Beauchesne-eDriversMS.Rmd

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title: Characterizing Exposure to and Sharing Knowledge of Drivers of Environmental Change in the St. Lawrence System in Canada
author: David Beauchesne^1,2,\*^, Rémi M. Daigle^2^, Steve Vissault^3^, Dominique Gravel^3^, Andréane Bastien^4^, Simon Bélanger^5^, Pascal Bernatchez^5^, Marjolaine Blais ^6^, Hugo Bourdages^6^, Clément Chion^7^, Peter S. Galbraith^6^, Benjamin Halpern^8,9^, Camille Lavoie^2^, Christopher W. McKindsey^6^, Alfonso Mucci^10^, Simon Pineault^11^, Michel Starr^6^, Anne-Sophie Ste-Marie^4^, Philippe Archambault^2^
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^1^Institut des sciences de la mer, Université du Québec à Rimouski, Rimouski, QC, Canada \newline
^2^ArcticNet, Québec Océan, Département de biologie, Université Laval, Québec, QC, Canada \newline
^3^Département de biologie, Université de Sherbrooke, Sherbrooke, QC, Canada \newline
^4^St. Lawrence Global Observatory, Rimouski, QC, Canada \newline
^5^Département de Biologie, Chimie et Géographie, Université du Québec à Rimouski, Rimouski, QC, Canada \newline
^6^Département des Sciences naturelles, Université du Québec en Outaouais, Gatineau, QC, Canada \newline
^7^Fisheries and Oceans Canada, Maurice Lamontagne Institute, Mont-Joli, QC, Canada \newline
^8^National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara, CA, United States \newline
^9^Bren School of Environmental Science and Management, University of California, Santa Barbara, CA, United States \newline
^10^Department of Earth & Planetary Sciences, McGill University, Montréal, QC, Canada \newline
^11^Ministère Environnement et Lutte contre les changements climatiques, Québec, QC, Canada  \newline

**Correspondence**: \newline
David Beauchesne \newline
david.beauchesne@uqar.ca \newline

**Number of figures**: 6 \newline
**Number of tables**: 1 \newline

\newpage

<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->
# Abstract
<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

The St. Lawrence is a vast and complex socio-ecological system providing a
wealth of services that sustain numerous economic sectors. ~~These ecosystems are~~ \textcolor{blue}{This ecosystem is} subject to significant human pressures that overlap and potentially interact with climate-driven environmental changes. Our objective in this paper is to systematically characterize the distribution and intensity of drivers of \textcolor{blue}{environmental} change \textcolor{blue}{(hereafter, drivers)} in the St. Lawrence System. ~~To do so, we launch *eDrivers*, an open knowledge platform gathering experts committed to structuring, standardizing and sharing knowledge on drivers of change in support of science and management.~~ We gathered data\textcolor{blue}{-based indicators for} ~~on~~ 22 coastal, climate, fisheries, and marine traffic drivers through collaborations, existing environmental initiatives and open data portals. We show that few areas of the St. Lawrence are free of cumulative exposure. The Estuary, ~~the~~ Anticosti Gyre, and coastal areas are particularly exposed, especially in the vicinity of urban centers. We identified 6 ~~areas of~~ distinct ~~cumulative exposure regimes~~ \textcolor{blue}{clusters with similar suites of co-occurring drivers} and show that certain driver \textcolor{blue}{combinations are inherent to} ~~typically co-occur in~~
different regions of the St. Lawrence and that coastal areas are exposed to
all driver types. Of particular concern are two ~~threat complexes~~\textcolor{blue}{clusters} capturing
most exposure hotspots \textcolor{blue}{and} that show the convergence of contrasting ~~exposure
regimes~~ \textcolor{blue}{cumulative exposure profiles} at the head of the Laurentian Channel.
\textcolor{blue}{Sharing the knowledge of drivers emerged as a priority to facilitate future environmental assessment efforts. We thus launch \textit{eDrivers}, an open knowledge platform gathering experts committed to structuring, standardizing and sharing knowledge on drivers of environmental change in support of holistic science and management.} *eDrivers* was built on a series of guiding principles upholding existing data management and open science standards. We therefore expect it to evolve through time to address knowledge gaps and refine current driver layers. Ultimately, we believe that *eDrivers* represents a much needed solution that could radically influence broad scale research and management practices by increasing knowledge accessibility and interoperability.

**Keywords**: ocean observing systems, St. Lawrence,
cumulative exposure, multiple stressors, global change

<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->
# Introduction
<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

The St. Lawrence System, formed by one of the largest estuaries in the world
and a vast interior sea, is a complex social-ecological system characterized by highly variable environmental conditions and oceanographic processes
[@el-sabh1990a; @white1997; @dufour2007]. It constitutes a unique
and heterogeneous array of habitats suited for the establishment of diverse and
productive ecological communities [@savenkoff2000]. As a result, the St. Lawrence System has
benefited the Canadian economy. It sustains a rich fisheries
industry targeting more than 50 species, serves as the gateway to eastern
North-America by granting access to more than 40 ports and is the most densely
populated Canadian region, hosts a booming tourism industry and an expanding
aquaculture production, fosters emerging activities, and boasts a yet untapped
hydrocarbon potential [@beauchesne2016; @schloss2017; @archambault2017]. With
major investments recently made and more forthcoming in economic and
infrastructure development and research [*e.g.* @governmentofquebec2015; @rqm2018], an intensification of the human footprint is likely in the St. Lawrence System.
~~As elsewhere in the world [see @halpern2015a; @halpern2019], this intensifying human footprint will likely result in increasingly intricate environmental exposure regimes, *i.e.* suites of overlapping drivers of environmental change threatening ecosystems, habitats and ecological communities.~~
\textcolor{blue}{Consequently, the St. Lawrence System is exposed to an increasing number of drivers of environmental change, as is observed across ecosystems globally} [see @halpern2015a; @halpern2019].
~~Drivers of environmental change, often referred to as stressors or pressures and hereafter called drivers for simplicity, are any externalities that affect
environmental processes and disturb natural systems.~~
\textcolor{blue}{We broadly define drivers of environmental change (hereafter, drivers) as any externality that affects environmental processes and disturbs natural systems.}
Drivers may originate from natural or human-induced biophysical processes (*e.g.* sea surface-water temperature anomalies and hypoxia) or directly from anthropogenic activities (*e.g.* fisheries and marine pollution). The potential for complex interactions between co-occurring drivers is the largest uncertainty when studying or predicting environmental impacts [@darling2008; @cote2016]. Multiple drivers can combine non-linearly and result in effects that are greater (synergistic effect) or lower (antagonistic effect) than the sum of individual effects [@crain2008; @darling2008; @cote2016].

