NatureScot Research Report 1404 – Active peatland and peatland condition

Published: 2026

Authors: Roxane Andersen, Mascha Bischoff, Heather Johnstone, David Large

Cite as: Roxane Andersen, Mascha Bischoff, Heather Johnstone, David Large. Active peatland and peatland condition. NatureScot Research Report 1404. 

Keywords

Development on peatland, Peatland condition, Peatland definition, Scottish Planning Biodiversity Metric.

Background

There is currently some uncertainty around peatland terms which are used in different situations, leading to ambiguity. This includes definitions within peatland classification, condition and use in development. These definitions will be reviewed as part of this work and some recommendations will be provided about how to reduce uncertainty and increase clarity. 

At present, there are also a wide range of methods deployed for peatland condition assessment, developed for different purposes. This work will look at some of the existing peatland habitat condition classification systems and assessment tools and determine if there is a suitable existing methodology or one that can be adjusted to determine peatland condition in the context of development. Such a tool may be used as part of the Scottish Planning Biodiversity Metric for understanding the impacts of a development on peatland habitats, and indicating the scale of offsetting required to compensate for the loss of peatland habitats.

This commissioned report will be used to improve consistency and understanding of terms and be used to support NatureScot’s project to develop the Scottish Planning Biodiversity Metric to help show significant biodiversity enhancements in line with Policy 3(b) of National Planning Framework 4.

To improve readability, the report doesn’t follow a typical structure of introduction – method – result – discussion. Instead, it is split in three parts: the first part addresses the issues of clarity of terminology and definition around active peatlands through a lens of development on peat, the second part addresses the issue of classification and tools, and the final part tackles the development of a bespoke tool that could be used in the context of the Scottish Planning Biodiversity Metric. 

Main findings

• In Part 1 of the report, we review issues around definitions associated with peat and peatland and on what is priority peatland, and we summarise some of the main classification systems relevant in the context of planning and development on peat.
• We find that the lack of clear and agreed definitions at the highest level (e.g. globally agreed definitions of peat and peatland) and the range of possible habitat classes arising from different systems can cause issues with mapping, governance and policy. At the national level where definitions may be agreed, a lack of clarity can cause further issues for decision-makers because it makes decisions easier to challenge and open to subjective interpretation. 
• We recommend that where definitions will not be modified in the short-term, then the interpretation should be unambiguous with appropriate guidance.
• There are known key problems with threshold-based definitions: organic matter thresholds between peat, peat soils and non-peat soils and a depth-based delineation between peatland and non-peatland creates artificial “hard edges” where different policies apply. In contrast, smooth and gradual transitions exist on the ground with interconnected processes operating at the landscape scale. We recommend that recognising the natural processes as part of the interpretation and guidance associated with the definition is more important and useful in the short-term than altering the definition(s). 
• Several definitions and classifications would exclude peatlands where a former peat deposit has been lost through human influence (e.g. peat extraction, human-induced peat slides, wildfire, severe erosion exacerbated by overgrazing, pollution, burning, or agricultural wastage of peat). Such areas represent both historic and potential future peatlands. Indeed, with restoration and appropriate management interventions, these areas may support peat formation in the future. In the context of planning, we recommend that these areas should be included and assessed because of their relevance to potential carbon sequestration and positive effects for biodiversity.
• UK Habitat Classification (UKHab) is the current classification system used in the English statutory biodiversity metric and is being considered for the Scottish Planning Biodiversity Metric. It is a relatively recent and evolving classification system. We recommend that open communication about changes or updates and clear guidance about its use should be provided to ensure consistency in recording peatland categories. 
• The Joint Nature Conservation Committee provides a statement describing “active blanket bog”. The statement underpins the protection of peat and peatland in the context of development in Scotland, which is achieved through a range of policies implemented within the National Planning Framework 4. We address issues around ambiguity of terminology used in the statement.
• We recommend clarifying the phrase “peat-forming” by explicitly recognising it to mean “assemblages of specialist plant species typically associated with bogs and fens”.
• We recommend a clear acknowledgement that these assemblages are likely to only include a subset of the full suite of possible species, and that species that are otherwise considered “non-peat forming” may still be desirable from a biodiversity and wider ecosystem function perspective. This is particularly relevant in the context of seeking to secure positive effects for biodiversity.
• We recommend that recognising “Sphagnum” as a key group is more important in the context of development on peat than differentiating individual Sphagnum species based on their likely contribution to future peat deposits. However, we acknowledge that where identification skills are available (or could be developed), it may be useful to differentiate Sphagnum at the species level, notably when assessing outcomes of management interventions for biodiversity.     
• We recommend avoiding any arbitrary area-based or proportion-based threshold when defining “significant area” and instead considering the potential of individual hydrological units (mesotopes) to return to a functional state, and to consider the way in which these mesotopes connect with the wider landscape (macrotope).
• In the National Planning Framework 4, Policy 5d, states that: ‘A peat management plan will be required to demonstrate that this approach has been followed, alongside other appropriate plans required for restoring and/ or enhancing the site into a functioning peatland system capable of achieving carbon sequestration’. We review the semantical issues associated with the statement and provide an overview of the approaches that may be considered to assess the outcome of a management plan. 
• We recommend that the definition of a functioning peatland is interpreted in the broadest sense, as a self-sustaining peatland that can deliver the full suite of ecosystem services to an extent that approaches or is equivalent to what can be expected of a near-natural system.
• We recommend that the essential attributes of a functional peatland are a high and stable water table with hydrological integrity at the landscape scale and where recruitment of specialist species across a range of taxa, and absence of undesirable species including non-native conifers, are achieved. 
• We recommend that the desirable attributes of a functional peatland include well-developed or protected environmental and archaeological archives, the potential for natural succession to take place, resilience to further impacts, notably those arising from climate change, and no need for further intervention. A range of variables may be assessed to provide further evidence of trajectories of change and outcomes for those attributes.
• We recommend that the interpretation of the statement should put the emphasis on the words “capable of”, and that focus should be on the potential to achieve carbon sequestration, rather than on any quantitative estimates of carbon sequestration. 
• We recommend that direct measures of carbon sequestration should be considered where they could fill a key knowledge and research gap, in collaboration with organisations with the appropriate skills and systems in place. We do not recommend that direct empirical measures of carbon sequestration are routinely implemented otherwise, as it would be impractical and not cost-effective, and would fail to capture the “potential”.
• We recommend that the estimation of greenhouse gas emissions by proxy should not be the focus of the interpretation of the definition in the context of development on peat. 
• We recommend that the potential should be assessed at both the mesotope and macrotope levels. Suitable intervention(s) and management should be adapted to local settings, land use legacies and wider landscape context. 
• Given the lifespan of renewable energy development on peat, we recommend that the potential is assessed and optimised at the mesotope level and that subsequent, regular assessments of the primary key functions (hydrology, vegetation) are used to determine whether the potential is likely to be realised, or not. Where it isn’t, appropriate management or intervention(s) should be considered.
• We recommend that for the purpose of planning, field-based assessment of vegetation and hydrology (specifically, water table depth) should be undertaken, noting that rapid advances in drone technology, remote-sensing tools and AI might also be used in combination with field-based assessments in the future. We recommend that where appropriate, collaborative partnerships should be considered to support the development and the rigorous validation and testing of emerging techniques, e.g., combining empirical data, multi-sensor platforms and AI models.
• We recommend that appropriate guidance and training are provided to ecologists and consultants undertaking any field-based assessments to improve the consistency of data collection and increase the potential for data re-use. 
• In Part 2 of the report, we review the broad contexts within which biodiversity metrics sit in Europe, including an overview of the English Statutory Biodiversity Metric. We provide an overview of the way in which peatland may be considered as part of the Scottish Planning Biodiversity Metric. 
• We then compare a range of peatland condition assessments for their potential to contribute to the Scottish Planning Biodiversity Metric. These are the Peatland ACTION condition assessment, the field-based assessment used for the validation of optical satellite models, Richard Lindsay’s “Peatland Condition Matrix” and the InSAR Rapid Assessment tool developed to validate “Bog Breathing” condition classification.
• We identify that the InSAR Rapid Assessment Tool has the strongest potential to be rapidly and readily adapted to meet the required considerations: easy to use and rapid to deploy in the field, separation into up to 5 classes, nuanced through a summed score, applicable to a wide range of peatland types, and sensitive enough to pick up changes in condition over time. 
• We recommend that the InSAR Field-based Rapid Assessment Tool is reviewed and adapted to suit the needs of the Scottish Planning Biodiversity Metric. We recommend that a new, bespoke template is created and it is then re-named to avoid confusion with the InSAR field-based assessment method. 
• In Part 3, we adapted the variables and scoring systems in the InSAR Rapid Assessment Tool to create a bespoke peatland condition classification tool fit for purpose in the context of the Scottish Planning Biodiversity Metric. We tested the system against a synthetic dataset of 29 peatland scenarios, created from a mixture of real field data and expert knowledge.
• We recommend that the current version of the tool under development [see Annex 1] is scrutinised by the Scottish Planning Biodiversity Metric team. This could include the creation of further synthetic data based on expertise and experience, or real data if available. 
• We recommend that the Scottish Planning Biodiversity Metric team defines the names associated with the condition classes 1-5, for example following the approach of the English Statutory Biodiversity Metric (which classes condition as good, fairly good, moderate, fairly poor, poor). But that in doing so, it is recognised that suitable outcomes of restoration or enhancement may fall under the current categories 1 or 2, and that not all trajectories or interventions will necessarily move through all the categories linearly. 
• We recommend that the tool is kept under regular review, especially when it becomes used more readily by end-users.
• We recommend that appropriate guidance, training and support is provided to facilitate uptake and ensure consistency.

Acknowledgements

We acknowledge funding from NERC (NE/T010118/1, NE/P014100/1), The Leverhulme Trust (RL-2019-002) and NatureScot Peatland ACTION (NS Research Report 1356, NS Research Report 1308 and NS Research Report 1269), which have all supported the development of the InSAR-derived “Bog Breathing” condition mapping and condition change assessment, including the development of the InSAR field-based assessment tool. We acknowledge funding from Innovate UK (Project number 10143811) that supports a Knowledge Transfer Partnership between UHI and RWE, which has funded Heather Johnstone’s time to help review the English Statutory Biodiversity Metric in the context of peatland.

