One Ecosystem :
Research Article
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Corresponding author: Eszter Tanács (tanacs.eszter@ecolres.hu)
Academic editor: Paula Rendon
Received: 01 Feb 2022 | Accepted: 04 Apr 2022 | Published: 05 May 2022
© 2022 Eszter Tanács, Ákos Bede-Fazekas, Anikó Csecserits, Lívia Kisné Fodor, László Pásztor, Imelda Somodi, Tibor Standovár, András Zlinszky, Zita Zsembery, Ágnes Vári
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:
Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
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The availability of robust and reliable spatial information on ecosystem condition is of increasing importance in informing conservation policy. Recent policy requirements have sparked a renewed interest in conceptual questions related to ecosystem condition and practical aspects like indicator selection, resulting in the emergence of conceptual frameworks, such as the System of Environmental-Economic Accounting - Ecosystem Accounting (SEEA-EA) and its Ecosystem Condition Typology (ECT). However, while such frameworks are essential to ensure that condition assessments are comprehensive and comparable, large-scale practical implementation often poses challenges that need to be tackled within stringent time and cost frames.
We present methods and experiences of the national-level mapping and assessment of ecosystem condition in Hungary. The assessments covered the whole country, including all major ecosystem types present. The methodology constitutes four approaches of quantifying and mapping condition, based on different interpretations of naturalness and hemeroby, complemented by two more using properties that ‘overarch’ ecosystem types, such as soil and landscape attributes. In order to highlight their strengths and drawbacks, as well as to help reconcile aspects of conceptual relevance with practical limitations, we retrospectively evaluated the six mapping approaches (and the resulting indicators) against the indicator selection criteria suggested in the SEEA-EA. The results show that the various approaches have different strengths and weaknesses and, thus, their joint application has a higher potential to address the specific challenges related to large-scale ecosystem condition mapping.
ecosystem condition, ecosystem condition mapping, national-scale mapping, naturalness, hemeroby
The availability of robust and reliable spatial information on ecosystem condition is important in informing conservation policy (
Many attempts have been made to clarify and formulate definitions of ecosystem condition. Two interpretations prevail (
The determination of naturalness, as well as that of ecosystem condition, is often based on biodiversity indicators (
Given the number of related concepts, approaches and indicators, the issue of indicator selection has been on the table for a long time (
In Hungary, a countrywide ecosystem condition assessment took place within the national Mapping and Assessment of Ecosystems and their Services (MAES-HU) between 2016 and 2021. It was conducted in two distinct parts, reflecting the two main interpretations of condition assessments. ‘Service-specific’ condition indicators, which directly determine ecosystem service supply, were selected and assessed by groups of experts for each ES (
The different types of ecosystem condition indicators as used in MAES-HU (based on the graphic by
We present a selection of the most relevant MAES-HU methods and results. Further documentation is available on the project website (
As a first step, we conducted an indicator selection in order to find appropriate indicators to describe the condition of the major ecosystems of Hungary. The following expectations, set up at the beginning of the project, defined the choice of methods and the indicator selection for the MAES-HU condition mapping:
it should be spatially explicit and cover the whole area of Hungary or as much of it as possible;
it should be based on existing, regularly updated databases - the use of one-off datasets should be avoided;
data type and quality should be consistent across the mapped area;
it should comply with the recommendations of the MAES group (e.g.
Indicators were selected and developed in an iterative process. Initial lists, based on available guidelines (
Three indicators of ecosystem condition were specifically pre-defined by the project targets: soil fertility, landscape diversity and naturalness/hemeroby. Given the complexity of the term ‘naturalness’, we used various approaches to describe it, highlighting different, but complementary aspects of condition: a direct biodiversity-based approach, one using the anthropogenic transformation of the vegetation and two more using direct or indirect composite indicators specifically developed for each ecosystem type (ET). These were complemented with two more, based on soil and landscape characteristics, which are relevant across all ecosystem types. Thus 'approach' is used here as an umbrella term for the different ways of describing ecosystem condition. Table
Different approaches to mapping ecosystem condition in the MAES-HU project.
