combining data to
model species distributions. Ecology \textbf{100} (6): e02710.
https://doi.org/10.1002/ecy.2710.

Franco, J.N., Tuya, F., Bertocci, I., Rodriguez, L., Martinez, B.,
Sousa-Pinto, I. and Arenas, F. 2018. The `golden kelp' Laminaria
ochroleuca under global change: Integrating multiple eco-physiological
responses with species distribution models. Journal of
Ecology\textbf{106} (1): 47-58. https://doi.org/10.1111/1365-2745.12810.

Franklin, J., Davis, F., Ikegami, M., Syphard, A., Flint, L., Flint, A.
and Hannah, L. 2013. Modeling plant species distributions under future
climates: How fine scale do climate projections need to be? Global
change biology \textbf{19} : 473-483. 10.1111/gcb.12051.

Fredston-Hermann, A., Selden, R., Pinsky, M., Gaines, S.D. and Halpern,
B.S. 2020. Cold range edges of marine fishes track climate change better
than warm edges. Global Change Biology \textbf{26} (5): 2908-2922.
https://doi.org/10.1111/gcb.15035.

Freeman, E. and Moisen, G. 2008. A Comparison of the Performance of
Threshold Criteria for Binary Classification in Terms of Predicted
Prevalence and Kappa. Ecological Modelling \textbf{217} : 48-58.
10.1016/j.ecolmodel.2008.05.015.

Frolicher, T.L., Rodgers, K.B., Stock, C.A. and Cheung, W.W.L. 2016.
Sources of uncertainties in 21st century projections of potential ocean
ecosystem stressors. Global Biogeochemical Cycles \textbf{30} (8):
1224-1243. https://doi.org/10.1002/2015GB005338.

Gamliel, I., Buba, Y., Guy-Haim, T., Garval, T., Willette, D., Rilov, G.
and Belmaker, J. 2020. Incorporating physiology into species
distribution models moderates the projected impact of warming on
selected Mediterranean marine species. Ecography \textbf{43} (7):
1090-1106. https://doi.org/10.1111/ecog.04423.

Gardner, A.S., Maclean, I.M.D. and Gaston, K.J. 2019. Climatic
predictors of species distributions neglect biophysiologically
meaningful variables. Diversity and Distributions \textbf{25} (8):
1318-1333. https://doi.org/10.1111/ddi.12939.

Gelman, A., Meng, X.-L. and Stern, H. 1996. Posterior predictive
assessment of model fitness via realized discrepancies. Statistica
sinica: 733-760.

Giorgi, F. and Gutowski, W.J. 2015. Regional Dynamical Downscaling and
the CORDEX Initiative. Annual Review of Environment and
Resources\textbf{40} (1): 467-490.
10.1146/annurev-environ-102014-021217.

Gomez, C., Nephin, J., Lang, S., Feyrer, L., Keyser, F. and Lazin, G.
2021. Spatial Data, Analysis and Modelling Forums: An initiative to
broaden the collaborative research potential at DFO. Can. Tech. Rep.
Aquat. Sci. . 3416, pp. v + 36 p.

Gottschalk, T.K., Aue, B., Hotes, S. and Ekschmitt, K. 2011. Influence
of grain size on species--habitat models. Ecological
Modelling\textbf{222} (18): 3403-3412.
https://doi.org/10.1016/j.ecolmodel.2011.07.008.

Guillera-Arroita, G., Lahoz-Monfort, J.J., Elith, J., Gordon, A.,
Kujala, H., Lentini, P.E., McCarthy, M.A., Tingley, R. and Wintle, B.A.
2015. Is my species distribution model fit for purpose? Matching data
and models to applications. Global Ecology and Biogeography\textbf{24}
(3): 276-292. https://doi.org/10.1111/geb.12268.

Hao, T., Elith, J., Lahoz\selectlanguage{english}-Monfort, J.J. and Guillera\selectlanguage{english}-Arroita, G. 2020.
Testing whether ensemble modelling is advantageous for maximising
predictive performance of species distribution models.
Ecography\textbf{43} (4): 549-558.

Hausfather, Z. and Peters, G. 2020. Emissions -- the `business as usual'
story is misleading. Nature \textbf{577} : 618-620.

