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On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)


Abstract and Figures

Conservation planning and management typically require accurate and spatially explicit data at scales that are relevant for conservation objectives. In marine conservation, these data are often combined with spatial analytical techniques to produce marine habitat maps. While marine habitat mapping is increasingly used to inform conservation efforts, this field is still relatively young and its methods are rapidly evolving. Because conservation efforts do not always specify standards or guidelines for the production of habitat maps, results can vary dramatically. As representations of real environmental characteristics, habitat maps are highly sensitive to how they are produced. In this review paper, I present four concepts that are known to cause variation in spatial representation and prediction of habitats: the methodology used, the quality and scale of the data, and the choice of variables in regards to fitness for use. I then discuss the potential antinomy associated with the use of habitat maps in conservation: while habitat maps have become an invaluable tool to inform and assist decision-making, maps of the same area built using different methods and data may provide dissimilar representations, thus providing different information and possibly leading to different decisions. Exploring the theories and methods that have proved effective in terrestrial conservation and the spatial sciences, and how they can be integrated in marine habitat mapping practices, could help improve maps used to support marine conservation efforts and result in more reliable products to inform conservation decisions. Having a strong, consistent, transparent, repeatable, and science-based protocol for data collection and mapping is essential for effectively supporting decision-makers in developing conservation and management plans. The development of user-friendly tools to assist in the application of such protocol is crucial to a widespread improvement in practices. I discuss the potential to use interactive and collaborative Geographic Information Systems (GIS) to encourage the conservation and management community, from data collectors and mapmakers to decision-makers, to move toward a digital resilience and to develop such science-based protocol. Until standards and protocols are developed, habitat maps should always be interpreted with care, and the methods and metadata associated with their production should always be explicitly stated.
Content may be subject to copyright.
published: 11 September 2017
doi: 10.3389/fmars.2017.00288
Frontiers in Marine Science | 1September 2017 | Volume 4 | Article 288
Edited by:
Brett Favaro,
Memorial University of Newfoundland,
Reviewed by:
Rochelle Diane Seitz,
Virginia Institute of Marine Science,
United States
Lydia Chi Ling Teh,
University of British Columbia, Canada
Vincent Lecours
Specialty section:
This article was submitted to
Marine Conservation and
a section of the journal
Frontiers in Marine Science
Received: 06 February 2017
Accepted: 23 August 2017
Published: 11 September 2017
Lecours V (2017) On the Use of Maps
and Models in Conservation and
Resource Management (Warning:
Results May Vary).
Front. Mar. Sci. 4:288.
doi: 10.3389/fmars.2017.00288
On the Use of Maps and Models in
Conservation and Resource
Management (Warning: Results May
Vincent Lecours *
Fisheries and Aquatic Sciences, School of Forest Resources and Conservation, University of Florida, Gainesville, FL,
United States
Conservation planning and management typically require accurate and spatially explicit
data at scales that are relevant for conservation objectives. In marine conservation,
these data are often combined with spatial analytical techniques to produce marine
habitat maps. While marine habitat mapping is increasingly used to inform conservation
efforts, this field is still relatively young and its methods are rapidly evolving. Because
conservation efforts do not always specify standards or guidelines for the production
of habitat maps, results can vary dramatically. As representations of real environmental
characteristics, habitat maps are highly sensitive to how they are produced. In this review
paper, I present four concepts that are known to cause variation in spatial representation
and prediction of habitats: the methodology used, the quality and scale of the data,
and the choice of variables in regards to fitness for use. I then discuss the potential
antinomy associated with the use of habitat maps in conservation: while habitat maps
have become an invaluable tool to inform and assist decision-making, maps of the
same area built using different methods and data may provide dissimilar representations,
thus providing different information and possibly leading to different decisions. Exploring
the theories and methods that have proved effective in terrestrial conservation and the
spatial sciences, and how they can be integrated in marine habitat mapping practices,
could help improve maps used to support marine conservation efforts and result in
more reliable products to inform conservation decisions. Having a strong, consistent,
transparent, repeatable, and science-based protocol for data collection and mapping
is essential for effectively supporting decision-makers in developing conservation and
management plans. The development of user-friendly tools to assist in the application of
such protocol is crucial to a widespread improvement in practices. I discuss the potential
to use interactive and collaborative Geographic Information Systems (GIS) to encourage
the conservation and management community, from data collectors and mapmakers to
decision-makers, to move toward a digital resilience and to develop such science-based
protocol. Until standards and protocols are developed, habitat maps should always be
interpreted with care, and the methods and metadata associated with their production
should always be explicitly stated.
Keywords: marine conservation, habitat mapping, data quality, scale, data selection, species distribution
modeling, GIS, management information systems
Lecours Using Maps? Proceed with Caution
In the past 25 years, the impacts of anthropogenic pressure on
marine environments (e.g., Fraschetti et al., 2010) and a growing
awareness of the significance of these ecosystems (Borja, 2014)
have steered many nations toward increasing efforts to better
manage and protect marine resources (Leslie, 2005; Laffoley
et al., 2016; Wells et al., 2016). However, these efforts have
not always been effective as they are sometimes based on
incomplete or inappropriate data for the targeted environments
(Broderick, 2015; Orlikowska et al., 2016), and can also be
manipulated by political processes that may induce bias (Edgar
et al., 2008; Devillers et al., 2014). It has been argued that
marine protected areas impacted by such bias have low value
for biodiversity conservation despite the high costs associated
with the conservation efforts (Edgar et al., 2008). In spite of a
wide recognition of the importance for conservation policy to be
based on scientific evidence (Gjerde and Rulska-Domino, 2012;
Adams and Sandbrook, 2013; Rose, 2015; Walsh et al., 2015),
it has been suggested that the use of science in conservation
and policy decision-making has declined (Parsons et al., 2015;
Borja et al., 2016; McConney et al., 2016). This paradox may
result, in part, from a lack of effective communication between
scientists and policy/decision makers (Holmes and Clark, 2008;
Grorud-Colvert et al., 2010; Duarte, 2014; Broderick, 2015).
Maps hold great potential for attempting to (re)connect
decision-making with science; maps are known to be an
effective means of communication, particularly when they
can be understood by a variety of audiences (Wright, 2016).
Maps are often required to assess and monitor marine
environments (see Table 1). In turn, such assessments can be
used to inform and assist conservation scientists, planners,
and other decision-makers, for instance in the implementation
of scientific management (Buhl-Mortensen et al., 2015), the
monitoring of environmental change (McCarthy and Halls,
2014), the assessment of impacts of anthropogenic disturbance
on ecosystems (Rolet et al., 2015), and the identification of
marine protected areas (Le Pape et al., 2014). Maps representing
the actual or predicted spatial distribution of organisms and
ecological features are currently among the best available spatial
decision-support tools (Guisan et al., 2013; Levin et al., 2014).
They have become key for data integration and synthesis to
inform decision-making in a variety of contexts (Costello, 2014);
in a meta-analysis of marine conservation planning approaches,
Leslie (2005) identified that 24 of the 27 cases examined used
maps to make decisions.
TABLE 1 | Examples with references of elements that are often mapped or
modeled to assess and monitor the marine environment.
Species and habitat distributions García-Alegre et al., 2014; Davies et al., 2015
Biodiversity hotspots Bedulli et al., 2002; Allen, 2008
Ecosystem services Galparsoro et al., 2014; Outeiro et al., 2015
Uses of marine resources Buhl-Mortensen et al., 2015; Hossain et al.,
Threats to biodiversity Andersen et al., 2004; Harris, 2012
While the ability of maps and models to communicate
relevant information about current and predicted states of the
environment makes them ideal candidates in any attempt to
(re)connect decision-making with science, how do we know
that these spatial decision-support tools are conveying the right
information? In fact, maps and models have downsides and
their use in conservation and policy-making should be carefully
examined (Reiss et al., 2015). In this contribution I ask, “What if
the information extracted from such tools is misleading?” There
may be very few ways to find out; marine habitat maps often
suffer from a lack of scrutiny from mapmakers and end-users,
a shortcoming that is particular to marine applications. For
instance, in agricultural and peri-urban settings, landowners and
other stakeholders often verify that the maps/models reflect the
reality of the landscape, forcing the mapmaker to ensure that
the appropriate data are collected and are representative of the
environment, that the method used to produce the maps/models
is sound and adequate, and that the output is robust (Wintle
et al., 2005). In contrast, data from the marine environment
that are used in the production of maps and models are not
always easily accessible for decision-makers and stakeholders
to further investigate (Lengyel et al., 2008; Cvitanovic et al.,
The consequences of making marine conservation decisions
based on information extracted from misleading maps would
be significant: loss of trust in science, conservation plans or
management plans that do not reach their objectives, financial
costs and temporal costs, etc. My objective with this contribution
is to advocate against the uncritical use of maps and models
by discussing the high variability of these spatial decision-
support tools and how they can become double-edge swords by
affecting conservation and management planning outcomes. I
first briefly review the use of habitat mapping in conservation and
management, and highlight the misconnection between habitat
mapping practices and concepts from the spatial sciences. In each
of the following sub-sections, I then present and discuss one of
four vectors of map variability that must be carefully taken into
account when making or using maps: the methodology used to
produce the map, the quality of the data that are used, scale,
and data selection in a context of fitness for use. I then discuss
how the concept of digital resilience recently introduced by
Wright (2016) can be applied to a context of marine conservation
and management through the use of Geographic Information
Systems (GIS) for more informed, robust and science-based
decision-making. To support my argument and propose ways
to move forward, I explore the theories and methods that have
proved effective in terrestrial conservation and spatial sciences,
and how they can be integrated in marine habitat mapping
practices to improve maps and models as spatial decision-support
tools for conservation and management. For the remainder of
this paper, I use the term “habitat maps” to encompass all types of
static maps and models that represent some characteristic of the
marine environment. Following the definitions by Brown et al.
(2011), those include abiotic maps used to characterize distinct
physical potential habitats, and single species and community
habitat maps (including but not limited to species distribution
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Lecours Using Maps? Proceed with Caution
As illustrated in Figure 1, the interest in marine habitat mapping
has grown continuously since the early 1990s (Smith and
McConnaughey, 2016). Developments in sampling technologies
such as multibeam echosounders often directly translated into
advances in the field of habitat mapping (Harris and Baker,
2012a). In addition, the realization of the extent of anthropogenic
impacts on marine environments (e.g., Matthaüs, 1995), which
has resulted in increased efforts by many nations to manage
and protect marine resources (e.g., the United States Magnuson-
Stevens Fishery Conservation and Management Act), has also
boosted the need for accurate and spatially explicit data for
baseline mapping, management, and conservation.
Scientists and decision-makers involved in conservation and
management also grew an interest for marine habitat mapping
(e.g., Ross and Howell, 2013; Selgrath et al., 2016): while habitat
maps can serve many purposes, statistics show that habitat
mapping studies are most often directly or indirectly set in
a context of conservation or management (Figure 1), and the
meta-analysis showed that 16 review articles were written on the
use of marine habitat mapping for conservation and management
between 2003 and 2016. The proportion of habitat mapping
studies set in a context of conservation or management is higher
in the marine environment (about 67%) than in other types
of environments (about 58%) (Figure 1). These numbers are
in line with observations made by Harris and Baker (2012a)
for the GeoHab Atlas of Seafloor Geomorphic Features and
Benthic Habitats (Harris and Baker, 2012b), in which about
64% of the case studies were performed for conservation or
management purposes (e.g., baseline mapping for management,
fisheries resources management, marine protected areas design).
They also identified conservation groups and governments and
industries that manage marine resources as the main users and
clients of habitat mapping.
Since marine habitats are broadly defined as distinct areas
characterized by a combination of specific chemical, physical
and/or biological characteristics (Lecours et al., 2015), their study
is by definition a multidisciplinary endeavor. By its spatial and
data-driven natures, marine habitat mapping strongly relies on
spatial sciences (Cogan et al., 2009). Through the development of
geospatial technologies and methods (e.g., GIS, remote sensing,
spatial analysis), spatial sciences have become very accessible to a
wide range of scientists involved in marine habitat mapping (e.g.,
biologists, ecologists, geologists, oceanographers) (e.g., Valavanis
et al., 2008; Reshitnyk et al., 2014). The near ubiquitous use of
these technologies and methods in the habitat mapping workflow
makes its practices directly influenced by spatial concepts such
as spatial scale, spatial autocorrelation, and spatial heterogeneity
(Lecours et al., 2015). However, the scientists from different
disciplines framing the questions related to the study of marine
habitats, and the managers and decision-makers applying the
answers are frequently not trained in the theories behind the
many tools and techniques that migrate from the spatial sciences
to habitat mapping. While spatial scientists have been studying
some of these concepts—some of which are addressed in this
FIGURE 1 | Cumulative number of publications (articles or reviews) listed in the Scopus database mentioning specific keywords (see legend) in their title, abstract, or
keywords, in July 2017.
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Lecours Using Maps? Proceed with Caution
paper—for a long time (Table 2), the understanding of their
role and their integration in the habitat mapping workflow
have received scant attention in the past. For example, of the
460 marine habitat mapping studies from Figure 1, very few of
them had the terms “data quality” (0.7%), “spatial scale” (7.4%),
or “temporal scale” (1.3%) in their title, abstract, or keywords.
