One Ecosystem :
Research Article
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Corresponding author: Stoyan Nedkov (snedkov@abv.bg)
Academic editor: Ignacio Palomo
Received: 12 Aug 2022 | Accepted: 14 Nov 2022 | Published: 21 Nov 2022
© 2022 Stoyan Nedkov, Mariyana Nikolova, Hristina Prodanova, Vanya Stoycheva, Desislava Hristova, Eugenia Sarafova
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Nedkov S, Nikolova M, Prodanova H, Stoycheva V, Hristova D, Sarafova E (2022) A multi-tiered approach to map and assess the natural heritage potential to provide ecosystem services at a national level. One Ecosystem 7: e91580. https://doi.org/10.3897/oneeco.7.e91580
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Natural heritage (NH) possesses an outstanding universal value that can be described as “natural significance” at a national level. The ecosystems can be considered as the spatial units which represent the NH of the particular area in terms of their value to people. Recreation and tourism are amongst the important values which are strongly dependent on the NH and they have a certain impact on the ecosystems' condition and the quality of the services they provide. The efforts through the Mapping and Assessment of Ecosystems and thier Services (MAES) process led to the development of a multi-tiered approach that considers different methods at different levels of detail and complexity and can be applied according to specific needs, data and resource availability. In this paper, we propose the development of this methodology for the specific need for mapping and assessment of the NH as a source of ecosystem services (ES) for recreation and tourism. The conceptual scheme of the study demonstrates how the MAES framework can be adapted to the specific needs of the work and arrange the methods into three tiers according to the data availability and resources. The mapping and assessment procedure is based on an algorithm for spatial data analyses which enables the evaluation of the NH potential to provide 15 ecosystem services. The results show that the NH of Bulgaria is a valuable source of ES which are well presented in most parts of the country. The areas with very high potential form several clusters that correspond to the country's tourist regions. The proposed approach is applicable on the national scale and solves the problem of data availability limitations for various ES. The algorithm ensures the optimal quality of the results using the available data and resources. Instead of an expert-based assessment for all services which is easier, but less accurate, the proposed approach provides the means how to define more precise indicators, based on statistical data or models where possible. The study provides appropriate data for analyses of the methods’ performance at different tiers.
ES indicators, recreation, tourism, modelling, spatial proxy, InVEST, ESTIMAP
Natural heritage (NH) refers to the elements of biodiversity, including flora and fauna, ecosystems and geological structures which are an important part of each country's natural resources. According to the World Heritage Convention (
The efforts to solve such problems have the potential to deliver sustainable benefits to people. However, regions for which conservation benefits both biodiversity (including NH) and ES cannot be identified unless ES can be quantified and valued and their areas of production mapped (
The multi-tiered approach covers a variety of ES aspects, as well as a wide range of possible applications. Therefore, this approach should be tested for different objectives and in different case studies to be validated and further developed into a comprehensive ES mapping and assessment methodology. Every ecosystem assessment has to be relevant to a certain theme and address a broad range of questions pertaining to decision-making processes that occur at different levels of decision-making and across different actors in society (
The ES matrix approach was proposed in a series of papers (
Recent studies in Bulgaria presented the NH as a spatial phenomenon conceptualised by the flows of benefits from ecosystems to people, contributing to human well-being (
In this paper, we propose a multi-tiered approach for mapping and assessment of ES, based on the MAES framework which is focused on the services provided by the NH at a national level. The main aim of the paper is to provide a deep insight into the whole process from the selection of ES through the indicators' quantification by using particular datasets and the estimation of the final scores for ES assessment. More specifically, we aim at: i) revealing what is the ES provided by NH and what is their potential to support recreation and tourism; ii) demonstrating which methods and indicators are used and how they are utilised in the ES assessment framework; iii) explaining the process of ES indicators quantification at different tiers; iv) analysing the ES potential and data quality at different tiers.
