Mapping Marine Ecosystem Services Potential Across an Oceanic Archipelago: Applicability and Limitations for Decision-Making

Abstract

Understanding the multiple benefits (i.e. Ecosystem Services, ES) that marine habitats provide to society is key for adequate decision-making that maintains our well-being in the long-term. The main objective of this research was to map and assess, in the context of marine spatial planning, the ES supply of shallow and deep-sea habitats in the Canary Islands across biological zones and substrate types. An ES-matrix was developed through a literature review to evaluate the supply potential, complemented with the habitats’ total extension to assess the supply capacity of each resulting ES. The matrix linked 34 habitats in relation to 42 ES, over ca. 485,000 km2. Cultural ES were the most abundant in the archipelago. On average, shallow habitats supplied potentially 25 ES compared to 17 ES by deep-sea habitats. This is likely to be explained by limitations regarding the available information suggesting that both provisioning ES and ES supply potential of the deep-sea were underestimated. The supply capacity analysis showed that particularly certain regulating and maintenance services may be at risk in the face of habitat degradation. Results enabled the extrapolation of already existing ES monetization, e.g. for those accounted for Cymodocea nodosa generating 25,633,919 € y-1 in the Canary Islands. This study provided the first comprehensive spatial assessment of ES supply potential in the Canary Islands, filling a regional knowledge gap. This enables accounting for previously overlooked ES in the region, strengthening the idea that coastal communities’ well-being in small islands depends on their marine ecosystems. Finally, results were discussed in relation to their applicability and limitations to marine spatial planning and protected area design informing on the potentially large societal benefits that may be at risk when allocating maritime activities spatially.

Full text

Ecosystem Services 60 (2023) 101517
Available online 22 February 2023
2212-0416/© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Full Length Article
Mapping marine ecosystem services potential across an oceanic
archipelago: Applicability and limitations for decision-making
Víctor Cordero-Penín
*
, Andrej Abramic, Alejandro García-Mendoza, Francisco Otero-Ferrer,
Ricardo Haroun
Biodiversity and Conservation Group, Research Institute ECOAQUA, Scientic & Technological Marine Park of the Univ. de Las Palmas de Gran Canaria, Crta. Taliarte
s/n, 3521 Telde, Spain
ARTICLE INFO
Keywords:
Marine Spatial Planning
Outermost regions
ES Matrix model
Canary Islands
Ecosystem services supply
Social-ecological systems
ABSTRACT
Understanding the multiple benets (i.e. Ecosystem Services, ES) that marine habitats provide to society is key
for adequate decision-making that maintains our well-being in the long-term. The main objective of this research
was to map and assess, in the context of marine spatial planning, the ES supply of shallow and deep-sea habitats
in the Canary Islands across biological zones and substrate types. An ES-matrix was developed through a liter-
ature review to evaluate the supply potential, complemented with the habitats’ total extension to assess the
supply capacity of each resulting ES. The matrix linked 34 habitats in relation to 42 ES, over ca. 485,000 km
2
.
Cultural ES were the most abundant in the archipelago. On average, shallow habitats supplied potentially 25 ES
compared to 17 ES by deep-sea habitats. This is likely to be explained by limitations regarding the available
information suggesting that both provisioning ES and ES supply potential of the deep-sea were underestimated.
The supply capacity analysis showed that particularly certain regulating and maintenance services may be at risk
in the face of habitat degradation. Results enabled the extrapolation of already existing ES monetization, e.g. for
those accounted for Cymodocea nodosa generating 25,633,919
€
y
-1
in the Canary Islands. This study provided the
rst comprehensive spatial assessment of ES supply potential in the Canary Islands, lling a regional knowledge
gap. This enables accounting for previously overlooked ES in the region, strengthening the idea that coastal
communities’ well-being in small islands depends on their marine ecosystems. Finally, results were discussed in
relation to their applicability and limitations to marine spatial planning and protected area design informing on
the potentially large societal benets that may be at risk when allocating maritime activities spatially.
1. Introduction
Ecosystem services (ES) concept was popularized as the contribu-
tions of ecosystems to human well-being (Millennium Ecosystem
Assessment, 2005). To equate the growing interest in the development
of a blue economy improving human welfare, and the conservation of
nature (de Groot, 1987), ES was thus gradually integrated into the
decision-making process.
The ES concept is embedded in the ecosystem-based approach (EBA),
which aims “to maintain an ecosystem in a healthy, productive and
resilient condition so that it can provide the services humans want and
need” (McLeod et al., 2005). For the marine environment, marine spatial
planning (MSP) has been identied as one of the main tools to implement
the EBA (Chalastani et al., 2021; Douvere, 2008). In other words, MSP
should ensure a reasonable use of the marine space to prevent the dete-
rioration of the ecological components that underpins the provision of ES
(i.e. the “service providing units”; Kremen, 2005; Luck et al., 2009).
Therefore, maintaining ES supply in the long-term. Additionally, ES as-
sessments, comprising both environmental and socio-economic informa-
tion, can contribute to the transparency of MSP processes. They may
provide a baseline to evaluate existing trade-offs between different eco-
nomic, ecological and social objectives while measuring their success
(Elliott and O’Higgins, 2020; García-Onetti et al., 2021; Tallis et al., 2012).
The provision of ES is underpinned by the overall functioning of eco-
systems (Potschin-Young et al., 2017). However, ES is an anthropogenic
concept, i.e. only exists in reference to human beneciaries (Armstrong
et al., 2012a; Armstrong et al., 2012b). This makes it necessary to consider
cultural values and human-made or built capital (Elliott et al., 2017) that
mediates the ES ‘ow’ from nature to society (Burkhard et al., 2014). In
turn, this ow depends (positively or negatively) on the governance sys-
tem (Spangenberg et al., 2014), and the society’s consumption habits,
perceptions, and values around ES, all of which may change over time
* Corresponding author.
E-mail address: victor.cordero@fpct.ulpgc.es (V. Cordero-Penín).