This problem is rooted in a disconnection between marine
habitat mapping practioners and the sciences (e.g., geography,
geomatics) that provide the spatial concepts that partly support
habitat mapping practices. The fast pace at which tools and
techniques are developing may commonly prevent scientists not
directly involved in spatial sciences from remaining apprised of
new developments in these fields. In addition, the development
of easily accessible GIS tools bring hidden dangers by facilitating
non-critical use by end-users who may have limited appreciation
of spatial concepts (Lecours et al., 2016b). Because the tools are
made to be intuitive, they often do not require end-users to
fully understand the characteristics of the underlying processes
and parameters implemented in the tools, and of the spatial
data that form the basis for analysis. The scientific concepts and
foundations are thus often hidden behind increasingly “black-
box,” user-friendly tools. The combination of these factors leads
to the danger of inappropriate use of geospatial data and tools
and the lack of appropriate consideration of important spatial
concepts, from which misinformed and potentially erroneous
interpretations or inferences could be made in contexts like
conservation and management. There is a need to reunite the
marine habitat mapping and spatial sciences communities to
encourage the production of habitat maps that take into account
the relevant spatial concepts to increase robustness and validity of
habitat distribution and predictions. The remainder of this paper
attempts to do so by reviewing four concepts from spatial sciences
that may cause issues when not properly considered in marine
habitat mapping for conservation and management.
This section discusses four vectors of map variability that need
to be considered when producing habitat maps: methodology,
data quality, scale, and data selection. As highlighted in Table 2,
each of these topics have been previously reviewed or discussed
in length in different contexts, including the spatial sciences and
both terrestrial and marine habitat mapping. However, these
reviews and discussions were often not directly set in a marine
conservation and management context. In order to prevent
repetition with published work, this section solely focuses
on the impacts of these issues on marine conservation and
management (as opposed to focusing on the issues themselves).
More information on these four issues can be found in the
literature listed in Table 2.
One of the first elements that requires a critical decision is
the methodology that is used to create a map or model. This
TABLE 2 | Examples of publications that discuss issues associated with methods selection, data quality, scale, and data selection, with their context.
Spatial sciences Terrestrial/general habitat
Marine habitat mapping
Methods Thomas et al., 2003
Lu et al., 2004
Ma et al., 2017
Maulik and Chakraborty, 2017
Guisan and Thuiller, 2005
Elith and Leathwick, 2009
Franklin, 2010
Li and Wang, 2013
Morris et al., 2016
Ashraf et al., 2017
de Rivera and López-Quílez, 2017
Bierman et al., 2011
Diesing et al., 2014
Finkl and Makowski, 2015
Piechaud et al., 2015
Data quality Van Oort and Bregt, 2005
Devillers and Goodchild, 2010
Li et al., 2012
Robertson et al., 2010
Lechner et al., 2012
Rocchini et al., 2011
Gallo and Goodchild, 2012
Moudrý and Šímová, 2012
Cros et al., 2014
Lecours et al., 2017a
Scale Woodcock, 1987
Quattrochi and Goodchild, 1997
Marceau and Hay, 1999
Duncan et al., 2002
Goodchild, 2011
Zhang et al., 2014
Wiens, 1989
Borcard et al., 2004
Rahbek, 2005
Seo et al., 2009
Austin and Van Niel, 2011
Moudrý and Šímová, 2012
Bradter et al., 2013
Wilson et al., 2007
Anderson et al., 2008
Brown et al., 2011
Harris and Baker, 2012b
Rengstorf et al., 2012
Lecours et al., 2015
Data selection Bellier et al., 2007
Lecours et al., 2017b
Araújo and Guisan, 2006
Blanchet et al., 2008
Murtaugh, 2009
Austin and Van Niel, 2011
Synes and Osborne, 2011
Lecours et al., 2016b
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Lecours Using Maps? Proceed with Caution
can be approached from two perspectives: the data collection
methods and the map production methods. Developments in
data collection techniques in the last few decades have increased
the types, amount and quality of data that can be collected
for marine environmental characterization, particularly in terms
of remotely sensed data (Brown et al., 2011; Kachelriess et al.,
2014; Lecours et al., 2016b). Despite all the benefits associated
with new data collection tools, Wells et al. (2016) have warned
against an over-dependence on technology, which can create a
false feeling of trust in the data (see Section Data Quality) to
the detriment of collecting appropriate ground-truthing data to
verify the maps. Ground-truthing should always be performed,
whether it is to train a model, to validate a final map, or to give an
ecological interpretation to patterns in the data. Data collection
methods and sampling patterns have to be carefully selected
as they define key elements, such as the type of information
collected and the observational scale of the data (Bradshaw
and Fortin, 2000; Lecours et al., 2015), in addition to logistical
elements like cost and surveying time. Even before any maps are
made, these elements directly impact the information provided
to decision-makers regarding the surveyed environment (Pinn
and Robertson, 2003). Data collection methods and sampling
patterns also need to align with the goals and needs of the
mapping exercise, or, in other words, the intended use of the map.
In terms of the map production approach, it has been
shown in many studies that given the same context and data,
using different mapping and modeling techniques produces
different outcomes (e.g., Keil and Hawkins, 2009; Marmion
et al., 2009; Murtaugh, 2009), which can inform conservation
and management differently (Jones-Farrand et al., 2011). While
the type of approach is selected based on the objectives of the
maps and the available data (Brown et al., 2012), the specific
method is open to subjectivity from mapmakers or modelers. For
instance, when performing unsupervised (i.e., self-classifying,
without training data) classifications on a set of data, it is unclear
whether algorithms of the “ISO cluster” family perform better
or worse than algorithms from the “K-means” family, and this
would presumably depend on the phenomenon being mapped.
While Table 3 gives examples of methods that have been applied
in marine habitat mapping studies, Figure 2 shows that even
when two algorithms are from the same family, they can still
introduce differences in mapping outcomes. In this figure, the
reference map was computed using the “k-Means” unsupervised
classification algorithm from WhiteBox GAT (v.3.4 “Montreal”),
and the altered map was produced with the “Modified k-Means”
algorithm from the same software. While the two algorithms are
very similar, they classified 4.1% of the entire area differently.
Algorithms that are not from the same family produce much
more discrepancies in habitat distributions.
The literature comparing different methods is much more
extensive for species distribution modeling, likely because of
the constant development of new methods (e.g., Elith et al.,
2006; Phillips et al., 2006; Crase et al., 2012). However, the
problem is not a lack of methods, but rather a question of
which method produces the most appropriate results for an
intended use (Elith and Graham, 2009; Jones-Farrand et al.,
2011). Unfortunately, no consensus has ever been reached on
which modeling techniques are best, and it has been recognized
TABLE 3 | Examples of classification methods used in marine habitat mapping and species distribution modeling, with examples.
Methods Supervised/Unsupervised Examples
Boosted regression trees Supervised Costa et al., 2014; Hewitt et al., 2015
Classification rule with unbiased interaction selection and estimation Supervised Ierodiaconou et al., 2011
Discriminant function analysis Supervised Degraer et al., 2008
Ecological niche factor analysis Supervised Tong et al., 2012; Sánchez-Carnero et al., 2016
Fuzzy k-means Unsupervised Falace et al., 2015
Generalized additive model Supervised Schmiing et al., 2013; Touria et al., 2015
Generalized linear model Supervised Lauria et al., 2011
ISO cluster Both Calvert et al., 2015
Iterative self-organizing data analysis technique (ISODATA) Unsupervised Mishra et al., 2006; Lauer and Aswani, 2008
k-means Unsupervised Hoang et al., 2016
Mahalanobis distance Supervised Hoang et al., 2016
Maximum entropy Supervised Brown et al., 2012; Piechaud et al., 2015; Poulos et al., 2016
Maximum likelihood Supervised Ierodiaconou et al., 2011; Hasan et al., 2012; Calvert et al., 2015
Minimum distance Supervised Hoang et al., 2016
Modified k-means Unsupervised Lecours et al., 2016a, 2017a
Multivariate adaptive regression splines (MARS) Supervised Bu ˇ
cas et al., 2013
Nearest neighbor Both Lucieer, 2008; Lucieer et al., 2013
Neural network Supervised Ojeda et al., 2004; Marsh and Brown, 2009
Parallelepiped Supervised Hoang et al., 2016
Quick, unbiased, efficient tree Supervised Ierodiaconou et al., 2011; Hasan et al., 2012
Random forest Both Hasan et al., 2012; Diesing et al., 2014; Piechaud et al., 2015
Support vector machine Supervised Hasan et al., 2012
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Lecours Using Maps? Proceed with Caution
FIGURE 2 | Example of how different methods can produce different outcomes. The input data were bathymetric data, backscatter data, and topographic data (i.e.,
slope, easterness, northerness, and relative deviation from mean value) (see Lecours et al., 2016b). The area shown is a 13.4 km by 12 km subset of the German
Bank study area presented in Brown et al. (2012) and Lecours et al. (2016b).
that little guidance is provided to users (Elith and Graham,
2009; Jones-Farrand et al., 2011). The reality is that no approach
is universally applicable (Hamil et al., 2016). However, map
producers should be able to explain why a particular method
was selected and how that method helps achieve the objectives of
the process. Model comparisons studies showing how different
techniques produce different outcomes should be limited until
they start addressing why techniques perform differently (Elith
and Graham, 2009): a better understanding of the mechanics of
the modeling techniques will enable the proposition of guidelines
to help map producers make a more informed choice of methods.
From a conservation and management perspective, the lack
of guidance in method selection has been criticized based on its
importance in the planning process (Jones-Farrand et al., 2011).
Differences in sampling methodologies make planning difficult
(Griffiths et al., 1999) and prevent transparency, comparability
and repeatability. Critiques on the lack of standardization of
methods for robust decision-making have been made for many
years in both terrestrial and marine conservation (e.g., Laffoley
and Hiscock, 1993; Griffiths et al., 1999; Cork et al., 2000). While
those early critiques may have contributed to encouraging many
countries to standardize mapping and decision-making processes
(Guarinello et al., 2010; e.g., Buhl-Mortensen et al., 2015),
judgment calls are still being made (Lengyel et al., 2008; Levin
et al., 2014); the inconsistency in the use of different methods
is still considered a challenge for decision-making and the
development of robust conservation and management measures
(Crossman et al., 2012; Gjerde et al., 2016). The most important
outcome of standardization is arguably the comparability of the
resulting maps (Howell, 2010; Davies et al., 2015) which enables,
for instance, the systematic identification of priority conservation
targets based on common criteria (Laffoley and Hiscock, 1993;
Edgar et al., 2008), comparisons across geographic areas (Borja
et al., 2014), and multi-temporal assessments as verification
process to ensure that conservation objectives are reached (Bisack
and Magnusson, 2016; Wells et al., 2016).
Standardization of protocols from data collection to
decision-making can minimize political bias and ensure that
conservation decisions are objective, data-driven and grounded
in sound science (Edgar et al., 2008). The main challenge for
standardization is the scale of management (see Section Scale):
while many individual countries are developing their own
protocols (Guarinello et al., 2010), calls are being made for
common frameworks for instance across the European Union
(Lengyel et al., 2008; Levin et al., 2014; Howell et al., 2016) and
at the international level for areas beyond national jurisdiction
or for the protection of migratory species (Di Sciara et al., 2016;
Gjerde et al., 2016; Wenzel et al., 2016). To save time, costs
and efforts, each level of protocol should be in line with the
others, which is quite challenging considering that no solution
is universally applicable at all scales and in all contexts (but see
Section Toward a Digital Resilience in Marine Conservation and
Data Quality
Another element that can significantly affect the outcomes of
mapping and modeling processes is data quality. Data quality
encompasses concepts of accuracy, precision, and uncertainty,
and can be of different types including spatial, thematic, and
temporal. The different components of data quality have been
reviewed in a number of publications (e.g., Jager and King, 2004;
Barry and Elith, 2006; Devillers et al., 2010; Moudrý and Šímová,
2012; Levin et al., 2014), and I thus focus here on their impacts
on habitat maps in a conservation context.
When integrated into a modeling exercise, each dataset carries
its own uncertainty and errors (Heuvelink, 1998). For instance,
species distribution data can be uncertain and associated with
positional and thematic accuracy (Edgar et al., 2008; Moudrý
and Šímová, 2012), while bathymetric data can also be associated
with uncertainty (Lecours and Devillers, 2015) and affected by
different kinds of artifacts (Hughes-Clarke, 2003; Yang et al.,
2007). Principles of error and uncertainty propagation make
errors and uncertainty likely to impact any subsequent analyses,
including mapping and modeling (Arbia et al., 1998; Heuvelink,
1998). The algorithms and modeling techniques themselves,
through their structure and parameters, can also introduce
uncertainty in the final outputs (Heuvelink, 1998). While the
concepts associated with error and uncertainty propagation have
been studied in the geospatial literature (Fisher and Tate, 2006;
Wilson, 2012), the attempts to raise end-users’ awareness in fields
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Lecours Using Maps? Proceed with Caution
like ecology and environmental modeling have failed (Brown and
Heuvelink, 2007; Devillers et al., 2010). With a few exceptions
from the terrestrial literature (e.g., van Niel and Austin, 2007;
Livne and Svoray, 2011), and despite repeated calls for the
appropriate consideration of error and uncertainty propagation
in environmental modeling and mapping (Rocchini et al., 2011;
Beale and Lennon, 2012; Lechner et al., 2012), these concepts
have yet to be better implemented, especially in a marine context
(Lecours et al., 2015).