Approach to map ecosystem condition | Examples of indicators |
Based on soil characteristics | Soil fertility |
Based on the anthropogenic transformation of vegetation * |
Departure of the actual vegetation from the potential natural vegetation |
Based on direct indicators of biodiversity * |
The ratio of the number of bird species present relative to the expected number (based on species list specific for ecosystem types) |
Ecosystem-specific evaluation - based on composite indicators (direct) * |
Composite indicator of forest condition, based on structural and compositional indices |
Ecosystem-specific evaluation - based on composite indicators (indirect) * |
Composite indicator of wetlands, based on proxy pressure indicators |
Based on landscape-level indicators | Shannon Diversity of ecosystem types within a 1-km radius |
MAES-HU did not allow primary data collection; all maps and assessments had to be based on existing national and international databases. Suppl. material
The mapping methods are described separately according to the six approaches presented in Table
Only one indicator was chosen within this approach, soil fertility. In order to describe it, we used an already existing national soil fertility map, expressing an overall fertility by scoring units of genetic soil classification (
This analysis was carried out only for two major ecosystem types, grasslands and wetlands. As the vegetation category system (Á-NÉR -
As a first step, a reference list of bird species was defined for the major ecosystem types, including those species whose presence is presumed in an area considered to be in good condition. In the next step, we differentiated between species, based on what type of observation should be included. To this end, we selected the nesting probability codes under which the species was considered present (Suppl. material
A biodiversity-based approach was applied also for water bodies, developed within the Water Framework Directive (WFD;
An important approach to represent ecosystem condition was to design ecosystem-specific composite indicators by combining the relevant individual indicators previously chosen in the selection process (see Suppl. material
In the first case, threshold limits were set for each relevant variable, based on the recommendations of experts and/or the relevant literature and scores were defined for each resulting category. By determining the scores, each variable was also weighted. The scores were then summed and the result was simplified to a 5-level ordinal scale, based on expert knowledge, considering the distribution and, in some cases, the quantiles of the summed scores. The different ecosystem types are, thus, not directly comparable with each other. In the second case (grasslands), a Classification and Regression Tree method (CART) was used. The naturalness maps described in Suppl. material
The applied methods also differ in terms of indicator type. Structural and compositional indicators were included where possible (forests). Where data were scarce, landscape characteristics and pressure-related variables (e.g. distance to roads) were used as proxies.
For forests, where detailed sectoral data were available, we developed a composite indicator including both structural and compositional components (Suppl. material
For wetlands, grasslands and arable lands, where data were particularly scarce, the ecosystem-specific composite indicator was based mainly on proxies (landscape and pressure indicators, most derived from the ecosystem type map and a few from other databases like the OpenStreetMap (OSM) or the Copernicus HRL layer Water and Wetness Probability Index (WWPI) (
Urban areas were characterised with simple indicators describing the proportion of green surfaces (see Suppl. material
Landscape-level indicators were calculated using the Ecosystem type map of Hungary, usually for a circle of 1000 m radius. For the indicators measuring change in ecosystem extent over time, the Corine Land Cover database (2000-2018) (
In order to provide some measure of quality for our result maps, we compared them to habitat maps, where each patch was assigned a naturalness score (modified Németh-Seregélyes /mNS/ naturalness) during field surveys by conservation experts (
Spearman’s rho correlations were calculated for the final result of the biodiversity-based approach and
in order to see to what extent these variables affect the results.
The six mapping approaches were evaluated against the indicator selection criteria of the SEEA-EA framework (
Altogether 52 indicators were selected and mapped in order to describe the condition of the major ecosystem types present in Hungary, using the six approaches described under 'Indicator selection'. Suppl. material
Summary of the main advantages and limitations of the different approaches to map ecosystem condition in MAES-HU.
Approach | Advantage | Limitation |
Based on soil characteristics |
- horizontal indicators for terrestrial ecosystems - directly relevant for many ES (→ instrumental relevance) |
- resource-intensive data acquisition → wall-to-wall maps based on models (higher uncertainty) → no or less frequent updates |
Based on (direct) biodiversity indicators |
- sensitive to subtle change - easy to interpret - close to the current, well-established practice of conservation - consistent method and reference state across ecosystem types |
- resource-intensive data acquisition - precise choice of taxa and indicators strongly affect the result - difficult to define a reference state - sampling bias issues |
Based on the anthropogenic transformation of the vegetation |
- can be used as direct input to conservation planning - may be useful in defining the reference state |
- PNV not necessarily available at the national level - PNV may differ in (thematic or spatial) resolution from the ecosystem type map - in some cases, PNV is used in ecosystem type mapping to fill in data gaps - nearly impossible to verify the result |
Ecosystem-specific evaluation - based on composite indicators (direct) |
- easier to measure than biodiversity - most components available from already existing (sectoral) databases at the national scale (→ ensured repeatability) |
- all relevant characteristics should be included (in order to define these, a framework like the SEEA-EA ECT can be used) - but existing (sectoral) databases may not hold all necessary information - may yield different results to the biodiversity-based approach → harder to communicate to conservation practice |
Ecosystem-specific evaluation - based on composite indicators (indirect) |
- better data availability - relatively easy to map at a large scale |
- results are rather risk maps, only indirectly reflect condition - may be less sensitive to slow, subtle changes |
Based on landscape-level indicators | - easy to map at a large scale (only requires ecosystem type map) |
- interpretation in terms of ecosystem condition is not evident - not sensitive to slow, subtle changes |
Example maps for each of the approaches applied to map ecosystem condition in MAES-HU: (a) map of soil fertility (approach based on soil characteristics); (b) percentage of bird species present compared to the expected (biodiversity-based approach); (c) forest condition map with simplified scores (ecosystem-specific approach using direct indicators); (d) proportion of semi-natural areas (approach based on landscape-level indicators); (e) wetland condition map with simplified scores (ecosystem-specific approach using indirect indicators); (f) departure of the potential from the actual vegetation (approach based on the anthropogenic transformation of the vegetation).