Hawkins, E. and Sutton, R. 2009. The Potential to Narrow Uncertainty in
Regional Climate Predictions. Bulletin of the American Meteorological
Society \textbf{90} (8): 1095-1108. 10.1175/2009BAMS2607.1.

Heltberg, R., Siegel, P.B. and Jorgensen, S.L. 2009. Addressing human
vulnerability to climate change: Toward a `no-regrets' approach. Global
Environmental Change \textbf{19} (1): 89-99.
https://doi.org/10.1016/j.gloenvcha.2008.11.003.

Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. and Jarvis, A.
2005. Very high resolution interpolated climate surfaces for global land
areas. International Journal of Climatology \textbf{25} (15): 1965-1978.
https://doi.org/10.1002/joc.1276.

Hilborn, R. 1987. Living with uncertainty in resource management. North
American Journal of Fisheries Management \textbf{7} (1): 1-5.

Hoegh-Guldberg, O. and Bruno John, F. 2010. The Impact of Climate Change
on the World's Marine Ecosystems. Science \textbf{328} (5985):
1523-1528. 10.1126/science.1189930.

Hof, A., Jansson, R. and Nilsson, C. 2012. The usefulness of elevation
as a predictor variable in species distribution modelling. Ecological
Modelling \textbf{246} : 86-90. 10.1016/j.ecolmodel.2012.07.028.

Holdsworth, A.M., Zhai, L., Lu, Y. and Christian, J.R. 2021. Future
Changes in Oceanography and Biogeochemistry Along the Canadian Pacific
Continental Margin. Frontiers in Marine Science \textbf{8} .

Holt, R.D. 1990. The microevolutionary consequences of climate change.
Trends in Ecology \& Evolution \textbf{5} (9): 311-315.
https://doi.org/10.1016/0169-5347(90)90088-U.

Homburg, K., Brandt, P., Drees, C. and Assmann, T. 2014. Evolutionarily
significant units in a flightless ground beetle show different climate
niches and high extinction risk due to climate change. Journal of Insect
Conservation \textbf{18} (5): 781-790. 10.1007/s10841-014-9685-x.

IPCC. 1996. A brief overview of the IPCC Second Assessment Report.
Climate Change Bulletin (10).

IPCC. 2021. Climate Change 2021: The Physical Science Basis.
Contribution of Working Group I to the Sixth Assessment Report of the
Intergovernmental Panel on Climate Change.

IPCC. 2022. Climate Change 2022 Impacts, Adaptation and Vulnerability:
Summary for Policymakers.

Iverson, L.R., Peters, M.P., Prasad, A.M. and Matthews, S.N. 2019.
Analysis of Climate Change Impacts on Tree Species of the Eastern US:
Results of DISTRIB-II Modeling. Forests \textbf{10} (4).
10.3390/f10040302.

Jarnevich, C.S., Stohlgren, T.J., Kumar, S., Morisette, J.T. and
Holcombe, T.R. 2015. Caveats for correlative species distribution
modeling. Ecological Informatics \textbf{29} : 6-15.

Johnson, K.F., Thorson, J.T. and Punt, A.E. 2019. Investigating the
value of including depth during spatiotemporal index standardization.
Fisheries Research \textbf{216} : 126-137.
https://doi.org/10.1016/j.fishres.2019.04.004.

Johnston, A., Moran, N., Musgrove, A., Fink, D. and Baillie, S.R. 2020.
Estimating species distributions from spatially biased citizen science
data. Ecological Modelling \textbf{422} : 108927.
https://doi.org/10.1016/j.ecolmodel.2019.108927.

Kearney, M. and Porter, W. 2009. Mechanistic niche modelling: combining
physiological and spatial data to predict species' ranges. Ecology
letters \textbf{12} (4): 334-350.

Kingsolver, J.G., Arthur Woods, H., Buckley, L.B., Potter, K.A.,
MacLean, H.J. and Higgins, J.K. 2011. Complex life cycles and the
responses of insects to climate change. \emph{In} Oxford University
Press.

Kordas, R.L., Harley, C.D.G. and O'Connor, M.I. 2011. Community ecology
in a warming world: The influence of temperature on interspecific
interactions in marine systems. Journal of Experimental Marine Biology
and Ecology \textbf{400} (1): 218-226.
https://doi.org/10.1016/j.jembe.2011.02.029.