Abbreviations 

Artificial Intelligence (AI)

Dissolved Organic Carbon (DOC)

Environmental Impact Assessment (EIA)

European Union (EU)

Global Guidance for Life Cycle Impact Assessment Indicators and Methods (GLAM)

Great Britain (GB)

Greenhouse Gases (GHG)

Gross Primary Productivity (GPP)

Department for Environment, Food and Rural Affairs (DEFRA) 

Ecosystem Respiration (ER)

Interferometric Synthetic Aperture Radar (InSAR)

Intergovernmental Panel on Climate Change (IPCC)

Joint Nature Conservation Committee (JNCC)

Land Use, Land Use Change and Forestry (LULUCF)

Life Cycle Assessment (LCA)

National Planning Framework 4 (NPF4)

National Vegetation Classification (NVC)

Net Ecosystem Exchange (NEE)

Particulate Organic Carbon (POC)

Photosynthetically Active Radiation (PAR)

Plant Functional Type (PFT)

Product Environmental Footprint (PEF)

Red-Amber-Green (RAG)

Scottish Planning Biodiversity Metric (SPBM)

Site of Special Scientific Interest (SSSI)

Special Area of Conservation (SAC)

UK Habitat Classification (UKHab)

United Kingdom (UK)

Water Table Depth (WTD)

World Heritage Site (WHS)

Part 1 –Definitions, classifications, and clarity of terms in the context of development on peatland

Definition of peat and peatland

Definition refers to a statement of the meaning of a word – in this context, peat and peatland. Definition can be based on specific defining criteria and thresholds, for instance regarding the physical, chemical or biological properties of the soil layer, habitat, etc. Currently, there is no unanimously agreed, single scientific or authoritative consensus definition or classification for peat or peatland (Lindsay, 2016; Lourenco et al., 2023). Instead, researchers, national authorities and peatland interest groups have developed and continue to use a range of definitions for peat and peatland that may vary across scientific discipline and according to the purpose for which the definitions have been created (Lourenco et al., 2023). In 2011, an extensive report titled “Towards an assessment of the state of UK Peatlands” included a review of relevant definitions, delineation and descriptions of UK peatland (Bruneau and Johnson, 2011). For the present report, we review some of the same concepts and definitions with a lens focusing more specifically on the Scottish planning context. 

“Peat” could be defined functionally as an organic soil with low or no mineral content, made up of partially decayed plant remains preserved due to the anoxic conditions created by a waterlogged environment. A range of thresholds relating to depth of the organic layer and/or the proportion of organic material also exist. For example, peat has been defined by the International Peatland Society (IPS) as “a sedentarily accumulated material consisting of at least 30% (dry mass) of dead organic material” (Joosten and Clarke, 2002), though this threshold itself is arbitrary and other thresholds have been used (Hobbs, 1987). 

The word “Peatland” is equally vaguely defined: it describes a subset of wetland habitats where peat has accumulated in layers. The Ramsar Convention (1971) definition of peatland is widely accepted, stating that they are 

“ecosystems with a peat deposit that may currently support a vegetation that is peat-forming, may not, or may lack vegetation entirely. Peat is dead and partially decomposed plant remains that have accumulated in situ under waterlogged conditions”.

In this case, the term ‘peatland’ is used to describe the collective and diverse range of ecosystems that have a peat substrate, but that vary in their underlying physical and ecological processes and properties, from tropical peat swamp forests to arctic palsa mires and everything in between (Gore, 1983; Lindsay, 2010). The Ramsar convention recognises “active peatland” as areas where peat is currently forming and accumulating and inactive peatlands are areas lacking current peat formation. 

Peatlands are a globally but unevenly distributed group of diverse ecosystems, with proportionally more peatlands in the northern hemisphere (United Nations Environment Programme, 2022). But like for peat, many current definitions in use are accompanied with depth threshold that vary between context and countries (Lourenco et al., 2022). 

Acknowledging that the lack of singular definition complicates the estimation of area and volumetric carbon content, peatlands are still globally recognised for their disproportionately large contribution to long-term soil C storage, holding an estimated ~600 of C or 1/3 of the soil C despite their small areal coverage at ~3% of the land surface (Xu et al., 2018; Yu et al., 2010). Beyond carbon, peatlands also support a uniquely adapted, specialist biodiversity, regulate and filter freshwater, are an archive of past climate and land use change and have cultural importance (Food and Agriculture Organization (FAO), 2020). However, peatlands globally face increasing, substantial and often multi-faceted pressures from land use and climate change (Page and Baird, 2016). This puts the long-term C store of peatland at risk, with peatland degradation estimated to account for up to 5% of anthropogenic greenhouse gas (GHG) emissions (Joosten et al., 2016), which could rise to 8% by 2050 (Urák et al., 2017) without large-scale restoration intervention. 

In this context, definitions are important: definitions that are too narrow could be misinterpreted, leading to management and legislative decisions that may lead to further degradation. In their review of the current state of global peat and peatland definitions, Lourenco et al., (2022) attempt to broaden the definition, and suggests that peat soil having at least 5% of organic carbon and a depth of at least 10 cm should be considered in scope to be defined as “peatland”, regardless of the vegetation and water level. They recommend including data on depth, extent, carbon content and bulk density alongside assessments. While the motivation of the authors – to protect organic soil from further degradation – is commendable, the recommendation is impractical on many levels and is not compatible with definitions already embedded in national policies and frameworks, as is the case in Scotland. And while ultimately desirable, creating a single definition or more likely, sets of definitions that would be applicable in most circumstances across most disciplines and for most purposes, will require time, consideration and extensive consultation.

Thus, where definitions and classifications are unlikely to be modified in the short-term, there should be complete clarity over the meaning of terms used in the definition(s) and over the context within which they apply.

Classification of peat and peatland 

While definition refers to semantics, peatland classification refers to the act of assigning peat or peatland to groups or orders designed on the basis of common relations or characteristics, from which the distribution of classes can be assessed (Lindsay, 2016). Numerous classification systems based on peatland characteristics have developed over time, and like definitions, there is still no single complete or authoritative classification system for peat and peatland. While early classification systems were derived from a land exploitation perspective with peat seen as a resource for fuel and energy potential (Birnie et al., 1991), further classifications focussed on ecological properties and incorporated elements of soil sciences and biology (Lindsay, 2016), with peatlands often described according to soil typology, vegetation-based typology or geological and process-based typology (Bruneau, 2014; Bruneau and Johnson, 2011). Ultimately, classification systems are created for a purpose and differences in classes are expected across disciplines or end user groups. 

In the UK, mapping and classification of soil type started in the early 1940s. As part of these mapping exercises, the National Soil Survey Institute of Scotland sought to adapt existing classification systems to describe the range of soils they encountered (Soil Survey of Scotland, 1984), and this is where the depth threshold (50 cm) and organic matter threshold (60%) that distinguishes peat from non-peat soil was introduced, with different thresholds for England and Wales and for Northern Ireland (Joint Nature Conservation Committee, 2011).

Further classification systems can then be applied to further differentiate these broad categories. Habitat-based classification of peatland is an example of such classification, generally designed to provide a simplified description of key features enabling a categorical classification. None of the current classification systems were designed specifically for supporting planning, but some may be more fit for purpose than others. Averis and Averis (2026) provide a recent review of the habitat classification systems for their potential use in a Scottish Planning Biodiversity Metric. Their report did not specifically focus on peatland classification but provides wider context. 

The National Vegetation Classification System

In the UK, the National Vegetation Classification (NVC) system has been used as a key framework for habitat-based classification. Commissioned in 1975 by the Nature Conservancy Council, it was created to provide a comprehensive and systematic catalogue and description of the plan communities of Britain (Rodwell, 2006).  Considering the lowest sub-division of each type, except variants, the NVC comprises 681 units, including 38 mire communities and 22 heath communities. It is based on a phytosociological approach and has been the main terrestrial habitat classification underpinning the selection of biological Sites of Special Scientific Interest, the UK Common Standards Monitoring Guidance for upland habitats, as well as the UK Interpretation of Annex I Habitats under the European Commission Habitats Directive. For each Annex I habitat in the UK, a definition is provided, based on broad vegetation types arising from NVC classification with additional terms relating to relevant processes. 

UK Habitat Classification System

In 2013, work began to develop a new unified and comprehensive approach to classifying habitats in the UK, the UK Habitat Classification system, or UKHab. The classification covers terrestrial and freshwater habitats and has been designed to be flexible enough to be use in a range of survey types from local to national scale habitat mapping. UKHab is simpler and has fewer, broader categories than the NVC. To deal with habitat mosaics or provide additional features to be included, secondary codes can be linked to each primary habitat. UKHab builds on existing classifications and is a fully translatable, hierarchical system that integrates with all major classifications in use in the UK and Europe. Legacy datasets from other classification systems can be translated into UKHab using a translation table. UKHab data can also integrate with large-scale GIS-based habitat datasets. It includes an attribute data model intended to help user collect and store data in a standardised, consistent way to facilitate exchange and re-use. 

The UKHab forms the basis of the classification used with the English Statutory Biodiversity Metric sometimes referred to as “the DEFRA metric”. It will form the basis of the system for use in the Scottish Planning Biodiversity Metric. 

Other Habitat Classification systems in the UK

Other habitat classification systems also exist, and are widely used, including vegetation-based systems (e.g. European Nature Information System (EUNIS) Habitat Classification system, the Phase 1 Habitat Classification, or the UK Biodiversity Action Plan broad habitat and priority habitat type. 

Recommendations on definition and classification issues

• The lack of clear and agreed definition at the highest level (e.g. globally agreed definitions of peat and peatland) and the range of possible habitat classes arising from different systems can cause issues with mapping, governance and policy. At the national level where definitions may be agreed, a lack of clarity can cause further issues for decision-makers because it makes decisions easier to challenge and open to subjective interpretation. We recommend that where definitions will not be modified in the short-term, then the interpretation should be unambiguous with appropriate guidance.
• There are known key problems with threshold-based definitions: a depth-based delineation between peat and non-peat (or peatland and non-peatland) creates artificial “hard edges” where different policies apply. In contrast, smooth and gradual transitions exist on the ground with interconnected processes operating at the landscape scale. We recommend recognising the natural processes as part of the interpretation and guidance associated with the definition is more important and useful in the short-term than altering the definition(s). 
• Several definitions and classifications would exclude peatlands where a former peat deposit has been lost as a result of human influence (e.g. peat extraction, human-induced peat slides, wildfire, severe erosion exacerbated by overgrazing, pollution, burning, or agricultural wastage of peat). Such areas represent both historic and potential future peatlands. Indeed, with restoration and appropriate management interventions, these areas may support peat formation in the future. In the context of planning, we recommend that these areas should be included and assessed because of their relevance to potential benefits for biodiversity.
• UK Habitat Classification (UKHab) is the current classification system used in the English statutory biodiversity metric and is being considered for the Scottish Planning Biodiversity Metric. It is a relatively recent and evolving classification system. As the SPBM develops, clear guidance about how the habitats recognised in the metric correlate to UKHab, and where they deviate from UKHab categories/methodologies should be provided to ensure consistency in recording peatland categories. 

