Extent accounts, developed using CORINE data (Table 2), show that the Dargle comprises 24 CLC Level 3 classes. Aggregating the CORINE data shows the full extent of each high-level ecosystem type (Freshwater etc.) and highlights the main trends over the period 2000-2018.

In terms of general trends, the data show that, between 2000 and 2018, there were overall declines in cover of freshwater, woodlands and forest, grasslands and croplands, while peatlands and heathlands and urban areas in the Dargle have increased (Table 3). Another way of visualising these data are via accounting tables: the EnSym change account for ecosystem extent in the Dargle, developed using the earliest (2000) and most recent (2018) CLC status layers data, highlights further the changes in CLC Level 3 classes (Table 4), aggregated to ecosystem type in Table 5. The change account highlights the expansion of Urban class by ca. 4% (781 ha) between 2000 and 2018 and a reduction in the total area of Grasslands and Croplands class by ca. 5% (839 ha) (we note slight differences in area relating to calculating the areas of CORINE using ArcGIS and the EnSym tool, in the order of 0-10 ha). More detail is shown in Suppl. material 3, which outlines the ecosystem change matrix and highlights what ecosystem changes to other types.

Detailed ecosystem extent accounts for the Dargle reveal which land classes (and high-level ecosystem types) have changed over the accounting period (Suppl. material 3. Combining CORINE data with relevant supplementary datasets helps to build a more informed narrative about the ecosystem composition and relative changes in the catchment. We describe the key messages emerging here, describing the trends highlighted in Tables 2, 3, 4, 5.

Freshwater: This ecosystem type includes the CLC Level 3 class Waterbodies (< 1% cover of the catchment). CORINE records the extent of one of two lakes in the Dargle, showing a relatively small change over the accounting periods. CORINE does not detect rivers, lakes and/or freshwater wetlands smaller than the minimum mapping unit (MMU); these features were supplemented to the CORINE layer using the EPA rivers dataset (Fig. 3).

Figure 3.  

Overview of CORINE Land Cover (CLC) Level 3 classes in the Dargle, with waterbodies overlain using data from the EPA. The ecosystem types are inferred by the CLC3 classes and the area of each ecosystem asset is aggregated to total area (Tables 2, 3, 4, 5).

Woodlands and Forest: As highlighted above, we distingush between Woodlands (all semi-natural types) and Forest (areas planted and managed for the purpose of commercial production) and we describe both types seperately. We note that the CLC classes coincide with commercially planted Forest in the main.

Forest: This high-level ecosystem type covers ca. 20% of the Dargle in 2000, showing a relative decline in 2018 to ca. 18.5%. The change may be attributed to refinement of CORINE, given that the changes correspond to a relative increase in the area of peat bogs (Suppl. material 3). There is an increase in Coniferous forest in the same period. CLC Level 3 classes outline the extent of Broad-leaved forest, Coniferous forest, Mixed forest and Transition woodland-shrub, which correspond to commercial forests in the catchment (verified with ancillary commercial datasets). Coniferous forest dominates, comprising more than half of the total cover in 2018. Transitional woodland scrub shows a decline in cover and corresponds to the maturation of conifer stands between 2000 and 2018. Commercial forest datasets highlight that afforestation on state lands within the catchment largely occurred between the 1950s and the 1990s, with planting in private plots mainly in the period 1980 to 2000.

Woodlands: There are no woodlands (hedgerows or patches of semi-natural native woodland types) detected by CORINE, despite an extensive network of hedgerows, parkland and riparian woodland areas obvious from aerial imagery. Overlaying the CORINE dataset with commercial forest datasets, the SWF and STL HRLs and catchment survey data, increased the total extent of woodlands and forest cover (taking overlaps into consideration) from ca. 18.5% to 40%. In particular, the SWF and STL HRLs highlighted the network of hedgerows and additional woody features undetected by CORINE (Figs 4, 5). Further overlaying combined woodlands and forest ecosystem extent with soil texture data highlighted that much of the commercial forest areas have been planted on peat soils. This may be a contributing factor in forestry being identified as a pressure on freshwater river ecological status in the Glencullen Valley (described under the Condition accounts).