Kujala, H., Burgman, M.A. and Moilanen, A. 2013a. Treatment of
uncertainty in conservation under climate change. Conservation
Letters\textbf{6} (2): 73-85.
https://doi.org/10.1111/j.1755-263X.2012.00299.x.

Kujala, H., Moilanen, A., Araujo, M.B. and Cabeza, M. 2013b.
Conservation Planning with Uncertain Climate Change Projections. PLOS
ONE \textbf{8} (2): e53315. 10.1371/journal.pone.0053315.

Legendre, P. and Fortin, M.J. 1989. Spatial pattern and ecological
analysis. Vegetatio \textbf{80} (2): 107-138.

Lenoir, J., Bertrand, R., Comte, L., Bourgeaud, L., Hattab, T.,
Murienne, J. and Grenouillet, G. 2020. Species better track climate
warming in the oceans than on land. Nature Ecology \&
Evolution\textbf{4} (8): 1044-1059. 10.1038/s41559-020-1198-2.

Liu, C., White, M. and Newell, G. 2011. Measuring and comparing the
accuracy of species distribution models with presence-absence data.
Ecography \textbf{34} (2): 232-243.

Lowen, J.B., Hart, D.R., Stanley, R.R.E., Lehnert, S.J., Bradbury, I.R.
and DiBacco, C. 2019. Assessing effects of genetic, environmental, and
biotic gradients in species distribution modelling. ICES Journal of
Marine Science \textbf{76} (6): 1762-1775. 10.1093/icesjms/fsz049.

Luoto, M. and Heikkinen, R.K. 2008. Disregarding topographical
heterogeneity biases species turnover assessments based on bioclimatic
models. Global Change Biology \textbf{14} (3): 483-494.
https://doi.org/10.1111/j.1365-2486.2007.01527.x.

Makino, A., Klein, C.J., Possingham, H.P., Yamano, H., Yara, Y., Ariga,
T., Matsuhasi, K. and Beger, M. 2015. The Effect of Applying Alternate
IPCC Climate Scenarios to Marine Reserve Design for Range Changing
Species. Conservation Letters \textbf{8} (5): 320-328.
https://doi.org/10.1111/conl.12147.

Maraun, D. 2016. Bias Correcting Climate Change Simulations - a Critical
Review. Current Climate Change Reports \textbf{2} (4): 211-220.
10.1007/s40641-016-0050-x.

Martinez, B., Arenas, F., Trilla, A., Viejo, R.M. and Carreno, F. 2015.
Combining physiological threshold knowledge to species distribution
models is key to improving forecasts of the future niche for macroalgae.
Global Change Biology \textbf{21} (4): 1422-1433.
https://doi.org/10.1111/gcb.12655.

Merow, C., Wilson, A.M. and Jetz, W. 2017. Integrating occurrence data
and expert maps for improved species range predictions. Global Ecology
and Biogeography \textbf{26} (2): 243-258.
https://doi.org/10.1111/geb.12539.

Monteiro, J.G., Jimenez, J.L., Gizzi, F., P\selectlanguage{polish}ř\selectlanguage{english}ikryl, P., Lefcheck, J.S.,
Santos, R.S. and Canning-Clode, J. 2021. Novel approach to enhance
coastal habitat and biotope mapping with drone aerial imagery analysis.
Scientific Reports \textbf{11} (1): 574. 10.1038/s41598-020-80612-7.

Moreno-Amat, E., Mateo, R.G., Nieto-Lugilde, D., Morueta-Holme, N.,
Svenning, J.-C. and Garc\selectlanguage{ngerman}ía-Amorena, I. 2015. Impact of model complexity
on cross-temporal transferability in Maxent species distribution models:
An assessment using paleobotanical data. Ecological
Modelling\textbf{312} : 308-317.
https://doi.org/10.1016/j.ecolmodel.2015.05.035.

Muha, T.P., Rodríguez-Rey, M., Rolla, M. and Tricarico, E. 2017. Using
Environmental DNA to Improve Species Distribution Models for Freshwater
Invaders. Frontiers in Ecology and Evolution \textbf{5} : 158.

