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Article
Mapping and Assessment of Ecosystems Services under the
Proposed MAES European Common Framework:
Methodological Challenges and Opportunities
Lia Laporta * , Tiago Domingos and Cristina Marta-Pedroso
Citation: Laporta, L.; Domingos, T.;
Marta-Pedroso, C. Mapping and
Assessment of Ecosystems Services
under the Proposed MAES European
Common Framework:
Methodological Challenges and
Opportunities. Land 2021,10, 1040.
https://doi.org/10.3390/
land10101040
Academic Editor: Shiliang Liu
Received: 28 July 2021
Accepted: 26 September 2021
Published: 2 October 2021
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Attribution (CC BY) license (https://
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4.0/).
MARETEC/LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1,
1049-001 Lisbon, Portugal; tdomingos@tecnico.ulisboa.pt (T.D.); cristina.marta@tecnico.ulisboa.pt (C.M.-P.)
*Correspondence: lia.laporta@tecnico.ulisboa.pt
Abstract:
The EU Biodiversity Strategy for 2020 was a driving force behind spatially explicit quantifi-
cations of Ecosystem Services (ES) in Europe. In Portugal, the MAES initiative (ptMAES–Mapping
and Assessment of Ecosystem and their Services) was conducted in 2014 to address Target 2 (Action 5)
of the Strategy, namely mapping and assessing ecosystems, ecosystems’ condition (EC), and ES. In
this study covering the NUTS II Alentejo region, EC was assessed and mapped based on four indica-
tors (soil organic matter, plant and bird diversity, and ecological value of plant communities) and
five ES were assessed and mapped (soil protection, carbon sequestration, and fiber/crop/livestock
production). Assessments were performed under a multi-tiered approach, ranging from spatial-
ization of statistical data to analytical modeling, based on the most detailed land-use/land-cover
cartography available. In this paper, we detail the methodological and analytical framework applied
in ptMAES and present its main outcomes. Our goal is to (1) discuss the main methodological
challenges encountered to inform future MAES initiatives in Portugal and other member states; and
(2) further explore the outcomes of ptMAES by looking into spatial relationships between EC and ES
supply. We highlight the advantages of the proposed analytical framework and identify constraints
that, among others, limited the number of ES and EC indicators analyzed. We also show that MAES
can provide useful insights to landscape planning at the regional scale, for instance, red-flagging
areas where ES supply may be unsustainable over time.
Keywords:
land-use planning; ecosystem services; ecosystem condition; MAES; methodological framework
1. Introduction
Ecosystem services (ES) are the benefits humans derive from ecosystems [
1
], which
are categorized into provisioning services (e.g., food production), regulating services (e.g.,
control of erosion rates), and cultural services (e.g., aesthetic enjoyment of nature) by the
Common International Classification of Ecosystem Services (CICES) [
2
], a mainstream
classification within the EU. Although the concept of ES is not exempt from criticism [
3
],
it brings into perspective that humans (and human actions by extension) largely depend
on well-functioning ecosystems, emphasizing the need for developing sustainable and
resilient interactions between human societies and the natural environment [
4
]. This
imposes new challenges when it comes to landscape planning, calling for an understanding
of how human actions across different landscapes can best safeguard well-functioning
ecosystems and maximize the services they generate [
5
]. A core concept in understanding
and addressing the sustainability of ES is ecosystem condition [
6
]. Here, we refer to the
ecological condition of ecosystems (EC) as the state of ecological systems, which includes
their physical, chemical, and biological characteristics and the processes and interactions
that connect them [
7
]. Biodiversity is commonly understood as one possible indicator of
the ecological condition of ecosystems [8,9].
Land 2021,10, 1040. https://doi.org/10.3390/land10101040 https://www.mdpi.com/journal/land
Land 2021,10, 1040 2 of 28
Spatially explicit quantifications of ES (i.e., mapping of ES) gained particular promi-
nence in Europe under the EU Biodiversity Strategy for 2020 [
10
]
1
. Spatial-based ap-
proaches facilitate decision making by providing an efficient way of conveying complex
information through visual representation [
11
] and are valuable in systematic conservation
planning to ensure the long-term capacity of ecosystems to provide ES [
3
]. Despite the
increasing number of ES mapping and spatial assessment exercises in the literature, only a
small amount have explored Action 5, Target 2 of the EU Biodiversity Strategy for 2020
2
from an integrated scientific perspective, namely by pointing out the relationship between
the ecological condition of ecosystems and their service supply capacity under a long-term
sustainability perspective [
12
]. A major challenge lies in the complexity of the relationships
between EC and ES [
13
] and in the fact that these relationships greatly vary depending on
the scale of analysis [
14
,
15
], which may not coincide with the scale of landscape planning.
In Portugal, the MAES initiative (ptMAES–Mapping and Assessment of Ecosystem
and their Services) was set out to address Target 2 of the EU Biodiversity Strategy by taking
the challenges imposed by its actions 5 and 7a), namely, “Member States, with the assistance
of the Commission, will map and assess the state of ecosystems and their services in their national
territory by 2014, assess the economic value of such services, and promote the integration of these
values into accounting and reporting systems at EU and national level by 2020”. The ptMAES
initiative was carried out in 2014 as a regional assessment targeting the NUTS II Alentejo
region, which covers about one-third of the total area of mainland Portugal. The goal
of ptMAES was to map the dominant ecosystems within the NUTS II Alentejo region
3
following the European Nature Information System (EUNIS) habitat classification and to
map and assess ecosystem’s condition and services provided by the agricultural, forest,
and agroforestry ecosystems in the region. The study comprised a multi-tiered approach
to spatially quantify EC and ES, following the standardized methodological approaches
proposed in the MAES guidelines to support the use of ES in planning [
8
,
16
,
17
]. With this
approach, a set of accurate and defendable methods were selected, intended to deliver
outcomes that could better assist decision making aimed at landscape planning under
nature conservation goals [
18
]. Although it presented a pioneering exercise in this regard,
the study fell short of providing further clues on the spatial relationships between EC and
ES, which may potentially assist the actual integration of ES into landscape planning in
the region.
In this paper, we detail the methodological and analytical framework developed
in ptMAES to map and assess EC and ES supply, explore spatial relationships between
EC and ES supply, and discuss the main challenges and opportunities encountered in
order to inform the implementation of future MAES initiatives in Portugal and other
member states. As such, the goal of this paper is two-fold. We aimed at developing a
methodological reference for national policies furthering the implementation of the EU
Biodiversity Strategy to 2020 (namely those concerned with Target 2, Action 5) and of the
key actions set out in the EU Biodiversity Strategy to 2030. These national policies include,
among others, the Portuguese Nature Conservation and Biodiversity Strategy 2015–2020
and 2020–2030, the National Strategy for Forests up to 2030, and the Commitment for
Green Growth 2020/2030. We also aimed at exploring the potentialities of the proposed
methodology by further looking into spatial relationships between EC and ES.