It has been argued that there is a general lack of concern about
data quality from both mapmakers and end-users (Devillers
et al., 2010), to the point where data quality issues are ignored
and tolerated by conservation planners as an inherent part of
data (Tulloch et al., 2016). Tulloch et al. (2016) found out
that conservation planners prefer simplicity of maps to their
accuracy, which is understandable to a certain point because
it is easier to make policy from a simple map than from a
complex one, or one with contingencies (Borja, 2014). However,
errors and uncertainty can result in inaccurate representation
of the environments, and thus inaccurate maps (Wintle et al.,
2005,Figure 3). While looking at the impact of artifacts in
bathymetric data on the habitat mapping workflow, Lecours
et al. (2017a) found that poor data quality affects the outcome
of the mapping process, although in an often unpredictable
way: the presence of artifacts can sometimes artificially increase
the measured map accuracy and model predictive power while
in other cases it can decrease them. A lack of awareness and
understanding of the concepts associated with data quality can
in turn lead to misinformed conservation and management
decisions (Rondinini et al., 2006; Beale and Lennon, 2012;
Katsanevakis et al., 2012) with dire results for biodiversity
(Regan et al., 2005). For instance, it can become an issue when
prioritizing conservation decisions (Tulloch et al., 2016): if one
area is identified as being a slightly more important target for
conservation than another, but the first is associated with a higher
level of uncertainty, which one should be prioritized?
Some methods exist to estimate the quality of resulting
maps. For example, when using unsupervised classifications of
environmental data to identify distinct habitats, the classification
accuracy can be evaluated using confusion matrices (e.g., Brown
et al., 2012; Lecours et al., 2016a). Confusion matrices identify
commission and omission errors, or the level of agreement and
disagreement between the classification results and ground-truth
data. Commission errors occur when a species or habitat is
mistakenly identified as present while omissions errors occur
when they are mistakenly identified as being absent. Rondinini
et al. (2006) identified the implications for conservation of these
two types of errors: commission errors may lead to areas being
identified as relevant for conservation when they are not, while
omission errors may miss the identification of relevant areas
for conservation, thus underestimating the needs for protection.
Consequently, these errors affect the adequacy and efficiency of
conservation, and the representativeness and comprehensiveness
of the areas that should be targeted or prioritized (Rondinini
et al., 2006).
Modeling error and uncertainty propagation through an
entire workflow is challenging because it is unclear how the
quality of each individual dataset influences the processing and
adds to the uncertainty of the modeling technique. In addition,
it is not fully understood how the combination of individual
components of data quality (e.g., positional accuracy, thematic
accuracy) can be translated into one overall measure of quality.
Perhaps it cannot, but different approaches exist in other fields
to combine multiple metrics (e.g., Borja et al., 2014) that could
likely be adapted to combine different components of data
quality. One of the current problems is transparency: it has
been argued that readily available data could affect the accuracy
of conservation planning outcomes (Rondinini et al., 2006) by
introducing uncertainty and subjectivity in the process (Larsen
and Rahbek, 2003). However, subjectivity would be removed
if complete metadata including quality information would be
associated with each dataset, enabling a robust data quality
assessment. Rocchini et al. (2011) and Diesing et al. (2016),
among other authors, have highlighted the need for maps of
ignorance, i.e., maps that spatially display uncertainty and errors
associated with the primary maps and can assist decision-makers
in assessing the reliability of predictions. While some tools have
been provided to assess individual components of data quality
[e.g., Combined Uncertainty Bathymetric Estimator (Calder and
Mayer, 2003); Data Uncertainty Engine (Brown and Heuvelink,
FIGURE 3 | Example of how using data of different quality can produce different outcomes. The reference map is the same as in Figure 2. The altered map used
bathymetric data impacted by artifacts caused by a 0.25 s mis-synchronization in the surveying system (see Lecours et al., 2017a). The presence of artifacts made the
classification vary of 2.1%. Motion artifacts, which are often present in bathymetric data, can cause much more impacts in mapping outcomes than time artifacts.
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Lecours Using Maps? Proceed with Caution
2007)], there is a need for a user-friendly tool that enables the
combined quantification of errors and uncertainty at the end of
an analysis workflow. In addition, an analysis of data quality of
each dataset at the beginning of the mapping workflow would
facilitate decisions regarding the validity of data integration in
the exercise. Research in geovisualization and cognition also
needs to be done to develop a geovisualization tool that would
enable a clear spatial representation of uncertainty and errors
associated with habitat maps and models. Such a tool would have
to be suitable for decision-makers without generating negative
perceptions of uncertainty and errors in the end-users: issues of
data quality are known to be a sensitive topic in management
as a lack of understanding of these issues can lead managers to
disqualify science on the sole basis that uncertainty and errors
were identified or acknowledged.
Awareness of data quality issues in conservation and
management must be improved, and many actions can be taken
to move toward resolving this issue. First, there should be a
focus on increasing awareness in the end-users community and
increasing the connection between data producers, mapmakers
and end-users (Cork et al., 2000; Devillers et al., 2010).
In addition, sources of errors and uncertainty and known
limitations of data and methods should be acknowledged and this
information should be associated with maps and models. Relating
to methods (as discussed in Section Methodology), Jones-
Farrand et al. (2011) warned against the use of only one habitat
model, indicating how risky it can be for conservation planning.
They advocate for the comparisons of outputs from different
models (e.g., Figure 2) to enable the identification of areas with
stronger potential for conservation and those associated with
more uncertainty, thus holding more risks for conservation. As
they explain, combining multiple maps can reduce uncertainty,
increase efficacy of conservation when maps agree with each
other, and increase understanding of the data and process when
maps do not agree with each other. Ensemble techniques are
promising methods to implement these solutions (Diesing and
Stephens, 2015; Robert et al., 2016). In all cases, mapmakers
should explicitly report commission and omission errors and
their meaning when relevant (Rondinini et al., 2006; Borja, 2014),
and in modeling processes probabilistic outputs accounting for
model uncertainty should be provided (Tulloch et al., 2013).
Finally, all of this information should be incorporated into
the decision-making processes (Moilanen et al., 2006; Tulloch
et al., 2016): Levin et al. (2014) argued that the lack of data
quality information is among the most significant impediments
to adequate conservation efforts.
The issues associated with thematic, spatial, and temporal
scales have been widely discussed in the ecological (e.g., Levin,
1992; Schneider, 2001; Hobbs, 2003) and geospatial literatures
(e.g., Stone, 1972; Atkinson and Tate, 2000; Goodchild, 2011),
including a recent review by Lecours et al. (2015) on scale
in marine habitat mapping. Here, I focus on how scale
impacts mapping outcomes, and the implications of scale for
decision-making. Planning processes and outcomes are directly
affected by scale (Rondinini et al., 2006; Wolff et al., 2016):
measuring the same phenomenon with data at different scales
produces different patterns (Keil and Hawkins, 2009; Ross
et al., 2015) which provides different information to decision-
makers. For instance, Figure 4 shows that by generating the
bathymetric and backscatter surfaces at 25 and 100 m spatial
resolution instead of 10 m resolution, the representations of
the distribution of habitats vary by 13.2% (482 km2) and
31.3% (1,144 km2) respectively. While generating sets of species
distribution models using the same data but at different spatial
resolutions, Seo et al. (2009) found that both the accuracy of the
models and the spatial distribution of predictions varied with
changing scale.
Ecological patterns and processes occur at multiple scales
(Buhl-Mortensen et al., 2015) and by often focusing on single
scales current habitat mapping practices regularly fail to capture
multiscale environmental drivers (Eidens et al., 2015; Lecours
et al., 2015). The situation becomes more complex when
the maps are used for conservation and management as the
scale of management has to match the ecological scales in
order to be effective. While many authors have recommended
shifting habitat mapping practices toward multiscale analyses,
Battista et al. (2017) suggested also moving toward multiscale
conservation and management planning that involves self-
replication across scales. If implemented properly, this would
solve a constant dilemma in conservation regarding the choice
between protection of large comprehensive areas or networks
of smaller areas (Di Sciara et al., 2016). However, cross-scale
interactions in social-ecological systems and exercises—like in
conservation and management—are complex and identified as
a key challenge (see Cash et al., 2006). While issues of scale
are well-known across sciences, it is unclear how different
stakeholders with different backgrounds perceive and interpret
the concept of scales (Apostolopoulou and Paloniemi, 2012).
This is more acute in conservation and management that is
the product of transdisciplinary collaboration and that requires
the appropriate matching of methodological (observational and
analytical), ecological, social (i.e., the scale at which people
or industry use the resources) and management scales (Edgar
et al., 2008; Levin et al., 2014). Mismatches between the
natural and social scales have been identified as causes for
failures in conservation and management (Crowder et al.,
2006; Cumming et al., 2006). Much work remains to fully
understand the underpinnings of attempting to match the
many different types of scale to produce sound science,
better inform decisions, and answer the needs of the different
stakeholders. As stated by Apostolopoulou and Paloniemi (2012),
“biodiversity conservation is an illuminating case for exploring
scale challenges, given that biodiversity is by definition a
multiscale phenomenon, and conservation expansion has at its
core several emerging multiscalar challenges.”
The incorporation of scientific knowledge regarding
ecological scale into conservation planning has been identified
as one of the most urgent scale challenge (e.g., Apostolopoulou
and Paloniemi, 2012). However, like for data quality, the lack
of translation between theory and practice is problematic; while
there are methods to properly account for scale in ecological
mapping and modeling (e.g., Detto and Muller-Landau, 2013;
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Matteo Sánchez et al., 2014), practical solutions are often
not implemented in user-friendly, easily accessible software
(Hamil et al., 2016). Until such tools become available, scale
information should always be explicitly stated in the metadata
and conservation and management plans to effectively support
decision-makers (Wolff et al., 2016).
Data Selection and Fitness for Use
Finally, in addition to data scale and quality, data selection
itself requires critical evaluation when being integrated into
the habitat mapping workflow for use in conservation and
management. While this may seem obvious, data content is a
matter that needs to be carefully approached: it has been reported
that data for marine characterization are not always carefully
selected, with data being selected primarily based on availability
rather than ecological meaning and fitness-for-use (Peterson
et al., 2011; Tulloch et al., 2016; Lecours et al., 2017a). It is
evident that selecting different variables, for instance broad-scale
currents instead of fine-scale substrate types, is likely to produce
different maps and influence models differently. However, it
has been shown that significant variations occur even when
selecting different variables that describe the same environmental
characteristics. Figure 5 provides an example in which significant
changes in habitat distribution were observed by simply replacing
one of eight variables by another one that was highly spatially
correlated with it (r=0.96). Based on that correlation, it
would be intuitive to not be expecting much change in mapping
outputs. However, 15.5% (566 km2) of the study area was
mapped differently. When more than one variable is replaced, the
changes can be more significant, with changes in habitats’ spatial
FIGURE 4 | Example of how using the same data at different scales can produce different outcomes. The reference map is the same as in Figure 2. The two altered
maps were computed using the same methods and the same data but generated at 25m (top) and 100m (down) spatial resolution.
FIGURE 5 | Example of how replacing only one variable as input to the mapping process can produce different outcomes. The reference map is the same as in
Figure 2. The altered map was produced with all the same data layers, except that the slope data was replaced by rugosity data. The slope and rugosity layers are
highly spatially correlated in this study area (r=0.96).
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distributions that can reach as much as thousands of square
kilometers (Lecours et al., 2016a). One of the main solutions that
has been discussed widely in the ecological literature is to select
variables with an ecological meaning for the studied area, species
or habitat (Howell, 2010; Borja et al., 2014; Lecours et al., 2015,
2016a). However, this makes comparability of maps in a broader
context difficult; as previously discussed, there is no universal
approach and variable selection is site- and case-specific (Pitcher
et al., 2012).
Selecting which variables to use amongst those for which
data are available is different than selecting which variables
should be used: easily accessible datasets often cause an over-
simplification of habitat maps by encouraging the lack of
consideration of important variables. The marine environment
is a complex, multidimensional environment, and mapping
exercises often do not include a comprehensive set of pertinent
data such as physical, geological, biological, and anthropogenic
characteristics (Guarinello et al., 2010; Boyd and Brown,
2015). Even more challenging is the integration of appropriate
ecological phenomena and interactions that limits our ability
to use this crucial information in decision-making, whether
it is the synergistic and antagonistic effects of drivers on
species distributions (Boyd and Brown, 2015), habitat edges and
transitions (Pinn and Robertson, 2003; Ries et al., 2004), or
the addition of temporal variability and the third dimension
(i.e., depth of phenomenon rather than depth of the seafloor,
see Duffy and Chown, 2017). It is time to recognize that
in order to improve the representation of marine habitats,
establishing or predicting their spatial extent and distribution
may not be enough (Spalding et al., 2016). When characterizing
the environment for conservation and management, we need
to recognize the importance of elements such as life history
(Kindsvater et al., 2016), prey-predator dynamics (Chakraborty
et al., 2013), community ecology (Pitcher et al., 2012), population
biology (Sherman et al., 2016), structure and dynamics (Borja
et al., 2014), connectivity (Hilário et al., 2015), dispersal (Beier
et al., 2011), sensitivity to changes (Borja et al., 2014), and
ecosystem services (Spalding et al., 2016).
