ArticlePDF Available

Linking ecosystem changes to their social outcomes: Lost in translation


Abstract and Figures

Ecosystem degradation represents one of today’s major global challenges, threatening human well-being and livelihoods worldwide. To reverse continuing degradation, we need to understand its socio-economic consequences so that these can be incorporated into ecosystem management decisions. This requires links to be made between our understanding of how ecosystems function and change, with socially meaningful representations of those changes. While increasing attempts are being made at such integration, the interface or translation between those two strands remains largely undiscussed. This carries the risk that key aspects of the socio-ecological interactions become ‘lost in translation’. In this paper, we document and discuss how models of ecosystem change may be combined with socially meaningful outcomes exposing and discussing the translation process itself (i.e. the ‘translation key’). For this, we use an exemplar based on peatland condition. We employ a process-based model, DigiBog, to simulate the effects of land use on blanket peatlands, which we relate to estimates of changes to the public’s well-being derived from peatland degradation and restoration, obtained as monetary values from a choice experiment survey in Scotland (UK). By quantifying linkages between environmental conditions and social values, we make the translation between these system components transparent and allow value estimates to be recalculated under different ecological scenarios, or as new evidence emerges. This enhances the replicability of the research and can better inform decision-making. By using peatlands as the exemplar ecosystem, this paper also contributes to a limited body of evidence on the socio-economic impacts of changes to the most space-effective carbon store in the terrestrial biosphere.
Content may be subject to copyright.
Ecosystem Services 50 (2021) 101327
Available online 3 July 2021
2212-0416/© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (
Linking ecosystem changes to their social outcomes: Lost in translation
Julia Martin-Ortega
, Dylan M. Young
, Klaus Glenk
, Andy J. Baird
, Laurence Jones
Edwin C. Rowe
, Chris D. Evans
, Martin Dallimer
, Mark S. Reed
Sustainability Research Institute, School of Earth and Environment, University of Leeds, Leeds, United Kingdom
School of Geography, University of Leeds, Leeds, United Kingdom
Department of Rural Economy, Environment and Society. SRUC Scotlands Rural College, Edinburgh, United Kingdom
UK Centre for Ecology & Hydrology, Bangor, United Kingdom
School of Natural and Environmental Sciences, University of Newcastle, Newcastle, United Kingdom
Thriving Natural Capital Challenge Centre, SRUC Scotlands Rural College, Edinburgh, United Kingdom
Choice experiment
Ecosystem services
Ecosystem degradation represents one of todays major global challenges, threatening human well-being and
livelihoods worldwide. To reverse continuing degradation, we need to understand its socio-economic conse-
quences so that these can be incorporated into ecosystem management decisions. This requires links to be made
between our understanding of how ecosystems function and change, with socially meaningful representations of
those changes. While increasing attempts are being made at such integration, the interface or translation between
those two strands remains largely undiscussed. This carries the risk that key aspects of the socio-ecological in-
teractions become ‘lost in translation. In this paper, we document and discuss how models of ecosystem change
may be combined with socially meaningful outcomes exposing and discussing the translation process itself (i.e.
the ‘translation key). For this, we use an exemplar based on peatland condition. We employ a process-based
model, DigiBog, to simulate the effects of land use on blanket peatlands, which we relate to estimates of
changes to the publics well-being derived from peatland degradation and restoration, obtained as monetary
values from a choice experiment survey in Scotland (UK). By quantifying linkages between environmental
conditions and social values, we make the translation between these system components transparent and allow
value estimates to be recalculated under different ecological scenarios, or as new evidence emerges. This en-
hances the replicability of the research and can better inform decision-making. By using peatlands as the
exemplar ecosystem, this paper also contributes to a limited body of evidence on the socio-economic impacts of
changes to the most space-effective carbon store in the terrestrial biosphere.
1. Introduction
With over 70% of the Earths land area being signicantly altered,
ecosystem degradation represents one of todays major global chal-
lenges, threatening human well-being and livelihoods worldwide (Díaz
et al., 2019). There is widespread consensus that, to counter ongoing
degradation, we need to understand its socio-economic consequences to
inform the design of effective management and conservation strategies,
and to gain public and nancial support for mitigation policies (CBD,
2011; Díaz et al., 2019; MA, 2005; Olander et al., 2018). Understanding
the socio-economic consequences of ecosystem degradation requires
knowledge about how an ecosystem works (i.e. the biophysical un-
derpinnings of ecosystem processes and functions); how ecosystem
processes are affected by land use and other drivers such as climate
change; and how these changes affect people and society. Building this
understanding therefore requires that the effects of changes in
ecosystem processes and functions are meaningfully translated into
outcomes that dene impacts on society (Barkmann et al., 2008; Car-
penter et al., 2009; Martin-Ortega et al., 2017).
Following the release of the Millennium Ecosystem Assessment (MA,
2005), the interplay between ecosystem change and human-well-being
has been explored in many publications, often using the ecosystem
services concept (Bateman et al., 2011; Costanza et al., 2017; Haines-
Young and Postchin, 2010; Liu et al., 2007; Yang et al., 2015). As a
result of this development, integrated interdisciplinary approaches that
couple both ecological and economic knowledge are now rmly
* Corresponding author.
E-mail address: (J. Martin-Ortega).
Contents lists available at ScienceDirect
Ecosystem Services
journal homepage:
Received 5 January 2021; Received in revised form 10 May 2021; Accepted 15 June 2021
Ecosystem Services 50 (2021) 101327
established in academic and environmental management agendas
(Bateman et al., 2016; Martin-Ortega et al., 2015a). This integration has
been attempted using a variety of approaches, often relying on the
development of models to establish and map the services delivered by
ecosystems under different management scenarios (Maes et al., 2012) in
combination with economic data elicited via various valuation methods
(Elwell et al., 2018). Some of these assessments are based on global
models, such GLOBIO, which was used as the basis of The Economics of
Ecosystems and Biodiversity (TEEB) Assessment (Hussain et al., 2011).
More detailed landscape scale models run at ner scales, often with local
data, are also used to show more realistic changes in ecosystem service
supply than global models (Maes et al., 2012). Such models are used in,
for example, the InVEST tool (Sharp, 2014), which was explicitly
designed to provide information on ecosystem change in terms of human
well-being. Quantication of well-being impacts in these assessments
often focus only on changes in agricultural/land use gross margins
(through yield changes) (e.g., see Sch¨
onhart et al. (2018) and further
references therein). Other studies include assessments of impacts on a
broader range of sources of social well-being. For example, Martin-
Ortega et al. (2015b) used hydro-chemical models to simulate the
effectiveness of interventions to reduce dissolved phosphorous reduc-
tion loads in water bodies in Scotland and related the changes to the
non-market benets derived from improving the ecological status of a
catchment. Grˆ
et-Regamey et al. (2008) also combined various process-
based models for characterising avalanche protection, scenic beauty,
timber production and habitat provided by an Alpine region to establish
public values for these ecosystem services.
Spatially explicit statistical models relating land use to economic
outputs have also been developed. One example is Bateman et al.
(2016), in which monetary valuation of climate driven land-use change
is underpinned by land-use modelling. The model predicts climate-
driven shifts in the protability of alternative uses of agricultural land,
including farm gross margins and monetary values for water quality and
recreation, at the individual and catchment scale. Other approaches
have integrated ecological and economic elements using Bayesian Belief
Networks. McVittie et al. (2015) used this method to assess and value the
delivery of ecosystem services from riparian buffer strips under alter-
native management options. Whereas Juutinen et al. (2020) combined
various biophysical models with an economic analysis of biodiversity,
climate impact and water emissions to identify cost-effective land-use
options of drained peatlands.
The approaches mentioned above have certainly represented sub-
stantial knowledge advancement. However, producing a set of model
outputs that can be linked in one way or another to one or more
ecosystem services does not guarantee the most appropriate represen-
tation of ecosystem change for assessing its societal impact (Elwell et al.,
2018). This link is generally loosely specied, since there is often a
mismatch between typical outputs of ecosystem models and the repre-
sentation of outcomes that are perceived to be relevant by the general
public (Martin-Ortega et al., 2017; McVittie et al., 2015). Furthermore,
models should be relevant to the scale at which changes in ecosystem
services delivery are relevant for the people who benet from it (Hein
et al., 2006).
To address the mismatch between how ecosystem change is repre-
sentated and the assessment of related social outcomes, recent sugges-
tions emphasise the need to place peoples perceptions at the centre of
the assessment process (Elwell et al., 2018; Jones et al., 2016; Martin-
Ortega et al., 2017). However, there is little evidence that this insight
is then used to help improve ecosystem services modelling in assess-
ments of changes to social well-being (Elwell et al., 2018), leaving the
values poorly supported by biophysical measures (Olander et al., 2018).
This remaining disconnect between the ways in which we address
our understanding of how ecosystems work and respond to change, and
our estimates of how this affects people, carries the risk that key aspects
of the socio-ecological interactions are ‘lost in translation. Published
work typically contains a great level of detail on one or both aspects (i.e.,
ecosystem modelling or valuation of social outcomes), but often neglects
to report and discuss sufciently the translation between the two, with
the interface remaining a ‘black box. Or, as Olander et al. (2018) note,
what is less clear is the hand-off between the biophysical measures and
valuation the link between the biophysical measure and a measure of
what that biophysical entity means to (or how it affects) people. Here,
we postulate that building, articulating and exposing this interface is
critical to enhance the robustness and usefulness of integrated assess-
ments of ecosystem change (Evans et al. 2014). That translation should
be a clearly identiable step in the research process that is open to
scrutiny, thus facilitating continued knowledge improvement and more
robust support to decision-making.
In this paper, we document and discuss how models of ecosystem
change may be combined with socially meaningful outcomes, making a
point of exposing and discussing the translation process itself. We use an
exemplar based on change in peatland condition, applying the DigiBog
development model (Baird et al., 2012; Young et al., 2017) to simulate
the effects of land use on the ecological functioning of blanket peatlands.
We categorise DigiBog model outputs describing peatland condition
according to public perceptions of, and preferences for, key ecosystem
services. Model outputs are linked to estimates of public values (in
monetary terms) derived from peatland restoration, obtained from a
choice experiment survey in Scotland. We highlight the additional
processing step used to make the quantitative link between direct model
outputs and aspects of peatland condition with their socially meaningful
outcomes. And we emphasise how the link was built and articulated:
what we refer to here as the ‘translation key.
The value of this paper lies in the exposure and discussion of the
translation process and on how the process model was adapted to match
socially meaningful outcomes. Our intention is to encourage reection
rather than prescription. The ultimate aim is not an ontological simpli-
cation of the problem i.e. socio-ecological interactions are and will
always be complex and there will always be limits to how much of that
complexity we can disentangle, or represent in models (Martin-Ortega
et al., 2017). Instead, the aim is to improve the development of tools and
processes that can support decision-making to reverse the current
degradation trend (Olander et al., 2018).
2. Methodology
We followed proposals by Elwell et al. (2018), Jones et al. (2016) and
Martin-Ortega et al. (2017) to place the identication of socially
meaningful outcomes at the core of the process via a transdisciplinary
process involving key stakeholders and the public, through which we
elicited public values of the ecosystem services provided by peatland
restoration as measures of well-being. The peatland model (DigiBog)
was adapted so that its functional representation of the biophysical
changes in the ecosystem could be linked explicitly to those socially
meaningful outcomes, establishing the ‘translation key.