Fig.
Comparing the departure of the actual from the potential vegetation to field naturalness maps, we also found that areas where there is no or only a small difference are more likely to have higher mNS naturalness values (signifying better condition) (see Suppl. material
The results, based on bird observation data, showed a significant correlation with both the extent of certain ecosystem types (agricultural land: r=-0.187, grasslands and wetlands both: r=0.23) and sampling effort (r=0.537). Only ~40% of all the squares could be evaluated.
Table
Evaluation of the mapping approaches applied in MAES-HU against the indicator selection criteria of the SEEA-EA framework (
SEEA EA - Criterion | Short description | MAES-HU mapping approach | |||||
Based on soil characteristics | Based on (direct) indicators of biodiversity | Based on the anthropogenic transformation of the vegetation | Based on ecosystem-specific composite indicators | Based on landscape level indicators | |||
Direct | Indirect | ||||||
Soil fertility | Ratio of the number of bird species present relative to the expected number | Departure of the actual vegetation from the potential natural vegetation | Composite indicator of forest condition, based on structural and compositional indices | For example, composite indicator of wetlands, based on proxy pressure indicators | For example, Shannon diversity of ecosystem types within a 1-km radius | ||
Conceptual criteria | |||||||
Intrinsic relevance | Reflective of existing scientific understanding of ecosystem integrity, supported by the ecological literature | ++ | +++ | +++ | +++ | ++ | ++ |
Instrumental relevance | Have the potential to be related to the availability of ecosystem services | +++ | + | ++ | ++ | + | ++ |
Sensitivity to human influence | Responsive to known socio-ecological leverage points (key pressures, management options) | + | +++ | ++ | +++ | ++ | ++ |
Directional meaning | It should be clear if a change is favourable or unfavourable | +++ | +++ | +++ | +++ | ++ | + |
Framework conformity | Differentiated from other components of the SEEA ecosystem accounting framework | + | +++ | +++ | +++ | + | ++ |
Practical criteria | |||||||
Validity | Metrics need to represent the characteristics they address in a credible and unbiased way | ++ | + | ++ | ++ | +++ | +++ |
Reliability | Scientifically valid representation of the characteristics they address | ++ | +++ | ++ | +++ | ++ | +++ |
Availability | Cover the studied spatial and temporal extents with the required resolution | ++ | + | ++ | ++ | +++ | +++ |
Simplicity | As simple as possible | +++ | ++ | ++ | ++ | +++ | +++ |
Compatibility | The same characteristics should be measured with the same (compatible) metrics in the different ecosystem types and/or different areas | +++ | +++ | +++ | + | ++ | - |
Ensemble criteria | |||||||
Comprehensiveness | The final set of metrics should cover all the relevant characteristics of the ecosystem | +++ | + | + | ++ | ++ | ++ |
Parsimony | The final set of metrics should be free of redundant (correlated) variables | +++ | +++ | +++ | ++ | ++ | +++ |
The soil fertility map, while having the huge advantage of being readily available, scored low on sensitivity to human influence as it is based on a one-off map. The (biodiversity) assessment, based on bird observations, obtained a low score on more than one criteria. Only one group could be examined (comprehensiveness) and the spatial resolution of the data is rather coarse (spatio-temporal reference). The results do not only reflect ecosystem condition, but also ecosystem extent and sampling effort (validity). On the other hand, it has the advantage of being sensitive to change and using the same methodology across ecosystem types (compatibility). Direct (compositional and structural) condition indicators were only available for forests, an ecosystem-specific approach that performed well against several criteria. Since most of these indicators are only relevant in forests, compatibility is not relevant. Some important aspects of forest condition could not be covered with the available database, which affects both comprehensiveness and validity. The approach using indirect (pressure) proxies for data-deficient ecosystem types scored generally high on practical criteria and simplicity, but lower on the conceptual criteria. Finally, landscape indicators are a challenge in terms of directionality, but, since they are calculated from the ecosystem type map, they perform well in all practical criteria.