Clarity of terms in the context of development on peatland

The protection of peat and peatland in the context of development in Scotland is achieved through a range of policies implemented within the National Planning Framework 4, including policies 3 (Biodiversity), 4 (Natural places), 5 (Soils) and 7l (World Heritage Site). NatureScot has also created guidance advising on peatland habitats and carbon-rich soils in development management. The guidance is in place to support NatureScot staff provide developers, planning authorities and Scottish Government with consistent advice on the assessment of effects of any development proposals on peatland, carbon-rich soils and priority peatland habitat, and is regularly reviewed. Within this context, definitions need to be clear and unambiguous – if not in their wording, in their interpretation. 

JNCC in the description of Annex 1 habitat H7130 Blanket bogs states that for a blanket bog to be a priority habitat it must be active. It then states: 

‘Active’ is defined as supporting a significant area of vegetation that is normally peat-forming. Typical species include the important peat-forming species, such as bog-mosses Sphagnum spp. and cottongrasses Eriophorum spp., or purple moor-grass Molinia caerulea in certain circumstances, together with heather Calluna vulgaris and other ericaceous species. Thus sites, particularly those at higher altitude, characterised by extensive erosion features, may still be classed as ‘active’ if they otherwise support extensive areas of typical bog vegetation, and especially if the erosion gullies show signs of recolonisation. 

Elsewhere, “active” is used to describe bogs that exhibit a two-layered structure (Bruneau, 2014) comprising an acrotelm (the layer within which the water table fluctuates, made up of living vegetation and newly decomposing plant matter) and catotelm (permanently saturated layer) as described by Ingram (1983) and Clymo (1992).

The JNCC definition raises both conceptual and semantical issues, creating uncertainty. In this section of the report, we address some of these issues. For simplicity, we provide a recommendation to improve clarity at the end of each sub-section.

What is ‘vegetation that is normally peat-forming’?

Issue

Conceptually, peat is not typically defined based on the identity of the plants from which the partially organic matter remains are derived. Some plants from which remains are found in peat deposits may not accumulate deposits without waterlogging conditions maintained over long periods of time. These timescales may exceed the 30-50 years typically associated with a development lease on a given site. It is also known that not all plant remains are preserved equally in the fossil records, including in peat, owing to differences in plant tissue properties. These differences lead to taxonomic bias, where some remains may be identifiable at the species level, while others may not (Kuder and Kruge, 1998).

Therefore, the term itself is conceptually misleading and potentially unhelpful, because the formation of peat is a consequence of complex mechanical, hydrological and ecological feedback loops operating over a range of timescales and spatial scales – not solely attributable to the botanical identity of the vegetation and certainly not tied to any depth-based threshold, as discussed in the previous section.

However, in practice, the term “peat-forming” appears to be used consistently to refer to assemblages of vegetation types associated with wet conditions and conducive to peat formation. We note that this mostly refers to specialist bog/fen species typically found in UK raised and blanket bog cores (as macrofossils) and/or typically associated with contemporary UK blanket bog habitats, as suggested by the examples provided in the definition, and the ones discussed in Bruneau (2014). These are derived from NVC habitat classes that make up the various UK raised bogs, blanket bogs, and fen categories. It should be noted that the list included in the definition is neither precise nor complete for practical reasons. 

However, this is also potentially problematic: any plant growing in waterlogged conditions that subsequently partially decomposes and creates a deposit could be termed “peat-forming” – as illustrated by the wide variety of peatland types found around the world. In the UK, this may include species that would not be typically considered “peat forming” today (e.g. various tree species) but may well have contributed to historical formation and accumulation of peat. Then, not all species found on a peatland will contribute equally (or at all) to the formation of peat itself but may still play essential roles in the maintenance of other functions, including supporting biodiversity among other taxa. For instance, only the fruits and seeds of Drosera sp. are likely to be preserved in the peat record and thus the species would not fit the concept of “peat-forming”– yet Drosera anglica is listed as “near threatened” on the GB red list species (JNCC Open Data) and may therefore be highly desirable within a “peat-forming” vegetation assemblage from a biodiversity perspective. As concluded by Barber (1993), the palaeo-record suggests that the present vegetation found on peatland gives only a partial view of their past biodiversity, for the reasons highlighted above. It is beyond the scope of this report to provide an exhaustive list of species that could match this description for all Scottish peatlands. Nevertheless, we caution that the interpretation of “peat-forming” should be broadened to avoid a narrow focus on a small subset of species. 

Recommendation

• We recommend that clarifying the phrase “peat-forming” by explicitly recognising it to mean “assemblages of specialist plant species typically associated with bogs and fens” would provide clarity. These species and assemblages are likely to vary, notably on account on climate and local settings. 
• We recommend a clear acknowledgement that these assemblages are likely to only include a subset of the full suite of possible species, and that species that are otherwise considered “non-peat forming” may still be desirable from a biodiversity and wider ecosystem function perspective. This is particularly relevant in the context of seeking to secure positive effects for biodiversity.

Should all ‘bog-mosses Sphagnum spp’ be included in this?

Issue

It is widely known that individual Sphagnum species differ in their habitat preferences (e.g. nutrient regime, altitude, etc), niche preferences (e.g. wetter or drier microtopographic positions), and plasticity (Turetsky et al., 2025). Some species that have historically contributed significantly to the formation of blanket bog peat may have much narrower distribution today as a result of land use changes and legacies (e.g. S. Austinii, formerly S. imbricatum) (Wieder and Vitt, 2006). Not all species of Sphagnum are associated with bog habitats, and not all will contribute equally to peat deposits. Some species adopt life strategies of faster growth and faster decomposition (e.g. S. cuspidatum, S. fallax) while other species have more conservative strategies, with slow growth and increased recalcitrance to decay (e.g. most of species in the acutifolia section) (Johnson et al., 2015; Turetsky et al., 2008). Some species are more readily able to colonize bare peat, such as S. tenellum but are poor competitors (Rydin, 1993). Other species can also tolerate higher nutrient status, like S. fallax and S. palustre (Turetsky et al., 2008). While these conditions could be associated with flushes or riparian habitats, they are also common in sites with elevated atmospheric N deposition (Limpens et al., 2003), or forest-to-bog sites where nutrient legacies persist (Hancock et al., 2018).

On the other hand, while not all Sphagnum species will ultimately form peat, all Sphagnum species need consistently high moisture as they lack the structures (roots, stomata) to actively regulate their internal water supply. While this can mainly be associated with a high and stable water level, certain combinations of orography and climate might provide enough moisture to support growth e.g. overhangs on the west coast of Scotland where species like S. quinquefarium or S. skyense can be found in dense colonies. There is also evidence that changes in environmental conditions can trigger succession, with the replacement of species where dispersal is not limited (e.g. donor populations still exist), and in the context of restoration, early “pioneer” species can facilitate the establishment and persistence of the bryophyte assemblages essential to the long-term maintenance of carbon accumulation and storage (Wieder and Vitt, 2006). Importantly, Sphagnum (and bryophytes, more generally) are cryptic species, whose taxonomical identification at the species level can be challenging. In peatlands, higher diversity of Sphagnum – regardless of future contribution to peat deposits - may be associated with a wider range of small-scale heterogeneity (e.g. hummocks, lawn, pool edges, pools) that underpin other biodiversity elements. Thus, higher diversity of Sphagnum is likely to support resilience and contributes to biodiversity, including by providing habitat for a wide range of microbes (Kostka et al., 2016). 

Beyond the formation of peat, there is extensive evidence that all Sphagnum species further support reduction in net climate forcing through their key contribution to oxidation of methane, linked to their methane-oxidising quasi-symbionts (Kip et al., 2010; Larmola et al., 2010). In other words, for a given similar water table depth position supporting methane production in the peat, a peatland with any Sphagnum will emit less methane than a peatland without Sphagnum.    

Recommendation

• We recommend that recognising “Sphagnum” as a key group is more important in the context of development on peat than differentiating individual Sphagnum species based on their likely contribution to the future peat deposits. However, we acknowledge that where identification skills are available (or could be developed), it may be useful to differentiate Sphagnum at the species level. In this way, it should be possible to identify sites and/or interventions where a broader suite of species is present (prior to any development) or have returned (following e.g. restoration intervention), which may in turn signal increased potential for resilience.

What is a ‘significant area’? How much of an area does ‘peat-forming’ vegetation need to cover for the area to be classed as active?

Issue

It is not clear from the definition as currently written what scale it applies to. This creates an issue with the interpretation of the definition because applying it to different scales may lead to different outcomes. In the context of development on peat, this may be problematic if the scale is interpretated as the footprint of the development, irrespective of the connectivity to the wider landscape. 

Conceptually, a key defining feature of blanket bog is that it can develop over slopes and bury the topography under unevenly distributed layers of peat. Nevertheless, all bog units, including blanket bog units, still have boundaries that dictate how the water might flow through or out of the system, and where peat may stop forming – for example, steep and well-draining slopes, rocky outcrops or changes in geological deposit that may affect the sub-surface impermeability (hydrological boundaries) or rivers and erosive floodplains incising the peat (erosive boundaries). The boundaries of the system may not be the current boundaries to the peat as defined by arbitrary depth definitions and may include “non-peat” areas of transition where peat naturally thins.

The largest unit of peatland usually recognised is commonly referred to as a “macrotope” or the peatland complex made up of several merged peatland units – but doesn’t not necessarily include well-defined boundaries as described above. This term forms part of a hierarchical feature classification after (Masing, 1974), also developed in Lindsay et al. (1988) and more recently in Minayeva et al. (2017). In that classification system, the next level down from macrotope is the “mesotope”, which differentiates individual units within a macrotope, followed by the microtope (e.g. individual pool systems, margins, hummock-hollow complex). The microform or nanotope follows, referring to a single hummock-hollow-pool-ridge. The smallest possible unit is the vegetation mosaic within a small area (one meter squared or less).