Contents lists available at ScienceDirect
Ecosystem Services
journal homepage: www.elsevier.com/locate/ecoser
https://doi.org/10.1016/j.ecoser.2023.101517
Received 24 May 2022; Received in revised form 20 January 2023; Accepted 10 February 2023

Ecosystem Services 60 (2023) 101517
2
(Hebel, 1999; Klain & Chan, 2012). All these make integrated ecosystem
services assessments challenging, being still under development within
MSP (Galparsoro et al., 2021; Townsend et al., 2018).
Tallis et al. (2012) proposes a way to structure and promote the
implementation of ES into MSP processes by clearly differentiating be-
tween (1) the potential and further capacity of ecosystems to provide ES
(i.e. supply metrics), (2) the ow of ES used or enjoyed by users (i.e.
service metrics), and (3) the benets that are perceived by society (i.e.
value metrics). In this sense, the present study is framed within the rst
supply-side step.
Commonly, ES supply is assessed through the ES-matrix approach,
which explores the linkages between the service providing units (SPU;
Kremen, 2005; Luck et al., 2009) and the different ES they provide (Cam-
pagne et al., 2020; Jacobs et al., 2015). Then, from all possible SPU with the
potential to provide ES, critical ones are identied based on their supply
capacity (Culhane et al., 2020a; Culhane et al., 2020b), which in turn will
depend on their ecological state or condition. For example, Teixeira et al.
(2019) score the overall ecosystem services supply of the SPU by dis-
tinguishing between three dimensions: (1) the supply potential, (2) the
supply capacity and (3) the supply condition. The idea of potential refers to
the theoretical ability of a certain SPU to provide particular ES (Tallis et al.,
2012). Whereas the ideas of capacity and condition refer to the ecosystem
functions of the SPU that underpins a particular ES and the general de-
scriptors of the status of the SPU, respectively (Potschin-Young et al., 2017).
Responding to which ecosystems provide which ES is a complex task,
which was identied as a particular need for European Atlantic Ocean
archipelagos (Galparsoro et al., 2014). Most studies map and assess the
ES supply at a regional scale, based on secondary data without validation
techniques to test the accuracy of the developed model (Martínez-Harms
& Balvanera, 2012), while relaying on expert-based knowledge to link
and weight the SPU-ES associations through the ES-matrix approach
(Campagne et al., 2020). ES are context-dependent and their analysis has
not always followed a uniform terminology across literature hindering
the compilation of empirical data about their supply potential and ca-
pacity (Bordt & Saner, 2019; Potschin-Young et al., 2018). Nonetheless,
Tempera et al. (2016) have consulted a number of corresponding authors
from various literature reviews to build cross-reference tables between
the different ES terminologies into the Common International
Classication of Ecosystem Services (CICES; Haines-Young & Potschin,
2018) enabling ES potential supply assessments and mapping in areas
where detailed benthic habitats cartography is available.
Moreover, the supply capacity of each SPU may be weighted by a
series of criteria including their relative importance in providing a given
ES (Galparsoro et al., 2014; Potts et al., 2014), their efciency in per-
forming the ecological processes or functions leading to the supply of ES
(Culhane et al., 2020), their spatial extent (Teixeira et al., 2019), or a
combination of the above (Geange et al., 2019). Here, despite recog-
nizing that ES do not increase linearly with habitats size (Barbier et al.,
2008; Koch et al., 2009), the community/habitat extension has been
directly related to the magnitude or capacity of ecosystem services
supply (Harrison et al., 2014). Additionally, the supply condition of each
SPU may be determined by eld measurements of biophysical processes
and functions (Geange et al., 2019) or derived from their conservation
status or state assessed by environmental policies such as European
Directives related to Habitats, Water and Marine Strategy (Culhane
et al., 2020a; Culhane et al., 2020b; Teixeira et al., 2019).
Thus, the aim of this study is to map and assess the ES supply, un-
dertaken for the rst time with this level of detail in a European North-
Atlantic outermost region, while assessing their implications for
decision-making and undergoing MSP processes. This study also intends
to strengthen the basis paving the way for other similar oceanic archi-
pelagos that will be required to advance in ES assessments encouraged
by the European legislation.
2. Methodology
MSP processes depend highly on spatial data, leading to the usage of
benthic marine habitats as the SPU for ES assessment and mapping
(Fletcher et al., 2012; Galparsoro et al., 2014; Potts et al., 2014; Tempera
et al., 2016). The ES supply potential of benthic marine habitats in the
Canary Islands was mapped and assessed through a literature review
carried out for the European regional seas (Agardy et al., 2005; Armstrong
et al., 2012a; Armstrong et al., 2012b; Galparsoro et al., 2014; Millennium
Ecosystem Assessment, 2005; Potts et al., 2014; Salomidi et al., 2012;
Tempera et al., 2016). The total extension of the habitats was used to
assess the supply capacity of each resulting ES. Fig. 1 shows the
Fig. 1. Methological steps followed to map the ecosystem service potential (ESP) of marine benthic habitats in the study area.
V. Cordero-Penín et al.

Ecosystem Services 60 (2023) 101517
3
methodological steps taken, which are further explained in the following
sections.
2.1. Study area
The Canary Island archipelago is located off the Northwest African
coast at about 28◦N (Fig. 2). Rising steeply from the seabed, they
represent a natural barrier to the southward ow of the Trade Winds and
the Canary Current, generating large mesoscale eddies south of the
islands (Arístegui et al., 1994). Besides, the archipelago is regularly
inuenced by cold water upwelling laments derived from the NW Af-
rican coastal upwelling system locating the islands in the so-called Ca-
naries-African Coastal Transition Zone (Barton et al., 1998).