2. Methods
2.1. Study Area
The ptMAES initiative was carried out as a regional study in Alentejo (Figure 1), which
presents a typically Mediterranean climate regime [
19
], dominated by rural landscapes
with high potential for tourism [
20
]. The study focused on the rural landscapes of Alentejo,
and as such, only forest, agroforestry, agricultural, and shrubland land use/land cover
(LULC) were targeted in the analysis (other LULC were classified as “no data”, Figure 1).
Land 2021,10, 1040 3 of 28
Figure 1. Location and dominant LULC in the study region of Alentejo.
2.2. Mapping Ecosystems’ Condition and Service Supply
Following MAES guidelines, the methodological framework chosen to map and assess
ecosystems’ condition and services consists of a multi-tiered approach, combining methods
of varying complexity given data availability. The decision on which EC indicators and
ES to assess was based on data availability to test the study under the limited time frame
usually associated with the implementation period set out in EU strategies/regulations.
As such, the selection here presented was supported by discussions with representatives of
the National Authority for Nature Conservation (Instituto da Conservação da Natureza e
Florestas–ICNF) to accommodate official requirements while accounting for data availabil-
ity and approved methods to be used under the proposed time frame for the development
of this first MAES initiative in Portugal. Figure 2presents a schematic representation of
our approach, and each method is detailed in the following subsections.
All assessments were based on the most detailed LULC cartography publicly available
in Portugal, from 2007 (hereinafter COS07), which has a minimum mapping unit of 25 ha.
Therefore, the unit of mapping and analysis was the polygons of the COS07 cartography
within the study area that belong to the broad LULC categories shown in Figure 1, namely
agriculture, agroforestry, forest, and shrubland (n = 149,013). We considered the lowest
legend level available for COS07 LULC classes, which reports 59 different LULC classes
under agriculture, agroforestry, forest, or shrubland categories (see Supplementary Material
Table S1).
Land 2021,10, 1040 4 of 28
Figure 2. Mapping ecosystems’ condition and service supply capacity: methodological overview.
2.3. Ecosystem Condition (EC)
Four indicators were considered to assess ecosystems’ condition: Soil Organic Matter,
Ecological Value of Plant Communities,Plant Diversity, and Bird Diversity. A summary of
the selected indicators is presented in Table 1. The methods are further detailed in the
following subsections.
Table 1. Final selection of ecosystem condition indicators and a brief description of biophysical mapping methods used.
Selected EC Indicators Unit Biophysical Mapping
Soil Organic Matter tonC.ha−1.year−1
Soil Organic Matter content was assessed based primarily on the information presented in
the National Greenhouse Gases Inventory Report (NIR), according to its land-use
typology (Kyoto Protocol classes) although minor adjustments have been introduced (i.e.,
changes in organic matter estimates in areas undergoing land-use change). Soil organic
matter is indicative of the ecological condition of soils, being essential to maintaining soil
ecosystem functions such as stabilization, water infiltration, and conservation of nutrients.
Ecological Value of Plant
Communities Semi-Quantitative Score (1 to 5)
The Ecological Value of Plant Communities represents the mean value of five parameters
(naturalness, replaceability, threat, rarity, and condition), scored from 1 to 5, was
attributed to each of the studied ecosystems (level n). The geobotanical models used, at
the geographical scale in which they were implemented, are indicative of the ecological
condition of ecosystems by providing integrative information on the structural quality,
phytocoenotic integrity, and successional maturity of the present plant communities.
Plant Diversity Semi-Quantitative Score (1 to 5)
Plant Diversity Assessment Assumed that Vegetation Series Maps Provide Information on
the Natural Communities Occurring at Different Locations. it is thus possible to consult
phytosociological tables of these communities and to know their average or characteristic
floristic composition, which reflects species richness and rarity, as well as the presence of
endemic or threatened species. Based on 3500 phytosociological inventories,
representative of Portuguese natural vegetation, plant diversity was estimated as the
weighted average of four different parameters attributed to each plant community
(presence of protected species, of other endemic species, of other rare species, and of
characteristic species). Plant diversity is an indicator of the ecological condition of
ecosystems by supporting their multi-functionality and resilience.
Land 2021,10, 1040 5 of 28
Table 1. Cont.
Selected EC Indicators Unit Biophysical Mapping
Bird Diversity Semi-Quantitative Score (1 to 5)
Indicator Assessment was Based on an Extensive and Publicly Available Dataset of
Observation Records (PortugalAves/eBIRD), Used to Obtain a Model (Multiple Logistic
Regression with 16 Explanatory Variables Related to Land Use, Temperature, Rainfall and
Elevation) that Resulted in a Map with the Potential Distribution of Bird Diversity in the
Study Area. This Indicator is thus given by the estimated number of species in grid cells (2
×2 km) covering the study area, which was reclassified into a 1 (low bird diversity) to 5
(high bird diversity) scale. as can be noted, this differs from the unit of analysis of the
other indicators (the LULC polygons from COS07), but this issue has been properly
addressed when accounting for spatial relationships. Birds have been widely
acknowledged as indicators of the ecological condition of forests and agroecosystems,
with bird diversity being one possible good measure of the general ecological condition
and overall biodiversity present in an ecosystem.
2.3.1. Soil Organic Matter
To map soil organic matter, we used average organic carbon content present at 0–40 cm
deep soil (Table 2) as obtained from the Portuguese National Greenhouse Gases Inventory
Report, hereinafter referred to as NIR (APA, 2004). These average value were estimated
based on the results of three international projects, namely ICP Forests grid (1995 and 2005),
Biosoil (1999) and LUCAS soil assessment (2009).
Table 2. Average soil organic carbon obtained from APA (2004).
Land-Use Classes Average tC/ha (0–40 cm)
01. Pinus pinaster 113
02. Quercus suber 66
03. Eucalyptus spp. 98
04. Quercus rotundifolia 65
05. Other Quercus spp. 89
06. Other broadleaves 107
07. Pinus pinea + 08. Other coniferous 93
09. Rain-Fed Crops 59
10. Irrigated Crops + 11. Rice 64
12. Vineyards 51
13. Olive 71
14. Other Permanent 56
15. Grassland 61
17. Settlements 87
18. Shrubland 107
The specific land-use classification adopted in the NIR (Kyoto Protocol land-use
classes, Table 3) was translated into land-use typologies (COS07) for harmonization of leg-
ends and further use in our study. This harmonization (Supplementary Material Table S4)
was later evaluated and approved by NIR authors in a meeting.
We also considered land-use transitions observed (1990 to 2007) to properly adjust
the average soil organic carbon content present in each polygon. This means that the soil
organic carbon content in polygons that underwent a land-use transition
i→j
is given
by the average of the content value of
i
and
j
. All calculations were performed in ArcGIS
(v9.3).
Land 2021,10, 1040 6 of 28
Table 3. Kyoto Protocol (KP) land-use categories, as defined by the United Nations Framework for Climate Change.