Muhling, B.A., Brodie, S., Smith, J.A., Tommasi, D., Gaitan, C.F.,
Hazen, E.L., Jacox, M.G., Auth, T.D. and Brodeur, R.D. 2020.
Predictability of Species Distributions Deteriorates Under Novel
Environmental Conditions in the California Current System. Frontiers in
Marine Science \textbf{7} (589). 10.3389/fmars.2020.00589.

Naimi, B., Hamm, N.A.S., Groen, T.A., Skidmore, A.K. and Toxopeus, A.G.
2014. Where is positional uncertainty a problem for species distribution
modelling? Ecography \textbf{37} (2): 191-203.
https://doi.org/10.1111/j.1600-0587.2013.00205.x.

Nature Editorials. 2022. Time to recognize authorship of open data.
Nature \textbf{604} (8).

Nephin, J., Gregr, E.J., St. Germain, C., Fields, C. and Finney, J.L.
2020. Development of a Species Distribution Modelling Framework and its
Application to Twelve Species on Canada's Pacific Coast. DFO Can. Sci.
Advis. Sec. Res. Doc. . 2020/004, pp. xii + 107 p.

O'Connor, M., Bruno, J., Gaines, S., Halpern, B., Lester, S., Kinlan, B.
and Weiss, J. 2007. Temperature control of larval dispersal and the
implications for marine ecology, evolution, and conservation.
Proceedings of the National Academy of Sciences of the United States of
America \textbf{104} : 1266-1271. 10.1073/pnas.0603422104.

Osborne, P.E. and Leitão, P.J. 2009. Effects of species and habitat
positional errors on the performance and interpretation of species
distribution models. Diversity and Distributions \textbf{15} (4):
671-681. https://doi.org/10.1111/j.1472-4642.2009.00572.x.

Pecl Gretta, T., Araújo Miguel, B., Bell Johann, D., Blanchard, J.,
Bonebrake Timothy, C., Chen, I.C., Clark Timothy, D., Colwell Robert,
K., Danielsen, F., Evengård, B., Falconi, L., Ferrier, S., Frusher, S.,
Garcia Raquel, A., Griffis Roger, B., Hobday Alistair, J.,
Janion-Scheepers, C., Jarzyna Marta, A., Jennings, S., Lenoir, J.,
Linnetved Hlif, I., Martin Victoria, Y., McCormack Phillipa, C.,
McDonald, J., Mitchell Nicola, J., Mustonen, T., Pandolfi John, M.,
Pettorelli, N., Popova, E., Robinson Sharon, A., Scheffers Brett, R.,
Shaw Justine, D., Sorte Cascade, J.B., Strugnell Jan, M., Sunday
Jennifer, M., Tuanmu, M.-N., Vergés, A., Villanueva, C., Wernberg, T.,
Wapstra, E. and Williams Stephen, E. 2017. Biodiversity redistribution
under climate change: Impacts on ecosystems and human well-being.
Science \textbf{355} (6332): eaai9214. 10.1126/science.aai9214.

Peña, M.A., Fine, I. and Callendar, W. 2019. Interannual variability in
primary production and shelf-offshore transport of nutrients along the
northeast Pacific Ocean margin. Deep Sea Research Part II: Topical
Studies in Oceanography \textbf{169-170} : 104637.
https://doi.org/10.1016/j.dsr2.2019.104637.

Petitgas, P., Rijnsdorp, A.D., Dickey-Collas, M., Engelhard, G.H., Peck,
M.A., Pinnegar, J.K., Drinkwater, K., Huret, M. and Nash, R.D.M. 2013.
Impacts of climate change on the complex life cycles of fish. Fisheries
Oceanography \textbf{22} (2): 121-139.
https://doi.org/10.1111/fog.12010.

Phillips, S.J., Anderson, R.P. and Schapire, R.E. 2006. Maximum entropy
modeling of species geographic distributions. Ecological
modelling\textbf{190} (3-4): 231-259.

Piironen, J. and Vehtari, A. 2017. Comparison of Bayesian predictive
methods for model selection. Statistics and Computing \textbf{27} (3):
711-735.

Pinsky, M.L. and Fogarty, M. 2012. Lagged social-ecological responses to
climate and range shifts in fisheries. Climatic Change \textbf{115} (3):
883-891. 10.1007/s10584-012-0599-x.