~~The uncertainty associated with complex driver interactions must therefore be taken into account when investigating environmental impacts [@cote2016], yet research on the effects of drivers in marine environments remains overwhelmingly focused on single driver assessments} [@obrien2019].~~  Increasing exposure and the experiences of past ecological tragedies in the St. Lawrence System such as the collapse of cod fisheries [@frank2005; @dempsey2018] and the decline of the beluga and right whale populations [@plourde2014] together urge the need to characterize the distribution, intensity and co-occurrence of drivers in system. \textcolor{blue}{Research on the effects of drivers in marine environments, nonetheless, remains overwhelmingly focused on single driver assessments} [@obrien2019]. \textcolor{blue}{Whereas co-occurring drivers may not interact, driver co-occurrence is a requirement for interactions to exist. Knowledge of their co-distribution can therefore identify areas where driver interactions are most likely observed.}
~~Describing drivers will therefore provide critical information on areas most exposed to drivers in the St. Lawrence. It~~

\textcolor{blue}{Characterizing drivers is also a necessary step for the application of holistic management approaches.} Holistic approaches typically involve, but are not limited to, selecting and describing valued ecosystem components (\textit{e.g.} habitats and species) and drivers (\textit{e.g.} marine traffic and ocean acidification), assessing the exposure and vulnerability of valued components to drivers, selecting a proper spatio-temporal scale, monitoring, and public and stakeholder participation [@dube2001].
Gathering environmental knowledge for holistic initiatives can, however, be a very challenging and time consuming –- not to say painful –- process. On one hand, there is an overwhelming and expanding wealth of data available. Such information overload may inhibit our ability to make decisions based on scientific information, promote massive duplication of effort, disproportionately appropriate research funds to certain sectors, and obscure knowledge gaps amid a sea of information [@eppler2004]. On the other hand, crucial data are lacking and remain largely unavailable or inaccessible for a variety of reasons, including proprietary rights, lack of organizational time, capacity and training, and, in some cases, an unwillingness to share; this curtails our ability for appropriate decision-making.

Current initiatives facilitate the data gathering process by assembling, organizing and sharing environmental knowledge, such as the Ocean Biogeographic Information System [OBIS; @obis2019] for biotic data and Bio-ORACLE [@assis2018] for abiotic data. However, equivalent platforms for ~~environmental~~ drivers have largely focused on single drivers (e.g. Global Fishing Watch) and platforms collating data\textcolor{blue}{-based indicators} and knowledge on multiple drivers in a comparable and interoperable way remain conspicuously missing [but see @halpern2015]. This is in spite of integrated management and assessment approaches requiring efficient data reporting, standardized data management practices, and tools tailored to the study of the effects of multiple drivers [@dafforn2016; @stock2018]. ~~An additional objective thus emerged in the process of addressing our initial goals: sharing information about the distribution and intensity of drivers of change in the St.Lawrence.~~

The main ~~objective~~ \textcolor{blue}{goal} of this study is to characterize the distribution and
intensity of drivers in the St. Lawrence System. More specifically, ~~we aim~~ \textcolor{blue}{our objectives are} to: 1)
identify areas of high cumulative exposure \textcolor{blue}{to drivers} and 2) characterize areas with similar cumulative exposure ~~regimes~~ \textcolor{blue}{profiles, \textit{i.e.} areas exposed to similar suites of co-occurring drivers}.
\textcolor{blue}{An additional objective emerged while addressing the main goal of this manuscript: sharing information about the distribution and intensity of drivers of environmental change in the St.Lawrence.}
~~We achieve these objectives with the development of an
open knowledge platform, named *eDrivers*. The platform was designed to
facilitate collaboration, real-time assessments of cumulative exposure and to
evolve with the addition of information and threats to the St-Lawrence ecosystems.~~
\textcolor{blue}{We achieve this through the development of an open knowledge platform, \textit{eDrivers}, that was designed to facilitate the widespread availability of driver characterization for holistic assessments and management approaches. Here, we present its guiding principles and accompanying tools.}

<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->
# Materials and Methods
<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

## ~~Estuary and Gulf of St. Lawrence~~ \textcolor{blue}{St. Lawrence System}

The St. Lawrence System is composed of the \textcolor{blue}{St. Lawrence} Estuary and the Gulf of St. Lawrence
(Figure \ref{egsl}). The Estuary is defined by the limit of seawater intrusion,
close to Îles d'Orléans, to the west and by its connection to the Gulf
near Pointe-des-Monts.
The surface layer is
composed of freshwater flowing seaward, primarily from the Great Lakes Basin
through the St. Lawrence River.
Atlantic waters intrude landwards at depth into the Gulf and Estuary from Cabot Strait, but as well as from the Strait of Belle Isle (see below).

The topology of the Northern Gulf is characterized by three deep channels (250-500 m).
The Laurentian Channel is the main channel connecting the Estuary to the
Atlantic through Cabot Strait. The Esquiman and Anticosti channels are two
secondary channels that branch off from the Laurentian Channel to the north
towards the Strait of Belle Isle and the Labrador and north of Anticosti Island, respectively.
The Southern Gulf hosts the Magdalen Shallows, a vast area with an
average depth of ~50 m. The water column in the Gulf and St. Lawrence Estuary includes a seasonal cold intermediate layer
that separates the surface and deep layers. Seasonal sea ice affects circulation
in the St. Lawrence. Finally, three islands impact the physical dynamics of the
St. Lawrence: the Anticosti Island to the north, the îles de la Madeleine in the
middle of the Magdalen Shallows and Prince Edward Island to the south. See
@saucier2003 and @galbraith2018 for more information on the physical oceanography
of the St. Lawrence.

The St. Lawrence drains over 25% of global freshwater reserves through its
connection to the Great Lakes Basin, which is home to over 45 million
North Americans, *i.e.* 15 and 30 million in Canada and the United States,
respectively. The coasts of St. Lawrence System, as delimited by our study area (Figure \ref{egsl}), boast a much lower
population of approximately 1 million Canadians living within 10 km of the
coast, with populations mainly located in a few coastal cities in the Estuary
and the Southern Gulf [@statistics-canada2017].