This approach differs from the practice of describing social outcomes
as the endpoints of a linear process that starts with the characterization
of ecosystem change, followed by quantication of changes in the pro-
vision of ecosystem services in biophysical terms and ultimately their
valuation. An approach that is implicit in the widely referred to
framework of the ecosystem services cascade (Haines-Young and Post-
chin, 2010), and the main large ecosystem assessments carried out so far
(e.g. Bateman et al., 2011; Kumar, 2010; MA, 2005). Fig. 1 highlights the
differences between approaches that use this implicit/un-managed
translation between biophysical outputs and social outcomes (Fig. 1a),
and the one used here, where the translation key is explicit and managed
(Fig. 1b).
We chose DigiBog for our biophysical model because it simulates
peatland development over different underlying landscape scale fea-
tures (e.g., slopes of varying gradients, plateaus, and hollows). The
model can be congured in either 2-D or 3-D to represent sections of a
landscape comprising some or all these features with contiguous,
J. Martin-Ortega et al.
Ecosystem Services 50 (2021) 101327
hydrologically connected columns that ‘talkto each other. Because of
these connections, which are not intrinsic to existing 1-D peatland
models (e.g., Frolking et al.,2010), peat properties can vary both verti-
cally and horizontally and can be affected by land uses in other parts of
the simulated landscape (see Young et al., 2017). As a result, overall
peatland condition can be determined from these smaller-scale
2.1. Estimating socially meaningful outcomes of peatland restoration
We use blanket peatlands in Scotland as the exemplar of this
research. Blanket peatlands are the most common peatland type in
Scotland, covering 20% of its land surface (Bain et al., 2011; Bruneau
and Johnson, 2014), mainly in the uplands. These peatlands provide
several important ecosystem services at global and national scales, such
as carbon storage and fresh water supply (Bain et al., 2011). Atmo-
spheric pollution, along with land uses, including drainage, conversion
to agriculture, burning for game shooting, and forestry, have caused
blanket peatlands to become degraded (Maltby, 2010). In the past,
peatlands in Scotland were mainly seen as either a source of peat for fuel
or as wastelands to be converted to other productive uses such as
forestry or agriculture (Rotherham, 2011). As a consequence, more than
two thirds of Scottish peatlands are thought to be damaged or degraded
to some degree, and degradation is projected to continue if no action is
taken to restore their ecological function (Bain et al., 2011). This has led
to a surge in policy interest to restore degraded peatlands in Scotland. In
its recent Climate Change Plan, the Scottish Government (2018) laid out
ambitious targets to restore 250,000 hectares of degraded peatland by
2030, supporting this aim through grants available to land managers.
Social outcomes from peatland restoration are represented here by
monetary values provided by improved ecological condition, as mea-
sures of well-being increase. These values were estimated as the publics
willingness to pay in a choice experiment (Adamowicz et al., 1998)
implemented in an online survey of a representative sample of Scot-
lands population (Glenk and Martin-Ortega, 2018). In the choice
experiment, survey respondents were asked to choose from two peatland
restoration alternatives and a third business as usual situation (i.e. no
restoration, with no cost). These alternatives were characterized by at-
tributes described as outcomes of a restoration programme in terms of
improved peatland condition to be attained at a cost by 2030. This
valuation was developed through a transdisciplinary process in a com-
bination of workshops, focus groups and bilateral interactions between
natural and social scientists, peatland restoration practitioners and
members of the public
(Table 1 summarizes the stages of the process
and Table 2 shows the organizations involved). Martin-Ortega et al.
(2017) provide a more detailed account of the process and the actors
involved, but of relevance to the present paper is that it was driven by
and shaped from the publics perspective. The process was able to
represent restoration outcomes and establish a restoration reference
(including temporal and spatial aspects) in a way that was meaningful
from the publics perspective and useful in terms of assisting manage-
ment decisions (see Martin-Ortega et al. (2017) for evidence on these
The design of the choice experiment included three ecosystem con-
ditions: bad, intermediate and good. The conditions were associated
with varying levels of ecosystem service provision related to climate
change mitigation (carbon storage), water quality improvement and
changes to wildlife habitat. These ecosystem services were chosen
following the transdisciplinary process. We also introduced productive
uses and related provisioning services including forestry, eld sports
(shooting) and livestock grazing in the contextual description of the
valuation scenario (rather than as attributes of the choice experiment).
Specically, respondents were reminded that some peatlands are
currently unused, while others are used for sheep grazing and deer
Fig. 1. (a) conventional linear approach to linking the understanding of ecosystem change and social outcomes in which biophysical outputs are linked to social
outcomes in an implicit unmanaged way (or in any case, under-discussed), creating a ‘black boxat their interface; (b) alternative approach (used in this research) in
which social outcomes are placed centrally and the interface or translation is explicitly articulated and discussed. The blue arrows in (b) indicate the matching of the
key characteristics of socially meaningful outcomes and a biophysical simulation of the ecosystem (i.e. the translation key based on ecosystem conditions in our case).
Three focus groups with members of the general public were conducted in
two locations in Scotland chosen due to their contrasting characteristics in
relation to peatlands and the different relationships and experiences that we
assumed people would have with peatlands. The focus groups were attended by
a total of 37 participants, covering both genders (over half were female), and a
wide range of ages (early 20s to 70s), socio-economic backgrounds, and reasons
for wanting to attend the focus groups (from a general interest in the envi-
ronment and outdoor recreation to being offered some food at the workshop or
having nothing better to do that day).
J. Martin-Ortega et al.
Ecosystem Services 50 (2021) 101327
management, forestry or eld sports like grouse shooting. Respondents
were also informed about the relation between condition and scope for
productive uses. Specically, they were told that peatlands which are in
intermediate condition can be used for livestock grazing and eld sports
(grouse), but if they deteriorate to poor condition all of these uses and ac-
tivities are severely impaired. Peatlands in good condition are not suitable for
livestock grazing and eld sports. The normative labelling of the peatland
conditions (i.e., bad, intermediate and good) were intentional in its
reference to the ecological condition of the ecosystem so that it can be
understood by the public.
Three stylized landscape representations (drawings) and de-
scriptions (narratives) portraying the three conditions were developed
in an iterative process with stakeholders and the general public. They
describe in simple terms how changes in ecosystem condition lead to
changes in ecosystem service provision for each of the peatland condi-
tions. The narratives served as mechanisms for conveying (at least in
part) the complexity of the ecosystem processes that lead to the delivery
of the ecosystem services mentioned above (Martin-Ortega et al., 2017)
and are shown in Table 3. This approach also allowed a straightforward
quantication of restoration monetary benets on a per hectare basis (i.
e. £/hectare/year), making it appealing to use for decision makers, and
facilitating further spatial analysis of benet estimates (Glenk and
Martin-Ortega, 2018).
The survey was implemented online using a professional market
research company with 585 adult Scottish citizens between February
and March 2016. A quota-based approach was used to sample from the
online panel with age and gender quotas. The sample was representative
of the population of Scotland in terms of gender, age, and the rural/
urban split. In terms of educational attainment, higher educational
levels are slightly over-represented, as well as respondents with higher
employment-based social grade (see Supplementary Material S1).
Further details of the choice experiment application (e.g., sampling
procedure and recruitment, survey structure, experimental design, etc.)
and detailed monetary estimations can be found in Glenk and Martin-
Ortega (2018). Of relevance here is that this process allowed us to un-
derstand what it means to people if peatlands become degraded and
restored, expressed as the trade-off that these changes represent in terms
of their well-being (in this case: the monetary value they ascribe to these
changes measured in £/ha/year).
2.2. Peatland model description
DigiBog simulates the development of a peatland in 2-D/3-D over
centuries, building up layers of peat within hydrologically connected
columns. Simulations begin with a mineral soil base and individual peat
layers are added to each model column on an annual basis. The pro-
cesses of peat formation, peat decomposition, and water movement in
the model can be summarized as follows: (1) the mass of each new layer
is determined by a plant litter productivity function. Peat formation
occurs as an annual addition of new plant litter to the top of each model
column, with the thickness of the new layer varying according to annual
average air temperature and the annual average water-table depth for
the column; (2) on a sub-annual basis, the peat in each layer of a column
is decomposed depending on its position relative to the water-table and
the annual air temperature; and (3) also on a sub-annual basis, water is
moved horizontally between columns to simulate water-table behaviour
(driven by net rainfall and the hydraulic properties of the peat).
Therefore, the addition of new peat varies from column to column, and
peat decomposition varies both horizontally (between columns) and
vertically (within a column). The degree of decomposition of a peat
layer determines its saturated hydraulic conductivity, which in turn
determines water movement between columns. These interactions mean
that there are feedbacks between peat accumulation, decomposition,
changes in hydraulic properties, and water movement.
We used a modied version of the 2-D/3-D DigiBog peatland
development model (Young et al., 2017) to simulate the accumulation of
blanket peat in response to land use. The model used by Young et al.
(2017) now includes an algorithm to reduce simulation times. The al-
gorithm aggregates neighbouring layers when below a user-dened
thickness, allowing virtual peatlands to be grown over signicantly
increased spatial scales (thousands of metres rather than tens of metres),
which enabled the use of the model for this study (also see Young et al.
2.3. Linking DigiBog outputs to monetary values of peatland restoration
We used three steps to link the monetary values of peatland resto-
ration into parameters for our peatland development model (Fig. 2). The
Table 1
Stages of the transdisciplinary process.
Aims Strands of
Format of interaction
1 Understand the current
knowledge base of peatlands
processes, functions and
ecosystem services delivery
Identication of the policy
and policy-
2 Dene the potential
challenges associated with
understanding peatland
restoration and its public
Bilateral dialogue
3 Development of a tool for
conveying simplied
restoration information
and policy-
Policy makers and
4 Testing and rening the tool
with the public
Public Focus groups
5 Validation and uptake Natural
and policy-
Expertsfocus groups
Bilateral dialogue
Policy events
Learning module and
condition assessment
support tool
Although stages are somewhat consecutive, there was some level of overlap
between them and some of the tasks were interspersed (e.g. focus groups with
the public also took place as part of stage 3).
The process is presented from the
perspective of the social scientists leading this research, and therefore should be
read as ‘strand of knowledge with which the social science strand interacts.
Table 2
Main organizations involved in the transdisciplinary process.
Name Type of organization
The James Hutton Institute Research (including hydrologists,
ecologists, soil scientists, economists,
environmental social scientists)
Scotlands Rural College (SRUC) Research (economists)
University of Leeds Research (including hydrologists,
wetland and peatland scientist,
University of Birmingham Research (social and natural sciences)
Centre for Ecology and Hydrology Research (including water ecologists,
hydrologists and peatland scientists)
Scottish Natural Heritage (SNH) Practice (peatland restoration
Scottish Government Policy-making (environmental and
strategic research managers)
International Union for the Conservation
of Nature (IUCN) UK National
Practice (nature conservation)
Scottish Environmental Protection
Agency (SEPA)
Policy-making (environmental
ClimateXChange Science-practice interface (climate
J. Martin-Ortega et al.
Ecosystem Services 50 (2021) 101327
Table 3
Socially meaningful description of peatland ecological conditions elaborated in a transdisciplinary process, including pictorial representation and accompanying
narratives. These were shown to the public in an interactive manner through an online survey (i.e. fragments of the narrative appeared as participants clicked at
relevant features in the drawings).