UNFCC Category KP Land-Use Category Description
Forest Land
Pinus pinaster Forests Dominated by Maritime Pine
Quercus suber Forests Dominated by Cork Oak
Eucalyptus spp. Forests Dominated by Eucalypt Species
Quercus rotundifolia Forests Dominated by Holm Oak
Quercus spp. Forests Dominated by Other Oaks
Other broadleaves Forests Dominated by any Other Broadleaf Species
Pinus pinea Forests Dominated by Umbrella Pine
Other Coniferous Forests Dominated by any Other Coniferous Species
Cropland
Rain-Fed Annual Crops Includes All Land Cultivated with Annual Crops without Irrigation
Includes Fallow-Land Integrated Into Crop-Rotations
Irrigated Annual Crops Includes All Land Cultivated with Annual Crops that is Under Irrigation (Except Rice) and
Greenhouses
Rice Paddies Includes All Land Prepared for Rice Cultivation
Vineyards Includes All Areas Used for Cultivation of Table and/or Wine Grapes
Olive Groves Includes All Areas Used for Cultivation of Olea Europea146
Other Permanent Crops Includes All Areas Used for Cultivation of all other Species of Woody Crops, Including Fruit
Orchards147
Grassland All Grasslands Includes All Lands Covered in Permanent Herbaceous Cover
Other land Shrubland Includes All Lands Covered in Woody Vegetation that do not meet the Forest or Permanent Crop
Definitions
2.3.2. Ecological Value of Plant Communities & Plant Diversity
These two EC indicators were assessed as presented in [
21
]. The indicator Ecological
Value of Plant Communities represents the mean value of five parameters (naturalness,
replaceability, threat, rarity, and condition), scored from 1 to 5, attributed to each of
the studied ecosystems (level N). The geobotanical models used, at the geographical
scale in which they were implemented, integrated information on the structural quality,
phytocoenotic integrity, and successional maturity of plant communities. Further details
on the methods used are provided in [21].
The Plant Diversity indicator was assessed assuming that vegetation series maps pro-
vide reliable information on the natural communities occurring at different locations. It
was thus possible to consult phytosociological tables of these communities and to know
their average or characteristic floristic composition, which reflects species richness and
rarity, as well as the presence of endemic or threatened species. Based on 3500 phytosocio-
logical inventories, representative of Portuguese natural vegetation, plant diversity was
estimated as the weighted average of four different parameters attributed to each plant
community (presence of protected species, of other endemic species, of other rare species,
and of characteristic species). Further information on the methods used is provided in [
21
].
2.3.3. Bird Diversity
As birds are usually referred to as an “indicator” group for several environmental
parameters [
22
], including biodiversity and the condition of ecosystems, bird diversity
was also chosen to assess EC. We performed a multiple logistic regression with pres-
ence/absence records of farmland and forest bird species and a set of explanatory variables
(land-use typology, topography, temperature, and rainfall) (Table 4). See Supplemetary
Material Table S1. for aggregation criteria for land-use typologies.
Bird data was collected from an extensive and publicly available dataset of observation
records (from the PortugalAves database, which now has been merged into eBIRD https://
ebird.org/portugal/home accessed on 1 September 2021). Data processing and validation
was required to select only records of true absence/presence and corresponding to a specific
temporal scale (2004–2011) close enough to the land use cartography used (2007). This
resulted in a final list of 50 species and 15,000 observations (Table 5). Bird observations
were recorded in 2
×
2 km cells, and thus we applied a 2
×
2 km grid over the study area
Land 2021,10, 1040 7 of 28
and used the cells as the unit of analysis for the Bird Diversity indicator. When comparing
with other indicators (see Section 2.5, we integrated the results into the LULC polygons
from COS07 by averaging grid cell values within each polygon.
Table 4. Logistic regression variables used to model Bird Diversity.
Variable Type Unit Temporal Scale Source
P/A–Presence/Absence of Each Bird
Species Bird data Factor (P/A) 2004–2011 eBIRD Database
tmax–Average Maximum Temperature
Climate and Topography
◦C
2004–2009
MM5 Model (9 km Resolution)
with Krigging (Standard ArcGIS,
1 km Pixel)
tmin–Average Minimum Temperature
rain–Total Rainfall mm
altm–Average Elevation m 2009 DEM (30 m Resolution) Supplied
by NASA (ASTER Sensor) *
flor–Forest
Land-use Factor (P/A) 2007
COS’07 Land-Use Cartography
(See Supplementary Material
Table S1)
floa–Open Forest
agrs–Rainfed Crops
agrr–Irrigated Crops
agrp–Permanent Crops
agrm–Mixed Crops
mont–Montado (Agroforestry
Ecosystems)
past–Grasslands
ncul–Shrublands
purb–Urban Settlements
plen–Lakes and Other Water Bodies
plot–Rivers
*http://gdem.ersdac.jspacesystems.or.jp/ (accessed on 1 September 2021).
Table 5. List of bird species and number of records considered for modeling.
Scientific Name Number of Records
Saxicola torquatus 264
Sylvia melanocephala 259
* Sturnus unicolor 247
Turdus merula 246
Parus caeruleus 240
Parus major 208
Emberiza calandra 183
Lanius meridionalis 182
Fringilla coelebs 175
Buteo buteo 169
Carduelis carduelis 167
Passer domesticus 163
Erithacus rubecula 157
* Streptopelia decaocto 149
* Alectoris rufa 139
Bubulcus ibis 138
Galerida cristata 138
Lullula arborea 138
Carduelis cannabina 133
Land 2021,10, 1040 8 of 28
Table 5. Cont.
Scientific Name Number of Records
Galerida theklae 133
Corvus corone 131
Phylloscopus collybita 131
Serinus serinus 128
Oenanthe oenanthe 125
Cisticola juncidis 124
Garrulus glandarius 123
Motacilla alba 118
* Pica pica 117
Falco tinnunculus 116
Upupa epops 116
Cyanopica cyanus 115
Carduelis chloris 112
Columba palumbus 107
Sylvia atricapilla 107
Ardea cinerea 106
Sitta europaea 104
Certhia brachydactyla 102
Anthus pratensis 97
Cettia cetti 94
Elanus caeruleus 81
Ficedula hypoleuca 80
* Troglodytes troglodytes 80
Hirundo rustica 78
* Vanellus vanellus 77
Egretta garzetta 74
* Anas platyrhynchos 72
Hirundo daurica 72
* Turdus philomelos 67
* Tringa ochropus 65
* Dendrocopos major 62
* Later excluded from analysis, see below.
We ran a separate multiple logistic regression model for each bird species, which
resulted in the probability of observation of each species at any given cell of our study area.
Ten out of the fifty species were excluded at this point since the models returned significant
errors (either false convergency or null convergency, see marked species in Table 5).