The MAES methodological framework provides typology for ecosystems, a set of indicators for the assessment of ecosystem condition and mapping of ES (
Study area and initial data
The multi-tiered approach, developed in this work, is designed for application at the national level. Therefore, the whole area of Bulgaria is selected as a case study. Due to the diverse climatic, geological, topographic and hydrological conditions, Bulgaria is amongst the richest countries in Europe in terms of biodiversity and geodiversity. The country accounts for about 2.5% of the total EU area, but in terms of species present on the territory, it hosts 26% of all European species, 70% of the protected bird species under the EU Birds Directive and 40% of the conservation habitats types (under Annex I,
Case study area. Tourist regions: D - Dunav (The Danube); DR - Dolina na Rozite (Valley of the Roses); RP - Rila and Pirin; R - Rodopi (The Rhodopes); SC - Severno Chernomorie (North Black Sea coast); S - Sofia; SP - Stara Planina (The Balkan); T - Trakia (Thrace); YC - Yuzhno Chernomorie (South Black Sea coast).
MAES implementation needs spatially-explicit datasets to address the key drivers, pressures and their different gradients and variations in space and time. Each ES is assessed by specific indicators which have to be supported with appropriate spatial data available at the national level corresponding to the whole territory of the country. The activities under MAES in Bulgaria led to the development of several datasets, but their use at the national level at this stage is hampered by two main problems. Firstly, the data for the nine ecosystem types are in separate datasets which do not fit topologically correctly if they are merged in a single GIS layer. Secondly, the mapping does not cover the Natura 2000 areas which is a significant gap that makes these data inappropriate for national scale mapping. Only for some services, which are assessed using municipality-based initial information, there are appropriate data that can be applied at the national level. For instance, the quantification of education and science service is based on a number of papers calculated per municipality (
The lack of full coverage at the national scale data of some ES can be overcome using models and modelling approaches. For instance, the ESTIMAP model for recreation uses easily available data on land cover, protected areas, water bodies, transport network and topography (
Data sources used for quantification and mapping of ES (for the numbers of ES, see Table
Data type |
Dataset |
Used in ES assessment |
Used for method |
Source |
Land cover |
CLC 2018 |
I, II, V, VIII, X, XI, XII, XIII, XV |
E.A. |
Copernicus dataset |
Rivers |
JICA dataset |
IV |
Sp. Pr. |
The study on integrated water management in the Republic of Bulgaria – MOEW by Japan International Cooperation Agency (JICA) |
Mineral water |
Mineral water |
IV |
Sp. Pr. |
NIGGG digital archive |
Ground water |
JICA dataset |
IV |
Sp. Pr. |
The study on integrated water management in the Republic of Bulgaria – MOEW by Japan International Cooperation Agency (JICA) |
Number of reared animals |
Registry of domestic animals in BG |
III |
Stat. |
Ministry of agriculture and forests |
DEM 50m |
JICA dataset |
VI |
Sp. Pr. |
The study on integrated water management in the Republic of Bulgaria – MOEW by Japan International Cooperation Agency (JICA) |
Soil data |
Soil data archive |
VI |
Sp. Pr. |
Ministry of Agriculture and Forests |
Local climate zones |
World Urban Database and (WUDAPT) |
VIII |
LCZ model |
|
Nationally designated areas (CDDA) |
CDDA (ArcGIS geodatabase file) |
IX |
ESTIMAP |
|
Bathing water quality (European Environment Agency - EEA) |
Bathing Water Directive - Status 1990 - 2018 |
IX |
ESTIMAP |
|
Urban areas in Republic of Bulgaria |
JICA dataset |
XIV |
ESTIMAP |
The study on integrated water management in the Republic of Bulgaria – MOEW by Japan International Cooperation Agency (JICA) |
The road network in Republic of Bulgaria |
JICA dataset |
XIV |
ESTIMAP |
The study on integrated water management in the Republic of Bulgaria – MOEW by Japan International Cooperation Agency (JICA) |
The multi-tiered approach for mapping and assessment of ES provided by the NH at the national level is based on the MAES framework (
The prioritisation of ES provided by the NH aims to identify the ES and rank them according to their significance for recreation and tourism. It is based on the application of the ES prioritisation matrix (ESPM) (Suppl. material
The approach consists of three tiers and both the level of detail of input data and the complexity of the analysis (i.e. methods) increase from tier 1 to tier 3 (
Indicators and methods at different Tiers. E.A. – expert assessment; Ec. – ecosystem subtype; Stat. – analysis of statistical data; Mun. – municipality; Sp. Pr. – spatial proxy model; Var. – various spatial units.