Peatlands Categories Ecosystem Services
Accompanying Narratives
In good condition, there is plenty of water, so it is visible on the
surface, slowly owing through larger and smaller pools.You will
see small grasses and especially the peat moss that grows well in
wet conditions. The moss stores lots of water and makes the
peatland appear in a typical red-greenbrown mosaic.Peatlands in
good condition continue to grow by adding more and more layers
of peat. While growing, carbon is taken up from the atmosphere as
carbon dioxide (CO
) and stored as peat.Water that ows from
peatlands that are in good ecological condition is usually clear and
of good quality. This means less need for water treatment. The
water quality is also good for sh living downstream, especially
salmon and trout.Peatlands in good condition are home to various
birds and wildlife species.This includes waterfowl and wading
birds such as curlew, and predators such as hen harrier and red
In peatlands in intermediate condition, water has been taken off
the land by creating channels for drainage. This allows activities
such as livestock grazing. Surface water is rarely visible.With less
water on the land, taller plants can grow, like cotton grass, or
small bushes like heather.Peatlands in this condition are not very
colourful. However, if heather grows in the area and is in bloom,
its purple colour stands out. Signs of bare peat start to appear as
dark patches. Sometimes peatland of intermediate condition is
burned regularly, to create conditions for grouse shooting. This
leaves characteristic patterns of burned and unburned land in the
landscape.Peatlands in intermediate condition have stopped
growing. No additional peat layers are added. Instead, peat layers
gradually shrink, releasing a moderate amount of carbon to the
atmosphere, where it contributes to climate change.Water owing
from such peatlands can be of lower quality. Water can be slightly
murky, especially after a heavy rainfall. This can affect the sh
population downstream, including salmon and trout, and increase
the need for water treatment.Peatlands in intermediate condition
may still harbour some of the wildlife that is present in peatlands
in good condition. However, it is less abundant and some of the
wildlife may not be found any more.It is also more likely that you
will see managed species such as deer, sheep and grouse.
(continued on next page)
J. Martin-Ortega et al.
Ecosystem Services 50 (2021) 101327
objective of this process was to: 1) identify the key characteristics of
peatland condition that are socially meaningful (as dened in Section
2.1) and match them to DigiBog model outputs, i.e. establishing what we
refer to here as the ‘translation key; 2) model a scenario of peatland
degradation (e.g. following drainage) and restoration intervention as an
exemplars; and 3) use the key characteristics dened in the matching
(translation) process, to assess the impact of the scenario on the change
in monetary values of peatland condition. This process (and Fig. 2)
represent the detailed version of the broad research approach proposed
in Fig. 1(1b).
Step 1 Establishing the translation key of socially meaningful peatland
condition descriptions into model outputs. The aim of this stage was to
identify how the descriptions of peatland condition could be translated
into biophysical outputs without being overly constrained by the
Table 3 (continued )
Peatlands Categories Ecosystem Services
Accompanying Narratives
Peatlands in bad condition have been drained for a longer time.
The forces of water and wind (erosion) have now exposed larger
areas of bare peat. Deep gullies and trenches are formed.Rarely
any plant grows on the areas that are exposed. Patches of grasses
or heather are still found on ‘islandsin between exposed bare
peat. The exposed bare peat areas will continue to grow, leaving
less plant cover as protection on the surface. Peat will continue to
be lost until the solid rock surface emerges.Peatlands in bad
condition lose carbon at a high rate. They have turned into a
severe ‘sourceof carbon to the atmosphere, where it contributes
to climate change.Water that ows downstream is of bad quality.
It is often murky and can be dark brown from soil components in
the water, especially after heavy rainfall events. The bad water
quality will affect sh downstream. It is not suitable for human
consumption and therefore needs a lot of treatment.Peatlands in
this condition are home to little wildlife. Not many plant and
animal species can be found.
Images and text open access under the Creative Common license and are freely downloadable from Martin-Ortega et al. (2017).
Fig. 2. The three-step process used to link the peatland development model with the socially meaningful outcomes as dened here in terms of monetary values for
changes in peatland condition (G: good; I: intermediate; B: bad). The numbered steps are shown to indicate the overall order of the steps we took (although in reality
the process was often iterative): [1] Translating descriptions of peatland conditions into model outputs, i.e. establishing the ‘translation key(see Table 4 for a
detailed description); [2] simulating ecosystem scenarios; and [3] assessing the impact of scenarios in terms of changes in well-being.
J. Martin-Ortega et al.
Ecosystem Services 50 (2021) 101327
existing DigiBog set up (Fig. 2[1]). To fully incorporate the key char-
acteristics into DigiBog, we used the model code described in Young
et al. (2017) with a modication to reduce simulation times (see Young
et al., 2019). To ensure that the model outputs for each peat column of
the simulated landscape included, directly or indirectly, the key char-
acteristics for the three condition classes, we reviewed the descriptions
of peatland condition dened by Martin-Ortega et al. (2017) both in
their diagrams and the associated narratives (Fig. 2and Table 3). In this
way, we identied two key characteristics on which the economic
valuation had been based, that could be derived either directly or
indirectly from DigiBog model outputs. The condition assessment
criteria are shown in Table 4.
These two characteristics are the vegetation cover and carbon (C)
sink status. We modied DigiBog to include litter fractions of four plant
functional types (PFTs) (Sphagnum mosses, shrubs, sedges, and grasses),
which comprise the mass of peat. The PFTs also indirectly represent the
domesticated and wild fauna associated with the narrative descriptions.
For example, the narrative for good condition states: You will see small
grasses and especially the peat moss that grows well in wet conditions
Peatlands in good condition are home to various birds and wildlife species.
This includes waterfowl and wading birds such as curlew, and predators such
as hen harrier and red kite(Table 3).
Carbon accumulation is an output of DigiBog (i.e., there is a direct
link between the model and the condition classes). However, because C
exchange takes place throughout the total depth of a peat column
(Clymo, 1984; Young et al., 2019), we could not infer the C sink status of
our simulations from the surface vegetation. We, therefore, calculated
separately the C sink status of the simulated peatland following the
method detailed in Young et al. (2019) and converted the output to CO
equivalent (CO
e). For our purposes, we used a conversion factor of 1 t
C equals 3.67 t CO
e. The last step allowed us to compare our results
with the emissions factors for peatlands (minus N
O) in different con-
dition statuses calculated by Evans et al. (2017).
The vegetation on the surface of a peatland is not simulated by the
model (i.e., there is an indirect link between the model and the condition
classes). DigiBog simulates the accumulation of layers of peat that are
made up of the PFTs described above, but these do not necessarily match
the vegetation cover on a peatlands surface. For example, some grasses
may be abundant on the surface but undergo signicant decay before
becoming part of the peat proper, meaning there could be a mismatch
between proportion of such a grass that makes up peat and that on the
surface. We therefore needed to develop a way of calculating the
proportions of surface cover for our PFTs. To link vegetation cover and
composition to the condition descriptions from Martin-Ortega et al.
(2017), we developed response relationships based on a dataset of eld
observations of PFTs and their corresponding water-table depths. The
set of equations predicts the proportion of cover for the four PFTs ac-
cording to the models simulated water-tables. In this two-step approach
vegetation composition was calculated using the MultiMOVE set of
niche models (Smart et al., 2010; Henrys et al., 2015; Alison et al.,
2020), using a relationship between plant composition and mean
Ellenberg moisture index. The relationship between moisture index and
water table depth was derived from co-located oristic and dipwell data
from blanket bog at Moor House, UK (see Supplementary Materials S2
for further detail).
To match the three condition categories with model outputs, we
transformed the qualitative descriptions within each condition class into
categorical variables (Table 4). We determined the categories for our
variables by matching the diagrams and narratives of Martin-Ortega
et al. (2017) Table 3with previous studies or other published infor-
mation on how to dene the ecological condition of blanket peatlands.
For example, the three C sink status categories are described in Table 3
as follows; good condition: Peatlands in good condition continue to grow
by adding more and more layers of peat.; intermediate condition: “… peat
layers gradually shrink, releasing a moderate amount of carbon to the at-
mosphere, where it contributes to climate change.; and bad condition:
Peatlands in bad condition lose carbon at a high rate. Therefore, we
dened our C sink status categories as ranging from the continued
accumulation of C (i.e. a sink good condition) to losing some C (i.e. a
small source intermediate condition) to losing a signicant amount of
C (i.e. a large source bad condition) (see Table 3). We used the emis-
sions factors (EF) (minus N
O) from Evans et al. (2017, Table 4.1 in
there) to determine the C condition status of our simulation. We selected
the EF of the ‘near natural bog condition category to be equivalent to
our good condition and the mean EF for their ‘eroded modied bog
condition category as the boundary between our intermediate and bad
conditions (the mean EF of the drained and undrained statuses)
(Table 4).
Similarly, for PFTs we assessed the condition status of our model
outputs by combining the relative habitat suitabilities of our PFTs into
two groups: one where greater suitability is associated with good peat-
land condition (Sphagnum mosses and sedges), which we refer to as
favourable PFTs, and a second comprised of grasses and shrubs where
greater suitability is associated with bad condition when they are the
Table 4
Translation key used to match the key characteristics of peatland condition dened by the transdisciplinary process with the public (i.e. socially meaningful outcomes
as per the narratives in Table 3) with DigiBog outputs. See the text for a description of how each key characteristic was determined.
Ecological condition
Good Intermediate Bad
Key characteristic
Carbon sink status
(socially meaningful
While growing, carbon is taken up from the
atmosphere as carbon dioxide (CO2) and
stored as peat
No additional peat layers are added. Instead, peat
layers gradually shrink, releasing a moderate
amount of carbon to the atmosphere
Peatlands in bad condition lose carbon at a high
rate. They have turned into a severe ‘sourceof
carbon to the atmosphere
Fifteen-year mean (t
CO2e ha-1 yr-1).
Negative values are a
=<0 [0, 4.1] >4.1
Cover of plant
functional types
(socially meaningful
You will see small grasses and especially
the peat moss that grows well in wet
With less water on the land, taller plants can
grow, like cotton grass, or small bushes like
Patches of grasses or heather are still found on
‘islandsin between exposed bare peat
Fifteen-year mean
Sphagnum and Sedges >0.40 fx6 Grasses and shrubs >0.50
J. Martin-Ortega et al.
Ecosystem Services 50 (2021) 101327
dominant vegetation cover (Averis et al., 2004), (Table 4). We refer to
this second group as unfavourable PFTs.
Step 2 - Simulating ecosystem scenarios. The purpose of this step was to
model the effects of our chosen land use and of restoration on the key
characteristics dened in step 1 (Fig. 2[2]). DigiBog was set up to
simulate peat accumulation over a published transect of blanket bog
considered typical for the UK (Tipping, 2008). The transect comprised
100 ×2 m ×2 m columns (400 m
), which included slopes and pla-
teaus of varying extents and slope angles. Our model was driven with a
time series of rainfall and temperature data, typical for UK uplands (see
Young et al. (2019) for information about these time series). We ran the
model with these driving data for a series of preliminary simulations
using different sets of oxic and anoxic decay parameters to ensure that
the model produced a plausible peatland. By plausible we mean that
peat on slopes was thinner than that on plateaus (for an example see
Tipping (2008); page 2104) and that peat thickness on plateaus was
similar to that reported for blanket peatlands in the UK (peat built up to
a maximum thickness of approximately 3.5 m). Our aim was not to
replicate the detailed development history of a specic peatland, but to
provide a set of outputs that could be used as an analogue for blanket
We identied drainage, grazing, afforestation, and managed burning
as the three main land use impacts that are implemented singly or in
combination across blanket peatlands in the UK: the reduction or
reversal of these activities has also been the focus of recent restoration
activities (Parry et al., 2014). Of these activities, we chose to investigate
the effects of drainage.