Pollock, L.J., O'Connor, L.M.J., Mokany, K., Rosauer, D.F., Talluto,
M.V. and Thuiller, W. 2020. Protecting Biodiversity (in All Its
Complexity): New Models and Methods. Trends in Ecology \&
Evolution\textbf{35} (12): 1119-1128.
https://doi.org/10.1016/j.tree.2020.08.015.

Pollock, L.J., Tingley, R., Morris, W.K., Golding, N., O'Hara, R.B.,
Parris, K.M., Vesk, P.A. and McCarthy, M.A. 2014. Understanding
co-occurrence by modelling species simultaneously with a Joint Species
Distribution Model (JSDM). Methods in Ecology and Evolution\textbf{5}
(5): 397-406. https://doi.org/10.1111/2041-210X.12180.

Pörtner, H.O. and Peck, M.A. 2010. Climate change effects on fishes and
fisheries: towards a cause-and-effect understanding. Journal of Fish
Biology \textbf{77} (8): 1745-1779.
https://doi.org/10.1111/j.1095-8649.2010.02783.x.

Raimi, K.T., Stern, P.C. and Maki, A. 2017. The Promise and Limitations
of Using Analogies to Improve Decision-Relevant Understanding of Climate
Change. PLOS ONE \textbf{12} (1): e0171130.
10.1371/journal.pone.0171130.

Randin, C.F., Engler, R., Normand, S., Zappa, M., Zimmermann, N.E.,
Pearman, P.B., Vittoz, P., Thuiller, W. and Guisan, A. 2009. Climate
change and plant distribution: local models predict high-elevation
persistence. Global Change Biology \textbf{15} (6): 1557-1569.
https://doi.org/10.1111/j.1365-2486.2008.01766.x.

Roberts, D.R., Bahn, V., Ciuti, S., Boyce, M.S., Elith, J.,
Guillera\selectlanguage{english}-Arroita, G., Hauenstein, S., Lahoz\selectlanguage{english}-Monfort, J.J., Schroder, B.
and Thuiller, W. 2017. Cross\selectlanguage{english}-validation strategies for data with
temporal, spatial, hierarchical, or phylogenetic structure.
Ecography\textbf{40} (8): 913-929.

Robinson, L.M., Elith, J., Hobday, A.J., Pearson, R.G., Kendall, B.E.,
Possingham, H.P. and Richardson, A.J. 2011. Pushing the limits in marine
species distribution modelling: lessons from the land present challenges
and opportunities. Global Ecology and Biogeography \textbf{20} (6):
789-802. https://doi.org/10.1111/j.1466-8238.2010.00636.x.

Roux, M.-J., Duplisea, D.E., Hunter, K.L. and Rice, J. 2022. Consistent
Risk Management in a Changing World: Risk Equivalence in Fisheries and
Other Human Activities Affecting Marine Resources and Ecosystems.
Frontiers in Climate \textbf{3} .

Rufener, M.C., Kristensen, K., Nielsen, J.R. and Bastardie, F. 2021.
Bridging the gap between commercial fisheries and survey data to model
the spatiotemporal dynamics of marine species. Ecological Applications:
e02453.

Schloss, C.A., Nunez, T.A. and Lawler, J.J. 2012. Dispersal will limit
ability of mammals to track climate change in the Western Hemisphere.
Proceedings of the National Academy of Sciences \textbf{109} (22):
8606-8611.

Seneviratne, S., Nicholls, N., Easterling, D., Goodess, C., Kanae, S.,
Kossin, J., Luo, Y., Marengo, J., McInnes, K., Rahimi, M., Reichstein,
M., Sorteberg, A., Vera, C. and Zhang, X. 2012. Changes in climate
extremes and their impacts on the natural physical environment: An
overview of the IPCC SREX report. \emph{In} p. 12566.

Seo, C., Thorne, J., Hannah, L. and Thuiller, W. 2008. Scale effects in
species distribution models: Implications for conservation planning
under climate change. Biology letters \textbf{5} : 39-43.
10.1098/rsbl.2008.0476.

Shelton, A.O., Thorson, J.T., Ward, E.J. and Feist, B.E. 2014. Spatial
semiparametric models improve estimates of species abundance and
distribution. Canadian Journal of Fisheries and Aquatic
Sciences\textbf{71} (11): 1655-1666.