While I recognize that many of these elements do not
necessarily lend themselves well to static cartographic
representation, we need to work with cartographers and
other spatial scientists toward finding a solution to do so. To
better understand how we can achieve such complex integration
of variables, more research and data are needed (Broderick, 2015;
Buhl-Mortensen et al., 2015; Hilário et al., 2015), in addition to
cross-disciplinary collaborations that will improve the quantity
and quality of information available for decision-making (Lent,
2015; Bisack and Magnusson, 2016). A promising endeavor
in that vein is the Ecological Marine Units (EMUs) project
(Sayre et al., 2017), which offers a baseline three-dimensional
global map of marine ecosystems. Finally, habitat mapping
methods that give a more accurate representation of the marine
environment should be explored. For instance, changes in habitat
types are often more characterized by gradual changes driven by
environmental gradients than by hard demarcation lines; many
traditional habitat mapping methods do not capture the presence
of these mixed, transitional habitats (Pinn and Robertson, 2003),
an issue also highlighted in the terrestrial literature (Ries et al.,
2004). However, habitat edges are ecologically important and
their study has potential for improving understanding of spatial
patterning in ecosystems and therefore can inform conservation
and management (Ries et al., 2004). Some classification methods
emerging from concepts of fuzzy logic hold great promises in
improving representation of habitat edges and should be further
explored (Kobryn et al., 2013; Lecours et al., 2015).
So far, this paper has highlighted impediments to a valid,
appropriate use of marine habitat maps in the context of marine
conservation and management. Methods and data selection,
scale, and data quality significantly influence the mapping
process, its outcomes, and consequently the decisions that are
being supported by the maps. In this section, I discuss the
potential to use interactive and collaborative GIS to encourage
the conservation and management community, from data
collectors and mapmakers to decision-makers, to move toward
a digital resilience. Digital resilience involves data and tools
that are “freely accessible, interchangeable, operational, of high
quality, and up-to-date” (Wright, 2016). In a context of human
adaptation to change, Wright (2016) discussed how innovations
in information technologies and analyses assist in decision-
making and help people become more resilient. The author
made three recommendations that I believe can be transferred
to the context of habitat mapping for marine conservation and
management and can help move toward resolving some of the
issues identified in this paper. The recommendations are to
“ (1) create and implement a culture that consistently shares
not only data, but workflows and use cases with the data,
particularly within maps and geographic information systems
or GIS; (2) use maps and other visuals to tell compelling
stories that many different kinds of audiences will understand
and remember; and (3) be more open to different kinds
of partnerships to reduce project costs, yield better results,
and foster public awareness and behavioral change.” In this
section, I propose GIS environments as a supporting tool for
the implementation of these recommendations in a habitat
mapping context.
Interactive GIS Framework to Improve
Critical Use of Maps in Marine Sciences
Many of the components of marine habitat mapping that I
have problematized and discussed in this paper could be better
integrated in the habitat mapping workflow, and thus help to
ground decision-making in sound science, if the maps and
data were interactive and embedded in a GIS environment
(Levin et al., 2014). In the last 25 years, innovations related to
GIS have contributed considerably to developments in marine
habitat mapping (Wright and Heyman, 2008; Brown et al., 2011).
Within a GIS environment, spatial analytical techniques can be
combined with environmental data and in situ observations to
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enable accurate quantification and representation of habitats,
providing a framework for mapping the distribution of species
and interpreting spatial patterns in biodiversity (Brown et al.,
2011; Lecours et al., 2015). In addition, existing GIS tools
provide options for the implementation of many habitat mapping
approaches, including tools based on concepts of fuzzy logic or
landscape ecology theory. In terms of science communication,
GIS environments facilitate the visualization of maps and data,
and queries on those maps and data and their associated
metadata. While most conservation and management decision-
making exercises are currently assisted by one or a few static maps
(Leslie, 2005), the use of GIS in facilitating dynamic interaction
with multiple maps could help resolve the issues discussed in this
paper: behavioral experiments have shown that the use of GIS
to analyze and interpret data and maps can improve decision-
making (Crossland et al., 1995). In addition, it is recognized
that GIS-based spatial decision-support tools remain among the
best options for environmental decision-making (Andersen et al.,
Characterized by three main components, namely database
management, data analysis, and mapping and geovisualization
(Table 4), GIS developed specifically for assisting decision-
making in marine conservation and management could help
increase awareness about the importance of varying methods,
data quality, scale and data selection, and move toward resolving
issues associated with them. First, geodatabases can facilitate
data selection by regrouping available data and identifying gaps
in relevant data. In addition, geodatabases enable the proper
integration of metadata for each dataset, including information
TABLE 4 | The different components of GIS that make them potential candidates
for informing decision-making in marine conservation and management while
facilitating awareness on the different issues highlighted in this paper.
of GIS
Examples of integrated solutions Issues being
Geodatabases Access to metadata
Combination of biological, ecological,
environmental and other types of data
Combination of data at multiple scales
Storage of uncertainty layers and quality
Data Quality
Data Selection
Analysis tools Data processing and modeling
Data quality analysis including error and
uncertainty propagation
Multiscale analyses
Combination of multiple methods of map
and model production (ensemble mapping)
Variable selection routines
Data Quality
Data Selection
3D display
Multi-layers interactive display
Multi-temporal and multiscale dynamic
Uncertainty and errors visualization
Data Quality
Data Selection
Such GIS combine many of the solutions proposed in previous sections (i.e., examples of
integrated solutions).
on data quality and scale. In terms of analysis, habitat mapping
and modeling tools (e.g., different algorithms for unsupervised
classifications, MaxEnt, boosted regressions) can be integrated
within GIS environments, in addition to proper tools for
error and uncertainty propagation analysis. By combining the
different algorithms and tools in one computing environment,
multiple scenarios could be run on the data and compared
to assess the reliability of predictions and maps, as previously
suggested. In terms of mapping, GIS offer versatility in visualizing
individual datasets, including spatial representations of errors
and uncertainty. In addition, GIS offer a scalable environment,
which can be useful for conservation planners that may need
broader-scale maps to serve as vision statements at the same
time as finer-scale maps to plan site-specific interventions
(Beier et al., 2011). GIS also allow the exploration of ways
to represent the four-dimensional nature of the environment,
whether it is for accounting for depth in the study of pelagic
species or for the temporal component of migration (Bentlage
et al., 2013; Kaplan et al., 2014). Tools to help make individual
maps or a combination of maps that tell a story could also
be incorporated in such GIS (Wright, 2016), and all potential
outcomes could be interrogated by decision-makers retrieving
different types of information (e.g., uncertainty associated with
a result, classification results for one area but given by different
models). Finally, GIS like this would be based on an adaptive
framework wherein data, tools, maps, and models could be
iteratively updated and refined with new developments. This
would offer replicable and transparent options for processing and
analysis, enabling valid comparisons to be made.
Such GIS would address the second recommendation made
by Wright (2016) by enabling the production of different
visuals (e.g., story maps) to effectively communicate the relevant
information to the appropriate audiences. Furthermore, it would
be possible to address Wright’s two other recommendations by
establishing specific management guidelines for these systems.
These GIS should be in line with the open-access, data-sharing
movement (Sedberry et al., 2011; Borja, 2014; Levin et al.,
2014), by being accessible to anyone, by making data and tools
accessible to anyone, and by allowing everyone to contribute
data and tools. Gjerde and Rulska-Domino (2012) suggested
the creation of an internationally accepted advisory body for
providing scientific and technical advice in conservation, which
could be in charge of such GIS. Similar to the controlled
environment of OpenStreetMap (Haklay and Weber, 2008), data
and tools would be quality-controlled before being integrated
into the systems. Whether members of these GIS communities
or members of their advisory body would perform this quality
control is up for debate among the different stakeholders and may
vary according to the needs of different conservation mandates. A
collaborative effort from different researchers across a variety of
fields and jurisdictions would also be needed (Gjerde and Rulska-
Domino, 2012; Dunstan et al., 2016; Jay et al., 2016) to ensure
sharing of resources and proper representation of ecological
processes that occur across and beyond political jurisdictions
(Poiani et al., 2000). As highlighted by Rudd (2014), such
efforts would benefit from the involvement of social scientists.
Suitable guidelines would need to be established to avoid conflicts
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between natural and social scientists, who may have different
priorities and definitions (e.g., in terms of data quality or validity)
(see discussion in Rudd, 2014). In addition, a committee on ethics
would be required within the advisory body. For example, the
inclusion of social data into a public geodatabase involves ethical
questions related to data ownership and access.
While such GIS may seem utopian, user-contributed websites
like OpenStreetMap (Haklay and Weber, 2008), collaborative
global initiatives like the Census for Marine Life (Alexander et al.,
2011; Vermeulen, 2013), projects like DEVOTES (http://www., OpenTopography (http://opentopography.
org) or the production of Ecological Marine Units (http://, and other calls for GIS
to support conservation and management (e.g., Levin et al.,
2014) put the proposed GIS in the realm of feasibility, if
the different stakeholders come together with the appropriate
resources to make it happen. In addition, there recently has been
a rise in similar GIS of smaller amplitude and complexity (e.g.,
Tyberghein et al., 2012; Karnatak et al., 2014).
Impediments to a Successful
While marine ecosystems and sciences are complex, decision-
makers and the public are drawn to simple scientific
communication (Borja et al., 2014; Hilário et al., 2015;
Tulloch et al., 2016), and sound and informed choices in
conservation require effective communication that reaches the
multiple audiences involved in the planning process (Fischhoff,
2013; Wright, 2016). We have a responsibility as scientists to
make our results and recommendations understandable for
the stakeholders and to fit their needs (Groffman et al., 2010;
Duarte, 2014; Broderick, 2015; Mea et al., 2016). The proposed
idea of open-access GIS that enable geodatabase building,
geovisualization tools, data quality control, and multiple habitat
mapping options is not a panacea. First, GIS have their own set
of issues, including those associated with mapping and identified
in this paper (e.g., data quality). The advantage of GIS over static
maps is the possibility of showing multi-dimensional datasets
that can include data quality information, metadata, temporal
variations, etc. Second, we cannot discount the possibility
that such GIS could be manipulated to display misleading
information, whether it is for a political purpose or simply due to
a lack of understanding of tools or data. Then, the most effective
solution still resides in improving scientific and geospatial
literacy among the different people involved in conservation and
management efforts (e.g., scientists, decision-makers, politicians,
the public). It is thus crucial to keep raising awareness of the
high variability of maps and models, and to continue proposing
solutions to better consider the issues discussed in this paper. By
including all who are involved at each step of conservation and
management efforts, and by working all together, we can start
reducing the gap between the priorities of scientists, society and
policy-makers, and ensuring that conservation policy is based on
sound scientific evidence (Adams and Sandbrook, 2013; Rudd,
2014; Rose, 2015).
Maps are critical to effectively communicate information about
the marine environment to the relevant decision-makers.
However, maps are highly variable and they depend strongly
on a variety of elements like the methodology used to produce
them, the data that were selected to make them, and the
scale and quality of these data. These elements have to be
acknowledged and appropriately dealt with as they are inherent
to map production and may cause issues for conservation and
management planning (Tulloch et al., 2016). In addition, they
are all related; for instance, there is often a relationship between
data quality and spatial scale (Braunisch and Suchant, 2010;
Lecours and Devillers, 2015), and between data quality and model
selection (Rondinini et al., 2006).
This contribution to the literature was written keeping this
antinomy in mind, which can be summarized with a quote
from Box and Draper (1987), who wrote that “essentially, all
models are wrong, but some are useful,” and a subsequent
quote by Devillers et al. (2010): “all spatial data are wrong,
but some are useful.” Despite their utility and their strong
potential for clear and understandable communication, maps
and models will always require scientific expert advice and
interpretation (Reiss et al., 2015): we must be transparent
with the mapping techniques and data, and recognize their
respective limitations (Kindsvater et al., 2016). In addition,
understanding the different trade-offs involved in the mapping
process is critical when maps and models are used in marine
conservation and management planning (Langford et al., 2009).
However, stakeholders involved in these practices are frequently
not trained to critically evaluate the maps they are presented,
in addition to the underlying methods and data. Mapmakers
also need to consider the end-use of the maps in order to
make appropriate decisions when producing the maps. While
geographers and other spatial scientists have been studying
concepts of scale and data quality for a long time, the
understanding of their role and integration in the marine habitat
mapping workflow has received scant attention in the past.
There is an urgent need to improve our understanding of how
these concepts influence the representation of ecosystems and
habitats, and to raise awareness among scientists, decision-
makers, and other stakeholders about their implications. Having
a strong, consistent, transparent, repeatable, and science-
based protocol for data collection and mapping that is truly
representative of the environment and ecological patterns
and processes is essential for effectively supporting decision-
makers in developing conservation and management plans.