We ran a single simulation for 5100 years. To simulate management,
we ran the model for 4900 years and then added six 60 cm deep ditch
drains (see Young et al. 2017) at 30 m intervals before allowing the
model to continue to run with drainage for a further 100 years. During
the ‘drainagephase, the depth of the ditch drains was maintained to
simulate active management of water tables. At the end of the drainage
period (i.e. after 5000 years) the ditch drains were restored with
simulated ditch dams set to a depth of 10 cm (see Young et al., (2017)
and the model run continued until 5100 years had elapsed.
Step 3 - Assessing the impact of scenarios in terms of changes in social
well-being. We calculated the change in monetary value associated with
peatland condition by comparing the classication of our simulated
peatland during the nal 15 years of each of the stages of our model run
(pre-drainage, drained, restored) (Fig. 2[3]). We used these timescales
as they matched those originally used to elicit the monetary values in the
choice experiment survey, established during the preparatory focus
groups of the valuation study (Glenk and Martin-Ortega, 2018). In a
reversal of Step 1, we used the translation key in Table 4 to classify the
simulated peatland into good, intermediate, or bad condition. We rst
determined the classication of each peat column making up the
modelled peatland by categorising the mean values of the peatland
condition (good, intermediate, or bad) for the 15-year assessment pe-
riods for both drainage and restoration. Then, for each of the changes in
peatland stage (i.e., from natural to drained, and from drained to
restored), the number of hectares undergoing a change in peatland
condition (e.g. from good to intermediate and to bad) was calculated.
These hectare changes were then related to per hectare values obtained
in the choice experiment survey.
3. Results
3.1. Condition assessment
The addition of ditch drains in our model caused a switch in the
surface vegetation from predominantly mosses and sedges (mean cover
across the peatland of 56%) to a cover dominated by shrubs and grasses
(mean cover of 75%) (Supplementary Material S2). The C balance also
reversed from 0.97 t CO
e ha
(negative values are a sink)
before drainage to 11.52 t CO
e ha
during drainage (means of all
model columns for the last 15 years of each period). During the last
15 years of the restoration period, 69% of surface vegetation was made
up shrubs and grasses and the C balance was 2.98 t CO
e ha yr
(Supplementary Material S2). During this time, the condition status of
the whole peatland changed from good (natural) to bad (drained) to
intermediate condition (restored). Fig. 3 shows the whole peatland C
sink status for the nal 300 years of our simulation. Whilst all peat
columns in the pre-drained period of the simulation were classed as in
good condition, all three conditions were present to a greater or lesser
extent in the drainage and restoration treatments showing a heteroge-
neous response to land use (Table 5).
Table 6 shows the number of hectares for each of the peatland stages
(natural, drained and restored) in the three conditions (bad, good and
intermediate). For illustrative purposes, the DigBog results have been
scaled up from the simulated 400 m
to a hypothetical landscape of
100 ha using the proportions of columns in each category (see Tables 5
and 6). These proportions were then used to establish changes in well-
being through measurement of social values quantied in monetary
terms (Section 3.2).
3.2. Changes in well-being
For each of the changes in peatland status, i.e. from natural condition
to drained and from drained to restored, the number of hectares moving
from one ecological condition to another, could then be related to the
per hectare values obtained in the choice experiment survey, relating
hectare changes from Table 6 to monetary values from Glenk and
Martin-Ortega (2018) (Table 7). The move from natural peatland to
drained peatlands represents a decrease in well-being, i.e., a decrease in
the value that the ecosystem can now deliver. In the case of restoration,
the change in value is an increase in well-being, reecting the value of
the increased services delivered.
What these values show us (Table 7) is that there is a clear well-being
gain in restoring peatlands; however, the initial loss from draining
peatlands in the rst place is far greater (by an order of magnitude).
4. Discussion
In this paper we have described an explicit process of linking socially
meaningful outcomes, dened in terms of changes to well-being quan-
tied in monetary terms, to the outputs of a process-based model (Dig-
iBog) for three different scenarios (natural, altered and restored
ecosystem), placing the emphasis on describing the ‘translation key
(Table 4). Such an explicit and articulated translation process increases
the replicability of research outputs and makes it possible to scrutinize
and update the translation key as new evidence emerges. An example of
the potential for updating the translation with new evidence is our
calculation of the relationship between water-table depth and vegeta-
tion cover of four PFTs. This was based on a robust dataset, but from a
single peatland site. Further development of this relationship would
benet from inclusion of data from more sites, and could be used to
parameterise a wider set of PFTs for inclusion in Digibog, or other
Of greater importance is the potential to challenge, and eventually
improve, the translation key itself. For example, a core aspect of this
process has been the linkage of ecosystem processes and functions to
discrete categories of ecological condition. This meant we had to dene
‘cut-offpoints (boundaries) to establish discrete changes in ecological
condition. Although somewhat articial, the use of discrete categories
made it simpler to assess ecological conditions because survey re-
spondents could easily attach well-being trade-offs to such discrete
changes. It also greatly helped to dene the extent of restoration efforts
in the valuation scenarios (Martin-Ortega et al., 2017). However, while
rich narratives that try to convey the idea of a gradual process occurring
within each of the categories are an improvement over more simplistic
descriptions often used in valuation studies, ultimately, the boundaries
J. Martin-Ortega et al.
Ecosystem Services 50 (2021) 101327
or cut-off points between condition categories require a value judgement
that can thus be questioned. The case for a cut-off point between the
good and the intermediate conditions with respect to the C sink status of
our virtual peatland is one such example. Only a peatland with a C sink
status falls into our good condition category, because the boundary
between good and intermediate conditions was explicitly designed to
represent the change from carbon sink to carbon source, based on
greenhouse gas emissions. But dening the boundary between inter-
mediate and bad conditions was less straightforward and could be
challenged. In the socially meaningful description, the intermediate
Fig. 3. Condition status assessment inputs
for each of the three peatland stages (natural,
drained, and restored). Values are the mean
of all simulated peat columns. The condition
assessment was carried out on all columns in
the last 15 years of each period (shown by
thick black lines on panels b and c. All panels
show the nal 300 years of the 5100 years
simulation. a) Water-table depth used for
predicting the plant functional types (PFTs)
on the surface of the virtual peatland, b)
Favourable and unfavourable PFTs surface
cover, c) C sink status. Mean C emissions
e) for the three stages of the peatland
simulation (natural, drained and restored).
The average emission factors for natural, and
drained and undrained eroded bog (Evans
et al., 2017) were used in the classication of
‘good, ‘intermediateand ‘bad C sink status
conditions as indicated. The combination of
both the criteria for PFTs and C sink status
(Table 4) were used to classify the overall
condition of the peatland stage.
Table 5
Proportion of peatland in good, intermediate and bad conditions.
Proportion of peat columns
Condition Natural Drained Restored
Good 1 0.03 0.04
Intermediate 0 0.29 0.79
Bad 0 0.68 0.17
J. Martin-Ortega et al.
Ecosystem Services 50 (2021) 101327
condition refers to [p]eatlands [that] have stopped growing. No additional
peat layers are added. Instead, peat layers gradually shrink, releasing a
moderate amount of carbon to the atmosphere, while the bad condition
refers to [p]eatlands [that] lose carbon at a high rate. They have turned into
a severe ‘sourceof carbon to the atmosphere(Table 3). Establishing when
carbon losses are moderate as opposed to high was a judgement based on
published information: we chose to dene the boundary between in-
termediate and bad conditions based on peatland emission factors in
Evans et al. (2017). As explained in section 2.3, we used the mean of the
emission factors for the eroded bog category (drained and not drained)
as the limit of the intermediate condition category. Whilst the categories
of the emissions factors (e.g., near natural peatland, modied peatland,
eroded peatland) were helpful in dening condition boundaries, they
are not necessarily socially meaningful. Furthermore, the PFTs are used
here as proxies for the attribute wildlife habitat that was used in the
choice experiment survey, which is another simplication.
An additional complication arises from the fact that the two
ecosystem services (carbon emissions and wildlife habitat) are not in-
dependent of each other, since they share underpinning ecosystem
functions and processes and affect each other. The econometric base of
environmental valuation using choice experiments rests on the
assumption that attributes reecting ecosystem service provision can
vary independently of each other (Holmes et al., 2017). This makes it
implausible to value changes in ecosystem services resulting from
peatland restoration on the same site independently if they are causally
related (Glenk et al., 2014). For example, water-table position is a key
driver of the different plant communities that develop and of the relative
abundance of their component species, but it is also an important control
of the peatlands C balance (the balance between addition of plant litter
and decomposition of peat, Clymo (1984)). Additionally, the ease with
which different plant litter decomposes is directly related to the accu-
mulation of C in peatlands. It is therefore unlikely that a peatland with
near natural vegetation and wildlife communities will lose signicant
amounts of carbon (see Fig. 3 and Evans et al., 2017). In cases like this,
where ecosystem services are correlated, it is arguably better to bundle
correlated services for valuation purposes (Glenk and Martin-Ortega,
. The peatland conditions used here allowed us to derive values
for these bundles of services in a way that is more aligned with how
ecosystem services are derived in reality. This inter-relation and de-
pendency of service delivery applies to any ecosystem and not just
peatlands (Bullock et al., 2011).
There were also socially meaningful outcomes that are not well
addressed in our assessment, because they are not provided by DigiBog
outputs. Water quality is affected by peatland condition (Martin-Ortega
et al., 2014), and was one of the relevant outcomes featured in the
narratives used in the valuation study (Table 4). However, DigiBog does
not model changes in water quality. This means that in our linking
process, water quality remains implicit, i.e. water-table depths and
vegetation are expected to affect the water quality levels described in the
narratives for the valuation, but this connection remains in the trans-
lation ‘black box(Fig. 1). Furthermore, we did not simulate the effect of
future climate. This made it easier to visualize the linkage between
ecosystem changes related to land management and the social outcomes
of such changes, but increasing temperatures due to climate change may
worsen peatland condition and affect the recovery time of restored areas
(Ferretto et al., 2019; Gallego-Sala et al., 2010; Gallego-Sala and Pren-
tice, 2013). Such climate change effects may translate into different
social outcomes, since there might be varying public preferences asso-
ciated with the timing of the delivery of the restored ecosystem services
(Glenk et al., 2018).
Nevertheless, by exposing and discussing the translation key, the
understanding of the connection between ecological change and its
related social outcomes can be improved. The exposure enhances the
possibility of continued improvement of integrated assessments, which
provide critical information to decision-makers for the management of
ecosystem change. This relates to other emerging suggestions, such as
that made by Olander et al. (2018), who introduce the concept of Benet
Relevant Indicators (BRIs) as measures that capture the connection be-
tween ecological change and social outcomes by considering what is
valued by people. BRIs are intended as indicators explicitly constructed
to reect an ecosystems capacity to provide benets to society. Their
aim is to support ecosystem service assessments by measuring outcomes
Table 6
Hectares of peatland in good, intermediate and bad
conditions per peatland stage (natural, drained and
restored), up-scaled to an hypothetical 100 hectare
Peatland condition Hectares
Good 100
Intermediate 0.00
Bad 0.00
Total 100
Drained peatland
Good 3.00
Intermediate 29.00
Bad 68.00
Total 100.00
Restored peatland
Good 4.00
Intermediate 79.00
Bad 17.00
Total 100.00
Table 7
Changes in well-being (in monetary values) derived from peatland degradation
and restoration for an illustrative 100 hectare landscape.