## Drivers

\textcolor{blue}{Drivers, as broadly defined in this study, are data-based indicators of environmental conditions and human activities that are often referred to as driving forces, stressors, pressures, or states in the scientific and environmental assessment litterature} [*e.g.* @kristensen2004; @halpern2019].
\textcolor{blue}{Defining such categories, however, can be difficult and is often context- and ecosystem-specific} [@gari2015; @dempsey2018].
\textcolor{blue}{As such, we refrain from articulating our work around a specific framework or imposing categories on data-based products that may change with a user's objective. Instead, we focus on available data-based indicators that contribute to evaluate the ecosystem's cumulative exposure to multiple threats.}

Drivers selection was informed by a global cumulative impact assessment initiative [@halpern2008a; @halpern2015a; @halpern2019] and available from the National Center for Ecological Analysis and Synthesis (NCEAS) online data repository [@halpern2015], regional holistic evaluations of the state of the St. Lawrence [@dufour2007;
@benoit2012], and communications with regional experts (Table 1).
~~We selected global data that were unavailable at the regional scale and that were available at a resolution adequate for use at the scale of the St. Lawrence (*e.g.* marine pollution).~~ \textcolor{blue}{Where regional data on drivers were unavailable, available global data at a resolution adequate for the scale of the St. Lawrence System were used instead (\textit{e.g.} marine pollution).}

We characterized the intensity and distribution of 22 drivers
(Table 1). Drivers incorporated in the analyses are varied in origin,
*i.e.* from terrestrial (*e.g.* nutrient input) to marine (*e.g.* shipping),
and from large scale biophysical processes (*e.g.* temperature anomalies) to
localized anthropogenic activities (*e.g.* fisheries). Drivers were divided
into 4 groups: coastal, climate, fisheries, and marine traffic (Table 1). All
data layers and methodologies are described in the supplementary materials.

~~Drivers with non-normal frequency distributions were log-transformed, as in
Halpern et al. 2008, to minimize the impact of extreme outliers that may or may
not be real observations (Figure S1), and all drivers were scaled between 0 and
1 to allow driver comparisons. The 99th quantile of individual driver
distribution was used as the upper bound for scaling.~~
\textcolor{blue}{As in Halpern et al. 2019, drivers with non-normal frequency distributions were log-transformed to avoid underestimating intermediate driver values. All drivers were scaled between 0 and 1 to allow comparisons. The 99th quantile of individual driver distribution was used as the upper limit for scaling to control for extreme values that may or may not represent real observations.}
~~All drivers were embedded in a regular grid composed of 245604 $1 km^2$
hexagonal cells to construct the integrated dataset used for the analyses
(Figure S2).~~
\textcolor{blue}{The St. Lawrence System was divided into a regular grid of
1 $km^2$ cells into which all drivers were integrated (Figure S2)}

## ~~Driver co-occurrence~~ {-}

~~As an introduction to indicators of cumulative exposure
(objective 1), we begin by presenting a simplifed 2-driver example that focuses
on hypoxia and demersal destructive fisheries, two drivers known to occur
mainly in deeper waters of the St. Lawrence. Driver co-ccurrence was evaluated
spatially by summing the scaled intensity of drivers in each grid cell. The
intensity at which pairs of drivers co-occur was evaluated using a two-dimensional kernel density.~~

## Cumulative exposure

~~Areas with high cumulative exposure were identified by comparing
areas on the basis of the number and relative intensity of drivers in each
grid cell (objective 1).~~
\textcolor{blue}{We begin by providing a simplified 2-driver example that focuses on
the co-occurrence of hypoxia and demersal destructive fisheries, two drivers that mostly
occur in deeper St. Lawrence waters. Driver co-occurrence was
evaluated spatially by summing the scaled intensity of drivers in each grid cell.
The intensity at which pairs of drivers co-occur was evaluated using a
two-dimensional kernel density. This example demonstrates how driver
co-occurrence was evaluated and serves as a stepping stone to the
integrative indicators used hereafter, \textit{i.e.} cumulative exposure and
cumulative hotspots (objective 1).}

~~Cumulative exposure ($E_C$), was defined as the sum of the scaled intensity of all
drivers in each grid cell:~~
\textcolor{blue}{We evaluated cumulative exposure ($E_C$) for each grid
cell as the sum of scaled driver intensities:}

$$E_{C_x} = \sum_{i=1}^{n} D_{i,x}$$

where $x$ is a grid cell, $i$ is a driver, and $D$ is the scaled intensity of
driver $i$. ~~The cumulative footprint provides an estimate of the total relative
footprint in each grid cell.~~ A grid cell with a high $E_C$ value is either
characterized by multiple drivers at low relative intensity, ~~limited~~ \textcolor{blue}{a few} drivers at
high relative intensity, or both.

~~We also identified cumulative hotspots ($H_C$) to explore the distribution of
cumulative exposure in the St. Lawrence (objective 1). Cumulative hotspots ($H$)
were defined as the number of drivers in each grid cell with scaled intensity
contained over their respective 80th percentile:~~

\textcolor{blue}{We also identified cumulative hotspots ($H_C$) -- \textit{i.e.} areas where drivers co-occur at high
relative intensities -- as the number of drivers in each grid cell with scaled
intensity contained over their respective 80th percentile:}

<!-- Indicator function -->
$$H_{C_x} = \sum_{i=1}^{n} \mathbbm{1} (D_{i,x} \; \epsilon \; P_{80, D_i})$$

where, $x$ is a grid cell, $i$ is a driver and $D$ is the scaled intensity of
driver $i$ and $P_{80, D_i}$ is the 80th percentile of driver $i$.
~~Hotspots thus identify areas where drivers are co-occurring at high relative intensities.~~


## ~~Threat complexes~~\textcolor{blue}{Cumulative exposure profiles}

~~In order to identify areas with similar exposure regimes (objective 2), we identify threat complexes using a clustering approach [*e.g.* see @bowler2019]. We use the term clusters in presenting the methods, but use threat complex when discussing the results on cumulative exposure regimes.~~

### Clustering

\textcolor{blue}{We identified areas with similar cumulative exposure profiles (objective 2) using a clustering approach} [@bowler2019].
~~Threat complexes were identified using~~ \textcolor{blue}{We used} a partional *k-medoids* clustering
algorithm, CLARA [CLustering for Large Applications; @kaufman1990], which was
designed for large datasets. The CLARA algorithm uses the PAM (Partition
Around Medoids) algorithm on a sample from the original dataset to identify a
set of $k$ objects that are representative of all other objects, *i.e.*
medoids and that are central to the cluster they represent. The goal of the
algorithm is to iteratively minimize intra-cluster dissimilarity. Iterations
are compared on the basis of the average dissimilarity between cluster objects
and representative medoid to select the optimal set of $k$ medoids that
minimizes average dissimilarity. We used the clustering algorithm with the
Manhattan distance since this measure is less affected by extreme values
[@legendre2012], as is the *k-medoids* clustering algorithm [@kaufman1990].
We used 100 iterations using samples of 10000 observations
(*i.e.* ~5% of observations) to identify clusters. Analyses were performed
using the *cluster* R package [@maechler2018].
Partitional clustering algorithms require a user-defined number of clusters.
Values of $k$ ranging from 2 to 10 were tested and validated by selecting
the number of clusters that maximized the average silhouette width [@kaufman1990]
and minimized the total within-cluster sum of squares (Figure S4).