The notion of “mesotope” within a macrotope, as a single functional hydrological landscape unit, therefore, makes sense to use as the spatial scale to which the definition of “active” blanket bog applies. The definition seems to suggest there may be an (undefined) area threshold beyond which a blanket bog unit would be considered “active” – and the logical follow on would be that below that threshold, it would be “inactive”. 

However, at the landscape scale (macrotope and beyond) and over long timescales, Scottish blanket bog should naturally include areas of accretion/net carbon gain (so-called “active”) and erosion/net carbon loss (so-called “inactive”). This is an inevitable consequence of the erosion of the Scottish landmass via river to incision and slope retreat into previously contiguous areas of peat, leading to loss of peat mass on slopes (erosive boundaries), incised peatland margins and loss of carbon due to lowering of water tables along peatland margins. If climatic conditions remain suitable for the continuing formation of peat, blanket bog systems may support successive cycles of accretion and erosion, as these areas self-heal as observed in the peatland record as recurrence surfaces. In other words, at the macrotope scale, peatland units are likely to go through phases where peat is actively formed, and phases where it is not, or inactive phases during which the net mass gains are balanced or exceed by the net mass losses. 

In Scotland, the land use legacies of burning, drainage, grazing, trampling have largely stopped the cycles of self-healing and accretion, instead exacerbating loss through oxidation and erosion – but these areas may still exhibit potential for future accretion if external stressors are removed and with appropriate intervention, depending on landscape and climate setting (Bruneau and Johnson, 2011). Over timescales typically associated with development on peat (<50 years), these processes may seem irrelevant. However, they matter conceptually by highlighting that in a functioning peatland, not all areas will be or can be actively accumulating peat at the same time. For example, within a mesotope, a soft wet margin adjacent to erosive boundary will be unstable, and drier margins (“inactive”) are essential if wetter interiors (“active”) are to exist. It is therefore unreasonable to expect an entire mesotope or a collection of mesotopes forming a macrotope, to be “active”. Rather, the integrity each individual mesotope – from the edges, through to margins and interior - should be considered. What, then, is the undefined threshold of significance?

 “Significant” is defined by the Oxford English Dictionary as sufficiently great or important to be worthy of attention. It could be argued that any area within a macrotope supporting vegetation assemblages typically associated with near-natural blanket bog could be deemed “significant”. Indeed, it would be worthy of attention and important, because this would suggest that eco-hydrological feedback loops within the macrotope are not disrupted enough to cause the vegetation assemblages to shift entirely (Nijp et al., 2026) and might have potential to support future accumulation with the right intervention and management plan. 

Recommendation

• We recommend avoiding any arbitrary area-based or proportion-based threshold when defining “significant area”, and instead, consider the potential of individual hydrological units (mesotopes) to return to a functional state, and to consider the way in which these mesotopes connect with the wider landscape (macrotope). 

Defining functional peatland

In the National Planning Framework 4, Policy 5d, states that:

 ‘A peat management plan will be required to demonstrate that this approach has been followed, alongside other appropriate plans required for restoring and/ or enhancing the site into a functioning peatland system capable of achieving carbon sequestration’. 

This statement raises semantical issues and could be interpreted in different ways depending on how key concepts are understood. 

Issue: what is a ‘functioning peatland’?

Broadly, a functioning peatland could be defined as a self-sustaining peatland that can deliver the full suite of ecosystem services to an extent that approaches or is equivalent to what can be expected of a near-natural system (Loisel and Gallego-Sala, 2022). In turn, the delivery of these ecosystem services is ultimately underpinned by key processes, operating across a range of scales (Waddington et al., 2015). Therefore, a functioning peatland has key essential attributes and may have further desirable attributes. 

Essential attributes of a functional peatland 

High stable water table, with hydrological integrity at the landscape scale

A high and stable water table is necessary to support the growth of key peatland specialist taxa, including Sphagnum. The position of the water table ultimately determines the volumes of peat exposed to oxygen where CO₂ is produced, and those permanently saturated where CH₄ dominates. The position of the water table is therefore a key determinant of net greenhouse gas emissions and carbon storage of a given peatland (Swindles et al., 2025), alongside temperature (Denager et al., 2026; Juszczak et al., 2013; Lafleur et al., 2005) and the nature of the organic matter itself (Leifeld et al., 2012). 

According to (Evans et al., 2021), there is a “Goldilocks” zone for water table depth of -5 to -13 cm, where the cooling effect of CO₂ sequestration exceeds the warming impact of CH₄ emissions over centennial timescales. Their analysis doesn’t fully account for temperature dependency relationship that may become increasingly important with a changing climate (Denager et al., 2026). It also uses annual modelled estimates of GHG that have their own inherent issues and uncertainties. However, it provides a useful point of reference to define “high and stable” as something that essentially fluctuates within this zone. Studies focusing on apparent peat accumulation rate, rather than contemporary greenhouse gas emissions, also found the highest rates associated with water table depths 5–10 cm of the peatland surface, reinforcing this concept (Swindles et al., 2025).

As well as a high and stable water table, for a peatland to be functional, the smallest scale at which hydrological processes should be considered is the mesotope, i.e. the largest individual functional hydrological unit. In the context of development-related interventions, restoration, enhancement and mitigation, it may be useful to identify these hydrological units and develop habitat management plans that support coherent interventions that maintain or enhance key feedback on water storage, such as flow, transmissivity and mechanical or elastic storage (Nijp et al., 2026; Waddington et al., 2015). 

This may be particularly relevant where a site footprint may not align with natural boundaries and neighbouring land management may need to be considered. It would also be a useful concept where infrastructure design is likely to disrupt natural hydrological flow, such as where tracks or structures create new “mesotopes” by cutting across larger units.

We note that for a mesotope to function as part of a wider macrotope, it will inevitably include features where peat thins and transitions to other habitats such as wet heath, riparian woodland, gorge woodland, etc. In these transitionary habitats, expectations around carbon and water storage may differ.  

Recruitment of specialist species across a range of taxa, and absence of undesirable species including non-native conifers 

A functional peatland should have conditions where specialist species, if not already present, can return, colonise, establish and expand. This may be facilitated by understanding the wider landscape context, species dispersal mechanisms and abilities, and species’ niche requirements and availability (Palmer et al., 1997). With the right species assemblages and stable hydrological condition, new peat formation and carbon sequestration should return. The uptake of carbon in peatlands relies on specialist plants and photosynthetic microbes, while the release of carbon through GHG and aqueous pathways is controlled by equally specialised microbial assemblages (Andersen et al., 2013; Gorham, 1991). However, beyond carbon, a functional peatland should also support the wider range of specialist species across a range of taxa, including invertebrates and vertebrates. Where appropriate, removal of barriers to dispersal or initial re-introduction (e.g. vegetation translocation) may be required, and suitable niches may need to be created through intervention (Allan et al., 2024; Rowland et al., 2021). In the context of Scottish peatland restoration, including as part of development on peat, a functional peatland should be one where the spread of non-native conifers is minimised, therefore adjacent land use may need to be considered at a wider, integrated landscape scale.

Desirable attributes of a functional peatland

A functional peatland would also typically display some of the following attributes:

Well-developed and conserved historical archives of past environmental and human change

Under stable hydrological conditions, enough of the peat profile should remain under permanently saturated condition to ensure the safekeeping of archaeological artefacts and palaeo-ecological archive. This may not apply in the context of development on peat where restoration or management is undertaken on areas with substantial land use legacy where some of the peat has been lost or modified (e.g. peat cutting, drainage and erosion). The planning process includes separate guidance on identifying and protecting archaeological artefacts, which should also be adhered to.

Potential for natural successional processes to take place

A functioning peatland should be one where succession can proceed, with early pioneer species replaced by later successional species. Scottish blanket bogs are considered a late-successional stage ecosystem (Klinger, 1996). It is not realistic to expect that restoration, rehabilitation or enhancement would allow ecosystems to transition rapidly into a steady state associated with ecological equilibrium. However, in a functioning peatland, the conditions should be stable enough to allow the successional processes to take place, and where landscape setting allow, for new peat to form and accumulate. This would include, over time, the formation of new peat and the development and evolution of macro- and micro-morphological structures, such as appropriate habitat mosaics and micro-topography. 

Resilience to new disturbance events

Resilience is defined as the ability to recover and maintain function in the face of disturbance. Near-natural peatlands are inherently resilient ecosystems (Loisel and Gallego-Sala, 2022) and can recover and maintain function following events like wildfires (Wilkinson et al., 2023) and droughts (Nijp et al., 2026), and this could be realistically expected from a functioning peatland. “Bog Breathing” – the dynamic movement of the surface in response to changes in volumes of water and gas in the peat – has long been described as a key self-regulating mechanism in peatland (Howie and Hebda, 2018). In Scotland, it has been identified as a key resilience mechanism in the context of drought, where the rapid, partially irreversible collapse of the porous upper layers of peat in pool systems tracks the water level, maintaining high moisture level post-drought, stimulating the growth of early successional species (Marshall et al., 2022). It has also been used as a diagnostic of peatland condition, identifying that actively degrading peatland are detectable by long-term trends of surface decline, and the lack of typical rise-and-fall “breathing” patterns (Large et al., 2025). Therefore, a functioning peatland could be thought of conceptually as one that is capable of bog breathing. In the context of development on peat and particularly following restoration of highly degraded areas (e.g. forest-to-bog, erosion), recovery of “bog breathing” patterns similar to those of equivalent near-natural systems and indeed increased resilience may only be achieved over time.

No need for ongoing intervention, but ongoing management may be needed

A functioning peatland should be self-sustaining. In the context of development, the peat management plan should indicate how, beyond initial intervention, additional stressors that may impede recovery will be managed at the appropriate level and without unintended consequences. In the context of Scottish peatlands and development on peat, this may include consideration around non-native conifers, deer management, wildfire (sources of ignition, spread to/from adjacent areas, access), cumulative impacts, etc. We note that this would also apply to margins and transitions to non-peatland systems, and that the level of management required may depend on whether they are natural or artificially maintained.

Recommendation

• We recommend that the definition of a functioning peatland is interpreted in the broadest sense, as a self-sustaining peatland that can deliver the full suite of ecosystem services to an extent that approaches or is equivalent to what can be expected of a near-natural system.
• We recommend that the essential attributes of a functional peatland are a high and stable water table with hydrological integrity at the landscape scale and where recruitment of specialist species across a range of taxa, and absence of undesirable species including non-native conifers are achieved. 
• We recommend that the desirable attributes of a functional peatland include well-developed or protected environmental and archaeological archives, the potential for natural succession to take place, resilience to further impacts, notably those arising from climate change, and no need for further intervention. A range of variables may be assessed to provide further evidence of trajectories of change and outcomes for those attributes.