Change in hectares
(as estimated by
the simulation,
Table 6) (A)
Per hectare value
(£/ha, as reported
in Glenk and
Value (£) for a
100 hectare
(A ×B)
Peatland degradation (from natural to drained)
Hectares that
deteriorate from
good to
29.00 190.90 5536.10
Hectares that
deteriorate from
good to bad
68.00 273.05 18,567.50
Change in well-
Peatland restoration (from drained to restored)
Hectares that
improve from
intermediate to
good condition
1.00 190.90 190.90
Hectares that
improve from
bad to
51.00 82.15 4189.65
Change in well-
Glenk and Martin-Ortega (2018) report a range of values for each of these
changes in peatland category according to changes in some spatial characteris-
tics. For illustrative purposes, here we use the average values. Negative values
indicate loss of well-being due to ecosystem deterioration.
It can still make sense to value ecosystem services individually if the focus is
on maximizing the delivery of one particular ecosystem service; for example,
for off-setting calculations for that particular service.
J. Martin-Ortega et al.
Ecosystem Services 50 (2021) 101327
that are demonstrably and directly relevant to human well-being by
using causal chains to make the connections between ecological condi-
tions and human use and enjoyment explicit. BRIs are a conceptual
proposition that resonates with the assertion that we make here that
those connections need to be central to and explicitly articulated in the
assessment process. Jones et al. (2018) also used a translation focussed
approach whereby ecosystem impacts, resulting from atmospheric ni-
trogen pollution, were linked to willingness to pay to maintain plant
species diversity. We argue that much more discussion about the
translation processes and keys used is still needed. Without it, the
linking of ecosystem change and its social outcomes risks, on the one
hand, loosing some of the rigour/scientic accuracy regarding the bio-
physical processes, rendering the assessments inaccurate or possibly
awed; or, on the other hand, not sufciently meaningful for the public
or useful for policy-making.
Finally, it is worth noting the policy relevance of the specic ndings
from our example and their implications for peatland restoration. By
using peatlands, this paper also contributes to a limited body of evidence
on the socio-economic consequences of changes in what is the most
space-effective carbon store of terrestrial ecosystems (Yu et al., 2010).
Across the world, peatlands are threatened by climate change and have,
in some places, been severely degraded by land use, changing from a
carbon sink to a carbon source (Joosten, 2009; Swindles et al., 2019). As
a result, they are now one of the largest sources of greenhouse gas
emissions from the terrestrial biosphere to the atmosphere (Leifeld and
Menichetti, 2018). Understanding the socio-economic consequences of
peatland degradation is therefore also key to advancing the global net
zero emissions agenda. The monetary values presented here for an
illustrative 100 hectare catchment are of the order of thousands of
pounds for restoration and tens of thousands for degradation (Table 7).
Considering that in Scotland alone there are over 1.5 million hectares of
blanket bog habitat (Aitkenhead and Coull, 2016), gives a measure of
the magnitude of the well-being implications of peatland degradation.
Compared with the cost of previous and future public investments into
peatland restoration in Scotland, peatland restoration is, overall, well-
being enhancing, i.e. it provides overall net benets (Glenk and
Martin-Ortega, 2018). These net benets strengthen the economic
rationale for climate change mitigation through improved peatland
5. Conclusions
Ecosystem degradation represents one of todays major global chal-
lenges that threatens human well-being and livelihoods worldwide. To
reverse continuing degradation, we need to understand its socio-
economic consequences so that they can be incorporated into
ecosystem management decision-making processes. For this, we need to
link our knowledge of how ecosystems function and change to socially
meaningful representations of those changes. While attempts are
increasingly being made at such integrations, the translation processes
required for effective linkage remain largely undiscussed. Therefore, key
aspects of the socio-ecological interactions may be ‘lost in translation.
We argue that, as we further our understanding of ecosystem change,
and in line with the aspiration to understand its social effects, the in-
tricacies of the translation processes need to be made explicit and be
made available for others to inspect, so that true advancements can be
Here we have described, detailed and discussed a process of estab-
lishing the social outcomes (in terms of well-being changes measured
quantied in monetary values) of ecosystem restoration, using peatlands
as an exemplar of a complex ecosystem of global relevance. This illus-
tration is limited in the extent that it only tests one single translation
key, while others could be tested and their various consequences
compared. Further, we do not claim that our process was perfect or
superior to other attempts (this should be obvious from our discussion of
the various unresolved and problematic issues in the previous section).
Rather, we suggest that precisely those unresolved and problematic is-
sues should be a focus of discussion in the study of global environmental
change, and currently such discussions are not taking place often
enough. Improving our ability to tackle the future effects of ecosystem
degradation will, to a great extent, depend on the usefulness of the
models used to understand ecosystem processes (e.g., models able to
cope with many ecosystem parameters and that are relevant at the level
of land management interventions), the quality of our valuation
methods, and our ability to tailor them to specic cases (Evans et al.,
2014). But there will always be a limit to the ‘real-world relevance of
these integrated approaches if the relationships between these aspects
remain in a black box.
Ideally, these integration processes should be co-developed with
stakeholders. Iterative, adaptive, and interdisciplinary processes should
help (Reed et al. 2013). For example, we used monetary values that had
been obtained prior to engaging with DigiBog modellers (although the
process was still transdisciplinary and co-developed with peatland sci-
entists). An iterative DigiBog co-development process would have
enabled us to make our narratives more consistent across peatland
conditions. Furthermore, rather than using an existing model, we could
have created an ad-hoc model from scratch although it is also impor-
tant to consider how realistic it is to construct new models for every
assessment process or to come up with models able to cope with a large
range of ecosystem types and conditions. Even so, the development of
new bespoke models would still not be sufcient to produce a break-
through without paying attention to the translation key between the two
strands of knowledge. The translation key that we have exposed in this
paper provides a way of interrogating the interface between our un-
derstanding of ecosystem changes and the estimation of their social
outcomes. If these strands of knowledge become more sophisticated
without paying attention to how ecological change is translated into
socially meaningful outcomes (i.e., without paying attention to the
‘translation key), we risk enhancing the divide between them and
hampering the robustness and rigor of integrated assessments.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
This work was co-funded by the Natural Environment Research
Council (NERC) through the project ‘Understanding ecosystem stocks
and tipping points in UK peatlands(grant number NE/P00783X/1) and
the Scottish Government Rural Affairs and the Environment Portfolio
Strategic Research Programme 2011-2016 and 20162021. Authors are
grateful to project colleagues Mark Whittingham, Gavin Stewart, James
Pearce-Higgins, Simone Martino and Jasper Kenter for fruitful discus-
sions that have enriched this work.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
Adamowicz, W., Boxall, P., Williams, M., Louviere., J, 1998. Stated preference
approaches to measuring passive use values: Choice experiments versus contingent
valuation. Am. J. Agric. Econ. 80, 864875.
Aitkenhead, M.J., Coull, M.C., 2016. Geoderma Mapping soil carbon stocks across
Scotland using a neural network model. Geoderma 262, 187198.
Alison, J., Jarvis, S., Rowe, E., Sier, A., Wilson, M., Smart, S. 2020. Find your niche! Plant
species model assessment.nd_your_niche/.
J. Martin-Ortega et al.
Ecosystem Services 50 (2021) 101327
Averis, A., Averis, B., Birks, J., Horseld, D., Thompson, D., Yeo, M. 2004. An Illustrated
Guide to British Upland Vegetation,. JNCC, Peterborough, UK.
Bain, C.G., Bonn, A., Stoneman, R., Chapman, S., Coupar, A., Evans, M. 2011. IUCN UK
Commission of Inquiry on Peatlands. IUCN UK. Peatland Programme.
Baird, Andy J., Morris, Paul J., Belyea, Lisa R., 2012. The DigiBog peatland development
model 1: Rationale, conceptual model, and hydrological basis. Ecohydrology 5 (3),
Barkmann, J., Glenk, K., Keil, A., Leemhuis, C., Dietrich, N., Gerold, G., Marggraf, R.,
2008. Confronting unfamiliarity with ecosystem functions: The case for an
ecosystem service approach to environmental valuation with stated preference
methods. Ecol. Econ. 65 (1), 4862.
Bateman, I., Agarwala, M., Binner, A., Coombes, E., Day, B., Ferrini, S., Fezzi, C.,
Hutchins, M., Lovett, A., Posen, P., 2016. Spatially explicit integrated modeling and
economic valuation of climate driven land use change and its indirect effects.
J. Environ. Manage. 181, 172184.
Bateman, Ian J., Mace, Georgina M., Fezzi, Carlo, Atkinson, Giles, Turner, Kerry, 2011.
Economic analysis for ecosystem service assessments. Environ. Resour. Econ. 48 (2),
Bruneau, P., Johnson, S.M., 2014. Scotlands Peatland - Denitions & Information
Resources. n Comm. Rep. Scottish Nat. Herit.
Bullock, James M., Aronson, James, Newton, Adrian C., Pywell, Richard F., Rey-
Benayas, Jose M., 2011. Restoration of ecosystem services and biodiversity: conicts
and opportunities. Trends Ecol. Evol. 26 (10), 541549.
Carpenter, S.R., Mooney, H.A., Agard, J., Capistrano, D., DeFries, R.S., Díaz, S., Dietz, T.,
Duraiappah, A.K., Oteng-Yeboah, A., Pereira, H.M., Perrings, C., Reid, W. V.,
Sarukhan, J., Scholes, R.J., Whyte, A. 2009. Science for managing ecosystem
services: Beyond the Millennium Ecosystem Assessment. Proc. Natl. Acad. Sci. 106,
CBD, 2011. The strategic plan for biodiversity 2011-2020 and the Aichi biodiversity
targets. Convention on Biological Diversity. Document UNEP/CBD/COP/DEC/X/2.,
COP Decision X/2.
Clymo, R.S., 1984. The limits to peat bog growth. Philos. Trans. R. Soc. Lond. B. Biol. Sci.
303, 605654.
Costanza, R., de Groot, R., Braat, L., Kubiszewski, I., Fioramonti, L., Sutton, P., Farber, S.,
Grasso, M., 2017. Twenty years of ecosystem services: How far have we come and
how far do we still need to go? Ecosyst. Serv. 28, 116.
Díaz, S., Settele, J., Brondízio, E., Hien T. Ngo (IPBES), Maximilien Gu`
eze (IPBES); John
Agard (Trinidad and Tobago), A.A., (Germany), Patricia Balvanera (Mexico), Kate
Brauman (United States of America), S.B., (United Kingdom of Great Britain and
Northern Ireland/BirdLife International), K.C. (Canada), Lucas Garibaldi
(Argentina), Kazuhito Ichii (Japan), Jianguo Liu (United States of America), S.,
Mazhenchery Subramanian (India/United Nations University), Guy Midgley (South
Africa), P., Miloslavich (Bolivarian Republic of Venezuela/Australia), Zsolt Moln´
(Hungary), D.O., (Kenya), Alexander Pfaff (United States of America), S.P. (United S.
of A., Andy Purvis (United Kingdom of Great Britain and Northern Ireland), J.R.,
(Bangladesh/United Kingdom of Great Britain and Northern Ireland), B.R. (South A.,
Rinku Roy Chowdhury (United States of America), Yunne-Jai Shin (France), Ingrid
VisserenHamakers (Netherlands/United States of America), K. 2019. Summary for
policymakers of the global assessment report on biodiversity and ecosystem services
of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem
Services - assessment Key messages.
Elwell, T.L., Gelcich, S., Gaines, S.D., L´
opez-Carr, D., 2018. Using peoples perceptions of
ecosystem services to guide modeling and management efforts. Sci. Total Environ.