### Inter-cluster dissimilarity

Differences between clusters were explored by measuring the total inter-cluster
dissimilarity and the contribution of each driver to the total inter-cluster
dissimilarity using a similarity percentage analysis (SIMPER) with Manhattan
distance [Figure S5; @clarke1993]. The Manhattan distance was again preferred for continuity
with the clustering analysis and to ensure that outliers did not have a strong
influence \textcolor{blue}{on} the analysis. As the drivers dataset is large (~ 250000 observations),
we used a bootstrap procedure for the SIMPER analysis, randomly selecting
5% of each cluster to run the analysis and repeating the process over 300
iterations. We also compared the mean intensity of each driver within each
cluster to better capture the inter-cluster dissimilarity.


### Intra-cluster similarity

Intra-cluster similarity was evaluated calculating the intra-cluster Manhattan
distance and by transforming the mean contribution to distance ($M_c$) of each
driver by $.1 / (.1 + M_c)$ to obtain a similarity measure for each driver
($S_d$). The total similarity is the sum of all $S_d$. (Figure S6).
As with the inter-cluster dissimilarity, we used a bootstrap procedure for the
intra-cluster similarity, randomly selecting 25% of each cluster observation to
run the analysis and repeating the process over 50 iterations. We only used the
bootstrap procedure for clusters with less than 40000 observations since
computation time was manageable.



<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->
# Results and Discussion
<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

## ~~Driver co-occurrence~~ {-}

~~Hypoxia is mainly distributed in the Laurentian, Anticosti and Esquiman Channels,
with the head of the Channels most exposed (Figure \ref{kernel}A). Demersal
destructive fisheries are located along the Laurentian Channel, the heads of the
Anticosti and Esquiman Channels and around the îles de la Madeleine (Figure \ref{kernel}B).
By combining both drivers, we can observe that hypoxia and demersal destructive
fisheries co-occur mostly at high relative intensity (Figure \ref{kernel}D) in
the vicinity of the Anticosti Gyre and the heads of the Esquiman and Anticosti
Channels (Figure \ref{kernel}C, Box 1).~~

~~Fisheries in the St. Lawrence have historically affected biodiversity
distribution and habitat quality [@moritz2015]. Concurrently, hypoxia decreases
overall habitat quality, but triggers species-dependent responses ranging from
adaptation [*e.g.* northern shrimp *Pandalus borealis* and Greenland halibut
*Reinhardtius hippoglossoides*; @pillet2016] to reduced growth rates
[@dupont-prinet2013] and avoidance of oxygen-depleted habitats [*e.g.* Atlantic
cod *Gadus morhua*; @chabot2008] to increased mortality [*e.g.* sessile benthic
invertebrates; @eby2005; @gilbert2007; @belley2010]. Certain species may thus be
adversely affected by fisheries and withstand hypoxia but still experience a
decrease in prey availability, while others may be deleteriously affected by
the compounded effect of both drivers [@deleo2017].~~


## Cumulative exposure

\textcolor{blue}{We first present the simplified hypoxia-fisheries example to demonstrate how driver co-occurrence was evaluated. Hypoxic bottom waters area mainly found at the head of the Laurentian, Anticosti and Esquiman channels (Figure \ref{kernel}A). Demersal destructive fisheries are concentrated along the Laurentian Channel, the heads of the Anticosti and Esquiman channels and around the îles de la Madeleine (Figure \ref{kernel}B). By combining both drivers, we observe that hypoxia and demersal destructive fisheries co-occur mostly at high relative intensity (Figure \ref{kernel}D) in the vicinity of the Anticosti Gyre and the heads of the Esquiman and Anticosti channels (Figure \ref{kernel}C); these are the areas where we might expect interactions between these drivers to be more likely}.

\textcolor{blue}{We now focus on the integrative exposure indicators.}
Apart from the northeastern Gulf, ~~the~~ cumulative ~~footprint~~\textcolor{blue}{exposure} ~~of drivers~~ is
ubiquitous in the St. Lawrence (Figure \ref{footprint}). Cumulative exposure is
generally highest along coasts (Figure \ref{footprint}), with hotspots
located in the vicinity of coastal cities (Figure \ref{hotspot}). In general,
offshore areas are less exposed to cumulative drivers, with the Estuary and the
Anticosti Gyre being notable exceptions (Figures \ref{footprint} and
\ref{hotspot}). This is not to say that offshore areas are free from exposure,
as most of the St. Lawrence is exposed to multiple overlapping drivers
(Figures \ref{footprint} and \ref{hotspot}). For example, \textcolor{blue}{the heads of the Anticosti and Esquiman channels are highly exposed to cumulative drivers} ~~it is worthy to note high cumulative exposure observed at the heads of the Anticosti and Esquiman
Channels~~ (Figure \ref{footprint}).

These results are consistent with observations elsewhere in the world, where
cumulative ~~driver~~ exposure conspicuously arises from and markedly intensifies
close to coastal cities and at the mouth of rivers draining highly populated
areas [e.g. @halpern2015a; @feist2016; @mach2017; @stock2018]. These are areas
where human activities (*e.g.* coastal development and shipping) and footprints
(*e.g.* pollution runoff) are most intense [@feist2016], and on which is
overlaid a background of natural disturbances [@micheli2016]. They are also the
areas in which the most dramatic increases in exposure are expected, with
populations increasing more rapidly along coasts than inland [@feist2016].
In the St. Lawrence, large coastal cities are mostly located along the Estuary
and the southwestern Gulf, whereas the northeastern Gulf is largely uninhabited
or home to small coastal communities. Certain smaller coastal communities with
high cumulative ~~footprint~~\textcolor{blue}{exposure} are characterized by large industries (*e.g.* Sept-Îles and Charlottetown).