Figure 4.  

Dargle Woodlands and Forest cover using CORINE Land Cover Level 3 classes (2018) only.

Figure 5.  

Cover of linear wooded areas in the Dargle, additional woody features and patchy wooded areas, as shown in SWF and STL HRLs.

Peatlands and Heathlands: In 2000, only Peat Bogs were detected by CORINE, while the data distinguished four CLC Level 3 classes in 2018, namely Peat Bogs, Moors and Heathlands, Sparsely vegetated areas (screes on mountain slopes) and Burnt areas. This is attributed to an improvement in the distinction between these closely-related systems by CORINE after 2000. In total, these ecosystems accounted for 23% of the catchment in 2000, with a marginal increase to 25% in 2018. Overlaying Article 17 habitat mapping (Fig. 6), showed that the extent of peatlands and heathlands, detected by CORINE, is aligned with Article 17 datasets, serving to refine the extent data to distinguish more specific ecosystem types, including active blanket bog, wet heath, dry heath and alpine heathland types, as well as patches of fen less than the CORINE MMU. In addition, these ecosystems lie largely within Natura 2000 designations (Fig. 7). Combining both CORINE and Article 17 data for peatlands and heathlands with the national soil datasets, showed that peat texture covers ca. 41% of the catchment, indicating that, prior to 2000, these ecosystems were more extensive (Fig. 8). This additional 16% area of peat soil is overlain predominantly by CLC Level 3 classes Coniferous Forest and Pastures.

Figure 6.  

The extent of datasets for the 24 habitats listed in Annex I of the EU Habitats Directive reported under Article 17 (2019 data) for the Dargle accounting area.

Figure 7.  

Designated nature areas (Natura 2000 and national designated areas) and rivers with protected status in the Dargle catchment.

Figure 8.  

Soil texture data for the Dargle show that peat texture is predominant and aligns with peatland and heathland habitats in uplands and forests along river valleys (see Fig. 3).

Grasslands and Cropland: Grasslands and Cropland cover ca. 33% of the catchment in 2018, showing an overall 5% decline in cover from 2000. The CLC Level 3 class Pastures is most extensive for all accounting periods, declining by ca. 500 ha between 2000 and 2018. CLC class Complex cultivation patterns shows a similar decline. For both of these classes, the area was converted to a range of CLC classes, including other grassland types, urban fabric, as well as golf courses (verified by aerial imagery). The area of the CLC Level 3 class Lands principally occupied by agriculture with significant areas of natural vegetation, showed an overall increasing trend for the same time period. Croplands (CLC Non-irrigated arable land) cover a relatively low area (< 3%) of the catchment, also showing a decline. Applying the HNVf layer shows that outside of urban areas, the Dargle lies in the High potential HNVf category (Fig. 9), reflecting the high density of semi-natural habitats, as well as conservation designations, inferring that agriculture in the catchment tends more towards extensive, rather than intensive, use. This was verified by overlaying the Department of Agriculture, Forest and Marine Land Parcel Identification System (or LPIS) dataset, (annual datasets gathered by the Department of Agriculture, Forest and Marine for the purposes of farm payment schemes under the Common Agricultural Policy), which showed a high area of low input permanent pasture for the area.

Figure 9.  

Potential of High Nature Value farmland in the Dargle.

Coastal: Accounting for less than 1% of the Dargle, the CLC classes, detected in this category (Sea and Ocean), align with coastal margins. These data were supplemented with Article 17 datasets for Annex I coastal habitats and show that this area includes a relatively small sand dune complex, a fringe of tidal mudflats along the eastern boundary and a section of sea cliffs south of Bray.