Regarding benthic habitats, canopy-forming macroalgae in tidal
pools are often found in rocky littoral platforms which, in general, are
dominated by turf-forming macroalgae, cirriped and scattered cyano-
bacterial colonies or mats in their lower, intermediate and upper bands,
respectively (Tuya et al., 2006). Subtidal benthic landscapes are
constituted by volcanic rocky bottoms presenting a variety of forms, e.g.
marine caves, canyons or large boulders, surrounded by coarse, sandy
and muddy sediment plains. Subtidal macroalgal assemblages structure
varies across islands together with the thermal gradient ranging from
Fucales dominated assemblages towards the eastern islands to ones
dominated by Dictyotales in the western islands. However, this natural
pattern is diffused by the pressure of the main herbivore Diadema afri-
canum (Sangil et al., 2011), which creates extensive urchin barrens in
rocky subtidal bottoms. The archipelago also hosts other important
‘ecological engineer’ species, such as extensive, but fragmented seasonal
seagrass meadows of Cymodocea nodosa on soft bottoms and maerl beds
(Otero-Ferrer et al., 2020; Tuya et al., 2014a). All of the above biological
characteristics support the idea that the Canary Islands are a marine
biodiversity hotspot. Furthermore, due to the highly developed coastal
tourism and the existing traditional community linkages with the marine
environment, this area is thought of as a social and natural “laboratory”
for the study of multiple ES.
2.2. Benthic habitats spatial distribution
The spatial distributions of marine habitats were gathered from two
spatial data set (see Table A.1 for more detail and links to the sources):
the so-called eco-cartography for shallow habitats (up to a depth of 50
m), and for deep habitats up to the outer limit of the study area (see
Fig. 2) obtained from the European Marine Observation and Data
Network (EMODnet).
The eco-cartographies of the Canary Islands were individually
mapped by islands through public tenders during 2000–2006. These
produced various maps with high spatial resolution, but that did not
follow a standardized classication terminology. Thus, this study used
the harmonized eco-cartography for the Canary Islands done through a
local expert group following the Spanish Inventory of marine species
and habitats (IEHEM). Subsequently, applying the IEHEM own cross-
walk tables, a cartography based on the 2012 revision of the pan-
European EUNIS habitat classication was produced (PLASMAR Con-
sortium, 2020).
According to the EUNIS available description of marine habitats,
these were categorized by their biological zone related to depth (littoral,
infralittoral, circalittoral, offshore circalittoral and deep-sea), and sub-
strate type (rock, coarse sediment, sand, mud, mixed sediment and
biogenic substrate). The spatial distribution analysis of benthic habitats
was done separately due to differences between the two data sets
employed in the assessment. Habitats from the eco-cartography were
mapped with a high level of detail (1 m resolution) up to 50 m of depth.
Whereas deeper habitats from EMODnet, although covering wider areas,
were mapped with less precision (200 m resolution).
2.3. Ecosystem services supply potential, capacity and condition
We adopted the following denition of ES supply potential (ESP) as
the “full potential of ecological functions or biophysical elements in an
ecosystem to provide a potential ecosystem service, irrespective of
whether humans actually use or value that function or element
currently” (Tallis et al., 2012), similarly to (Caro et al., 2020). The
Fig. 2. The Canary Island archipelago located off the northwest African coastal upwelling system. The grey line represents the outer limit of the study area coinciding
with the technical application of the Spanish Marine Strategy Framework Directive (2008/56/EC).
V. Cordero-Penín et al.

Ecosystem Services 60 (2023) 101517
4
identication of the multiple ES potentially provided by the marine
habitats was done based on Tempera et al. (2016). Their cross-reference
tables were used to translate the ES terminology presented by the con-
sulted literature reviews (see Table A.2 for more details) into the latest
CICES version 5.1. following the guidance on the application of the
revised structure and its corresponding equivalence table (Haines-Young
& Potschin, 2018).
The CICES (and EUNIS) classication is hierarchical, gaining in
detail towards lower levels, i.e. from section, division, group to class.
Meaning that a given section, includes all its corresponding divisions,
and the latter their groups, and these their classes (i.e. higher levels
include the lower levels). Similarly to Tempera et al. (2016), in the
present study, ‘parents’ were considered to inherit the ESP from their
‘children’ (see Tables A6 to A8). Terminology around specic ES tend to
be translated or broadened to facilitate dialogue and consensus among
actors (van Oudenhoven et al., 2018). Given the difculty of translating
ESP evidence into ES at a class level, all ES across the hierarchy were
considered equally. In line with Potschin-Young et al. (2017), we argued
that the ES-related ‘double counting’ issue should be conned to the
accounting of the usage/enjoyed services and derived values, while
preventing the screening of the ecological functional characteristics that
the ES supply assessments aim to explain. This was a necessary approach
to avoid falling into “all ecosystems provide all ES”.
For the ESP of those benthic habitats in our study area not assessed
by Tempera et al. (2016) (e.g. littoral habitats, EUNIS coded as #A1 and
#A2), we followed their same methodology to assign ESP (i.e. applying
qualitative categories of presence, absence or no data) through the
reviewed literature (see Table A.3). Finally, for the habitats not assessed
in any of the previously mentioned literature, i.e. the Cystoseira spp.
habitat (EUNIS code A3.151) and “faunal communities on low energy
infralittoral rock” (EUNIS code A3.35), their ESP was assigned according
to local scientic literature and the authors’ knowledge on the Canary
Islands, as in Galparsoro et al. (2014) and Potts et al. (2014) (see ESP
assignment explanation in section A.4).
The services supply capacity (ESC) was considered as the habitats’
“true spatial representativeness” (Teixeira et al., 2019) meaning the
contributing area to a given ES, which was calculated resembling
Geange et al. (2019)’s approach:
aESPijk =∑
n
h=1
aijk (1)
where a is the area, h is habitat type, i is the service. Note that this study
did not weight the ESC through a ranking scale (i.e., j), due to the dif-
culty in harmonizing these semi-quantitative scores from the various
literature sources. Besides, providing habitat quality information (i.e., k)
at a regional scale for the Canary Islands was out of the scope of this
study. Moreover, using environmental policies to assess the condition of
the evaluated habitats was not possible considering that the Good
Environmental Status (EU Directive 2008/56/EC) for the seaoor
integrity and benthic habitats was only partially assessed for the Canary
Islands (Abramic et al., 2020).