As for offshore exposure, the Estuary, along with the St. Lawrence River,
provide access to and serves as the primary drainage outflow of the Great Lakes
Basin, which is home to over 45 million North Americans and is the most densely
populated region in Canada [@statistics-canada2017]. Most marine
traffic thus converges into the Estuary.

Whereas we cannot ascertain that high exposure areas are the most impacted, we
can safely predict that these are the areas where studying ecosystem state will
be the most complex due to the uncertainty associated with driver
co-occurrence, an uncertainty bound to increase rapidly with the number of co-occurring drivers [@cote2016].


## ~~Threat complexes~~ \textcolor{blue}{Cumulative exposure profiles}

While informative, the hypoxia-fisheries example focuses on a single pair of
drivers and falls short of the number of drivers typically overlapping at
high intensities throughout the St. Lawrence (Figure \ref{hotspot}).
The number of drivers overlapping in the St. Lawrence increases with cumulative
exposure (Figure S3). Areas with high exposure such as the Estuary, the
Anticosti Gyre, and the southwestern Gulf coastline (Figure \ref{footprint} and
\ref{hotspot}) are thus areas where driver interactions are
most likely and where they can arise between a host of different drivers.
~~The identification of threat complexes~~ \textcolor{blue}{Identifying areas with similar cumulative exposure profiles} provides a crucial tool to simplify the multi-dimensional complexity of overlapping drivers ~~to areas exposed to similar suites of drivers~~ [@bowler2019]. This ~~may prove critical for a better understanding~~ \textcolor{blue}{could facilitate assessments of} the state of species, habitats and ecosystems located within or
moving through ~~threat complexes and exposed to the combined effects of all
drivers inherent to those areas~~ \textcolor{blue}{areas exposed to similar suites of drivers}.

Six distinct ~~threat complexes~~\textcolor{blue}{clusters} were identified in the St. Lawrence (Figures S4, S5). Based on their
distribution and representative drivers, ~~threat complexes~~\textcolor{blue}{clusters} can be divided into
3 offshore and 3 coastal ~~complexes~~\textcolor{blue}{clusters} (Figures \ref{cluster}, S6 and S7).
Coastal ~~threat complexes~~\textcolor{blue}{clusters} (1 to 3; Figure \ref{cluster}) include all types
of drivers other than hypoxia; they are also the most exposed ~~threat complexes~~\textcolor{blue}{clusters}, both in
terms of driver overlap and intensity. ~~Threat complex~~\textcolor{blue}{Cluster} 1 encompasses the
coastline and is characterized by higher direct human impact (*i.e.* population
density). ~~Threat complex~~\textcolor{blue}{Cluster} 2 is differentiated from other ~~complexes~~\textcolor{blue}{clusters} by the
presence of aquaculture sites. ~~Threat complex~~\textcolor{blue}{Cluster} 3 is the most exposed ~~complex~~ and
has a distribution similar to the most exposed coastal hotspots (Figure
\ref{hotspot}). This ~~complex~~\textcolor{blue}{cluster} is characterized by high intensities of land-based
drivers (*e.g.* nutrient input), demersal non-destructive high bycatch fisheries
(*e.g.* trap fishing), climate drivers and marine traffic drivers in
the vicinity of ports.

Offshore ~~threat complexes~~\textcolor{blue}{clusters} (4 to 6; Figure \ref{cluster}) are generally
characterized by high intensity climate and marine traffic drivers.
~~Threat complex~~\textcolor{blue}{Cluster} 4 is differentiated by demersal non-destructive high bycatch
fisheries, higher marine traffic drivers compared to ~~complex~~\textcolor{blue}{cluster} 5, and generally
corresponds to the whole Southern Gulf. ~~Threat complex~~\textcolor{blue}{Cluster} 5 is characterized by
more fisheries types (*i.e.* demersal destructive and pelagic high bycatch),
generally lower intensity marine traffic drivers, and is located almost
exclusively in the Northern Gulf. Finally, ~~threat complex~~\textcolor{blue}{cluster} 6 is the most exposed
offshore ~~threat complex~~\textcolor{blue}{cluster} and includes all offshore hotspots (Figure \ref{hotspot}).
It is characterized by high intensity hypoxia, marine traffic and
pollution, as well as demersal destructive and pelagic high bycatch fisheries.
This ~~threat complex~~\textcolor{blue}{cluster} corresponds primarily to the Laurentian Channel and
incorporates parts of the Esquiman and Anticosti channels.

\textcolor{blue}{Clusters} 3 and 6 capture most coastal and offshore hotspots and are the two most exposed clusters in the St. Lawrence.
They offer some insight into the potential importance of considering
spatial dynamics in areas intersecting multiple ~~threat complexes~~\textcolor{blue}{clusters}. For example,
~~threat complexes~~\textcolor{blue}{clusters} 3 and 6 meet at the mouth of the Saguenay River. This area
is particularly dynamic, with deep Atlantic waters advected through estuarine
circulation mixing with surface waters from the St. Lawrence and
Saguenay rivers [@dufour2007]. This results in the convergence of climate
drivers from the bottom of the Laurentian Channel and marine traffic drivers
(~~threat complex~~\textcolor{blue}{cluster} 6) with terrestrial run-off from river outflows and direct
human impacts (*i.e.* population density; ~~threat complex~~\textcolor{blue}{cluster} 3).


<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->
# Open Knowledge Platform: *eDrivers*
<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

Sharing the knowledge acquired through the description of drivers in the
St. Lawrence emerged as a priority to curtail the need to contact dozens
of experts across multiple organizations and over extensive periods of time to
assemble the data needed for integrated research and management. It is also
a requirement to ensure that this manuscript will not quickly become an outdated
snapshot of drivers distribution and intensity in the St. Lawrence System,
but rather serve as a stepping stone towards an adaptive and ever-improving
collection of knowledge.

As such, we are launching *eDrivers*, an open knowledge platform focused on
sharing knowledge on the distribution and intensity of drivers and on gathering
a community of experts committed to structuring, standardizing and sharing
knowledge on drivers in support of science and management. In launching this
initiative, our objective is to uphold the highest existing standards of data
management and open science. We identified four guiding principles \textcolor{blue}{(Section 5.1)} to meet this
objective and ~~that guide the~~ structure of the ~~platform~~ \textcolor{blue}{initiative} (Figure \ref{diag}).