All calculations were performed in R (v. 2.15.13). The multiple logistic regression model
was adjusted with the glm (family = binomial logit) function, with step (glw = backwards). AIC
(Akaike’s information criteria) values were compared before and after stepwise adjustment
to ensure the appropriate selection of variables. The Hosmer–Lemeshow goodness-of-fit
test [
23
] was used to compare the log-likelihood of observed and estimated values through
the hoslem.test(fitted(backwards)) function (library ResourceSelection). Results for each
model are provided in Supplementary Materials (Table S8). We mapped results considering
Land 2021,10, 1040 9 of 28
that a species would occur in a given cell if the probability returned by our model for that
cell was over 50%. This resulted in a map with the potential distribution of bird diversity
in the study area. We reclassified our results on a scale from 1 (low diversity) to 5 (high
diversity) (Table 6).
Table 6. Bird Diversity Scale.
Bird Diversity Scale # of Species Present (p> 0.5)
1 [0;5]
2 [6;9]
3 [10;13]
4 [14;17]
5 [18; . . . ]
2.4. Ecosystem Services (ES)
Five ES were quantified and mapped: control of erosion rates, climate regulation through
carbon sequestration, fiber production, crops, and extensive livestock production. A summary
of the selected ES, including its designation under the CICES [
2
] classification system, is
presented in Table 7. The methods are further detailed in the following subsections.
Table 7.
Final selection of ES (classification following CICES and specifications for this study) and a brief description of the
biophysical mapping methods used.
Selected ES Biophysical Mapping
ES Classification Following CICES (v5.1) Specifications
Section Section Class (Code) ES Designation Indicator Unit Description
Provisioning Biomass
Cultivated Crops
(1.1.1.1) Crop Production ton.ha−1.yr−1
Crop Production was mapped based on the
total annual production of main cultures
present within the study area. Information
obtained per municipality, based on official
national agriculture statistics (Instituto
Nacional de Estatística, INE). Spatialization
of this information was possible based on the
harmonization of culture classes with LULC
classes.
Reared Animals and
Their Outputs
(1.1.1.2)
Extensive Livestock
Production L LU.ha−1.yr−1
Extensive Livestock Production was mapped
based on the effective support capacity of
extensive pastures, considering the average
livestock unit (LU) within the study area.
Information obtained per municipality, based
on official national agriculture statistics
(Instituto Nacional de Estatística, INE).
Spatialization of this information was
possible based on the harmonization of
pasture classes with LULC classes
Fibers and Other
Materials for Direct
Use or Processing
(1.2.1.1)
Fiber Production m3.ha−1.yr−1
Fiber Production mapping was based on
yearly biomass increments per species, as
reported in the Portuguese National
Greenhouse Gases Inventory Report (NIR),
According to its land-use typology (Kyoto
Protocol Classes). classes of species
considered were: Pinus pinaster,Pinus pinea,
Quercus spp, Quercus suber,Quercus
rotundifolia,Eucalyptus spp, Mixed
broadleaves forests, and mixed coniferous
forests. average biomass losses due to natural
mortality were discounted. spatialization of
this information was possible based on the
harmonization of kp classes legend with
LULC classes from national cartography.
Land 2021,10, 1040 10 of 28
Table 7. Cont.
Selected ES Biophysical Mapping
ES Classification Following CICES (v5.1) Specifications
Section Section Class (Code) ES Designation Indicator Unit Description
Regulating
Regulation of
Physical, Chemical,
Biological Conditions
Global Climate
Regulation by
Reduction of
Greenhouse Gas
Concentrations
(2.3.5.1)
Carbon
Sequestration tonCO2.ha−1.yr−1
Carbon Sequestration mapping was based on
input/output balances in biomass (above
and below ground). Annual emission and
retention coefficients for each land-use
change (considering changes observed in a
17-year period) were estimated based on the
National Inventory Report results (NIR).
Spatialization of this information was
possible based on the harmonization of KP
classes legend with LULC classes from
national cartography.
Stabilization and
Control of Erosion
Rates (2.2.1.1)
Control of Erosion
Rates ton.ha−1.yr-−1
Control of Erosion Rates was modeled and
mapped based on the Universal Soil Loss
Equation (USLE), integrated into a GIS
platform, which allowed determining the
difference between erosion rates in the
current scenario (i.e., erosion rates given
actual land cover type) and erosion rates for a
worst-case scenario (considering a maximum
erosion cover type), as first suggested by [24]
2.4.1. Control of Erosion Rates
Control of Erosion Rates was estimated considering the contribution of a given soil
cover to reduce soil erosion by comparing it with a worst-case scenario (i.e., the land cover
that would generate the highest erosion rate at a given point). Soil erosion rates were
estimated using the Universal Soil Loss Equation [25], which consists of a tier 3 approach.
The equation estimates average soil loss (A) such as:
A=R×K×LS ×C×P
where,(in SU):
A=average soil loss in ton ·ha−1·year−1
R=rai nf all ero sivity f actor Mj.mm ·ha−1·h−1·year−1.
K=Erodibility f actor (soil resistance),in t.h.M j−1·mm−1
LS =Dimensionless f actor given by pixel length and slope
C=Dimensionless f actor ranging f rom 0to 1, given by soil cover ty pology
P=Dimensionless f actor ranging f rom
0
to
1,
given by land management practices
(here kept constant as 1)
As no information was available to estimate factor P accurately, it was kept constant
as 1. For our approach, we considered that the
Control of Erosion Rates
service was given
by avoided erosion (
Aa
) when factor
Ca
= 1 (soil without any natural cover), which can be
re-written as:
Aa=RKLS(1−Ca)
Factor K was obtained by combining information from national soil cartography avail-
able at http://sniambportal.apambiente.pt (accessed on 1 September 2021) and national
literature. European estimates for Factor C [
26
] were refined per Portuguese LULC classes
based on [
27
] (see Supplementary Material Table S2). Factor R was based on national statis-
tics publicly available at http://geo.snirh.pt/AtlasAgua/ (accessed on 1 September 2021)
(required conversion of American to SI units based on [
28
]. Factors LS (combined) are
given by:
LS =λ
22, 13 0.4 sin β
0, 0896 1.3
Land 2021,10, 1040 11 of 28
where:
λ=a∗p;a=f lo w a ccumul ation mod el (ArcG I S);p=pixel size given by DEM
β=slope (in degrees)given by DEM
All calculations were performed in ArcGIS using raster calculation operations. We
selected the finest resolution possible for the raster cells (250
×
250 m), but, by definition,
when performing raster calculations our results were not mapped using the LULC polygons
from the COS07 cartography as unit of analysis (same as explained for Bird Diversity).
However, when depicting spatial relationships (see Section 2.1), we averaged raster cell
values per LULC polygon to make results comparable.
2.4.2. Climate Regulation through Carbon Sequestration
Carbon Sequestration was estimated as the balance of gains and losses of carbon in
both biomass (above and belowground) and soil, considering the land-use transitions that
occurred between 1990 and 2007 (and assuming these transitions occurred at a constant
rate). IPCC guidelines point to a 20-year transition period to be considered for carbon
balances, for the stabilization of carbon fluxes that are slow and/or occurring between two
different states [
29
,
30
]. Owing to the LULC cartography available (from 1990 and 2007),
the analysis was limited to a period of 17 years.