№ |
Ecosystem Services |
n Indicators |
Tier 1 |
Tier 2 |
Tier 3 |
|||
Method |
Sp.unit |
Method |
Sp.unit |
Method |
Sp.unit |
|||
I |
Cultivated plants and animals used for nutrition |
1 |
E.A. |
Ec. |
||||
II |
Wild plants used for nutrition |
1 |
E.A. |
Ec. |
||||
III |
Animals reared to provide energy |
1 |
Stat. |
Mun. |
||||
IV |
Water for drinking |
3 |
Sp. Pr. |
Var. |
||||
V |
Regulation of pollution and other harmful impacts |
1 |
E.A. |
Ec. |
||||
VI |
Regulation of natural hazards |
1 |
Sp. Pr. |
Var. |
||||
VII |
Maintaining populations and habitats |
2 |
Sp. Pr. |
Var. |
||||
VIII |
Local climate regulation |
1 |
E.A. |
Ec. |
LCZ model |
Var. |
||
IX |
Conditions for recreation by biotic systems |
2 |
ESTIMAP |
Var. |
||||
X |
Science and education value |
2 |
E.A. |
Ec. |
Stat. |
Mun. |
||
XI |
Cultural heritage |
1 |
E.A. |
Ec. |
||||
XII |
Aesthetic experiences |
2 |
E.A. |
Ec. |
InVEST |
|||
XIII |
Symbolic and spiritual value by biotic systems |
1 |
E.A. |
Ec. |
||||
XIV |
Conditions for recreation by abiotic systems |
2 |
ESTIMAP |
Var. |
||||
XV |
Symbolic and spiritual value by abiotic systems |
1 |
E.A. |
Ec. |
ES indicators' quantification at tier 1
The indicators at tier 1 compensate for the lack of uniform data at the national level in Bulgaria. They are derived from ecosystems' spatial database and expert judgement. An expert-based assessment was applied for mapping the potential of NH to supply ES for recreation and tourism and the mapping was performed through a widely-used matrix approach. Twelve experts participated in the expert-based assessment by filling individual matrices for the potential of the NH to provide ES (
ES indicators' quantification at tier 2
The indicators at tier 2 relied on statistical data or biophysical parameters used to derive more complex indicators that were combined to estimate ES at the national level using GIS spatial analyses. Two services, animals reared to provide energy and science and education value, were quantified in this way. The information for both services is aggregated at a municipality level and integrated into the spatial dataset using GIS techniques. The indicator for animals reared to provide energy is the number of equines per municipality and the data are provided by the Ministry of Agriculture and Forestry. The indicators for science and education values are the number of publications (for science value) and the number of centuries-old trees (for education value), both of them calculated at the municipality level (
ES indicators' quantification at tier 3
According to the methodological framework, the indicators at tier 3 are selected for more detailed analyses by modelling biophysical processes (
The water for drinking purposes ES integrates two CICES 5.1 classes: surface water for drinking purposes and groundwater for drinking purposes. The quantification is based on data about water bodies (surface and groundwater) and water sources (mineral water springs) which were processed in GIS to generate spatial data layers. The spatial proxy model, in this case, includes spatial analyses of proximity and overlay arranged in a specific algorithm to generate the spatial distribution and calculate the potential of the NH elements to provide this service.