Issue: How to determine whether a peatland is ‘capable of achieving carbon sequestration’?

The definition suggests that a functioning peatland should be capable of achieving carbon sequestration, but there is currently no clear guidance on how this should be determined. There are a broadly two types of possible approaches: quantitative estimates, or qualitative assessments. For quantitative estimates, the carbon sequestration could be measured empirically or estimated using a proxy through modelling from empirical relationships with a range of variables. For qualitative assessment, the carbon sequestration potential could be assumed to be taking place or not based on environmental variables and context. 

Empirical measures of C sequestration

The net carbon balance of a peatland is the sum of all the net gains (Gross Primary Productivity or GPP from photosynthesis) and losses (Ecosystem respiration or ER, Methane emissions or CH₄, Dissolved Organic Carbon or DOC, Particulate Organic Carbon or POC). In near-natural peatland systems, C sequestration is caused by an imbalance where gains consistently exceed losses over time. Therefore, the most direct way of assessing the C sequestration of a peatland is to measure and sum all the fluxes. However, this would not address the potential, only the current state.

The largest contribution to the net carbon balance comes from the land-atmosphere exchanges of greenhouse gases (GPP, ER and CH₄). These can be measured empirically with two methods more commonly applied: static chambers or eddy covariance (flux towers).

Static chambers are small plexiglass boxes (open at one end) that are typically used to capture the GHG emissions of a small, discrete area rarely exceeding 1m2. The chamber is placed on top of the small area often delineated by a collar (a small open-ended box of the same area) and encloses the vegetation (Alm et al., 2007). The GHG concentrations are monitored within the chamber over a short period of time with a gas analyser, typically once with a shroud to capture ER and CH₄ and once without a shroud to capture the Net Ecosystem Exchange (NEE). From this, GPP can be derived, as the difference between NEE and ER. Typically, chamber-based measurements are replicated with multiple collars within a given area, and are repeated over time. Alongside direct measurements, sites can be instrumented with e.g. continuous temperature, moisture and water level loggers. These measures of environmental variables can then be combined with direct flux measurements to derive modelled averages over e.g. monthly or annual timescales (Alm et al., 2007). They require a high level of expertise and specialist equipment, cannot capture landscapes fully, and are time intensive.

Eddy Covariance Flux Towers are used to capture the GHG emissions over small (<1 ha), homogeneous areas within wider landscapes. Eddy Covariance is a technique that effectively models fluxes by combining high resolution (30 min interval) micro-meteorological inputs (wind direction, wind speed, temperature, pressure, photosynthetically active radiation or PAR) and CO₂ and CH₄ concentration as measured by sensors (Laurila et al., 2012). This is a highly specialised, high-cost and high-maintenance technique. 

Aquatic POC and DOC losses can be measured empirically at the catchment level in streams where both concentration and flow need to be established (Alm et al., 2007). They typically make a smaller contribution to the overall carbon balance compared with GHG (Billett et al., 2010). While the techniques for measuring POC and DOC concentration and flow are well-established and relatively accessible, catchments are rarely uniform and the relative contributions from different land uses and ecosystems cannot be distinguished readily. 

Aeolian losses of POC (wind erosion) are difficult to measure but could be significant in exposed landscapes with significant areas of bare peat (Campbell et al., 2002). They are generally not included in Scottish peatland carbon balance.   

Estimating carbon sequestration or emissions by proxy

When direct measurements of greenhouse gas emissions and other carbon losses are not possible or realistically achievable at scale, proxies can be used to infer emissions from known relationships with other variables. Estimation of carbon emissions by proxy is by far the most common approach used in peatland.

Mapping and monitoring variables in peatland that could be used as proxies for greenhouse gas emissions can be done at a range of scales and with a range of techniques. Many of these have been recently reviewed by (Minasny et al., 2024), and it is beyond the scope of this report to report on all of those. Instead, we focus here on the approaches and methods that may be most relevant in the context of development on peat.

Water level

The use of water table depth (WTD) as a proxy for interannual GHG emissions from peatlands has been well established (Evans et al., 2021). Therefore, with reliable measures of water table depth, it should be possible to predict greenhouse gas emissions, and therefore, whether any given site is likely to sequester or release carbon. Remote sensing methods or data product that measure WTD by proxy have so far only been validated at local or regional scales (Harris and Bryant, 2009; Kalacska et al., 2018; Reddin et al., 2025). However, there is promising work underway on remote-sensing products that could predict water table depth over much larger areas (national, Europe) and it may be that in the future, one or several products are developed that can be readily deployed (Koch et al., 2023; Toca, 2023).  

In the meantime, monitoring water level in situ can provide WTD data, and is relevant in the context of renewable energy development, where it is already commonly deployed. We note that field-based assessment of WTD will still be needed for the validation of any remote-sensing products. In the field, WTD measurement is typically achieved by setting up a network of dipwells where manual readings can be taken at regular intervals, and/or where automated pressure transducers (water level loggers) can provide high frequency data. When used in the context of intervention (e.g. restoration), including controls and monitoring of other variables that may impact the water budget, such as rainfall and temperature can add value, as these variables may help differentiate e.g. land-use effects from climate-driven changes (Bonnett et al., 2009).

Land cover classes or condition categories based on vegetation

Rather than water table level, emissions can also be assigned to broader categories or classes of peatland based on features including degradation, restoration intervention, vegetation or land cover classes. Such an alternative approach is used notably for reporting the national contribution from wetlands as part of the National Greenhouse gas Inventories under the Land Use, Land Use Change and Forestry (LULUCF). The Intergovernmental Panel on climate change’s Wetland supplement, published in 2013, provides a framework to assign emissions to categories of wetlands including peatlands. The default method (Tier 1) can be improved where data and knowledge are available to better constrain emission factors to key categories. Such a methodology has been developed for the UK, based on land cover categories, with the method regularly reviewed and updated as knowledge and data increase (Evans et al., 2017).The Peatland CODE, a voluntary certification standard developed and managed by the IUCN UK Peatland Programme, also estimates emissions and carbon credits by proxy, using a framework of key categories all associated with emission factors. Categories are assigned by field-based assessments undertaken by certified assessors and focus mainly on restoration features and vegetation. 

In both cases, the problem then largely becomes one of classification once again, though for the different purpose of assigning emissions. It is beyond the scope of this report to review extensively the wide range of methods, approaches and techniques that can be used across the range of scales (plot- to landscape) to classify and monitor peatland. Rather we point to recent reviews (Lees et al., 2021; Minasny et al., 2024). We note that as well as remote sensing products, there are rapid developments combining various sensor products (optical, radar, etc) with AI models, developed for the purpose of monitoring restoration trajectories, including in Scotland (Ball et al., 2023). These may or not be aligned with the IPCC or Peatland CODE and may not readily translate into classes for which emission factors are assigned. 

We also point to key highlights emerging from these reviews, notably that the heterogeneity of peatlands at a range of scales is challenging for large-scale monitoring and that remote-sensed models require robust and extensive field validation to perform well, and that long-term datasets are needed to draw valid conclusions on changes in peatland condition as measured by proxies. Alongside water table, vegetation is seen as critical variable. In the context of development on peat, it seems like field-based assessment of vegetation is likely to be the most robust and transparent method of assessing condition. However, it is likely that field based assessments might work well alongside other approaches using e.g. drone-based imagery or remote-sensing products. 

Qualitative assessment of carbon sequestration “potential”

It may be that in the context of the development on peat where decadal timescales are involved, and where carbon units derived from land management interventions are not counted or traded, it is desirable to consider the definition by putting more emphasis on the word “capable of”. From this perspective, rather than focusing on a quantitative assessment, an area could be assumed to be capable of achieving carbon sequestration provided that the landscape setting, climate, hydrology and vegetation have the potential to support the necessary processes as described in the previous section.  

Given that peatland macrotopes and mesotopes are heterogeneous by nature, we recognise that the above conditions may be met by areas where peat is currently <50cm e.g. This may be because past land use (e.g. peat cutting) has removed peat from an area where it has otherwise a strong potential to form. On the other hand, appropriate management of margins and non-peatland habitats may also enhance the potential by improving hydrological integrity of individual mesotopes and the connectivity within macrotopes. For the purposes of a qualitative assessment of potential, the variables identified as relevant proxies (hydrology, vegetation) could be used. In this case, standardised approaches and protocols would be useful. Standardisation would increase transparency, and support potential for data to be re-used, e.g. as validation dataset for remote sensing products. We recognise that developing reliable technologies requires comprehensive field validation, and that a key challenge going forward will be the retention of field knowledge and expertise that will be essential to identify errors and improve models in the future.

Recommendation

• We recommend that the interpretation of the statement should put the emphasis on the word “capable of”, and that focus should be on the potential to achieve carbon sequestration, rather than on any quantitative estimates of carbon sequestration. 
• We recommend that direct measures of carbon sequestration should be considered where they could fill a key knowledge and research gap, in collaboration with organisations with the appropriate skills and systems in place. We do not recommend that direct empirical measures of carbon sequestration are routinely implemented otherwise, as it would be impractical and not cost-effective, and would fail to capture the “potential”.
• We recommend that the estimation of greenhouse gas emissions by proxy should not be the focus of the interpretation of the definition in the context of development of peat. However, we recognise the opportunity around remote-sensing and we recommend that where appropriate, collaborative partnerships should be considered to support the development and the rigorous validation and testing of emerging techniques, e.g., combining multi-sensor platforms and AI models. 
• We recommend that the potential should be assessed at both the mesotope and macrotope levels. Suitable intervention(s) and management should be adapted to local settings, land use legacies and wider landscape context.
• Given the lifespan of renewable energy development on peat, we recommend that the potential is assessed and optimised at the mesotope level and that subsequent, regular assessments of the primary key functions (hydrology, vegetation) are used to determine whether the potential is likely to be realised, or not. Where it isn’t, appropriate management or intervention should be considered.
• We recommend that for the purpose of planning, field-based assessment of vegetation and hydrology (specifically, water table depth) should be undertaken, noting that rapid advances in drone technology, remote-sensing tools and AI might also be used in combination with field-based assessments in the future. 
• We recommend that appropriate guidance and training are provided to ecologists and consultants undertaking any field-based assessments to improve the consistency of data collection and increase the potential for data re-use. 