2.4. Ecosystem services assessment
To map and compare ESP supply between ES aggregated by section
level (i.e., provisioning, regulation and maintenance (hereafter both
referred to as regulating), and cultural), ve classes of Jenks natural
breaks classication were applied through a geographic information
system (GIS) approach. For this, the ES abundance for each habitat was
calculated as in Caro et al. (2020), based on the number of ES provided
by that habitat in relation to the maximum number of ES within CICES, i.
e. provisioning (n =28), regulating (n =27); and cultural (n =18).
Finally, to analyse patterns in the spatial distribution of ESP supply, like
Galparsoro et al. (2014), the total area of each habitat and its relative
extension to the total marine study area were mapped.
2.5. Statistical analysis
A Friedman test, followed by post-hoc Wilcoxon tests, as in the just
aforementioned study, were done to explore statistical differences be-
tween ESP aggregated at a section level (i.e. provision, regulating, and
cultural). Then, Kruskal-Wallis non-parametric tests and post-hoc tests
between pairs, corrected through the Bonferroni test, were applied to
analyse the inuenced of the biological zones and substrate type on the
ESP.
3. Results
The harmonized eco-cartography for the Canary Islands resulted in
23 shallow marine benthic habitats. These were characterized for the
ESP analysis of this study together with the 11 deeper habitats from
EMODnet (see Table A.5). The literature reviewed supplemented with
the authors’ knowledge, resulted in a matrix summarizing the ESP of 34
habitats in relation to 43 ES (see Tables A.6, A.7 and A.8) over a marine
extension of approximately 485,000 km
2
. The ESP matrix can be read
both horizontally to see all ES potentially provided by a particular
habitat (Tables 1 and 2), and vertically to see all habitats with the po-
tential to provide a particular service (Table 3). In general, 57.6 % of the
cells within the matrix were assessed conrming either the presence or
absence of ESP (see Table A.9 for more details). Particularly, there is
greater scientic knowledge about the ESP of cultural ES (69 % of cells,
n =476) than of regulating ES (56 % of cells, n =612) or provisioning
ES (47 % of cells, n =374). Moreover, shallower habitats were more
extensively assessed (64 % of cells, n =989) than deeper habitats (45 %
of cells, n =473).
Reading the ESP-matrix horizontally, none of the 34 habitats iden-
tied in the Canary Islands provide all the 43 ES considered in this study
(Table 1 and 2). Shallower habitats supply on average a total of 25 ES, i.
e. an ES abundance of 35 %. The habitats better covered by the literature
and, thus, presenting very high ESP are the Cymodocea and Halophila
seagrass beds, and “Cystoseira spp. on exposed infralittoral bedrock and
boulders” (i.e. supplying 31, 31 and 30 ES respectively). Conversely, the
habitats with the lowest ESP were infralittoral ne sand and faunal
communities on low energy infralittoral rock (i.e. supplying 19 and 21
ES respectively). In turn, deeper habitats present signicantly lower ESP
than shallower habitats (H=20.401,p<0.001), supplying on average
17 ES (an ES abundance of 23 % of the 73 possible ES included in
CICES). “Sponge communities on deep circalittoral rock” and broad
“deep-sea bed” are the deeper habitats with the highest ESP, i.e. asso-
ciated with 22 and 21 different ES, respectively.
Overall, for all aggregated ES at the section level (i.e. provisioning,
regulating, and cultural ES), the habitats’ ESP differ signicantly across
the hierarchical levels of the EUNIS habitat classication (Kruskal-
Wallis H=15.673,p<0.003), and across biological zones (H=20.972,
p<0.001). Although rock and biogenic types of seaoor substrate show
the highest abundances of ES (Table 1), there were no signicant ESP
differences among substrate types. The just presented signicant dif-
ferences were seen for both regulating, and cultural ES, but not for
provisioning ES.
Particularly for regulating and cultural ES, benthic habitats inform-
ing on the dominant communities (i.e. EUNIS level 4) are signicantly
associated with less ESP than those habitats characterized for specic
marine species (i.e. EUNIS 6) (post-hoc tests between pairs p<0.033 in
all cases). Besides, offshore circalittoral habitats show signicant lower
ESP than infralittoral habitats (post-hoc tests between pairs p<0.038
and p<0.001, respectively for regulating and cultural ES).
Signicant differences are also observed regarding the spatial dis-
tribution of ESP aggregated by sections (i.e. provisioning, regulating and
cultural) (Friedman test
χ
2
=54.672, p <0.001) (Figs. 3, 4, 5). Cultural
ES are signicantly more abundant than both regulating and provi-
sioning ES (Wilcoxon post-hoc test z = − 2.984, p <0.003; and z =
−5.108, p <0.001, respectively). In turn, regulating ES are supplied
V. Cordero-Penín et al.

Ecosystem Services 60 (2023) 101517
5
signicantly more than provisioning ES (z = − 5.023, p <0.001).
Collectively, marine benthic habitats in the Canary Islands present
ESP for a wide range of different ES (Fig. 6). However, the supply ca-
pacity of benthic habitats, as their spatial representativeness to an ES,
varies greatly across ES (see Table 3). For example, only infralittoral
shallow habitats presented the supply potential of regulating ES such as
“regulation of soil quality” (code 2.2.4), “ltration/sequestration/stor-
age/accumulation by micro-organisms, algae, plants, and animals”
(code 2.1.1.2), or “decomposition and xing processes and their effect
on soil quality” (code 2.2.4.2). In turn, the latter habitats only presented
a capacity to supply these ES in ca. 16 km
2
(i.e. 0.003 %) of our study
area. Another example would be the 89 km
2
and 105 km
2
that underpins
the capacity to supply the ES “control of erosion rates” (code 2.2.1.1),
and “maintaining nursery populations and habitats (Including gene pool
protection)” (code 2.2.2.3), respectively. The latter examples of the total
area capable to contribute to each ES may indicate the susceptibility to
lose their supply capacity in case of habitat degradation.
The eco-cartography data was harmonized from individual mapping
for each island (see Table A.5). Thus, considering all habitats with a high
or very high ESP (see Table 1), we can analyse the relative area per is-
land upon which the provision of most ES depends upon (Fig. 7).