## \textcolor{blue}{Guiding principles}

### \textcolor{blue}{Unity and inclusiveness}

***Why***: Operating over such large scales in time, space, and subject matter
requires a vast and diverse expertise that cannot possibly be possessed by any
one individual or organization. Consequently, we envision an initiative that
seeks to mobilize all individuals and entities with relevant expertise.

***How***: By promoting, consolidating and working with experts involved in
existing and highly valuable environmental initiatives already in place in the
St. Lawrence. Notable examples of environmental initiatives are the annual
review of physical [@galbraith2018], chemical, and biological [@blais2019]
oceanographic conditions in the St. Lawrence, the fisheries monitoring program
[@dfo2016], the annual groundfish and shrimp multidisciplinary survey
[@bourdages2018], the characterization of benthic [@dutil2011], epipelagic and
coastal [@dutil2012] habitats of the St. Lawrence, and Canada's shoreline
classification [@eccc2018]. There are also nascent efforts to share
information on several human activities in the St. Lawrence such as the Marine
Spatial Data Infrastructure portal, which provides data on zoning, shipping,
port activities, and other human activities in Canadian waters, including the
St. Lawrence system [@governmentofcanada2018].

By working with existing data portals whose objective is to share
environmental data. We are thus collaborating actively with the St. Lawrence
Global Observatory (SLGO) to develop the initiative and to host the
platform on their web portal. The mission of SLGO is to
promote and facilitate the accessibility, dissemination and exchange of official
and quality data and information on the St. Lawrence ecosystem through the
networking of organizations and data holders to meet their needs and those of
users, to improve knowledge, and to assist decision-making in areas such as
public safety, climate change, transportation, resources and biodiversity
conservation. SLGO is also one of three regional associations spearheading
the Canadian Integrated Ocean Observing System (CIOOS[^cioos]),
which will focus on integrating oceanographic data from multiple sources to
make them accessible to end-users and to enable the national coordination of
ocean observing efforts by integrating isolated or inaccessible data, and by
identifying gaps or duplications in observations and research efforts.
We are also developing collaborations with the Portal on water knowledge[^melcc],
an initiative from the Québec provincial government. The aim of this portal is to collect and share accurate, complete, and up-to-date resources on water and
aquatic ecosystems to support the mandate of relevant actors and stakeholders
working in water and aquatic ecosystems management.

[^cioos]: https://cioos.ca
[^melcc]: http://www.environnement.gouv.qc.ca/eau/portail/

By actively inviting, seeking, and developing collaborations as well as
encouraging constructive criticism from the inception and throughout the
lifetime of the platform.

By inviting external community contributions (Figure \ref{diag}).
External researchers or entities wishing to submit marine data will be able to
do so through SLGO web portal. Submissions through
other data portals will also be accepted either through the development of data
sharing agreements or with the caveat that shared data are under an open source
license and that they adhere to the platform data standards.


### \textcolor{blue}{Findability, accessibility, interoperability and reusability}

***Why***: Open knowledge has been propelled to the forefront of scientific research
in an era of open, collaborative and reproducible science. By moving towards
large scale, cross-disciplinary research and management projects, there is a
growing need to increase the efficiency of data discovery, access,
interoperability and analysis [@reichman2011; @wilkinson2016]. Our goal is to
foster efficient and functional open science by creating a fully open,
transparent and replicable open knowledge platform.

***How***:
By building an infrastructure adhering to the FAIR Data Principles,
which states that data and metadata must be Findable, Accessible, Interoperable
and Reusable. These \textcolor{blue}{(sub)}principles focus on the ability of humans and machines to
automatically find and (re)use data and knowledge [@wilkinson2016].
\textcolor{blue}{As the FAIR Data Principles already exist as a unified set of
principles, we adopt them as a set of guiding subprinciples to our initiative.}

By making data and associated tools accessible through a variety of ways:
the SLGO web portal, two R packages called *eDrivers*[^eDR] and *eDriversEx*[^eDX]
to access the data through SLGO's API and to provide analytical tools to explore
data, respectively, and a Shiny application[^eDapp] to explore drivers data
interactively (Figure \ref{diag}). Note that the data are currently contained
within and accessible through the *eDrivers* R package only, as we are actively
working to allow users to download selected layers from SLGO's web portal and
geoserver. The functions available in *eDrivers* to access the data have however
been developed to ensure forward compatibility once the data are migrated to SLGO's
geoserver.

[^eDR]: https://github.com/orgs/eDrivers/eDrivers
[^eDX]: https://github.com/orgs/eDrivers/eDriversEx
[^eDapp]: https://david-beauchesne.shinyapps.io/eDriversApp/

By defining clear data and metadata standards and specifications to support
the regional standardization of current and future protocols and practices and
to favour interoperability with national and international initiatives like the
Essential Ocean Variables (EOV) identified by the Global Ocean Observing System
(GOOS). As such, we will adopt the metadata standard
currently targeted for CIOOS, *i.e.* the North American Profile
of ISO 19115:2014 - Geographic information - Metadata, a schema favoured
for geospatial data in Canada and the United States.

By providing version control and code access to the workflows set up to generate
driver layers from raw data, the R packages and the Shiny application through
a GitHub organization called *eDrivers*[^eD].

[^eD]: https://github.com/orgs/eDrivers/


### \textcolor{blue}{Adaptiveness}

***Why***: In the face of uncertainty and in an effort to address impending
environmental changes, adaptive management has been identified as the chief
strategy to guide efficient decision-making
[*e.g.* @margules2000; @keith2011; @jones2016; @chion2018] and has already
been discussed in the context of multi-drivers and cumulative impact assessments
[@halpern2015a; @beauchesne2016; @cote2016; @schloss2017]. Adaptive management can only be
truly achieved through a commitment to adaptive monitoring and data reporting
[@margules2000; @halpern2012; @lubchenco2015]. We further contend that adaptive
management requires the development of adaptive monitoring tools and
infrastructures, which we seek to address through a continuously-evolving platform.

***How***:
By setting up mechanisms structuring cyclic reviews of platform content,
for the integration of new material (*e.g.* data and methods) as it becomes
available or accessible, and by striving to provide time-series data that are
crucial to assess temporal trends and potentially early-warning signals of
ecosystem change (Figure \ref{diag}).