Skroblin, A., Carboon, T., Bidu, G., Chapman, N., Miller, M., Taylor,
K., Taylor, W., Game, E.T. and Wintle, B.A. 2021. Including indigenous
knowledge in species distribution modeling for increased ecological
insights. Conservation Biology \textbf{35} (2): 587-597.
https://doi.org/10.1111/cobi.13373.

Sofaer, H.R., Jarnevich, C.S., Pearse, I.S., Smyth, R.L., Auer, S.,
Cook, G.L., Edwards Jr, T.C., Guala, G.F., Howard, T.G. and Morisette,
J.T. 2019. Development and delivery of species distribution models to
inform decision-making. BioScience \textbf{69} (7): 544-557.

Stammer, D., Engels, A., Marotzke, J., Gresse, E., Hedemann, C. and
Petzold, J. 2021. Hamburg Climate Futures Outlook 2021. Assessing the
plausibility of deep decarbonization by 2050. Hamburg, Germany.

Stoklosa, J., Daly, C., Foster, S.D., Ashcroft, M.B. and Warton, D.I.
2015. A climate of uncertainty: accounting for error in climate
variables for species distribution models. Methods in Ecology and
Evolution \textbf{6} (4): 412-423.

Sunday, J., Bates, A. and Dulvy, N. 2012. Thermal tolerance and the
global redistribution of animals. Nature Climate Change \textbf{2} :
686--690. 10.1038/nclimate1539.

Thompson, P.L. and Gonzalez, A. 2017. Dispersal governs the
reorganization of ecological networks under environmental change. Nature
Ecology \& Evolution \textbf{1} (6): 1-8.

Thompson, P.L. and Fronhofer, E.A. 2019. The conflict between adaptation
and dispersal for maintaining biodiversity in changing environments.
Proceedings of the National Academy of Sciences \textbf{116} (42):
21061-21067.

Thompson, P.L., Guzman, L.M., De Meester, L., Horvath, Z., Ptacnik, R.,
Vanschoenwinkel, B., Viana, D.S. and Chase, J.M. 2020. A process-based
metacommunity framework linking local and regional scale community
ecology. bioRxiv: 832170. 10.1101/832170.

Thompson, P.L., Anderson, S.C., Nephin, J., Haggarty, D.R., Pena, M.A.,
English, P.A., Gale, K.S.P. and Rubidge, E. 2022a. Disentangling the
impacts of environmental change and commercial fishing on demersal fish
biodiversity in a northeast Pacific ecosystem. Marine Ecology Progress
Series \textbf{689} : 137-154.

Thompson, P.L., Nephin, J., Davies, S.C., Park, A.E., Lyons, D.A.,
Rooper, C.N., Pena, M.A., Christian, J.R., Hunter, K.L., Rubidge, E. and
Holdsworth, A.M. 2022b. Groundfish biodiversity change in northeastern
Pacific waters under projected warming and deoxygenation. bioRxiv:
2022.2005.2004.490690. 10.1101/2022.05.04.490690.

Thorson, J.T. and Minto, C. 2014. Mixed effects: a unifying framework
for statistical modelling in fisheries biology. ICES Journal of Marine
Science \textbf{72} (5): 1245-1256. 10.1093/icesjms/fsu213.

Thorson, J.T., Shelton, A.O., Ward, E.J. and Skaug, H.J. 2015.
Geostatistical delta-generalized linear mixed models improve precision
for estimated abundance indices for West Coast groundfishes. ICES
Journal of Marine Science \textbf{72} (5): 1297-1310.
10.1093/icesjms/fsu243.

Thorson, J.T., Barbeaux, S.J., Goethel, D.R., Kearney, K.A., Laman,
E.A., Nielsen, J.K., Siskey, M.R., Siwicke, K. and Thompson, G.G. 2021.
Estimating fine-scale movement rates and habitat preferences using
multiple data sources. Fish and Fisheries \textbf{22} (6): 1359-1376.
https://doi.org/10.1111/faf.12592.

Thuiller, W., Brotons, L., Araujo, M. and Lavorel, S. 2004. Effects of
restricting range of data to project current and future species
distributions. Ecography \textbf{27} : 165-172.
10.1111/j.0906-7590.2004.03673.x.