4. Discussion
4.1. Ecosystem services supply in the Canary Islands
Cultural ES are the most widely supplied in the Canary Islands in line
with ES literature reviews for other small islands (Balzan et al., 2018).
However, this contrast with the European North Atlantic Ocean where
provisioning ES were dominant (Galparsoro et al., 2014). Cultural ES are
easier to identify in the absence of scientic literature than other types
of services (Caro et al., 2020), which was also noted during the
Table 2
Ecosystem services supply potential, aggregated at section level, of deep-sea benthic habitats in the Canary Islands.
Offshore circalittoral and deep-sea habitats from EMODnet (EUNIS) Ecosystem Services (CICES V5.1)
Provision Regulation Cultural Total
(n=28) (n=27) (n=18) (n=73)
Code Name km
2
% N◦% N◦% N◦% N◦%
A6 Deep-sea bed 472959.5 98.15 5 18 7 26 9 50 21 29
A6.3 Deep-sea sand 1975.71 0.41 5 18 7 26 7 39 19 26
A6.4 Deep-sea muddy sand 4107.15 0.85 5 18 7 26 7 39 19 26
A6.11 Deep-sea bedrock 1619.79 0.34 1 4 3 11 5 28 9 12
A4.12 Sponge communities on deep circalittoral rock 386.38 0.08 4 14 8 30 10 56 22 30
A5.14 Circalittoral coarse sediment 18.63 0.004 4 14 5 19 5 28 14 19
A5.15 Deep circalittoral coarse sediment 54.71 0.01 4 14 5 19 5 28 14 19
A5.27 Deep circalittoral sand 726.28 0.15 4 14 7 26 5 28 16 22
A5.35 Circalittoral sandy mud 0.41 0.0001 4 14 6 22 5 28 15 21
A5.37 Deep circalittoral mud 9.78 0.002 4 14 6 22 5 28 15 21
A Marine (unknown) habitats 1402.87 0.29
Total area/average ES 481858.31 100 4 14 6 22 7 38 17 23
Note: Habitat code “A”, which is classied as “unknown habitats”, is not summing to the total extension of the study area, nor to the percentage of the relative area of
each habitat type. Unknown habitats correspond to the category “not habitat info” in to 6.
Table 1
Ecosystem services supply potential, aggregated at section level, of shallow benthic habitats in the Canary Islands.
Littoral, infralittoral and circalittoral habitats from the Eco-cartography (EUNIS) Ecosystem Services (CICES V5.1)
Provision Regulation Cultural Total
(n=28) (n=27) (n=18) (n=73)
Code Name km
2
% N◦% N◦% N◦% N◦%
A1 Littoral rock and other hard substrata 0.07 0.002 4 14 12 44 11 61 27 37
A3 Infralittoral rock and other hard substrata 575.6 19.52 4 14 11 41 12 67 27 37
A4 Circalittoral rock and other hard substrata 109.25 3.76 4 14 11 41 10 56 25 34
A1.2 Moderate energy littoral rock 0.002 0.0001 4 14 12 44 11 61 27 37
A1.4 Features of littoral rock 3.05 0.11 4 14 10 37 9 50 23 32
A2.2 Littoral sand and muddy sand 0.36 0.01 4 14 12 44 11 61 27 37
A3.1 Atlantic and Mediterranean high energy infralittoral rock 0.012 0.0004 4 14 9 33 10 56 23 32
A3.2 Atlantic and Mediterranean moderate energy infralittoral rock 484.7 16.63 4 14 9 33 11 61 24 33
A3.3 Atlantic and Mediterranean low energy infralittoral rock 5.32 0.18 4 14 9 33 11 61 24 33
A4.2 Atlantic and Mediterranean moderate energy circalittoral rock 0.01 0.0003 4 14 9 33 10 56 23 32
A5.1 Sublittoral coarse sediment 4.2 0.14 6 21 9 33 11 61 26 36
A5.2 Sublittoral sand 730.7 24.64 6 21 7 26 10 56 23 32
A5.3 Sublittoral mud 0.39 0.01 6 21 7 26 10 56 23 32
A2.11 Shingle (pebble) and gravel shores 0.74 0.03 4 14 10 37 9 50 23 32
A3.24 Faunal communities on moderate energy infralittoral rock 10.27 0.35 4 14 8 30 8 44 20 27
A3.35 Faunal communities on low energy infralittoral rock 3.85 0.13 1 4 1 4 9 50 11 15
A5.13 Infralittoral coarse sediment 62.9 2.16 6 21 6 22 11 61 23 32
A5.23 Infralittoral ne sand 410.5 16.77 4 14 5 19 10 56 19 26
A5.51 Maerl beds 118.8 4.12 6 21 9 33 11 61 26 36
A5.52 Kelp and seaweed communities on sublittoral sediment 212.7 7.98 4 14 11 41 10 56 25 34
A3.151 Cystoseira spp. on exposed infralittoral bedrock and boulders 15.95 0.55 6 21 13 48 10 56 29 40
A5.5311 Macaronesian Cymodocea beds 82.6 2.82 4 14 14 52 13 72 31 42
A5.5321 Canary Island Halophila beds 2.48 0.09 4 14 12 44 13 72 29 40
Total area/average ES 2834.5 100 5 16 10 37 11 61 25 35
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Ecosystem Services 60 (2023) 101517
6
discussion rounds undertaken in this study to include the authors
knowledge on the ESP assessment. This may inuence the results of
studies which are solely based on expert knowledge, reason why ES
supply assessments would greatly benet from the incorporation of
empirical evidence (Geange et al., 2019).
The statistically signicant ESP decreasing gradient towards sea-
wards and deeper habitats were not noted for the Canary Islands,
although it has been reported for the European North Atlantic Ocean
(Galparsoro et al., 2014). Despite that, higher ESP near the coastline is
visually appreciable in the ESP maps as expected in volcanic archipel-
agos with limited and abrupt continental platforms. We argue this is due
to other factors such as more limited information on ESP for deeper
habitats as compared to shallower ones (Thiele, 2019; Tyler et al.,
2016).