For estimating carbon balances, we first determined carbon gains by calculating
mean annual biomass increments as reported in NIR (Tables 8and 9), converted into
tonC
·
ha
−1·
year
−1
using the appropriate conversion factors (root-to-shoot, biomass ex-
pansion factor, and carbon fraction) (see Supplementary Material Table S3.). Next, we
determined carbon losses by using data on timber harvesting, observed land-use transi-
tions (from 1990 to 2007), maps of burned areas, and information on natural mortality
to determine:
•
Mortality by timber harvesting (spatialization of statistical data allowed determining
harvesting rate for the given years at each polygon of interest, i.e., polygons with
timber harvesting plantations).
•
Mortality by fire (total loss of biomass in polygons that experienced fire events, based
on official fire maps for 2007, available at http://www2.icnf.pt/portal/florestas/dfci/
inc/mapa (accessed on 1 September 2021)).
•
Mortality by transition (total or partial loss of biomass due to land-use transition, from
1990 to 2007, observed in a given polygon).
•Natural mortality, as determined and reported by the NIR (Table 10).
Table 8. Biomass increments for forest land-uses.
Forest KP Classes Mean Annual Increment (m3/ha)
01. Pinus Pinaster 5.6
02. Quercus Suber 0.5
03. Eucalyptus 9.5
04. Quercus Rotundifolia 0.5
05. Other Quercus 2.9
06. Other Broadleaves 2.9
07. Pinus Pinea 5.6
08. Other Coniferous 5
Land 2021,10, 1040 12 of 28
Table 9. Biomass increment estimates for non-forest land-uses.
Non-Forest KP Classes Aboveground Mean Annual
Increment
Belowground Mean Annual
Increment
12. Vineyards 0.17 0.14
13. Olive 0.39 0.06
14. Other Permanent 0.42 0.07
15. Grassland 0.53 0.94
18. Shrubland 0.44 0.25
Table 10. Natural mortality rates for forest land-uses. Source APA 2014.
Forest KP Classes Mortality (% of Annual Increment)
01. Pinus Pinaster 0.77%
02. Quercus Suber 0.97%
03. Eucalyptus 0.83%
04. Quercus Rotundifolia 0.8%
05. Other Quercus 0.93%
06. Other Broadleaves 1.23%
07. Pinus Pinea 0.23%
08. Other Coniferous 1.1%
All information was converted to tonC
·
ha
−1·
year
−1
to allow comparisons. The
Carbon sequestration service was given by the positive carbon flows estimated (above
0 tonC
·
ha
−1·
year
−1
). Data was managed in a specific geodatabase, and calculations
were performed in ArcGIS and Access (Microsoft Office). Emission/sequestration co-
efficients were thus obtained for each land-use transition, resulting in a carbon emis-
sion/sequestration map.
2.4.3. Provisioning ES
Fiber Production was estimated as mean annual increments of forests trees as presented
in the NIR (reported for KP forest classes, see carbon sequestration above), deducing
biomass losses due to natural mortality. Spatialization of ES supply was possible after
harmonization of legends between KP classes and COS07 (see Supplementary Material
Table S4.)
Mapping of crop production was based on the establishment of a correspondence
between the main crops in the study area (as listed in the official statistics) and COS07
classes. We first listed and systematized the main crops, areas (ha) and productivity
(ton/ha) in the study area as extracted from the statistics bureau. Once the correspondence
with land use classes was made (in certain cases, more than one crop was attributed to the
same land-use class; only “pure” land use classes were considered), average productivity
was estimated for each land-use class (COS07)–see Supplementary Material Table S5 for
established relationships.
Extensive livestock production (or the capacity of ecosystems to support extensive live-
stock production) was quantified and mapped by determining average livestock densities
(for calves, dairy cattle, and sheep) in pasture areas within the study region, using offi-
cial national statistics at the municipality level. As it was not possible to geographically
identify pastures where each unit of livestock production occurs, average livestock density
was estimated considering the two main species together (cattle and sheep) for the given
pasture area in national statistics for each municipality. Next, average livestock values
were spatialized by attributing them to meadow and pasture areas as defined by COS’07.
Land 2021,10, 1040 13 of 28
Due to the identified disparities between pasture areas reported in COS’07 and pasture
areas reported in the national statistics, the resulting values presented in the map should
not be aggregated.
2.5. Spatial Relationships and Interactions
The analysis of spatial relationships and interactions between ecosystem condition
(EC) and the supply of ecosystem services (ES) is intended to explore the outcomes of the
MAES initiative envisioning its potential contribution to landscape planning in the region.
This analysis was conducted from two different perspectives: the assessment of statistical
correlations and the analysis of the spatial distributions and relationships between EC
and ES.
The models used for bird diversity and control of erosion rates required mapping using
raster/grid cells of finer resolution as the unit of analysis (as detailed in the corresponding
section), and here they were analyzed along with the other variables at the polygon level
by averaging cell values per LULC polygon of the COS07 cartography.
For the statistical correlations, we first considered five levels for each EC indicator.
We took the semi-quantitative 1 to 5 scale already assessed for bird and plant diversity, as
well as the ecological value of plant communities. For the soil organic matter indicator,
we categorized the values into five levels using Jenks/Natural breaks [
31
]: very low
(0–34 tC/ha), low (34–56), medium (56–69), high (69–82) and very high (82–113). These
levels are in accordance with the range of values for soil organic matter previously reported
in Alentejo [
32
]. We then analyzed ES supply for each EC level by applying the Kruskal–
Wallis rank test [
33
] on the EC levels’ medians to detect significant differences of ES supply
between each level (i.e., to evaluate whether different levels of EC result in different ES
supply). We also performed a Jonckheere–Terpstra test [
34
,
35
] to identify positive or
negative trends in the relationship between the supply of each ES and EC level (i.e., to
evaluate whether increasing/decreasing EC levels result in higher/lower ES supply). The
statistical work was conducted in RStudio (version 4.0.5).
To show an integrated overview of the distribution of ES supply and EC levels in
Alentejo, we normalized all the variables into a 0 to 100 scale to make them comparable.
To spatially visualize the relationships between the ES and EC indicators, we overlapped
the normalized ES supply and the normalized sum of all EC indicators. We also indicate
the relationship per LULC typology, differentiating between agriculture, agroforestry, and
forest/shrubland classes. This analysis allowed us to identify how EC condition is related
to ES supply in Alentejo.
3. Results
The results of the assessment are presented in maps showing the distribution of the
indicators for ecosystem condition (EC, Figure 3) and the ecosystem services supply (ES,
Figure 4) within Alentejo. The breakdown of results per ecosystem type is summarized in
Supplementary Material Table S6. Other graphs and maps show the spatial relationships
and interactions between EC and ES (Figures 5–9).
Land 2021,10, 1040 14 of 28
Figure 3.