To reach this goal, I recommend: (1) standardizing methods,
(2) implementing multiscale data collection and analyses,
(3) using ensemble mapping and prediction techniques, (4)
quantifying and spatially representing errors, uncertainty, and
their propagation, (5) explicitly addressing scale components in
analyses and interpretations, (6) producing complete metadata,
and (7) developing a digital resilience through the integration
of interactive and dynamic GIS in support of decision-
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Lecours Using Maps? Proceed with Caution
The author confirms being the sole contributor of this work and
approved it for publication.
Many thanks are due to Emma LeClerc for her insightful
comments on the different versions of this manuscript, and to
Benjamin Misiuk for his assistance with classification methods.
Publication of this article was funded in part by the University of
Florida Open Access Publishing Fund. The ideas for this paper
emerged from my work and enlightening discussions with Drs.
Rodolphe Devillers, Craig Brown, Vanessa Lucieer, and Evan
Edinger, and I thank them for that. A data-based presentation
on some of the elements presented in this paper was given
at the 4th International Marine Conservation Congress, under
the title “Assessing marine habitat maps’ sensitivity to variable
selection and data quality,” by VL, R. Devillers, C. Brown, V.
Lucieer, and E. Edinger. I would also like to thank the two
reviewers for their comments that significantly improved this
Adams, W. M., and Sandbrook, C. (2013). Conservation, evidence and policy. Oryx
47, 329–335. doi: 10.1017/S0030605312001470
Alexander, V., Miloslavich, P., and Yarincik, K. (2011). The census of marine
life – evolution of worldwide marine biodiversity research. Mar. Biodivers. 41,
545–554. doi: 10.1007/s12526-011-0084-1
Allen, G. R. (2008). Conservation hotspots of biodiversity and endemism for indo-
Pacific coral reef fishes. Aquatic Conserv. Mar. Freshw. Ecosyst. 18, 541–556.
doi: 10.1002/aqc.880
Andersen, M. C., Thompson, B., and Boykin, K. (2004). Spatial risk
assessment across large landscapes with varied land use: lessons from
a conservation assessment of military lands. Risk Anal. 24, 1231–1242.
doi: 10.1111/j.0272-4332.2004.00521.x
Anderson, J. T., Van Holliday, D., Kloser, R., Reid, D. G., and Simard, Y. (2008).
Acoustic seabed classification: current practice and future directions. ICES J.
Mar. Sci. 65, 1004–1011. doi: 10.1093/icesjms/fsn061
Apostolopoulou, E., and Paloniemi, R. (2012). Frames of scale challenges
in Finnish and Greek biodiversity conservation. Ecol. Soc. 17, 1–15.
doi: 10.5751/ES-05181-170409
Araújo, M. B., and Guisan, A. (2006). Five (or so) challenges for species distribution
modelling. J. Biogeogr. 33, 1677–1688. doi: 10.1111/j.1365-2699.2006.01584.x
Arbia, G., Griffith, D., and Haining, R. (1998). Error propagation and modelling
in raster GIS: overlay operations. Int. J. Geogr. Inform. Sci. 12, 145–167.
doi: 10.1080/136588198241932
Ashraf, U., Peterson, A. T., Chaudhry, M. N., Ashraf, I., Saqib, Z., Ahmad,
S. R., et al. (2017). Ecological niche model comparison under different
climate scenarios: a case study of Olea spp. in Asia. Ecosphere 8:e01825.
doi: 10.1002/ecs2.1825
Atkinson, P. M., and Tate, N. J. (2000). Spatial scale problems and geostatistical
solutions: a review. Prof. Geogr. 52, 607–623. doi: 10.1111/0033-0124.00250
Austin, M. P., and Van Niel, K. P. (2011). Improving species distribution models
for climate change studies: variable selection and scale. J. Biogeogr. 38, 1–8.
doi: 10.1111/j.1365-2699.2010.02416.x
Barry, S., and Elith, J. (2006). Error and uncertainty in habitat models. J. Appl. Ecol.
43, 413–423. doi: 10.1111/j.1365-2664.2006.01136.x
Battista, W., Tourgee, A., Wu, C., and Fujita, R. (2017). How to achieve
conservation outcomes at scale: an evaluation of scaling principles. Front. Mar.
Sci. 3:278. doi: 10.3389/fmars.2016.00278
Beale, C. M., and Lennon, J. J. (2012). Incorporating uncertainty in predictive
species distribution modelling. Philos. Trans. R. Soc. B 367, 247–258.
doi: 10.1098/rstb.2011.0178
Bedulli, D., Bassignani, F., and Bruschi, A. (2002). Use of biodiversity hotspots for
conservation of marine molluscs: a regional approach. Mediterr. Mar. Sci. 3,
113–121. doi: 10.12681/mms.250
Beier, P., Spencer, W., Baldwin, R. F., and McRae, B. H. (2011). Toward best
practices for developing regional connectivity maps. Conserv. Biol. 25, 879–892.
doi: 10.1111/j.1523-1739.2011.01716.x
Bellier, E., Monestiez, P., Durbec, J. P., and Candau, J. N. (2007). Identifying
spatial relationships at multiple scales: principal coordinates of neighbour
matrices (PCNM) and geostatistical approaches. Ecography 3, 385–399.
doi: 10.1111/j.0906-7590.2007.04911.x
Bentlage, B., Perterson, A. T., Barve, N., and Cartwright, P. (2013). Plumbing
the depths: extending ecological niche modelling and species distribution
modelling in three dimensions. Glob. Ecol. Biogeogr. 22, 952–961.
doi: 10.1111/geb.12049
Bierman, P., Lewis, M., Ostendorf, B., and Tanner, J. (2011). A review of methods
for analyzing spatial and temporal patterns in coastal water quality. Ecol. Indic.
11, 103–114. doi: 10.1016/j.ecolind.2009.11.001
Bisack, K. D., and Magnusson, G. M. (2016). Measuring management success for
protected species: looking beyond biological outcomes. Front. Mar. Sci. 3:61.
doi: 10.3389/fmars.2016.00061
Blanchet, F. G., Legendre, P., and Borcard, D. (2008). Forward selection of
explanatory variables. Ecology 89, 2623–2632. doi: 10.1890/07-0986.1
Borcard, D., Legendre, P., Avois Jacquet, C., and Tuomisto, H. (2004). Dissecting
the spatial structure of ecological data at multiple scales. Ecology 85, 1826–1832.
doi: 10.1890/03-3111
Borja, A. (2014). Grand challenges in marine ecosystems ecology. Front. Mar. Sci.
1:1. doi: 10.3389/fmars.2014.00001
Borja, A., Elliot, M., Snelgrove, P. V. R., Austen, M. C., Berg, T., Cochrane,
S., et al. (2016). Bridging the gap between policy and science in
assessing the health status of marine ecosystems. Front. Mar. Sci. 3:6.
doi: 10.3389/978-2-88945-004-6
Borja, A., Prins, T. C., Simboura, N., Andersen, J. H., Berg, T., Marques, J.-C., et al.
(2014). Tales from a thousand and one ways to integrate marine ecosystem
components when assessing the environmental status. Front. Mar. Sci. 1:72.
doi: 10.3389/fmars.2014.00072
Box, G. E. P., and Draper, N. R. (1987). Empirical Model-Building and Response
Surfaces. Hoboken, NJ: Wiley.
Boyd, P. W., and Brown, C. J. (2015). Modes of interactions between
environmental drivers and marine biota. Front. Mar. Sci. 2:9.
doi: 10.3389/fmars.2015.00009
Bradshaw, G., and Fortin, M. J. (2000). Landscape heterogeneity effects on scaling
and monitoring large areas using remote sensing data. Geogr. Inf. Sci. 6, 61–68.
doi: 10.1080/10824000009480534
Bradter, U., Kunin, W. E., Altringham, J. D., Thom, T. J., and Benton, T. G. (2013).
Identifying appropriate spatial scales of predictors in species distribution
models with the random forest algorithm. Methods Ecol. Evol. 4, 167–174.
doi: 10.1111/j.2041-210x.2012.00253.x
Braunisch, V., and Suchant, R. (2010). Predicting species distributions based on
incomplete survey data: the trade-off between precision and scale. Ecography
33, 826–840. doi: 10.1111/j.1600-0587.2009.05891.x
Broderick, A. C. (2015). Grand challenges in marine conservation and sustainable
use. Front. Mar. Sci. 2:11. doi: 10.3389/fmars.2015.00011
Brown, C. J., Sameoto, J. A., and Smith, S. J. (2012). Multiple methods, maps,
and management applications: purpose made seafloor in support of ocean
management. J. Sea Res. 72, 1–13. doi: 10.1016/j.seares.2012.04.009
Brown, C. J., Smith, S. J., Lawton, P., and Anderson, J. T. (2011). Benthic habitat
mapping: a review of progress towards improved understanding of the spatial
ecology of the seafloor using acoustic techniques. Estuar. Coast Shelf Sci. 92,
502–520. doi: 10.1016/j.ecss.2011.02.007
Brown, J. D., and Heuvelink, G. B. M. (2007). The Data Uncertainty Engine (DUE):
a software tool for assessing and simulating uncertain environmental variables.
Comput. Geosci. 33, 172–190. doi: 10.1016/j.cageo.2006.06.015
Frontiers in Marine Science | 13 September 2017 | Volume 4 | Article 288
Lecours Using Maps? Proceed with Caution
cas, M., Bergström, U., Downie, A. L., Sundblad, G., Gullström, M., Von
Numers, M., et al. (2013). Empirical modelling of benthic species distribution,
abundance, and diversity in the Baltic Sea: evaluating the scope for predictive
mapping using different modelling approaches. ICES J. Mar. Sci. 70, 1233–1243.
doi: 10.1093/icesjms/fst036
Buhl-Mortensen, L., Buhl-Mortensen, P., Dolan, M. F. J., and Gonzalez-Mirelis,
G. (2015). Habitat mapping as a tool for conservation and sustainable use
of marine resources: some perspectives from the MAREANO programme,
Norway. J. Sea Res. 100, 46–61. doi: 10.1016/j.seares.2014.10.014
Calder, B. R., and Mayer, L. A. (2003). Automatic processing of high-rate, high-
density multibeam echosounder data. Geochem. Geophy. Geosy. 4, 1048–1069.
doi: 10.1029/2002GC000486
Calvert, J., Strong, J. A., Service, M., McGonigle, C., and Quinn, R. (2015). An
evaluation of supervised and unsupervised classification techniques for marine
benthic habitat mapping using multibeam echosounder data. ICES J. Mar. Sci.
72, 1498–1513. doi: 10.1093/icesjms/fsu223
Cash, D. W., Adger, W., Berkes, F., Garden, P., Lebel, L., Olsson, P., et al. (2006).
Scale and cross-scale dynamics: governance and information in a multilevel
world. Ecol. Soc. 11, 1–12. doi: 10.5751/ES-01759-110208
Chakraborty, K., Das, K., and Kar, T. K. (2013). An ecological perspective
on marine reserves in prey-predator dynamics. J. Biol. Phys. 39, 749–776.
doi: 10.1007/s10867-013-9329-5
Cogan, C. B., Todd, B. J., Lawton, P., and Noji, T. T. (2009) The role of
marine habitat mapping in ecosystem-based management. ICES J. Mar. Sci. 66,
2033–2042. doi: 10.1093/icesjms/fsp214
Cork, S. J., Clark, T. W., and Mazur, N. (2000). Conclusions and
recommendations for koala conservation. Conserv. Biol. 14, 702–704.
doi: 10.1046/j.1523-1739.2000.99392.x
Costa, B., Taylor, J. C., Kracker, L., Battista, T., and Pittman, S. (2014). Mapping
reef fish and the seascape: using acoustics and spatial modeling to guide coastal
management. PLoS ONE 9:e85555. doi: 10.1371/journal.pone.0085555
Costello, M. J. (2014). Long live marine reserves: a review of experiences and
benefits. Biol. Conserv. 176, 289–296. doi: 10.1016/j.biocon.2014.04.023
Crase, B., Liedloff, A. C., and Wintle, B. A. (2012). A new method for dealing with
residual spatial autocorrelation in species distribution models. Ecography 35,
879–888. doi: 10.1111/j.1600-0587.2011.07138.x
Cros, A., Venegas-Li, R., Teoh, S. J., Peterson, N., Wen, W., and Fatan, N. A. (2014).
Spatial data quality control for the Coral Triangle Atlas. Coast. Manage. 42,
128–142. doi: 10.1080/08920753.2014.877760
Crossland, M. D., Wynne, B. E., and Perkins, W. C. (1995). Spatial decision support
systems: an overview of technology and a test of efficacy. Decis. Support Syst. 14,
219–235. doi: 10.1016/0167-9236(94)00018-N
Crossman, N. D., Burkhard, B., and Nedkov, S. (2012). Quantifying and
mapping ecosystem services. Int. J. Biodiv. Sci. Ecosyst. Serv. Manage. 8, 1–4.
doi: 10.1080/21513732.2012.695229
Crowder, L. B., Osherenko, G., Young, O. R., Airamé, S., Norse, E. A., Baron,
N., et al. (2006). Resolving mismatches in U.S. ocean governance. Science 313,
617–618. doi: 10.1126/science.1129706
Cumming, G. S., Cumming, D. H. M., and Redman, C. L. (2006). Scale mismatches
in social-ecological systems: causes, consequences, and solutions. Ecol. Soc. 11,
1–20. doi: 10.5751/ES-01569-110114
Cvitanovic, C., Fulton, C. J., Wilson, S. K., van Kerkhoff, L., Cripps, I. L., and
Muthiga, N. (2014). Utility of primary scientific literature to environmental
managers: an international case study on coral-dominated marine protected
areas. Ocean Coast. Manage. 102, 72–78. doi: 10.1016/j.ocecoaman.2014.