The deep-sea is generally considered out of reach for direct or in-situ
interactions in contrast to more accessible shallower habitats, especially
regarding cultural ES (Galparsoro et al., 2014; Milcu et al., 2013). Its
fundamental role in providing habitat for a great diversity of commer-
cial species is nonetheless recognized (Armstrong et al., 2012a; Arm-
strong et al., 2012b). Additionally, provisioning ESP was the less
assessed, followed by regulating ESP (i.e. 53 % and 47 % left unassessed,
respectively). This suggests that they would particularly benet from a
local extensive literature review, e.g. on ES related to biotechnological
applications of seaweeds (Haroun et al., 2019). This may suggest that
both provisioning and regulating ES, and deep-sea ESP were under-
estimated in this study. This is likely due to the use of derived goods and
services (i.e. services metrics) rather than biophysical functions and
processes (i.e. supply metrics) as a proxy for ESP (La Notte et al., 2017).
Assuming the good environmental status of the assessed benthic
habitats enabled us to consider their total area as a proxy of their ES
supply capacity in the Canary Islands. Although ES capacity is recog-
nized not to increase linearly with the size of habitats (Barbier et al.,
2008; Koch et al., 2009), this simplication was necessary to provide an
approximation to the ES supply. However, habitats do not support the
provision of ES directly. It is the numerous biodiversity interactions
within these habitats that ultimately account for the structures and
functions underpinning ES supply (Culhane et al., 2019; de Groot et al.,
2002). Besides, ES capacity depends on the condition and attributes of
the considered SPUs, which may vary across places. For example, sea-
grass meadows’ carbon sequestration capacity was found different
across various locations even at a local scale for the island of Gran
Canaria (Ba˜
nolas et al., 2020).
The EUNIS habitats classication generally includes information
regarding oceanographic conditions, species distribution and abiotic
characteristics of the environment. But our 2D maps disregard pelagic
habitats and their dynamic spatial–temporal variability. The inclusion of
Table 3
Supply capacity of each ES summarized by the cumulative extension (km
2
) and relative area values (%) across biological zones of the benthic habitats (N◦) that
potentially provide each ES in particular. Empty cells indicate cero value. Ecosystem service codes are translated in Table A.2.
Ecosystem service Shallow habitats (Eco-cartography) Deep habitats (EMODnet)
Littoral Infralittoral Circalittoral Offshore circalittoral Deep-sea
CICES N◦Area N◦Area N◦Area N◦Area N◦Area
V5.1 km
2
% km
2
% km
2
% km
2
% km
2
%
1 5 4.25 100 16 2721 100 2 109 100 7 1296 100 4 480,521 100
1.1 5 4.25 100 16 2721 100 2 109 100 7 1296 100 3 475,072 99
1.1.5 5 4.25 100 16 2721 100 2 109 100 7 1296 100 3 475,072 99
1.1.6 5 4.25 100 16 2721 100 2 109 100 7 1296 100 3 475,072 99
1.1.5.2 6 933 34
1.1.6.1 3 475,072 99
1.1.6.2 6 933 34
2 5 4.25 100 16 2721 100 2 109 100 7 1296 100 4 480,521 100
2.1 5 4.25 100 14 2192 81 2 109 100 4 1143 88
2.2 5 4.25 100 16 2721 100 2 109 100 7 1296 100 4 480,521 100
2.2.1 5 4.25 100 14 2248 83 1 109 100 2 1133 87 3 475,072 99
2.2.2 3 0.43 10 16 2721 100 2 109 100 7 1296 100 4 480,521 100
2.2.3 5 4.25 100 6 996 37 1 109 100 1 407 31
2.2.4 1 16 1
2.2.5 5 4.25 100 16 2721 100 2 109 100 7 1296 100 3 475,072 99
2.2.6 5 4.25 100 11 1512 56 2 109 100
2.1.1.2 1 16 1
2.2.1.1 3 89 3
2.2.1.2 1 4
2.2.1.3 5 4.25 100 9 1486 55 2 109 100 3 475,072 99
2.2.2.3 4 105 4
2.2.3.2 5 4.25 100 3 661 24 1 109 100
2.2.4.2 1 16 1
2.2.5.2 5 4.25 100 16 2721 100 2 109 100 7 1296 100 3 475,072 99
2.2.6.1 3 0.43 10 4 314 12
3 5 4.25 100 16 2721 100 2 109 100 7 1296 100 4 480,521 100
3.1 5 4.25 100 16 2721 100 2 109 100 1 407 31 4 480,521 100
3.2 5 4.25 100 16 2721 100 2 109 100 7 1296 100 1 468,989 98
3.1.1 5 4.25 100 16 2721 100 2 109 100 1 407 31
3.1.2 5 4.25 100 16 2721 100 2 109 100 7 1296 100 4 480,521 100
3.2.1 3 661 24 1 109 100
3.2.2 10 1632 60 2 109 100 1 407 31 1
3.1.1.1 16 2721 100 2 109 100
3.1.1.2 5 4.25 100
3.1.2.1 5 4.25 100 16 2721 100 2 109 100 7 1296 100 4 480,521 100
3.1.2.2 3 0.43 10 13 2087 77 2 109 100 1 407 31 3 475,072 99
3.1.2.4 5 4.25 100 5 433 16
3.2.2.1 5 4.25 100 16 2721 100 2 109 100 7 1296 100 4 480,521 100
3.2.2.2 5 4.25 100 16 2721 100 2 109 100 7 1296 100 4 480,521 100
V. Cordero-Penín et al.

Ecosystem Services 60 (2023) 101517
7
Fig. 3. Illustrates the potential of marine benthic habitats to provide provisioning services in the Canaries.
Fig. 4. Illustrates the potential of marine benthic habitats to provide regulation and maintenance services in the Canaries.