Part 2 – Peatland value assessment

Introduction 

In the National Planning Framework 4 (NPF4), Policy 3b states: 

‘Development proposals for national or major development, or for development that requires an Environmental Impact Assessment will only be supported where it can be demonstrated that the proposal will conserve, restore and enhance biodiversity, including nature networks so they are in a demonstrably better state than without intervention’.  

Policy 3biii requires that 

‘an assessment of potential negative effects which should be fully mitigated in line with the mitigation hierarchy prior to identifying enhancements’, 

with Policy 3biv stating that

‘significant biodiversity enhancements are provided, in addition to any proposed mitigation. This should include nature networks, linking to and strengthening habitat connectivity within and beyond the development, secured within a reasonable timescale and with reasonable certainty’.

NPF4 Policy 3b does not specify or require a particular assessment approach or methodology to demonstrate the delivery of significant biodiversity enhancements, though the policy is clear that best practice assessment methods should be used. Assessment may be qualitative or quantitative (for example through use of a biodiversity metric). A biodiversity metric can be used as a method of identifying baseline habitats, assessing their condition, and identifying what weighting these habitats should receive in terms of carrying out offsetting measures. 

Biodiversity modelling and metrics

The Kunming–Montreal Global Biodiversity Framework calls for transformative action to embed biodiversity conservation and sustainable use considerations into decision-making processes across both public and private sectors. In response, biodiversity accounting approaches are rapidly emerging, aiming to translate the complex and multidimensional nature of biodiversity into quantitative metrics that can support practical decision-making. Such approaches are expected to play a key role in enabling expanding policy and management practices, including nature-based solutions, corporate biodiversity management, no-net-loss commitments, and the planning of sustainable infrastructure (van Rees et al., 2026). However, a recent analysis of 129 biodiversity assessment tools (Constantino-Panopio et al., 2025) revealed that less than half of recommended tools were classified as a database or software tool providing standardised outputs to queries. Moreover, only 36 (28%) of the listed tools contained ecosystem (31) or species data (18) with global coverage, and only 13 (10%) tools used data on individual species to enable risk to be assessed at a species level. There is an urgent need for guardrails when using these tools to avoid unintended consequences like biodiversity loss and environmental injustice (van Rees et al., 2026). Tools should not be used to replace good decision making, and sound ecological understanding (by competent metric tool users) will always be crucial to ensure that the best ecological outcomes are being delivered for any given site.

Biodiversity accounting tools are needed in all sectors of society, yet different stakeholders use different approaches. For instance, Life cycle assessment (LCA) is a modelling tool to quantify the overall environmental impacts of products, processes, services or policy scenarios across their entire life cycle, including resource extraction, production, transport, use, and disposal. The approach enables the assessment of environmental pressures linked to the triple planetary crisis: climate change, biodiversity loss, and pollution (Hellweg et al., 2023). LCA allows the linkage of product or service responsibility with biodiversity outcomes and enables producers and consumers to make informed decisions.

Efforts to integrate biodiversity impacts into LCA modelling have been ongoing for some time. The UNEP–SETAC Life Cycle Initiative was launched in 2002 and initiated the Global Guidance for Life Cycle Impact Assessment Indicators and Methods (GLAM) in 2013. GLAM published the first guidance volume in 2016 to provide globally harmonised recommendations for Life Cycle Impact Assessment indicators, including approaches addressing biodiversity-related impacts (UNEP-SETAC, 2023). The key challenge in LCA is the requirement that potential biodiversity impacts should be modelled independent of a specific geographic context. Data availability has long been the limiting factor. Land use associated with a product or service is relatively easy to quantify but the impacts on biodiversity resulting from land use change or different land use intensities are complex. The current GLAM guidance published in 2024 recommends the method “Biodiversity Impact Assessment Considering Land Use Intensities and Fragmentation” by (Scherer et al., 2023). This refined method calculates relative species loss as the dimensionless metric Potential Disappeared Fraction (PDF) at the global scale, by multiplying the regional species loss and the global species extinction potential, thus having the global preservation of species as the focus of impact assessments. 

Recent developments, such as the spatially differentiated characterisation factors proposed by Scherer et al. (2023), introduce improvements in representing land-use intensity and habitat fragmentation in biodiversity impact assessment within LCA. While such approaches contribute to ongoing methodological discussions under initiatives such as UNEP-SETAC GLAM, they are not yet implemented in the current European Product Environmental Footprint (PEF) impact assessment framework, which continues to rely on soil-function-based land-use indicators. Although method development is ongoing, LCA has great potential to contribute to the understanding and quantification of land use impacts on biodiversity and allow producers and consumers to make informed decisions and choices.

When it comes to biodiversity accounting from a planning perspective, a wide range of offset programmes exist, with over 100 public policies now incorporating no net loss of biodiversity principles (Marshall et al., 2020). Approaches in the European Union (EU) have been far from unified. Across the EU, biodiversity compensation or offsetting is governed more by shared principles than by a shared metric - primarily through the mitigation hierarchy, with offsetting representing the final step to address residual impacts (Mechin et al., 2023). In their recent review, Marshall et al. (2020) identified a notable disconnect between how biodiversity is measured at the offset planning stage and how outcomes are subsequently evaluated, highlighting the need to reassess how offset policies and programmes define, value and measure biodiversity to ensure that key ecological processes are captured and no net loss is achieved. Careful consideration of local and regional biodiversity is key, with a one-size-fits-all approach for biodiversity compensation unlikely to succeed.

Developing the Scottish Planning Biodiversity Metric

In this context, it is not surprising that NatureScot is in the process of developing its own Scottish Planning Biodiversity Metric for use in development falling under Policy 3b NPF4 (e.g. national, major, and EIA development). The Scottish metric will primarily adapt the English Statutory Biodiversity metric for Biodiversity Net Gain. 

The English Statutory Biodiversity metric is used to create a baseline from which pre-development Biodiversity Units (BU) can be estimated. These units are based on a series of multipliers combining habitat type, habitat area, habitat condition, distinctiveness, and strategic significance. To determine the post-development BU, additional risk multipliers are included: difficulty to create/enhance, time to target condition and spatial risk. The difference between post-development BU and pre-development BUs determines the required biodiversity offsetting and enhancement. By comparing the pre-development BU value with the post-development BU value, the metric tool can be used to help understand what level of intervention is required to fully offset the biodiversity lost and deliver additional biodiversity enhancement.  

In a peatland context, there are some areas in which the Scottish Planning Biodiversity Metric proposes to differ from the approach taken in England. For example, the “difficulty to create/enhance” multiplier represents a technical risk associated with the uncertainty in the effectiveness of techniques to create or enhance habitats. In the English Statutory Biodiversity metric, a fixed ‘High’ difficulty risk multiplier applies to all blanket bog interventions. In Scotland, it is recognised that this doesn’t capture the advances in restoration techniques and differences in starting condition, and a more flexible approach to assigning risk multiplier values may be beneficial. There are sites where lower levels of degradation combined with well-established techniques would bring high likelihood of success, suggesting that a lower risk multiplier may be appropriate. For complex or more degraded sites, a higher technical risk multiplier may be desirable. 

For peatland habitats, the Scottish Planning Biodiversity Metric may also differ in the way the temporal risk multiplier is applied. This risk multiplier represents the average time between the start of habitat creation/enhancement works and the target outcome. In the English Statutory metric, developers can deviate from standard metric values, if they believe they can successfully create/enhance a habitat quicker than the average value assigned (with evidence) and the consenting body agrees. In Scotland, it is recognised that the timescales of habitat recovery following restoration are likely to be non-linear, with early transitions likely to be faster than achieving diversity comparable to the original system, which may be much slower or impossible. For the Scottish Planning Biodiversity Metric, clear definition around the acceptable end point of successful restoration will need to be provided and may impact how the risk multiplier is applied.

Another area where the metrics will differ between Scotland and England relates to the condition of peatlands habitats. The English Statutory Biodiversity Metric uses the same six core condition assessment criteria for peatlands as for all other wetland habitat types, with an additional criteria applicable to bog habitats only. Given the extent of peatland habitats in Scotland and the frequency with which they are likely to be encountered/impacted by development in the uplands, it is proposed that a more specific condition assessment approach for peatland habitats would be beneficial.

Ideally, a condition assessment for peatland habitats in Scotland will address several important considerations:

1. The method should be relatively simple and quick to assess in the field.
2. The Defra statutory metric condition assessments have 3 main condition classes with 2 intermediate steps applied based on professional judgement (poor, fairly poor, moderate, fairly good, good). Other habitats in the Scottish Planning Biodiversity Metric will likely be scored in 5 classes (poor, fairly poor, moderate, fairly good, good). Therefore, a similar structure should be mirrored by a bespoke peatland condition assessment.
3. The Scottish Planning Biodiversity Metric will require baseline condition assessment criteria to be summarised into an overall value (number), with this value then added into the metric calculation formula to determine the baseline Biodiversity Unit value of the peatland habitat. The condition assessment approach will therefore have implications for the scale of offsetting requirement indicated by the metric tool to fully compensate for peatland habitat losses. More nuances may be useful, for instance summed scores as opposed to categorical classes only.
4. It should cover as many peatland types as possible.
5. It should be sensitive enough to be used as part of post-development to document progress against targets for offsetting interventions.

Preferably, the metric would use, or add to, an existing method, rather than introducing anything new. Thus, the next section of this report describes some of the current methods used for peatland condition assessment in Scotland and assesses their suitability for use in the context of the Scottish Planning Biodiversity Metric. We note that it is thought that the common standards monitoring for upland blanket bog is too detailed for use in the development context and is therefore out of scope for comparison. We have excluded the Peatland CODE field assessment because of its purposes (determine carbon credits associated with emission reduction based on changes between discrete condition classes).

Existing condition assessment systems for peatland condition 

1. Peatland ACTION Peatland Condition Assessment 

Brief description: 

The Peatland ACTION Peatland Condition Assessment is designed for blanket bog and is used to assign one of four categories of peatland conditions to a given site, with a primary purpose to identify potential for restoration intervention. It focuses on “indicators” including Sphagnum moss cover, other vegetation, bare peat, drains, burning and grazing.

Step-by-step: 

• Desk based survey and site walkover to identify features as above.
• Assign category based on the presence (and dominance) or absence of features.
• Refine categories on map using appropriate buffers (e.g. drained category only applies within 30m either side of a drain or a re-vegetated hagg/gully system).

Advantage: 

Simple, doesn’t require extensive botanical knowledge, accessible. Well-defined categories relevant to management intervention. 