Ecosystem condition (EC) in Alentejo assessed based on four indicators: soil organic matter (
a
), ecological value of plant communities (
b
), plant diversity (
c
), and bird diversity (
d
).
Land 2021,10, 1040 15 of 28
Figure 4.
Ecosystem service (ES) supply in Alentejo. Maps showing supply distribution for two regulating ES (control of erosion rates (
a
) and carbon sequestration (
b
)) and three
provisioning ES (fiber (c), crop (d), and extensive livestock production (e)).
Land 2021,10, 1040 16 of 28
Figure 5.
Relationships between the organic matter indicator and ecosystem services (ES) supply. (
a
) Supply over different levels of organic matter (very low to very high) for each ES
assessed. (b) Representation (% area) of different land-use typologies (agriculture, agroforestry, and forest/shrubland) per organic matter level.
Land 2021,10, 1040 17 of 28
Figure 6.
Relationships between the ecological value of plant communities indicator (EV) and ecosystem services (ES) supply. (
a
) Supply over different levels of EV (scored 1 to 5, as low to
high) for each ES assessed. (b) Representation (% area) of different land-use typologies (agriculture, agroforestry, and forest/shrubland) per EV level (1–5).
Land 2021,10, 1040 18 of 28
Figure 7.
Relationships between the plant diversity indicator and ecosystem services (ES) supply. (
a
) Supply over different levels of plant diversity (scored 1 to 5, as low to high) for each
ES assessed. (b) Representation (% area) of different land-use typologies (agriculture, agroforestry, and forest/shrubland) per plant diversity level (1–5).
Land 2021,10, 1040 19 of 28
Figure 8.
Relationships between the bird diversity indicator and ecosystem services (ES) supply. (
a
) Supply over different levels of bird diversity (scored 1 to 5, low to high) for each ES
assessed. (b) Representation (% area) of different land-use typologies (agriculture, agroforestry, and forest/shrubland) per bird diversity level (1–5).
Land 2021,10, 1040 20 of 28
Figure 9.
Spatial representation of the overlap between ecosystem condition (normalized sum of all indicators) and ecosystem services supply (normalized): (
a
) fiber production;
(
b
) crop production; (
c
) extensive livestock production. Breakdown of results shows number of polygons (dots) per LULC (green, cropland/pastures; yellow, agroforestry; purple,
forest/shrubland).
Land 2021,10, 1040 21 of 28
3.1. Ecosystem Condition (EC)
Soil organic matter mapping showed average organic matter content in Alentejo at
around 50–75 tC/ha (Figure 3a). Only 12% of the study area presented OM content above
85 tC/ha. OM concentrations were overall higher in the north and southwest of the region,
mainly in forest and shrubland ecosystems. Results for the EC indicator ecological value of
plant communities (Figure 3b) shows most of the region presented lower scores (1–2). Areas
with the highest score (5) can be sparsely found along the west coast (in dune/paleodune
and sea cliff ecosystems with a higher preservation value or presence of successional
evolution) and to the northeast and southeast of the region (in Quercus suber dominated
agroforestry ecosystems with low agricultural intensity). Higher plant diversity was found
in the center and southwest of Alentejo (Figure 3c), mainly in agroforestry and forest
ecosystems characterized by sandy soils. As for bird diversity (Figure 3d), over 13% of the
study area presented the highest score (5). Higher bird diversity scores were found mainly
in broadleaved deciduous forests, temperate Mediterranean scrubs, and in inland rock
cliff ecosystems.
3.2. Ecosystem Services (ES)
In terms of control of erosion rates (Figure 4a), higher service supply (over 50 t/ha/year)
was found along mountainous regions to the southwest and northeast of the region. Grass-
lands and forests (Quercus suber forests) are the most erosion controlling ecosystems. Carbon
sequestration was determined using carbon balances, and results show positive rates (se-
questration) mainly present throughout the region but predominantly in the southwest
(Figure 4b). Negative rates (carbon emission) dominate the eastern part of Alentejo. Higher
service supply (i.e., carbon sequestration rates) are associated with transitions to forest
ecosystems, namely areas converted to broadleaf forests and Pinus pinea, while the highest
emissions rates were found in areas that were converted from forest to non-forest uses over
the 17 years analyzed (1990–2007).
Three provisioning ES were assessed based on the mean annual increment of forest
species of interest (fiber production) and spatialization of statistic data reports (crop and
extensive livestock production). Spatialization of data was limited to specific land-use ty-
pologies (e.g., fiber production is not accounted for in grasslands or croplands), which
explains the larger areas where ES supply is absent (“0”) in these maps (Figure 4c–e, re-
spectively). Fiber production in Alentejo is mostly present at lower rates in large extents of
agroforestry ecosystems and Quercus suber and Quercus rotundifolia forests, mainly in central
Alentejo (Figure 4c). Nevertheless, other ecosystems dominated by fast-growing species
(Eucalyptus sp. and Pinus pinaster) can be found in the eastern part of the region, where
much higher rates of service supply were found (up to 9.4 m
3
/ha). As for crop production,
a higher service supply can be found along the Tagus Valley (northwest) and sparsely
throughout the region where cropland is present. Finally, results for livestock production
shows the level of service supply ranging from 0.2 to over 1LU/ha. Levels higher than the
0.6–0.8 ha category are found mainly in grassland ecosystems, whereas lower levels are
present in agroforestry ecosystems (extensive grazing).
3.3. Spatial Relationships and Interactions
Figures 5–8show the relationship between each ecosystem condition (EC) indicator
and the supply of the different ecosystem services (ES) assessed. The candlestick charts in
Figures 5–8indicate how ES supply changes with increasing levels of EC—each figure refers
to one EC indicator. Kruskal- Wallis tests for all variables indicate significant differences
(p> 0.05) in ES supply for the different levels of EC. Based on the Jonckheere–Terpstra
tests, results indicate a positive relationship (p> 0.05) between the supply of regulating
ES (control of erosion rates and carbon sequestration) and higher levels of bird diversity and
soil organic matter (Figures 5and 8, respectively). Contrastingly, the supply of CE and CS
seems to decrease with increasing plant diversity and ecological value of plant communities
(Figures 6and 7, respectively).
Land 2021,10, 1040 22 of 28
As for provisioning ES, supply levels mainly decrease with increasing levels of EC
indicators, except for fiber production (which increases with increasing organic matter,
Figure 5) and extensive livestock production (which increases with increasing ecological value
of plant communities, Figure 6). These results were expected since (1) higher levels of
organic matter were found in production forests and (2) the higher supply of extensive
livestock refers mainly to medium range values (around 0.5 LU/ha), which occur in
agroforestry systems that scored medium-high in the ecological value EC indicator.