Davies, J. S., Stewart, H. A., Narayanaswamy, B. E., Jacobs, C., Spicer, J.,
Golding, N., et al. (2015). Benthic assemblages of the Anton Dohrm Seamount
(NE Atlantic): defining deep-sea biotopes to support habitat mapping and
management efforts with a focus on vulnerable marine ecosystems. PLoS ONE
10:e0124815. doi: 10.1371/journal.pone.0124815
de Rivera, Ó., and López-Quílez, A. (2017). Development and comparison of
species distribution models for forest inventories. ISPRS Int. J. Geo Inf. 6, 1–14.
doi: 10.3390/ijgi6060176
Degraer, S., Verfaillie, E., Willems, W., Adriaens, E., Vincx, M., and Van Lancker,
V. (2008). Habitat suitability modelling as a mapping tool for macrobenthic
communities: an example from the Belgian part of the North Sea. Cont. Shelf
Res. 15, 369–379. doi: 10.1016/j.csr.2007.09.001
Detto, M., and Muller-Landau, H. C. (2013). Fitting ecological process models to
spatial patterns using scalewise variances and moment equations. Am. Nat. 181,
68–82. doi: 10.1086/669678
Devillers, R., and Goodchild, H. (2010). Spatial Data Quality, from Process to
Decisions. Boca Raton, FL: CRC Press, 222.
Devillers, R., Pressey, R. L., Grech, A., Kittinger, J. N., Edgar, G. J., Ward, T.,
et al. (2014). Reinventing residual reserves in the sea: are we favouring ease of
establishment over need for protection? Aquat. Conserv. Mar. Freshw. Ecosyst.
25, 480–504. doi: 10.1002/aqc.2445
Devillers, R., Stein, A., Bédard, Y., Chrisman, N., Fisher, P., and Shi, W. (2010).
Thirty years of research on spatial data quality: achievements, failures, and
opportunities. Trans. GIS 14, 387–400. doi: 10.1111/j.1467-9671.2010.01212.x
Di Sciara, G. N., Hoyt, E., Reeves, R., Ardron, J., Marsh, H., Vongraven, D.,
et al. (2016). Place-based approaches to marine mammal conservation. Aquat.
Conserv. Mar. Freshw. Ecosyst. 26, 85–100. doi: 10.1002/aqc.2642
Diesing, M., and Stephens, D. (2015) A multi-model ensemble approach to seabed
mapping. J. Sea Res. 100, 62–69. doi: 10.1016/j.seares.2014.10.013
Diesing, M., Green, S. L., Stephens, D., Lark, R. M., Stewart, H. A., and Dove,
D. (2014). Mapping seabed sediments: comparison of manual, geostatistical,
object-based image analysis and machine learning approaches. Cont. Shelf Res.
84, 107–119. doi: 10.1016/j.csr.2014.05.004
Diesing, M., Mitchell, P., and Stephens, D. (2016). Image-based seabed
classification: what can we learn from terrestrial remote sensing? ICES J. Mar.
Sci. 73, 2425–2441. doi: 10.1093/icesjms/fsw118
Duarte, C. M. (2014). Global change and the future ocean: a grand challenge for
marine sciences. Front. Mar. Sci. 1:63. doi: 10.3389/fmars.2014.00063
Duffy, G. A., and Chown, S. L. (2017). Explicitly integrating a third dimension
in marine species distribution modelling. Mar. Ecol. Prog. Ser. 564, 1–8.
doi: 10.3354/meps12011
Duncan, J. L., Perry, J. N., Dale, M. R. T., Legendre, P., Citron-Pousty, S., Fortin, M.
J., et al. (2002). A balanced view of scale in spatial statistical analysis. Ecography
25, 626–640. doi: 10.1034/j.1600-0587.2002.250510.x
Dunstan, P. K., Bax, N. J., Dambacher, J. M., Hayes, K. R., Hedge, P. T., Smith,
D. C., et al. (2016). Using ecologically or biologically significant marine areas
(EBSAs) to implement marine spatial planning. Ocean Coast. Manage. 121,
116–127. doi: 10.1016/j.ocecoaman.2015.11.021
Edgar, G. J., Langhammer, P. F., Allen, G., Brooks, T. M., Brodie, J., Crosse,
W., et al. (2008). Key biodiversity areas as globally significant target sites for
the conservation of marine biological diversity. Aquat. Conserv. Mar. Freshw.
Ecosyst. 18, 969–983. doi: 10.1002/aqc.902
Eidens, C., Hauffe, T., Bayraktarov, E., Wild, C., and Wilke, T. (2015). Multi-scale
processes drive benthic community structure in upwelling-affected coral reefs.
Front. Mar. Sci. 2:2. doi: 10.3389/fmars.2015.00002
Elith, J., and Graham, C. H. (2009). Do they? How do they? Why do they differ?
on finding reasons for differing performances of species distribution models.
Ecography 32, 66–77. doi: 10.1111/j.1600-0587.2008.05505.x
Elith, J., and Leathwick, J. R. (2009). Species distribution models: ecological
explanation and prediction across space and time. Ann. Rev. Ecol. Evol. System.
40, 677–697. doi: 10.1146/annurev.ecolsys.110308.120159
Elith, J., Graham, C. H., Anderson, R. P., Dudik, M., Ferrier, S., Guisan, A.,
et al. (2006). Novel methods improve prediction of species’ distributions from
occurrence data. Ecography 29, 129–151. doi: 10.1111/j.2006.0906-7590.04596.x
Falace, A., Kaleb, S., Curierl, D., Miotti, C., Galli, G., Querin, S., et al.
(2015). Calcareous bio-concretions in the Northern Adriatic Sea: habitat
types, environment factors that influence habitat distributions, and predictive
modeling. PLoS ONE 10:e0140931. doi: 10.1371/journal.pone.0140931
Finkl, C. W., and Makowski, C. (2015). Autoclassification versus cognitive
interpretation of digital bathymetric data in terms of geomorphological
features for seafloor characterization. J. Coast. Res. 31, 1–16.
doi: 10.2112/JCOASTRES-D-14A-00006.1
Fischhoff, B. (2013). The sciences of science communication. Proc. Natl. Acad.
Sci.U.S.A. 110, 14033–14039. doi: 10.1073/pnas.1213273110
Fisher, P. F., and Tate, N. J. (2006). Causes and consequences of
error in digital elevation models. Prog. Phys. Geogr. 30, 467–489.
doi: 10.1191/0309133306pp492ra
Franklin, J. (2010). Moving beyond static species distribution models in
support of conservation biogeography. Divers. Distrib. 16, 321–330.
doi: 10.1111/j.1472-4642.2010.00641.x
Frontiers in Marine Science | 14 September 2017 | Volume 4 | Article 288
Lecours Using Maps? Proceed with Caution
Fraschetti, S., Terlizzi, A., Guarnieri, G., Pizzolante, F., D’Ambrosio, P., Maiorano,
P., et al. (2010). Effects of unplanned development on marine biodiversity: a
lesson from Albania (Central Mediterranean Sea). J. Coast. Res. 58, 106–115.
doi: 10.2112/SI_58_10
Gallo, J., and Goodchild, M. (2012), Mapping uncertainty in
conservation assessment as a means toward improved conservation
planning and implementation. Soc. Nat. Resour. 25, 22–36.
doi: 10.1080/08941920.2011.578119
Galparsoro, I., Borja, A., and Uyarra, M. C. (2014). Mapping ecosystem services
provided by benthic habitats in the European North Atlantic Ocean. Front.
Mar. Sci. 1:23. doi: 10.3389/fmars.2014.00023
García-Alegre, A., Sánchez, F., Gómez-Ballesteros, M., Hinz, H., Serrano, A., and
Parra, S. (2014). Modelling and mapping the local distribution of representative
species on the Le Danois Bank, El Cachucho marine protected area (Cantabrian
Sea). Deep Sea Res. II 106, 151–164. doi: 10.1016/j.dsr2.2013.12.012
Gjerde, K. M., and Rulska-Domino, A. (2012). Marine protected areas beyond
national jurisdiction: some practical perspectives for moving ahead. Int. J. Mar.
Coast. Law 27, 351–373. doi: 10.1163/157180812X633636
Gjerde, K. M., Nordtvedt Reeve, L. L., Harden-Davies, H., Ardron, J.,
Dolan, R., Durussel, C., et al. (2016). Protecting Earth’s last conservation
frontier: scientific, management and legal priorities for MPAs beyond
national boundaries. Aquat. Conserv. Mar. Freshw. Ecosyst. 26, 45–60.
doi: 10.1002/aqc.2646
Goodchild, M. F. (2011). Scale in GIS: an overview. Geomorphology 130, 5–9.
doi: 10.1016/j.geomorph.2010.10.004
Griffiths, G. H., Eversham, B. C., and Roy, D. B. (1999). Integrating species and
habitat data for nature conservation in Great Britain: data sources and methods.
Global Ecol. Biogeogr. 8, 329–345. doi: 10.1046/j.1365-2699.1999.00143.x
Groffman, P. M., Stylinski, C., Nisbet, M. C., Duarte, C. M., Jordan, R., Burgin,
A., et al. (2010). Restarting the conversation: challenges at the interface
between ecology and society. Front. Ecol. Environ. 8, 284–291. doi: 10.1890/
Grorud-Colvert, K., Lester, S. E., Airamé, S., Neeley, E., and Gaines, S. D. (2010).
Communicating marine reserve science to diverse audiences. Proc. Natl. Acad.
Sci. U.S.A. 107, 18306–18311. doi: 10.1073/pnas.0914292107
Guarinello, M. L., Shumchenia, E. J., and King, J. W. (2010). Marine habitat
classification for ecosystem-based management: a proposed hierarchical
framework. Environ. Manage. 45, 793–806. doi: 10.1007/s00267-010-9430-5
Guisan, A., and Thuiller, W. (2005). Predicting species distribution:
offering more than simple habitat models. Ecol. Lett. 8, 993–1009.
doi: 10.1111/j.1461-0248.2005.00792.x
Guisan, A., Reid, T., Baumgartner, J. B., Naujokaitis-Lewis, I., Sutcliffe, P. R.,
Tulloch, A. I. T., et al. (2013). Predicting species distributions for conservation
decisions. Ecol. Lett. 16, 1424–1435. doi: 10.1111/ele.12189
Haklay, M., and Weber, P. (2008). OpenStreetMap: user-generated street maps.
IEEE Perv. Comput. 7, 12–18. doi: 10.1109/MPRV.2008.80
Hamil, K. A. D., Iannone, B. V. III, Huang, W. K., Fei, S., and Zhang, H. (2016).
Cross-scale contradictions in ecological relationships. Landscape Ecol. 31, 7–18.
doi: 10.1007/s10980-015-0288-z
Harris, P. T. (2012). “Anthropogenic threats to benthic habitats,” in Seafloor
Geomorphology as Benthic Habitats, eds P. T. Harris and E. K. Baker
(Amsterdam: Elsevier), 39–60.
Harris, P. T., and Baker, E. K. (eds.). (2012a). “GeoHab atlas of seafloor geomorphic
features and benthic habitats: synthesis and lessons learned,” in Seafloor
Geomorphology as Benthic Habitats (Amsterdam: Elsevier), 871–890.
Harris, P. T., and Baker, E. K. (2012b). Seafloor Geomorphology as Benthic Habitats.
Amsterdam: Elsevier, 900.
Hasan, R. C., Ierodiaconou, D., and Monk, J. (2012). Evaluation of four supervised
learning methods for benthic habitat mapping using backscatter from multi-
beam sonar. Remote Sens. 4, 3427–3443. doi: 10.3390/rs4113427
Heuvelink, G. B. M. (1998) Error Propagation in Environmental Modelling with
GIS. London: Taylor and Francis, 127.
Hewitt, J. E., Wang, D., Francis, M., Lundquist, C., and Duffy, C. (2015).
Evaluating demersal fish richness as a surrogate for epibenthic richness in
management and conservation. Divers. Distrib. 21, 901–912. doi: 10.1111/ddi.