Urban: Urban fabric in the Dargle is extensive in 2018 (ca. 23.5% of the catchment) increasing by ca. 4.5% between 2000 and 2018 across the urban CLC classes. The greatest increase was between 2000 and 2006, corresponding to a period of intense economic growth and expansion of infrastructural developments in Ireland. Focusing on green spaces, between 2000 and 2018, Green Urban areas showed a gradual decline to 93 ha (reduced by over half), while Sports and leisure facilities double in extent to over 800 ha (comprising golf courses mainly). Supplementing these data with the Urban Atlas HRL increased the extent of Urban green space from a few scattered patches (< 0.5% using CORINE) to ca. 3% of the catchment. Applying the Urban Atlas STL HRL also highlighted that urban areas in the catchment have a significant network of wooded areas, not detected by CORINE.

In the absence of a detailed ecosystem map and/or other higher resolution data, CORINE datasets provided the necessary contiguous, time-series data to support the development of indicative ecosystem extent accounts at catchment scale. This reflects the use of CORINE for high-level ecosystem and landcover reporting across the EU Region at Tier I (EU Region, using CLC Level 2 classes) and Tier II levels (national regions, using CLC Level 3 classes) (Burkhard 2018, EEA 2016, La Notte 2017). While we note the accuracy of CORINE has improved between 2000 and 2018, reflected particularly in the distinction of peatland and heathland areas, the ongoing limitations of the CORINE data for catchment scale (Tier III level) accounting relate to the resolution of the data, outlined specifically as follows:

Distinction of ecosystem types: In this study, we broadly aligned CLC Level 3 classes to Level 1 of the national ecosystem typology (Fossitt 2000). Recognising that Level 3 of the national typology (Suppl. material 1) provides greater insight and detail as to ecosystem sub-types and variants (for example, distinguishing improved grassland from semi-natural grassland types), the data available limited our discussions and findings to high level ecosystem types and high level trends only. This will limit further discussions relating to the flows of services, which vary considerably between more detailed ecosystem types. For example, biomass provision from improved grassland is likely to be higher compared to that from wet, semi-natural grassland types which are likely to provide a greater level of water and sediment retention services than improved grassland types.

Detection of linear features: rivers, hedgerows and landscape features less than the MMU or minimum mapping width of CORINE (such as locally-important wetlands and woodlands) were not included in the CORINE, based core extent accounts for the Dargle. Supplementary datasets are effective in refining and providing detail but, in general, these are gathered at varying intervals and scales and are generally not consistent either with each other or the available CORINE time series.

These limitations extend across all scales of reporting, however, presenting recurring challenges in building ecosystem accounts at any level, as shown across the EU Region (EEA 2016, Grêt-Regamey 2017, Grunewald 2020, Hein 2020a, La Notte 2017). A national landcover map (in development for Ireland) (Wall 2020) due to have a resolution of 10 m, is likely to provide finer detail on ecosystem extent and will be aligned with the national ecosystem typology. In the absence of a national ecosystem map and/or contiguous catchment scale ecosystem mapping, this will support both catchment and national levels of reporting in Ireland, streamlining further the ecosystem extent accounting process. In the meantime, work in the Dargle reflects approaches to developing ecosystem accounts in other studies, requiring the alignment of disparate datasets, such as habitat survey, remote sensing and land use data to present the best available information relating to ecosystem extent (Burkhard 2018, EEA 2016, Grunewald 2020, Parker 2016, Hein 2020a, Perennes 2020). We note that applying ancillary datasets proved useful to contextualise findings within the accounting area relative to changes occurring outside of the accounting period.

Further alignment with the IUCN Global Ecosystem Typology (Keith 2020) highlighted that ecosystem types, recorded in the Dargle, are largely grouped in the Intensive Land-use category, T7. This comprises artificially-managed areas, such as sown pastures, urban areas and plantations. Only scattered framents of semi-natural ecosystem types are present, reflecting the steady and increasing conversion of natural lands, such as temperate woodlands, heathlands and wetlands (peatlands and fens), to intensive agricultural use in former centuries, as well as the more recent expansion of urban areas in the late 19^th and early 20^th centuries (Mitchell 1997). Agriculture is the dominant land-use in Ireland and, while there are subtle changes in recent years towards intensification, this has largely occurred outside the Dargle which shows an opposing trend towards extensification in agricultural areas. Aligining the SEEA-EA accounting approach with the IUCN Global Ecosystem Typology will facilitate effective comparison across the EU Region and globally (UNSD 2021) in terms of the extent of intensively used ecosystems and natural lands, providing information to plan targeted restoration to rebuild natural networks and re-connect isolated areas protected for nature, a key action identified in the EU Biodiversity Strategy for 2030 (EC 2020).