V. Cordero-Penín et al.

Ecosystem Services 60 (2023) 101517
8
mobile biotic groups and their association to multiple habitats in ES
supply assessments would be a step forward (Armoˇ
skait˙
e et al., 2020;
Culhane et al., 2018, 2020). This would additionally reinforce the
intrinsic value of shared ES, and enable consideration of new bundles of
ES resulting from their interrelations (Tempera et al., 2016). In the
Canaries, for instance, local studies modelling species of commercial
interest could be incorporated in future ES supply studies, e.g. using
spatial units through different depth ranges including both benthic and
pelagic habitats (Couce-Montero et al., 2015; Couce Montero et al.,
2021).
We acknowledge the need to incorporate more holistic approaches to
marine ES supply studies, e.g. system dynamics based on the “cells of
ecosystem functioning” (Boero et al., 2019). For example, through the
identication of signicant ecological interconnected units that explain
the main biogeochemical cycles, life cycles and food webs interactions
covering the instability of marine systems. To advance in the under-
standing of habitat’s ES supply is, thus, improving our knowledge on
such ecological interactions and processes. This was dened in 2010 as
one of the main pending tasks regarding ES assessments of the decade
(Perrings et al., 2010), and we believe it is still pending in the current
2020 to 2030 decade.
4.2. Applicability and limitations to marine planning
ES assessments can promote understanding of human activity-
ecosystem interactions informing MSP processes while favouring
stakeholder’s engagement (Friedrich et al., 2020). In turn, broad
ambiguous ES terms may help bringing political will and trans-
disciplinary efforts together (van Oudenhoven et al., 2018), depicting
marine benthic habitats and ES to their lowest possible level in our study
favour promoting further assessments of ES goods and benets
(Schaafsma & Turner, 2015). Thus, this study may also serve to pave the
way for similar ES mapping and assessment in other insular regions.
Degradation of ecosystems depends upon public attention to be
considered environmental problems worth managing (Downs, 1972).
This suggests that the connection to our oceans (i.e. nature), and thus
also their management, may depend on our ability to perceive the
benets derived from them. Therefore, reinforcing the role of society in
the co-production of ES alongside nature. For example, shark observa-
tion through snorkelling or scuba diving tended to favour positive atti-
tudes towards them, which in turn promoted conservation management
responses (Acu˜
na-Marrero et al., 2018).
Greater biodiversity and more complex habitat structures (i.e.
“good” condition) are positively related with ES of all types (Harrison
et al., 2014) including, for example, greater species abundance for
wildlife watching. However, this connection is stronger across provi-
sioning and regulating ES, which are associated with tangible benets
than for cultural ES, which tend to produce intangible benets (La Notte
et al., 2017) and are mainly assessed through social-cultural dimensions
and world-views (Jefferson et al., 2021; Jobstvogt et al., 2014; Klain &
Chan, 2012; Tonge et al., 2013).
Through the analysis of the main factors underpinning cultural ES
associated with woodlands in the UK and Ireland, Irvine & Herrett
(2018) found that the enhancement of elements with social meaning,
rather than ecological characteristics, guided conservation initiatives.
This may be due to the fact that the concept of ‘naturalness’ varies with
public perception (Nawaz & Sattereld, 2022). Due to the ndings of
Irvine & Herrett (2018), Caro et al. (2020) suggested that decision-
makers may be undervaluing ecosystem characteristics in the manage-
ment of a Portuguese estuary where cultural ES were the most observed.
If we assume the same for this case study in the Canary Islands, MSP
processes in the region may risk pursuing socio-cultural enhancement
Fig. 5. Illustrates the potential of marine benthic habitats to provide cultural services in the Canaries.
V. Cordero-Penín et al.

Ecosystem Services 60 (2023) 101517
9
rather than ecosystem health.
Incorporating littoral habitats and improving the spatial resolution
(i.e. up to 1 m) of benthic habitats enabled to produce nuanced ESP maps
that can be used as new communication tools for policy guidance ac-
counting for ES previously overlooked in the archipelago. This can
reinforce the recognition that coastal communities’ well-being in small
islands depends on their marine ecosystems.
However, it is understood that the reliability of the obtained ES
supply maps depends on the quality of the benthic habitats’ available
spatial data (Galparsoro et al., 2014; Tempera et al., 2016). Accordingly,
the eco-cartographies are more than 16 years old, which implies that
habitat extensions most likely differ nowadays, e.g. as reported for
Cystoseira spp. (Valdazo et al., 2017). Besides, the usage of broad cate-
gories within EMODnet was noticed to underestimate deep-sea habitats,
e.g. the extension of seamounts located in the North-eastern part of the
Canaries, and thus their ESP (Agardy et al., 2005). Nevertheless, these
datasets represent the most comprehensive, updated and a legitimate
geospatial source for both shallow and deeper habitats (Tempera et al.,
2016). In the future, as new spatial data become available, more accu-
rate ES-supply mapping and assessments could be done, e.g. to incor-
porate regulating ES of offshore circalittoral black coral habitats
(Czechowska et al., 2020). The above mentioned limitations coincided
with what other ES studies have named as sources of uncertainty (Sousa
et al., 2016), which must be considered when communicating and
applying the results.
Fig. 7. Represents, in spatial terms, the sum of shallower benthic habitats
contributing to supply 26 ES or more in relation with the total area extension.
Sum of the area of habitats presenting high or very high ESP (dark grey) is
showed on top of the total area of marine shallower benthic habitats (light grey)
of each of the Canary Islands sorted from the highest to the lowest extension. The
relative habitat’s area with high to very high ESP values (%) are denoted inside
bars. Islands’ abbreviations mean: FV =Fuerteventura, GC =Gran Canaria, LZ
=Lanzarote, TN =Tenerife, LP =La Palma, LG =La Gomera, EH =El Hierro.
Fig. 6. Illustrates the overall potential of marine benthic habitats to provide multiple ecosystem services in the Canaries.
V. Cordero-Penín et al.

Ecosystem Services 60 (2023) 101517
10
Furthermore, our ndings could inform existing regional MSP pro-
cesses on the potentially large societal benets that may be at risk by
allocating maritime activities and, thus, transparently favour outcomes
that benet more people (Tallis et al., 2012). Spatially overlapping the
resulting ES supply hotspots with the areas where maritime activities
(and derived pressures) concentrate could inform on the area and on the
percentage of ES that are being demanded and may be at risk (Tempera
et al., 2016). This type of assessments could be particularly of interest
while analysing the existing conditions or evaluating future scenarios for
MSP (Ehler & Douvere, 2009).