Hilário, A., Metaxas, A., Gaudron, S. M., Howell, K. L., Mercier, A.,
Mestre, N. C., et al. (2015). Estimating dispersal distance in the deep
sea: challenges and applications to marine reserves. Front. Mar. Sci. 2:6.
doi: 10.3389/fmars.2015.00006
Hoang, T. C., O’Leary, M. J., and Fotedar, R. K. (2016). Remote-sensed mapping of
Sargassum spp. Distribution around Rottnest Island, Western Australia, using
high-spatial resolution WorldView-2 satellite data. J. Coast. Res. 32, 1310–1321.
doi: 10.2112/JCOASTRES-D-15-00077.1
Hobbs, N. T. (2003). Challenges and opportunities in integrating
ecological knowledge across scales. For. Ecol. Manage. 181, 223–238.
doi: 10.1016/S0378-1127(03)00135-X
Holmes, J., and Clark, R. (2008). Enhancing the use of science in
environmental policy-making and regulation. Environ. Sci. Policy 11, 702–711.
doi: 10.1016/j.envsci.2008.08.004
Hossain, M. S., Bujang, J. S., Zakaria, M. H., and Hashim, M. (2016). Marine
and human habitat mapping for the Coral Triangle Initiative region of
Sabah using Landsat and google Earth imagery. Mar. Policy 72, 176–191.
doi: 10.1016/j.marpol.2016.07.003
Howell, K. L. (2010). A benthic classification system to aid in the implementation
of marine protected area networks in the deep/high seas of the NE Atlantic.
Biol. Conserv. 143, 1041–1056. doi: 10.1016/j.biocon.2010.02.001
Howell, K. L., Piechaud, N., Downie, A. L., and Kenny, A. (2016). The distribution
of deep-sea sponge aggregations in the North Atlantic and implications
for their effective spatial management. Deep Sea Res. I 115, 309–320.
doi: 10.1016/j.dsr.2016.07.005
Hughes-Clarke, J. E. (2003). Dynamic motion residuals in swath sonar data:
ironing out the creases. Int. Hydrogr. Rev. 4, 6–23.
Ierodiaconou, D., Monk, J., Rattray, A., Laurenson, L., and Versace, V. L. (2011).
Comparison of automated classification techniques for predicting benthic
biological communities using hydroacoustics and video observations. Cont.
Shelf. Res. 31, S28–S38. doi: 10.1016/j.csr.2010.01.012
Jager, H. I., and King, A. W. (2004). Spatial uncertainty and ecological models.
Ecosystems 7, 841–847. doi: 10.1007/s10021-004-0025-y
Jay, S., Alves, F. L., O’Mahony, C., Gomez, M., Rooney, A., Almodovar,
M., et al. (2016). Transboundary dimensions of marine spatial planning:
fostering inter-juridictional relations and governance. Mar. Policy 65, 85–96.
doi: 10.1016/j.marpol.2015.12.025
Jones-Farrand, D. T., Fearer, T. M., Thogmartin, W. E., Thompson, F. R. III.,
Nelson, M. D., and Tirpak, J. M. (2011). Comparison of statistical and
theoretical habitat models for conservation planning: the benefit of ensemble
prediction. Ecol. Appl. 21, 2269–2282. doi: 10.1890/10-1047.1
Kachelriess, D., Wegmann, M., Gollock, M., and Pettorelli, N. (2014). The
application of remote sensing for marine protected area management. Ecol.
Indic. 36, 169–177. doi: 10.1016/j.ecolind.2013.07.003
Kaplan, D. M., Chassot, E., Amandé, J. M., Dueri, S., Demarcq, H., Dagorn, L., et al.
(2014). Spatial management of Indian Ocean tropical tuna fisheries: potential
and perspectives. ICES J. Mar. Sci. 71, 1728–1749. doi: 10.1093/icesjms/fst233
Karnatak, H., Pandey, K., Oberai, K., Roy, A., Joshi, D., Singh, H., et al.
(2014). Geospatial data sharing, online spatial analysis and processing of
Indian biodiversity data in internet and GIS domain – a case study
for raster based online geo-processing. Int. Arch. ISPRS 40, 1133–1137.
doi: 10.5194/isprsarchives-XL-8-1133-2014
Katsanevakis, S., Weber, A., Pipitone, C., Leopold, M., Cronin, M., Scheidat,
M., et al. (2012). Monitoring marine populations and communities:
methods dealing with imperfect detectability. Aquat. Biol. 16, 31–52.
doi: 10.3354/ab00426
Keil, P., and Hawkins, B. A. (2009). Grids versus regional species lists: are broad-
scale patterns of species richness robust to the violation of constant grain size?
Biodivers. Conserv. 18, 3127–3137. doi: 10.1007/s10531-009-9631-5
Kindsvater, H. K., Mangel, M., Reynolds, J. D., and Dulvy, N. K. (2016).
Ten principles from evolutionary ecology essential for effective marine
conservation. Ecol. Evol. 6, 2125–2138. doi: 10.1002/ece3.2012
Kobryn, H. T., Wouters, K., Beckley, L. E., and Heege, T. (2013). Ningaloo Reef:
shallow marine habitats mapped using a hyperspectral sensor. PLoS ONE
8:e70105. doi: 10.1371/journal.pone.0070105
Laffoley, D., and Hiscock, K. (1993). The classification of benthic estuarine
communities for nature conservation assessments in Great Britain. Neth. J.
Aquat. Ecol. 27, 181–187. doi: 10.1007/BF02334781
Laffoley, D., Lundin, C., and Simard, F. (2016). Introduction. Aquat. Conserv. Mar.
Freshw. Ecosyst. 26, 5–6. doi: 10.1002/aqc.2682
Frontiers in Marine Science | 15 September 2017 | Volume 4 | Article 288
Lecours Using Maps? Proceed with Caution
Langford, W. T., Gordon, A., and Bastin, L. (2009). When do conservation
planning methods deliver? Quantifying the consequences of uncertainty. Ecol.
Inform. 4, 123–135. doi: 10.1016/j.ecoinf.2009.04.002
Larsen, F. W., and Rahbek, C. (2003). Influence of scale on conservation
priority setting – a test on African mammals. Biodivers. Conserv. 12, 599–614.
doi: 10.1023/A:1022448928753
Lauer, M., and Aswani, S. (2008). Integrating indigenous ecological knowledge
and multi-spectral image classification for marine habitat mapping in Oceania.
Ocean Coast. Manage. 51, 495–504. doi: 10.1016/j.ocecoaman.2008.04.006
Lauria, V., Vaz, S., Martin, C. S., Mackinson, S., and Carpentier, A. (2011). What
influences European plaice (Pleuronectes platessa) distribution in the eastern
English Channel? Using habitat modelling and GIS to predict habitat utilization.
ICES J. Mar. Sci. 68, 1500–1510. doi: 10.1093/icesjms/fsr081
Le Pape, O., Delavenne, J., and Vaz, S. (2014). Quantitative mapping of fish
habitat: a useful tool to design spatialised management measures and marine
protected area with fishery objectives. Ocean Coast. Manage. 87, 8–19.
doi: 10.1016/j.ocecoaman.2013.10.018
Lechner, A. M., Langford, W. T., Bekessy, S. A., and Jones, S. D. (2012). Are
lansdscape ecologists addressing uncertainty in their remote sensing data?
Landscape Ecol. 27, 1249–1261. doi: 10.1007/s10980-012-9791-7
Lecours, V., and Devillers, R. (2015). “Assessing the spatial data quality paradox
in the deep-sea,” in Spatial Knowledge and Information Canada (Banff, AB:
SKI-Canada), 1–8.
Lecours, V., Brown, C. J., Devillers, R., Lucieer, V. L., and Edinger,
E. N. (2016b). Comparing selections of environmental variables for
ecological studies: a focus on terrain attributes. PLoS ONE 11:e0167128.
doi: 10.1371/journal.pone.0167128
Lecours, V., Devillers, R., Edinger, E. N., Brown, C. J., and Lucieer, V. L.
(2017a). Influence of artefacts in marine digital terrain models on habitat maps
and species distribution models: a multiscale assessment. Remote Sens. Ecol.
Conserv. doi: 10.1002/rse2.49. [Epub ahead of print].
Lecours, V., Devillers, R., Schneider, D. C., Lucieer, V. L., Brown, C. J., and
Edinger, E. N. (2015). Spatial scale and geographic context in benthic habitat
mapping: review and future directions. Mar. Ecol. Prog. Ser. 535, 259–284.
doi: 10.3354/meps11378
Lecours, V., Devillers, R., Simms, A. E., Lucieer, V. L., and Brown, C. J. (2017b).
Towards a framework for terrain attribute selection in environmental studies.
Environ. Model. Softw. 89, 19–30. doi: 10.1016/j.envsoft.2016.11.027
Lecours, V., Dolan, M. F. J., Micallef, A., and Lucieer, V. L. (2016a). A review of
marine geomorphometry, the quantitative study of the seafloor. Hydrol. Earth
Syst. Sci. 20, 3207–3244. doi: 10.5194/hess-20-3207-2016
Lengyel, S., Kobler, A., Kutnar, L., Framstad, E., Henry, P. Y., Babij,
V., et al. (2008). A review and a framework for the integration of
biodiversity monitoring at the habitat level. Biodivers. Conserv. 17, 3341–3356.
doi: 10.1007/s10531-008-9359-7
Lent, R. J. (2015). Conservation benefits of an interdisciplinary approach to marine
mammal science. Front. Mar. Sci. 2:67. doi: 10.3389/fmars.2015.00067
Leslie, H. M. (2005). A synthesis of marine conservation approaches. Conserv. Biol.
19, 1701–1713. doi: 10.1111/j.1523-1739.2005.00268.x
Levin, N., Coll, M., Fraschetti, S., Gal, G., Giakoumi, S., Göke, C., et al. (2014).
Biodiversity data requirements for systematic conservation planning in the
Mediterranean Sea. Mar. Ecol. Prog. Ser. 508, 261–281. doi: 10.3354/meps
Levin, S. A. (1992). The problem of pattern and scale in ecology. Ecology 73,
1943–1967. doi: 10.2307/1941447
Li, D., Zhang, J., and Wu, H. (2012). Spatial data quality and beyond. Int. J. Geogr.
Inf. Sci. 26, 2277–2290. doi: 10.1080/13658816.2012.719625
Li, X., and Wang, Y. (2013). Applying various algorithms for species distribution
modelling. Int. Zool. 8, 124–135. doi: 10.1111/1749-4877.12000
Livne, E., and Svoray, T. (2011). Components of uncertainty in primary production
model: the study of DEM, classification and location error. Int. J. Geogr. Inf. Sci.
25, 473–488. doi: 10.1080/13658816.2010.517752
Lu, D., Mausel, P., Batistella, M., and Moran, E. (2004). Comparison of land-cover
classification methods in the Brazilian Amazon Basin. Photogram. Eng. Remote
Sens. 70, 723–731. doi: 10.14358/PERS.70.6.723
Lucieer, V. L. (2008). Object-oriented classification of sidescan sonar data
for mapping benthic marine habitats. Int. J. Remote Sens. 29, 905–921.
doi: 10.1080/01431160701311309
Lucieer, V. L., Hill, N. A., Barrett, N. S., and Nichol, S. (2013). Do marine substrates
‘look’ and ‘sound’ the same? Supervised classification of multibeam acoustic
data using autonomous underwater vehicle images. Estuar. Coast. Shelf Sci. 117,
94–106. doi: 10.1016/j.ecss.2012.11.001
Ma, L., Manchun, L., Ma, X., Cheng, L., Du, P., and Liu, Y. (2017). A review
of supervised object-based land-cover image classification. ISPRS J. Photogr.
Remote Sens. 130, 277–293. doi: 10.1016/j.isprsjprs.2017.06.001
Marceau, D. J., and Hay, G. J. (1999). Remote sensing contributions to the
scale issue. Can. J. Remote Sens. 25, 357–366. doi: 10.1080/07038992.1999.108
Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K., and Thuiller, W. (2009).
Evaluation of consensus methods in predictive species distribution modelling.
Divers. Distrib. 15, 59–69. doi: 10.1111/j.1472-4642.2008.00491.x
Marsh, I., and Brown, C. (2009). Neural network classification of multibeam
backscatter and bathymetry data from Stanton Bank (Area IV). Appl. Acoust.
70, 1269–1276. doi: 10.1016/j.apacoust.2008.07.012
Matteo Sánchez, M. C., Cushman, S. A., and Saura, S. (2014). Scale dependence
in habitat selection: the case of the endangered brown bear (Ursus arctos)
in the Cantabrian Range (NW Spain). Int. J. Geogr. Inf. Sci. 28, 1531–1546.
doi: 10.1080/13658816.2013.776684
Matthaüs, W. (1995) Natural variability and human impacts reflected in
longterm changes in the Baltic deep water conditions–a brief review. Deutsche
Hydrographische Zeitschrift 47, 47–65. doi: 10.1007/BF02731990
Maulik, U., and Chakraborty, D. (2017). Remote sensing image classification:
a survey of support-vector-machine-based advanced techniques. IEEE Geosc.
Remote Sens. Mag. 5, 33–52. doi: 10.1109/MGRS.2016.2641240
McCarthy, M. J., and Halls, J. N. (2014). Habitat mapping and change assessment of
coastal environments: an examination of WorldView-2, Quickbird, and Ikonos
satellite imagery and airborne lidar for mapping barrier island habitats. ISPRS
Int. J. Geo Inform. 3, 297–325. doi: 10.3390/ijgi3010297
McConney, P., Fanning, L., Mahon, R., and Simmons, B. (2016). A first look at the
science-policy interface for ocean governance in the wider Caribbean Region.