The regulation of natural hazards ES is quantified using the modelling approach developed for flood regulation (
The maintaining population and habitats ES is quantified using two indicators: the hemeroby index and protected areas. The hemeroby index is a proxy of the naturalness of the area. Hemeroby is used in ecological studies to express the degree of human influence on ecosystems, the higher degree representing more harmful human influence (
The local climate regulation ES is considered in CICES 5.1 as the regulation of temperature and humidity, including ventilation and transpiration which is performed by the mediation of ambient atmospheric conditions by virtue of the presence of plants that improves living condition for people. Here, we consider this ES following the understanding of
The condition for recreation in CICES 5.1 is split into two service classes according to the source of the service provision: condition for recreation by biotic systems and condition for recreation by abiotic systems. The ESTIMAP recreation model provides a framework for a spatially-explicit assessment of local outdoor recreation (
Modelling through InVEST provides a rapid way to value selected ES, such as aesthetic experiences. The InVEST module "Visitation: Recreation and Tourism" was applied in recent regional studies assessing the recreational-tourist potential in Bulgaria (
The 15 priority ES were assessed using different methods and spatial units, as well as a different number of indicators. First, the results from indicators' quantification for each ES were integrated into a single layer. All datasets were converted into 50 m raster layers to ensure the correct spatial overlay. Thus, 15 layers with 50 m resolution representing the priority ES were generated. However, the importance of the different ES for recreation and tourism is not equal. Therefore, the results from the prioritisation were used to define weighted indices that represent these differences. The values of the weighted indices are given in Table
№ |
Ecosystem Services |
Weighted index |
I | Cultivated plants and animals used for nutrition | 0.6 |
II | Wild plants used for nutrition | 0.7 |
III | Animals reared to provide energy | 0.6 |
IV | Water for drinking | 0.8 |
V | Regulation of pollution and other harmful impacts | 0.7 |
VI | Regulation of natural hazards | 0.6 |
VII | Maintaining populations and habitats | 0.8 |
VIII | Local climate regulation | 0.6 |
IX | Conditions for recreation by biotic systems | 1 |
X | Science and education value | 0.8 |
XI | Cultural heritage | 1 |
XII | Aesthetic experiences | 1 |
XIII | Symbolic and spiritual value by biotic systems | 1 |
XIV | Conditions for recreation by abiotic systems | 0.9 |
XV | Symbolic and spiritual value by abiotic systems | 1 |
The application of the multi-tiered approach enabled us to develop a GIS database containing layers for each of the 15 priority services (Suppl. material
The areas with high potential cover about 24% of the country (Table
ES score |
n/area/% |
Provisioning |
Regulating |
Cultural |
Overall |
0 |
n poly |
1104 |
2198 |
1561 |
4960 |
area km2 |
573 |
1242 |
1023 |
4552 |
|
% |
0.5 |
1.1 |
0.9 |
4 |
|
1 |
n poly |
12439 |
17273 |
6964 |
10948 |
area km2 |
24096 |
45147 |
24017 |
16594 |
|
% |
22 |
41 |
22 |
15 |
|
2 |
n poly |
4780 |
84767 |
14801 |
19755 |
area km2 |
77525 |
21638 |
29883 |
34687 |
|
% |
70 |
20 |
27 |
32 |
|
3 |
n poly |
6810 |
102418 |
12708 |
20025 |
area km2 |
8798 |
16281 |
10483 |
18174 |
|
% |
8 |
15 |
10 |
17 |
|
4 |
n poly |
0 |
15215 |
6682 |
13132 |
area km2 |
0 |
26501 |
32611 |
26070 |
|
% |
0 |
24 |
30 |
24 |
|
5 |
n poly |
0 |
1 |
2150 |
6606 |
area km2 |
0 |
0.01 |
11640 |
9578 |
|
% |
0 |
0.0 |
11 |
9 |
The maps of provisioning, regulating and cultural services visualise quite different patterns of ES potential throughout the country. The overall potential of the provisioning services (Fig.
The ES assessment of the NH for recreation and tourism enabled also the estimation of the ES potential per tourist region. We recalculated the overall ES scores for each tourist region estimating an average ES score as well as the distribution of the 0-5 scores. The average scores show quite similar results for all regions with figures ranging from 2.45 to 3.08 (Fig.