637638, 10141025.
Evans, C., Artz, R., Moxley, J., Smyth, M.-A., Taylor, E., Archer, N., Burden, A.,
Williamson, J., Donnelly, D., Thomson, A., Buys, G., Malcolm, H., Wilson, D., Renou-
Wilson, F., Potts, J. 2017. Implementation of an Emissions Inventory for UK
Evans, C.D., Bonn, A., Holden, J., Reed, M.S., Evans, M.G., Worrall, F., Couwenberg, J.,
Parnell, M., 2014. Relationships between anthropogenic pressures and ecosystem
functions in UK blanket bogs: Linking process understanding to ecosystem service
valuation. Ecosyst. Serv. 9, 519.
Ferretto, Anna, Brooker, Rob, Aitkenhead, Matt, Matthews, Robin, Smith, Pete, 2019.
Potential carbon loss from Scottish peatlands under climate change. Reg. Environ.
Chang. 19 (7), 21012111.
Frolking, S., Roulet, N.T., Tuittila, E., Bubier, J.L., Quillet, A., Talbot, J., Richard, P.J.H.,
2010. A new model of Holcene peatland net primary production, decomposition,
water balance and peat accumulation. Earth Syst. Dyn. 1, 121.
Gallego-Sala, A.V., Clark, J.M., House, J.I., Orr, H.G., Prentice, I.C., Smith, P.,
Farewell, T., Chapman, S.J., 2010. Bioclimatic envelope model of climate change
impacts on blanket peatland distribution in Great Britain. Clim. Res. 45, 151162.
Gallego-Sala, Angela V., Colin Prentice, C.I., 2013. Blanket peat biome endangered by
climate change. Nat. Clim. Chang. 3 (2), 152155.
Glenk, K., Faccioli, M., Martin-Ortega, J., 2018. Report on ndings from a survey on
public preferences for peatlands restoration: timing and long term resilience of
peatlands under climate change. SEFARI Report.
Glenk, K., Martin-Ortega, J. 2018. The economics of peatland restoration. https://doi.
Glenk, K., Schaafsma, M., Moxey, A., Martin-Ortega, J., Hanley, N., 2014. A framework
for valuing spatially targeted peatland restoration. Ecosyst. Serv. 9, 2033. https://
et-Regamey, Adrienne, Bebi, Peter, Bishop, Ian D., Schmid, Willy A., 2008. Linking
GIS-based models to value ecosystem services in an Alpine region. J. Environ.
Manage. 89 (3), 197208.
Haines-Young, R., Postchin, M., 2010. The links between biodiversity, ecosystem services
and human well-being. In: Raffaelli, D., Frid, C. (Eds.), Ecosystem Ecology: A New
Synthesis. Cambridge University Press, Cambridge.
Hein, Lars, van Koppen, Kris, de Groot, Rudolf S., van Ierland, Ekko C., 2006. Spatial
scales, stakeholders and the valuation of ecosystem services. Ecol. Econ. 57 (2),
Henrys, P.A., Smart, S.M., Rowe, E.C., Evans, C.D., Emmett, B.A., Butler, A., Jarvis, S.G.,
Fang, Z. 2015. Niche models for British plants and lichens obtained using an
ensemble approach. New Journal of Botany 5, 89100.
Holmes, T., Adamowicz, W., Carlsson, F. 2017. Choice Experiments, in: Champ P., Boyle
K., B.T. (Ed.), A Primer on Nonmarket Valuation. The Economics of Non-Market
Goods and Resources. Springer, Dordretch.
Hussain, S., McVittie, A., Brander, L., Vardakoulias, O., Wagtendonk, A., Verburg, P., De
Groot, R.S., Tinch, R., Fofana, A., Baulcomb, C., Mathieu, L., Ozdemiroglu, E., Phang,
Z. 2011. The Economics of Ecosystems and Biodiversity. The quantitative
Assessment. Final Report to the United Nations Environment Programme.
Jones, L., Norton, L., Austin, Z., Browne, A.L., Donovan, D., Emmett, B.A., Grabowski, Z.
J., Howard, D.C., Jones, J.P.G., Kenter, J.O., Manley, W., Morris, C., Robinson, D.A.,
Short, C., Siriwardena, G.M., Stevens, C.J., Storkey, J., Waters, R.D., Willis, G.F.,
2016. Stocks and ows of natural and human-derived capital in ecosystem services.
Land Use Policy 52, 151162.
Jones, L., Milne, A., Hall, J., Mills, G., Provins, A., Christie, M., 2018. Valuing
improvements in biodiversity due to controls on atmospheric nitrogen pollution.
Ecol. Econ. 152, 358366.
Joosten, H. 2009. The Global Peatland CO2 Picture: peatland status and drainage related
emissions in all countries of the world. Wetl. Int. 35.
Juutinen, Artti, Tolvanen, Anne, Saarimaa, Miia, Ojanen, Paavo, Sarkkola, Sakari,
Ahtikoski, Anssi, Haikarainen, Soili, Karhu, Jouni, Haara, Arto, Nieminen, Mika,
a, Timo, Nousiainen, Hannu, Hotanen, Juha-Pekka, Minkkinen, Kari,
Kurttila, Mikko, Heikkinen, Kaisa, Sallantaus, Tapani, Aapala, Kaisu,
Tuominen, Seppo, 2020. Cost-e ff ective land-use options of drained peatlands
integrated biophysical- economic modeling approach. Ecol. Econ. 175, 106704.
Leifeld, J., Menichetti, L. 2018. The underappreciated potential of peatlands in global
climate change mitigation strategies. Nat. Commun.
Liu, J., Dietz, T., Carpenter, S.R., Alberti, M., Folke, C., Moran, E., Pell, A.N., Deadman,
P., Kratz, T., Lubchenco, J., Ostrom, E., Ouyang, Z., Provencher, W., Redman, C.L.,
Schneider, S.H., Taylor, W.W. 2007. Complexity of Coupled Human and Natural
Systems\n10.1126/science.1144004. Science (80-.). 317, 15131516.
MA, 2005. Millennium Ecosystem Assessment. Island Press, Washington, DC.
Maes, Joachim, Egoh, Benis, Willemen, Louise, Liquete, Camino, Vihervaara, Petteri,
agner, Jan Philipp, Grizzetti, Bruna, Drakou, Evangelia G., Notte, Alessandra La,
Zulian, Grazia, Bouraoui, Faycal, Luisa Paracchini, Maria, Braat, Leon,
Bidoglio, Giovanni, 2012. Mapping ecosystem services for policy support and
decision making in the European Union. Ecosyst. Serv. 1 (1), 3139.
Maltby, E., 2010. Effects of climate change on the societal benets of UK upland peat
ecosystems : applying the ecosystem approach. Clim. Res. 45, 249259. https://doi.
Martin-Ortega, J., Allott, T.E.H., Glenk, K., Schaafsma, M., 2014. Valuing water quality
improvements from peatland restoration: Evidence and challenges. Ecosyst. Serv. 9,
Martin-Ortega, Julia, Glenk, Klaus, Byg, Anja, Zia, Asim, 2017. How to make complexity
look simple? Conveying ecosystems restoration complexity for socio-economic
research and public engagement. PLoS One 12 (7), e0181686.
Martin-Ortega, Julia, Jorda-Capdevila, Diídac, Glenk, Klaus, Holstead, Kirsty L., Martin-
Ortega, Julia, Ferrier, Robert C., Gordon, Iain J., Khan, Shahbaz, 2015a. In: Water
Ecosystem Services: A Global Perspective. Cambridge University Press, Cambridge,
pp. 314.
Martin-Ortega, J., Perni, A., Jackson-Blake, L., Balana, B.B., Mckee, A., Dunn, S.,
Helliwell, R., Psaltopoulos, D., Skuras, D., Cooksley, S., Slee, B., 2015b.
A transdisciplinary approach to the economic analysis of the European Water
Framework Directive. Ecol. Econ. 116, 3445.
McVittie, A., Norton, L., Martin-Ortega, J., Siameti, I., Glenk, K., Aalders, I., 2015.
Operationalizing an ecosystem services-based approach using Bayesian Belief
Networks: An application to riparian buffer strips. Ecol. Econ. 110, 1527. https://
Olander, L.P., Johnston, R.J., Tallis, H., Kagan, J., Maguire, L.A., Polasky, S., Urban, D.,
Boyd, J., Wainger, L., Palmer, M., 2018. Benet relevant indicators: Ecosystem
services measures that link ecological and social outcomes. Ecol. Indic. 85,
Parry, L.E., Holden, J., Chapman, P.J., 2014. Restoration of blanket peatlands.
J. Environ. Manage. 133, 193205.
Reed, M.S., Hubacek, K., Bonn, A., Burt, T.P., Holden, J., Stringer, L.C., Beharry-borg, N.,
Buckmaster, S., Chapman, D., Chapman, P.J., Clay, G.D., Cornell, S.J., Dougill, A.J.,
Evely, C., Fraser, E.D.G., Jin, N., Irvine, B.J., Kirkby, M.J., Kunin, W.E., Prell, C.
J. Martin-Ortega et al.
Ecosystem Services 50 (2021) 101327
2013. Anticipating and Managing Future Trade-offs and Complementarities between
Ecosystem Services.
Rotherham, I.D. 2011. Peat and Peat Cutting. Oxford: Shire Library.
onhart, M., Trautvetter, H., Parajka, J., Blaschke, A.P., Hepp, G., Kirchner, M.,
Mitter, H., Schmid, E., Strenn, B., Zessner, M., 2018. Modelled impacts of policies
and climate change on land use and water quality in Austria. Land use policy.
Scottish Government Climate Change Plan: third report on proposals and policies 2018
20182032 (RPP3).
Sharp, R. 2014. InVEST users guide.The Natural Capital Project. Stanford, CA, USA.
Smart, S.M., Scott, W.A., Whitaker, J., Hill, M.O., Roy, D.B., Critchley, C.N., Marini, L.,
Evans, C., Emmett, B.A., Rowe, E.C., Crowe, A., Le Duc, M., Marrs, R.H., 2010.
Empirical realised niche models for British higher and lower plants - development
and preliminary testing. J. Vegetation Sci. 21, 643656.
Swindles, Graeme T., Morris, Paul J., Mullan, Donal J., Payne, Richard J.,
Roland, Thomas P., Amesbury, Matthew J., Lamentowicz, Mariusz, Turner, T.
Edward, Gallego-Sala, Angela, Sim, Thomas, Barr, Iestyn D., Blaauw, Maarten,
Blundell, Antony, Chambers, Frank M., Charman, Dan J., Feurdean, Angelica,
Galloway, Jennifer M., Gałka, Mariusz, Green, Sophie M., Kajukało, Katarzyna,
Karofeld, Edgar, Korhola, Atte, Lamentowicz, Łukasz, Langdon, Peter,
Marcisz, Katarzyna, Mauquoy, Dmitri, Mazei, Yuri A., McKeown, Michelle M.,
Mitchell, Edward A.D., Novenko, Elena, Plunkett, Gill, Roe, Helen M.,
Schoning, Kristian, Sillasoo, Ülle, Tsyganov, Andrey N., van der Linden, Marjolein,
aliranta, Minna, Warner, Barry, 2019. Widespread drying of European peatlands in
recent centuries. Nat. Geosci. 12 (11), 922928.
Tipping, Richard, 2008. Blanket peat in the Scottish Highlands: Timing, cause, spread
and the myth of environmental determinism. Biodivers. Conserv. 17 (9), 20972113.