Thuiller, W., Gueguen, M., Renaud, J., Karger, D.N. and Zimmermann, N.E.
2019. Uncertainty in ensembles of global biodiversity scenarios. Nature
Communications \textbf{10} (1): 1-9.

Tittensor, D., Blanchard, J., Fulton, E., Cheung, W., Novaglio, C.,
Harrison, C., Heneghan, R., Barrier, N., Bianchi, D., Bopp, L.,
Bryndum-Buchholz, A., Britten, G., Buchner, M., Christensen, V., Coll,
M., Dunne, J., Eddy, T., Everett, J., Fernandes, J. and Stock, C. 2021.
Next-generation ensemble projections reveal higher climate risks for
marine ecosystems. Nature Climate Change. 10.1038/s41558-021-01173-9.

Tyberghein, L., Verbruggen, H., Pauly, K., Troupin, C., Mineur, F. and
De Clerck, O. 2012. Bio-ORACLE: a global environmental dataset for
marine species distribution modelling. Global Ecology and
Biogeography\textbf{21} (2): 272-281.
https://doi.org/10.1111/j.1466-8238.2011.00656.x.

Urban, M.C. 2019. Projecting biological impacts from climate change like
a climate scientist. WIREs Climate Change \textbf{10} (4): e585.
https://doi.org/10.1002/wcc.585.

Urban, M.C., Bocedi, G., Hendry, A.P., Mihoub, J.B., Pe'er, G., Singer,
A., Bridle, J.R., Crozier, L.G., De Meester, L., Godsoe, W., Gonzalez,
A., Hellmann, J.J., Holt, R.D., Huth, A., Johst, K., Krug, C.B.,
Leadley, P.W., Palmer, S.C.F., Pantel, J.H., Schmitz, A., Zollner, P.A.
and Travis, J.M.J. 2016. Improving the forecast for biodiversity under
climate change. Science \textbf{353} (6304): aad8466.
10.1126/science.aad8466.

Urli, M., Brown, C.D., Narvaez Perez, R., Chagnon, P.L. and Vellend, M.
2016. Increased seedling establishment via enemy release at the upper
elevational range limit of sugar maple. Ecology \textbf{97} (11):
3058-3069.

US National Research Council. 2008. Public participation in
environmental assessment and decision making. National Academy Press,
Washington, USA.

Valladares, F., Matesanz, S., Guilhaumon, F., Araujo, M.B., Balaguer,
L., Benito-Garzon, M., Cornwell, W., Gianoli, E., van Kleunen, M., Naya,
D.E., Nicotra, A.B., Poorter, H. and Zavala, M.A. 2014. The effects of
phenotypic plasticity and local adaptation on forecasts of species range
shifts under climate change. Ecology Letters \textbf{17} (11):
1351-1364. https://doi.org/10.1111/ele.12348.

Vehtari, A., Gelman, A. and Gabry, J. 2017. Practical Bayesian model
evaluation using leave-one-out cross-validation and WAIC. Statistics and
computing \textbf{27} (5): 1413-1432.

Vellend, M. 2016. The theory of ecological communities (MPB-57).
Princeton University Press.

Villero, D., Pla, M., Camps, D., Ruiz-Olmo, J. and Brotons, L. 2017.
Integrating species distribution modelling into decision-making to
inform conservation actions. Biodiversity and Conservation \textbf{26} .
10.1007/s10531-016-1243-2.

Virkkala, R., Marmion, M., Heikkinen, R., Thuiller, W. and Luoto, M.
2010. Predicting range shifts of northern bird species: Influence of
modelling technique and topography. Acta Oecologica \textbf{36} :
269-281. 10.1016/j.actao.2010.01.006.

Wallingford, P.D. and Sorte, C.J.B. 2022. Dynamic species interactions
associated with the range-shifting marine gastropod Mexacanthina
lugubris. Oecologia. 10.1007/s00442-022-05128-5.

Warton, D.I., Blanchet, F.G., O'Hara, R.B., Ovaskainen, O., Taskinen,
S., Walker, S.C. and Hui, F.K.C. 2015. So many variables: Joint modeling
in community ecology. Trends in Ecology \& Evolution \textbf{30} (12):
766-779. https://doi.org/10.1016/j.tree.2015.09.007.