Potential limitation: 

Primarily designed for blanket bog and specifically designed to inform restoration plans. Somewhat subjective as no framework to collect field data systematically, instead based on expert judgement and site walkover. Doesn’t include any measures of peat depth. Might not be appropriate because of its intended purpose with four classes that may not be easily altered to meet the requirement of the Scottish Planning Biodiversity Metric. 

2. Richard Lindsay’s “Peatland Condition matrix”

Brief description: 

The “Condition Matrix” was created by Richard Lindsay to look at habitat condition across peatlands while taking into consideration plant distribution across microtopography. It is mentioned in the Peatland CODE white paper on methods for monitoring biodiversity credits. The matrix is it based on a walk-over survey over a series of pre-identified areas within a wider site. The report’s lead author has first-hand experience of using the matrix in the field over several sites. 

Step-by-step:

• The site to be surveyed is split into smaller areas, based on texture and essentially capturing the “microtope” features of the site. One condition matrix will be completed for each of those smaller areas. 
• In the field, the surveyor, equipped with the condition matrix template, walks over each small area and ticks each box that applies (columns), for each micro-topographic feature that is present (lines). The micro-topographic features are assigned based on height and moisture and follow Lindsay et al (2016). Within the matrix, the boxes are colour coded using a red-amber-green (RAG) system, with desirable species in green and species associated with degradation in red. In each box, a list of species is included, and the user can tick all the species that apply or simply tick the genus, if botanical knowledge is limited. 
• Each area and the whole site can then be visually assessed using the RAG scheme. Repeated visits can be used to determine whether the site condition is improving (more green, more micro-topographic features) or degrading (more red, fewer micro-topographic features).
• Where botanical expertise is available, a synusial survey may be used to complement the condition matrix. In this survey, every species found in every micro-topographic feature is recorded using a semi-quantitative scale (rare, occasional, present, abundant, dominant).

Advantage:

The system is relatively simple and doesn’t require any set up in the field, only a copy of the condition matrix (paper or electronic) for each area to be assessed and a hand-held moisture probe. It could be used in theory by surveyors with varying range of botanical identification skills. The visual assessment based on a RAG system is simple and accessible and could be used to track change over time. It can be converted to a 0-100 scale, so could potentially be adapted to fit a 5-class system. 

Potential limitations:

The separation of the site into smaller areas is based on remote sensing classification of textures (or reliable identification of microtopes). The method is not in the peer-reviewed literature yet and so may not be repeatable with consistency. While botanical skills may not be needed, the system is underpinned by reliable identification of discrete micro-topographic features that some surveyors may not be familiar with and may not distinguish readily between degraded classes. Moisture-based measures are influenced by weather and may be unreliable. Although not strictly required, the synusial survey is time consuming and requires in depth botanical skills. Doesn’t include any measures of peat depth.

3. Remote-sensing vegetation-based field validation

Brief description: 

Various organisations have developed field-based vegetation surveys for the purpose of validating remote-sensing assessment of condition (e.g. using optical sentinel-2 data). Perhaps an example of evolution convergence, most of these surveys share the same basic principles and approaches. One such survey was designed by the lead author of this report to support a vegetation classification of Sentinel 2 pixels as part of a study by Alshammari et al. (2020). A similar approach has also been used to build up field validation datasets for classification models by The James Hutton Institute and by The UK Centre for Ecology and Hydrology. 

Step-by-step:

• Selecting coordinate(s) within a site of interest either randomly or based on site knowledge or method needs.
• In the field, walking to the selected coordinates/area of interest and setting up a 10 m x 10m quadrat using e.g. stakes and rope. 
• Undertaking a survey of Plant Functional Type (PFTs, e.g. Sphagnum, sedges, shrubs, grasses, trees, other mosses, lichen, etc.) within the 10 x 10m quadrat, accompanied with sketches and/or photography to illustrate the distribution of the PFTs. Percent covers are either estimated to the nearest % or assessed semi quantitative scales e.g. Braun-Blanquet or Domin scale and entered manually (field sheets or tablet).
• The data are collated, digitised, and used to derive or map e.g. dominant PFT(s), which may then be used for validation purposes as proxies for condition categories.

Advantage: 

Relatively simple to implement and undertake. PFTs do not require advanced botanical identification skills. Semi-quantitative or quantitative data allow for robust comparison over time. 

Potential limitation: 

Method requires set up and dismantling of quadrat in the field and could not be realistically described as a “rapid” assessment, especially if it were to be repeated for a large number of points, and given the small area surveyed (10 x 10 m). Doesn’t include wider landscape details or peat depth. Doesn’t lead to a summed-up score. Currently used to match land-surface condition classes than may not fit with the desired 3-5 condition categories.

4. InSAR Rapid Field-based Assessment Tool

Brief description: 

The InSAR Rapid Field-based Assessment Tool, first described in (Bradley et al., 2022), was designed specifically to capture a range of hydrological, ecological, and land management features that might impact condition as perceived by InSAR-derived surface displacement or “Bog Breathing”. It was initially developed to form a large-scale validation dataset, where 216 small areas of blanket bog (0.3-0.6 km²) across a range of conditions and setting were visited and assessed using a qualitative (present, absent) and semi-quantitative scale (absent, present, dominant) for a range of features. It was then refined as part of NatureScot funded research (Bradley et al., 2025), with a scoring system to align the field-based condition assessment outputs to the 3-point InSAR-based condition classes (good, stiff, degrading) with field validation (80 field data points).

Step-by-step:

• Design a grid for survey (random or structured).
• Walk to the point designed for survey and walk over around the point to cover roughly a 30 x 30 m area. 
• Using the template, tick all the features that apply (presence, absence) or score the features that need scoring (vegetation), using the guidance as needed. Scores can be recorded on a tablet where survey questions have already been added. 
• The score is automatically calculated based on the entry and mapped over condition classes using a colour coding (condition) and shading (probability). 

Advantage: 

Peer-reviewed and published method that is simple and doesn’t require any set up in the field. Template is intuitive and guidance is in place to support users. Rapid: untrained users could independently complete assessment in <5 min after brief training, with high reproducibility (different users reaching same scores) (Bradley et al., 2025). Can be matched to existing survey (peat depth) or have a bespoke design. Is flexible, for instance, the spreadsheet has been adapted by a PhD student for transect-based study of peatland margins (Webber, 2025). A version of the tool has also already been used by some developers as part of Environmental Impact Assessment (EIA), so could be seen as “industry-friendly”. Has demonstrated potential for remote-sensing validation so could be seen as “future proof”. 

Potential limitation: 

Currently primarily designed to validate InSAR condition categories (only 3) that do not map or align with other condition classes.

Other tools

Discussions with colleagues in Canada and Iceland revealed that similar rapid assessment tools are being developed there as well, mostly with a purpose to inform restoration design and post-restoration assessment, rather than in response to development on peat. In both cases, the rapid assessment tools focussed largely on vegetation, with the Icelandic tool developed around 5 categories of outcome ranging from near-natural to highly degraded. 

We are not aware of other field-based rapid assessment tools specifically designed for peatland in the context of planning and development. However, we acknowledge that it was beyond the scope of the report to conduct an in-depth review of all available methods and tools relevant to peatland condition assessment, and that the list above is likely to be incomplete. Almost certainly, other methods or tools exist, which may share features of the ones included here, or may differ.

All the methods reviewed here have merit and have clearly been designed thoughtfully for their specific purpose. However, none of the peatland condition assessment methods contrasted in the report have been designed specifically to fit the context of the Scottish Planning Biodiversity Metric and could be transferred without alteration. 

Recommendation

• We recommend that the InSAR Field-based Rapid Assessment Tool has the strongest potential to be rapidly and readily adapted to meet the required considerations (easy to use and rapid to deploy in the field, separation into up to 5 classes, nuanced through a summed score, applicable to wide range of peatland types, and sensitive enough to pick up changes in condition over time). 
• We recommend that the InSAR Field-based Rapid Assessment Tool is reviewed and adapted to suit the needs of the Scottish Planning Biodiversity Metric. We recommend that a new, bespoke template is created and it is then re-named to avoid confusion with the InSAR field-based assessment method. 

Part 3. Adapting the InSAR Rapid Assessment Tool for the Scottish Planning Biodiversity Metric 

Categories, variables and field scoring

As a first step, we reviewed the broad categories of variables included in the tool available at Bradley et al., (2025): stiffness, vegetation, hydrology and land use. All the categories were deemed relevant in the context of the Scottish Planning Biodiversity Metric tool, and no gaps were identified. Therefore, the same four categories were maintained. 

Then, the individual variables within each category were critically assessed to determine whether all were needed and appropriate, identifying gaps or potential issues. We also considered whether the field scoring system for the variables was adequate. This was done through discussion with NatureScot leads on the project. It was clear that some changes were needed to reflect the specific needs of the Scottish Planning Biodiversity Metric. The variables were assessed and updated as follows:

Stiffness 

This relates to how the peat surface feels when walking over. There are three variables (Spongy, Soft, Firm). In the field, the categories are mutually exclusive and are present (1) or absent (0).

This category was left unchanged with the same three variables. The multipliers applied are: strong positive (5) for “Spongy”, a small positive (1) for “Soft” and neutral (0) for “Firm”. 

Vegetation

This relates to the abundance and co-dominance of vegetation within an area of approximately 20-30 m surrounding the observation point. It doesn’t involve looking at length for single individual of a species. There are 10 variables (Sphagnum, Shrubs, Sedges, Mosses, Rushes, Molinia, Other Grasses, Scrub, Conifers, Bare peat) and in the field, a total of 10 points are allocated among all possible variables with more points for the dominant group(s) and 0 points for groups that only have small number of individuals and/or absent.

There were significant updates in this category. The “shrub” category was split in two, with “Ericaceous and other peatland shrubs excluding Calluna vulgaris” and “Calluna vulgaris” separated. This was done to provide further nuance and reflect better the range of NVC classes where the identity of the ericaceous species matters. For example, it allows some distinction between a peatland dominated by Calluna vulgaris that would be strongly associated with management regimes involving muirburn (Maltby et al., 1990) from a peatland in a similar setting but without the same land use legacy, where Calluna may be present but co-dominant with other species like Erica tetralix or Empetrum nigrum, for example. The variable “lichen” was added as a recognition of its prevalence in certain context (e.g. montane bog) and because its known sensitivity to e.g. wildfires (Miller et al., 2018) and air pollution (Insarova et al., 1992), allied with a slow response time post-intervention. The descriptor for the variable “conifers” was modified to only mean self-seeding non-native conifers, to avoid double counting with the land-use category “Forestry” (see below).