Yang, Wu, Dietz, Thomas, Kramer, Daniel Boyd, Ouyang, Zhiyun, Liu, Jianguo, 2015. An
integrated approach to understanding the linkages between ecosystem services and
human well-being. Ecosyst. Heal. Sustain. 1 (5), 112.
Young, D.M., Baird, A.J., Charman, D.J., Evans, C.D., Gallego-Sala, A.V., Gill, P.J.,
Hughes, P.D.M., Morris, P.J., Swindles, G.T., 2019. Misinterpreting carbon
accumulation rates in records from near-surface peat. Sci. Rep. 9, 18. https://doi.
Young, Dylan M., Baird, Andy J., Morris, Paul J., Holden, Joseph, 2017. Simulating the
long-term impacts of drainage and restoration on the ecohydrology of peatlands.
Water Resour. Res. 53 (8), 65106522.
Yu, Zicheng, Loisel, Julie, Brosseau, Daniel P., Beilman, David W., Hunt, Stephanie J.,
2010. Global peatland dynamics since the Last Glacial Maximum. Geophys. Res. Lett.
37 (13), n/an/a.
J. Martin-Ortega et al.
Ecosystem services are often omitted from climate policy owing to difficulties in estimating the economic value of climate-driven ecosystem changes. However, recent advances in data and methods can help us overcome these challenges and move towards a more comprehensive accounting of climate impacts.
Full-text available
Combining natural capital accounting tools and ecosystem restoration approaches builds on existing frameworks to track changes in ecosystem stocks and flows of services and benefits as a result of restoration. This approach highlights policy relevant benefits that arise due to restoration efforts and helps to maximize opportunities for return on investment. Aligning the System of Environmental Economic Accounting – Ecosystem Accounting (SEEA EA) framework with risk assessment tools, we developed a risk register for peatlands in two contrasting catchments in Ireland, based on available information relating to peatland stocks (extent and condition) and flows (services and benefits), as well as knowledge of pressures. This approach allowed for identification of areas to target peatland restoration, by highlighting the potential to reduce and reverse negative trends in relation to provisioning, regulating and cultural services, flows relating to non-use values, as well as abiotic flows. We also highlighted ways to reduce and reverse the effects of historical and ongoing pressures through restoration measures, aligning our approach with that outlined in the SER International Principles and Standards for the Practice of Ecological Restoration. Building on the synergies between the SEEA EA and the SER Standards is highlighted as a means to develop trans-disciplinary collaboration, to assist in setting and achieving targets set out under the UN Decade on Ecosystem Restoration as well as integrating regional policy targets set under the EU Biodiversity Strategy for 2030, and the related EU Habitats and EU Water Framework Directives. This article is protected by copyright. All rights reserved.
Full-text available
The United Nations System of Environmental and Economic Accounting - Ecosystem Accounting (SEEA EA) is a geospatial approach, whereby existing data on ecosystem stocks and flows are collated to show changes over time. The framework has been proposed as a means to track and monitor ecosystem restoration targets across the EU. Condition is a key consideration in the conservation assessment of habitats protected under the EU Habitats Directive and ecosystem condition accounts are also integral to the SEEA EA. While SEEA EA accounts have been developed at EU level for an array for ecosystem types, condition accounts remain the least developed. Collating available datasets under the SEEA EA framework, we developed extent and rudimentary condition accounts for peatland ecosystems at catchment scale in Ireland. Information relating to peatland ecosystem sub-types or habitat types was collated for peatland habitats listed under Annex I of the EU Habitats Directive, as well as degraded peatlands not included in EU nature conservation networks. While data relating to peatland condition were limited, understanding changes in ecosystem extent and incorporating knowledge of habitat types and degradation served as a proxy for ecosystem condition in the absence of more comprehensive data. This highlighted the importance of the ecosystem extent account, which underpins all other accounts in the SEEA EA framework. Reflecting findings at EU level, drainage, disturbance and land conversion were identified as the main pressures affecting peatland condition. We highlighted a number of options to gather data to build more robust, time-series extent and condition accounts for peatlands at varying accounting scales. Overall, despite the absence of comprehensive data, bringing information under the SEEA EA framework is considered a good starting point, with the integration of expert ecological opinion considered essential to ensure development of reliable accounts, particularly when working at ecosystem sub-type (habitat type) and catchment scale.
Full-text available
Peatlands are globally important stores of carbon (C) that contain a record of how their rates of C accumulation have changed over time. Recently, near-surface peat has been used to assess the effect of current land use practices on C accumulation rates in peatlands. However, the notion that accumulation rates in recently formed peat can be compared to those from older, deeper, peat is mistaken – continued decomposition means that the majority of newly added material will not become part of the long-term C store. Palaeoecologists have known for some time that high apparent C accumulation rates in recently formed peat are an artefact and take steps to account for it. Here we show, using a model, how the artefact arises. We also demonstrate that increased C accumulation rates in near-surface peat cannot be used to infer that a peatland as a whole is accumulating more C – in fact the reverse can be true because deep peat can be modified by events hundreds of years after it was formed. Our findings highlight that care is needed when evaluating recent C addition to peatlands especially because these interpretations could be wrongly used to inform land use policy and decisions.
Full-text available
Climate warming and human impacts are thought to be causing peatlands to dry, potentially converting them from sinks to sources of carbon. However, it is unclear whether the hydrological status of peatlands has moved beyond their natural envelope. Here we show that European peatlands have undergone substantial, widespread drying during the last ~300 years. We analyse testate amoeba-derived hydrological reconstructions from 31 peatlands across Britain, Ireland, Scandinavia and Continental Europe to examine changes in peatland surface wetness during the last 2,000 years. We find that 60% of our study sites were drier during the period 1800–2000 ce than they have been for the last 600 years, 40% of sites were drier than they have been for 1,000 years and 24% of sites were drier than they have been for 2,000 years. This marked recent transition in the hydrology of European peatlands is concurrent with compound pressures including climatic drying, warming and direct human impacts on peatlands, although these factors vary among regions and individual sites. Our results suggest that the wetness of many European peatlands may now be moving away from natural baselines. Our findings highlight the need for effective management and restoration of European peatlands.
Full-text available
The Scottish Government is committed to reduce carbon emissions by 80% by 2050 (compared to a 1990–1995 baseline). Peatlands have been recognised as a key environment for the carbon balance as they sequester and store great quantities of carbon, but they also have the potential to release it. In Scotland, peatlands cover more than 20% of the surface (more than 90% of which is blanket bog) and store more than 2500 Mt of carbon. Blanket bogs are very climate reliant, and as a consequence of climate change, many areas in Scotland may not be able to support peatlands in the near future. In this study, two bioclimatic envelope models (Linsday Modified model and Blanket Bog Tree model) have been used to obtain a first estimate of how the distribution of blanket bogs in Scotland could vary according to climate change in the 2050s and in the 2080s. The potential losses of carbon arising from climate change have then been calculated. Results showed that in 2050, more than half of the carbon currently stored in Scottish blanket bogs will be at risk of loss. This is 4.4–6.6 times the amount of carbon emitted in 2016 from all the sectors in Scotland and, if emissions from peatland occur and are taken into account, it will greatly hamper efforts to meet emission reduction targets set out in the Climate Change (Scotland) Act of 2009.
Full-text available
Technical Report
This report summarises work undertaken on behalf of the Department for Business, Energy and Industrial Strategy (BEIS) to develop and implement a new method for reporting greenhouse gas (GHG) emissions from peatlands in the UK’s emissions inventory. The work builds on the Intergovernmental Panel on Climate Change (IPCC) 2013 Wetlands Supplement, by providing empirically-based and UK-specific ‘Tier 2’ estimates of emissions from a representative range of peat land-use and condition categories. It collates consistent spatial information on peat extent and condition from each of the four UK administrations, as well as the most peat-rich Crown Dependencies and Overseas Territories (Isle of Man and Falkland Islands respectively). These data were used to assess the overall extent and condition of UK peatlands; to estimate change in condition over the period from 1990 to 2013; to implement the first UK-wide inventory of peatland GHG emissions over this period; and to project future peat-derived GHG emissions through to 2050 based on a set of five illustrative scenarios. Key findings were:  Based on updated figures obtained during this project, the UK’s peatlands are estimated to occupy a total area of around 3.0 million hectares (12.2 % of the total UK land area). Another 280,000 ha of peat are believed to be present in the Falkland Islands (around one quarter of the land area).  Of the UK’s total peat area, approximately 640,000 ha (22%) is estimated to remain in a near-natural condition. This area of near natural bog and fen is believed to be continuing to act as a significant net sink for CO2, of approximately 1,800 kt CO2 yr-1. This CO2 sink is however counterbalanced by similar emissions of methane (CH4) when its greater 100-year Global Warming Potential is taken into account making near-natural peatlands close to carbon neutral. Over longer time-horizons, natural peatlands have a strong net cooling impact on climate, due to the longer atmospheric lifetime of CO2 compared to CH4. While near-natural bogs are very small net GHG sources, for near-natural fens, CO2 uptake exceeds CH4 emission on a CO2-equivalent basis making them a very small net GHG sink. However the areas that could be definitely mapped as near-natural fen from available data were small.  A further 1,213,000 ha (41%) of the UK peat area remains under some form of semi-natural peatland vegetation, but has been affected to varying degrees by human activities including drainage, burn- management, and livestock grazing. This has led to drying of the peat, loss of peat-forming species and erosion, converting these areas into net GHG sources. Although the emissions per unit area of modified peatland are relatively low, their great extent makes them significant contributors to overall UK peatland GHG emissions (3,400 kt CO2e yr-1, 15% of total emissions).  Arable cropland occupies just 7% of the UK’s peat area, but has the highest GHG emissions per unit area of any land-use, with high rates of both CO2 and N2O emissions as a result of drainage and fertilisation. As a result, cropland is estimated to emit 7,600 kt CO2e yr-1, 32% of total UK peat GHG emissions. Around two thirds of the cropland area is on ‘wasted’ peat (shallow residual organic soils where much of the original peat has already been lost), predominantly in the Fenlands of East Anglia. The true extent and rate of GHG emission from wasted peatlands is not well quantified, making this component of the total cropland emission particularly uncertain.  Peatlands converted to Grassland occupy a further 8% of the UK’s peat area, and emit ~6,300 kt CO2e yr-1, 27% of total UK peat emissions. Drained intensive grasslands in lowland areas are the primary source of these emissions.  Around 16% of the UK peat area is covered by woodland, the majority of which is drained conifer plantation. The UK inventory currently applies a model-based (‘Tier 3’) approach to inventory reporting for forests, but data collated for this study were used to derive empirically-based ‘Tier 2’ emissions estimates for comparative purposes. Both the area estimates and emissions factors associated with afforested peatlands are uncertain, and the Tier 2 emission factors cannot take into account factors such as the age of forest, differences between tree species or forest management practices. However 1 the Tier 2 emission estimates suggest that peat under forestry in the UK could be emitting around 4,600 kt CO2e yr-1 (20% of the UK total). This figure does not take into account CO2 uptake into tree biomass, or the after-use of harvested timber.  Industrial peat extraction for horticultural use occupies a comparatively small proportion of the UK’s peat area (4,600 ha). A much larger area (mainly in Northern Ireland and Scotland) has been affected by current or historic domestic peat cutting for fuel (145,000 ha), and the resulting modification of vegetation and hydrology is thought (in the absence of subsequent restoration) to have converted these areas into sustained GHG sources. The combined total GHG emission from extracted areas of ~1,200 kt CO2e yr-1 derives mainly from these domestic extraction areas, despite the higher emissions per unit area of industrial extraction sites.  In total, the UK’s peatlands are estimated to be emitting approximately 23,100 kt CO2e yr-1 of GHG emissions. This emission is sufficient to convert the UK LULUCF inventory as a whole from a net GHG sink into a net GHG source.  