Additionally, we identified overlaps between ES supply and EC indicators. Figure 9a–c
show the distribution of normalized ES supply for fiber,crop, and livestock production, respectively,
in relation to the normalized sum of all EC indicators (ecological value of plant communities,plant
and bird diversity, and soil organic matter). Overlap results for the two regulating ES (control
of erosion rates and carbon sequestration) are shown in Supplementary Material Table S7. We
also present the breakdown of the overlap between ES and EC per land-use/land cover
(LULC), indicating which LULC class is more predominant under different combinations
of ES supply and EC level. For instance, in Figure 9c, we see that intermediate levels of EC
condition and ES supply are more predominant in agroforestry areas (yellow dots) than in
pastures/grasslands (green dots). Areas of high ES supply and below-average EC level
(green shades on the top left of the legend) can be found throughout Alentejo for the three
provisioning ES, particularly to the north and northeast of the region (Figure 9a,b) but also
sparsely in the central part of Alentejo (Figure 9b,c). This indicates a high supply of ES in
areas of poor ecological condition.
4. Discussion
Our discussion is framed by the objectives earlier stated: first by considering the
proposed analytical framework and outcomes of the ptMAES study in the context of future
MAES initiatives, and second by discussing the assessed relationships between ecosystem
services (ES) and ecosystems’ condition (EC).
4.1. Proposed Analytical Framework
We identify several advantages and challenges of the proposed analytical framework
for implementing national policies targeting the EU Biodiversity Strategy to 2020 and 2030,
as well as assisting the launch of MAES initiatives in Portugal.
The methods proposed for assessing the ES control of erosion rates bring novelty to the
soil arena, being a first attempt to detail a C-factor for the national land-use cartography
classes (COS07) and a GIS-based application of the USLE at a regional scale in a context
of ES assessment. This resulted in a highly detailed map (Figure 4a), capturing spatial
variability with the potential to assist landscape planning at multiple scales. Our estimates
proved to be more detailed and reliable than soil erosion maps produced at the European
scale (JRC PESERA) based on remote sensing/LULC data, although our range of estimates
falls within the reported values for Portugal. However, the proposed approach fails
to incorporate the temporal variability of ES supply and the impacts of different land
management options, which can impact observed erosion rates [23].
The approach for mapping and assessing the ES carbon sequestration (Figure 4b) pre-
sented several advantages: it uses information already produced for national reporting
(UN Framework Convention on Climate Change and Kyoto Protocol), innovatively applied
on a spatial-based approach, and it accounts for LULC changes (i.e., temporal and spatial
dimension), allowing the ES to be genuinely quantified as flows [
36
]. Accounting for
LULC change is critical as its potential impact on ES supply at different scales has already
been called out in the literature [
37
–
39
], and LULC changes in Alentejo are significant and
mainly driven by agricultural policies [40].
Though the use of regional process-based models could significantly improve out-
comes [
41
], they provide additional challenges in terms of data requirements that might
not be readily available for the scale of interest.
Land 2021,10, 1040 23 of 28
Lack of disaggregated data (i.e., at the farm or local scale) publicly available was also
a significant limitation observed in the assessment of provisioning services (Figure 4c–e).
Other authors have reported this to be a common limitation of tier-1 approaches for ES
mapping [
42
,
43
], which is usually the case when mapping provisioning services. In the
case of fiber production, this limitation may be solved by using more spatialized annual
increment coefficients [
44
]. This refinement can also be possible based on literature and/or
expert opinion while keeping the cost-efficient nature of the proposed framework [
45
].
Better results can be achieved by integrating Land Parcel Identification System information,
which allows for a more detailed spatialization of crop and livestock production [
46
].
However, using this information in research agendas requires disclosure of personal data
and raises confidentiality and data protection issues.
Concerning EC indicators, the advantages of the proposed approach to mapping
soil organic matter (Figure 3a) are the same as reported for carbon sequestration, in the
sense that it relies on information that is available and collected for national reporting
on GHG emissions (UNFCC and Kyoto Protocol), but innovatively spatialized based on
LULC cartography while accounting for LULC change. The estimates efficiently account
for spatial variability and have the potential to assist landscape planning at multiple scales
while falling within the range of values previously assessed based on regional models
using sampling data [
32
]. We also highlight the potential of the proposed biodiversity
indicators, namely plant diversity and ecological value of plant communities (Figures 3b,c,
respectively), in assisting decision making regarding landscape planning (e.g., protected
areas management), as they focus on ecological and functional measures complemented
with expert judgment rather than on the presence or absence of species alone. We argue that
such integrated solutions conveying expert knowledge to mapping could better improve
the assessment of biodiversity-related indicators with increased potential to assist decision-
making at multiple scales [
47
,
48
]. As for the bird diversity indicator (Figure 3d), although
the method proposed is suitable for upscaling, rare species with conservation interest were
not included in the analysis due to their low observation frequency in the study area. This
limitation should be addressed in future applications if the purpose of the assessment is
to support nature conservation policies targeting protected species [
49
]. Moreover, it has
been argued that high biodiversity levels in managed landscapes are more likely to be
maintained for reasons of intrinsic value (e.g., traditional land management practices),
cultural values (e.g., “bequest” ES) or use values (“direct use”) than for its functional
values or its role in maintaining good ecosystem condition [
50
]. Our findings in this
paper support that it is meaningful to include biodiversity assessments both as ES supply
(biodiversity with conservation interest) as well as EC indicators (functional biodiversity)
in MAES initiatives aimed at the integration of ES into planning/policy design in human-
dominated landscapes. Finally, when assessing EC, we argue that priority should be given
to indicators that could be related to the ES flow measurement (e.g., soil organic matter
for crop production, infiltration rate for water availability, etc.), such as demonstrated by
recent findings [51,52].
Overall, the proposed framework can be easily applied at different scales subject to
specific refinements dictated by territory spatial variability. This is clearly a major ad-
vantage of performing a multi-tiered spatial assessment based on LULC cartography as
proposed in the ptMAES study, which has been also pointed out by others [
45
], However,
we acknowledge that in the absence of data limitations (in terms of scale, availability,
and coverage), a more comprehensive assessment would be possible, including a larger
number of ES and a wider array of EC indicators. Cultural ES, in particular, should not
be overlooked as they play an important role in motivating public support and assisting
decision-making [
53
,
54
]. To make the best out of the available information, certain ES and
EC indicators were based on tier-1 approaches (i.e., spatialization of statistical information)
(Figure 2), which limited the portrayal of spatial variability and consequent explanatory
power of the assessment to better assist decision-making [
52
]. These issues have been
discussed by [
55
], and our analysis supports the review and directions proposed by the au-
Land 2021,10, 1040 24 of 28
thors. We highlight that the ptMAES also produced a thematic EUNIS habitat cartography
based on interpreting LULC units as territorial units of ecological succession. This was an
innovative approach in Portugal, and the outcomes are presented in [
22
]. Results of EC
and ES supply per EUNIS habitat are presented in Supplementary Material Table S6.
4.2. Spatial Relationships and Interactions
In this paper, we analyzed the spatial relationships and interactions between EC and
ES supply in Alentejo. Our goal was to explore the outcomes of the analytical framework
proposed and discuss how they could potentially support landscape planning.