The initial scoring system used in the InSAR validation version was deemed somewhat complex for rapid field-based assessment. Instead, a semi-quantitative scale of 0 (absent), 1 (a few individuals are present), 2 (several individuals or patches are visible), 3 (Dominant with numerous individuals and/or extensive cover) was adopted. The multipliers were as follows: Sphagnum (7), Ericaceous other (3), sedges (3), Calluna (0.5), Mosse (0.5) Lichen (0.5), Molinia (0.5), Rushes (-1), Other grasses (-1), Scrub (-1), Self-seeded conifers (-4) and Bare peat (-4). 

Hydrology

This relates to features that impact water storage within the vicinity (20-30m) of the point. It includes 9 variables: Pools, Stream, Drains, Erosion, Flat, Gentle slope, Moderate-to-steep slope, Shallow, Deep. We note here that “Pools” refers to groups of generally smaller bodies of water underlain by peat and are recorded here as a feature that is either present or absent. Some pool systems and even amalgamated pools themselves can be extensive, but here we note that bodies of water that would be recorded as habitat in their own rights as “lochans” would be assessed separately in the SPBM. The scoring system is a presence (1) or absence (0), with some categories mutually exclusive (only one of “Flat”, “Gentle slope”, or “Moderate-to-steep slope”, and only one of “Rocky outcrops, bedrock or mineral soil” or “Only peat”).

This category was updated to include a new variable “Micro-topography”, reflecting Richard Lindsay’s condition matrix emphasis on this feature of near natural peatland. The category “Streams” was renamed “Watercourses” to avoid any confusion in nomenclature (e.g. burn or stream) or size (e.g. streams or rivers). We note here that the watercourses are recorded principally as a way to denote boundaries of the peatland systems (i.e. as a feature of the habitats that is either present or absent). The assessment of the watercourses as habitats are handled in a separate module of the SPBM. The variable “shallow” was re-named “Rocky outcrops, bedrock of mineral soil” and the variable “deep” was re-named “Only peat visible” to reflect that this category relates to water storage primarily, rather than any threshold-based definition. It was deemed that actual measures of peat depths would be complementary to the walk over assessment, rather than embedded within it. The scoring system was unchanged.

The multipliers applied were as follows: Pools (5), micro-topography (5), Watercourses (1), Active drains (-2), Erosion features (-3), Flat (4), Gentle slope (-1), moderate-to-steep slope (-3), Rocky outcrops, bedrock or mineral soil (-1), only peat (3). 

Land use (including historical) 

This category relates to current land use(s) and/or past land use(s). It includes seven variables: Peat cutting, Road or Track, Grazing, Burning, Forestry, Restoration, Near natural. The scores are based on presence (1) or absence (0) and some categories are mutually exclusive (e.g. only one of Forestry, Restoration or Near Natural).

This category was updated, with the variable “Windfarm” removed given that the method is likely to be deployed in a range of pre-development assessments, that may or not be associated with windfarms. Instead, a new variable was introduced “Road or Track”, given that many developments are likely to use existing tracks and/or develop new ones, and that tracks are known to have biodiversity implications, particularly where they are associated with forestry (Hancock et al., 2020).

The categories were re-ordered in the tool to have all the variables associated with human impacts first, then “Restoration”, then “Near-natural”. The multipliers applied were Peat cutting (-1), Road or track (-1), Grazing (sheep, deer, cattle) (-1), Burning (-1), Forestry (-5), Restoration (1), Near-Natural (5). 

A score for each category is calculate as the sum of the variables within that category and the specific multiplier for that variable, with the total score as the sum of each categorical score.

Multipliers, summed scores and condition classes

Iterative development of multipliers with synthetic data

The next step was to adapt the method into a tool that works for the range of possible field conditions that may be encountered in Scotland’s peatland in the context of development, including under different landscape and climate settings and with different land use legacies. To do so, we created a set of 29 “synthetic” field scores, based on extensive field survey data available from previous projects as well as expert knowledge and experience. These represented 27 possible peatland scenarios, and two “impossible peatland” scenarios setting the boundaries of the tool, i.e. the absolute maximum score and the absolute minimum score. 

We entered the synthetic data in the tool and iteratively adjusted the formulas from the Bradley et al. (2025) version to reflect the changes in variables and scoring (for vegetation) and to capture the nuances required in the context of planning. This iterative approach included an interactive session with the NatureScot project leads. The formulas are based on field scores and multipliers, which can be positive or negative. Positive multipliers are associated with variables expected to be found in near natural systems, whereas negative multipliers are associated with variables expected to be found in degraded systems. There is one formula for each broad category, and the four category scores are summed into a total score. As part of the refinement of the tool, we also created a “ratio” of variables strictly associated with near-natural sites to variables strictly associated with degraded sites. This was inspired by the Icelandic metric, which uses a similar principle to derive its condition categories. In this case, the ratio doesn’t form part of the finalised method but was useful as an internal check. 

From our synthetic dataset the absolute maximum score was (73) and the absolute minimum score was (-27). However, the realistic maximum score of 58.5 was achieved by both raised bog pool system and blanket bog pool system. The lowest score achieved was from an actively eroding blanket bog on steep slope (-13.5) closely followed by a conifer plantation on peat (-12).

Condition classes

The final step was to define suitable condition class boundaries, based on the range of possible values but grounded in our understanding of peatland systems, restoration potential and restoration trajectories. The system appears to work best with 5 unevenly split condition classes where natural break points appeared. For the purpose of the report, they are simply labelled numerically from 1 to 5. 

Condition class 1 (scores of 38 and above)

This condition class captured a range of near natural states and systems, such as raised bog with pool system, blanket bog with pool system, Sphagnum-sedge dominated large pool, blanket bog area with ridges, lawns and seasonal small pools, Sphagnum-dominated floating mat or lawn, montane bog, and gently sloping blanket bog with some micro-topographic features. This condition class may represent suitable end point of restoration for some degraded sites, depending on landscape setting and land use legacies.

Condition class 2 (scores of 23 to 37.5)

Interestingly, this condition class captured a range of near natural states associated with slopes, transitions or boundaries (natural blanket bog margin on a gentle slope, naturally stiff peat on a moderate to steep slope, flush typical of a riparian margin or an area of seepage). This category represents areas that may be naturally part of a functioning mesotope. As a result, this condition class may also represent a suitable end point of restoration for some degraded sites where the landscape setting or land use legacy may not be suited for a trajectory towards Condition class 1. This Condition class 2 also included scenarios of legacy land-use, notably blanket bog area without pools with historical burning and raised bog with scrub encroachment. As part of the assessment, understanding whether a given score is attributable to landscape setting, land use legacies or a combination of both will help determine suitable trajectories and end points.

Condition class 3 (scores of 10 to 22.5)

This condition class captured two types of scenarios. It picked up scenarios with some degradation features, but that retained some of the properties associated with their former selves, such as micro-erosion on a high plateau peatland with Racomitrium lanuginosum hummocks, drained gently sloping site with grazing, raised bog with drains and encroaching scrub, top of a hag with blanket bog vegetation and wet heath vegetation developing around legacy peat cutting. It also picked up scenarios of early stages of post-restoration succession, such as a drained blocked blanket bog with grazing and trampling near an access track and forest-to-bog site 2-3 years after re-profiling.

Condition class 4 (scores 0 to 9.5)

This condition class mostly captured sites with land use legacies, such as drained margin with historical peat cutting, historically burnt drained site, and heather dominated high altitude peatland with a history of muirburn. Scenarios from this condition class would likely move towards class 3, then 2 or 1, with suitable intervention and management. 

Condition class 5 (scores < 0)  

This condition class captured the most degraded types of scenarios. It included bare peat pans, drained agricultural conversion with high grazing, conifer plantations on peat and actively eroding blanket bog on steep slopes. Of these, conifer plantations have a high potential to move up condition classes over time with forest-to-bog restoration intervention. In Scotland, we note 1) the scale of afforestation on peat; 2) the extensive experience of delivering large-scale forest-to-bog restoration and 3) the applicability of the monitoring tool to meaningfully capture transition(s) from forestry to peatland habitats. In this context, we note that it might be more appropriate to treat commercial forestry on peat as peatland habitat in the SPBM, rather than as a coniferous woodland (as per English Statutory Metric). If instead the commercial forestry on peat is recognised as a distinct habitat category from either peatland or woodland, then we note that the current tool would still apply and could still be useful in determining suitable end points following restoration using the same scoring system.

Next steps 

We recognise that the current summed scores range from negative to positive values, which may complicate the way in which they integrate with other elements of the tool. Therefore, we identify that this may need to be addressed but could be readily solved – for example by adding the minimal possible score to all values, creating a summed score range starting at 0. 

We believe that the use of synthetic data derived from existing datasets and extensive knowledge and experience of using rapid assessment tool provides a strong foundation. We acknowledge fully that it was beyond the scope of this report to collect field data for further testing the new version of the tool in the real world. This will be an important and critical next step. We envisage that a combination of scrutiny and possibly an implementation pilot where data collected can be shared would work well. It is essential that end users have an opportunity to try the tool, ideally with guidance and training provision to support appropriate use.

We believe that in practice, it is likely that end-users will want to use GIS enabled apps or software to capture data in the field, rather than to capture data on paper and digitise later as it would make it more cost-effective. The tool should be compatible with Qfield for QGIS but may also be compatible with other similar applications. It was beyond the scope of this report to check which ones. Recording scores directly in the tool and then uploading scores on a GIS software would still work, though would require coordinates to be recorded for each point, as well as scores. 

Recommendation 

• We recommend that the current version of the tool under development is scrutinised by the Scottish Planning Biodiversity Metric team. This could include the creation of further synthetic data based on expertise and experience, or real data if available. 
• We recommend that the Scottish Planning Biodiversity Metric team defines the names associated with the categories 1-5, for example following the approach of the English Statutory Biodiversity Metric (which classes condition as good, fairly good, moderate, fairly poor, poor). But that in doing so, it is recognised that suitable outcomes of restoration or enhancement may fall under the current categories 1 or 2, and that not all trajectories or interventions will necessarily move through all the categories linearly. 
• We recommend that the tool is kept under regular review, especially when it becomes used more readily by end-users.
• We recommend that appropriate guidance, training and support is provided to facilitate uptake and ensure consistency.