We developed a set of approaches and indicators for Hungary to quantify and map ecosystem condition at the national level, based on the different interpretations of the related concepts of naturalness and hemeroby. These are complementary, grasping different aspects of condition, covering all broad ecosystem types within Hungary and nearly the entire area. Indicators relevant across all ecosystem types, such as the characteristics of soil and landscape, were added to those targeting specific ecosystem types.
In recent decades, as an answer to the unfolding biodiversity crisis, a multitude of indicators has been designed and published to describe and monitor biodiversity (related to ecosystem integrity/condition) for different scales and ecosystem types (see
In MAES-HU, the available options were overviewed and considered at the indicator selection phase (in 2017). As detailed, high-quality, spatially explicit data were needed for the whole area of Hungary, many otherwise relevant indicators had to be discarded due to data availability (e.g. deadwood) or quality (e.g. grazing/mowing activities in grasslands) issues. Others, especially indicators based on remote sensing data (e.g. grassland management intensity), would have needed a development of new methods or the adaptation of existing methods to the national scale, which was not possible due to time or resource constraints. However, the indicators that were considered important, but needed to be omitted due to such constraints, were highlighted in the project reports (
In order to describe the anthropogenic transformation of the vegetation, the departure of the actual from the potential natural vegetation was calculated in an experimental manner, for two major ecosystem types (grasslands and wetlands) (see Suppl. material
As potential natural vegetation models are based on the site requirements of vegetation, a PNV is probably the best approximation for ‘natural state’ and, thus, can have strong implications concerning the sustainability of the present land use (
Whereas the condition map based on bird observations clearly outlines some of the most valuable areas of nature conservation in Hungary, the results reflect a mixed effect of ecosystem extent and condition. Sampling effort (expressed with the duration of observation) also strongly defines the patterns. Unfortunately both condition assessments, based on biodiversity indices (for terrestrial ecosystems and water bodies), display significant data gaps.
Biodiversity-based indicators are amongst the most favoured ones to assess ecosystem condition. They represent a plurality of the values of nature and underpin several ecosystem functions (
As the database we used is mostly derived from a planned survey carried out by volunteers, the strong effect of sampling effort is probably related to factors, such as volunteer density and site popularity, which depend on both site quality and accessibility (
The large-scale patterns of forest condition, identifiable on the forest condition map developed in MAES-HU (Suppl. material
In order to validate the expert model developed for wetlands in MAES-HU, we compared the results with field naturalness maps. We found that there is a similar overall tendency, but there are some areas where the results significantly diverge. As the mNS naturalness index strongly relies on indicator species, extinction debt (
The use of easy-to-map pressure proxies is ambiguous. They may not be sensitive to slow, subtle degradation (loss of species or homogenisation of forest structure), only to fast, dramatic changes like habitat fragmentation. According to some preliminary feedback from conservation experts, the divergence of pressure-based condition maps from their perception of the value of a certain area may negatively influence their views and acceptance of large-scale ecosystem condition maps. The recent development of user-friendly GIS tools promotes an increasing use of spatial data by end-users who may not be aware of the underlying concepts, which could lead to misinterpretations (
On the other hand, detailed data collection, suitable to support a ‘direct’ approach to describe ecosystem condition, rarely has nationwide coverage, but usually focuses on protected areas. Yet, dominantly agricultural land, occupying much of the landscape, has its own role in preserving biodiversity (
With regard to the above, the use of pressure proxies must be handled with care and it is especially important to effectively communicate their specific nature to potential users. On the other hand, maps created on the basis of pressure indicators can be used as risk maps and, thus, provide an opportunity for early intervention.
In MAES-HU, we used several landscape-level metrics (covering indicators irrelevant at the habitat patch level), which, in the SEEA-ECT, are either listed as landscape characteristics, as the ratio of embedded subtypes or as pressure (see Suppl. material
Landscape patterns partly define and partly reflect ecological processes, thus being related to biodiversity (
In the MAES-HU ecosystem condition assessment, reference levels were, in most cases, defined as threshold values for individual variables, based on scientific literature and expert knowledge. In the case of the ecosystem-specific assessments, we used an additive method for the aggregation of variables. The aggregated scores had to be simplified, in part to ensure some measure of comparability, but mostly for easier communication to stakeholders. In order to create these simplified scores, further thresholds were needed, qualifying condition based on all characteristics, including their interactions, for which there is a general lack of empirical evidence (
However, all methods used for defining reference levels have weaknesses (
We avoided combining the results of the ecosystem-specific mapping into one map, partly to avoid strengthening the above-mentioned false sense of consistency and partly to avoid misunderstandings about the actual values of different ecosystem types. Arable lands were assigned five classes, the same as forests; however, the term ‘most favourable condition’ has a different meaning for the two.