Our analysis indicates overall changes in ES supply rate for varying levels of EC
(Figures 5–8). Generally speaking, we expected regulating ES supply to increase with
increasing EC (as ecosystems under better ecological conditions potentially sequester
more carbon and provide more soil protection) and provisioning ES to decrease with
increasing EC (as higher production rates are usually found in monocultural crops or
intensive pastures under poorer ecological condition) [
56
]. Despite this, we found that
carbon sequestration rates decrease with increasing levels of plant-related EC indicators
(Figures 6and 7). Two underlying factors explain this result: (1) the great representation of
agroforestry systems in good EC (scoring 3 and higher, see Figures 5b and 6b); and (2) the
lower carbon sequestration rates attributed to Q. suber/Q. rotundifolia agroforestry systems,
which are given by the lower mean annual increment considered for these species in the
estimated biomass balance, when compared to other forest species. This means that carbon
sequestration is potentially underestimated in these ecosystems and is not true a reflection
of their ecological condition [
43
]. In addition, we found crop and fiber production to increase
with increasing EC levels (particularly bird diversity, Figure 8a). We believe this is caused by:
(1) the consideration of common farmland and arable land bird species in the distribution
models, which estimate high bird presence in cropland and harvested forests alike; and
(2) fiber and crop production levels are not real estimates collected in situ but rather result
from the specialization of statistical data. This result should be interpreted with caution
since bird presence is actually expected to decrease in intensive monocultures [57].
Despite the considerations presented above, when looking at spatial overlaps between the
supply of provisioning ES and the normalized sum of all EC indicators (Figure 9), we identify
high ES supply rates in areas under below-average EC (green shades in the maps). Since good
ecological conditions ultimately underpin the capacity of an ecosystem to supply ES [
58
,
59
],
these results red-flag areas where the sustainability of ecosystems (and its capacity to
supply ES) is threatened in the long term. The breakdown of these results per LULC
typology shows that agroforestry land-uses promote higher levels of ES supply (namely
crop and extensive livestock production) under best (above-average) ecological conditions
(see, for instance, the representation of agroforestry in Figure 9b,c, respectively). This is
less evident for fiber production as higher production levels are limited to fast-growing species
(Eucalyptus sp. and Pinus pinaster), overshadowing fiber production in Q. suber-/Q. rotundifolia-
dominated agroforestry systems. However, the bundle of ES supplied by the agroforestry
systems present in Alentejo has been widely documented in the literature [60–63].
Our outcomes show examples of how MAES could potentially assist landscape plan-
ning. For instance, our results point to planning interventions that could: (i) target and
help improve management practices in croplands, timber forests, and grasslands where
ES supply is high, but EC is not optimal, and (ii) target agroforestry areas in good EC and
which have the potential to supply many ES in the central part of Alentejo. Notwithstand-
ing, we point out that our achievements are specific to this case study and should not be
readily transposed to other research or policy contexts.
5. Conclusions
In this paper, we present an analytical framework based on LULC cartography to
assess ecosystem condition (EC) and ecosystem services (ES) as tested in the ptMAES study.
Innovative, multi-tiered information-processing approaches were used, which proved to be
Land 2021,10, 1040 25 of 28
effective and suitable for upscaling. Our findings leverage new approaches to the potential
integration of ES into landscape planning at the regional and national scales. We highlight
the usefulness of integrated approaches (e.g., conveying expert knowledge to spatial as-
sessments), as it can better depict spatial variability in the absence of data available at the
desirable scale. However, we identify relevant caveats in the proposed analytical frame-
work that should be addressed in the future for a more comprehensive MAES assessment:
(1) data availability (in terms of aggregation, scale, and coverage) limited the inclusion
of process-based modeling; (2) refinement of results could be achieved with the use of
information collected by the public administration if data protection issues are overcome;
(3) a wider range of EC indicators and ES should be considered (particularly cultural
services); and (5) the selection of EC indicators should better reflect their relationship to ES.
Supplementary Materials:
The following are available online at https://www.mdpi.com/article/10
.3390/land10101040/s1: Table S1: LULC CLASSES—AGREGATION CRITERIA; Table S2: FACTOR C
(USLE)—ADAPTATION FROM PIMENTA 1999; Table S3: CONVERSION FACTOR FOR BIOMASS
TO CARBON; Table S4: HARMONIZATION OF KYOTO PROTOCOL LAND-USE CLASSES WITH
COS; Table S5: “PURE“CROPS AND LULC; Table S6: RESULTS PER ECOSYSTEM TYPE/EUNIS
HABITAT; Table S7: SPATIAL OVERLAP REGULATING ES; Table S8: BIRD DIVERSITY MODELS.
Author Contributions:
L.L.: conceptualization, methodology, formal analysis, investigation, writing—
original draft, writing—review and editing; T.D.: conceptualization, methodology, writing—review
and editing, supervision; C.M.-P.: conceptualization, methodology, writing—review and editing,
supervision. All authors have read and agreed to the published version of the manuscript.
Funding:
This work has been conducted under the financial support of the National Authority
for Biodiversity and Forest Conservation (ICNF) under contract AD 288/2014/ICNF/SEDE and
of the Portuguese Foundation for Science and Technology (FCT) through FCT/MCTES (PIDDAC)
project UIDB/EEA/50009/2020. The work of L.L. was supported by FCT through the PhD grant
SFRH/BD/94195/2013.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
Publicly available datasets were analyzed in this study. This data can be
found here: https://www.ine.pt/xportal/xmain?xpgid=ine_main&xpid=INE;https://apambiente.
pt/sites/default/files/_Clima/Inventarios/NIR20210415.pdf;https://ebird.org/portugal/home;
http://www2.icnf.pt/portal/florestas/dfci/inc/mapa (accessed on 15 September 2021).
Acknowledgments:
The authors would like to thank the extended ptMAES working team who
greatly contributed to the production of the original data used in this study: Sandra Mesquita, Jorge
Capelo, Ivo Gama, Miguel Alves, Vânia Proença, Paulo Canaveira and especially Marco Reis, who,
although no longer with us, continues to inspire by his example and dedication to the people he
worked with over the course of his fruitful career.
Conflicts of Interest: The authors declare that they have no conflict of interest.
Notes
1
The 2020 EU Biodiversity Strategy (COM 2011) was built around six mutually supportive and inter-dependent targets that
addressed the main drivers of biodiversity loss. They aimed to reduce key pressures on nature and ecosystem services in the
EU by setting up efforts to fully implement existing EU nature legislation, anchoring biodiversity objectives into key sectoral
policies, and closing important policy gaps. Each target was accompanied by a set of focused, time-bound actions to ensure these
ambitions are fully realized.
2
The goal of Target 2 of the EU Biodiversity Strategy 2020 is to “maintain and restore ecosystems and their services”, with Action
5 set out to “improve knowledge of ecosystems and their services in the EU”.
3NUTS II refers to the second level of the Nomenclature of Territorial Units for Statistics (NUTS) that is used in Portugal.
Land 2021,10, 1040 26 of 28
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