Watson, J., Joy, R., Tollit, D.J., Thornton, S.J. and Auger Methe, M.
2019. Estimating animal utilization distributions from multiple data
types: a joint spatio-temporal point process framework. arXiv:
Methodology. https://doi.org/10.48550/arXiv.1911.00151.

Weiskopf, S.R., Rubenstein, M.A., Crozier, L.G., Gaichas, S., Griffis,
R., Halofsky, J.E., Hyde, K.J.W., Morelli, T.L., Morisette, J.T., Munoz,
R.C., Pershing, A.J., Peterson, D.L., Poudel, R., Staudinger, M.D.,
Sutton-Grier, A.E., Thompson, L., Vose, J., Weltzin, J.F. and Whyte,
K.P. 2020. Climate change effects on biodiversity, ecosystems, ecosystem
services, and natural resource management in the United States. Science
of The Total Environment \textbf{733} : 137782.
https://doi.org/10.1016/j.scitotenv.2020.137782.

Wiens, J.A., Stralberg, D., Jongsomjit, D., Howell, C.A. and Snyder,
M.A. 2009. Niches, models, and climate change: Assessing the assumptions
and uncertainties. Proceedings of the National Academy of
Sciences\textbf{106} (Supplement 2): 19729. 10.1073/pnas.0901639106.

Wiens, J.J. 2016. Climate-Related Local Extinctions Are Already
Widespread among Plant and Animal Species. PLOS Biology \textbf{14}
(12): e2001104. 10.1371/journal.pbio.2001104.

Willis, K. and Bhagwat, S. 2009. Biodiversity and Climate Change.
Science (New York, N.Y.) \textbf{326} : 806-807.
10.1126/science.1178838.

Xu, Z., Han, Y., Tam, C.-Y., Yang, Z.-L. and Fu, C. 2021. Bias-corrected
CMIP6 global dataset for dynamical downscaling of the historical and
future climate (1979--2100). Scientific Data \textbf{8} (1): 293.
10.1038/s41597-021-01079-3.

Young, M. and Carr, M.H. 2015. Application of species distribution
models to explain and predict the distribution, abundance and assemblage
structure of nearshore temperate reef fishes. Diversity and
Distributions \textbf{21} (12): 1428-1440.
https://doi.org/10.1111/ddi.12378.

Zangiabadi, S., Zaremaivan, H., Brotons, L., Mostafavi, H. and Ranjbar,
H. 2021. Using climatic variables alone overestimate climate change
impacts on predicting distribution of an endemic species. PLOS
ONE\textbf{16} (9): e0256918. 10.1371/journal.pone.0256918.

Zarnetske, P.L., Skelly, D.K. and Urban, M.C. 2012. Biotic multipliers
of climate change. Science \textbf{336} (6088): 1516-1518.

Zurell, D., Franklin, J., Konig, C., Bouchet, P.J., Dormann, C.F.,
Elith, J., Fandos, G., Feng, X., Guillera-Arroita, G., Guisan, A.,
Lahoz-Monfort, J.J., Leitao, P.J., Park, D.S., Peterson, A.T.,
Rapacciuolo, G., Schmatz, D.R., Schroder, B., Serra-Diaz, J.M.,
Thuiller, W., Yates, K.L., Zimmermann, N.E. and Merow, C. 2020. A
standard protocol for reporting species distribution models.
Ecography\textbf{43} (9): 1261-1277. https://doi.org/10.1111/ecog.04960.

\textbf{Hosted file}

\verb`Addressing_uncertainty_figue.docx`  available at \url{https://authorea.com/users/568358/articles/614231-addressing-uncertainty-when-projecting-marine-species-distributions-under-climate-change}

\textbf{Hosted file}

\verb`Addressing_uncertainty_table1.docx`  available at \url{https://authorea.com/users/568358/articles/614231-addressing-uncertainty-when-projecting-marine-species-distributions-under-climate-change}

\textbf{Hosted file}

\verb`Addressing_uncertainty_table2.docx`  available at \url{https://authorea.com/users/568358/articles/614231-addressing-uncertainty-when-projecting-marine-species-distributions-under-climate-change}

\selectlanguage{english}
\FloatBarrier
\end{document}