Potential of the tourist regions to provide ES. A - Mean potential of the tourist regions to provide ES, B - Distribution of the potential scores within the tourist regions by area. Tourist regions: D - Dunav (The Danube); DR - Dolina na Rozite (Valley of the Roses); RP - Rila and Pirin; R - Rodopi (The Rhodopes); SC - Severno Chernomorie (North Black Sea coast); S - Sofia; SP - Stara Planina (The Balkan); T - Trakia (Thrace); YC - Yuzhno Chernomorie (South Black Sea coast).
In contrast to the relatively uniform average scores, the distributions of the 0-5 scores amongst the regions show quite different patterns. Each region has specific distribution and only the first two (Rila and Pirin and the Rhodopes) show a similar pattern in the distribution diagram with high and very high potential covering more than half of the area, moderate and low potential covering the rest, while the areas with 0 and 1 score have limited extent. The Danube Region has predominantly low and very low potential as they cover more than 75% of the area. The North Black Sea coast has a similar pattern with a slightly higher share of the area with moderate potential. This is in contrast with the South Black Sea coast which has a significantly higher share of the areas with high and very high potential than the North Black Sea coast.
The results for the ES potential were obtained using various methods at three different tiers. From the methodological point of view, it is important to compare the results at different tiers. There are only two ES assessed by methods at tier 2 which is not enough for appropriate conclusions. Therefore, the analyses were made only for tier 1 and tier 3. The mapping results (in the form of GIS layers) were re-arranged into two groups corresponding to these tiers. The layers were processed to recalculate the ES potential derived from the method at different tiers. At tier 1, the scores for the nine ES from the expert assessment were recalculated to estimate mean values for each ES. Then, the mean values were normalised to the 0 to 5 assessment scale. At tier 3, there were seven layers produced by the different modelling methods. They were processed using the same procedure which was performed for the integrated layer of the overall ES potential. Thus, we had two resulting layers representing the results about ES potential, calculated using the methods at tiers 1 and 2. These scores could not be treated as another way to define the potential of the NH to provide ES. They are just for analysing the results at different tiers and to obtain data for discussion about their advantages and disadvantages from the methodological point of view. This enabled us to generate maps of the ES potential derived from methods at tier 1 and tier 3, as well as the differences between them (Fig.
ES potential and the differences at tier 1 and tier 3. Tourist regions: D - Dunav (The Danube); DR - Dolina na Rozite (Valley of the Roses); RP - Rila and Pirin; R - Rodopi (The Rhodopes); SC - Severno Chernomorie (North Black Sea coast); S - Sofia; SP - Stara Planina (The Balkan); T - Trakia (Thrace); YC - Yuzhno Chernomorie (South Black Sea coast).
The two maps of the ES potential show a similar pattern which correlates relatively well with the overall ES map presented in Fig.
ES score |
n/area/% |
Tier 1 |
Tier 3 |
0 |
n poly |
1639 |
13358 |
area km2 |
1255 |
4484 |
|
% |
1 |
4 |
|
1 |
n poly |
125 |
40012 |
area km2 |
134 |
32999 |
|
% |
0.1 |
29 |
|
2 |
n poly |
30615 |
72538 |
area km2 |
64415 |
23984 |
|
% |
58 |
21 |
|
3 |
n poly |
13017 |
88667 |
area km2 |
15762 |
15350 |
|
% |
14 |
13 |
|
4 |
n poly |
4091 |
53782 |
area km2 |
6448 |
23451 |
|
% |
6 |
21 |
|
5 |
n poly |
5428 |
16146 |
area km2 |
22974 |
10772 |
|
% |
20 |
9 |
The comparison between the results obtained by methods at tier 1 and tier 3 were analysed by overlay between the two layers. First, the scores at tier 3 were recalculated to negative values. Then, an overlay procedure by a simple adding operation between the two layers was applied. Thus, in the areas where the scores are equal, the resulting value would be 0, in the areas where the tier 1 score exceeds the tier 2 score, the result will be a positive value between 1 and 5 depending on the excess value, in the areas with a higher score for tier 3, the result would be a negative value with the same gradient. The result of this procedure was a new layer presenting the differences in the scores between tier 1 and tier 3 (Fig.