There are large inter-regional variations in the main sources of peatland GHG emissions. In Scotland, with the largest total peat area, the largest sources are modified blanket bog and forests. In England, the smaller (and partly wasted) peat area makes a larger overall contribution to total UK emissions, as a result of intensive arable and grassland cultivation, predominantly in lowland areas. In Northern Ireland, intensive grassland in the lowlands and domestic peat extraction in the uplands are major sources, and in Wales sources include intensive and extensive grasslands and modified bogs. It was not yet possible to develop an inventory for the large area of peat in the Falkland Islands, but a significant proportion of this area is thought to be modified by grazing, erosion and fire.  Since 1990, an estimated 95,000 ha of UK peatland have been subject to some form of active restoration intervention, of which around 70,000 ha has involved some form of re-wetting. These activities have occurred in all of the UK administrations, with the majority having taken place in areas of modified blanket bog. Some re-wetting and restoration to peatland vegetation has also occurred in areas of plantation forest, cropland, grassland and peat extraction. In total, these activities are estimated to have generated an emissions reduction since 1990 of 423 kt CO2e yr-1. It is likely that other unrecorded restoration activities, land-use changes and management activities (for example as part of agri-environment schemes) have had an additional influence on peatland emissions, but available data were insufficient to allow these changes to be reported.  The emissions estimates obtained during this project represent a major (more than tenfold) increase in the total peat-derived emissions captured in the current UK inventory. This reflects a significant development in the IPCC methodology following publication of the 2013 Wetland Supplement, which allows for more complete reporting of peatland emissions than was previously possible. This new approach by IPCC has led to much more detailed reporting of peatland emissions in the LULUCF inventory, incorporating improved data on peat condition including the extent of peat mapped; peat condition classification and mapping; estimated emission factors; treatment of wasted peats; and methodology applied to forest on peat.  Future emissions projections to 2050 based on a set of illustrative scenarios suggest that currently legislated peat restoration measures (mainly the phasing out of peat extraction in England) will have limited impact on emissions, but that current levels of ambition on peat restoration in all four countries could deliver over 4 Mt CO2e yr-1 of emissions reductions by 2050. A more ambitious restoration scenario, including removal of 50% of forest planted on peat since 1980, could deliver over 8 Mt CO2e yr-1 of emissions abatement. However none of our scenarios incorporated large-scale cessation of drainage-based agriculture on lowland peat, which (as it accounts for 60% of all current emissions) placed effective limits on the degree of emissions abatement that could be achieved. 2 In summary, although around 70% of UK peatlands retain some form of semi-natural vegetation cover, over three quarters are in a modified state, ranging from relatively minor changes to vegetation cover and hydrology, through to the complete replacement of wetland vegetation by arable and horticultural crops, agricultural grasses and non-native conifers, with accompanying deep drainage. As a result, UK peatlands have transitioned from modest historical net GHG sinks (an estimated pre-anthropogenic sink, based on 100 year Global Warming Potentials, in the region of 0.25 Mt CO2e yr-1) into large emission sources (exceeding 23 Mt CO2e yr-1). The contrast between these two values highlights that the priority for peatland management should be to reduce current high emissions; it is unlikely that so-called ‘negative emissions’ from peat formation will be able to offset emissions from other sectors. Widespread and ongoing peat restoration across the UK has contributed to a reduction in total emissions, but to date the majority of restoration has taken place within modified upland bogs, which produce modest emissions sources per unit area, rather than categories with higher Tier 2 emission factors per unit area such as cropland, lowland grassland and plantation forest. Addressing continued emissions from these areas could provide a high degree of emissions abatement, but would face significant logistical and socio- economic barriers. Mitigation measures that reduce emissions from cultivated peatlands without leading to large-scale loss of income to farmers and landowners, or to a decrease in UK food security, thus represent a key scientific and policy challenge. In the meantime, the continued restoration of modified upland bogs, notably higher-emitting categories such as actively eroding areas and heavily degraded former domestic peat cutting sites, may represent more tractable options for emissions reduction. Whilst many individual components of the peatland emissions inventory remain uncertain, due to limitations in the number of primary measurement studies and difficulties in translating available soils and land-cover data into reliable peat area and condition estimates, the data and methods set out in this report provide the basis for initial inclusion of peatlands in the UK emissions inventory. To support the future development of this inventory, there is a need for new field-scale measurements of GHG fluxes from under-studied peatland types, and for the development of consistent, UK-scale condition mapping and monitoring approaches, potentially based on new earth observation data.
Full-text available
Although ecosystem service (ES) approaches are showing promise in moving environmental decision-making processes toward better outcomes for ecosystems and people, ES modeling (i.e., tools that estimate the supply of nature's benefits given biophysical constraints) and valuation methods (i.e., tools to understand people's demand for nature's benefits) largely remain disconnected, preventing them from reaching their full potential to guide management efforts. Here, we show how knowledge of environmental perceptions explicitly links these two lines of research. We examined how a diverse community of people with varying degrees of dependencies on coastal and marine ecosystems in southern Chile perceived the importance of different ecosystem services (ESs), their states (e.g., doing well, needs improvement), and management options. Our analysis indicates that an understanding of people's perceptions may usefully guide ecosystem modeling and management efforts by helping to: (1) define which ESs to enter into models and tradeoff analyses (i.e., what matters most?), (2) guide where to focus management efforts (i.e., what matters yet needs improvement?), and, (3) anticipate potential support or controversy surrounding management interventions. Finally, we discuss the complexity inherent in defining which ESs matter most to people. We propose that future research address how to design ES approaches and assessments that are more inclusive to diverse world views and notions of human wellbeing.
Full-text available
Soil carbon sequestration and avoidable emissions through peatland restoration are both strategies to tackle climate change. Here we compare their potential and environmental costs regarding nitrogen and land demand. In the event that no further areas are exploited, drained peatlands will cumulatively release 80.8 Gt carbon and 2.3 Gt nitrogen. This corresponds to a contemporary annual greenhouse gas emission of 1.91 (0.31–3.38) Gt CO2-eq. that could be saved with peatland restoration. Soil carbon sequestration on all agricultural land has comparable mitigation potential. However, additional nitrogen is needed to build up a similar carbon pool in organic matter of mineral soils, equivalent to 30–80% of the global fertilizer nitrogen application annually. Restoring peatlands is 3.4 times less nitrogen costly and involves a much smaller land area demand than mineral soil carbon sequestration, calling for a stronger consideration of peatland rehabilitation as a mitigation measure.
Full-text available
Restoration offers opportunities for securing and enhancing critical ecosystem services provided by peatlands, such as carbon storage, water retention and water quality, and support for biodiversity and wildlife. A comprehensive valuation encompassing the relevant public benefits of restoration and how these compare with it is lacking to date, leaving policy makers with little guidance with respect to the economic efficiency of restoring this climate-critical ecosystem. Using Scotland as a case study, this paper quantifies the non-market benefits of changes in peatland ecological condition associated with changes in ecosystem service provision and depending on the location of restoration efforts. Benefits on a per hectare basis are compared to varying capital and recurrent cost in a net present value space, providing a benchmark to be used in decision making on investments into peatland restoration. The findings suggest that peatland restoration is likely to be welfare enhancing. Benefits also exceed cost in appraisals of previous and future public investments into peatland restoration. The results thus strengthen the economic rationale for climate change mitigation through improved peatland management.
Peatlands provide habitats for many species and a variety of ecosystem services worldwide. In this study we used an integrated biophysical-economic modeling approach with multi-objective optimization to investigate how alternative land-use and land-management (LULM) options jointly affect economic returns from marketed (timber, energy peat, restoration costs) and non-marketed public goods (water quality, GHG emissions, biodiversity) in a typical landscape dominated by peatlands in northern Finland. We considered several LULM options including no action (the current state will continue), bioenergy wood harvesting, intensive forest management, restoration, and energy peat extraction with three after use options (no after use, reforestation, rewetting). Our study revealed strong tradeoffs between biodiversity and ecosystem services in drained peatlands. Optimal LULM depended strongly on the chosen objectives, i.e. whether marketed or non-marketed goods were preferred. For example, when the objective was carbon neutral land-use, the no action option was mostly chosen, while bioenergy wood harvesting was mostly chosen when the objective was to provide economic and environmental benefits at the same time. The strong tradeoff between biodiversity and ecosystem services indicates that compromises are unavoidable in order to obtain a multi-functional landscape which provides biodiversity conservation, climate change mitigation and water protection in a cost-effective manner.
Atmospheric nitrogen pollution has severe impacts on biodiversity, but approaches to value them are limited. This paper develops a spatially explicit methodology to value the benefits from improvements in biodiversity resulting from current policy initiatives to reduce nitrogen emissions. Using the UK as a case study, we quantify nitrogen impacts on plant diversity in four habitats: heathland, acid grassland, dunes and bogs, at fine spatial resolution. Focusing on non-use values for biodiversity we apply value-transfer based on household's willingness to pay to avoid changes in plant species richness, and calculate the benefit of projected emission declines of 37% for nitrogen dioxide (NO2) and 6% for ammonia (NH3) over the scenario period 2007?2020. The annualised benefit resulting from these pollutant declines is ?32.7?m (?4.4?m to ?109.7?m, 95% Confidence Interval), with the greatest benefit accruing from heathland and acid grassland due to their large area. We also calculate damage costs per unit of NO2 and NH3 emitted, to quantify some of the environmental impacts of air pollution for use alongside damage costs for human health in policy appraisal. The benefit is ?103 (?33 to ?237) per tonne of NO2 saved, and ?414 (?139 to ?1022) per tonne of NH3 saved
Climate change is a major driver of land use with implications for the quality and quantity of water resources. We apply a novel integrated impact modelling framework (IIMF) to analyze climate change impacts until 2040 and stakeholder driven scenarios on water protection policies for sustainable management of land and water resources in Austria. The IIMF mainly consists of the sequentially linked bio-physical process model EPIC, the regional land use optimization model PASMA[grid], the quantitative precipitation/runoff TUWmodel, and the nutrient emission model MONERIS. Three climate scenarios with identical temperature trends but diverging precipitation patterns shall represent uncertainty ranges from climate change, i.e. a dry and wet situation. Water protection policies are clustered to two policy portfolios WAP_I and WAP_II, which are targeted to regions (WAP_I) or applied at the national scale (WAP_II). Policies cover agri-environmental programs and legal standards and tackle management measures such as restrictions in fertilizer, soil and crop rotation management as well as establishment of buffer strips. Results show that average national agricultural gross margin varies by ±2%, but regional impacts are more pronounced particularly under a climate scenario with decreasing precipitation sums. WAP_I can alleviate pressures compared to the business as usual scenario but does not lead to the achievement of environmental quality standards for P in all rivers. WAP_II further reduces total nutrient emissions but at higher total private land use costs. At the national average, total private land use costs for reducing nutrient emission loads in surface waters are 60-200 €/kg total N and 120-250 €/kg total P with precipitation and the degree of regional targeting as drivers. To conclude, the IIMF is able to capture the interfaces between climate change, land use, and water quality in a policy context. Despite efforts to improve model linkages and the robustness of model output, uncertainty propagations in integrated modelling frameworks need to be tackled in subsequent studies.