The validation of the result maps is a specific challenge. The dataset available for validation is spatially biased, as it mostly covers protected areas. The mNS naturalness we used is itself a composite indicator (
The assessment was intended as a first attempt, as well as a baseline, using methods and data to ensure future repeatability. Therefore, the results are suitable for studying spatial patterns and relationships between different descriptors and aspects of ecosystem condition, but studying temporal change will need a repeated assessment.
We evaluated and compared the six different condition mapping approaches applied in MAES-HU using the indicator selection criteria introduced within the SEEA-EA framework (
The aims of MAES-HU included the assessment of ecosystem services as well as ecosystem condition, with the different focuses requiring different considerations and different sets of indicators. Therefore, we differentiated between ‘service-specific’ condition indicators and ‘general’ condition indicators. This separation corresponds to the two main strands of ecosystem integrity concepts identified earlier (
While there is an evident need to optimise resources, no single set of indicators is suitable for all purposes (
Relying on available (and regularly updated) data is time and cost-efficient (
We presented the results of a first wall-to-wall mapping and assessment of ecosystem condition in Hungary. The methods and maps will be further developed in the future, but useful conclusions can be already drawn. A realistic picture of ecosystem condition can only be obtained with the help of data collection that is continuous over time, methodologically well-founded and of sufficient scope, but such spatial databases are not necessarily available. Since a regular wall-to-wall field mapping of ecosystem condition is unlikely, national condition assessments need to be based mainly on existing databases, which will always have shortcomings either in terms of spatial extent, resolution, data quality or data content. However, using complementary approaches with different strengths and weaknesses mitigates the effects of the resulting uncertainty. Comparing the results from direct and indirect approaches allows for a better understanding of the relationship between human pressures and their effects on ecosystem condition, which, in turn, increases our ability to estimate condition in data-scarce regions. The use of multiple approaches also allows for a flexible use of the condition indicators, enabling a change of emphasis on the examined aspects. It helps to satisfy the information needs of both ‘traditional’ conservation and ecosystem accounting. The constant development of new methods, for example, based on remote sensing or citizen science, opens up ever new possibilities. However, the most important data gaps need to be addressed by targeted data collection. Lack of data cannot be a reason to completely ignore any important aspect of ecosystem condition in the long term, especially if the aim is to use the results of these assessments in natural capital accounting systems.
We would like to express our thanks to everyone who contributed to this work, especially to András Schmidt, Gergő Gábor Nagy and Mihály Nyúl, who helped to compile the bird species reference lists and to Márta Belényesi, Róbert Lehoczki, Róbert Pataki and Ottó Petrik, who strongly supported the data acquisition and preparation work.
The assessment was carried out in the framework of the EU co-financed project ‘Strategic Assessments supporting the long term conservation of natural values of community interest as well as the national implementation of the EU Biodiversity Strategy to 2020’ (KEHOP-4.3.0-VEKOP-15-2016-00001). The programme is financed as part of the Széchenyi 2020 Development Program and implemented within the framework of the Environmental and Energy Efficiency Operational Programme. The writing process was supported by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the Hungarian Scientific Research Fund (projects OTKA/134329 and OTKA/135252).
List and short description of the datasets used in the MAES-HU EC mapping.
Crosswalk between the categories of the Ecosystem type map of Hungary and the national vegetation classification system (Á-NÉR).
The list of bird species used for the MAES-HU biodiversity approach, the nesting probability codes and weights.
The final set of indicators developed for the mapping and assessment of ecosystem condition in the Hungarian MAES for the major ecosysytem types (ET), along with their SEEA-ECT condition type.
Summary of the condition assessment of forests in MAES-HU (indicators, scores and threshold values).
Summary of the condition assessment of wetlands in MAES-HU (indicators, scores and threshold values).
Summary of the condition assessment of arable lands in MAES-HU (indicators, scores and threshold values).
Further results from the MAES-HU EC assessments - Anthropogenic transformation of the vegetation.
Further results of the MAES-HU forest condition assessment.
approaches based on different interpretations of naturalness/hemeroby