Data availability and accuracy of the resulting ES maps are amongst the most important issues in the application of the tiered approach (
Spatial data characteristics of the resulting layers at the different tiers.
Parameter |
Tier 1 |
Tier 2 |
Tier 3 |
n polygons |
54910 |
264 |
284503 |
min. polygon area |
0.01 |
44 |
0.006 |
max. polygon area |
5451.5 |
1365.7 |
2552.1 |
mean polygon area |
2.0 |
420.4 |
0.4 |
The results of the ES layers generated at tier 3 significant differences in the spatial resolution (Table
Parameter |
ES IV |
ES VI |
ES VII |
ES VIII |
ES IX |
ES XII |
n polygons |
15894 |
36070 |
57590 |
180348 |
50485 |
7601 |
min. polygon area |
0.002 |
0.0016 |
0.002 |
0.0001 |
0.0016 |
0.3 |
max. polygon area |
11958.1 |
15464.4 |
24688.1 |
22981.5 |
27791.2 |
97039.9 |
mean polygon area |
7.0 |
3.1 |
1.9 |
0.6 |
2.2 |
14.4 |
The assessment of multiple ES at the national level is a challenging task because it necessitates a variety of data that should be available for the whole country and the application of various methods that requires a large team of experts with different expertise. This is possible only for large and well-funded projects that are not easy to be achieved. Even the ES matrix (which is easy and not resource-intensive) is more often used at the local and regional than at the national level (
The results demonstrate that the NH of Bulgaria is a valuable resource that ensures the generation of various ES which are important for the development of tourism activities in the country. The areas with very high potential can be found throughout the country which proves the hypothesis behind tourism regionalisation which covers the whole country and distinguishes the regions depending on their specialisation. The clusters of very high potential correspond to six out of nine tourist regions. The Rhodopes Region contains two clusters and has also one of the highest overall ES potential scores. Stara Planina is the other region with two clusters, but its overall score is lower. The reason behind this difference could be explained by the more compact mountainous character of the Rhodopes Region and the high forest cover. Both mountain relief and forests cover stand out as the main factors for the high ES potential. Thus, the Stara Planina Region contains also some lowland areas with a higher anthropogenic impact which reduces the overall score of the region. Although the increase in the elevation tends to refer to an increase in the ES potential, the highest areas in Rila and Pirin are not assessed with the highest potential. In this case, the lack of forest in the alpine and subalpine areas is the factor for the decrease in the overall potential. This could be defined as one of the limitations of the approach that needs to be studied in more detail in the future. The application of some kind of a rapid assessment approach that exploits available datasets and triggers more detailed and disciplined specific studies on ecosystem condition indicators (
In this work, we develop and apply an approach for mapping and assessment of the NH as a source of ES for recreation and tourism. It is based on the multi-tiered approach proposed by
The integration of the ES matrix into the approach allows for the assessment of more ES, especially at tier 1, as it helps to overcome the limitations of data availability and the lack of proper proxies for quantification (
The multi-tiered approach was applied predominantly at different levels of scales. The most representative example is provided by
The spatial data resolution at the different tiers can be used as an indicator of the data quality and consequently of the accuracy of the results. The spatial resolution of the tier 2 data is quite low due to the specifics of the data which is available at the municipality level. The multi-tiered approach could be further developed by considering the specifics of the ecosystem types, especially the necessity of finer-scale mapping of urban and freshwater ecosystems. As
The multi-tiered approach for ES mapping and assessment developed to facilitate the MAES process in the EU countries considers different levels of details and complexity and can be applied according to specific needs, data and resources availability (
This research was funded by the BG05M2OP001-1.001-0001 Project “Creation and development of “Heritage BG” Centre of Excellence”, Operational Programme Science and Education for Smart Growth, Priority axis 1, Procedure BG05M2OP001-1.001, Component 4 “New technologies in creative and recreation industries”.