### \textcolor{blue}{Recognition}

***Why***: Like peer-reviewed publications, data must also be given its due
importance in scientific endeavours and thus be considered as legitimate citable
products contributing to the overall scientific output of data providers
[@taskgroupondatacitationstandardsandpractoutofciteoutofmindthecurrentsices2013; @force112014].
Appropriate citations should therefore be provided for all data layers used and
shared by the platform.

***How***: By adhering to the Data Citation Principles [@force112014], which
focus on citation practices that provide appropriate credit to data products.

## \textcolor{blue}{Using \textit{eDrivers}}

\textcolor{blue}{Using \textit{eDrivers} is simplified through the tools already
in place and will be increasingly accessible as the initiative evolves and other
tools are developed to ease user experience. We provide an example of  the ease
with which the data can be accessed and used with the \textit{eDrivers} R package
to reproduce figure \ref{kernel} (Box 1). The code using the \textit{eDrivers}
R package to reproduce all the analyses and figures in this manuscript is also
available on GitHub.}[^ms]

[^ms]: https://github.com/orgs/eDrivers/eDriversMS

<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->
# Perspectives
<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

Understanding how ecosystem state will be affected by global change requires
a comprehensive understanding of how threats are distributed and interact in
space and time, which in turn hinges on appropriate data tailored to
multi-driver studies [@dafforn2016; @stock2018; @bowler2019]. In the St.
Lawrence, we found that few areas are free from cumulative exposure and that the
whole Estuary, the Anticosti Gyre, and coastal southwestern Gulf are particularly
exposed to cumulative drivers, especially close to urban areas. We also
identified six geographically distinct ~~threat complexes~~\textcolor{blue}{areas} that display similar
cumulative exposure ~~regimes~~\textcolor{blue}{profiles}; these ~~complexes~~ reveal that coastal areas are
particularly exposed to all types of drivers and that ~~multiple~~\textcolor{blue}{certain} driver \textcolor{blue}{combinations are inherent to certain regions of the St. Lawrence}~~typically co-occur in space~~. These results allow us to efficiently identify
areas in need of heightened scrutiny from a science and management perspective.

Through *eDrivers*, these observations will be iteratively improved towards an
increasingly robust assessment of cumulative exposure and ~~threat complexes~~\textcolor{blue}{areas with similar cumulative exposure profiles} as
gaps in knowledge are addressed or approaches to describe drivers are refined.
Arguably, the most meaningful benefit anticipated from *eDrivers* will be the
gain in efficient access to comparable data-based knowledge on the exposure of
ecosystems to multiple threats. This could pay quick scientific
and management dividends by \textcolor{blue}{efficiently} drawing on the
knowledge and efforts of a wide range of contributors, by expanding avenues
of scientific inquiry, by decreasing overall effort duplication and research
costs, and by increasing research efficiency [@franzoni2014].

Critically, *eDrivers* will allow the scientific and governmental communities to identify key knowledge gaps that will assist in prioritizing and optimizing research efforts. Ultimately, we believe that *eDrivers* will operationalize evidence-based decision-making by streamlining data management and research, allowing science output to be available and interpretable on a time scale relevant to management [see @sutherland2004; @reichman2011]. The platform will thus greatly facilitate the application of broad scale, holistic research and management approaches such as marine spatial planning, ecosystem-based management, marine spatial planning and strategic environmental assessments [*e.g.* @rice2011; @halpern2015a; @jones2016].

The next step will be the inclusion of other types of knowledge to our initiative. Our focus has been on a single element required for fully operational impact assessments. Data that provide knowledge on the exposure of ecosystems to drivers are called stressor-based indicators [@dube2001; @dube2003]. These indicators efficently identify potential local impacts and can be proactively linked to decision-making, yet assume complete knowledge of drivers and fail to diagnose impacts on valued components or non-additive effects. In contrast, effect-based indicators are direct measurements of valued components (\textit{e.g.} species abundance and biodiversity) and inherently capture the effects of multiple drivers [@dube2001; @dube2003]. Whereas effect-based indicators are considered superior to stressor-based indicators, they fail to ascribe observed effects to specific drivers. Stressor-based and effect-based indicators are, therefore, both required to diagnose causes of ecosystem change [@jones2016]. As a collection of knowledge on stressor-based indicators, \textit{eDrivers} should be weaved with other, comparable, collections of knowledge describing valued ecosystem components that can be linked to drivers and allow for a better understanding of cumulative impacts. Ultimately, interdisciplinary collections of knowledge could be weaved together through social-ecological metanetworks analyses [@dee2017]. \textcolor{blue}{In turn, these could be used in conceptual frameworks to help to establish causal relationships between drivers and valued ecosystem components such as the DPSIR (Driving forces -- Pressure -- State -- Impact -- Response) framework} [@kristensen2004; @gari2015]. \textcolor{blue}{Within such frameworks, data-based indicators provided through \textit{eDrivers} could be categorized as driving forces, pressures or states, depending on the objective and context of a study.}

Significant effort is still needed to bring our vision to fruition. Foremost
is to maintain our efforts to foster collaborations, develop platform
content and identify key knowledge gaps. A fair and efficient organizational
structure will be developed in order to manage *eDrivers* as a community and
appropriate funding must be secured to continue building this community and
ensure the long-term viability of the initiative, although the partnership with
SLGO partly addresses this issue. We also wish to provide users with enhanced capabilities and flexibility in using the interactive tool and R package. This could include creating automatic reports and more flexibility for user-defined driver-based indicators.

Finally, terrestrial and coastal environments must be incorporated, as
sources of stress within those habitats extend to the marine environments.
Moreover, despite coastal areas being recognized as the most exposed to
environmental threats, we continue to delineate
terrestrial and marine realms, considering coastlines as an impermeable
barrier. Whereas there is a sensible rationale for this division, we must strive
to eliminate it if we are to appropriately study and predict the
impacts of global change [*e.g.* see @bowler2019].

Despite the challenges and work ahead, we are hopeful that this initiative will
be very successful. Ultimately, *eDrivers* represents a much needed solution to
address important issues in data management that could radically shift broad
scale research and management practices towards efficient, adaptive and holistic
ecosystem-based management in the St. Lawrence and elsewhere in the world. All
it requires to be successful is for the scientific and political communities to
fully commit to open knowledge, adaptive monitoring and, most of all, an
integrated vision of ecosystem management.