Front. Mar. Sci. 2:119. doi: 10.3389/fmars.2015.00119
Mea, M., Newton, A., Uyarra, M. C., Alonso, C., and Borja, A. (2016). From science
to policy and society: enhancing the effectiveness of communication. Front.
Mar. Sci. 3:168. doi: 10.3389/fmars.2016.00168
Mishra, D., Narumalani, S., Rundquist, D., and Lawson, M. (2006). Benthic habitat
mapping in tropical marine environments using Quickbird multispectral data.
Photogr. Eng. Remote Sens. 72, 1037–1048. doi: 10.14358/PERS.72.9.1037
Moilanen, A., Wintle, B. A., Elith, J., and Burgman, M. (2006). Uncertainty
analysis for regional-scale reserve selection. Conserv. Biol. 20, 1688–1697.
doi: 10.1111/j.1523-1739.2006.00560.x
Morris, L. R., Proffitt, K. M., and Blackburn, J. K. (2016). Mapping resource
selection functions in wildlife studies: concerns and recommendations. Appl.
Geogr. 76, 173–183. doi: 10.1016/j.apgeog.2016.09.025
Moudrý, V., and Šímová, P. (2012). Influence of positional accuracy, sample size
and scale on modelling species distributions: a review. Int. J. Geogr. Inf. Sci. 26,
2083–2095. doi: 10.1080/13658816.2012.721553
Murtaugh, P. A. (2009). Performance of several variable-selection methods applied
to real ecological data. Ecol. Lett. 12, 1–8. doi: 10.1111/j.1461-0248.2009.01361.x
Ojeda, G. Y., Gayes, P. T., Van Dolah, R. F., and Schwab, W. C. (2004).
Spatially quantitative seafloor habitat mapping: example from the northern
South Carolina inner continental shelf. Estuar. Coast. Shelf Sci. 59, 399–416.
doi: 10.1016/j.ecss.2003.09.012
Orlikowska, E. H., Roberge, J.-M., Blicharska, M., and Mikusi´
nski, G. (2016).
Gaps in ecological research on the world’s largest internationally coordinated
network of protected areas: a review of Natura 2000. Biol. Conserv. 200,
216–227. doi: 10.1016/j.biocon.2016.06.015
Outeiro, L., Häussermann, V., Viddi, F., Hucke-Gaete, R., Försterra, G., Oyarzo,
H., et al. (2015). Using ecosystem services mapping for marine spatial planning
in southern Chile under scenario assessment. Ecosyst. Serv. 16, 341–353.
doi: 10.1016/j.ecoser.2015.03.004
Parsons, E. C. M., DellaSala, D. A., and Wright, A. J. (2015). Is marine
conservation science becoming irrelevant to policy makers? Front. Mar. Sci.
2:102. doi: 10.3389/fmars.2015.00102
Peterson, A. T., Soberón, J., Pearson, R. G., Anderson, R. P., Martínez-Meyer, E.,
Nakamura, M., et al. (2011). Ecological Niches and Geographic Distributions.
Princeton, NJ: Princeton University Press.
Frontiers in Marine Science | 16 September 2017 | Volume 4 | Article 288
Lecours Using Maps? Proceed with Caution
Phillips, S. J., Anderson, R. P., and Schapire, R. E. (2006). Maximum entropy
modeling of species geographic distributions. Ecol. Modell. 190, 231–259.
doi: 10.1016/j.ecolmodel.2005.03.026
Piechaud, N., Downie, A., Stewart, H. A., and Howell, K. L. (2015). The impact
of modelling method selection on predicted extent and distribution of deep-
sea benthic assemblages. Earth Environ. Sci. Trans. R. Soc. 105, 251–261.
doi: 10.1017/S1755691015000122
Pinn, E. H., and Robertson, M. R. (2003). Effect of track spacing and
data interpolation on the interpretation of benthic community distribution
derived from RoxAnn acoustic surveys. ICES J. Mar. Sci. 60, 1288–1297.
doi: 10.1016/S1054-3139(03)00134-6
Pitcher, C. R., Lawton, P., Ellis, N., Smith, S. J., Incze, L. S., Wei, C. L., et al.
(2012). Exploring the role of environmental variables in shaping patterns of
seabed biodiversity composition in regional-scale ecosystems. J. Appl. Ecol. 49,
670–679. doi: 10.1111/j.1365-2664.2012.02148.x
Poiani, K. A., Richter, B. D., Anderson, M. G., and Richter, H. E.
(2000). Biodiversity conservation at multiple scales: functional sites,
landscapes, and networks. Bioscience 50, 133–146. doi: 10.1641/0006-
Poulos, D. E., Gallen, C., Davis, T., Booth, D. J., and Harasti, D. (2016). Distribution
and spatial modelling of a soft coral habitat in the port stephens-great lakes
marine park: implications for management. Mar. Freshw. Res. 67, 256–265.
doi: 10.1071/MF14059
Quattrochi, D. A., and Goodchild, M. F. (1997). Scale in Remote Sensing and GIS.
Boca Raton, FL: Lewis Publishers.
Rahbek, C. (2005). The role of spatial scale and the perception
of large-scale species-richness patterns. Ecol. Lett. 8, 224–239.
doi: 10.1111/j.1461-0248.2004.00701.x
Regan, H. M., Ben-Haim, Y., Langford, B., Wilson, W. G., Lundberg, P.,
Andelman, S. J., et al. (2005). Robust decision-making under severe uncertainty
for conservation management. Ecol. Appl. 15, 1471–1477. doi: 10.1890/
Reiss, H., Birchenough, S., Borja, A., Buhl-Mortensen, L., Craeymeersch,
J., Dannheim, J., et al. (2015). Benthos distribution modelling and its
relevance for marine ecosystem management. ICES J. Mar. Sci. 72, 297–315.
doi: 10.1093/icesjms/fsu107
Rengstorf, A. M., Grehan, A., Yesson, C., and Brown, C. (2012). Towards high-
resolution habitat suitability modeling of vulnerable marine ecosystems in the
deep-sea: resolving terrain attribute dependencies. Mar. Geod. 35, 343–361.
doi: 10.1080/01490419.2012.699020
Reshitnyk, L., Costa, M., Robinson, C., and Dearden, P. (2014) Evaluation of
WorldView-2 and acoustic remote sensing for mapping benthic habitats
in temperate coastal Pacific waters. Remote Sens. Environ. 153, 7–23.
doi: 10.1016/j.rse.2014.07.016
Ries, L., Fletcher, R. J. Jr., Battin, J., and Sisk, T. D. (2004). Ecological responses to
habitat edges: mechanisms, models, and variability explained. Annu. Rev. Ecol.
Evol. Syst. 35, 491–522. doi: 10.1146/annurev.ecolsys.35.112202.130148
Robert, K., Jones, D. O. B., Roberts, J. M., and Huvenne, V. A. I. (2016)
Improving predictive mapping of deep-water habitats: considering multiple
model outputs and ensemble techniques. Deep Sea Res. I 113, 80–89.
doi: 10.1016/j.dsr.2016.04.008
Robertson, M. P., Cumming, G. S., and Erasmus, B. F. N. (2010).
Getting the most out of atlas data. Divers. Distrib. 16, 363–375.
doi: 10.1111/j.1472-4642.2010.00639.x
Rocchini, D., Hortal, J., Lengyel, S., Lobo, J. M., Jiménez-Valverde, A.,
Ricotta, C., et al. (2011). Accounting for uncertainty when mapping species
distributions: the need for maps of ignorance. Prog. Phys. Geogr. 35, 211–226.
doi: 10.1177/0309133311399491
Rolet, C., Spilmont, N., Dewarumez, J. M., and Luczak, C. (2015). Linking
macrobenthic communities structure and zonation patterns on sandy shores:
mapping tool toward management and conservation perspectives in Northern
France. Cont. Shelf Res. 99, 12–25. doi: 10.1016/j.csr.2015.03.002
Rondinini, C., Wilson, K. A., Boitani, L., Grantham, H., and Possingham,
H. P. (2006). Tradeoffs of different types of species occurrence data
for use in systematic conservation planning. Ecol. Lett. 9, 1136–1145.
doi: 10.1111/j.1461-0248.2006.00970.x
Rose, C. (2015). The case for policy-relevant conservation science. Conserv. Biol.
29, 748–754. doi: 10.1111/cobi.12444
Ross, L. K., Ross, R. E., Stewart, H. A., and Howell, K. L. (2015). The influence
of data resolution on predicted distribution and estimates of extent of
current protection of three ‘listed’ deep-sea habitats. PLoS ONE 10:e0140061.
doi: 10.1371/journal.pone.0140061
Ross, R. E., and Howell, K. L. (2013) Use of predictive habitat modelling to assess
the distribution and extent of the current protection of ‘listed’ deep-sea habitats.
Divers. Distrib. 19, 433–445. doi: 10.1111/ddi.12010
Rudd, M. A. (2014). Scientists’ perspectives on global ocean research priorities.
Front. Mar. Sci. 1:36. doi: 10.3389/fmars.2014.00036
Sánchez-Carnero, N., Rodríguez-Pérez, D., Couñago, E., Le Barzik, F., and Freire,
J. (2016). Species distribution models and local ecological knowledge in marine
protected areas: the case of Os Miñarzos (Spain). Ocean Coast. Manage. 124,
66–77. doi: 10.1016/j.ocecoaman.2016.02.008
Sayre, R. G., Wright, D. J., Breyer, S. P., Butler, K. A., Van Graafeiland, K.,
Costello, M. J., et al. (2017) A three-dimensional mapping of the ocean
based on environmental data. Oceanography 30, 90–103. doi: 10.5670/oceanog.
Schmiing, M., Afonso, P., Tempera, F., and Santos, R. S. (2013). Predictive habitat
modelling of reef fishes with contrasting trophic ecologies. Mar. Ecol. Prog. Ser.
474, 201–216. doi: 10.3354/meps10099
Schneider, D. C. (2001). The rise of the concept of scale in ecology. Bioscience 51,
545–553. doi: 10.1641/0006-3568(2001)051[0545:TROTCO]2.0.CO;2
Sedberry, G. R., Fautin, D. G., Feldman, M., Fornwall, M. D., Goldstein, P., and
Guralnick, R. P. (2011). A data-sharing legacy of the census of marine life.
Oceanography 24, 166–173. doi: 10.5670/oceanog.2011.36
Selgrath, J. C., Roelfsema, C., Gergel, S. E., and Vincent, A. C. J. (2016) Mapping
for coral reef conservation: comparing the value of participatory and remote
sensing approaches. Ecosphere 7:e01325. doi: 10.1002/ecs2.1325
Seo, C., Thorne, J. H., Hannah, L., and Thuiller, W. (2009). Scale effects in species
distribution models: implications for conservation planning under climate
change. Biol. Lett. 5, 39–43. doi: 10.1098/rsbl.2008.0476
Sherman, K. D., Dahlgren, C. P., Stevens, J. R., and Tyler, C. R. (2016).
Integrating population biology into conservation management for endangered
Nassau grouper Epinephelus striatus.Mar. Ecol. Prog. Ser. 554, 263–280.
doi: 10.3354/meps11771
Smith, T. A., and McConnaughey, R. A. (2016) The Applicability of Sonars for
Habitat Mapping: A Bibliography. US Dep. Commer. NOAA Tech. Memo.
NMFS-AFSC-317, 129.
Spalding, M. D., Meliane, I., Bennett, N. J., Dearden, P., Patil, P. G., and
Brumbaugh, R. D. (2016). Building towards the marine conservation end-game:
consolidating the role of MPAs in a future ocean. Aquat. Conserv. Mar. Freshw.
Ecosyst. 26, 185–199. doi: 10.1002/aqc.2686
Stone, K. H. (1972). A geographer’s strength: the multiple scales approach. J. Geogr.
71, 354–362. doi: 10.1080/00221347208981686
Synes, N. W., and Osborne, P. E. (2011). Choice of predictor variables
as a source of uncertainty in continental-scale species distribution
modelling under climate change. Glob. Ecol. Biogeogr. 20, 904–914.
doi: 10.1111/j.1466-8238.2010.00635.x
Thomas, N., Hendrix, C., and Congalton, R. G. (2003). A comparison of urban
mapping methods using high-resolution digital imagery. Photogram. Eng.
Remote Sens. 69, 963–972. doi: 10.14358/PERS.69.9.963
Tong, R., Purser, A., Unnithan, V., and Guinan, J. (2012). Multivariate
statistical analysis of distribution of deep-water gorgonian corals in relation
to seabed topography on the Norwegian margin. PLoS ONE 7:e43534
doi: 10.1371/journal.pone.0043534
Touria, B., Sébastien, R., Martial, L., Axel, E., Anouar, H., and Philippe, L. N.
(2015). Multi-approach mapping to help spatial planning and management
of the kelp species L. digitata and L. hyperborean: case study of the Molène
Archipelago, Brittany. J. Sea Res. 100, 2–21. doi: 10.1016/j.seares.2015.04.004
Tulloch, A. I. T., Sutcliffe, P., Naujokaitis-Lewis, I., Tingley, R., Brotons,
L., Ferraz, K. M. P. M. B., et al. (2016). Conservation planners tend