Condition accounts are the least developed within the European Region and at national levels, though efforts are becoming more focused (Czúcz 2020, Keith 2020, Maes 2020). The lack of condition data across the full range of ecosystem types indicates that only bespoke condition accounts can be developed at catchment and/or national scale in Ireland at this time.

In relation to our case study, WFD data provides a comprehensive resource to develop ecosystem condition accounts for waterbodies in general and, by extension, as highlighted already, this can be extended to the related sub-basins in the absence of condition data for other ecosystem types. Ecological status is a pre-aggregated index which may be used as a sub-index as part of Stage 3 of condition accounting and has been used in ecosystem accounting in European case studies (Rendon 2019, Maes 2020, Hein 2020a, EEA 2016). Other condition datasets available for habitats listed under Annex I of the EU Habitats Directive, as well as for sites within the Natura 2000 network, are available, though site-specific data relating to catchment level are very limited. Use of these and other datasets (such as National Forest Inventory data gathered at national scale) data are appropriate for condition accounts developed at national scale rather than catchment scale, as used in other studies (Maes 2020, Rendon 2019).

Aligning ancillary datasets with the core extent accounts data in the Dargle illustrated the effective use of soils data to infer the historical extent of peatlands and heathlands. This is an important consideration for the use of cultivated peat soils and the resultant contribution to climate regulation. In this way, ancillary data and proxies can be used to effect, serving as placeholders to highlight data gaps until more appropriate data are gathered (Burkhard 2018, Geijzendorffer 2015, Maes 2020, Vačkářů and Grammatikopoulou 2019, Grunewald 2020). The use of these data will provide information for further analyses in relation to changes in peat soils that occur when converted to plantation or cultivation, such as changes in carbon stocks and flows over time and trade-offs in terms of ecosystem service (for example, biomass provisioning versus climate regulation services) (UNSD 2021).

The challenges identified in this case study reflect those identified in other studies and include the lack of data to build condition accounts, the absence of targeted and reliable time-series data on structure and function, as well as the need for agreed reference levels (Maes 2020). Despite clear guidance provided in the SEEA-EA, a number of questions remain to be addressed and require multi-disciplinary efforts, particularly from ecologists with specialist knowledge from across the range of ecosystem types of relevance, to guide and develop the links between condition, capacity to deliver services and sustainable use (Czúcz 2020, Keith 2020, Rendon 2019, Maes 2020).

In relation to peatlands, data relating to drainage and vegetation cover, is often reflected in the name of the peatland ecosystem type (Level 3 of the national ecosystem typology). Within the Dargle, a desktop survey of the Wicklow Mountains SAC highlights areas of active blanket bog (considered to be good condition), as well as cutover bog and eroding bog (considered to be drained and eroding, therefore inferring poor condition) within the SAC area. Linking these data with remote sensing approaches detecting peatland drainage (Connolly and Holden 2013), would provide information about potential peatland ecosystem condition indicators. A similar approach, working with ecosystem experts, would make information available for selection of relevant ecosystem condition variables and condition indicators for other ecosystem types (woodlands, grasslands, freshwater etc.), particularly in the local and regional context. Efforts to combine advances in remote sensing at the EU level to develop Essential Biodiversity Variables as well as national efforts, will facilitate alignment with local ecosystem types and contribute to filling data gaps, ultimately facilitating effective ways of tracking and accounting for changes in a standardised comparable way.

While challenges remain, following the examples of other studies (Maes 2020, Rendon 2019) and proposed condition variables set out in the SEEA-EA guidance (UNSD 2021), more focused work at the individual ecosystem type level to incorporate and provide information for other datasets, such as survey data commissioned for development and planning projects and/or species data collated by NGOs and citizen science programmes, will facilitate gathering of relevant condition data and, thereby, development of more robust condition accounts.