<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->
# Acknowledgements
<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

We thank the Fond de Recherche Québécois Nature et Technologie (FRQNT) and the
Natural Science and Engineering Council of Canada (CRSNG) for financial support.
This project is also supported by Québec Océan, the Quebec Centre for
Biodiversity Science (QCBS), Takuvik, and the Notre Golfe networks. This
research is also sponsored by the NSERC Canadian Healthy Oceans Network and its
Partners: Department of Fisheries and Oceans Canada and INREST (representing the
Port of Sept-Îles and City of Sept-Îles).


<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->
# Author contributions statement
<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

DB, RD, DG and PA conceived the manuscript and the underlying objectives. DB
prepared/formatted the data, performed the analyses, was in charge of technical
developments and lead the drafting of the manuscript. All co-authors contributed
to data, analyses and writing based on their respective expertise and
contributed to the revision of the manuscript.


<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->
# Conflict of interest statement
<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

The authors declare that the submitted work was carried out in the absence of
any personal, professional or financial relationships that could potentially be
construed as a conflict of interest.


\newpage

<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->
# Listings
<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

Box 1. Code snippet demonstrating how to use the *eDrivers* R package to
reproduce figure \ref{kernel}.

\begin{lstlisting}[frame=single]
# Install and load eDrivers package
devtools::install_github('eDrivers/eDrivers')
library(eDrivers)

# Load data
drivers <- fetchDrivers(drivers = c('hypoxia','fishDD'))

# Get data from `eDrivers` class object
driverData <- getData(drivers)

# Normalize data
driverData <- driverData / cellStats(driverData, 'max')

# Visualize data and combination
plot(driverData$fishDD)   # Demersal destructive fisheries
plot(driverData$hypoxia)  # Hypoxia
plot(sum(driverData))     # Combination

# Identify values > 0 and not NAs
driverData$fishDD[driverData$fishDD < 0] <- NA
driverData$fishDNH[driverData$hypoxia < 0] <- NA
id0 <- !is.na(values(driverData$fishDD)) &
       !is.na(values(driverData$hypoxia))

# 2D kernel for driver co-intensity
library(MASS)
coInt  <- kde2d(x = values(driverData$fishDD)[id0],
                y = values(driverData$hypoxia)[id0],
                n = 500, lims = c(0, 1, 0, 1))
image(coInt, zlim = c(0,max(coInt$z)))

# Driver density distribution
plot(density(driverData$fishDD[id0]))   # Demersal destructive
plot(density(driverData$hypoxia[id0]))  # Hypoxia
\end{lstlisting}

\newpage

<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->
# Figures
<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

\begin{figure}[H]
\centering
\includegraphics{./Figure1.jpg}
\caption{Description of the St. Lawrence System in Eastern Canada, composed of the St. Lawrence Estuary and the Gulf of St. Lawrence. The Estuary is defined by the limit of seawater intrusion, close to Île d'Orléans, to the west and by its connection to the Gulf near Pointe-des-Monts. The Gulf is an interior sea connected to the Atlantic by Cabot Strait and the Strait of Belle Isle to the south and north of Newfoundland, respectively.}
\label{egsl}
\end{figure}

\newpage

\begin{figure}[H]
\centering
\includegraphics{./Figure2.jpg}
\caption{\textcolor{blue}{Simplified 2-driver example of driver co-occurrence between hypoxia and demersal destructive fisheries in the St. Lawrence}. An index of hypoxia ($A$) was created using bottom-water dissolved oxygen between 2013 and 2017 (Blais et al., 2018). Demersal destructive fisheries ($i.e.$ trawl and dredges) ($B$) intensity was evaluated from fisheries catch data collected between 2010 and 2015 used to measure annual area weighted total biomass ($kg$) in 1 $km^2$ grid cells (DFO, 2016b). See supplementary materials for more information on specific methodologies. Relative hypoxic stress and demersal destructive fisheries intensity was summed ($C$) to visualize their combined spatial distribution and intensity. Finally, individual density and the co-intensity of hypoxia and demersal destructive fisheries was investigated with a two-dimensional kernel analysis ($D$).}
\label{kernel}
\end{figure}

\newpage

\begin{figure}[H]
\centering
\includegraphics{./Figure3.jpg}
\caption{Distribution of cumulative \sout{footprint} \textcolor{blue}{exposure} in the St. Lawrence System.}
\label{footprint}
\end{figure}

\newpage

\begin{figure}[H]
\centering
\includegraphics{./Figure4.jpg}
\caption{Distribution of cumulative hotspots in the St. Lawrence System.}
\label{hotspot}
\end{figure}

\newpage


\begin{figure}[H]
\centering
\includegraphics{./Figure5.jpg}
\caption{Distribution of areas with similar cumulative exposure profiles in the Estuary and Gulf of St. Lawrence, identified through a clustering approach (Top). Mean intensity of all coastal (red), climate (green), fisheries (blue), and marine traffic (purple) drivers within each cluster (Bottom). Refer to Table 1 for acronym meaning and to the Supplementary Materials for more details.}
\label{cluster}
\end{figure}

\newpage

\begin{figure}[H]
\centering
\includegraphics{./Figure6.jpg}
\caption{Diagram of the platform structure. Community input in the form of raw data is accessed through the St. Lawrence Global Observatory (SLGO; https://ogsl.ca/en) repository - the platform host - or through open access repositories ($e.g.$ NASA data). The raw data are then processed through a workflow hosted on the $eDrivers$ GitHub organization (https://github.com/orgs/eDrivers/). Data processing may be as simple as data rescaling\sout{ ($e.g.$ night lights)} or make use of more complex methodologies\sout{ ($e.g.$ acidification)}. All data is then hosted on SLGO's geoserver and accessible through their API. We developed a R package called $eDrivers$ to access the driver layers through R and we are actively developing a second R package called $eDriversEx$ that will include analytical tools to explore drivers data. Finally, we have developed a Shiny application, \textcolor{blue}{\textit{eDrivers} app,} that allows users to explore drivers data interactively (https://david-beauchesne.shinyapps.io/eDriversApp/). All R components of the project are hosted and available on the $eDrivers$ GitHub organization. Iterative and adaptive processes are identified by circular arrows.}
\label{diag}
\end{figure}

\newpage
\blandscape

<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->
# Tables
<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

Table 1. List of drivers available on *eDrivers* and used for the analyses presented in this paper. Further details on methods and data are available in the Supplementary Materials.

```{r table1, echo=F, fontsize=4}
load('../tables/table1.RData')
table1
```

\elandscape
\newpage




<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->
# References
<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->