In this sense, the present ES supply assessment may also suggest the
risk of losing supply capacity, e.g. particularly of certain regulating ES
(Table 3), and thus the related potential benets for human well-being in
case of habitat degradation. For example, Valdazo et al. (2017) reported
a “massive decline” of Cystoseira abies-marina, passing from 9.28 km
2
in
1987–1989 to 0.077 km
2
in 2016 in the island of Gran Canaria. This
habitat loss, only protected regionally within Nature 2000 sites (Law 4/
2010, of June 4, 2010, of the Canary Islands Catalogue of Protected
Species), will result in a supply decrease of its associated ES, e.g. as
nursery grounds for shes of commercial interest (Chemin´
ee et al.,
2013). Translating ecological information in social terms (e.g. losses for
shermen) can foster stakeholder engagement to promote the levelling
of power relationships. Thus, fostering a more equitable distribution of
benets derived from marine ecosystems in regional/national MSP
processes (von Thenen et al., 2021). Additionally, the presented ES ca-
pacity results could improve marine protected areas (MPAs) design
(Schill et al., 2021) or help track the expected benets provided by
existing MPAs (Geange et al., 2019).
This study may also serve to meet the rst step (i.e. the ecosystem
extent account) of the System of Environmental-Economic Accounting
(SEEA) methodology proposed by United Nations (United Nations,
2021). For example, based on our results, the ca. 8260 ha corresponding
to the extension of Cymodocea nodosa beds in the present study (EUNIS
#A5.5311 in Table 1) can be used to extrapolate existing monetization
of ES done for the island of Gran Canaria. Thus, C. nodosa accounts, in
instrumental terms, for 25,633,919
€
y
-1
in the Canary Islands. This
resulted by adding 17,689,864
€
derived from estimations of organic
carbon sequestration (i.e. regulating chemical composition of oceans
CICES code #2.2.6.1) using maximum market carbon prices (Ba˜
nolas
et al., 2020); and 7,944,055
€
y
-1
derived from estimations of biomasses
of adult sh (i.e. wild animals for nutrition CICES code #1.1.6.1) and
juvenile sh (i.e. nursery grounds CICES code #2.2.2.3) of those species
of interest for coastal sheries (Tuya et al., 2014b). Note the usage of
CICES class level for the monetization of the ES to avoid double count-
ing. In this sense, similar future extrapolations could be devised from our
results.
However, we highlight, as noted by SEEA, that monetary valuation is
not a necessary feature of ES accounting (United Nations, 2021). Thus,
we recommend acknowledging within cost-benet analysis that price is
an approximation of value (Vatn & Bromley, 1994), e.g. part of the
above monetization refer to the instrumental value for local artisanal
sheries and do not cover all species to which seagrass meadows offer
nursery and feeding grounds. This statement implies that the under-
standing, measuring, and leveraging of the diverse values of nature
should be embedded into the decision-making process (IPBES, 2022).
Particularly, in the establishment of social objectives within MSP pro-
cess, which tend to lag behind blue economic objectives (Jones et al.,
2016). Socio-economic evaluations within MSP should aim to recognize
the different world-views and the intrinsic and relational values of na-
ture apart from instrumental values (see IPBES, 2022). Otherwise, un-
intended social inequity and ecological degradation may occur (see e.g.
Pascual et al., 2014; Spash & Aslaksen, 2015).
Moreover, this study entails a practical example of the utility of
standardised classication systems (e.g. EUNIS and CICES) applicability
to MSP processes, and more in particular, to the planning process of
regional applicability to the Canary Islands. The benthic habitat
harmonisation done for our case study following the principles of the
INSPIRE Directive (PLASMAR Consortium, 2020) enabled the combi-
nation of the national (i.e. Spanish) benthic habitat mapping efforts with
the European EMODnet products. This, as highlighted by the interna-
tional guide on MSP (UNESCO-IOC/European Commission, 2021), is an
example of the importance of data harmonisation within national MSP
processes as well as for cross-border cooperation initiatives.
5. Conclusion
This study provided the rst comprehensive spatial assessment of the
ES supply of the benthic habitats in the Canary Islands, lling a regional
knowledge gap (Galparsoro et al., 2014). The followed ESP approach
and the cross-reference tables of Tempera et al. (2016) resulted in a
exible, easy-to-apply and updatable method to assess ESP from multi-
ple literature sources. The ES-matrix resulted in a useful tool to syn-
thesize the available knowledge, and thus to map and assess the ES
supply. However, it might promote falling into a mechanistic approach
that fails to inform on the ecological characteristics of the SPUs, and how
they interrelate to originate the ES supply. Therefore, this study serves as
a useful rst approximation that can be further expanded by gathering
more detailed ecological information that explores the interconnections
between the SPUs, as well as between these and their contributions to
our human well-being.
This study produced nuanced ESP maps that can be used as new
communication tools for policy guidance accounting for previously
overlooked ES in the Canary Islands. This can reinforce the recognition
that coastal communities’ well-being in small islands depends on their
marine ecosystems. Moreover, this study intends to inform existing MSP
processes on the potentially large societal benets that may be at risk
when allocating maritime activities spatially. Finally, the present results
may be used to include another nature dimension to the planning pro-
cesses beyond existing and future planned MPAs.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgments
This work was supported by the project PLASMAR+(grant number
MAC2/1.1a/347) under the INTERREG V-A Spain-Portugal MAC
2014–2020 (Madeira-Azores-Canarias) Program from the European
Regional Development Fund (ERDF) of the European Union. The authors
would like to deeply thank the three anonymous reviewers who pro-
vided constructive comments and helped to substantially improve the
original manuscript, as well as to Laura Marin, Juan Fern´
andez, Fer-
nando Tuya, Inma Herrera, and Lourdes Trujillo for their writing advice
and proof reading.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.ecoser.2023.101517.
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